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ISSN 1664-8714
ISBN 978-2-88919-486-5
DOI 10.3389/978-2-88919-486-5

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OLFACTORY MEMORY NETWORKS: FROM EMOTIONAL LEARNING TO SOCIAL BEHAVIORS

Topic Editors:

Regina M. Sullivan, Emotional Brain Institute, Nathan Kline Institute, Orangeburg, NY 10962, USA

Donald A. Wilson, Emotional Brain Institute, Nathan Kline Institute, Orangeburg, NY 10962, USA

Nadine Ravel, Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR5292, University Lyon1, France

Anne-Marie Mouly, Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR5292, University Lyon1, France

Odors are powerful stimuli that can evoke emotional states, and support learning and memory. Decades of research have indicated that the neural basis for this strong “odor-emotional memory” connection is due to the uniqueness of the anatomy of the olfactory pathways. Indeed, unlike the other sensory systems, the sense of smell does not pass through the thalamus to be routed to the cortex. Rather, odor information is relayed directly to the limbic system, a brain region typically associated with memory and emotional processes. This provides olfaction with a unique and potent power to influence mood, acquisition of new information, and use of information in many different contexts including social interactions. Indeed, olfaction is crucially involved in behaviors essential for survival of the individual and species, including identification of predators, recognition of individuals for procreation or social hierarchy, location of food, as well as attachment between mating pairs and infant-caretaker dyads. Importantly, odors are sampled through sniffing behavior. This active sensing plays an important role in exploratory behaviors observed in the different contexts mentioned above. Odors are also critical for learning and memory about events and places and constitute efficient retrieval cues for the recall of emotional episodic memories.

This broad role for odors appears highly preserved across species. In addition, the consistent early developmental emergence of olfactory function across diverse species also provides a unique window of opportunity for analysis of myriad behavioral systems from rodents to nonhuman primates and humans. This, when combined with the relatively conserved organization of the olfactory system in mammals, provides a powerful framework to explore how complex behaviors can be modulated by odors to produce adaptive responses, and to investigate the underlying neural networks.

The present research topic brings together cutting edge research on diverse species and developmental stages, highlighting convergence and divergence between humans and animals to facilitate translational research.

Table of Contents

Olfactory Memory Networks: From Emotional Learning to Social Behaviors

Regina M. Sullivan, Donald A. Wilson, Nadine Ravel and Anne-Marie Mouly

An fMRI Study on the Influence of Sommeliers’ Expertise on the Integration of Flavor

Lionel Pazart, Alexandre Comte, Eloi Magnin, Jean-Louis Millot and Thierry Moulin

A Review on the Neural Bases of Episodic Odor Memory: From Laboratory-Based to Autobiographical Approaches

Anne-Lise Saive, Jean-Pierre Royet and Jane Plailly

A Unique Memory Process Modulated by Emotion Underpins Successful Odor Recognition and Episodic Retrieval in Humans

Anne-Lise Saive, Jean-Pierre Royet, Nadine Ravel, Marc Thévenet, Samuel Garcia and Jane Plailly

Learning to Smell Danger: Acquired Associative Representation of Threat in the Olfactory Cortex

Wen Li

Sweet Reward Increases Implicit Discrimination of Similar Odors

Eva Pool, Sylvain Delplanque, Christelle Porcherot, Tatiana Jenkins, Isabelle Cayeux and David Sander

Olfactory Preference Conditioning Changes the Reward Value of Reinforced and Non-Reinforced Odors

Nicolas Torquet, Pascaline Aimé, Belkacem Messaoudi, Samuel Garcia, Elodie Ey, Rémi Gervais, A. Karyn Julliard and Nadine Ravel

Modulation of Olfactory Sensitivity and Glucose-Sensing by the Feeding State in Obese Zucker Rats

Pascaline Aimé, Brigitte Palouzier-Paulignan, Rita Salem, Dolly Al Koborssy,
Samuel Garcia, Claude Duchamp, Caroline Romestaing and A. Karyn Julliard

The Orexinergic System Influences Conditioned Odor Aversion Learning in the Rat: A Theory on the Processes and Hypothesis on the Circuit Involved

Barbara Ferry

Olfactory Tubercle Stimulation Alters Odor Preference Behavior and Recruits Forebrain Reward and Motivational Centers

Brynn J. FitzGerald, Kara Richardson and Daniel W. Wesson

Interactions with the Young Down-Regulate Adult Olfactory Neurogenesis and Enhance the Maturation of Olfactory Neuroblasts in Sheep Mothers

Maïna Brus, Maryse Meurisse, Matthieu Keller and Frédéric Lévy

Differential Memory Persistence of Odor Mixture and Components in Newborn Rabbits: Competition between the Whole and Its Parts

Gérard Coureaud, Thierry Thomas-Danguin, Frédérique Datiche, Donald A. Wilson and Guillaume Ferreira

Olfactory Bulb Encoding During Learning Under Anesthesia

Alister U. Nicol, Gabriela Sanchez-Andrade, Paloma Collado, Anne Segonds-Pichon and Keith M. Kendrick

Olfactory Systems and Neural Circuits that Modulate Predator Odor Fear

Lorey K. Takahashi

Chronic Exposure to a Predator or Its Scent Does Not Inhibit Male–Male Competition in Male Mice Lacking Brain Serotonin

Ying Huo, Qi Fang, Yao-Long Shi, Yao-Hua Zhang and Jian-Xu Zhang

Mouse Grueneberg Ganglion Neurons Share Molecular and Functional Features with C. Elegans Amphid Neurons

Julien Brechbühl, Fabian Moine and Marie-Christine Broillet

Sniff Adjustment in an Odor Discrimination Task in the Rat: Analytical or Synthetic Strategy?

Emmanuelle Courtiol, Laura Lefèvre, Samuel Garcia, Marc Thévenet,
Belkacem Messaoudi and Nathalie Buonviso

The Olfactory Bulb Theta Rhythm Follows All Frequencies of Diaphragmatic Respiration in the Freely Behaving Rat

Daniel Rojas-Líbano, Donald E. Frederick, José I. Egaña and Leslie M. Kay

Infant Rats Can Learn Time Intervals before the Maturation of the Striatum: Evidence From Odor Fear Conditioning

Julie Boulanger Bertolus, Chloe Hegoburu, Jessica L. Ahers, Elizabeth Londen, Juliette Rousselot, Karina Szyba, Marc Thévenet, Tristan A. Sullivan-Wilson,
Valérie Doyère, Regina M.Sullivan and Anne-Marie Mouly

Rodent Ultrasonic Vocalizations Are Bound to Active Sniffing Behavior

Yevgeniy B. Sirotin, Martín Elias Costa and Diego A. Laplagne

Repeated Exposure to Odors Induces Affective Habituation of Perception and Sniffing

Camille Ferdenzi, Johan Poncelet,Catherine Rouby and Moustafa Bensafi

Properties and Mechanisms of Olfactory Learning and Memory

Michelle T. Tong, Shane T. Peace and Thomas A. Cleland

Sleep and Olfactory Cortical Plasticity

Dylan C. Barnes and Donald A. Wilson

Beta and Gamma Oscillatory Activities Associated with Olfactory Memory Tasks: Different Rhythms for Different Functional Networks?

Claire Martin and Nadine Ravel

Context-Driven Activation of Odor Representations in the Absence of Olfactory Stimuli in the Olfactory Bulb and Piriform Cortex

Nathalie Mandairon, Florence Kermen, Caroline Charpentier, Joelle Sacquet, Christiane Linster and Anne Didier

Comparative Chemosensory Cognition

Alan Gelperin

EDITORIAL
published: 17 February 2015
doi: 10.3389/fnbeh.2015.00036

Olfactory memory networks: from emotional learning to social behaviors

Regina M. Sullivan1,2, Donald A. Wilson1,2,3, Nadine Ravel[4] and Anne-Marie Mouly[4][*]

1Emotional Brain Institute, Nathan Kline Institute, Orangeburg, NY, USA

2Child and Adolescent Psychiatry, The Child Study Center, New York University Langone Medical Center, New York, NY, USA

3Neuroscience and Physiology, Sackler Institute, New York University School of Medicine, New York, NY, USA

4Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR5292, University Lyon1, Lyon, France

* Correspondence: ammouly@olfac.univ-lyon1.fr

Edited and reviewed by:
Nuno Sousa, University of Minho, Portugal

Keywords: olfaction, olfactory memory, social odors, sniffing behavior, odor preference, odor aversion

Odors are powerful stimuli that can evoke emotional states, and support learning and memory. Decades of research have indicated that the neural basis for this strong “odor-emotional memory” connection is due to the uniqueness of the anatomy of the olfactory pathways. Indeed, unlike the other sensory systems, the sense of smell does not pass through the thalamus to be routed to the cortex. Rather, odor information is relayed directly to the limbic system, a brain region typically associated with memory and emotional processes. This provides olfaction with a unique and potent power to influence mood, acquisition of new information, and use of information in many different contexts including social interactions. Indeed, olfaction is crucially involved in behaviors essential for survival of the individual and species, including identification of predators, recognition of individuals for procreation or social hierarchy, location of food, as well as attachment between mating pairs and infant-caretaker dyads. Importantly, odors are sampled through sniffing behavior. This active sensing plays an important role in exploratory behaviors observed in the different contexts mentioned above. Odors are also critical for learning and memory about events and places and constitute efficient retrieval cues for the recall of emotional episodic memories.

This broad role for odors appears highly preserved across species. In addition, the consistent early developmental emergence of olfactory function across diverse species also provides a unique window of opportunity for analysis of myriad behavioral systems from rodents to nonhuman primates and humans. This, when combined with the relatively conserved organization of the olfactory system in mammals, provides a powerful framework to explore how complex behaviors can be modulated by odors to produce adaptive responses, and to investigate the underlying neural networks.

The present research topic brings together cutting edge research on diverse species and developmental stages, highlighting convergence and divergence between humans and animals to facilitate translational research. It is composed of 25 articles and encompasses 5 sections: human olfaction, odor preferences and aversions, odors and social behavior, olfaction and sniffing, and olfactory memory.

HUMAN ODOR MEMORY AND PERCEPTION

Pazart et al. (2014) designed an fMRI study to describe the differences in brain activation in wine experts compared to control subjects. They observed specific areas activated in the experts' brains during all phases of wine tasting and reported that wine experts showed a more immediate and targeted sensory reaction to wine stimulation than control subjects. Saive et al. (2014a) reviewed and discussed the different ways of investigating episodic memory in human, ranging from autobiographical approaches to laboratory-based approaches including more ecological paradigms. In an accompanying article, Saive et al. (2014b) presented a novel laboratory-based approach to study episodic memory, which led to the formation and subsequent retrieval of an integrated and multimodal memory of episodes comprising odors (What) localized spatially (Where) within a visual context (Which context). Li (2014) then reviewed rodent and human research in olfactory aversive conditioning, promoting a sensory cortical model of threat perception. By elucidating threat encoding in the sensory cortex, this proposed model may provide new insights into the pathophysiology of emotional disorders, pointing to a concrete clinical intervention target in the sensory cortex. Pool et al. (2014) investigated whether odor discrimination abilities could be enhanced in humans through learning. They used an appetitive Pavlovian conditioning paradigm, during which one of two perceptually non-distinguishable odors was associated with a rewarding taste. They reported a dissociation between explicit perception and physiological reactions: although participants were not able to explicitly perceive a difference, they reacted faster, inhaled more and had higher skin conductance responses when confronted with the reward-associated odor compared to its similar version that was never associated with the reward.

ODOR PREFERENCES AND AVERSION

Torquet et al. (2014) designed a new paradigm to study conditioned olfactory preference (odor-sugar) in rats and investigate resulting changes in the hedonic valence of the odors. They showed that discriminative appetitive conditioning induces not only a preference for the reinforced odor, but also a stable devaluation of the non-reinforced stimulus. Aimé et al. (2014) investigated the modulation of olfactory sensitivity and glucose-sensing by the feeding state in obese Zucker rats using a conditioned odor aversion learning paradigm. The data show that obese rats displayed a higher olfactory sensitivity and a 2 fold higher concentration of glucose in the olfactory bulb and cortex than in control lean rats, thus providing strong arguments toward establishing the OB glucose-sensing as a key factor for sensory olfactory processing. Using the same odor aversion paradigm, Ferry (2014) further showed that the orexinergic system which triggers food intake, acts like fasting to increase not only olfactory sensitivity, but also the strength of the odor-malaise association. Finally, Fitzgerald et al. (2014) explored the role of the olfactory tubercle, a structure with known anatomical connectivity with both brain reward and olfactory structures, in regulating odor-motivated behaviors. They confirmed that electrical stimulation of the olfactory tubercle was rewarding and possessed the capacity to alter odor-directed preference behaviors, supporting the notion that the olfactory tubercle is integral to motivated behaviors and likely involved in odor hedonics.

ODORS AND SOCIAL BEHAVIOR

Brus et al. (2014) investigated the influence of parturition and learning of the lamb odor on neurogenesis in sheep mothers. They reported that learning of the olfactory signature of the lamb, which occurs during the first mother/young interactions, induces a down-regulation in olfactory neurogenesis associated with an enhancement of olfactory neuroblasts maturation. The authors made the assumption that fewer new neurons decrease cell competition in the olfactory bulb and enhance maturation of those new neurons selected to participate in the learning of the young odor. Using another animal model of mother-young interaction in rabbits, Coureaud et al. (2014) addressed the question of memory persistence of odor mixture and components in newborn rabbits. Interacting with the mother during the daily nursing, newborn rabbits experience her body odor cues including the mammary pheromone which promotes the very rapid appetitive learning of simple or complex stimuli (odorants or mixtures) through associative conditioning. The data showed that newborn rabbits have access to both elemental and configural information in certain odor mixtures, and competition between these distinct representations of the mixture influences the persistence of their memories. Social transmission of food preference is another example of social learning involving the olfactory modality. Nicol et al. (2014) showed that this learning can occur under anesthesia and promotes extensive, but spatially distinct, changes in mitral cell networks to both cued and uncued odors as well as in evoked glutamate and GABA release. These data suggest that olfactory learning under anesthesia in mice is supported by broadly similar changes in olfactory bulb encoding as shown previously in conscious paradigms. Another kind of social odor is provided by predator odors. Takahashi (2014) reviewed current research on the olfactory and neural systems that activate fear elicited by predator odors. Interconnections from olfactory systems to brain circuits activated by predator odor are discussed in relation to autonomic, endocrine, and fear-related unconditioned and conditioned fear. Using chronic exposure to a predator or its scent as a social stress, Huo et al. (2014) investigated the role of serotonin as a modulator of the effects of this stress on male-male aggression and sexual attractiveness of urine odor. They found that exposure to a predator or its scent causes down-regulation of male–male aggression and sexual attractiveness of urine odor, and that such inhibitory effects were reduced or abolished in mice lacking brain serotonin. Finally in mice the Grueneberg ganglion located at the tip of the nose close to the entry of the naris, has been identified as a chemodetector of alarm pheromones. Brechbühl et al. (2013) investigated the conserved multisensory modalities of mouse Grueneberg ganglion neurons. They found striking similarities between mouse Grueneberg ganglion neurons and nematode amphid neurons, suggesting that the ability of an organism to detect cues from similar origin occurs in a cluster of specialized olfactory neurons that has been conserved throughout evolution.

OLFACTION AND SNIFFING

Olfaction and respiration are tightly linked since odors are sampled through sniffing behavior. Courtiol et al. (2014) reported that sniffing behavior is not only a matter of odorant sorption properties. Indeed a given odorant was sniffed differently depending on the odor pair in which it was presented. These data suggest that sniffing is adjusted in a synthetic manner that is dependent on the context in which the odorant is presented. Rojas-Líbano et al. (2014) monitored sniffing behavior, simultaneously with the local field potential of the olfactory bulb in rats moving freely in a familiar environment. They found that the local field potential in the olfactory bulb represents the sniff cycle with high reliability at every sniff frequency and can therefore be used to study the neural representation of motor drive in a sensory cortex. Under some circumstances, sniffing behavior can also be used as an index of cognitive processes providing a useful tool at very young ages. Indeed, Boulanger Bertolus et al. (2014) monitored freezing and sniffing behavior during odor fear conditioning at different ages of the rat's life, in order to investigate the learning of interval duration between two events in this paradigm. They observed temporal patterns for freezing and/or respiration curves in pups as young as 12 days post-natal, suggesting that infant rats are able to encode time durations as well as, and as quickly as, adults despite their immature brain. In active rodents, sniffing is often phase locked with other orofacial sensorimotor behaviors like whisking, and head movements. Sirotin et al. (2014) further examined the relationship between sniffing and ultrasonic vocal output of rats in a social environment. They found that ultrasonic vocalization of the 50 kHz family is largely restricted to periods of active sniffing (5–10 Hz), with the calls produced exclusively during exhalations and causing an instantaneous reduction in sniff rate. These results show that ultrasonic vocalizations are an integral part of the rhythmic orofacial behavioral ensemble. In humans, there is psychophysiological evidence that sniffing is modulated by subjective pleasantness of an odor: sniff duration and sniff volume increase when pleasant odors are sampled compared to unpleasant ones. Ferdenzi et al. (2014) investigated how repeated exposure to odors can affect their pleasantness. For this, sniff duration and volume were recorded and paired with ratings of odor pleasantness and intensity. The data showed that affective habituation occurs with repeated exposure, which can be observed both at the self-reported level and on sniffing behavior.

OLFACTORY MEMORY

Tong et al. (2014) reviewed some established molecular and structural mechanisms of memory with a focus on the time courses of post-conditioning molecular processes. They described the properties of odor learning intrinsic to the olfactory bulb and reviewed the utility of the olfactory system of adult rodents as a memory system in which to study the cellular mechanisms of cumulative learning. Recent work has begun exploring the role of sleep in olfactory memory. Barnes and Wilson (2014) reviewed recent evidence of sleep-dependent changes in olfactory system structure and function which contribute to odor memory and perception. During slow-wave sleep, the piriform cortex becomes hypo-responsive to odor stimulation and instead displays sharp-wave activity similar to that observed within the hippocampal formation. Furthermore, the functional connectivity between the piriform cortex and other cortical and limbic regions is enhanced during slow-wave sleep compared to waking. This combination of conditions may allow odor memory consolidation to occur during a state of reduced external interference and facilitate association of odor memories with stored hedonic and contextual cues. Olfactory structures have been early reported to exhibit oscillatory population activities which have been proposed to subserve memory processes such as encoding, consolidation and retrieval. Martin and Ravel (2014) reviewed our current knowledge about the conditions in which two main oscillatory rhythms linked to odor processing, namely beta (15–40 Hz) and gamma (60–100 Hz) are observed at the first stages of olfactory processing, the olfactory bulb and the piriform cortex. Based on a critical reexamination of those data, the authors propose hypotheses on the functional involvement of beta and gamma oscillations for odor perception and memory. It is well known from the literature that sensory neural activity is highly context dependent and shaped by experience and expectation. Mandairon et al. (2014) further showed that association of an odorant with a visual context in mice allowed the visual context alone to elicit a behavioral response similar to that elicited by the olfactory stimulus when it was initially presented, and a neural activation pattern in the olfactory bulb that was highly correlated with that elicited by the associated odorant, but not other odorants. The authors concluded that in rodents, neural representation of an odorant in primary sensory areas can be elicited in its absence by exposure to the context to which the odorant was previously associated, further suggesting that rodents can build internal representation of the olfactory stimulus. Finally, Gelperin (2014) underlined that chemosensory processing, and particularly olfactory information processing, is a particularly attractive modality within which to seek comparative insights into cognitive processes underlying learning and memory. He concluded that the study of olfactory information processing may be the most general and fruitful approach to the study of comparative cognition, including consciousness, in the majority of vertebrate animal species.

REFERENCES

Aimé, P., Palouzier-Paulignan, B., Salem, R., Al Koborssy, D., Garcia, S., Duchamp, C., et al. (2014). Modulation of olfactory sensitivity and glucose-sensing by the feeding state in obese Zucker rats. Front. Behav. Neurosci. 8:326. doi: 10.3389/fnbeh.2014.00326

Barnes, D. C., and Wilson, D. A. (2014). Sleep and olfactory cortical plasticity. Front. Behav. Neurosci. 8:134. doi: 10.3389/fnbeh.2014.00134

Boulanger Bertolus, J., Hegoburu, C., Ahers, J. L., Londen, E., Rousselot, J., Szyba, K., et al. (2014). Infant rats can learn time intervals before the maturation of the striatum: evidence from odor fear conditioning. Front. Behav. Neurosci. 8:176. doi: 10.3389/fnbeh.2014.00176

Brechbühl, J., Moine, F., and Broillet, M. C. (2013). Mouse Grueneberg ganglion neurons share molecular and functional features with C. elegans amphid neurons. Front. Behav. Neurosci. 7:193. doi: 10.3389/fnbeh.2013.00193

Brus, M., Meurisse, M., Keller, M., and Levy, F. (2014). Interactions with the young down-regulate adult olfactory neurogenesis and enhance the maturation of olfactory neuroblasts in sheep mothers. Front. Behav. Neurosci. 8:53. doi: 10.3389/fnbeh.2014.00053

Coureaud, G., Thomas-Danguin, T., Datiche, F., Wilson, D. A., and Ferreira, G. (2014). Differential memory persistence of odor mixture and components in newborn rabbits: competition between the whole and its parts. Front. Behav. Neurosci. 8:211. doi: 10.3389/fnbeh.2014.00211

Courtiol, E., Lefevre, L., Garcia, S., Thevenet, M., Messaoudi, B., and Buonviso, N. (2014). Sniff adjustment in an odor discrimination task in the rat: analytical or synthetic strategy? Front. Behav. Neurosci. 8:145. doi: 10.3389/fnbeh.2014.00145

Ferdenzi, C., Poncelet, J., Rouby, C., and Bensafi, M. (2014). Repeated exposure to odors induces affective habituation of perception and sniffing. Front. Behav. Neurosci. 8:119. doi: 10.3389/fnbeh.2014.00119

Ferry, B. (2014). The orexinergic system influences conditioned odor aversion learning in the rat: a theory on the processes and hypothesis on the circuit involved. Front. Behav. Neurosci. 8:164. doi: 10.3389/fnbeh.2014.00164

Fitzgerald, B. J., Richardson, K., and Wesson, D. W. (2014). Olfactory tubercle stimulation alters odor preference behavior and recruits forebrain reward and motivational centers. Front. Behav. Neurosci. 8:81. doi: 10.3389/fnbeh.2014.00081

Gelperin, A. (2014). Comparative chemosensory cognition. Front. Behav. Neurosci. 8:190. doi: 10.3389/fnbeh.2014.00190

Huo, Y., Fang, Q., Shi, Y. L., Zhang, Y. H., and Zhang, J. X. (2014). Chronic exposure to a predator or its scent does not inhibit male-male competition in male mice lacking brain serotonin. Front. Behav. Neurosci. 8:116. doi: 10.3389/fnbeh.2014.00116

Li, W. (2014). Learning to smell danger: acquired associative representation of threat in the olfactory cortex. Front. Behav. Neurosci. 8:98. doi: 10.3389/fnbeh.2014.00098

Mandairon, N., Kermen, F., Charpentier, C., Sacquet, J., Linster, C., and Didier, A. (2014). Context-driven activation of odor representations in the absence of olfactory stimuli in the olfactory bulb and piriform cortex. Front. Behav. Neurosci. 8:138. doi: 10.3389/fnbeh.2014.00138

Martin, C., and Ravel, N. (2014). Beta and gamma oscillatory activities associated with olfactory memory tasks: different rhythms for different functional networks? Front. Behav. Neurosci. 8:218. doi: 10.3389/fnbeh.2014.00218

Nicol, A. U., Sanchez-Andrade, G., Collado, P., Segonds-Pichon, A., and Kendrick, K. M. (2014). Olfactory bulb encoding during learning under anesthesia. Front. Behav. Neurosci. 8:193. doi: 10.3389/fnbeh.2014.00193

Pazart, L., Comte, A., Magnin, E., Millot, J. L., and Moulin, T. (2014). An fMRI study on the influence of sommeliers' expertise on the integration of flavor. Front. Behav. Neurosci. 8:358. doi: 10.3389/fnbeh.2014.00358

Pool, E., Delplanque, S., Porcherot, C., Jenkins, T., Cayeux, I., and Sander, D. (2014). Sweet reward increases implicit discrimination of similar odors. Front. Behav. Neurosci. 8:158. doi: 10.3389/fnbeh.2014.00158

Rojas-Líbano, D., Frederick, D. E., Egana, J. I., and Kay, L. M. (2014). The olfactory bulb theta rhythm follows all frequencies of diaphragmatic respiration in the freely behaving rat. Front. Behav. Neurosci. 8:214. doi: 10.3389/fnbeh.2014.00214

Saive, A. L., Royet, J. P., and Plailly, J. (2014a). A review on the neural bases of episodic odor memory: from laboratory-based to autobiographical approaches. Front. Behav. Neurosci. 8:240. doi: 10.3389/fnbeh.2014.00240

Saive, A. L., Royet, J. P., Ravel, N., Thevenet, M., Garcia, S., and Plailly, J. (2014b). A unique memory process modulated by emotion underpins successful odor recognition and episodic retrieval in humans. Front. Behav. Neurosci. 8:203. doi: 10.3389/fnbeh.2014.00203

Sirotin, Y. B., Costa, M. E., and Laplagne, D. A. (2014). Rodent ultrasonic vocalizations are bound to active sniffing behavior. Front. Behav. Neurosci. 8:399. doi: 10.3389/fnbeh.2014.00399

Takahashi, L. K. (2014). Olfactory systems and neural circuits that modulate predator odor fear. Front. Behav. Neurosci. 8:72. doi: 10.3389/fnbeh.2014.00072

Tong, M. T., Peace, S. T., and Cleland, T. A. (2014). Properties and mechanisms of olfactory learning and memory. Front. Behav. Neurosci. 8:238. doi: 10.3389/fnbeh.2014.00238

Torquet, N., Aime, P., Messaoudi, B., Garcia, S., Ey, E., Gervais, R., et al. (2014). Olfactory preference conditioning changes the reward value of reinforced and non-reinforced odors. Front. Behav. Neurosci. 8:229. doi: 10.3389/fnbeh.2014.00229

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 16 January 2015; Accepted: 01 February 2015; Published online: 17 February 2015.

Citation: Sullivan RM, Wilson DA, Ravel N and Mouly A-M (2015) Olfactory memory networks: from emotional learning to social behaviors. Front. Behav. Neurosci. 9:36. doi: 10.3389/fnbeh.2015.00036

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2015 Sullivan, Wilson, Ravel and Mouly. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 16 October 2014
doi: 10.3389/fnbeh.2014.00358

An fMRI study on the influence of sommeliers' expertise on the integration of flavor

Lionel Pazart[1][*][†], Alexandre Comte1,2,3[†], Eloi Magnin2,3, Jean-Louis Millot[2] and Thierry Moulin1,2,3

1Inserm Clinical Investigation Centre 1431, Clinical Investigation Centre, Besançon University Hospital, Besancon, France

2Laboratoire de Neurosciences, (EA-481), University of Franche-Comté, Besancon, France

3Département de Recherche en Imagerie Fonctionnelle, Besançon University Hospital, Besancon, France

* Correspondence: Lionel Pazart, Inserm Clinical Investigation Centre 1431, Besançon University Hospital, 2 Place St Jacques, 25030 Besancon, France e-mail: lpazart@chu-besancon.fr

[†]These authors have contributed equally to this work.

Edited by:
Donald A. Wilson, New York University School of Medicine, USA

Reviewed by:
Daniel W. Wesson, Case Western Reserve University, USA
Wen Li, University of Wisconsin-Madison, USA

Flavors guide consumers' choice of foodstuffs, preferring those that they like and meet their needs, and dismissing those for which they have a conditioned aversion. Flavor affects the learning and consumption of foods and drinks; what is already well-known is favored and what is new is apprehended. The flavor of foodstuffs is also crucial in explaining some eating behaviors such as overconsumption. The “blind” taste test of wine is a good model for assessing the ability of people to convert mouth feelings into flavor. To determine the relative importance of memory and sensory capabilities, we present the results of an fMRI neuro-imaging study involving 10 experts and 10 matched control subjects using wine as a stimulus in a blind taste test, focusing primarily on the assessment of flavor integration. The results revealed activations in the brain areas involved in sensory integration, both in experts and control subjects (insula, frontal operculum, orbitofrontal cortex, amygdala). However, experts were mainly characterized by a more immediate and targeted sensory reaction to wine stimulation with an economic mechanism reducing effort than control subjects. Wine experts showed brainstem and left-hemispheric activations in the hippocampal and parahippocampal formations and the temporal pole, whereas control subjects showed activations in different associative cortices, predominantly in the right hemisphere. These results also confirm that wine experts work simultaneously on sensory quality assessment and on label recognition of wine.

Keywords: fMRI, flavor, expertise, wine, olfaction pathways, taste

INTRODUCTION

Flavor is a crucial subject of study for understanding eating behavior, for the prevention of obesity, overdrinking and other eating disorders, and for the foods and drinks industry. The cortical integration of olfactory and gustatory information could modulate mechanisms involved in food selection and emotional reactions relating to the chemical senses (Fu et al., 2004). Flavor of foodstuffs refers to this combination of sensations perceived inside the mouth, combining taste (savor) and smells (aromas), as well as trigeminal somatosensory perception and thermal perception (Auvray and Spence, 2008; Prescott, 2012). Several previous neuroimaging studies analyzed brain regions activated by intrinsic cues of flavor (Cerf-Ducastel and Murphy, 2001; O'Doherty et al., 2001; Kobayashi et al., 2004; Kikuchi et al., 2005; Boyle et al., 2007a,b) and the convergence of taste and retronasal olfaction was mainly elicited the anterior part of the orbitofrontal cortex (de Araujo et al., 2003; Small et al., 2004; Small and Prescott, 2005). More precisely, flavor integration following retronasal stimulation may involve brain structures like the insula, the frontal operculum and the caudal orbitofrontal cortex (OFC) (Cerf-Ducastel and Murphy, 2001; de Araujo et al., 2003; Small et al., 2004; Small and Prescott, 2005) but also the amygdala, and cerebellum (Cerf-Ducastel and Murphy, 2001).

Other neuroimaging studies confirmed the high effects on taste and flavor perception by different extrinsic cues such as the appearance of the foodstuff, packaging design, brand name, geographical origin, price, subjective flavor preferences (McClure et al., 2004; Plassmann et al., 2008; Kühn and Gallinat, 2013; Okamoto and Dan, 2013; Van den Bosch et al., 2014) and also the simple evocation of the odor or product name (Royet et al., 2013a; Bensafi et al., 2014).

One main factor that might influence the effect of either intrinsic or extrinsic cues could be the strength of the taste/flavor memory associated with the cue (Okamoto and Dan, 2013). Indeed, the extraordinary performance of experts in many matters (chess, bridge, music, wine etc.) raises the question of the origin of their faculties. It is often found that experts and novices use different criteria to categorize domain-specific problems, in that novices use simplistic surface features whereas experts use underlying principles (Vicente and Wang, 1998).

Wine expertise provides an interesting field in which to test theories of skill acquisition since it is generally believed to be based mainly on advanced perceptual skills rather than cognitive ones, such as categorical knowledge or episodic memory (Hughson and Boakes, 2002; Saive et al., 2014). Perceived quality of a wine is dependent on consumers' level of expertise (Sáenz-Navajas et al., 2013). Experience tends to generate idiotypic recollections, to which new wines are compared (Hughson and Boakes, 2002). Accordingly, odor experts who are trained daily can acquire better olfactory sensitivity, and thus olfactory mental imagery capacities develop with practice and do not result from innate skill (Plailly et al., 2012). During the creation of mental images of odors, expertise influences not only the primary olfactory area (piriform cortex) but also the OFC and the hippocampus, regions that are involved in memory and the formation of complex sensory associations, respectively (Royet et al., 2013b; Saive et al., 2014). In these areas, the magnitude of activation was negatively correlated with experience: the greater the level of expertise, the lower the activation of these key regions (Royet et al., 2013b). Nevertheless, in wine expertise several behavioral studies (Brochet and Dubourdieu, 2001; Morrot et al., 2001; Hughson and Boakes, 2002; Parr et al., 2002; Ballester et al., 2008; Brand and Brisson, 2012) have compared wine experts and novices, and surprisingly no difference in olfactory sensitivity was revealed between them. This discrepancy between odor experts and wine experts could be explained by the needed integration of several sensory modalities for wine: sight, orthonasal olfaction, somesthesia, and chemical senses including trigeminal sensitivity, taste, and retronasal olfaction (Brochet and Dubourdieu, 2001; Morrot et al., 2001). Parr et al. (2002) demonstrated that wine experts have similar sensitivity for wine-related components such as tannin or alcohol, and similar odorant naming abilities compared to those of wine novices but superior explicit identification and memory recognition for wine-relevant odorants. For some authors, this greater ability of wine experts to recognize and identify odors is probably due to better semantic knowledge (Hughson and Boakes, 2002), however this remains an open question (Parr et al., 2004; Ballester et al., 2008). In this way, in addition to the structures involved in taste and retro-olfaction, those mechanisms might involve in wine experts a predominance of the left hemisphere, involved in analytic and linguistic treatment, and probably the temporal lobes and hippocampus, involved in episodic and semantic memory, related to previous experiences. In contrast, Parr et al. demonstrated the importance of perceptual skill, namely sensory memory for odorant and trigeminal perception, rather than semantic memory ability, in wine-relevant olfactory expertise (Parr et al., 2004).

To determine the relative importance of different types of memory and sensory capabilities in wine expertise, few studies have been performed with neuroimaging techniques and until present Castriota-Scanderbeg's study might be the only study involving wine experts and novices (Castriota-Scanderbeg et al., 2005).

Castriota-Scanderbeg's study compared via fMRI the brain activations of two groups of 7 wine experts (sommeliers) and 7 novices who received randomly 2 ml bolus of 3 Italian wine or glucose solution via a multi-channel device inserted in the mouth. Sommeliers showed greater activity in the left insula and orbitofrontal cortex than the novices. The principal areas activated in the novices were the primary gustatory cortex and the regions associated with emotional processing (Castriota-Scanderbeg et al., 2005). This study confirmed the involvement of olfactory memory in wine assessment by wine professionals. However, the primary olfactory area (piriform cortex) was not activated either in wine experts or novices, and surprisingly no activation occurred between the wine and glucose solution in mouth, i.e., during “taste phase.” Significant differences were observed only after swallowing a bolus, i.e., during the after-taste phase. Thus, the integration of flavor seems to be delayed contrary to what is usually observed by sommeliers and reserved to expert unlikely results of behavioral experiments.

Our hypothesis is that a specific effect of expertise may be observed early during the taste phase anticipating the after-taste phase, and requiring flavor memory and episodic memory rather than semantic memory. Therefore, we would like to replicate Castriota-Scanderbeg's study with a tasteless comparator to avoid the possible overshadowed effect of glucose solution on the taste part of brain activation. Thus, our study objective was to evaluate brain activity with fMRI during the taste and after-taste phases of wine tasting vs. a tasteless water comparator directly delivered into the mouth of a sample of matched-pair experts and novices tasters in order to determine the relative importance of memory and sensory capacities in wine flavor integration.

MATERIALS AND METHODS

SUBJECTS

We recruited 20 subjects, 10 famous sommeliers (seven men and three women) from France and Switzerland and 10 matched controls. All sommeliers had been active professionals for at least 5 years. Most had received awards such as “Best sommelier in the world,” “Best sommelier in Europe,” “Best sommelier in France,” or “Best sommelier in Switzerland” and were working in prestigious restaurants in either France or Switzerland. Each sommelier was matched with a control subject of the same sex and same age (±5 years) because chemosensory abilities can vary with gender and age (Doty, 1989; Yousem et al., 1999; Brand and Millot, 2001; Wang et al., 2005; Lundstrom and Hummel, 2006). Subjects were all aged between 24 and 67 years. In addition, the control subjects were from the same region as their matched expert in order to limit bias induced by the difference of regional flavor habits in each pair. All subjects were right-handed and non-smokers, as smoking habits can also influence sensory abilities (Katotomichelakis et al., 2007). All subjects underwent a medical examination to screen for MRI contra-indication and for any possible gustatory or olfactory dysfunctions before the study. The protocol was approved by the local ethics committee (Comité de Protection des Personnes CPP Est II) and declared to the national authority (N° UF: 1013; DGS 2006/0494). Written informed consent was obtained from all participants.

CHOICE OF STIMULI AND STIMULUS DELIVERY

Two wines (one white chardonnay variety, Arbois 2004, and one red “black pinot” variety, Côte du Jura 2006) were chosen for their good sensorial qualities from an expert point of view by an experienced sommelier (C. Menozzi) who was not participating in the study. He also tasted different types of water (including distilled water and physiologic serum) and deemed the local water to be the most suitable for control (in terms of salinity, tastelessness, and low minerality) and rinsing. A multi-channel custom-built gustometer was used to deliver the wine and water to the subjects (Andrieu et al., 2014). This device comprises three reservoirs, one for each type of liquid, and a computer-controlled pneumatic distributor, which dispatches air toward several exits. Each reservoir has its own opaque polythene tube, which transports the liquid toward the subject's mouth. The bolus was delivered in two stages: (1) the pneumatic distributor injected air into the appropriate reservoir in order to push the required volume of liquid into the tube; (2) air was injected into the connected tube, pushing the liquid into the subject's mouth. The bolus (2 ± 0.11 ml) was delivered in 0.34 ± 0.06 s. The three tubes were contained within a larger silicon tube (10 mm exterior diameter). Consequently, the subject only felt one tube in their mouth and was unable to see which liquid was being delivered.

Before undergoing MRI, each participant tested the device used for the administration of wine samples with the reference solution (2 ml samples of water) to familiarize him or herself with the experimental tasting procedure. This involved lying in a horizontal position and limiting any lip or jaw movements. Subjects were asked to swish water in their mouths, just as they were required to do with wine or water during the scan session. The same paradigm was used for the acquisition of images.

EXPERIMENTAL PROTOCOL

To avoid confusion between “taste as in common usage,” and “taste as a unimodal sensation,” hereafter we will follow the convention in chemosensory studies where “taste” refers to a unimodal sensation.

During the first phase (taste), subjects had to swish either wine or water (bolus of 2 ml) in their mouth for 7 s. At the end of the taste phase, an auditory cue prompted the tasters to swallow the liquid, signaling the beginning of the second phase (after-taste), which lasted 13 s. Each 2 ml bolus of wine was repeated twice before rinsing. There was a rest period of 15 s after each rinsing, resulting in a block of 1 min 15 s ([7 + 13 s] × 3 + 15 s). Each subject performed five blocks. The durations of the taste and after-taste periods were the same as in a previously validated study (Castriota-Scanderbeg et al., 2005). During the scan, subjects were asked to keep their eyes closed. Participants received no information on the number of wines chosen or their characteristics. In addition, the order in which the red and white wines were delivered was randomized according to test at least once each variety of wine and was therefore unknown to the subjects.

MRI DATA ACQUISITION

The functional MRI study was performed on a 3-Tesla (GE Healthcare Signa HDxt, Milwaukee, WI) MR system with a standard 40 mT/m gradient using blood–oxygen level-dependent (BOLD) fMRI. Foam cushions were used to minimize head movements within the coil. The experiment began with the acquisition of a high-resolution, T1-weighted, 3-dimensional anatomical scan (BRAVO sequence). This scan was acquired in 134 slices with 0.47 × 0.47 × 1.2 mm resolution. Functional images were then obtained parallel to the anterior-posterior commissure line, covering the entire cerebrum (30 slices) using an echo planar imaging (EPI) sequence (slice thickness = 4.5 mm; TR = 2500 ms; TE = 35 ms, matrix = 128 × 128; FoV = 256 mm; Flip Angle = 90°; phase acceleration factor = 2; auto-shimming).

FMRI DATA ANALYSIS

Image time-series analysis was performed using BrainVoyager QX 2.1 (Brain Innovation, Maastricht, The Netherlands). The time-series were corrected for slice acquisition time, realigned with their corresponding T1 volumes, warped into standard space (Talairach and Tournoux, 1988), re-sampled into 2 mm isotropic voxels, motion-corrected using Levenberg-Marquarts's least square fit for six spatial parameters, highpass-filtered for removal of low frequency drifts, corrected voxel-wise for linear drifts, and spatially smoothed using a 5-mm full-width at half-maximum Gaussian kernel.

The general linear model (GLM) was computed from the 20 z-normalized volume time courses. For all stimuli of interest, i.e., rest period, taste period and after-taste period, box-car time courses with a value of 1 for the stimuli of interest and values of 0 for the remaining time points were convolved with a theoretical hemodynamic response function (Boynton et al., 1996) and were entered as predictors into the design matrix of the study. Contrast analyses were based on random effects GLMs of the z-normalized volume time courses.

Analyses of the taste and after-taste periods were firstly performed for the entire group (20 subjects) using a statistical threshold of q(FDR) < 0.01 corrected for multiple comparisons. A minimum cluster size of 48 mm^3 was set. As suggested by Zald and Pardo (2000) for controlling in-mouth non-gustatory factors, we considered the water after swallowing period as a reference for the contrasts of wine vs. water for both periods.

The same analyses were then performed for each group separately (experts and controls). As the number of subjects in each sub-group is half the entire group, another statistic was chosen.

A cluster size threshold yielding the equivalent of a whole-brain corrected for a multiple comparison significance level of P < 0.05 was used after voxel-wise thresholding at P < 0.005 (uncorrected). The BrainVoyager Cluster-Level Statistical Threshold Estimator plug-in estimating the overall significance level by determining the probability of false detection through Monte Carlo simulation was used (with 10,000 Monte Carlo iterations).

Finally, for both the taste and after-taste periods, a group comparison was carried out to identify the brain areas affected by the level of expertise in wine appreciation and perception. A statistical extent threshold of P < 0.05 corrected for multiple comparisons after a voxel-wise thresholding at P < 0.005 was used.

RESULTS

Table 1 shows the cerebral activations for the whole sample of subjects (experts and controls) obtained for the contrast “wine minus water” during taste phase and after-taste phase. During the taste phase, activations were found in the insula, the frontal lobe (bilateral motor area and right superior and dorso-lateral prefrontal cortex), pallidum, left parahippocampic gyrus and left thalamus. During the after-taste phase, activations were again present in the insula and in various areas of the frontal lobe including the orbito-frontal cortex.

Table 1. Wine minus water contrast for all subjects (simple main effects).

When considering experts and controls separately, the same contrasts revealed that certain activations were specific to one group of subjects in both the taste and the after-taste periods (Table 2). Consequently, further analyses focused on the contrasts between experts and controls during the wine taste phase and the wine after-taste phase, which was the aim of the study.

Table 2. Wine minus Water contrast by group (simple main effects).

Table 3A shows the activated regions when contrasting experts and control subjects during the wine taste phase. When contrasting experts minus control subjects, activations were observed in the brainstem (left bulbo-pontic junction extended to left trigeminal nucleus), the cerebellum and subcortical areas (locus niger, globus pallidus). Cortical activations were present in the hippocampi, parahippocampal gyri, amygdalae, periamygdal cortex (entorhinal and perirhinal cortex), temporal and occipital lobes and in the right anterior insula. There were more widespread bilateral activations of the parietal lobes in control subjects compared to experts. During the wine taste phase, control subjects activated 18 regions vs. 9 for experts.

Table 3. Group comparison analysis during wine taste and wine after-taste.

Table 3B shows the activated regions when contrasting experts and control subjects during the wine after-taste phase. Experts exhibited fewer cerebral activations (in terms of number and size of clusters) compared to controls. These activations involved the right temporal lobe and left hippocampus. In the reverse contrast (control subjects minus experts), activations involved the frontal, temporal and parietal (postcentral gyrus) cortices and the anterior insula. Subcortical activations were restricted to the caudate nucleus.

DISCUSSION

This study was designed to describe the differences in brain activity in wine experts compared to control subjects, especially during wine tasting as stimuli compared to water, in order to confirm the influence of expertise on flavor integration. In addition, we would like to identify the type of memory used by experts in order to demonstrate experts requiring flavor memory and episodic memory rather than semantic memory. As expected, we observed specific areas activated in the experts' brains during all phases of wine tasting. Structures involved in the sensory and cognitive tasks of the expertise-related process were activated either during the taste phase, corresponding to gustatory and trigeminal sensations, and the after-taste phase, corresponding to retro-olfactory sensation. In addition, an unexpected and interesting result during the taste phase was that specific brainstem activation was observed in the expert group, suggesting that expertise can modify sensory treatment in addition to cortical cognitive processes.

ABILITY OF THE PARADIGM TO FOCUS ON FLAVOR

Choice of reference stimulus

Direct comparison between our results and those from the only neuroimaging study on wine experts vs. novices, by Castriota-Scanderbeg et al. (2005), should be interpreted with caution because of the difference of wines and reference stimulus in both studies. Instead of using glucose as the reference stimulus, we chose neutral water as the reference stimulus used currently in neuroimaging (Zald and Pardo, 2000) and wine behavioral experiments. The authors of this previous study argued their choice in order to adequately control the sweet components of the gustatory stimulus (but only one of their three tested wines was sweet) and to avoid somatosensory and motor components of the task (Castriota-Scanderbeg et al., 2005). Accordingly, the use of a glucose drink as reference stimulus might have overshadowed the taste part of brain activation while a sweet drink might present similar taste and trigeminal characteristics as tested wines. Furthermore, there is a great consumer preference for sweet wines in many countries and wine experts seem to prefer wines with less added glucose than the novices (Blackman et al., 2010). Sweet drinks might therefore appear more “pleasant” for novices than for experts and introduce another bias since a subjectively pleasant stimulus would have preferentially activated the medial OFC whereas an unpleasant stimulus would have preferentially activated the lateral OFC (Rolls et al., 2003). Moreover, comparing activations in response to a sweet solution or a bitter solution, tasting sweet solution caused greater activations in the OFC whereas tasting a bitter solution resulted in greater activations in the cingulate cortex, operculum and precentral gyrus (Van den Bosch et al., 2014). So in order to minimize bias, we chose exclusively dry wines for stimuli and a tasteless and odorless comparator (neutral water).

Isolation of in-mouth stimulation

We replicated the design of Castriota-Scanderbeg's study (Castriota-Scanderbeg et al., 2005) using a multi-channel custom-built gustometer to deliver the wine or comparator directly into subjects' mouths, with identical volume (2 ml) and duration of bolus stimuli, the same frame of run and similar fMRI analysis. This design tries to isolate the influence of in-mouth sensations on the flavor integration without orthonasal olfactory stimulation or external cues. Similar designs have been adopted in numerous neuroimaging studies on gustation (Kühn and Gallinat, 2013; Van den Bosch et al., 2014) despite well-known bias due to the supine conditions of the experiment, the lowering perceived intensity functions for taste stimuli affected by the stimulus delivery technique in the MRI scanner (Haase et al., 2009) and by the small amount of stimulus (2 vs. 5–20 ml for sip and spit techniques of behavioral experiments).

In the case of gustatory stimulation, we cannot exclude the possible contribution of other factors, since intraoral stimuli involve several types of processing in addition to gustation: olfaction, somatosensation, and oral movements. This complex interaction makes it difficult to identify the brain regions selectively processing gustation (Kobayashi, 2006).

Rozin (1982) first suggested that olfaction is a dual sense modality because it contributes to the perception of external and internal substances (via orthonasal and retronasal olfaction, respectively). Orthonasal perception can identify objects at a distance, and retronasal perception contributes to flavor and hence food identification in the mouth. These two “olfactory senses” differ physiologically in terms of delivery of odors to the olfactory epithelium (Pierce and Halpern, 1996), but also in terms of connections between senses and cognitive impact. Small and Prescott (2005) have demonstrated that routes of delivery produced differential activations in the insula/operculum, amygdala, thalamus, hippocampus, and caudolateral orbitofrontal cortex in orthonasal > retronasal and in the perigenual cingulate and medial orbitofrontal cortex in retronasal > orthonasal in response to chocolate, but not lavender, butanol, or farnesol, so that an interaction of route and odorant may be inferred. These findings demonstrate differential neural recruitment depending upon the route of odorant administration (orthonasal or retronasal) and suggest that its effect is influenced by whether an odorant represents a food or not (Small and Prescott, 2005). Small and Prescott (2005) explain these observations by the fact that taste perception is almost always accompanied by olfactory and oral somatosensory perception in the context of eating, whereas olfaction often occurs separately outside the context of eating. Thus, it would appear logical, in the identification of food in the mouth, to combine the food's qualities (savor, palatability and aromas) into a unitary perception (Prescott, 1999). Wine tasting in the mouth typically involves simultaneous gustatory, trigeminal and retro-olfactory information (Brochet and Dubourdieu, 2001). Wine could be a very good model to test the ability of people to perform this convergence of sensorial information in flavor integration. Like Castriota-Scanderbeg, we adopt the terminology of “taste phase” and “after-taste phase” to indicate the period before and after swallowing instead of dissociating responses to taste and smell stimuli. An issue is the succession of both phases (taste and after-taste). In this experiment (and in every experiment studying these two distinct periods) it is unavoidable that both phases are consecutive. In fact, flavor compounds are progressively released from the wine during the mouth process before swallowing particularly with the impregnation by saliva (Salles et al., 2011). So, even if the main perception during the taste phase remains from the gustation and trigeminal stimulation, there is an overlapping with the beginning of retronasal olfaction stimulation. Secondly, mouth movements and swallowing play a role in the enhancement of retronasal odor perception analogous to that played by sniffing in orthonasal perception (Burdach and Doty, 1987). This may lead to a diminishing of observed effects during the after-taste phase due to retro-nasal stimulation (even weak) during the taste phase, and due to memory processes (particularly with experts). Accordingly, the after-taste phase corresponds mainly to retronasal olfactory stimulation with a residual part of the preceding taste stimuli. In addition the 7 s taste phase duration may also lead to the situation where a slow response to the first process can fall into the time window of the after-taste phase and be interpreted as reflecting this second process. However, there is little chance that areas of interest (that is, those involved in olfaction, gustation and memory), if recruited during the taste phase, start to be active only during the after-taste phase. As such, responses even time-locked to the two events are still convoluted. One has then to keep in mind this issue when interpreting results with such paradigms.

In order to secure the exclusivity of in-mouth stimulation, no information was given about the color, types or number of wine since exposure to visual or verbal semantic odor/taste information alone could activate the piriform cortex, the amygdala or the insula (Kobayashi et al., 2004; González et al., 2006; Bensafi et al., 2014).

Nevertheless, wine tasting typically involves three phases contributing to a final synthesis of flavor analysis: firstly, wine tasters normally appreciate the sight of the wine and mainly the color, activating occipital visual areas, secondly they sniff the wine and orthonasal olfaction impacts on gustation and olfactory areas, finally they absorb a small amount of wine in their mouth, trill the wine, aerating it and allowing the flavors to be perceived by retro-olfaction before being spat. In our design, we shunted sight and ortho-nasal olfaction and so flavor integration is not complete.

SENSORY/FLAVOR INTEGRATION

Taste phase and in-mouth sensations

The main difference between our results and Castriota-Scanderbeg's study is the presence of taste phase activations, especially brain stem responses which suggest that expertise also impacts on basic taste processing. The modification of the paradigm by using water as a control explores this part of flavor processing that was possibly overshadowed by glucose control in Castriota-Scanderbeg's study, as previously explained.

Although no significant differences emerged between wine and glucose either in controls or in sommeliers during the taste period in Castriota-Scanderbeg's study, we found significant cerebral activations in the insula, frontal lobe (bilateral motor area and right superior and dorso-lateral prefrontal cortex), cerebellum, pallidum, left parahippocampic gyrus and left thalamus for the whole sample of subjects (Table 1) obtained for the contrast “wine minus water” during taste phase. These regions correspond to an area commonly activated by gustatory stimulation (Kobayashi, 2006): the superior frontal, middle frontal, inferior frontal, precentral, and postcentral gyri, insula/frontal operculum, inferior parietal lobe, and cerebellum and in addition to these regions, the thalamus and the region including the putamen.

Our study showed that the volume of activated regions in the insula/frontal operculum during gustatory stimulation was higher in the left hemisphere than in the right. Although most neuroimaging studies have shown that the right insula is more intensively activated than the left (Cerf-Ducastel and Murphy, 2001; Small and Prescott, 2005; Kobayashi, 2006) some studies have shown that activation in the left insula could be equal to or more dominant than the right insula (Kinomura et al., 1994; Francis et al., 1999).

There is no activation of the OFC during the taste phase either for experts or novices (Table 2), but a passive gustatory stimulus may not always be sufficient to activate the region (Kobayashi et al., 2004).

When considering the wines as stimuli and comparing experts to control subjects (experts minus control subjects, Table 3), there appeared to be involvement of the bulbo-pontic junction and trigeminal nuclei in the brainstem, suggesting specific chemosensory information processing in experts than controls. Motor activity induced by swallowing may have engaged brainstem activation. However, both groups had to swish and swallow the bolus of wine and no activations were found in the motor cortical area for this contrast. Although rarely mentioned, this result highlights the importance of trigeminal sensitivity in addition to gustatory perception in wine analysis. Indeed, several descriptors used in wine tasting (such as astringent, bitter, spicy, sharp or sweet) are typically trigeminal-type descriptors (Laska et al., 1997). Trigeminal activations were not mentioned by Castriota-Scanderbeg et al. (2005), which may have been due to the sweet type of reference stimuli and one of the three tested wines. It is somewhat surprising to find different sensory processing in experts compared to controls in this first stage of brainstem integration of the stimuli.

Activity was observed in the amygdala, enthorhinal and perirhinal cortices and anterior insula. Activation of the anterior insula is congruent with the taste phase as this area is involved in the integration of multimodal input such as olfactory (Sobel et al., 2003), gustatory (Small et al., 1997, 1999) and trigeminal (Lombion et al., 2009) stimuli. Its role in the hedonic evaluation of chemosensory stimulations (Fulbright et al., 1998; Small et al., 2001) and discrimination processes (Bengtsson et al., 2001) has also been suggested. Activity in the amygdala may have corresponded to the selective perception of olfactory stimuli via the retronasal pathway which started in experts before swallowing, since its activation strongly characterizes olfactory processes (Zatorre et al., 1992; Sobel et al., 2003). Moreover, psychophysical investigations in humans and behavioral work in animals have shown that the taste system plays an integral role in odor processing. While there is evidence to support the anticipation of taste-like properties by odors, there have been few reports of the acquisition of odor-like properties by taste (Prescott, 2012). In animals, some authors have demonstrated that taste input affects olfactory processing via a specialized “association” area (Desgranges et al., 2010). However, other works in conscious rats have shown that the gustatory system directly influences olfactory processing in the primary olfactory cortex (Maier et al., 2012). These results identify the posterior olfactory (piriform) cortex as a likely site for gustatory influences on olfactory processing.

We were surprised by the apparent lack of activation of the piriform cortex in the study by Castriota-Scanderbeg, in novices and experts, in neither the taste phase nor the after-taste phase (Castriota-Scanderbeg et al., 2005). Surprisingly, we found a similar absence in our study. However, as the threshold we used was maybe too strict; to enhance the piriform cortex we performed a ROI analysis centered on it. At a low threshold an activation is observed in the right piriform cortex for the contrast wine minus water for all subjects during the after-taste phase. Peak is obtained at 25, 9, −14 (Talairach coordinates), t = 2.24, p = 0.037. A cluster size of 11 voxels of 1 mm^3 is observed when the threshold is set at p = 0.05 (uncorrected), and a cluster size of 2 voxels of 1 mm^3 at p = 0.04 (uncorrected). With such a small activated volume combined with such a poor statistic, we could consider that there is no activation of the piriform cortex in our study.

The piriform cortex is a small structure in humans and its proximity to the insular lobe may render identification of activations in this area difficult. This area corresponds to the retro-nasal olfactory process mainly during the after-taste phase. Although animal studies have identified the posterior olfactory (piriform) cortex as a likely site for gustatory influences on olfactory processing (Maier et al., 2012), the piriform cortex may respond preferentially to orthonasal odors, reflecting its role in olfaction, enhanced by sniffing (Zatorre et al., 1992; Sobel et al., 1998). Piriform activation by an odorant stimulus administered in solution form into the mouth (retronasal olfactory pathway) was found inconsistently in the literature (Small and Prescott, 2005). The reference study by Cerf-Ducastel and Murphy (2001) showed activation of right piriform cortex in the group analysis of the 6 involved subjects, but there was inconstancy at an individual level with activation of the left piriform in one subject, the right piriform in another, and both sides for a third subject, i.e., in less than half of the sample, and activations were found only with one stimulant (citral) among four. Some authors discuss in detail the inconstancy of the activation of this region in neuroimaging studies of olfaction (Small and Prescott, 2005). The inconstancy of activation of primary olfactory structures could be due to many reasons including the anatomical variability of the inferior frontal and lateral temporal areas, technical conditions, type of stimulus used (odorant in aqueous solution), the single retronasal pathway and the short process with adaptation and/or habituation effects. In addition, the initial amplitude of the activation decreases from block to block when using a block paradigm (Sobel et al., 2003). In this way, our results were similar with previous studies that showed that the piriform cortex would not be activated during the taste and flavor integration phase (Small et al., 1997; O'Doherty et al., 2001). This result could also be explained by the results of Small and Prescott (2005) who demonstrated differential neural recruitment depending on the route of odorant administration (ortho or retronasally) and by some behavioral experiments exploring the extent to which the aroma or non-volatile fractions are responsible for the overall flavor differences of wines perceived in-mouth (Sáenz-Navajas et al., 2012; Villamor and Ross, 2013). A study performed under three different conditions (nose-close, retronasal perception only and retro- and orthonasal perception) have clearly shown that, globally, aroma perception is not the major driver of in-mouth sensory perception of red wine, which is undoubtedly primarily driven by the perception of astringency (Sáenz-Navajas et al., 2012). So we can attribute the absence of piriform activation mostly to the technical condition of supine administration of a small bolus of odorant in solution via a tube in mouth by shunting the orthonasal pathway. Perhaps wine experts can dissociate in the brain the three classical assessments of a wine (sight, orthonasal olfaction and then in-mouth sensations) before they make a synthesis.

After-taste phase and flavor integration

Firstly, our results in all subjects (experts and controls), showed activations in the insula, the operculum and the orbito-frontal cortex (Figure 1), which are all involved in taste/odor integration (Small et al., 1997, 2007; Rolls, 2008; Bender et al., 2009) as the key nodes of the “flavor network” (Small and Prescott, 2005). These regions represent the primary, secondary, and tertiary gustatory areas, and secondary and tertiary olfactory areas of the brain. Nevertheless, the piriform cortex was not activated during after-taste phase in our study, nor in the study by Castriota-Scanderbeg.

FIGURE 1. Example of activations involving flavor integration (from Table 2). Visible activations are left pars opercularis and left and right insula for controls during the taste phase, right and left anterior insula and right orbitofrontal cortex for controls during the after-taste phase, left insula for experts during the taste phase, orbitofrontal cortex and the associative occipital cortex for experts during the after-taste phase.

Common activations during the after-taste phase that are observed in both studies are the greater involvement of the right side in the control group while bilateral and especially left frontal activation can be observed in the expert group. This suggests that our paradigm is effective in exploring expertise of flavor integration. Castriota-Scanderbeg et al. (2005) found in sommeliers a higher activation in the anterior insular, which is presumed to be involved in the integration of olfaction and gustation, as well as in the LPFC areas. In our study, experts were mainly characterized by recruitment of the hippocampal formation, regions of the temporal lobe and associative visual cortex. This could be explained by the persistence of memory processes. Neither the dorsolateral prefrontal cortex nor the orbito-frontal or anterior insular cortices were recruited during the after-taste phase in experts, as was the case in the study by Castriota-Scanderbeg et al. (2005). In their study, the integration of sensory processes appeared to continue during the after-taste period whereas, in our study, only memory structures persisted during the second phase. This difference between the two studies may be explained by the methodological designs, particularly the use of glucose as a reference stimulus.

Control subjects showed predominant activations in the right hemisphere. Temporal activations were more numerous than in the previous phase but no hippocampal or parahippocampal activations were observed. Frontal, parietal and occipital regions were involved to a lesser degree than previously and may have corresponded to the persistence of ineffective retrieval strategies. In control subjects, the anterior insula was only activated in the after-taste phase, whereas in experts, sensory integration-related regions were no longer activated during this phase. This result indicates that experts showed a more immediate and targeted sensory reaction to wine stimulation than control subjects (Figure 2).

FIGURE 2. Beta-value averages for the areas involved in flavor, based on Table 2. For each area from a group, the average values are given for the experts Group (red) and the Controls group (blue). The error bars indicate standard error. All regions of interest are from the Controls group, except for the two marked with * which are taken from the experts.

Olfactory and gustatory pathways appeared to be reciprocally connected. Asymmetrical involvement of the gustatory and olfactory regions in flavor processing is also supported by perceptual experiences and neuroimaging studies of taste/odor integration. The “flavor network” model involves multisensory integration, and the system can be subsequently engaged by unimodal stimulation (Prescott, 2012; Small, 2012).

Finally, it is proposed that there is asymmetric contribution of olfaction and gustation to flavor, such that only retronasally perceived odors (via the mouth) and odors previously experienced with taste (irrespective of mode of delivery) engage the flavor system (Prescott, 2012). To improve the understanding of the expertise on wine flavor integration, further studies should take into account the three phases of wine tasting, and neuroimaging protocol design should integrate the sight of wine, then orthonasal stimulation and finally in-mouth sensations. Our protocol, as well as Castriota-Scanderbeg's study, could be too restrictive, and this could explain the lack of important activations such as the piriform cortex activations.

However, and despite limitations of our study, our results are compatible with clear evidence for the overlapping and integration of gustatory, tactile and olfactory inputs in the insular cortex (Small, 2012). The core flavor percept is then conveyed to upstream regions in the brainstem and thalamus, as well as downstream regions in the amygdala, orbitofrontal cortex and anterior cingulate cortex to produce the rich flavorful experiences that guide our feeding behavior (Small, 2012).

PARTICIPATION OF VARIOUS MEMORIES

Although olfaction is the least easily categorizable and recognizable sensory modality (Richardson and Zucco, 1989), sommeliers have the unique ability to verbalize descriptors with all their senses. Sensory perceptions enable experts to provide an analytical description by referring to a large corpus of previously memorized and categorized data (Vedel et al., 1972), while novices cannot find the vocabulary to describe their olfactory and gustatory sensations.

Experts, but not novices, can write descriptions that they themselves or other experts can later match to the appropriate samples (Lawless, 1984; Solomon, 1990). A lot of behavioral studies tend to attribute the greater ability of wine experts to recognize and identify wine-relevant odorants to better semantic knowledge (Parr et al., 2002; D'Alessandro and Pecotich, 2013), and authors suggest that wine expertise may be more cognitive, rather than perceptual, expertise (Ballester et al., 2008). These mechanisms in wine experts might imply a predominance of the left hemisphere involved in analytic and linguistic treatment and probably the temporal lobes and hippocampus involved in episodic and semantic memory related to previous experiences. Other studies suggest the importance of perceptual skill, namely sensory memory for odorant and trigeminal perception, rather than semantic memory ability, in wine-relevant olfactory expertise (Parr et al., 2004).

In our study, numerous activations were observed in areas involved in memory processes, predominantly in the left hemisphere (Figures 3, 4). Hippocampal and parahippocampal activations were observed during retrieval tasks eliciting episodic or autobiographic memory or familiarity (Stark and Squire, 2001; Spaniol et al., 2009). Activity in the anterior temporal lobe corresponds to semantic memory (Rogers et al., 2006; Visser et al., 2010a,b) and left side involvement may particularly characterize the verbal knowledge used by experts when describing and labeling the wine in the recognition task.

FIGURE 3. Example of activations involving memory (from Table 3) for the contrast experts minus controls. Activations in green are obtained during the taste phase; those in orange are obtained during the after-taste phase. Visible activations are both in the amygdala/hippocampus complex (taste phase), parahippocampal gyri (right and left for the taste phase, right for the after-taste phase), occipital associative cortex (right during the taste phase, left during the after-taste phase).

FIGURE 4. Beta-values of the contrast experts minus controls calculated for the regions involved in memory. Two conditions are concerned, in green for the wine and in blue for water. The error bars indicate standard error. The regions marked with # are activated during the after-taste phase, the others during the taste phase.

Bilateral activation of the occipital cortex (associative area) may also have been related to mental imagery. Activations of the occipital gyri were also noted by Royet et al. (1999) in judgments of edibility of different odorants.

In some professional activities and hobbies, tasks requiring expertise are supported by specific sensory and cognitive skills. Cerebral imaging studies have provided insight into the adaptive cerebral networks underlying these abilities. With expertise, an “economic” mechanism may result in enhanced efficiency, reduced effort and increased spontaneity (Hund-Georgiadis and von Cramon, 1999; Maguire et al., 2000; Lotze et al., 2003; Plailly et al., 2012). Accordingly, during the after-taste phase, fewer activations are observed in experts vs. novices and mainly in episodic and semantic memory network (temporal pole, hippocampus and parahippocampal gyrus) and mental imaging (occipital associative area) suggesting that experts classify and compare this sensorial stimulus with their own episodic experience and semantic knowledge. The comparison of control minus expert did not show specific activation of the olfactory or gustatory structures during taste phase and then only insula during the after-taste phase suggests a slower and incomplete analysis of the stimulus. During the after-taste phase diffuse activation probably suggests an ineffective retrieval strategy for the novice. For the novice, the treatment requires deeper thinking and passes very quickly to a high level in the cortical areas of the brain, using a much more diffuse and therefore less specific network. Unable to recognize or recall experts' knowledge, they quickly use their episodic memory (Tulving, 1995). They try to associate taste and perceived memories of places and people, in order to contextualize and identify it, and associate it with emotion. From an information processing perspective, this would suggest that experts could have more attention (working memory capacity) to direct to the task at hand, unencumbered by the associated semantic and affective input (Parr et al., 2004).

The hypothesis on memory mechanisms involved in experience should be explored further by time-series analysis methods such as Dynamic Causal Modeling or Granger Causality Analysis (Seth et al., 2013) to better understand the dynamic and interaction of the different kind of memory during wine flavor analysis.

ROLE OF EXPERTISE

The perceived quality of a wine is dependent on consumers' level of expertise (Sáenz-Navajas et al., 2013). Wine tasting expertise involves advanced discriminative and descriptive abilities with respect to wine. Cortical and brainstem activations showed two different and complementary mechanisms of wine expertise: a perceptive mechanism of modulation of the afferent input of information corresponding to the activated gustatory and trigeminal brainstem structures, and specialized cognitive analysis, with focalized cortical activations especially in the left structures involved in memory, language and chemosensory analysis.

A sommelier can distinguish a subtle difference of taste in wine by training their ability to integrate information from gustatory and olfactory senses with past experience.

During the wine tasting phase, control subjects showed fewer but larger activated regions than experts. In control subjects, these activations predominantly occurred in the right hemisphere, and were widespread in the parietal, occipital and frontal cortices. Frontal activations included the frontal operculum which is, like the above mentioned adjoining anterior insula, a putative primary taste cortex (Rolls and Scott, 2003; Pritchard and Norgren, 2004). It is also considered to be a secondary cortex related to odor memory (Savic, 2002). Right occipital activation including the associative visual cortex and parieto-occipital junction was found. Once again, we can hypothesize that this corresponds to mental imaging of past wine tasting experiences and of terms used to describe the taste of wine (fruity for example) or even its color (Qureshy et al., 2000). These associative visual areas were more activated in controls than in experts (in terms of cluster size). This could have been because most of this information corresponded to the experts' semantic knowledge (especially verbal), making them less likely to need to refer to visual images.

During after-taste phase, experts were mainly characterized by recruitment of the hippocampal formation, regions of the temporal lobe and associative visual cortex (Figure 4). This could be explained by the persistence of memory processes. Neither the dorsolateral prefrontal cortex nor the orbito-frontal or anterior insular cortices were recruited during the after-taste phase in experts, as was the case in the study by Castriota-Scanderbeg et al. (2005). In their study, the integration of sensory processes appeared to continue during the after-taste period whereas, in our study, only memory structures were persistent during the second phase. This difference between the two studies may be explained by the methodological designs, particularly the use of glucose as a reference stimulus. Control subjects showed predominant activations in the right hemisphere. Temporal activations were more numerous than in the previous phase but no hippocampal or parahippocampal activations were observed. Frontal, parietal and occipital regions were involved to a lesser degree than previously, but again this widespread recruitment of cerebral areas might correspond to the persistence of ineffective retrieval strategies. In control subjects, the anterior insula was only activated in the after-taste phase, whereas in experts, sensory integration-related regions were no longer activated during this phase. This result indicates that experts showed a more immediate and targeted sensory reaction to wine stimulation than control subjects. This analysis delay in the control subjects is a logical consequence of their level of expertise. Similarly, Plailly et al. (2012) demonstrated that the right anterior insula is more activated in students than in professionals in perfumery during odor imagery tasks.

EXTERNAL VALUE FOR OTHER ALCOHOLIC BEVERAGES

The discrepancy of our results with the previous ones from Castriota-Scanderbeg raise questions about the role of expertise during the initial taste phase which should be explored by further studies using a direct comparison between water, glucose solution, salty solution, sweet wine and dry wine to improve the understanding of the expertise of flavor integration and the modification induced by those factors. Otherwise, one might think that our results are largely generalizable to different test situations of alcoholic beverages comparing experts and novices. The involvement of brain structures involved in memory is indeed expected in experts regardless of the type of beverage, as well as the participation of different associative cortical areas in novice subjects. However, the specific compounds and sensory qualities of wine can marginally change significant differences (Villamor and Ross, 2013). The technical conditions of taste (taste phase) and olfaction (after-taste phase) may have some effects (Sáenz-Navajas et al., 2012) and because the same brain areas are not equally concerned whatever the flavor (sweet, bitter, sour) or aroma (Rolls et al., 2003; Small and Prescott, 2005), special attention should focus on different positive hedonic valence such as dry or sweet wine. Slightly different locations of the orbitofrontal cortex may be suspected in the case of strong bitterness such as beer relative to sweet flavors (Small et al., 2007). Trigeminal sensations during the test phase can however vary greatly (depending on alcohol content, tannin, astringency, pH…) and therefore recruit more or less the secondary somatosensory cortex S II (Bensafi et al., 2008; Billot et al., 2011; King et al., 2013).

CONCLUSION

This study describes the differences in brain activity of sommeliers compared to “naive” subjects during a blind taste test of wine samples as stimuli relative to water, in order to confirm the influence of expertise on the integration of flavor.

The present study was based on different stimuli compared to the only previous study with sommeliers and used a tasteless reference. Expertise impacts basic taste processing, and when comparing expert minus control, during the taste phase, early sensorial and hedonic structures (trigeminal nucleus, amygdala, insula) and familiarity structures (parahippocampal gyrus) are activated suggesting early analysis of the stimulus. Interestingly, brain stem responses have been observed in experts during this taste phase. During the after-taste phase, the experts showed greater activation in the left orbitofrontal cortex, left insular cortex and dorsolateral prefrontal cortex. Our results are consistent with previous studies, particularly in terms of the lateralization of the expertise-related process (predominantly restricted to the left hemisphere in experts). As suggested, the leftward specialization occurs at the expense of normal rightward activity.

Our study also revealed more involvement in experts of hippocampal, parahippocampal, anterior temporal regions and associative occipital area associated with different types of memory, suggesting that wine experts probably process by similitude to try to recognize all characteristics (country origin, cepage, “appellation,” millesime) of the wine, and further analyses such as Dynamic Causal Modeling or Granger Causality Analysis are needed to deeply understand the chronological functioning of brain networks of wine experts.

More generally, our results indicate that wine experts showed a more immediate and targeted sensory reaction to wine stimulation than control subjects. The influence of expertise on flavor integration may mainly comprise quicker sensorial integration with an economic mechanism reducing effort and increasing efficacy. Experts seem to also activate sensory memory and episodic memory as well as working memory and semantic memory. These results confirm that wine experts work simultaneously on sensory quality assessment and on label recognition of wine. To improve the understanding of the effect of expertise on wine flavor integration, further studies should take into account the three phases of wine tasting, and neuroimaging protocol design should integrate the sight of the wine, then orthonasal stimulation and finally in-mouth sensations.

ACKNOWLEDGMENTS

We would like to thank Holly Sandu (Medical and scientific translator) for her assistance in translating and proofreading our manuscript. We would like to thank Christophe Menozzi (2001, Maître Sommelier de France UDSF) for his contribution to the establishment of the experimental scheme and the choice of wines. We would like to thank the wine domains that provided the wine: Arbois 2004, Chardonnay “Les Brulées” Domaine St Pierre, Mathenay, FR; Côte du Jura 2006, Pinot noir “Cuvée dédicacée,” Benoît Badoz, Poligny, FR. We wish to give special thanks to the “Conseil Regional de Franche-Comte” for its financial support allowing the use of the MR-3T Scanner for this research.

REFERENCES

Andrieu, P., Bonnans, V., Meneses, J., Millot, J. L., Moulin, T., and Gharbi, T. (2014). A modular, computer-controlled system for olfactory stimulation in the MRI environment. Behav. Res. Methods 46, 178–184. doi: 10.3758/s13428-013-0362-x

Auvray, M., and Spence, C. (2008). The multisensory perception of flavor. Conscious. Cogn. 17, 1016–1031. doi: 10.1016/j.concog.2007.06.005

Ballester, J., Patris, B., Symoneaux, R., and Valentin, D. (2008). Conceptual vs. perceptual wine spaces: does expertise matter? Food Qual. Prefer. 19, 267–276. doi: 10.1016/j.foodqual.2007.08.001

Bender, G., Veldhuizen, M. G., Meltzer, J. A., Gitelman, D. R., and Small, D. M. (2009). Neural correlates of evaluative compared with passive tasting. Eur. J. Neurosci. 30, 327–338. doi: 10.1111/j.1460-9568.2009.06819.x

Bengtsson, S., Berglund, H., Gulyas, B., Cohen, E., and Savic, I. (2001). Brain activation during odor perception in males and females. Neuroreport 12, 2027–2033. doi: 10.1097/00001756-200107030-00048

Bensafi, M., Croy, I., Phillips, N., Rouby, C., Sezille, C., Gerber, J., et al. (2014). The effect of verbal context on olfactory neural responses. Hum. Brain Mapp. 35, 810–818. doi: 10.1002/hbm.22215

Bensafi, M., Iannilli, E., Gerber, J., and Hummel, T. (2008). Neural coding of stimulus concentration in the human olfactory and trigeminal systems. Neuroscience 154, 832–838. doi: 10.1016/j.neuroscience.2008.03.079

Billot, P. E., Comte, A., Galliot, E., Andrieu, P., Bonnans, V., Tatu, L., et al. (2011). Time course of odorant- and trigeminal-induced activation in the human brain: an event-related functional magnetic resonance imaging study. Neuroscience 189, 370–376. doi: 10.1016/j.neuroscience.2011.05.035

Blackman, J., Saliba, A., and Schmidtke, L. (2010). Sweetness acceptance of novices, experienced consumers and winemakers in Hunter Valley Semillon wines. Food Qual. Prefer. 21, 679–683. doi: 10.1016/j.foodqual.2010.05.001

Boyle, J. A., Frasnelli, J., Gerber, J., Heinke, M., and Hummel, T. (2007a). Cross-modal integration of intranasal stimuli: a functional magnetic resonance imaging study. Neuroscience 149, 223–231. doi: 10.1016/j.neuroscience.2007.06.045

Boyle, J. A., Heinke, M., Gerber, J., Frasnelli, J., and Hummel, T. (2007b). Cerebral activation to intranasal chemosensory trigeminal stimulation. Chem. Senses 32, 343–353. doi: 10.1093/chemse/bjm004

Boynton, G. M., Engel, S. A., Glover, G. H., and Heeger, D. J. (1996). Linear systems analysis of functional magnetic resonance imaging in human V1. J. Neurosci. 16, 4207–4221.

Brand, G., and Brisson, R. (2012). Lateralisation in wine olfactory threshold detection: comparison between experts and novices. Laterality 17, 583–596. doi: 10.1080/1357650X.2011.595955

Brand, G., and Millot, J. L. (2001). Sex differences in human olfaction: between evidence and enigma. Q. J. Exp. Psychol. B. 54, 259–270. doi: 10.1080/02724990143000045

Brochet, F., and Dubourdieu, D. (2001). Wine descriptive language supports cognitive specificity of chemical senses. Brain Lang. 77, 187–196. doi: 10.1006/brln.2000.2428

Burdach, K. J., and Doty, R. L. (1987). The effects of mouth movements, swallowing, and spitting on retronasal odor perception. Physzol. Behav. 41, 353–356. doi: 10.1016/0031-9384(87)90400-8

Castriota-Scanderbeg, A., Hagberg, G. E., Cerasa, A., Committeri, G., Galati, G., Patria, F., et al. (2005). The appreciation of wine by sommeliers: a functional magnetic resonance study of sensory integration. Neuroimage 25, 570–578. doi: 10.1016/j.neuroimage.2004.11.045

Cerf-Ducastel, B., and Murphy, C. (2001). fMRI activation in response to odorants orally delivered in aqueous solutions. Chem. Senses 26, 625–637. doi: 10.1093/chemse/26.6.625

D'Alessandro, S., and Pecotich, A. (2013). Evaluation of wine by expert and novice consumers in the presence of variations in quality, brand and country of origin cues. Food Qual. Prefer. 28, 28–303.

de Araujo, I. E., Rolls, E. T., Kringelbach, M. L., McGlone, F., and Phillips, N. (2003). Taste-olfactory convergence, and the representation of the pleasantness of flavour, in the human brain. Eur. J. Neurosci. 18, 2059–2068. doi: 10.1046/j.1460-9568.2003.02915.x

Desgranges, B., Ramirez-Amaya, V., Ricano-Cornejo, I., Levy, F., and Ferreira, G. (2010). Flavor preference learning increases olfactory and gustatory convergence onto single neurons in the basolateral amygdala but not in the insular cortex in rats. PLoS ONE 5:e10097. doi: 10.1371/journal.pone.0010097

Doty, R. L. (1989). Influence of age and age-related diseases on olfactory function. Ann. N.Y. Acad. Sci. 561, 76–86. doi: 10.1111/j.1749-6632.1989.tb20971.x

Francis, S., Rolls, E. T., Bowtell, R., McGlone, F., O'Doherty, J., Browning, A., et al. (1999). The representation of pleasant touch in the brain and its relationship with taste and olfactory areas. Neuroreport 10, 453–459.

Fu, W., Sugai, T., Yoshimura, H., and Onoda, N. (2004). Convergence of olfactory and gustatory connections onto the endopiriform nucleus in the rat. Neuroscience 126, 1033–1041. doi: 10.1016/j.neuroscience.2004.03.041

Fulbright, R. K., Skudlarski, P., Lacadie, C. M., Warrenburg, S., Bowers, A. A., Gore, J. C., et al. (1998). Functional MR imaging of regional brain responses to pleasant and unpleasant odors. AJNR Am. J. Neuroradiol. 19, 1721–1726.

González, J., Barros-Loscertales, A., Pulvermuller, F., Meseguer, V., Sanjuan, A., Belloch, V., et al. (2006). Reading cinnamon activates olfactory brain regions Neuroimage 32, 906–912. doi: 10.1016/j.neuroimage.2006.03.037

Haase, L., Cerf-Ducastel, B., and Murphy, C. (2009). The effect of stimulus delivery technique on perceived intensity functions for taste stimuli: implications for fMRI studies. Atten. Percept. Psychophys. 71, 1167–1173. doi: 10.3758/APP.71.5.1167

Hughson, A. L., and Boakes, R. A. (2002). The knowing nose: the role of knowledge in wine expertise. Food Qual. Prefer. 13, 463–472. doi: 10.1016/S0950-3293(02)00051-4

Hund-Georgiadis, M., and von Cramon, D. Y. (1999). Motor-learning-related changes in piano players and non-musicians revealed by functional magnetic-resonance signals. Exp. Brain Res. 125, 417–425. doi: 10.1007/s002210050698

Katotomichelakis, M., Balatsouras, D., Tripsianis, G., Davris, S., Maroudias, N., Danielides, V., et al. (2007). The effect of smoking on the olfactory function. Rhinology 45, 273–280.

Kikuchi, S., Kubota, F., Nisijima, K., Washiya, S., and Kato, S. (2005). Cerebral activation focusing on strong tasting food: a functional magnetic resonance imaging study. Neuroreport 16, 281–283. doi: 10.1097/00001756-200502280-00016

King, E. S., Dunn, R. L., and Heymann, H. (2013). The influence of alcohol on the sensory perception of red wines. Food Qual. Prefer. 28, 235–243. doi: 10.1016/j.foodqual.2012.08.013

Kinomura, S., Kawashima, R., Yamada, K., Ono, S., Itoh, M., Yoshioka, S., et al. (1994). Functional anatomy of taste perception in the human brain studied with positron emission tomography. Brain Res. 659, 263–266. doi: 10.1016/0006-8993(94)90890-7

Kobayashi, M. (2006). Functional organization of the human gustatory cortex. J. Oral Biosci. 48, 244–260. doi: 10.1016/S1349-0079(06)80007-1

Kobayashi, M., Takeda, M., Hattori, N., Fukunaga, M., Sasabe, T., Inoue, N., et al. (2004). Functional imaging of gustatory perception and imagery: “top-down” processing of gustatory signals. Neuroimage 23, 1271–1282. doi: 10.1016/j.neuroimage.2004.08.002

Kühn, S., and Gallinat, J. (2013). Does taste matter? How anticipation of cola brands influences gustatory processing in the brain. PLoS ONE 8:e61569. doi: 10.1371/journal.pone.0061569

Laska, M., Distel, H., and Hudson, R. (1997). Trigeminal perception of odorant quality in congenitally anosmic subjects. Chem. Senses 22, 447–456. doi: 10.1093/chemse/22.4.447

Lawless, H. T. (1984). Flavor description of white wine by “expert” and nonexpert wine consumers. J. Food Sci. 49, 120–123. doi: 10.1111/j.1365-2621.1984.tb13686.x

Lombion, S., Comte, A., Tatu, L., Brand, G., Moulin, T., and Millot, J. L. (2009). Patterns of cerebral activation during olfactory and trigeminal stimulations. Hum. Brain Mapp. 30, 821–828. doi: 10.1002/hbm.20548

Lotze, M., Scheler, G., Tan, H. R., Braun, C., and Birbaumer, N. (2003). The musician's brain: functional imaging of amateurs and professionals during performance and imagery. Neuroimage 20, 1817–1829. doi: 10.1016/j.neuroimage.2003.07.018

Lundstrom, J. N., and Hummel, T. (2006). Sex-specific hemispheric differences in cortical activation to a bimodal odor. Behav. Brain Res. 166, 197–203. doi: 10.1016/j.bbr.2005.07.015

Maguire, E. A., Gadian, D. G., Johnsrude, I. S., Good, C. D., Ashburner, J., Frackowiak, R. S., et al. (2000). Navigation-related structural change in the hippocampi of taxi drivers. Proc. Natl. Acad. Sci. U.S.A. 97, 4398–4403. doi: 10.1073/pnas.070039597

Maier, J. X., Wachowiak, M., and Katz, D. B. (2012). Chemosensory convergence on primary olfactory cortex. J. Neurosci. 32, 17037–17047. doi: 10.1523/JNEUROSCI.3540-12.2012

McClure, S. M., Li, J., Tomlin, D., Cypert, K. S., Montague, L. M., and Montague, P. R. (2004). Neural correlates of behavioral preference for culturally familiar drinks. Neuron 44, 379–387. doi: 10.1016/j.neuron.2004.09.019

Morrot, G., Brochet, F., and Dubourdieu, D. (2001). The color of odors. Brain Lang. 79, 309–320. doi: 10.1006/brln.2001.2493

O'Doherty, J., Rolls, E. T., Francis, S., Bowtell, R., and McGlone, F. (2001). Representation of pleasant and aversive taste in the human brain. J. Neurophysiol. 85, 1315–1321. doi: 10.1.1.23.1477

Okamoto, M., and Dan, I. (2013). Extrinsic information influences taste and flavor perception: a review from psychological and neuroimaging perspectives. Semin. Cell Dev. Biol. 24, 247–255. doi: 10.1016/j.semcdb.2012.11.001

Parr, W. V., Heatherbell, D., and White, K. G. (2002). Demystifying wine expertise: olfactory threshold, perceptual skill and semantic memory in expert and novice wine judges. Chem. Senses 27, 747–755. doi: 10.1093/chemse/27.8.747

Parr, W. V., White, K. G., Heatherbell, D. A. (2004). Exploring the nature of wine expertise: what underlies wine experts' olfactory recognition memory advantage? Food Qual. Prefer. 15, 411–420. doi: 10.1016/j.foodqual.2003.07.002

Pierce, J., and Halpern, B. (1996). Orthonasal and retronasal odorant identification based upon vapor phase input from common sub-stances. Chem. Senses 21, 529–543. doi: 10.1093/chemse/21.5.529

Plailly, J., Delon-Martin, C., and Royet, J. P. (2012). Experience induces functional reorganization in brain regions involved in odor imagery in perfumers. Hum. Brain Mapp. 33, 224–234. doi: 10.1002/hbm.21207

Plassmann, H., O'Doherty, J., Shiv, B., and Rangel, A. (2008). Marketing actions can modulate neural representations of experienced pleasantness. Proc. Natl. Acad. Sci. U.S.A. 105, 1050–1054. doi: 10.1073/pnas.0706929105

Prescott, J. (1999). Flavor as a psychological construct: implications for perceiving and measuring the sensory qualities of foods. Food Qual. Prefer. 10, 349–356. doi: 10.1016/S0950-3293(98)00048-2

Prescott, J. (2012). “Multimodal chemosensory interactions and perception of flavour,” in The Neural Bases of Multisensory Processes, eds M. M. Murray and M. T. Wallace (Boca Raton, FL: CRC Press), Chap 35.

Pritchard, T. C., and Norgren, R. (2004). “Gustatory system,” in The Human Nervous System, eds G. Paxinos and J. K. Mai (Amsterdam: Elsevier), 1171–1196. doi: 10.1016/B978-012547626-3/50032-6

Qureshy, A., Kawashima, R., Imran, M. B., Sugiura, M., Goto, R., Okada, K., et al. (2000). Functional mapping of human brain in olfactory processing: a PET study. J. Neurophysiol. 84, 1656–1666.

Richardson, J. T., and Zucco, G. M. (1989). Cognition and olfaction: a review. Psychol. Bull. 105, 352–360. doi: 10.1037/0033-2909.105.3.352

Rogers, T. T., Hocking, J., Noppeney, U., Mechelli, A., Gorno-Tempini, M. L., Patterson, K., et al. (2006). Anterior temporal cortex and semantic memory: reconciling findings from neuropsychology and functional imaging. Cogn. Affect. Behav. Neurosci. 6, 201–213. doi: 10.3758/CABN.6.3.201

Rolls, E. T., and Scott, T. (2003). “Central taste anatomy and neurophysiology,” in Handbook of Olfaction and Gustation, ed R. L. Doty (New York, NY: Marcel Dekker), 679–705.

Rolls, E. T. (2008). Functions of the orbitofrontal and pregenual cingulate cortex in taste, olfaction, appetite and emotion. Acta Physiol. Hung. 95, 131–164. doi: 10.1556/APhysiol.95.2008.2.1

Rolls, E. T., Kringelbach, M. L., and de Araujo, I. E. T. (2003). Different representations of pleasant and unpleasant odours in the human brain. Eur. J. Neurosci. 18, 695–703. doi: 10.1046/j.1460-9568.2003.02779.x

Royet, J. P., Delon-Martin, C., and Plailly, J. (2013a). Odor mental imagery in non-experts in odors: a paradox? Front. Hum. Neurosci. 7:87. doi: 10.3389/fnhum.2013.00087

Royet, J. P., Koenig, O., Gregoire, M. C., Cinotti, L., Lavenne, F., Le Bars, D., et al. (1999). Functional anatomy of perceptual and semantic processing for odors. J. Cogn. Neurosci. 11, 94–109. doi: 10.1162/089892999563166

Royet, J. P., Plailly, J., Saive, A. L., Veyrac, A., and Delon-Martin, C. (2013b). The impact of expertise in olfaction. Front Psychol. 4:928. doi: 10.3389/fpsyg.2013.00928

Rozin, P. (1982). “Taste-smell confusions” and the duality of the olfactory sense. Percept. Psychophys. 31, 397–401. doi: 10.3758/BF03202667

Sáenz-Navajas, M. P., Ballester, J., Pêcher, C., Peyron, D., and Valentin, D. (2013). Sensory drivers of intrinsic quality of red wines: effect of culture and level of expertise. Food Res. Int. 54, 1506–1518. doi: 10.1016/j.foodres.2013.09.048

Sáenz-Navajas, M. P., Campo, E., Avizcuri, J. M., Valentin, D., Fernández-Zurbano, P., and Ferreira, V. (2012). Contribution of non-volatile and aroma fractions to in-mouth sensory properties of red wines: wine reconstitution strategies and sensory sorting task. Anal. Chim. Acta. 732, 64–72. doi: 10.1016/j.aca.2011.12.042

Saive, A. L., Royet, J. P., and Plailly, J. (2014). A review on the neural bases of episodic odor memory: from laboratory-based to autobiographical approaches. Front. Behav. Neurosci. 8:240. doi: 10.3389/fnbeh.2014.00240

Salles, C., Chagnon, M. C., Feron, G., Guichard, E., Laboure, H., Morzel, M., et al. (2011). In-mouth mechanisms leading to flavor release and perception. Crit. Rev. Food Sci. Nutr. 51, 67–90. doi: 10.1080/10408390903044693

Savic, I. (2002). Brain imaging studies of the functional organization of human olfaction. Neuroscientist 8, 204–211. doi: 10.1177/1073858402008003006

Seth, A. K., Chorley, P., and Barnett, L. C. (2013). Granger causality analysis of fMRI BOLD signals is invariant to hemodynamic convolution but not downsampling. Neuroimage 65, 540–555. doi: 10.1016/j.neuroimage.2012.09.049

Small, D. M. (2012). Flavor is in the brain. Physiol. Behav. 107, 540–552. doi: 10.1016/j.physbeh.2012.04.011

Small, D. M., Bender, G., Veldhuizen, M. G., Rudenga, K., Nachtigal, D., and Felsted, J. (2007). The role of the human orbitofrontal cortex in taste and flavor processing. Ann. N.Y. Acad. Sci. 1121, 136–151. doi: 10.1196/annals.1401.002

Small, D. M., Jones-Gotman, M., Zatorre, R. J., Petrides, M., and Evans, A. C. (1997). Flavor processing: more than the sum of its parts. Neuroreport 8, 3913–3917. doi: 10.1097/00001756-199712220-00014

Small, D. M., and Prescott, J. (2005). Odor/taste integration and the perception of flavor. Exp. Brain Res. 166, 345–357. doi: 10.1007/s00221-005-2376-9

Small, D. M., Voss, J., Mak, Y. E., Simmons, K. B., Parrish, T., and Gitelman, D. (2004). Experience-dependent neural integration of taste and smell in the human brain. J. Neurophysiol. 92, 1892–1903. doi: 10.1152/jn.00050.2004

Small, D. M., Zald, D. H., Jones-Gotman, M., Zatorre, R. J., Pardo, J. V., Frey, S., et al. (1999). Human cortical gustatory areas: a review of functional neuroimaging data. Neuroreport 10, 7–14. doi: 10.1097/00001756-199901180-00002

Small, D. M., Zatorre, R. J., Dagher, A., Evans, A. C., and Jones-Gotman, M. (2001). Changes in brain activity related to eating chocolate: from pleasure to aversion. Brain 124, 1720–1733. doi: 10.1093/brain/124.9.1720

Sobel, N., Bradley, N. J., Mainland, J., and Yousem, D. M. (2003). “Functional neuroimaging of human olfaction,” in Handbook of Olfaction and Gustation, ed R. L. Doty (New York, NY: Marcel Dekker), 251–273.

Sobel, N., Prabhakaran, V., Desmond, J. E., Glover, G. H., Goode, R. L., Sullivan, E. V., et al. (1998). Sniffing and smelling: separate subsystems in the human olfactory cortex. Nature 392, 282–286. doi: 10.1038/32654

Solomon, G. (1990). Psychology of novice and expert wine talk. Am. J. Psychol. 105, 495–517. doi: 10.2307/1423321

Spaniol, J., Davidson, P. S., Kim, A. S., Han, H., Moscovitch, M., and Grady, C. L. (2009). Event-related fMRI studies of episodic encoding and retrieval: meta-analyses using activation likelihood estimation. Neuropsychologia 47, 1765–1779. doi: 10.1016/j.neuropsychologia.2009.02.028

Stark, C. E., and Squire, L. R. (2001). Simple and associative recognition memory in the hippocampal region. Learn. Mem. 8, 190–197. doi: 10.1101/lm.40701

Talairach, J., and Tournoux, P. (1988). Co-Planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging. New York, NY: Thieme Medical Publishers.

Tulving, E. (1995). “Organisation of memory: quo vadis?” in The Cognitive Neurosciences, ed M. S. Gazzaniga (Cambridge, MA: MIT Press), 839–847.

Van den Bosch, I., Dalenberg, J. R., Renken, R., Van Langeveld, A. W., Smeets, P. A., Griffioen-Roose, S., et al. (2014). To like or not to like: neural substrates of subjective flavor preferences. Behav. Brain Res. 269, 128–137. doi: 10.1016/j.bbr.2014.04.010

Vedel, A., Charle, G., Charnay, P., and Tourmeau, J. (1972). Essai sur la Dégustation des Vins. Mâcon: Institut National des Appellations d'Origine des Vins et Eaux-de-Vie.

Vicente, K. J., and Wang, J. H. (1998). An ecological theory of expertise effects in memory recall. Psychol. Rev. 105, 33–57. doi: 10.1037/0033-295X.105.1.33

Villamor, R. R., and Ross, C. F. (2013). Wine matrix compounds affect perception of wine aromas. Annu. Rev. Food Sci. Technol. 4, 1–20. doi: 10.1146/annurev-food-030212-182707

Visser, M., Embleton, K. V., Jefferies, E., Parker, G. J., and Ralph, M. A. (2010a). The inferior, anterior temporal lobes and semantic memory clarified: novel evidence from distortion-corrected fMRI. Neuropsychologia 48, 1689–1696. doi: 10.1016/j.neuropsychologia.2010.02.016

Visser, M., Jefferies, E., and Lambon Ralph, M. A. (2010b). Semantic processing in the anterior temporal lobes: a meta-analysis of the functional neuroimaging literature. J. Cogn. Neurosci. 22, 1083–1094. doi: 10.1162/jocn.2009.21309

Wang, J., Eslinger, P. J., Smith, M. B., and Yang, Q. X. (2005). Functional magnetic resonance imaging study of human olfaction and normal aging. J. Gerontol. A Biol. Sci. Med. Sci. 60, 510–514. doi: 10.1093/gerona/60.4.510

Yousem, D. M., Maldjian, J. A., Siddiqi, F., Hummel, T., Alsop, D. C., Geckle, R. J., et al. (1999). Gender effects on odor-stimulated functional magnetic resonance imaging. Brain Res. 818, 480–487. doi: 10.1016/S0006-8993(98)01276-1

Zald, D. H., and Pardo, J. V. (2000). Cortical activation induced by intraoral stimulation with water in humans. Chem. Senses 25, 267–275. doi: 10.1093/chemse/25.3.267

Zatorre, R. J., Jones-Gotman, M., Evans, A. C., and Meyer, E. (1992). Functional localization and lateralization of human olfactory cortex. Nature 360, 339–340. doi: 10.1038/360339a0

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 30 July 2014; Accepted: 26 September 2014; Published online: 16 October 2014.

Citation: Pazart L, Comte A, Magnin E, Millot J-L and Moulin T (2014) An fMRI study on the influence of sommeliers' expertise on the integration of flavor. Front. Behav. Neurosci. 8:358. doi: 10.3389/fnbeh.2014.00358

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Pazart, Comte, Magnin, Millot and Moulin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

REVIEW ARTICLE
published: 07 July 2014
doi: 10.3389/fnbeh.2014.00240

A review on the neural bases of episodic odor memory: from laboratory-based to autobiographical approaches

Anne-Lise Saive, Jean-Pierre Royet and Jane Plailly*

Olfaction: from Coding to Memory Team, Lyon Neuroscience Research Center, CNRS UMR 5292—INSERM U1028—University Lyon1, Lyon, France

* Correspondence: Jane Plailly, Olfaction: from Coding to Memory Team, Lyon Neuroscience Research Center, CNRS UMR 5292—INSERM U1028—University Lyon1, 50 Avenue Tony Garnier, 69366 Lyon Cedex 07, France e-mail: plailly@olfac.univ-lyon1.fr

Edited by:
Donald A. Wilson, New York University School of Medicine, USA

Reviewed by:
Maria Larsson, Stockholm University, Sweden
Rachel Herz, Brown University, USA

Odors are powerful cues that trigger episodic memories. However, in light of the amount of behavioral data describing the characteristics of episodic odor memory, the paucity of information available on the neural substrates of this function is startling. Furthermore, the diversity of experimental paradigms complicates the identification of a generic episodic odor memory network. We conduct a systematic review of the literature depicting the current state of the neural correlates of episodic odor memory in healthy humans by placing a focus on the experimental approaches. Functional neuroimaging data are introduced by a brief characterization of the memory processes investigated. We present and discuss laboratory-based approaches, such as odor recognition and odor associative memory, and autobiographical approaches, such as the evaluation of odor familiarity and odor-evoked autobiographical memory. We then suggest the development of new laboratory-ecological approaches allowing for the controlled encoding and retrieval of specific multidimensional events that could open up new prospects for the comprehension of episodic odor memory and its neural underpinnings. While large conceptual differences distinguish experimental approaches, the overview of the functional neuroimaging findings suggests relatively stable neural correlates of episodic odor memory.

Keywords: episodic memory, recognition memory, autobiographical memory, olfaction, behavior, approaches, neural bases, human

INTRODUCTION

Human episodic memory is the long-term memory process that enables one to mentally and consciously relive specific, personal events from the past (Tulving, 1972, 1983). It is associated with a feeling of mental time travel, a sense of self, and the autonoetic consciousness that allows one to be aware of the subjective time at which events happened (Tulving, 2001, 2002). Although this definition is accepted, episodic memory is experimentally studied through a large set of paradigms that differ in all dimensions of the memory. The content of the memory and the procedures for encoding and retrieval vary in complexity and ecological validity, while the retention time varies in delay. As a consequence, “episodic memory” refers to an ensemble of memory processes. To provide a general picture of episodic memory, it is thus of interest to orient this investigation by the experimental approach. Two different approaches are usually employed to investigate the explicit retrieval of past events: laboratory-based approaches and autobiographical approaches (McDermott et al., 2009). In the first case, experimenters test the memorization of artificial episodes created in the laboratory, whereas in the second case, experimenters test the retrieval of real-life memories encoded in the participants’ past. McDermott et al. (2009) further emphasized that the two methods differ in time “not only in that the events of interest have occurred on different timescales (weeks or years for studies in the autobiographical memory tradition compared with minutes/hours in the laboratory memory tradition): It can take people on the order of 8–12 s to construct a vivid autobiographical memory (Robinson, 1976), compared to recognition memory decisions, which often occur in a second or two”.

Episodic memory depends on the medial temporal lobe, which is composed of different interconnected subregions, including the hippocampus and adjacent parahippocampal, perirhinal and entorhinal cortices (Milner et al., 1968; Squire, 1992; Cohen and Eichenbaum, 1993). The contribution of each of the medial temporal lobe components to the memory process and their connectivity with the neocortex has been widely investigated (Suzuki and Amaral, 1994; Burwell and Amaral, 1998; Witter et al., 2000; Squire et al., 2004; Davachi, 2006; Diana et al., 2007; Eichenbaum et al., 2007). In summary, the cortical projections encompass two parallel pathways. In one pathway, sensory areas project inputs that are critically involved in object perception onto the perirhinal cortex and hence onto the lateral entorhinal cortex. In the other pathway, the parahippocampal cortex and then the medial entorhinal cortex receive visuospatial information. Both entorhinal cortices then converge onto the hippocampus and allow for the representation of the object in the visuospatial context in which it was experienced.

Phenomenologically, the sense of smell demonstrates a close relationship with episodic memory. Odors are well known to be particularly powerful memory cues. Among all sensorial stimuli, odors appear to trigger the most vivid and emotional memories (e.g., Hinton and Henley, 1993; Chu and Downes, 2002; Herz and Schooler, 2002; Larsson and Willander, 2009). This property is usually explained from an anatomical point of view. The olfactory input has direct connections via the olfactory bulb and the primary olfactory (piriform) cortex onto two key structures involved in emotion and memory: the amygdala and hippocampus (Figure 1; Carmichael et al., 1994; Insausti et al., 1997; Haberly, 1998). In contrast with other sensory modalities, projections from the sensory input onto these two structures do not pass via the thalamus. From these areas, information is then conveyed to the secondary olfactory cortices composed of the orbitofrontal cortex (OFC) and the insular cortex.

FIGURE 1. Schematic view of the human olfactory system. The primary and secondary olfactory cortices are represented in blue and green, respectively. Amyg, amygdala; Ento, entorhinal cortex; Hipp, hippocampus; OFC, orbitofrontal cortex; PC, piriform cortex; Thal, thalamus (adapted from Royet et al., 2014).

The strong anatomical connection between olfactory and memory structures makes olfaction a privileged sense for accessing memories. However, in light of the amount of behavioral data describing the characteristics of episodic odor memory, the paucity of information available on the neural substrates of this function is startling. The purpose of this review is threefold: (1) to assess and discuss the current knowledge of the neural correlates of episodic odor memory by presenting functional data from healthy participants; (2) to describe the diversity of paradigms and therefore the diversity of cognitive processes by focusing on laboratory-based approaches, such as odor recognition memory and odor-associative memory, and on autobiographical approaches, such as the evaluation of odor familiarity and odor-evoked autobiographical memory; and (3) to point to new experimental and theoretical directions that episodic odor memory research could profitably pursue. To fulfill this triple objective, we choose to present the literature data according to experimental approaches and not to follow the chronological order of publications.

LABORATORY-BASED APPROACHES FOR STUDYING THE NEURAL BASES OF EPISODIC ODOR MEMORY

In laboratory-based approaches for studying episodic odor memory, participants artificially encounter odors in laboratory settings during a first phase (named the “encoding phase”), and then, the memory trace of this odor is questioned in a second phase (named the “test phase”). We will describe in detail three types of laboratory-based approaches to test episodic odor memory, with the level of complexity increasing from the memory of a single item (i.e., the odor recognition) to the memory of an odor using its verbal label (i.e., the odor-verbal recognition memory) and finally to the memory of an association between two items of different modalities (i.e., the crossmodal odor associative memory).

ODOR RECOGNITION MEMORY

Recognition memory for odors received very little attention until the 1970s. The first study was led by Engen and Ross (1973). In this typical odor recognition paradigm, the participants were exposed to target odors in laboratory settings and, after a retention interval, were asked to decide whether the odor probe was an old stimulus (target odor) or a new one (distractor odor). This paradigm can be defined as investigating the explicit recognition of laboratory odors. The authors demonstrated that the memory of odors has very little long-term loss. Laboratory odors were less well recognized than laboratory pictures after a short interval of time (73% correct recognition), but they were better recognized than these laboratory pictures after 4 months (Figure 2A; Engen, 1987). However, this specificity of odor recognition memory has been challenged more recently and significant forgetting of odors over time was observed (e.g., Murphy et al., 1991; Larsson, 1997; Olsson et al., 2009).

FIGURE 2. Odor recognition memory. (A) Ability to recognize laboratory pictures and odors over a span of 1 year. The hypothetical curve of the ability to recognize episodic odors (odors associated with significant real-life experiences) is shown for comparison (adapted from Engen, 1987). (B) Impact of semantic processing on odor recognition memory performances. Memory scores for odors that were previously associated with no labels, chemical labels, labels generated by the participants or veridical labels. ** p < 0.01; *** p < 0.001 (adapted from Jehl et al., 1997).

The robust ability to accurately recognize odors has been consistently demonstrated (e.g., Lawless and Cain, 1975; Lawless, 1978; Rabin and Cain, 1984; Goldman and Seamon, 1992). Nevertheless, as highlighted in Herz and Engen (1996), odor recognition performance strongly depends on the experimental conditions. First, the odor set size and odor similarities both affect odor recognition: a greater number of odors and a closer similarity among odors result in lower scores (Engen and Ross, 1973; Lawless and Cain, 1975; Jones et al., 1978; Schab, 1991). Second, the perceived qualities of odors influence recognition memory. For example, evidence suggests that the unpleasantness of odors and their high intensity improve the robustness of memories (Larsson et al., 2009). Third, performances in odor recognition are strongly and positively dependent on the amount of semantic information regarding the odor source, as observed in the influence of odor familiarity (Figure 2B) and odor-naming ability (e.g., Rabin and Cain, 1984; Lesschaeve and Issanchou, 1996; Jehl et al., 1997; Larsson and Backman, 1997; Bhalla et al., 2000; Frank et al., 2011). Fourth, recognition memory performances can also be affected by the type of procedure engaged in encoding. While no differences emerge for odors learned intentionally or incidentally (Engen and Ross, 1973; Larsson et al., 2003, 2006), the processing task used to encode odor affects the subsequent recognition of odors. Odors are better recognized after elaborative processing (verbal definition, association with a life episode) than after pure odor perceptual processing (Lyman and McDaniel, 1986, 1990). Thus, the importance of semantic processing in odor recognition must be taken into account and, as Schab (1991) previously noted, “A more realistic assessment of the odor-recognition data reported in the literature, therefore, acknowledges that recognition performance is the joint result of memory for perceptual odor information and memory for covertly generated verbal associations to the odors”.

Two states of awareness are thought to be involved in recognition memory retrieval: recollection, which involves the remembering of an item along with contextual and associative details, and familiarity, where an item is seen as familiar but no other contextual information is remembered (Mandler, 1980). The recollective experience is experimentally approached through the Remember/Know procedure (Tulving, 1985) in order to determine how much recollection and familiarity contribute to different kinds of recognition. The recollective experience occurring in odor recognition memory is influenced by several factors: odor familiarity and identifiability, and gender (Larsson et al., 2003, 2006; Olsson et al., 2009). For instance, Larsson et al. (2006) showed that recognition is more based on recollection than familiarity for familiar odors, and is more based on familiarity and guessing than on recollection for unfamiliar odors.

The neural basis of odor recognition memory has been approached in four studies using standard recognition memory tests. Two positron emission tomography (PET) studies, which were among the first neuroimaging studies on olfactory cognitive processes, highlighted the brain regions specifically involved in long-term odor recognition memory in comparison with short-term odor memory processes (Savic et al., 2000; Dade et al., 2002). These two studies noted the importance of the prefrontal and posterior-parietal regions in long-term odor memory. They also revealed the role of the PC, especially its right part, in odor recognition. This right-hemisphere superiority in odor recognition has also been reported in patients with brain lesions. Despite a few discrepancies (Hudry et al., 2003), either patients with right temporal lobe or right orbitofrontal lesions or those with right temporal lobe epilepsy perform more poorly than do patients with left-sided lesions in odor recognition tests (Rausch et al., 1977; Carroll et al., 1993; Jones-Gotman and Zatorre, 1993).

Two of our studies recently further elucidated odor recognition memory by investigating the neural basis of this process as a function of task performance using event-related functional magnetic resonance imaging (fMRI; Royet et al., 2011; Meunier et al., 2014). Recognition memory performances were assessed using parameters from signal detection theory, which has widely dominated recognition memory theory since the 1950s (Swets, 1964; Lockhart and Murdock, 1970). From the experimental conditions (target vs. distractor) and the participants’ behavioral responses (“Yes” vs. “No”), four response categories were defined: Hit or Miss when the target items were accurately recognized or incorrectly rejected, respectively, and Correct Rejection (CR) or False Alarm when the distractor items were correctly rejected or incorrectly recognized, respectively. Using both standard and multivariate analyses, we observed that correct and incorrect recognition and rejection induced distinct neural signatures (Royet et al., 2011). Mainly, activity in the hippocampus and the parahippocampal gyrus was associated with the correct recognition of odors, whereas the perirhinal cortex was associated with errors in recognition and rejection. More strikingly, we observed a decreased involvement of the anterior hippocampus when memory performances increased during correct recognition and rejection (Figure 3A). These findings led to the hypothesis that a greater ease when performing the task results in less activation in the hippocampus. Recently, we explored the functional connectivity of the networks underpinning correct and incorrect olfactory memories using graph theory (Meunier et al., 2014). We found that among 36 regions of interest, the hippocampus, caudate nucleus, anterior cingulate and medial temporal gyrus were more frequently connected together during correct odor recognition and thus formed a specific module of this condition (Figure 3B). The poor odor recognition performances observed in patients with hippocampal lesions (Levy et al., 2004) agrees with the essential role of the hippocampus in odor recognition memory.

FIGURE 3. Neural basis of odor recognition memory. (A) Decreased activation intensity in the right and left hippocampus as a function of memory scores (d’L) for Hit and CR in all participants (adapted from Royet et al., 2011). (B) The module in dark blue shows four regions functionally connected during the Hit condition. Other modules were also found during the CR, Miss or False alarm conditions. aCing, anterior cingulate; Caud, caudate nucleus; Hipp, hippocampus; IFg, Inferior frontal gyrus; Ins, insula; LOg, lateral orbital gyrus; MTg, medial temporal gyrus; pPC, posterior piriform cortex; Puta, putamen; Tha, thalamus (adapted from Meunier et al., 2014).

ODOR RECOGNITION MEMORY FROM VERBAL LABELS

Odor recognition memory has also been investigated through the recognition of odor verbal labels where the odors are presented during the encoding phase and the odor labels are retrieval cues (Buchanan et al., 2003; Cerf-Ducastel and Murphy, 2006; Lehn et al., 2013). This paradigm can be defined as testing the explicit recognition of the verbal labels of laboratory odors and addresses the label-odor association. Although no statistical comparison was performed, the behavioral results depicted by Buchanan et al. (2003) suggested that the odor-verbal recognition paradigm leads to lower memory scores than those for the odor-odor recognition paradigm. This empirical observation indicates that odor recognition is more difficult when triggered by a label than by the odor itself.

The neural substrates of odor retrieval through odor name recognition have been investigated a couple of times (Cerf-Ducastel and Murphy, 2006; Lehn et al., 2013). The two studies were consistent with regards to the ensemble of brain regions involved in this odor memory process and revealed consistent activation in the hippocampus, PC, amygdala, OFC and cerebellum. However, comparing odor-name and object-name recognition memories, Lehn et al. (2013) further showed that the hippocampus was activated during the recognition memory of both types of cues, thus providing clear evidence for modality-independent functions of the hippocampus. In turn, a region encompassing the left anterior insula, PC and amygdala, in addition to the left OFC, the left frontal pole and the right cerebellum, were specific to the olfactory modality (Figure 4).

FIGURE 4. Neural basis of odor recognition through verbal label. Brain activations specific to olfactory modality (in comparison with object-verbal recognition). a, Left insula/amygdala/piriform cortex; b, left orbitofrontal cortex; c, left frontal pole (adapted from Lehn et al., 2013).

An advantage of using verbal cues is the facilitation of crossmodal comparisons because identical sensory inputs (retrieval cues) are used for different types of stimuli (Lehn et al., 2013). However, the main drawback of this technique is the typically weak link between an odor and its verbal label (Lawless and Cain, 1975; Engen, 1987). Humans perform poorly when identifying common odors from smell alone (Engen and Pfaffmann, 1960; Cain, 1979). This difficulty makes the recognition more complex. When a verbal label is presented during the retrieval phase, two strategies can be implemented. The participants can compare the label they were reading to all the labels explicitly or implicitly generated during the encoding phase, a task that involves semantic-based recognition memory. They can also decide whether the odor evoked by the test label matches the memory trace of the encoded odors, a task that refers to an episodic-based recognition memory. Thus, the use of a verbal label to test odor recognition obscures the nature of the memory processes involved during retrieval.

CROSSMODAL ODOR ASSOCIATIVE MEMORY

In contrast to odor recognition memory from the odor label, crossmodal odor associative memory is related to the association of an odor with a non-odor item. The capacity of healthy adult volunteers to retrieve associations between two items, including an odor, has been demonstrated through two main paradigms. The paired-associate paradigm tests the ability to recall the item previously associated with an odor during explicit encoding. Davis (1975, 1977) showed a disadvantage for odors as associative stimuli in comparison with abstract visual stimuli. However, they also observed that this disadvantage decreased with higher odor familiarity and with higher dissimilarity within odor sets, a result that is consistent with the observations reported above in terms of the impact of familiarity and qualitative similarity on odor recognition memory performances (see Section Odor Recognition Memory). The odor source paradigm tests the ability to retrieve limited contextual information associated with the odor perception during encoding. For instance, participants were asked to explicitly remember either a specific room (Takahashi, 2003) or a specific space on a board (Gilbert et al., 2008; Pirogovsky et al., 2009) in which the odors were initially presented or to remember the gender of the experimenter presenting the odors during the encoding phase (Gilbert et al., 2006; Pirogovsky et al., 2006; Hernandez et al., 2008). Overall, these studies demonstrated that odor recognition is superior to the recognition of the source, that explicit vs. implicit encoding improves the memory for the source but not for the odor itself, and that aging affects odor source memory than on odor recognition (Takahashi, 2003; Gilbert et al., 2006, 2008; Pirogovsky et al., 2006, 2009; Hernandez et al., 2008).

Functionally, crossmodal odor associative memory has been investigated only twice using the paired-associate paradigm. In the study led by Gottfried et al. (2004), objects were paired with odors, and the participants were instructed to imagine a link between each object and the smell (a priori, the objects had no explicit link with odor). The effect of “odor context” on the neural responses was then examined during retrieval when these same objects were presented among distractors. In other words, this paradigm studied the implicit recall of the odor through the explicit recognition of the object that was previously paired with the odor but not the conscious retrieval of the odor. This memory process can be defined as an implicit crossmodal recall of laboratory odor context. Gottfried et al. (2004) showed evidence for the reactivation of the right posterior PC during successful object recognition in the absence of olfactory stimulation, just by the specific reactivation of the association between the recognized object and its paired odor. The authors further demonstrated that the involvement of the primary olfactory cortex is independent of the odor valence and that this structure is more sensitive to the retrieval of odor than the retrieval of visual stimuli. More importantly, the authors found that odor retrieval involved the right anterior hippocampus, and hence hypothesized that this structure has an important role in the binding between both items. A recent neuropsychology study supports this hypothesis and shows that amnesic subjects with hippocampal damage have impaired odor-place memory but intact odor recognition (Goodrich-Hunsaker et al., 2009). Yeshurun et al. (2009) also suggested a specific role of the hippocampus for odor associative memory. They based their study on the finding that the first odor-to-object association is stronger than subsequent associations of the same odor with other objects (Lawless and Engen, 1977). They paired object photos twice with a different odor, a different sound or a different odor-sound stimulus each time. One week later, the participants were presented with the object photos and had to explicitly recognize, among distractors, the odor associated with the object during encoding through odor labels. This task can be defined as investigating the explicit crossmodal recognition of laboratory odor context. Yeshurun et al. (2009) observed hippocampal activation for early olfactory but not auditory associations regardless of whether they were pleasant or unpleasant. These findings confirmed the hypothesis that the first olfactory associations enjoy a privileged brain representation that is underlined by the hippocampus.

The odor associative memory paradigms allow the examination of long-term odor memory involving more complex processes than those implicated in the memory of a single item (i.e., odor recognition memory). In these paradigms, the memory concerns the association between an item and a given context. However, the richness of the context is usually limited and materialized by a single other dimension. Therefore, the gap between odor associative memory and odor autobiographical memory is still wide. As highlighted by Schab (1991) “the conditions under which an odor often is reported to evoke the recollection of past episode differ significantly from those of a paired-associate task. In the former, a single ambient odor triggers the remembrance of a personal episode of which the odor itself was an integral part, whereas in the latter a series of different odors is presented, typically in small bottles, and the learning task is deliberate and requires the acquisition of unrelated and personally irrelevant information”.

AUTOBIOGRAPHICAL APPROACHES FOR STUDYING THE NEURAL BASIS OF EPISODIC ODOR MEMORY

In odor-evoked autobiographical approaches, the content of the memory refers to the participants’ past, and its retrieval is triggered with odors. First, we will present the experiments that questioned the memory of previously encountered odors and investigated the feeling of familiarity and unfamiliarity. Then, we will present the studies that addressed the recall of real-life events and investigated odor-evoked autobiographical memories.

FEELING OF FAMILIARITY OF ODORS

Odor autobiographical memory can be investigated through the feeling of familiarity generated by odors that are presented in laboratory settings. This paradigm refers to the explicit recognition of self-relevant odor. As we previously described, “The feeling of familiarity is a long-term recognition memory process referring to a subjective state of awareness based on judgments of the item’s prior occurrence. It involves the recognition of the item’s perceptual features and eventually of conceptual or semantic features, without the confirmatory conscious recollection of contextual information and/or without identification” (Plailly et al., 2007). A consensus emerges from the evaluation of odor perceptual characteristics. There is consistent evidence for positive correlations between the ratings of odor familiarity and those of intensity and pleasantness (e.g., Jellinek and Köster, 1983; Ayabe-Kanamura et al., 1998; Distel et al., 1999; Royet et al., 1999). Familiar odors have also been described as more simple, in terms of ease of interpreting an odor meaningfully (Sulmont et al., 2002). Recently, Delplanque et al. (2008) argued that the relation between pleasantness and familiarity is nonlinear: pleasantness ratings were positively correlated with familiarity ratings for pleasant odors, but not for unpleasant odors, a result that has been subsequently replicated (Plailly et al., 2011; Ferdenzi et al., 2013).

Our research team was the first to address the neural basis of the familiarity process. In the first studies, we compared periods of brain activity recorded when participants rated the familiarity of a large set of familiar or unfamiliar odors to periods when they detected the presence of odors (Royet et al., 1999, 2001; Plailly et al., 2005). Participants were instructed to make familiarity judgments based on their life experiences (i.e., “Does this odor seem familiar to you?”). This paradigm avoided the need for an initial experimental encoding phase. Greater activation of the right OFC and the right PC was observed when the participants evaluated odor familiarity compared with when they detected odors (Royet et al., 1999, 2011; Plailly et al., 2005). The lateralization of this memory process (Royet and Plailly, 2004) was consistent with the higher familiarity of odors presented to the right nostril than those presented to the left nostril (Broman et al., 2001). This could also explain the right hemisphere lateralization of the odor process observed in the first studies when odorants were passively perceived because the odorants were familiar and could have automatically triggered recognition (e.g., Zatorre et al., 1992; Yousem et al., 1997; Sobel et al., 1998; Savic et al., 2000; Poellinger et al., 2001). Our studies on odor familiarity evaluation further emphasized the role of the left inferior frontal gyrus, a key region for semantic processing, which is most likely activated in an attempt to gather semantic information to identify the smell (Royet et al., 1999, 2011; Plailly et al., 2005). Additional activations were observed in the brain regions involved in emotion (amygdala), visual mental imagery (fusiform and occipital gyri) and memory (hippocampus and parahippocampal gyrus) processes, reflecting the large set of cognitive processes engaged during the evaluation of odor familiarity (Plailly et al., 2005).

Savic and Berglund (2004) and Plailly et al. (2007) revealed that familiar and unfamiliar odors are processed by different neural circuits. Savic and Berglund (2004) reported that the passive perception of odorants selected to be familiar vs. unfamiliar elicited specific activation of the right parahippocampal gyrus, right middle and inferior temporal gyri, and the left parietal cortex covering the precuneus. In addition, the familiarity ratings obtained after functional acquisitions were positively correlated with activation in the left inferior frontal gyrus and the right parahippocampal gyrus (Figure 5A), suggesting that the smelling of familiar, but not that of unfamiliar, odors engages neural circuits mediating semantic association and episodic retrieval functions. Our research team completed the preceding results by unveiling the existence of a bimodal neural system engaged in the feeling of familiarity vs. unfamiliarity (Plailly et al., 2007). The neural correlates of self-rated familiarity evoked by items of two modalities, odors and musical excerpts, overlapped within an extensive bimodal neural system that included the prefrontal, inferior frontal, parieto-occipital and medial temporal lobe brain regions in the left hemisphere (Figure 5B). We further concluded that because this system also overlaps with the familiarity processing of other types of stimuli (i.e., faces, voices, pictures and verbal items), a multimodal neural network might underlie the feeling of familiarity. Interestingly, we revealed the existence of neural processes specific to the feeling of unfamiliarity, which might be related to the detection of novelty, with a main bimodal activation in the right insula.

FIGURE 5. Neural basis of odor familiarity. (A) Correlations between familiarity ratings and activation in the right parahippocampus and left inferior frontal gyrus. The y-axis denotes differences in regional cerebral blood flow (rCBF) between the familiarity and baseline conditions (FAM–AIR). The x-axis shows the mean familiarity ratings of four familiar and four unfamiliar odorants for each participant (adapted from Savic and Berglund, 2004). (B) Bimodal neural basis of the feeling of familiarity evoked by odor and music (in comparison with the feeling of unfamiliarity). a, superior frontal gyrus; b, precuneus; c, angular gyrus; d, superior frontal gyrus bordering the cingulate gyrus; e, superior/middle frontal gyrus; f, inferior frontal gyrus. All regions were in the left hemisphere. The hippocampus and parahippocampal gyrus were regions of interests and hence were not displayed (adapted from Plailly et al., 2007).

ODOR-EVOKED AUTOBIOGRAPHICAL MEMORY

Odor-evoked autobiographical memory can be investigated through the recall of the life episode associated with an odor. This paradigm refers to the explicit recall of autobiographical memories evoked by self-relevant odor. Odors are exceptional cues for evoking personal autobiographical memories. Behavioral evidence has demonstrated that odors are more effective triggers of emotional memories than the same cue presented in other sensory formats or even in the form of odor labels (Hinton and Henley, 1993; Chu and Downes, 2002; Herz and Schooler, 2002; Herz, 2004, 2012; Herz et al., 2004; Larsson and Willander, 2009; Arshamian et al., 2013). Another specificity of odor-evoked autobiographical memories is that they produce a unique age distribution and favor childhood memories stemming from the first decade of life rather than from young adulthood, which is the typical reminiscence bump for memories evoked by verbal and visual information (Chu and Downes, 2000; Willander and Larsson, 2006; Larsson and Willander, 2009; Miles and Berntsen, 2011). Furthermore, empirical evidence indicates that odor-evoked memories are associated with stronger feelings of being brought back in time (Herz and Schooler, 2002; Herz, 2004; Willander and Larsson, 2006, 2007; Arshamian et al., 2013) and are thought of and talked about less than memories elicited by visual or verbal variants of the same items (Rubin et al., 1984). Finally, odors may also be more likely than visual or verbal cues to elicit perceptual-based memories; visual or verbal cues in turn provide more conceptual-based memories (Herz and Cupchik, 1992; Goddard et al., 2005; Willander and Larsson, 2007).

Although the high potential of odors to generate the successful recall of autobiographical memories has been behaviorally demonstrated, the neural basis remains little explored. Only two studies have investigated the neural underpinnings of odor-evoked autobiographical memories. Herz et al. (2004) explored whether the brain correlates of personal memories elicited by the smell of a perfume were different from those elicited by the sight of this perfume. Arshamian et al. (2013) compared memories evoked by either personally meaningful odors or pleasant control odors. In both studies, the authors observed activation in the parahippocampal gyrus, the amygdala, and the middle occipital gyrus. These regions play a crucial role in memory, emotion and visual mental imagery, and their engagement could explain the fact that odors are especially potent reminders of autobiographical experiences. Interestingly, Arshamian et al. (2013) raised two important issues. The first was inspired by the debate opposing the multiple memory trace theory consolidation model that postulates that the hippocampus and neocortex are in constant interaction (Nadel and Moscovitch, 1997, 1998) and the standard model of memory consolidation where the passage of time leads to a disengagement of the hippocampus and an additional recruitment of the prefrontal cortex (Marr, 1971; Squire et al., 1984). Arshamian et al. (2013) observed that hippocampal activation did not vary as a function of memory remoteness, which supports the notion of a permanent role of the hippocampus in the retrieval of odor-evoked autobiographical memories (Figure 6). Second, because of the early reminiscence bump in olfaction, the authors tested whether odors were differentially coded depending on the decade in which the stimulus was encoded. They observed a greater involvement of regions devoted to perceptual processes (e.g., the orbitofrontal cortex) during the recall of first-decade odor-evoked memories and a greater recruitment of regions involved in semantic processing (the left inferior frontal gyrus) during the recall of second-decade odor-evoked memories. This result suggests that the autobiographical recall is based more on perceptual processing and less on semantic processing when memories refer to early life experiences.

FIGURE 6. Neural basis of odor-evoked autobiographical memory. The blood oxygen level-dependent signal in the hippocampus region of interest did not significantly vary with the time from the event (adapted from Arshamian et al., 2013).

LABORATORY-ECOLOGICAL APPROACHES FOR STUDYING THE NEURAL BASIS OF EPISODIC ODOR MEMORY

The two main approaches for studying episodic memory developed above, the laboratory-based and autobiographical approaches, each have pros and cons. In the laboratory-based approach, artificial and simple episodes are encoded and recalled in controlled conditions in the laboratory. This method enables the manipulation of the encoding conditions and the retention time and allows the oversight of recall veracity. However, the to-be-remembered materials that are developed by experimenters are poor in comparison with a real-life episode. In the autobiographical approach, the retrieval of real-life memories that are encoded in the participants’ past is triggered by an experimental cue. This approach allows for the recall of real-life events in quite ecological conditions, but the veracity of the recalled events cannot be controlled. McDermott et al. (2009) have underscored the interest in proposing a new approach to study and understand human episodic memory, one that is halfway between the two traditional approaches and retains the respective advantages of each. Fulfilling those expectations, several laboratory-ecological approaches have been recently devised to study episodic memory (Pause et al., 2010, 2013; Holland and Smulders, 2011; Milton et al., 2011; Easton et al., 2012; Saive et al., 2013). On the one hand, these approaches are close to Tulving’s definition of episodic memory (Tulving, 1972, 1983) by allowing the conscious and controlled recollection of specific and complex events from the past. On the other hand, they are derived from content-based approaches developed in animals proposing to define the content of episodic memory as What happened, Where and When (Clayton and Dickinson, 1998; Griffiths and Clayton, 2001; Babb and Crystal, 2006; Crystal, 2009). In addition to the three-dimensional content of the episodic memories, Clayton et al. (2003) argued that these memories must also be integrated, flexible and trial unique. Subsequently, Easton and Eacott (2008; Eacott and Easton, 2010) enriched this operational definition of episodic memory by considering an alternative to the temporal dimension. They proposed replacing this dimension by the specific occasion or context in which the event occurred (Which context); this context encompasses the time when important but also the emotion, semantic knowledge, visual scene, or auditory and olfactory environments.

In the study of episodic odor memory, the laboratory-ecological approaches are still rare, although the necessity to elaborate new paradigms has been raised for more than 20 years. Schab (1991) wrote that “discrepancy between experience and past experimental research is due to less than optimal choice of procedures in the laboratory studies. One means of studying odor-cued recall in the laboratory is to ‘create’ a personal significant event”. This insight led Schab and Cain (1992) to suggest an example of a laboratory-based, personally significant event, which consisted of a scenario during which the participants witness a specific emotional event in the context of ambient odor and sound. This emotional event could be tested later to investigate the power of odor vs. sound to evoke episodic memory retrieval. The authors hypothesized that “Such an experiment might support the popular expectation regarding odor-evoked retrieval because it may stimulate the environmentally realistic event more faithfully”. However, their reflections did not give rise to any experiment. Sometime later, Aggleton and Waskett (1999) imagined an ingenious experiment where visitors to a museum were re-exposed to the ambient smell of a previous exhibition and were questioned about their memories of this exhibition. The odor specifically acted as an effective retrieval cue and improved their memory performances. This approach allowed for the investigation of the retrieval of a real-world episode but not in its entirety. The authors only tested the content of the exhibition and not the context or the emotion associated with the event. Along the same lines, Herz and Cupchik (1995) and Herz (1998) attempted to address the power of emotion triggered by odor to induce the recall of a memories-like association created in the laboratory. They used a paired-associate paradigm in which emotional paintings or pictures were paired with emotional odors or a verbal, visual, musical or tactile variant of the same cue. The mean percentages of paintings or pictures correctly recalled were similar across modalities, but the odor-evoked memories were significantly more emotionally loaded than the memories cued by the other modalities. The directions toward which this experiment went were exciting, but they were not further developed. Additionally, the paradigm was never enriched to match the content-based episodic-like memory definitions (Tulving, 1972; Easton and Eacott, 2008).

To investigate odor-evoked episodic memory, we recently developed an original laboratory-ecological approach deeply inspired by episodic-like memory tasks performed by animals (Saive et al., 2013). It was as ecologically valid as possible, yet the encoding and retrieval conditions were fully controlled. The to-be-remembered episodes were trial-unique, rich, close to real-life episodes, and in agreement with the definitions of episodic memory proposed by Tulving (1972) and Easton and Eacott (2008). During the encoding phase, the participants freely explored three unique episodes, one episode per day. Each unique episode was composed of three unfamiliar odors (What) positioned at three specific locations (Where) within a visual context (i.e., a picture of a landscape; Which context). We intentionally selected unfamiliar and largely unidentifiable odors and arbitrarily linked the odors, spatial locations and visual contexts in each episode to limit associative semantic processes. On the fourth day, the odors were used to trigger the retrieval of the complex episodes in a recall test. The participants were asked to recognize odors and to correctly remember the visuospatial context in which they were encountered, ensuring the evaluation of the memory content accuracy (Figure 7A). The participants were highly proficient in recognizing the target odors among distractors and retrieving the spatio-contextual environment of the episode with a rather high confidence level (Saive et al., 2013). This observation suggests that when an association between odors, spatial locations and contexts is encoded, the association forms an integrated representation retrievable by the participants. More recently, using a similar procedure, we observed that memory performances were influenced by the emotional content of the odor, regardless of their valence; both pleasant and unpleasant odors generated greater recognition and episodic retrieval than did neutral odors (Figure 7B; Saive et al., 2014). Our new approach is adapted to fMRI constraints and should permit further investigations of the neural basis of episodic odor memory.

FIGURE 7. Laboratory-ecological approach for studying episodic memory. (A) Episodic-memory task design. The memory of the episodes was tested using an odor recognition task followed for the “Yes” trials by an episodic memory retrieval (selection of a visual context and a location). (B) Number of accurate odor recognitions (Hit) and accurate episodic retrievals (WWW) as a function of odor pleasantness. Neut, neutral; Pleas, pleasant; Unp, unpleasant. * p < 0.05; ** p < 0.01 (adapted from Saive et al., 2014).

CONCLUSION AND FUTURE DIRECTIONS

Episodic odor memory is experimentally studied through a large set of paradigms and, as a consequence, the concept of “episodic odor memory” refers to an ensemble of memory processes which varied in complexity from the recognition of a single odor to the autobiographical memory evoked by odor. While large conceptual differences distinguish the laboratory-based and the autobiographical approaches, each approach has specificities that are complementary to the understanding of the neural underpinnings of the episodic odor memory. In laboratory-based approaches, the content of the memory is fully controlled and brain signals can be analyzed regarding the accuracy of the participants’ responses, allowing for the distinction between the neural substrates related to memory success or to memory failures. For example, a module of tightly-connected brain regions (hippocampus, caudate nucleus, anterior cingulate and medial temporal gyrus) is specifically involved when odors are accurately recognized (Meunier et al., 2014), while the perirhinal cortex is specifically associated with memory errors (Royet et al., 2011). In autobiographical approaches, the access to real-life memories allows for the involvement of a wider ensemble of cognitive processes. The personal significance of the cue item generates the engagement of semantic processes, as highlighted by the role of the inferior frontal gyrus (Royet et al., 1999, 2011; Savic and Berglund, 2004; Plailly et al., 2005, 2007), and of emotional and visual imagery processes reflecting the vividness of the recalled memories (Herz et al., 2004; Plailly et al., 2005). Studying autobiographical memories also enables addressing consolidation process over time and suggests a continuous engagement of the hippocampus whatever the age of the memory (Arshamian et al., 2013).

While the two experimental approaches differ in their conception of episodic memory, the overview of the functional neuroimaging findings suggests a core of relatively stable neural correlates of episodic odor memory regardless of the approach. The major role of the PC in human episodic odor memory is consensual. This finding agrees with the associational properties of the primary olfactory cortex observed in animals (Litaudon et al., 1997; Haberly, 2001; Wilson and Stevenson, 2003) and its role in working odor memory in humans (Zelano et al., 2009). The involvement of the PC in episodic odor memory is modality-specific (Gottfried et al., 2004; Lehn et al., 2013), it is independent of odor valence (Gottfried et al., 2004; Yeshurun et al., 2009), and it tends to be lateralized to the right (vs. left) hemisphere (Savic et al., 2000; Dade et al., 2002; Gottfried et al., 2004; Plailly et al., 2005; Cerf-Ducastel and Murphy, 2006). The hippocampus is also consistently observed in both approaches, which is consistent with a large amount of literature that stresses the importance of this brain region in episodic memory (e.g., Suzuki and Amaral, 1994; Burwell and Amaral, 1998; Witter et al., 2000; Squire et al., 2004; Davachi, 2006; Diana et al., 2007; Eichenbaum et al., 2007). The literature involving the olfactory modality further shows that hippocampal activation reflects the memory performance (Royet et al., 2011; Lehn et al., 2013), and that while the hippocampus is engaged in the episodic memory of different sensory modalities (Plailly et al., 2007; Lehn et al., 2013), it has a privileged role for the first olfactory associations (Yeshurun et al., 2009). Additionally to the PC and hippocampus, laboratory-based and autobiographical approaches are concordant in the role of prefrontal, infero-temporal, postero-parietal and medial temporal lobe brain regions in odor episodic memory. Thus, the present review agrees with previous report demonstrating that brain networks involved in classical autobiographical studies partially overlap with those found in more controlled laboratory episodic memory tasks (Cabeza et al., 2004; Burianova and Grady, 2007; Cabeza and St Jacques, 2007).

We believe that the development of laboratory-ecological approaches that control the encoding and retrieval of specific and multidimensional laboratory episodes can yield new discoveries for the comprehension of episodic memory. By controlling each aspect of the to-be-remembered event and of its retrieval, specific questions can be addressed. For example, the close relationship between olfaction, emotion and memory, commonly illustrated as the Proust phenomenon (Chu and Downes, 2000), can be further explored by manipulating the emotional strength of the episode during encoding and by manipulating the sensory modality of the cue that triggers episodic retrieval during the test phase. Furthermore, Mitchell and Johnson (2009) stressed the importance to rate amount of details of various types or vividness, emotional valence, arousal, because they provide specific information that explain the complex inter-play of cognitive processes that are characteristic when retrieving rich memories and that can be related to brain activity. Such features are relatively easy to measure and can be crucial in the understanding of the different processes underlying episodic memory. We further suggest the investigation of the brain as whole through the use of specific analysis techniques. Most cerebral imaging functional studies have used univariate statistical analyses to localize individual aspects of brain function, and have restricted investigation to specialized cognitive sub-systems. Various techniques for measuring functional connectivity are to date available and their use can represent a considerable improvement in the understanding of episodic memory. This sum of efforts will be the basis of real advances in this field and will bring substantial progress in the understanding of the behavioral specificities of episodic odor memory.

ABBREVIATIONS

fMRI, functional magnetic resonance imaging; PET, positron emission tomography.

ACKNOWLEDGMENTS

This work was supported by the Centre National de la Recherche Scientifique (CNRS), the LABEX Cortex (NR-11-LABX-0042) of Université de Lyon within the program “Investissements d’Avenir” (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR) and research grants from the Région Rhône-Alpes (CIBLE 10 015 772 01). Anne-Lise Saive was funded by the Roudnitska Foundation.

REFERENCES

Aggleton, J. P., and Waskett, L. (1999). The ability of odours to serve as state-dependent cues for real-world memories: can Viking smells aid the recall of Viking experiences? Br. J. Psychol. 90, 1–7. doi: 10.1348/000712699161170

Arshamian, A., Iannilli, E., Gerber, J. C., Willander, J., Persson, J., Seo, H.-S., et al. (2013). The functional neuroanatomy of odor evoked autobiographical memories cued by odors and words. Neuropsychologia 51, 123–131. doi: 10.1016/j.neuropsychologia.2012.10.023

Ayabe-Kanamura, S., Schicker, I., Laska, M., Hudson, R., Distel, H., Kobayakawa, T., et al. (1998). Differences in perception of everyday odors: a Japanese-German cross-cultural study. Chem. Senses 23, 31–38. doi: 10.1093/chemse/23.1.31

Babb, S. J., and Crystal, J. D. (2006). Episodic-like memory in the rat. Curr. Biol. 16, 1317–1321. doi: 10.1016/j.cub.2006.05.025

Bhalla, M., Marcus, K. M., and Cornwell, J. M. (2000). Odor recognition and identification: effect of labels over time. Psychol. Rep. 86, 565–574. doi: 10.2466/pr0.86.2.565-574

Broman, D. A., Olsson, M. J., and Nordin, S. (2001). Lateralization of olfactory cognitive functions: effects of rhinal side of stimulation. Chem. Senses 26, 1187–1192. doi: 10.1093/chemse/26.9.1187

Buchanan, T. W., Tranel, D., and Adolphs, R. (2003). A specific role for the human amygdala in olfactory memory. Learn. Mem. 10, 319–325. doi: 10.1101/lm.62303

Burianova, H., and Grady, C. L. (2007). Common and unique neural activations in autobiographical, episodic and semantic retrieval. J. Cogn. Neurosci. 19, 1520–1534. doi: 10.1162/jocn.2007.19.9.1520

Burwell, R. D., and Amaral, D. G. (1998). Cortical afferents of the perirhinal, postrhinal and entorhinal cortices of the rat. J. Comp. Neurol. 398, 179–205. doi: 10.1002/(sici)1096-9861(19980824)398:2<179::aid-cne3>3.0.co;2-y

Cabeza, R., and St Jacques, P. (2007). Functional neuroimaging of autobiographical memory. Trends Cogn. Sci. 11, 219–227. doi: 10.1016/j.tics.2007.02.005

Cabeza, R., Prince, S. E., Daselaar, S. M., Greenberg, D. L., Budde, M., Dolcos, F., et al. (2004). Brain activity during episodic retrieval of autobiographical and laboratory events: an fMRI study using a novel photo paradigm. J. Cogn. Neurosci. 16, 1583–1594. doi: 10.1162/0898929042568578

Cain, W. S. (1979). To know with the nose: keys to odor identification. Science 203, 467–470. doi: 10.1126/science.760202

Carmichael, S. T., Clugnet, M. C., and Price, J. L. (1994). Central olfactory connections in the macaque monkey. J. Comp. Neurol. 346, 403–434. doi: 10.1002/cne.903460306

Carroll, B., Richardson, J. T., and Thompson, P. (1993). Olfactory information processing and temporal lobe epilepsy. Brain Cogn. 22, 230–243. doi: 10.1006/brcg.1993.1036

Cerf-Ducastel, B., and Murphy, C. (2006). Neural substrates of cross-modal olfactory recognition memory: an fMRI study. Neuroimage 31, 386–396. doi: 10.1016/j.neuroimage.2005.11.009

Chu, S., and Downes, J. J. (2000). Odour-evoked autobiographical memories: psychological investigations of proustian phenomena. Chem. Senses 25, 111–116. doi: 10.1093/chemse/25.1.111

Chu, S., and Downes, J. J. (2002). Proust nose best: odors are better cues of autobiographical memory. Mem. Cognit. 30, 511–518. doi: 10.3758/bf03194952

Clayton, N. S., Bussey, T. J., and Dickinson, A. (2003). Can animals recall the past and plan for the future? Nat. Rev. Neurosci. 4, 685–691. doi: 10.1038/nrn1180

Clayton, N. S., and Dickinson, A. (1998). Episodic-like memory during cache recovery by scrub jays. Nature 395, 272–274. doi: 10.1038/26216

Cohen, N. J., and Eichenbaum, H. (1993). Memory, Amnesia, and the Hippocampal System. Cambridge, MA: MIT Press.

Crystal, J. D. (2009). Elements of episodic-like memory in animal models. Behav. Processes 80, 269–277. doi: 10.1016/j.beproc.2008.09.009

Dade, L. A., Zatorre, R. J., and Jones-Gotman, M. (2002). Olfactory learning: convergent findings from lesion and brain imaging studies in humans. Brain 125, 86–101. doi: 10.1093/brain/awf003

Davachi, L. (2006). Item, context and relational episodic encoding in humans. Curr. Opin. Neurobiol. 16, 693–700. doi: 10.1016/j.conb.2006.10.012

Davis, R. G. (1975). Acquisition of verbal associations to olfactory stimuli of varying familiarity and to abstract visual stimuli. J. Exp. Psychol. Hum. Learn. 104, 134–142. doi: 10.1037//0278-7393.1.2.134

Davis, R. G. (1977). Acquisition and retention of verbal associations to olfactory and abstract visual stimuli of varying similarity. J. Exp. Psychol. Hum. Learn. 3, 37–51. doi: 10.1037//0278-7393.3.1.37

Delplanque, S., Grandjean, D., Chrea, C., Aymard, L., Cayeux, I., Le Calve, B., et al. (2008). Emotional processing of odors: evidence for a nonlinear relation between pleasantness and familiarity evaluations. Chem. Senses 33, 469–479. doi: 10.1093/chemse/bjn014

Diana, R. A., Yonelinas, A. P., and Ranganath, C. (2007). Imaging recollection and familiarity in the medial temporal lobe: a three-component model. Trends Cogn. Sci. 11, 379–386. doi: 10.1016/j.tics.2007.08.001

Distel, H., Ayabe-Kanamura, S., Martinez-Gomez, M., Schicker, I., Kobayakawa, T., Saito, S., et al. (1999). Perception of everyday odors—correlation between intensity, familiarity and strength of hedonic judgement. Chem. Senses 24, 191–199. doi: 10.1093/chemse/24.2.191

Eacott, M. J., and Easton, A. (2010). Episodic memory in animals: remembering which occasion. Neuropsychologia 48, 2273–2280. doi: 10.1016/j.neuropsychologia.2009.11.002

Easton, A., and Eacott, M. J. (2008). “Chapter 2.3 a new working definition of episodic memory: replacing ‘when’ with ‘which’,” in Handbook of Behavioral Neuroscience, ed A. E. Ekrem Dere (Düsseldorf: Elsevier),185–196.

Easton, A., Webster, L. A. D., and Eacott, M. J. (2012). The episodic nature of episodic-like memories. Learn. Mem. 19, 146–150. doi: 10.1101/lm.025676.112

Eichenbaum, H., Yonelinas, A. P., and Ranganath, C. (2007). The medial temporal lobe and recognition memory. Annu. Rev. Neurosci. 30, 123–152. doi: 10.1146/annurev.neuro.30.051606.094328

Engen, T. (1987). Remembering odors and their names. Amer. Scientist. 75, 497–503.

Engen, T., and Pfaffmann, C. (1960). Absolute judgments of odor quality. J. Exp. Psychol. 59, 214–219. doi: 10.1037/h0043912

Engen, T., and Ross, B. M. (1973). Long-term memory of odors with and without verbal descriptions. Exp. Psychol. 100, 221–227. doi: 10.1037/h0035492

Ferdenzi, C., Roberts, S. C., Schirmer, A., Delplanque, S., Cekic, S., Porcherot, C., et al. (2013). Variability of affective responses to odors: culture, gender and olfactory knowledge. Chem. Senses 38, 175–186. doi: 10.1093/chemse/bjs083

Frank, R. A., Rybalsky, K., Brearton, M., and Mannea, E. (2011). Odor recognition memory as a function of odor-naming performance. Chem. Senses 36, 29–41. doi: 10.1093/chemse/bjq095

Gilbert, P. E., Pirogovsky, E., Ferdon, S., and Murphy, C. (2006). The effects of normal aging on source memory for odors. J. Gerontol. B Psychol. Sci. Soc. Sci. 61, P58–P60. doi: 10.1093/geronb/61.1.p58

Gilbert, P. E., Pirogovsky, E., Ferdon, S., Brushfield, A. M., and Murphy, C. (2008). Differential effects of normal aging on memory for odor-place and object-place associations. Exp. Aging Res. 34, 437–452. doi: 10.1080/03610730802271914

Goddard, L., Pring, L., and Felmingham, N. (2005). The effects of cue modality on the quality of personal memories retrieved. Memory 13, 79–86. doi: 10.1080/09658210344000594

Goldman, W. P., and Seamon, J. G. (1992). Very long-term memory for odors: retention of odor-name associations. Am. J. Psychol. 105, 549–563. doi: 10.2307/1422910

Goodrich-Hunsaker, N. J., Gilbert, P. E., and Hopkins, R. O. (2009). The role of the human hippocampus in odor-place associative memory. Chem. Senses 34, 513–521. doi: 10.1093/chemse/bjp026

Gottfried, J. A., Smith, A. P., Rugg, M. D., and Dolan, R. J. (2004). Remembrance of odors past: human olfactory cortex in cross-modal recognition memory. Neuron 42, 687–695. doi: 10.1016/S0896-6273(04)00270-3

Griffiths, D. P., and Clayton, N. S. (2001). Testing episodic memory in animals: a new approach. Physiol. Behav. 73, 755–762. doi: 10.1016/s0031-9384(01)00532-7

Haberly, L. B. (1998). “Olfactory cortex,” in The Synaptic Organization of the Brain, ed G. M. Shepherd (New York: Oxford University Press), 377–416.

Haberly, L. B. (2001). Parallel-distributed processing in olfactory cortex: new insights from morphological and physiological analysis of neuronal circuitry. Chem. Senses 26, 551–576. doi: 10.1093/chemse/26.5.551

Hernandez, R. J., Bayer, Z. C., Brushfield, A. M., Pirogovsky, E., Murphy, C., and Gilbert, P. E. (2008). Effect of encoding condition on source memory for odors in healthy young and older adults. Gerontology 54, 187–192. doi: 10.1159/000121377

Herz, R. S. (1998). Are odors the best cues to memory? A cross-modal comparison of associative memory stimuli. Ann. N Y Acad. Sci. 855, 670–674. doi: 10.1111/j.1749-6632.1998.tb10643.x

Herz, R. S. (2004). A naturalistic analysis of autobiographical memories triggered by olfactory visual and auditory stimuli. Chem. Senses 29, 217–224. doi: 10.1093/chemse/bjh025

Herz, R. S. (2012). “Odor memory and the special role of associative learning,” in Olfactory Cognition: From Perception and Memory to Environmental Odours and Neuroscience, eds G. M. Zucco, R. S. Herz and B. Schaal (Amsterdam, Holland: John Benjamins Publishing Company), 95–114.

Herz, R. S., and Cupchik, G. C. (1992). An experimental characterization of odor-evoked memories in humans. Chem. Senses 17, 519–528. doi: 10.1093/chemse/17.5.519

Herz, R. S., and Cupchik, G. C. (1995). The emotional distinctiveness of odor-evoked memories. Chem. Senses 20, 517–528. doi: 10.1093/chemse/20.5.517

Herz, R. S., and Engen, T. (1996). Odor memory: review and analysis. Psychon. Bull. Rev. 3, 300–313. doi: 10.3758/BF03210754

Herz, R. S., Eliassen, J., Beland, S., and Souza, T. (2004). Neuroimaging evidence for the emotional potency of odor-evoked memory. Neuropsychologia 42, 371–378. doi: 10.1016/j.neuropsychologia.2003.08.009

Herz, R. S., and Schooler, J. W. (2002). A naturalistic study of autobiographical memories evoked by olfactory and visual cues: testing the Proustian hypothesis. Am. J. Psychol. 115, 21–32. doi: 10.2307/1423672

Hinton, P. B., and Henley, T. B. (1993). Cognitive and affective components of stimuli presented in three modes. Bull. Psychon. Soc. 31, 595–598. doi: 10.3758/bf03337365

Holland, S. M., and Smulders, T. V. (2011). Do humans use episodic memory to solve a what-where-when memory task? Anim. Cogn. 14, 95–102. doi: 10.1007/s10071-010-0346-5

Hudry, J., Perrin, F., Ryvlin, P., Mauguiere, F., and Royet, J. P. (2003). Olfactory short-term memory and related amygdala recordings in patients with temporal lobe epilepsy. Brain 126, 1851–1863. doi: 10.1093/brain/awg192

Insausti, R., Herrero, M. T., and Witter, M. P. (1997). Entorhinal cortex of the rat: cytoarchitectonic subdivisions and the origin and distribution of cortical efferents. Hippocampus 7, 146–183. doi: 10.1002/(sici)1098-1063(1997)7:2<146::aid-hipo4>3.0.co;2-l

Jehl, C., Royet, J. P., and Holley, A. (1997). Role of verbal encoding in short- and long-term odor recognition. Percept. Psychophys. 59, 100–110. doi: 10.3758/bf03206852

Jellinek, J. S., and Köster, E. P. (1983). Perceived fragrance complexity and its relation to familiarity and pleasantness II. J. Soc. Cosmet. Chem. 34, 83–97.

Jones, F. N., Roberts, K., and Holman, E. W. (1978). Similarity judgments and recognition memory for some common spices. Percept. Psychophys. 24, 2–6. doi: 10.3758/bf03202967

Jones-Gotman, M., and Zatorre, R. J. (1993). Odor recognition memory in humans: role of right temporal and orbitofrontal regions. Brain Cogn. 22, 182–198. doi: 10.1006/brcg.1993.1033

Larsson, M. (1997). Semantic factors in episodic recognition of common odors in early and late adulthood: a review. Chem. Senses 22, 623–633. doi: 10.1093/chemse/22.6.623

Larsson, M., and Backman, L. (1997). Age-related differences in episodic odour recognition: the role of access to specific odour names. Memory 5, 361–378. doi: 10.1080/741941391

Larsson, M., Lovden, M., and Nilsson, L. G. (2003). Sex differences in recollective experience for olfactory and verbal information. Acta Psychol. (Amst) 112, 89–103. doi: 10.1016/s0001-6918(02)00092-6

Larsson, M., Oberg, C., and Backman, L. (2006). Recollective experience in odor recognition: influences of adult age and familiarity. Psychol. Res. 70, 68–75. doi: 10.1007/s00426-004-0190-9

Larsson, M., Oberg-Blavarg, C., and Jonsson, F. U. (2009). Bad odors stick better than good ones: olfactory qualities and odor recognition. Exp. Psychol. 56, 375–380. doi: 10.1027/1618-3169.56.6.375

Larsson, M., and Willander, J. (2009). Autobiographical odor memory. Ann. N Y Acad. Sci. 1170, 318–323. doi: 10.1111/j.1749-6632.2009.03934.x

Lawless, H. T. (1978). Recognition of common odors, pictures and simple shapes. Percept. Psychophys. 24, 493–495. doi: 10.3758/bf03198772

Lawless, H. T., and Cain, W. S. (1975). Recognition memory for odors. Chem. Senses 1, 331–337. doi: 10.1093/chemse/1.3.331

Lawless, H., and Engen, T. (1977). Associations to odors: interference, mnemonics and verbal labeling. J. Exp. Psychol. Hum. Learn. 3, 52–59. doi: 10.1037//0278-7393.3.1.52

Lehn, H., Kjønigsen, L. J., Kjelvik, G., and Håberg, A. K. (2013). Hippocampal involvement in retrieval of odor vs. object memories. Hippocampus 23, 122–128. doi: 10.1002/hipo.22073

Lesschaeve, I., and Issanchou, S. (1996). Effects of panel experience on olfactory memory performance: influence of stimuli familiarity and labeling ability of subjects. Chem. Senses 21, 699–709. doi: 10.1093/chemse/21.6.699

Levy, D. A., Hopkins, R. O., and Squire, L. R. (2004). Impaired odor recognition memory in patients with hippocampal lesions. Learn. Mem. 11, 794–796. doi: 10.1101/lm.82504

Litaudon, P., Mouly, A. M., Sullivan, R., Gervais, R., and Cattarelli, M. (1997). Learning-induced changes in rat piriform cortex activity mapped using multisite recording with voltage sensitive dye. Eur. J. Neurosci. 9, 1593–1602. doi: 10.1111/j.1460-9568.1997.tb01517.x

Lockhart, R. S., and Murdock, B. B. (1970). Memory and the theory of signal detection. Psychol. Bull. 74, 100–109. doi: 10.1037/h0029536

Lyman, B. J., and McDaniel, M. A. (1986). Effects of encoding strategy on long-term memory for odours. Quart. J. Exp. Psychol. 38A, 753–765. doi: 10.1080/14640748608401624

Lyman, B. J., and McDaniel, M. A. (1990). Memory for odors and odor names: modalities of elaborating and imagery. J. Exp. Psychol. Learn. 16, 656–664. doi: 10.1037/0278-7393.16.4.656

Mandler, G. (1980). Recognizing: the judgment of previous occurrence. Psychol. Rev. 87, 252–271. doi: 10.1037/0033-295x.87.3.252

Marr, D. (1971). Simple memory: a theory for archicortex. Philos. Trans. R. Soc. Lond. B Biol. Sci. 262, 23–81. doi: 10.1098/rstb.1971.0078

McDermott, K. B., Szpunar, K. K., and Christ, S. E. (2009). Laboratory-based and autobiographical retrieval tasks differ substantially in their neural substrates. Neuropsychologia 47, 2290–2298. doi: 10.1016/j.neuropsychologia.2008.12.025

Meunier, D., Fonlupt, P., Saive, A.-L., Plailly, J., Ravel, N., and Royet, J.-P. (2014). Modular structure of functional networks in olfactory memory. Neuroimage 95C, 264–275. doi: 10.1016/j.neuroimage.2014.03.041

Miles, A. N., and Berntsen, D. (2011). Odour-induced mental time travel into the past and future: do odour cues retain a unique link to our distant past? Memory 19, 930–940. doi: 10.1080/09658211.2011.613847

Milner, B., Corkin, S., and Teuber, H.-L. (1968). Further analysis of the hippocampal amnesic syndrome: 14-year follow-up study of H.M. Neuropsychologia 6, 215–234. doi: 10.1016/0028-3932(68)90021-3

Milton, F., Muhlert, N., Butler, C. R., Smith, A., Benattayallah, A., and Zeman, A. Z. (2011). An fMRI study of long-term everyday memory using SenseCam. Memory 19, 733–744. doi: 10.1080/09658211.2011.552185

Mitchell, K. J., and Johnson, M. K. (2009). Source monitoring 15 years later: what have we learned from fMRI about the neural mechanisms of source memory? Psychol. Bull. 135, 638–677. doi: 10.1037/a0015849

Murphy, C., Cain, W. S., Gilmore, M. M., and Skinner, R. B. (1991). Sensory and semantic factors in recognition memory for odors and graphic stimuli: elderly versus young persons. Am. J. Psychol. 104, 161–192. doi: 10.2307/1423153

Nadel, L., and Moscovitch, M. (1997). Memory consolidation, retrograde amnesia and the hippocampal complex. Curr. Opin. Neurobiol. 7, 217–227. doi: 10.1016/s0959-4388(97)80010-4

Nadel, L., and Moscovitch, M. (1998). Hippocampal contributions to cortical plasticity. Neuropharmacology 37, 431–439. doi: 10.1016/s0028-3908(98)00057-4

Olsson, M. J., Lundgren, E. B., Soares, S. C., and Johansson, M. (2009). Odor memory performance and memory awareness: a comparison to word memory across orienting tasks and retention intervals. Chemosens. Percept. 2, 161–171. doi: 10.1007/s12078-009-9051-7

Pause, B. M., Jungbluth, C., Adolph, D., Pietrowsky, R., and Dere, E. (2010). Induction and measurement of episodic memories in healthy adults. J. Neurosci. Methods 189, 88–96. doi: 10.1016/j.jneumeth.2010.03.016

Pause, B. M., Zlomuzica, A., Kinugawa, K., Mariani, J., Pietrowsky, R., and Dere, E. (2013). Perspectives on episodic-like and episodic memory. Front. Behav. Neurosci. 7:33. doi: 10.3389/fnbeh.2013.00033

Pirogovsky, E., Gilbert, P. E., and Murphy, C. (2006). Source and item memory for odors and objects in children and young adults. Dev. Neuropsychol. 30, 739–752. doi: 10.1207/s15326942dn3002_5

Pirogovsky, E., Murphy, C., and Gilbert, P. E. (2009). Developmental differences in memory for cross-modal associations. Dev. Sci. 12, 1054–1059. doi: 10.1111/j.1467-7687.2009.00857.x

Plailly, J., Bensafi, M., Pachot-Clouard, M., Delon-Martin, C., Kareken, D. A., Rouby, C., et al. (2005). Involvement of right piriform cortex in olfactory familiarity judgments. Neuroimage 24, 1032–1041. doi: 10.1016/j.neuroimage.2004.10.028

Plailly, J., Luangraj, N., Nicklaus, S., Issanchou, S., Royet, J. P., and Sulmont-Rosse, C. (2011). Alliesthesia is greater for odors of fatty foods than of non-fat foods. Appetite 57, 615–622. doi: 10.1016/j.appet.2011.07.006

Plailly, J., Tillmann, B., and Royet, J. P. (2007). The feeling of familiarity of music and odors: the same neural signature? Cereb. Cortex 17, 2650–2658. doi: 10.1093/cercor/bhl173

Poellinger, A., Thomas, R., Lio, P., Lee, A., Makris, N., Rosen, B. R., et al. (2001). Activation and habituation in olfaction - an fMRI study. Neuroimage 13, 547–560. doi: 10.1006/nimg.2000.0713

Rabin, M. D., and Cain, W. S. (1984). Odor recognition: familiarity, identifiability and encoding consistency. J. Exp. Psychol. Learn. Mem. Cogn. 10, 316–325. doi: 10.1037//0278-7393.10.2.316

Rausch, R., Serafetinides, E. A., and Crandall, P. H. (1977). Olfactory memory in patients with anterior temporal lobectomy. Cortex 13, 445–452. doi: 10.1016/s0010-9452(77)80024-5

Robinson, J. A. (1976). Sampling autobiographical memory. Cogn. Psychol. 8, 578–595. doi: 10.1016/0010-0285(76)90020-7

Royet, J. P., and Plailly, J. (2004). Lateralization of olfactory processes. Chem. Senses 29, 731–745. doi: 10.1093/chemse/bjh067

Royet, J. P., Hudry, J., Zald, D. H., Godinot, D., Gregoire, M. C., Lavenne, F., et al. (2001). Functional neuroanatomy of different olfactory judgments. Neuroimage 13, 506–519. doi: 10.1006/nimg.2000.0704

Royet, J. P., Koenig, O., Gregoire, M. C., Cinotti, L., Lavenne, F., Le Bars, D., et al. (1999). Functional anatomy of perceptual and semantic processing for odors. J. Cogn. Neurosci. 11, 94–109. doi: 10.1162/089892999563166

Royet, J.-P., Morin-Audebrand, L., Cerf-Ducastel, B., Haase, L., Issanchou, S., Murphy, C., et al. (2011). True and false recognition memories of odors induce distinct neural signatures. Front. Hum. Neurosci. 5:65. doi: 10.3389/fnhum.2011.00065

Royet, J.-P., Saive, A.-L., Plailly, J., and Veyrac, A. (2014). “Etre parfumeur, une question de prédisposition ou d’entraînement?,” in Art Olfactif Contemporain, ed C. Jacquet (Paris: Classiques Garnier), in press.

Rubin, D. C., Groth, E., and Goldsmith, D. J. (1984). Olfactory cuing of autobiographical memory. Am. J. Psychol. 97, 493–507. doi: 10.2307/1422158

Saive, A.-L., Ravel, N., Thévenet, M., Royet, J.-P., and Plailly, J. (2013). A novel experimental approach to episodic memory in humans based on the privileged access of odors to memories. J. Neurosci. Meth. 213, 22–31. doi: 10.1016/j.jneumeth.2012.11.010

Saive, A.-L., Royet, J.-P., Ravel, N., Thévenet, M., Garcia, S., and Plailly, J. (2014). A unique memory process, modulated by emotion, underpins successful odor recognition and episodic retrieval in humans. Front. Behav. Neurosci. 8:203. doi: 10.3389/fnbeh.2014.00203

Savic, I., and Berglund, H. (2004). Passive perception of odors and semantic circuits. Hum. Brain Mapp. 21, 271–278. doi: 10.1002/hbm.20009

Savic, I., Gulyas, B., Larsson, M., and Roland, P. (2000). Olfactory functions are mediated by parallel and hierarchical processing. Neuron 26, 735–745. doi: 10.1016/s0896-6273(00)81209-x

Schab, F. R. (1991). Odor memory: taking stock. Psychol. Bull. 109, 242–251. doi: 10.1037/0033-2909.109.2.242

Schab, F. R., and Cain, W. S. (1992). “Memory for odors,” in The Human Sense of Smell, eds D. G. Laing, R. L. Doty and W. Breipohl (Berlin: Springer-Verlag), 217–240.

Sobel, N., Prabhakaran, V., Desmond, J. E., Glover, G. H., Goode, R. L., Sullivan, E. V., et al. (1998). Sniffing and smelling: separate subsystems in the human olfactory cortex. Nature 392, 282–286. doi: 10.1038/32654

Squire, L. R. (1992). Memory and the hippocampus: a synthesis from findings with rats, monkeys and humans. Psychol. Rev. 99, 195–231. doi: 10.1037/0033-295x.99.2.195

Squire, L. R., Cohen, N. J., and Nadel, L. (1984). “The medial temporal region and memory consolidation: a new hypothesis,” in Memory Consolidation: Psychobiology of Cognition, eds H. Weingartner and E. Parker (Hillsdale, NJ: Lawrence Erlbaum and Associates), 185–210.

Squire, L. R., Stark, C. E. L., and Clark, R. E. (2004). The medial temporal lobe. Annu. Rev. Neurosci. 27, 279–306. doi: 10.1146/annurev.neuro.27.070203.144130

Sulmont, C., Issanchou, S., and Koster, E. P. (2002). Selection of odorants for memory tests on the basis of familiarity, perceived complexity, pleasantness, similarity and identification. Chem. Senses 27, 307–317. doi: 10.1093/chemse/27.4.307

Suzuki, W. A., and Amaral, D. G. (1994). Topographic organization of the reciprocal connections between the monkey entorhinal cortex and the perirhinal and parahippocampal cortices. J. Neurosci. 14, 1856–1877.

Swets, J. A. (1964). Signal Detection and Recognition by Human Observers. New York: Wiley.

Takahashi, M. (2003). Recognition of odors and identification of sources. Am. J. Psychol. 116, 527–542. doi: 10.2307/1423659

Tulving, E. (1972). “Episodic and semantic memory,” in Organization of Memory, eds E. Tulving and W. Donaldson (New York: Academic Press), 381–403.

Tulving, E. (1983). Elements of Episodic Memory. Oxford: Clarendon Press.

Tulving, E. (1985). Memory and consciousness. Can. Psychol. 26, 1–12. doi: 10.1037/h0080017

Tulving, E. (2001). Episodic memory and common sense: how far apart? Philos. Trans. R. Soc. Lond. B Biol. Sci. 356, 1505–1515. doi: 10.1098/rstb.2001.0937

Tulving, E. (2002). Episodic memory: from mind to brain. Annu. Rev. Psychol. 53, 1–25. doi: 10.1146/annurev.psych.53.100901.135114

Willander, J., and Larsson, M. (2006). Smell your way back to childhood: autobiographical odor memory. Psychon. Bull. Rev. 13, 240–244. doi: 10.3758/bf03193837

Willander, J., and Larsson, M. (2007). Olfaction and emotion: the case of autobiographical memory. Mem. Cognit. 35, 1659–1663. doi: 10.3758/bf03193499

Wilson, D. A., and Stevenson, R. J. (2003). Olfactory perceptual learning: the critical role of memory in odor discrimination. Neurosci. Biobehav. Rev. 27, 307–328. doi: 10.1016/s0149-7634(03)00050-2

Witter, M. P., Naber, P. A., Van Haeften, T., Machielsen, W. C., Rombouts, S. A., Barkhof, F., et al. (2000). Cortico-hippocampal communication by way of parallel parahippocampal-subicular pathways. Hippocampus 10, 398–410. doi: 10.1002/1098-1063(2000)10:4<398::aid-hipo6>3.3.co;2-b

Yeshurun, Y., Lapid, H., Dudai, Y., and Sobel, N. (2009). The privileged brain representation of first olfactory associations. Curr. Biol. 19, 1869–1874. doi: 10.1016/j.cub.2009.09.066

Yousem, D. M., Williams, S. C. R., Howard, R. O., Andrew, C., Simmons, A., Allin, M., et al. (1997). Functional MR imaging during odor stimulation: preliminary data. Radiology 204, 833–838.

Zatorre, R. J., Jones-Gotman, M., Evans, A. C., and Meyer, E. (1992). Functional localization and lateralization of human olfactory cortex. Nature 360, 339–340. doi: 10.1038/360339a0

Zelano, C., Montag, J., Khan, R., and Sobel, N. (2009). A specialized odor memory buffer in primary olfactory cortex. PLoS One 4:e4965. doi: 10.1371/journal.pone.0004965

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 14 April 2014; Accepted: 20 June 2014; Published online: 07 July 2014.

Citation: Saive A-L, Royet J-P and Plailly J (2014) A review on the neural bases of episodic odor memory: from laboratory-based to autobiographical approaches. Front. Behav. Neurosci. 8:240. doi: 10.3389/fnbeh.2014.00240

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Saive, Royet and Plailly. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 06 June 2014
doi: 10.3389/fnbeh.2014.00203

A unique memory process modulated by emotion underpins successful odor recognition and episodic retrieval in humans

Anne-Lise Saive[*], Jean-Pierre Royet, Nadine Ravel, Marc Thévenet, Samuel Garcia and Jane Plailly

Lyon Neuroscience Research Center, CNRS UMR 5292 - INSERM U1028 - University Lyon1, Lyon, France

* Correspondence: Anne-Lise Saive, Lyon Neuroscience Research Center, CNRS UMR 5292 - INSERM U1028 - University Lyon1, 50 Avenue Tony Garnier, 69366 Lyon, France e-mail: anne-lise.saive@olfac.univ-lyon1.fr

Edited by:
Donald A. Wilson, New York University School of Medicine, USA

Reviewed by:
Max L. Fletcher, University of Tennessee Health Science Center, USA
Yaara Yeshurun, Princeton University, USA

We behaviorally explore the link between olfaction, emotion and memory by testing the hypothesis that the emotion carried by odors facilitates the memory of specific unique events. To investigate this idea, we used a novel behavioral approach inspired by a paradigm developed by our team to study episodic memory in a controlled and as ecological as possible way in humans. The participants freely explored three unique and rich laboratory episodes; each episode consisted of three unfamiliar odors (What) positioned at three specific locations (Where) within a visual context (Which context). During the retrieval test, which occurred 24–72 h after the encoding, odors were used to trigger the retrieval of the complex episodes. The participants were proficient in recognizing the target odors among distractors and retrieving the visuospatial context in which they were encountered. The episodic nature of the task generated high and stable memory performances, which were accompanied by faster responses and slower and deeper breathing. Successful odor recognition and episodic memory were not related to differences in odor investigation at encoding. However, memory performances were influenced by the emotional content of the odors, regardless of odor valence, with both pleasant and unpleasant odors generating higher recognition and episodic retrieval than neutral odors. Finally, the present study also suggested that when the binding between the odors and the spatio-contextual features of the episode was successful, the odor recognition and the episodic retrieval collapsed into a unique memory process that began as soon as the participants smelled the odors.

Keywords: episodic memory, recognition memory, encoding, olfaction, visuospatial context, emotion, breathing, human

INTRODUCTION

Human episodic memory is the memory that permits the conscious re-experience of specific personal events from the past (Tulving, 1972, 1983) and is associated with a feeling of mental time travel (Tulving, 2001, 2002). Because the investigation of this ability in animals is controversial, content-based approaches have been developed that focus on the different types of information stored in memory: What happened, Where and When (Clayton and Dickinson, 1998; Griffiths and Clayton, 2001; Babb and Crystal, 2006; Crystal, 2009). Subsequently, based on human phenomenological experiences of event recall, Easton and Eacott (2008; Eacott and Easton, 2010) enriched this refined definition of episodic memory. They widened its third dimension, replacing the temporal dimension with the specific occasion or context in which the event occurred, thereby leading to a “What, Where, Which occasion, or Which context” definition. The authors considered episodic memory as a “snapshot” of an episode in which time can form a part of the context but is not the only contextual marker. Emotion, semantic knowledge, the visual scene, or auditory and olfactory environments can also define the context of the episode. For example, when you remember the last time you went to a restaurant, you can recall where and when it was, as well as the occasion for which you were there, with whom, what you ate, and if you had a good evening. Importantly, these approaches did not consider the memory in terms of autonoetic consciousness, and therefore, were referred to as episodic-like memory (Clayton and Dickinson, 1998; Clayton et al., Easton and Eacott (2003).

In humans, two approaches are usually used to study past event retrieval. In the ecological approach, experimenters test autobiographical memory by interrogating participants about real-life memories encoded in their past (Fink et al., 1996; Levine et al., 2004; Piolino et al., 2004; Nadel et al., 2007; Janata, 2009). This approach is quite ecological because it is close to real-life recall, but the veracity of the recalled events cannot be controlled for. In the laboratory-based approach, experimenters test the memorization of artificial episodes created in the laboratory using recognition tasks (Konishi et al., 2000; Daselaar et al., 2003; Donaldson et al., 2010; Royet et al., 2011; Herholz et al., 2012), thereby permitting control of the encoding conditions, the retention time and the veracity of the retrieval. However, the information to be remembered is often one-dimensional (e.g., What) and is therefore poor in comparison with a real-life episode. To limit the drawbacks of such methods, new laboratory-ecological approaches halfway between these two traditional methods have recently been devised to explore human episodic memory (Pause et al., 2010, 2013; Holland and Smulders, 2011; Milton et al., 2011; Saive et al., 2013). We proposed such an intermediate approach that was deeply inspired by tasks developed to study episodic-like memory in animals to determine the experimental conditions that best evaluate episodic memory while remaining ecologically valid (Saive et al., 2013). This approach allowed the controlled study of trial-unique free encoding, retention delay and the retrieval of rich and complex episodes composed of unnamable odors (What) located spatially (Where) within a visual context (Which context).

Phenomenologically, olfaction, memory and emotion are closely linked. Odors are particularly evocative reminders of past events. Among all sensorial stimuli, odors trigger more vivid and emotional memories (Hinton and Henley, 1993; Herz and Cupchik, 1995; Chu and Downes, 2002; Larsson et al., 2009). This phenomenon can be explained because the functions of olfaction, memory and emotion involve anatomically tight brain areas. The primary olfactory cortex includes the piriform-periamygdaloid cortex, which gives way gradually to the lateral entorhinal cortex. From these areas, the olfactory signal is respectively transmitted to the amygdala and to the CA1 of the hippocampus (Price, 1973; De Olmos et al., 1978; Shipley and Reyes, 1991) before being sent to the secondary olfactory cortices, the orbitofrontal and insular cortices. Therefore, from its birth in the olfactory epithelium, the olfactory signal is relayed through two or three neurons to the brain structures critical for emotion and memory (for review, Eichenbaum, 2000; Sergerie et al., 2008). Despite some consensus on odor pleasantness especially for very pleasant and very unpleasant odors (Moncrieff, 1966), the emotion generated by odors can greatly differ between individuals (Ferdenzi et al., 2013). The differences in emotional responses to odors can result from variations in genetic backgrounds (Keller et al., 2007) but likely mainly result from differences in personal experience (Engen, 1991; Robin et al., 1998; Herz, 2001; Herz et al., 2004). The association between an odor and the emotional content in which it occurs determines its future hedonic tone and explains why the same odor can be perceived as either pleasant or unpleasant.

The objective of the current study was first to investigate the cognitive processes of episodic memory by combining in an original way the laboratory and autobiographical approaches. Second, it was to test the still-unexplored hypothesis that the emotion carried by odors facilitates the memory of specific unique events. To investigate this idea, we adapted our episodic memory task and addressed the episodic retrieval of episodes comprising three different odors positioned at specific locations within a visual context to create rich multidimensional episodes (Saive et al., 2013). To identify the differential influence of emotion on episodic memory, we tested the effects of emotion carried by odors on the behavioral and physiological responses of the participants during encoding and retrieval.

MATERIALS AND METHODS

PARTICIPANTS

Twenty-five healthy participants [13 women; age: 21.4 ± 2.1 years (mean ± standard deviation)] consented to participate in the experiment. All participants were right-handed and reported normal senses of smell and no visual impairments. They provided written informed consent as required by the local Institutional Review Board in accordance with French regulations for biomedical experiments with healthy volunteers [Ethical Committee of CPP Sud-Est IV (CPP 11/007), ID RCB: 2010-A-01529-30, January 25, 2011] and received financial compensation. The study was conducted in accordance with the Declaration of Helsinki.

STIMULI AND MATERIALS

Odorants

Eighteen odorants consisting of essential oils and single or mixtures of monomolecular chemical compounds were selected a priori based on their distinctiveness and relatively low identifiability and familiarity. The odorants were subdivided into two sets (Sets 1 and 2) of nine odors each. Set 1 was composed of butanol, calone, carrot, cis-3-hexenyl salicylate, dihydromyrcenol, methyl octine carbonate, musk, rosemarel and stemone. Set 2 was composed of allyl amyl glycolate, basil, birch oil, citronellol, ethyl acetyl acetate, linalyl acetate, rose oxide, styrallyl acetate and tobacco.

The odorants were presented using a 20-channel computer-controlled olfactometer adapted from an olfactometer previously described by Sezille et al. (2013). Briefly, this odor diffusion system was developed to synchronize odorous stimuli with breathing. Undiluted odorants were contained in a 10-ml U-shaped Pyrex® tube (VS Technologies, France) filled with odorized microporous substances. Odorized airflows and air carrier were sent to and mixed in a homemade mixing head made of polytetrafluoroethylene and connected to the nostrils. The participant's respiratory signal was acquired using a nasal cannula and was used to trigger the odor stimulation through an airflow sensor. The airflow rate was set at 3 l/min, and the odorants were delivered over 4 s.

Spatio-contextual environment

The spatio-contextual environment was presented within the experimental setup previously described by Saive et al. (2013), but modified for the present study. Three landscape pictures presented full-screen (1280 × 1024 pixels, 72 dpi) constituted the visual contexts (a coastal cliff, a lavender field and a mountain landscape; Figure 1A). For each of the three contexts, circles symbolized nine spatial locations: 6 were colored in gray, and 3 were colored in orange. When the circle was orange, it was associated with an odor; otherwise, it was gray. All spatial locations of the orange circles and all odors differed between the contexts.

FIGURE 1. Episodic-memory task design. (A) The three spatio-contextual environments of the episodes. Orange circles represent the spatial locations associated with an odor. (B) The temporal course of the encoding and retrieval sessions. During the encoding, the participants discovered one episode per day over 3 days. On the fourth day, the memory of the episodes was tested using an odor-recognition task followed for the “Yes” trials by an episodic memory retrieval. T, Trial.

Multidimensional episodes

Three multidimensional episodes were created, which were each composed of three odors (What) associated with specific locations (Where) within a given visual context (Which context). Three multidimensional episodes were created, which were each composed of three odors (What) associated with specific locations (Where) within a given visual context (Which context). To limit associative semantic processes, the odors, spatial locations and visual context were arbitrary linked.

An in-house LabView software (version 8.6 or higher) controlled the presentation of odors, pictures and circles and recorded the participants' responses and breathing throughout the experiment. The participants were requested to breathe normally and avoid sniffing behaviors (Figure 2). To interact with the software, the participants used a trackball (Kensington, Redwood Shores, CA, USA). When the participants clicked on a circle, the odor stimulus was delivered at the beginning of the subsequent expiration, enabling the odor to be perceived at the beginning of the next inspiration (on average 2 s later). The volume, amplitude and duration of each inspiratory cycle were recorded, and the respiratory frequency was calculated.

FIGURE 2. Breathing signal. Course of a typical breathing signal depicting successive expirations and inspirations. The odor was sent at the beginning of the participant's expiration to ensure odor perception at the beginning of the next inspiration, which occurred approximately 2 s later (in green, period of odor perception, fading with time).

EXPERIMENTAL PROCEDURE

The experimental procedure consisted of four sessions performed over the course of 4 successive days. The first three sessions were used for encoding, and the retrieval occurred in the fourth session (Figure 1B). A full night of sleep followed each of the encoding sessions to promote consolidation and to reduce interference (Maquet, 2001; Stickgold, 2005). Participants completed the four sessions at the same time of the day to limit the differential influence of internal states (hunger, satiety) on olfactory and cognitive processes between sessions (Jiang et al., 2008; Plailly et al., 2011).

There were two groups of participants: G1 and G2. For G1, the Set1 odorants were defined as the targets, and the Set2 odorants were defined as the distractors. For G2, the Set2 odorants were defined as the targets, and the Set1 odorants were defined as the distractors.

Encoding

During encoding, the participants freely discovered one episode per day for 7 min (Figure 1B). They were asked to explore all dimensions of the episode as much as possible by paying attention to the background picture, the circles superimposed on this background, and the odors that are delivered when clicking on the orange circles. No memorization instruction was given, thereby ensuring free encoding, similar to what arises in real-life situations. The participants were only informed that they would be questioned about their perception of the episodes on the fourth day. The order of the three episodes was randomized between the participants.

Retrieval

Retrieval was performed on the fourth day. The session consisted of three blocks of 24 trials, and each block corresponded to the presentation of 15 target odors and 9 distractor odors. Each target odor was presented five times, and each distractor odor was presented three times. For a given block, the target and distractor odors were presented in a pseudorandom order such that two presentations of the same odor were separated by at least two trials. The odor presentation order was counterbalanced between the participants.

Each trial began with an odor recognition task (Figure 1B). The participants were presented the odors and had to determine whether they recognized the smell (“Do you recognize this smell?”) as having been previously presented during the encoding. Two situations could happen. 1) If the participants responded “Yes,” they were then asked to retrieve the entire episode associated with the odorant and to press on the trackball if they succeeded in less than 20 s after the odor was sent (“Press when you remember the context”). After this delay, they were given up to 10 s to choose both the accurate visual context and the exact location of the odor by selecting one of the three pictures, followed by one of the nine circles. A response was considered correct when the participants selected both the accurate context and the specific location previously associated with the odor during the encoding. 2) If the participants responded “No,” they had to press on the trackball (“Press the button”) and rest until the next trial.

Following this retrieval task, the strength of the association between the spatial location and the visual context of an event was tested. The participants had to recall the three locations (orange circles) associated with the odors in every visual context during the encoding.

Rating of odor intensity, pleasantness, and familiarity

At the end of the experiment, the participants were asked to rate the odorants in terms of intensity, pleasantness and familiarity using non-graduated scales. The pleasantness scale was divided into two equal parts by a “neutral” value separating the ratings of unpleasantness and pleasantness. The intensity, pleasantness and familiarity ratings were a posteriori transformed into scores from 0 to 10.

DATA ANALYSIS

Encoding

For each participant, the number of clicks was computed per odor. For each odor, the time periods between two consecutive clicks (delay) were measured, and the mean delay was then determined. The time window between the two clicks served as the time frame for the analyses of breathing parameters (e.g., the volume, amplitude and duration of the inspiratory cycles and the respiratory frequency). The influence of the odor characteristics (intensity, pleasantness and familiarity) on the behavioral and physiological (breathing) data was tested. The relationship between the encoding and the retrieval was investigated by analyzing the behavioral and physiological data during the encoding as a function of the subsequent memory performances.

Retrieval

Recognition memory performance was assessed using parameters from the signal detection theory (Lockhart and Murdock, 1970). From the experimental conditions (target vs. distractor) and the participants' behavioral responses (“Yes” vs. “No”), four response categories were defined: Hit and Miss occurred when the target items were accurately recognized or incorrectly rejected, respectively, and correct rejection (CR) and false alarm (FA) occurred when the distractor items were correctly rejected or incorrectly recognized, respectively. In the framework of the signal-detection theory, a memory score (d'L) reflected the participant's ability to discriminate between the target and distractor items. This score was determined from the Hit and FA scores and was calculated as follows:

Where HR represents the Hit rate [(Hit + 0.5)/(Nt + 1)], FR represents the false alarm rate [(FA + 0.5)/(Nd + 1)] and Nt and Nd represent the number of target and distractor odors, respectively, for which the participants provided an answer. Memory scores may be good or poor (positive or negative values, respectively).

In the episodic retrieval test, we focused the analyses on the participants' accurate responses for the target odors (Hit). Four types of responses were then defined depending on the recall accuracy. When the participants correctly recognized the target odors, they could accurately remember both the location and the context (WWW), the location only (WWhere), or the context only (WWhich) or they could be mistaken about both dimensions (What). These different scenarios were named episodic combinations. The theoretical proportions of these episodic combinations resulting from responses given randomly were 0.019 for WWW [1 response (“Yes/No”) out of 2 * 1 context out of 3 * 1 location out of 9], 0.148 for WWhich [1 response (“Yes/No”) out of 2 * 1 context out of 3 * 8 locations out of 9], 0.037 for WWhere [1 response (“Yes/No”) out of 2 * 2 contexts out of 3 * 1 location out of 9] and 0.296 for What [1 response (“Yes/No”) out of 2 * 2 contexts out of 3 * 8 locations out of 9].

The response times for odor recognition and episodic retrieval were considered. The response times corresponded to the durations between the first inspiration after the odor was delivered and 1) the “Yes/No” response for the odor recognition task and 2) the “I remember the context” response for the episodic retrieval task. The same time boundaries were used to record and analyze breathing parameters during the odor recognition and episodic retrieval tasks.

STATISTICAL ANALYSIS

Behavioral and physiological data were z-scored [(x−μ)/σ ] at the individual level to remove bias based on inter-individual differences. The number of each response given during the odor recognition and episodic retrieval tasks was further normalized by the number of trials after removal of one odor a posteriori from the data (“Odor intensity, pleasantness and familiarity”). The statistic main effects of the factors and interactions were determined using repeated measurements ANOVAs followed by post-hoc bilateral Student t-tests when main effects and/or interactions were significant. The effects were considered significant at p < 0.05. The relation between perceptual ratings of odors (intensity, pleasantness, familiarity) or memory performances with behavioral measures (number of clicks, delay between clicks) or breathing parameters was tested using Pearson tests. In these cases, to control for the Type I error rate associated to multiple comparisons, we applied the Bonferroni correction by dividing the probability alpha by the number of comparisons. Statistical analyses were performed using Statistica (StatSoft®, Tulsa, OK, USA).

RESULTS

ODOR INTENSITY, PLEASANTNESS, AND FAMILIARITY

On average, the odorants were perceived as moderately intense (5.31 ± 1.44; range: 1.49–7.15), relatively neutral (4.85 ± 1.38 range: 2.22–6.92) and unfamiliar (4.54 ± 1.61; range: 1.60–7.33). The intensity of the allyl amyl glycolate was rated as weak (1.49 ± 1.93) when compared with that of the other odorants. The Grubbs test, which was used to test for outliers, indicated that this intensity value abnormally deviated from the mean (G = 2.66, p = 0.04). As a consequence, the data related to allyl amyl glycolate were excluded from further analyses.

MEMORY PERFORMANCES

The effects of the set of target odors (Set1 vs. Set2) selected for the participants of G1 and G2 and of the age of the episodes (1–3 days) on the behavioral and breathing responses observed during the encoding and retrieval sessions were evaluated. The influence of the repetition of the odors (5 times for targets and 3 times for distractors) on memory performances, response times, and breathing during retrieval was also tested. No significant main effects or interactions were found, and thus we did not take these factors into account in the subsequent analyses. Second, as the effect of context (coastal cliff, lavender field, and mountain landscape) was confounded with the nature of the three odors associated with each context, we could not specifically analyze it.

Encoding

The investigation of the odors during the encoding was analyzed as a function of the odor characteristics. The participants smelled, on average, each odor 5.5 (±2.6) times by clicking on the circles. The number of clicks for each odor for all participants was significantly negatively correlated with the odor intensity [r = −0.22, t(1,210) = 3.30, p = 0.001, αadjusted = 0.017] but not the odor familiarity and pleasantness (ps > 0.11). The mean delay between the two odor investigations was 29.8 (±13.5) s. These delays were not correlated with the intensity, pleasantness, or familiarity of the odors (ps > 0.05, αadjusted = 0.017). The duration, amplitude and volume of the inspirations and the respiratory frequency did not vary significantly as a function of the odor's intensity, pleasantness and familiarity (ps > 0.04, αadjusted = 0.017).

Odor recognition

The participants were presented the target and distractor odors and were asked whether they had smelled them during the encoding phase. The memory score was high (d'L = 2.33 ± 1.18), which indicated that the participants were very proficient in recognizing old odors and rejecting new ones. The proportions of the different response categories (Hit, Miss, CR, and FA) are shown in Figure 3A. The proportion of correct responses (Hit + CR) was significantly higher than the proportion of incorrect responses (Miss + FA) [F(1, 24) = 135.29, p = 0.0001]. While odor type (target vs. distractor) and response accuracy significantly interacted [F(1, 24) = 4.11, p = 0.045], no significant differences were observed between Hit and CR and between Miss and FA (ps > 0.06).

FIGURE 3. Odor recognition. (A) Mean distribution and (B) mean response times of correct (black) and incorrect (gray) responses for the target (Hit, Miss) and distractor (CR, FA) odors. (C) Mean normalized inspiration duration and respiratory frequency during odor recognition (“Yes” responses) and odor rejection (“No” responses). The dashed horizontal line indicates the random level. Vertical bars represent the SD. ** p < 0.01; *** p < 0.001.

Figure 3B represents the influence of response accuracy (correct vs. incorrect) and odor type (target vs. distractor) on the response times. Response accuracy [F(1, 24) = 29.33, p = 0.001] but not odor type [F(1, 24) = 1.98, p = 0.17] significantly impacted the response times; the participants responded more rapidly when answering accurately (Hit + CR: 4.75 ± 1.71 s) than inaccurately (Miss + FA: 6.10 ± 2.44 s). Response accuracy and odor type significantly interacted [F(1, 24) = 9.17, p = 0.004]; the participants gave correct responses more rapidly than incorrect responses when the target odors were presented (p = 0.001) but not when the distractor odors were presented (p = 0.19). The participants also answered more rapidly for the Hit responses than for the Miss, CR, and FA responses (ps < 0.001).

The breathing variations were analyzed as a function of response accuracy and odor type. No significant effects of response accuracy and odor type on the duration, amplitude and volume of the inspiration (ps > 0.23) or the respiratory frequency (p = 0.07) were found. However, a significant interaction was identified between both factors and the duration [F(1, 24) = 13.85, p = 0.001] and respiratory frequency [F(1, 24) = 7.51, p = 0.008] but not the amplitude and volume of the inspirations (ps > 0.18). As shown in Figure 3C, the duration of the participants' breath was shorter and their respiratory frequency was higher when they recognized the odors (“Yes” responses: Hit, FA) than when they rejected them (“No” responses: Miss, CR).

The recognition performances did not depend on the exploratory behavior of the odors during the encoding. The number of accurate odor recognitions (Hit) was not correlated with the number of clicks (p = 0.62, αadjusted = 0.025) and the mean delay between the clicks (p = 0.62, αadjusted = 0.025).

Episodic retrieval

When the participants recognized an odor as the target, they were asked to retrieve the spatio-contextual environment in which it occurred. We focused our analysis on the responses following correct odor recognition (Hit). The proportions of the episodic combinations are represented in Figure 4A. The proportions of WWW, WWhich and What were significantly higher than the proportion of WWhere [F(3, 66) = 20.55, p = 0.001; post-hoc, ps < 0.001]. The proportions of complete accurate (WWW) and partially accurate responses (WWhich, WWhere) that were given by the participants differed significantly from the random responses (ps < 0.017), while the proportion of inaccurate responses (What) did not differ from the proportion of random responses (p = 0.19). Thus, the participants were able to retrieve the spatio-contextual environment of the episodes using the recognition of an odor, they recalled only a part of the episode, or they did not recall anything and responded randomly. The subsequent analysis did not include the responses associated with the WWhere episodic combination because of the small amount of data.

FIGURE 4. Episodic retrieval. (A) Mean proportions of episodic combinations (WWW, WWhich, WWhere, What). (B) Mean response times for each episodic combination, with the delay between the odor recognition and episodic retrieval responses represented in black crosses. (C) Mean normalized inspiration duration and volume for each episodic combination. The dashed horizontal lines indicate the random levels computed for the episodic combinations. Vertical bars represent the SD; * p < 0.05; ** p < 0.01; *** p < 0.001.

The response times were then analyzed (Figure 4B). A significant main effect of the episodic combinations was found [F(2, 46) = 18.56, p = 0.001]. The response times of the participants were significantly faster for perfect accurate responses (WWW) than for partially inaccurate responses (WWhich: p = 0.016). The response times were even faster for WWhich than for inaccurate What responses (p = 0.001). In other words, the more incorrect the answers, the slower the participants answered. Interestingly, the time interval between the odor recognition and the episodic retrieval responses did not significantly vary with the episodic combinations [F(2, 46) = 2.11, p = 0.14].

The mean durations and volumes of the inspirations are given for the episodic combinations WWW, WWhich and What in Figure 4C. These durations and volumes significantly varied with the episodic combinations [F(2, 46) = 5.31, p = 0.008 and F(2, 46) = 4.88, p = 0.011, respectively]. The duration and volume of the inspirations were greater when the participants remembered the spatio-contextual environment associated with the odor (WWW) than when they did not remember it (What, ps < 0.001). No significant differences in the respiratory frequency and amplitude of the inspirations were observed (ps > 0.15).

The influence of the exploratory behavior of odors during encoding on the episodic performances was investigated. The number of accurate episodic retrievals (WWW) was not correlated with the number of clicks (p = 0.70), and the mean delay between clicks (p = 0.69).

Following this episodic retrieval, the strength of the association between the spatial location and the visual context of an episode was tested. On average, the participants accurately recollected 80 ± 7% of the spatial locations associated with each visual context. These performances did not significantly depend on the visual context [F(2, 46) = 1.76, p = 0.19], which indicated that no difference in the strength of the visuospatial associations biased the episodic performances.

INFLUENCE OF EMOTION

To investigate the influence of emotion on the memory performances, we created three odor pleasantness categories. Given that the pleasantness ratings of the odors widely varied among the participants (Figure 5A), we selected the two more pleasant, the two more neutral and the two more unpleasant odors for each participant. The odors selected for these three pleasantness categories differed significantly in terms of intensity [F(2, 46) = 15.14, p = 0.001] and familiarity [F(2, 46) = 20.37, p = 0.001]: the unpleasant odors were perceived as more intense and less familiar (6.36 ± 1.85; 3.05 ± 2.20, respectively) than the neutral odors (4.25 ± 2.01; 3.74 ± 2.38, respectively), while the pleasant odors (6.29 ± 1.45; 6.69 ± 2.09, respectively) were perceived as more intense and familiar than the neutral odors (ps < 0.001).

FIGURE 5. Emotion. (A) Pleasantness ratings of the 17 odors for the 25 participants. Each odorant is represented by a different color. Number of (B) Hits, (C) WWWs, (D) WWhichs, and (E) Whats as a function of the odor's pleasantness (more unpleasant, neutral and more pleasant). Unp, unpleasant; Neut, neutral; Pleas, pleasant. The dashed horizontal lines indicate the random levels computed for each response. Vertical bars represent the SD; * p < 0.05; ** p < 0.01.

On memory performances

During the encoding, the number of clicks and the mean delay between two clicks did not differ between the pleasantness categories (ps > 0.71), indicating that the emotions carried by the odors did not influence their exploration.

The proportions of correct recognition (Hit) of odors differed significantly from the random responses whatever the emotion of odors (ps < 0.002), but it significantly varied as a function of the pleasantness category [F(2, 46) = 5.42, p = 0.007; Figure 5B]. The pleasant and unpleasant odors were recognized more accurately than the neutral odors (p = 0.024 and p = 0.003, respectively).

Considering episodic retrieval performances, the proportions of complete accurate responses (WWW) differed significantly from the random responses when triggered by pleasant and unpleasant (ps < 0.042) but not neutral odors (p = 0.72). The proportion of partial accurate responses (WWhich) significantly varied from random responses when triggered by pleasant odors only (p = 0.042; neutral and unpleasant odors, ps > 0.12), while the proportion of inaccurate responses (What) did not differ from the proportion of random responses whatever the pleasantness category of the odors (ps > 0.20). We observed a significant effect of the pleasantness category on the number of accurate episodic retrieval (WWW) responses [F(2, 46) = 3.27, p = 0.046, Figure 5C] but not on the number of partial episodic retrieval (WWhich, Figure 5D) or inaccurate episodic retrieval (What, Figure 5E) responses (ps > 0.56). The number of WWW was significantly higher when the odors that triggered the memory were more pleasant or more unpleasant than neutral (p = 0.047 and p = 0.024, respectively). No significant difference was found between the pleasant and unpleasant odors (p = 0.79). Thus, the emotion carried by the odors only improved the retrieval of accurate episodic memories, regardless of the positive or negative valence of the emotion. Importantly, while odor pleasantness categories differed in terms of familiarity and intensity, the accurate odor recognition (Hit) and episodic retrieval (WWW) performances were not significantly related to these ratings (ps > 0.49).

On response time and breathing

Regardless of the performances, the participants answered with similar response times regardless of the pleasantness category of the odors during odor recognition [F(2, 46) = 0.97, p = 0.39] and episodic retrieval [F(2, 46) = 1.26, p = 0.30]. Regardless of the performances, the participants answered with similar response times regardless of the odor pleasantness category during odor recognition [F(2, 46) = 0.97, p = 0.39] and episodic retrieval [F(2, 46) = 1.26, p = 0.30]. Performing two-way Session x Category ANOVAs on breathing data, we found a significant effect of pleasantness category on inspiration volume and duration [F(2, 48) = 5.42, p = 0.008 and F(2, 48) = 5.66, p = 0.006, respectively], and significant effects of pleasantness category and sessions on respiratory frequency [F(2, 48) = 3.34, p = 0.044 and F(2, 48) = 6.56, p = 0.003, respectively]. No significant effect was found for amplitude, and no significant interaction between factors was found whatever the breathing parameters. Thus, participants inspired more deeply, with longer inspirations, and less frequently for neutral and pleasant odors than unpleasant odors, whatever the session (ps = 0.017). They inspired also less frequently during episodic retrieval than during encoding (p = 0.018).

DISCUSSION

The present novel laboratory-based episodic memory approach, which was adapted from a previous paradigm developed by our team (Saive et al., 2013), succeeded in the formation and subsequent retrieval of an integrated and multimodal memory of episodes comprising odors (What) localized spatially (Where) within a visual context (Which context). Successful odor recognition and episodic memory were not related to differences in the odor investigation at encoding and were paralleled by modifications in both the response time and breathing patterns. However, memory performances were influenced by the emotional content of the odor, with both pleasant and unpleasant odors generating higher recognition and episodic retrieval than neutral odors.

RECOGNITION AND EPISODIC MEMORY PROCESSES

The behavioral data revealed a high ability to recognize odors previously encountered in laboratory settings. The unfamiliar odors freely encoded during episode discovery were proficiently recognized among the new odors encountered afterwards, as indicated by a very high memory score. The good memory recognition performances were supported by the behavioral measures. The participants answered more rapidly when they successfully recognized the target odors than for all the other responses. Moreover, the duration of the participants' breath was shorter and their respiratory frequency was higher when they accurately recognized the odors than when they rejected them. These response times and breathing observations are consistent with previous reports (Jehl et al., 1997; Olsson and Cain, 2003; Royet et al., 2011) and could be evidence for a serial identity matching process between the memory trace and the actual percept (Bamber, 1969). Until a match was found between the odor cue and the odor memory traces, the participants needed to follow the memory search (which ended in higher response times for No than Yes responses) and keep the odor “in their nose,” which led to expanded respiratory cycles. These results demonstrate the efficiency of our paradigm in generating the encoding of unknown odors and their later recognition.

The old odors were not only very well recognized but they also triggered the retrieval of past unique episodes at a level far above chance. From the accurate recognition of an odor, the participants were able either to retrieve the complete visuospatial context of the episodes or correctly recall only the context of the episodes. Otherwise, they did not remember any information related to the episode and answered randomly. Two scenarios are possible to explain the cognitive processes engaged in episodic retrieval: a serial recollection of the three dimensions (What, Where, and Which context) or an immediate recall of the whole episode. In the first scenario, when an odor was recognized, the participants interrogated their memories until the exact position of the odor in the exact context was recalled. In the second scenario, the episode was fully recovered from odor perception, all of its dimensions at once. The analysis of the response times revealed that the more information the participants retrieved about the episode, the faster they answered. However, the time period between odor recognition and episodic retrieval remained constant regardless of the accuracy of the episodic retrieval; this finding suggests that the content of the memory was already fully recovered from the odor recognition or that the episodic retrieval was already fairly advanced. Therefore, the response time data more strongly support the retrieval of the whole episode at once rather than a serial recall of its dimensions. The detailed analyses of the cognitive processes involved in our paradigm led us to support for the collapse of the recognition and episodic retrieval processes into a unique memory retrieval process when the binding between the odors and the spatio-contextual features of the episode is successful. The odor perception might generate the simultaneous recognition of the odor and the recall of other episodic features, such as the characteristics of the odor, the localization of the orange circle on the visual background or the mood the participants were in. These memories seem to be triggered as soon as the participants smelled the odor. Therefore, the odor recognition of the odor would be included in the episodic retrieval as one feature of the episode. Otherwise, when unsuccessful, the recognition and episodic retrieval memory process might be distinct.

Recognition and episodic performances were independent of the way the odors were investigated at encoding and the odors' intrinsic characteristics. The only exception was the odors that were less intense and were investigated more often, most likely to better characterize them. Given the amount of evidence indicating a serial position effect on recognition memory, with first and more recent items more likely to be recognized (Deese and Kaufman, 1957; Murdock, 1962), as well as on autobiographical memory, with events from late childhood or young adulthood and recent events more likely to be remembered (Crovitz and Schiffman, 1974; Crovitz and Quina-Holland, 1976), we might have expected primacy and recency effects to be observed. However, our data demonstrated that odor recognition and episodic memories were similar whether the day of encoding was the first, second or the last day, thereby confirming previous results (Saive et al., 2013). Thus, these performances were stable over time and were not dependent on the age of the retrieved episode. Furthermore, the performances were not impacted by the multiple presentations of the odors during the retrieval phase, although it has been demonstrated that repeated presentations of odors increase their familiarity (e.g., Jehl et al., 1995). These high and stable memory performances might reflect the influence of the multimodality and the episodic nature of our task. Odors are better recognized when associated with indices of other modalities or when associated with an episode of life during encoding (Lyman and McDaniel, 1986, 1990). When exploring the episodes, the participants were experiencing a new, rich and complex event, very similar to real-life encoding situations, which enhanced the strength of the whole memory trace. The full nights of sleep obtained between the encoding sessions may also have strengthened the consolidation of the memory traces and limited the interference between the episodes (Maquet, 2001; Stickgold, 2005; Alger et al., 2012; Abel and Bäuml, 2014).

Odors that triggered the retrieval of the spatio-contextual environment were associated with increased duration and volume of inspirations compared with odors that did not trigger any recall. These data are consistent with previous studies investigating breathing during autobiographical retrieval (Masaoka et al., 2012a,b). The current variation in breathing during memory construction raises interesting questions. Were the physiological responses a consequence of a successful episodic search or were they necessary for the search to be successful? In other words, were the breathing characteristics modified by the retrieval of the elements of the episodes or did they reflect an intense memory search? These questions are reminiscent of findings that showed attention and mental imagery processes are associated with larger sniffs when participants succeed in the tasks (Bensafi et al., 2003, 2005; Plailly et al., 2008). It is further possible that the reconstruction of the memory necessitated a relaxed state that was reflected in slower respiration. A previous study showed that yoga breathing specifically increased spatial memory performances (Naveen et al., 1997).

IMPACT OF EMOTION GENERATED BY ODOR ON MEMORY RETRIEVAL

Compared to neutral odors, both pleasant and unpleasant odors generated increased recognition and more complete episodic retrieval. This suggests that the intensity of the emotion, also called emotional arousal, but not the valence (pleasant vs. unpleasant) enhanced memory retrieval. Many studies have indicated an emotional arousal benefit on memory in humans (Burke et al., 1992; Cahill and McGaugh, 1995; Laney et al., 2004). For example, Cahill and McGaugh (1995) have shown that the higher the arousal content of a story, the better the long-term retention. This beneficial aspect of human memory would be highly adaptive, enabling more efficient accessibility of emotional memory, and is strongly dependent on the amygdala (Hamann, 2001). Interestingly, the effect of emotion on accurate odor recognition was in fact only observed when the complete episode was accurately recalled. Incomplete or inaccurate recalls of the episodes were not influenced by emotion. The fact that the accurate recognition of the odor and the accurate retrieval of the episodes were affected the same way by emotion is another argument favoring the idea that, in the case of an efficient episodic retrieval, these two memory processes might be collapsed into a unique memory process.

When did emotion influence episodic memory? Emotion can modulate the creation, storage and recollection phases of episode processing (Holland and Kensinger, 2013). First, arousing items are noticed quickly, and attention is preferentially directed toward them, potentially promoting their encoding (Kensinger and Corkin, 2004; MacKay et al., 2004; Leclerc and Kensinger, 2008). Furthermore, both pleasant and unpleasant odors trigger the modulation of skin conductance and heart rate measures (Alaoui-Ismaïli et al., 1997a,b; Bensafi et al., 2002; Royet et al., 2003). Thus, in the present study, the odors might have generated automatic emotional responses that might have modulated the participant's attention and induced improved encoding of all associated information. Second, emotional arousal could also influence the memory consolidation. Indeed, it has been shown that sleep not only promotes the general consolidation of new acquired memory traces (Maquet, 2001; Stickgold, 2005) but also specifically supports emotional memories (Wagner et al., 2006; Holland and Lewis, 2007; Groch et al., 2013). Finally, emotion can modulate retrieval by increasing how easily the memory comes to mind following cue perception and by increasing the amount of remembered details (Kensinger, 2009; Melcher, 2010). In the current experiment, odor pleasantness influenced the accurate retrieval of olfactory episodes. Importantly, odor pleasantness did not differentially impact the exploratory behavior (number of clicks and delays between clicks) during encoding and its influence on breathing did not differ between sessions. Therefore, in the frame of the experimental conditions of our study, we can suggest that odor pleasantness had only an impact on the consolidation or memory retrieval but not on the encoding of the episodes.

Which memory process was influenced by emotion? In our case, the emotion triggered by odors enhanced both the odor recognition itself and the retrieval of the entire episode. Emotional arousal enhances the binding of contextual details or dimensions when they are an integral part of the emotional stimulus (Mather, 2007; Mather and Nesmith, 2008; Nashiro and Mather, 2011). In our study, we suggest that the dimensions of the episodes were encoded as features of the emotional odors and were combined in an integrated unique memory trace. Taken together, remembering how the features of an event were associated together is a critical aspect of episodic memory that seems to be promoted by emotion.

In conclusion, our study represents the first laboratory-ecological approach involving olfactory dimension that allows the conscious and controlled recollection of specific and complex events from the past. It combines in a very original way the advantages of the laboratory-based approaches that allow the control of encoding and recall conditions, and of autobiographical-based approaches that enable the retrieval of real life episodes (Saive et al., in revision). Furthermore, of interest to the entire neuroscientist community devoted to the study of memory, our paradigm enables the ecological and direct comparison between episodic and recognition memory processes, rather than indirect assessment based on the comparison between recollection and familiarity processes engaged in simpler memory tasks.

It demonstrates that humans are capable of encoding and remembering rich and unique laboratory episodes triggered by odors. The episodic nature of the task generates high and stable memory performances, accompanied by slower and deeper breathing. It shows for the first time that the emotion carried by odors, regardless of their valence, does not influence encoding behavior but promotes their accurate recognition and the accurate retrieval of the visuospatial context of the episodes. Importantly, this study also suggests that when the binding between the odors and the spatio-contextual features of the episode is successful, the odor recognition and episodic retrieval collapse into a unique memory process that begins as soon as the participants smell the odors. However, further investigations are needed to validate this observation. The use of cerebral imaging techniques represents the ideal tool to test it. We hypothesize that the neural signature of the successful retrieval of episodic information will be observed from the mere odor perception.

ACKNOWLEDGMENTS

This work was supported by the Centre National de la Recherche Scientifique (CNRS), the LABEX Cortex (NR-11-LABX-0042) of Université de Lyon within the program “Investissements D'Avenir” (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR) and research grants from the Région Rhône-Alpes (CIBLE 10 015 772 01). Anne-Lise Saive was funded by the Roudnitska Foundation.

ABBREVIATIONS

CR, Correctre jection; FA, False alarm; WWW, Retrieval of the three dimensions (What, Where, Which context) of the episode; WWhich, Retrieval of the What and Which context dimensions of the episode; WWhere, Retrieval of the What and Where dimensions of the episode; What, Retrieval of the What dimension of the episode

REFERENCES

Abel, M., and Bäuml, K.-H. T. (2014). Sleep can reduce proactive interference. Memory 22, 332–339. doi: 10.1080/09658211.2013.785570

Alaoui-Ismaïli, O., Robin, O., Rada, H., Dittmar, A., and Vernet-Maury, E. (1997a). Basic emotions evoked by odorants: comparison between autonomic responses and self-evaluation. Physiol. Behav. 62, 713–720. doi: 10.1016/S0031-9384(97)90016-0

Alaoui-Ismaïli, O., Vernet-Maury, E., Dittmar, A., Delhomme, G., and Chanel, J. (1997b). Odor hedonics: connection with emotional response estimated by autonomic parameters. Chem. Senses 22, 237–248. doi: 10.1093/chemse/22.3.237

Alger, S. E., Lau, H., and Fishbein, W. (2012). Slow wave sleep during a daytime nap is necessary for protection from subsequent interference and long-term retention. Neurobiol. Learn. Mem. 98, 188–196. doi: 10.1016/j.nlm.2012.06.003

Babb, S. J., and Crystal, J. D. (2006). Episodic-like memory in the rat. Curr. Biol. 16, 1317–1321. doi: 10.1016/j.cub.2006.05.025

Bamber, D. (1969). Reaction times and error rates for “same”-“different” judgments of multidimensional stimull. Percept. Psychophys. 6, 169–174. doi: 10.3758/BF03210087

Bensafi, M., Porter, J., Pouliot, S., Mainland, J., Johnson, B., Zelano, C., et al. (2003). Olfactomotor activity during imagery mimics that during perception. Nat. Neurosci. 6, 1142–1144. doi: 10.1038/nn1145

Bensafi, M., Pouliot, S., and Sobel, N. (2005). Odorant-specific patterns of sniffing during imagery distinguish “Bad” and “Good” olfactory imagers. Chem. Senses 30, 521–529. doi: 10.1093/chemse/bji045

Bensafi, M., Rouby, C., Farget, V., Bertrand, B., Vigouroux, M., and Holley, A. (2002). Autonomic nervous system responses to odours: the role of pleasantness and arousal. Chem. Senses 27, 703–709. doi: 10.1093/chemse/27.8.703

Burke, A., Heuer, F., and Reisberg, D. (1992). Remembering emotional events. Mem. Cogn. 20, 277–290. doi: 10.3758/BF03199665

Cahill, L., and McGaugh, J. L. (1995). A novel demonstration of enhanced memory associated with emotional arousal. Conscious. Cogn. 4, 410–421. doi: 10.1006/ccog.1995.1048

Chu, S., and Downes, J. J. (2002). Proust nose best: odors are better cues of autobiographical memory. Mem. Cogn. 30, 511–518. doi: 10.3758/BF03194952

Clayton, N. S., Bussey, T. J., and Dickinson, A. (2003). Can animals recall the past and plan for the future? Nat. Rev. Neurosci. 4, 685–691. doi: 10.1038/nrn1180

Clayton, N. S., and Dickinson, A. (1998). Episodic-like memory during cache recovery by scrub jays. Nature 395, 272–274. doi: 10.1038/26216

Crovitz, H. F., and Quina-Holland, K. (1976). Proportion of episodic memories from early childhood by years of age. Bull. Psychon. Soc. 7, 61–62. doi: 10.3758/BF03337122

Crovitz, H. F., and Schiffman, H. (1974). Frequency of episodic memories as a function of their age. Bull. Psychon. Soc. 4, 517–518. doi: 10.3758/BF03334277

Crystal, J. D. (2009). Elements of episodic-like memory in animal models. Behav. Processes 80, 269–277. doi: 10.1016/j.beproc.2008.09.009

Daselaar, S. M., Veltman, D. J., Rombouts, S. A. R. B., Raaijmakers, J. G. W., and Jonker, C. (2003). Neuroanatomical correlates of episodic encoding and retrieval in young and elderly subjects. Brain 126, 43–56. doi: 10.1093/brain/awg005

Deese, J., and Kaufman, R. A. (1957). Serial effects in recall of unorganized and sequentially organized verbal material. J. Exp. Psychol. 54, 180–187. doi: 10.1037/h0040536

De Olmos, J., Hardy, H., and Heimer, L. (1978). The afferent connections of the main and the accessory olfactory bulb formations in the rat: an experimental HRP-study. J. Comp. Neurol. 181, 213–244. doi: 10.1002/cne.901810202

Donaldson, D. I., Wheeler, M. E., and Petersen, S. E. (2010). Remember the source: dissociating frontal and parietal contributions to episodic memory. J. Cogn. Neurosci. 22, 377–391. doi: 10.1162/jocn.2009.21242

Eacott, M. J., and Easton, A. (2010). Episodic memory in animals: remembering which occasion. Neuropsychologia 48, 2273–2280. doi: 10.1016/j.neuropsychologia.2009.11.002

Easton, A., and Eacott, M. J. (2008). “Chapter 2.3 A new working definition of episodic memory: replacing ‘when’ with ‘which’,” in Handbook of Behavioral Neuroscience, ed A. E. Ekrem Dere (Düsseldorf: Elsevier), 185–196.

Eichenbaum, H. (2000). A cortical-hippocampal system for declarative memory. Nat. Rev. Neurosci. 1, 41–50. doi: 10.1038/35036213

Engen, T. (1991). Odor Sensation and Memory. New York, NY: Praeger.

Ferdenzi, C., Roberts, S. C., Schirmer, A., Delplanque, S., Cekic, S., Porcherot, C., et al. (2013). Variability of affective responses to odors: culture, gender, and olfactory knowledge. Chem. Senses 38, 175–186. doi: 10.1093/chemse/bjs083

Fink, G. R., Markowitsch, H. J., Reinkemeier, M., Bruckbauer, T., Kessler, J., and Heiss, W. D. (1996). Cerebral representation of one's own past: neural networks involved in autobiographical memory. J. Neurosci. 16, 4275–4282.

Griffiths, D. P., and Clayton, N. S. (2001). Testing episodic memory in animals: a new approach. Physiol. Behav. 73, 755–762. doi: 10.1016/S0031-9384(01)00532-7

Groch, S., Wilhelm, I., Diekelmann, S., and Born, J. (2013). The role of REM sleep in the processing of emotional memories: evidence from behavior and event-related potentials. Neurobiol. Learn. Mem. 99, 1–9. doi: 10.1016/j.nlm.2012.10.006

Hamann, S. (2001). Cognitive and neural mechanisms of emotional memory. Trends Cogn. Sci. 5, 394–400. doi: 10.1016/S1364-6613(00)01707-1

Herholz, S. C., Halpern, A. R., and Zatorre, R. J. (2012). Neuronal correlates of perception, imagery, and memory for familiar tunes. J. Cogn. Neurosci. 24, 1382–1397. doi: 10.1162/jocn_a_00216

Herz, R. (2001). Ah, sweet skunk! Why we like or dislike what we smell. Cerebrum 34, 31–47.

Herz, R. S., Beland, S. L., and Hellerstein, M. (2004). Changing odor hedonic perception through emotional associations in humans. Int. J. Comp. Psych. 17, 315–338.

Herz, R. S., and Cupchik, G. C. (1995). The emotional distinctiveness of odor-evoked memories. Chem. Senses 20, 517–528. doi: 10.1093/chemse/20.5.517

Hinton, P. B., and Henley, T. B. (1993). Cognitive and affective components of stimuli presented in three modes. Bull. Psychon. Soc. 31, 595–598. doi: 10.3758/BF03337365

Holland, A. C., and Kensinger, E. A. (2013). “Emotion in episodic memory: the effects of emotional content, emotional state, and emotional goals.,” in The Cambridge Handbook of Human Affective Neuroscience, eds J. Armony and P. Vuilleumier (New York, NY: Cambridge University Press), 465–488. doi: 10.1017/CBO9780511843716.025

Holland, P., and Lewis, P. A. (2007). Emotional memory: selective enhancement by sleep. Curr. Biol. 17, R179–R181. doi: 10.1016/j.cub.2006.12.033

Holland, S. M., and Smulders, T. V. (2011). Do humans use episodic memory to solve a What-Where-When memory task? Anim. Cogn. 14, 95–102. doi: 10.1007/s10071-010-0346-5

Janata, P. (2009). The neural architecture of music-evoked autobiographical memories. Cereb. Cortex 19, 2579–2594. doi: 10.1093/cercor/bhp008

Jehl, C., Royet, J. P., and Holley, A. (1995). Odor discrimination and recognition memory as a function of familiarization. Percept. Psychophys. 57, 1002–1011. doi: 10.3758/BF03205459

Jehl, C., Royet, J. P., and Holley, A. (1997). Role of verbal encoding in short- and long-term odor recognition. Percept. Psychophys. 59, 100–110. doi: 10.3758/BF03206852

Jiang, T., Soussignan, R., Rigaud, D., Martin, S., Royet, J.-P., Brondel, L., et al. (2008). Alliesthesia to food cues: heterogeneity across stimuli and sensory modalities. Physiol. Behav. 95, 464–470. doi: 10.1016/j.physbeh.2008.07.014

Keller, A., Zhuang, H., Chi, Q., Vosshall, L. B., and Matsunami, H. (2007). Genetic variation in a human odorant receptor alters odour perception. Nature 449, 468–472. doi: 10.1038/nature06162

Kensinger, E. A. (2009). Remembering the details: effects of emotion. Emot. Rev. 1, 99–113. doi: 10.1177/1754073908100432

Kensinger, E. A., and Corkin, S. (2004). Two routes to emotional memory: distinct neural processes for valence and arousal. Proc. Natl. Acad. Sci. U.S.A. 101, 3310–3315. doi: 10.1073/pnas.0306408101

Konishi, S., Wheeler, M. E., Donaldson, D. I., and Buckner, R. L. (2000). Neural correlates of episodic retrieval success. Neuroimage 12, 276–286. doi: 10.1006/nimg.2000.0614

Laney, C., Campbell, H. V., Heuer, F., and Reisberg, D. (2004). Memory for thematically arousing events. Mem. Cogn. 32, 1149–1159. doi: 10.3758/BF03196888

Larsson, M., Oberg-Blavarg, C., and Jonsson, F. U. (2009). Bad odors stick better than good ones: olfactory qualities and odor recognition. Exp. Psychol. 56, 375–380. doi: 10.1027/1618-3169.56.6.375

Leclerc, C. M., and Kensinger, E. A. (2008). Effects of age on detection of emotional information. Psychol. Aging 23, 209–215. doi: 10.1037/0882-7974.23.1.209

Levine, B., Turner, G. R., Tisserand, D., Hevenor, S. J., Graham, S. J., and McIntosh, A. R. (2004). The functional neuroanatomy of episodic and semantic autobiographical remembering: a prospective functional MRI study. J. Cogn. Neurosci. 16, 1633–1646. doi: 10.1162/0898929042568587

Lockhart, R., and Murdock, B. (1970). Memory and the theory of signal detection. Psychol. Bull. 74, 100–109. doi: 10.1037/h0029536

Lyman, B. J., and McDaniel, M. A. (1986). Effects of encoding strategy on long-term memory for odours. Q. J. Exp. Psychol. 38A, 753–765. doi: 10.1080/14640748608401624

Lyman, B. J., and McDaniel, M. A. (1990). Memory for odors and odor names: modalities of elaborating and imagery. J. Exp. Psychol. Learn. 16, 656–664. doi: 10.1037/0278-7393.16.4.656

MacKay, D. G., Shafto, M., Taylor, J. K., Marian, D. E., Abrams, L., and Dyer, J. R. (2004). Relations between emotion, memory, and attention: evidence from taboo stroop, lexical decision, and immediate memory tasks. Mem. Cogn. 32, 474–488. doi: 10.3758/BF03195840

Maquet, P. (2001). The role of sleep in learning and memory. Science 294, 1048–1052. doi: 10.1126/science.1062856

Masaoka, Y., Sugiyama, H., Katayama, A., Kashiwagi, M., and Homma, I. (2012a). Remembering the past with slow breathing associated with activity in the parahippocampus and amygdala. Neurosci. Lett. 521, 98–103. doi: 10.1016/j.neulet.2012.05.047

Masaoka, Y., Sugiyama, H., Katayama, A., Kashiwagi, M., and Homma, I. (2012b). Slow breathing and emotions associated with odor-induced autobiographical memories. Chem. Senses 37, 379–388. doi: 10.1093/chemse/bjr120

Mather, M. (2007). Emotional arousal and memory binding: an object-based framework. Perspect. Psychol. Sci. 2, 33–52. doi: 10.1111/j.1745-6916.2007.00028.x

Mather, M., and Nesmith, K. (2008). Arousal-enhanced location memory for pictures. J. Mem. Lang. 58, 449–464. doi: 10.1016/j.jml.2007.01.004

Melcher, D. (2010). Accumulating and remembering the details of neutral and emotional natural scenes. Perception 39, 1011–1025. doi: 10.1068/p6670

Milton, F., Muhlert, N., Butler, C. R., Smith, A., Benattayallah, A., and Zeman, A. Z. (2011). An fMRI study of long-term everyday memory using SenseCam. Memory 19, 733–744. doi: 10.1080/09658211.2011.552185

Moncrieff, R. W. (1966). Odour Preferences. London: Leonard Hill.

Murdock, B. B. Jr. (1962). The serial position effect of free recall. J. Exp. Psychol. 64, 482–488. doi: 10.1037/h0045106

Nadel, L., Campbell, J., and Ryan, L. (2007). Autobiographical memory retrieval and hippocampal activation as a function of repetition and the passage of time. Neural Plast. 2007:90472. doi: 10.1155/2007/90472

Nashiro, K., and Mather, M. (2011). How arousal affects younger and older adults' memory binding. Exp. Aging Res. 37, 108–128. doi: 10.1080/0361073X.2011.536746

Naveen, K. V., Nagarathna, R., Nagendra, H. R., and Telles, S. (1997). Yoga breathing through a particular nostril increases spatial memory scores without lateralized effects. Psychol. Rep. 81, 555–561. doi: 10.2466/pr0.1997.81.2.555

Olsson, M. J., and Cain, W. S. (2003). Implicit and explicit memory for odors: hemispheric differences. Mem. Cogn. 31, 44–50. doi: 10.3758/BF03196081

Pause, B. M., Jungbluth, C., Adolph, D., Pietrowsky, R., and Dere, E. (2010). Induction and measurement of episodic memories in healthy adults. J. Neurosci. Meth. 189, 88–96. doi: 10.1016/j.jneumeth.2010.03.016

Pause, B. M., Zlomuzica, A., Kinugawa, K., Mariani, J., Pietrowsky, R., and Dere, E. (2013). Perspectives on episodic-like and episodic memory. Front. Behav. Neurosci. 7:33. doi: 10.3389/fnbeh.2013.00033

Piolino, P., Giffard-Quillon, G., Desgranges, B., Chételat, G., Baron, J.-C., and Eustache, F. (2004). Re-experiencing old memories via hippocampus: a PET study of autobiographical memory. Neuroimage 22, 1371–1383. doi: 10.1016/j.neuroimage.2004.02.025

Plailly, J., Howard, J. D., Gitelman, D. R., and Gottfried, J. A. (2008). Attention to odor modulates thalamocortical connectivity in the human brain. J. Neurosci. 28, 5257–5267. doi: 10.1523/JNEUROSCI.5607-07.2008

Plailly, J., Luangraj, N., Nicklaus, S., Issanchou, S., Royet, J.-P., and Sulmont-Rossé, C. (2011). Alliesthesia is greater for odors of fatty foods than of non-fat foods. Appetite 57, 615–622. doi: 10.1016/j.appet.2011.07.006

Price, J. L. (1973). An autoradiographic study of complementary laminar patterns of termination of afferent fibers to the olfactory cortex. J. Comp. Neurol. 150, 87–108. doi: 10.1002/cne.901500105

Robin, O., Alaoui-Ismaïli, O., Dittmar, A., and Vernet-Maury, E. (1998). Emotional responses evoked by dental odors: an evaluation from autonomic parameters. J. Dent. Res. 77, 1638–1646. doi: 10.1177/00220345980770081201

Royet, J.-P., Morin-Audebrand, L., Cerf-Ducastel, B., Haase, L., Issanchou, S., Murphy, C., et al. (2011). True and false recognition memories of odors induce distinct neural signatures. Front. Hum. Neurosci. 5:65. doi: 10.3389/fnhum.2011.00065

Royet, J. P., Plailly, J., Delon-Martin, C., Kareken, D. A., and Segebarth, C. (2003). fMRI of emotional responses to odors: influence of hedonic valence and judgment, handedness, and gender. Neuroimage 20, 713–728. doi: 10.1016/S1053-8119(03)00388-4

Saive, A.-L., Ravel, N., Thévenet, M., Royet, J.-P., and Plailly, J. (2013). A novel experimental approach to episodic memory in humans based on the privileged access of odors to memories. J. Neurosci. Meth. 213, 22–31. doi: 10.1016/j.jneumeth.2012.11.010

Sergerie, K., Chochol, C., and Armony, J. L. (2008). The role of the amygdala in emotional processing: a quantitative meta-analysis of functional neuroimaging studies. Neurosci. Biobehav. Rev. 32, 811–830. doi: 10.1016/j.neubiorev.2007.12.002

Sezille, C., Messaoudi, B., Bertrand, A., Joussain, P., Thevenet, M., and Bensafi, M. (2013). A portable experimental apparatus for human olfactory fMRI experiments. J. Neurosci. Meth. 218, 29–38. doi: 10.1016/j.jneumeth.2013.04.021

Shipley, M. T., and Reyes, P. (1991). “Anatomy of the human olfactory bulb and central olfactory pathways,” in The Human Sense of Smell, eds D. G. Laing, R. L. Doty, and W. Breipohl (Amsterdam: Springer-Verlag), 29–60.

Stickgold, R. (2005). Sleep-dependent memory consolidation. Nature 437, 1272–1278. doi: 10.1038/nature04286

Tulving, E. (1972). “Episodic and semantic memory,” in Organization of Memory, eds E. Tulving and W. Donaldson (New York, NY: Academic Press), 381–405.

Tulving, E. (1983). Elements of Episodic Memory. Oxford: Clarendon.

Tulving, E. (2001). Episodic memory and common sense: how far apart? Philos. Trans. R. Soc. Lond. B Biol. Sci. 356, 1505–1515. doi: 10.1098/rstb.2001.0937

Tulving, E. (2002). Episodic memory: from mind to brain. Annu. Rev. Psychol. 53, 1–25. doi: 10.1146/annurev.psych.53.100901.135114

Wagner, U., Hallschmid, M., Rasch, B., and Born, J. (2006). Brief sleep after learning keeps emotional memories alive for years. Biol. Psychiatry 60, 788–790. doi: 10.1016/j.biopsych.2006.03.061

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 24 March 2014; Accepted: 19 May 2014; Published online: 06 June 2014.

Citation: Saive A-L, Royet J-P, Ravel N, Thévenet M, Garcia S and Plailly J (2014) A unique memory process modulated by emotion underpins successful odor recognition and episodic retrieval in humans. Front. Behav. Neurosci. 8:203. doi: 10.3389/fnbeh.2014.00203

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Saive, Royet, Ravel, Thévenet, Garcia and Plailly. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

MINI REVIEW ARTICLE
published: 07 April 2014
doi: 10.3389/fnbeh.2014.00098

Learning to smell danger: acquired associative representation of threat in the olfactory cortex

Wen Li1,2*

1Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA

2Waisman Center, University of Wisconsin-Madison, Madison, WI, USA

* Correspondence: Wen Li, Department of Psychology, University of Wisconsin-Madison, Brogden Hall, 1202 W. Johnson Street, Madison, WI 53706, USA e-mail: wenli@psych.wisc.edu

Edited by:
Anne-Marie Mouly, Centre de Recherche en Neurosciences de Lyon, France

Reviewed by:
Jonas K. Olofsson, Stockholm University, Sweden
Tyler Lorig, Washington and Lee University, USA

Neuroscience research over the past few decades has reached a strong consensus that the amygdala plays a key role in emotion processing. However, many questions remain unanswered, especially concerning emotion perception. Based on mnemonic theories of olfactory perception and in light of the highly associative nature of olfactory cortical processing, here I propose a sensory cortical model of olfactory threat perception (i.e., sensory-cortex-based threat perception): the olfactory cortex stores threat codes as acquired associative representations (AARs) formed via aversive life experiences, thereby enabling encoding of threat cues during sensory processing. Rodent and human research in olfactory aversive conditioning was reviewed, indicating learning-induced plasticity in the amygdala and the olfactory piriform cortex. In addition, as aversive learning becomes consolidated in the amygdala, the associative olfactory (piriform) cortex may undergo (long-term) plastic changes, resulting in modified neural response patterns that underpin threat AARs. This proposal thus brings forward a sensory cortical pathway to threat processing (in addition to amygdala-based processes), potentially accounting for an alternative mechanism underlying the pathophysiology of anxiety and depression.

Keywords: threat encoding, olfactory sensory cortex, acquired associative representation, aversive conditioning, olfaction, anxiety

INTRODUCTION

Whether it is roaming on the safari or dozing by the fireplace, the ability to quickly detect threat (e.g., a tiger or a burning rug) and initiate appropriate responses can mean life or death for an organism. Decades of neuroscience research in emotion processing has reached a strong consensus: the amygdala extracts biological significance of a sensory cue and initiates and controls affective and motivational responses to the stimulus (LeDoux, 2000, 2012; Adolphs, 2013). In terms of threat perception, a widely held view is that the amygdala projects emotionally charged outputs to the sensory cortex, thereby enabling perceptual analysis of potential danger (Phelps and LeDoux, 2005; Vuilleumier and Pourtois, 2007). These theories have been incorporated into neural models of emotional disorders, shedding important light on the pathophysiology of anxiety and depression (Davis, 1992; Rauch et al., 2006; Clark and Beck, 2010).

However, striking findings have arisen recently, suggesting that threat processing can operate independently of the amygdala. Patient S.M. an individual with complete bilateral amygdala lesions, demonstrated intact early threat perception (Tsuchiya et al., 2009); she and two other patients with similar amygdala lesions also developed panic attacks and intense fear when challenged with high concentration CO_2 (Feinstein et al., 2013). In addition, extensive research indicates very swift (at latencies around 100 ms) threat processing in the visual cortex (Li et al., 2007, 2008b; Krusemark and Li, 2011, 2013; Forscher and Li, 2012; also cf. Vuilleumier and Pourtois, 2007), preceding the latencies of threat processing measured with depth-electrode recording in the amygdala (Oya et al., 2002; Krolak-Salmon et al., 2004). Accordingly, instead of relying on amygdala input, early threat perception may depend on inputs from other brain areas (e.g., the orbitofrontal cortex; Barrett and Bar, 2009), or simply consummate during the initial sensory feedforward sweep. Indeed, a seminal paper by Pessoa and Adolphs (2010) promotes a multi-path framework, arguing for extra-amygdala neural circuits in threat processing.

Towards that end, this article draws evidence from long-standing animal research and recent human data (reviewed below), proposing a sensory cortical model of threat perception (i.e., sensory-cortex-based threat perception): the sensory cortex stores threat codes/representations, thereby enabling perceptual encoding of threat information once an environmental input reaches the sensory cortex. This model highlights an active, independent role of the sensory cortex in threat perception, as opposed to the conventionally understood role of passively processing/integrating threat-laden inputs generated elsewhere in the brain (e.g., the amygdala; Phelps and LeDoux, 2005; Vuilleumier and Pourtois, 2007). This model makes clear evolutionary sense by permitting categorization of biological significance in the stage of sensory analysis, prompting an organism to respond with minimal delay. Importantly, by putting forward a sensory-based mechanism, in addition to limbic-based threat processing, this model would help reconcile controversial findings in the literature as discussed above, such as the very swift threat perception, even in the absence of intact amygdala.

A (OLFACTORY) SENSORY CORTICAL MODEL OF THREAT PERCEPTION

To provide a mechanistic explication of this account, a few principles need to be emphasized. As James (1890) asserted, “every perception is an acquired perception,” human perception is largely learned and depends on long-term memory (Goldstone, 1998; Stevenson and Boakes, 2003). This proposed model thus takes a learning perspective, building on mnemonically-based threat codes/representations acquired through life experiences. Indeed, except for a limited set of innate phobic objects (e.g., snakes; Ohman and Mineka, 2001), the copious repertoire of threat cues in humans appears to be learned and accumulated over the course of life, varying from the concrete (e.g., germs and guns) to the abstract (e.g., disease and death).

In addition, given the associative nature of memory and threat processing, this model centers on the associative (secondary) sensory cortex, characterized by dense intrinsic and extrinsic neural connections, as a primary site of threat code storage and threat encoding. In particular, extrinsic top-down connections transmitting contextual information can modulate sensory cortical activity to facilitate context-relevant behavior (Cohen and Maunsell, 2011; Harris and Mrsic-Flogel, 2013); this focus on associative sensory cortex would thus permit context- or state-dependent flexibility (adaptive to affective, motivational and physiological states) in perceiving threat while ensuring sensory fidelity (Proffitt, 2006; Barsalou, 2008; Krusemark and Li, 2013; Krusemark et al., 2013).

Furthermore, I chose olfaction as a model system for this account. The olfactory cortex has served as a model system for the cortical representation of associative memory (Gluck and Granger, 1993; Haberly, 1998), owing to the fact that olfactory perception is deeply rooted in memory (Stevenson and Boakes, 2003; Wilson and Stevenson, 2003) and that olfactory cortical processing is highly associative (Wilson and Sullivan, 2011). Moreover, akin to threat encoding specifically, olfaction is uniquely related to emotion in function and anatomy (Schiffman, 1974; Carmichael et al., 1994), given their phylogenetic proximity. Studies have shown that olfactory perception shifts readily with a perceiver’s affective state (Herz et al., 2004; Chen and Dalton, 2005; Herz, 2005; Pollatos et al., 2007; Krusemark et al., 2013); odor affective value (vs. odor character, “lemon” or “orange”) may even represent the dominant dimension in olfactory perception (Yeshurun and Sobel, 2010). Finally, odor hedonicity is posited to be borne directly out of emotional experiences attached to an odor (Herz, 2005). Taken together, these properties of olfactory perception represent a particularly close fit to the model here.

Mechanistically, this proposed account rests on long-term plasticity (as a form of long-term memory storage) in the sensory cortex, consequent to aversive associative learning. As accruing evidence suggests that the sensory cortex contains richly interconnected neurons, whose patterns of firing as a whole encode sensory input (Harris and Mrsic-Flogel, 2013), this model highlights modified neural response patterns induced by long-term plasticity in the sensory cortex. Critically, these patterns would reflect acquired associative representations (AARs) that encode the threat meanings learned via negative experiences (in addition to the sensory features of the stimuli). As such, constituting sensory neural codes of acquired threats, these threat AARs would underpin threat perception in the sensory cortex.

Based on the fear learning literature reviewed below, the genesis of threat-relevant sensory cortical long-term plasticity and threat AARs could involve two components (Figure 1): (1) acquisition/consolidation of aversive associative learning in the amygdala, thereby attaching threat meanings to innocuous odors; and (2) over time, the initial amygdala-based learning gives rise to long-term sensory cortical plasticity. That is, the associative olfactory (posterior piriform) cortex (PPC) undergoes plastic changes, resulting in an updated neural response pattern (i.e., a threat AAR) to the conditioned odor. Accordingly, subsequent encounters of the conditioned odor will activate this threat AAR in the PPC, supporting olfactory cortical encoding of threat. Finally, outputs from this sensory process (i.e., threat-laden sensory impulses) can trigger fear responses via projections to a wide range of associative neural networks (especially the amygdala, prefrontal cortex and brain stem structures).

FIGURE 1. A sensory cortical model of olfactory threat encoding. (A) When an odor is co-experienced with an aversive event, associative learning may occur. Long-term plasticity induced by such learning results in long-lasting changes in the olfactory (posterior piriform) cortical response pattern to the CS odor. As such, original representation of the odor (O) turns into acquired associative representation/AAR (O’). Such threat AARs constitute the basis of sensory cortical encoding of threat. Later encounters of the same odor will activate O’ to directly support threat encoding and trigger emotion responding. (B) Neural mechanisms. Initial association between the odor and aversive experience is formed in the lateral amygdala (LA), which projects directly or indirectly (via the basal nucleus of amygdala/BA) to the central nucleus (CE) to initiate and control fear responses. Over time, the acquired association is converted into a long-term memory stored in the PPC in the form of a threat AAR. Possible mediating mechanisms are increases in amygdala theta oscillation, cholinergic activity and amygdala efferents to the PPC. APC = anterior piriform cortex; OB = olfactory bulb; PFC = prefrontal cortex; Amyg. = amygdala; Hippo = hippocampus; CM = corticomedial nucleus of amygdala.

AMYGDALA MEDIATES THE ACQUISITION/CONSOLIDATION OF OLFACTORY AVERSIVE CONDITIONING

It is a well-known fact that repeated paired stimulation of a stimulus (CS, e.g., a tone) and a salient stimulus (US, e.g., an electric shock or a drop of water) often result in conditioning (e.g., Pavlovian or emotional conditioning), and the CS thus acquires a new threat/reward meaning (Pavlov, 1927/1960). In terms of the neural mechanism, extensive research has ascribed a key role to the amygdala, especially the basolateral complex (comprising the lateral, basal and accessory basal nuclei), in aversive conditioning. As described in influential reviews (LeDoux, 2000, 2012; Maren and Quirk, 2004; Myers and Davis, 2007), the lateral nucleus of amygdala reliably exhibits increased spike firing and long-term potentiation during conditioning, underscoring the lateral nucleus as a primary site of conditioning acquisition and consolidation. Furthermore, pre-training damage to the lateral nucleus directly impairs fear conditioning, indicative of its causal role in this process. Via direct or indirect intra-amygdala connections, the lateral nucleus triggers activation of the central nucleus of the amygdala, which initiates and controls the expression of the acquired fear via projections to a set of midbrain and brainstem structures (e.g., hypothalamus and periaqueductal gray). Finally, it is worth noting that for auditory fear conditioning, the magnocellular medial geniculate nucleus could mediate the initial learning (Weinberger, 2011).

The aversive conditioning literature has primarily involved the auditory sense and, to a lesser extent, the visual sense. Nevertheless, olfactory conditioning research has yielded similar conclusions (cf. Mouly and Sullivan, 2010). Electrophysiological studies in rodents indicate that the basolateral amygdala exhibits potentiated responses during olfactory aversive conditioning (Rosenkranz and Grace, 2002) and shortly after (Rattiner et al., 2004; Sevelinges et al., 2004). Highlighting its critical role in olfactory conditioning acquisition, pre-training lesions or pharmacological inactivation/inhibition of the basolateral amygdala significantly reduces conditioned fear or aversion to the CS odor (Cousens and Otto, 1998; Wallace and Rosen, 2001; Walker et al., 2005; Miranda et al., 2007; Sevelinges et al., 2009). Furthermore, post-training inactivation of the basolateral amygdala (Kilpatrick and Cahill, 2003; Sevelinges et al., 2009) would largely attenuate conditioned aversion, implicating this area in the consolidation of olfactory aversive associative learning. Using biomarkers of synaptic plasticity to reflect fear learning, research also further reveals plastic changes, during or shortly after conditioning, in the basolateral amygdala of rodents exposed to paired odor-shock stimulation, in the form of heightened expression of brain derived neurotrophic factor (BDNF; Jones et al., 2007) or increased concentrations of glutamate and GABA (Hegoburu et al., 2009).

A substantial body of human neuroimaging research in aversive conditioning has emerged, albeit largely concerning auditory and visual CS (Sehlmeyer et al., 2009). Human neuroimaging research of olfactory aversive conditioning remains scant. Functional magnetic resonance imaging (fMRI) data from our lab indicate conditioned responses evoked by the CS odor during the acquisition phase (Figure 2A, Li et al., 2008a). That is, conforming to an exponential decay observed in prior imaging studies of visual aversive conditioning (Büchel et al., 1998; LaBar et al., 1998), the amygdala response to the CS odor (vs. CS- odor) increases sharply in early trials and declines in later trials. In addition, pairing odors with painful (CO_2) trigeminal stimulation in human subjects, a new fMRI study reveals significant response enhancement to the CS odor in the amygdala during conditioning (Moessnang et al., 2013). These extant findings thus concur with conclusions of the general literature, confirming the role of amygdala in the acquisition of human olfactory conditioning.

FIGURE 2. Olfactory aversive learning and neural circuitry adaptation in humans. (A) During the conditioning phase, the amygdala exhibits conditioned response to the conditioned odor and its extremely similar enantiomer counterpart. (B) Perceptual discrimination between the CS and its enantiomer counterpart improves markedly after conditioning whereas the unconditioned enantiomer pair remains indistinguishable. (C) In parallel, response patterns for the CS pair become divergent (relative to the non-conditioned/nCS pair). Differential odor maps (spatial configurations of response intensities in all active PPC voxels) within each pair are displayed at the top of the bar graph, with strong-colored voxels reflecting large disparities between the counterparts. Notably, the post-conditioning differential map for the CS pair contains far more voxels of strong colors. (D) Plasticity in the PPC—enhanced response to the target odor after prolonged mere exposure. (E) The olfactory sensory pathway adapts readily with induced anxiety, characterized by strengthened APC efforts to amygdala and PPC, and amplified amygdala efferent to the PPC. This olfactory circuitry reorganization is accompanied by a significant negative shift in perceived pleasantness of odors (not shown here). This enhanced amygdala-olfactory-cortex connection may facilitate the transfer of learning from the amygdala to the olfactory cortex. Yellow lines represent intrinsic connections initially significant, green lines those that become significant in anxiety and red intercepting lines modulation by odors in anxiety. OFC = orbitofrontal cortex; PPC = posterior piriform cortex; pgACC = pregenual anterior cingulate cortex; Amyg. = amygdala; olf. = olfactory; APC = anterior piriform cortex. Panels A–C are adapted from Li et al. (2008a), Panel D from Li et al. (2006) and Panel E from Krusemark et al. (2013).

Notably, the olfactory anatomy is fairly distinct from other senses; it lacks the thalamic relay critical for signal transmission in other modalities, and its inputs terminate in the corticomedial nucleus of amygdala (Carmichael et al., 1994) versus the lateral nucleus for other sensory inputs (Luskin and Price, 1983; Savander et al., 1996). Despite these disparities, fear learning in the amygdala is fairly generic, contrasting with sensory-specific plasticity in the olfactory cortex.

OLFACTORY CORTEX SUPPORTS ACQUIRED ASSOCIATIVE REPRESENTATIONS (AARS) VIA OLFACTORY AVERSIVE CONDITIONING

TRANSFER OF AVERSIVE ASSOCIATIVE LEARNING FROM THE AMYGDALA TO THE OLFACTORY CORTEX

As discussed earlier, the current model requires long-term learning-based plasticity in the sensory cortex to substantiate threat codes (threat AARs). In fact, although the role of amygdala in long-term fear memory is still debatable (LeDoux, 2000; McGaugh, 2004), the sensory cortex has long been implicated as a site of storage and retrieval of remote associative memory (Mishkin, 1982; Squire, 1987; Damasio, 1989). Namely, long-standing views posit that as the memory of an object becomes fully consolidated in mediotemporal structures (e.g., the hippocampus), the object-specific sensory cortex (e.g., auditory cortex to a tone or olfactory cortex to an odor) gradually takes over to support long-term storage of the memory (Haberly and Bower, 1989; Gluck and Granger, 1993; Gluck and Myers, 1993; Squire and Wixted, 2011). Regarding aversive associative learning in particular, the sensory cortex could undergo plastic changes to serve as a primary storage site of long-term fear memory, following the initial fear learning (Weinberger, 2004).

Three mechanisms may mediate this transfer (Figure 1B). Firstly, long-range, low-frequency (theta) oscillatory activity in the amygdala is potentiated following conditioning, thereby facilitating amygdala interaction with sensory cortical storage sites to induce plasticity in these regions (Haberly and Bower, 1989; Gluck and Granger, 1993; Paré et al., 2002). Secondly, aversive conditioning potentiates amygdala efferents to the nucleus basalis, driving its acetylcholine release in the sensory cortex to mediate long-lasting sensory cortical plasticity (McGaugh et al., 2002; Weinberger, 2007). Thirdly, negative affective states induced by conditioning can intensify amygdala efferents to the sensory cortex to induce cortical plasticity, as suggested by a recent human fMRI in our lab (Krusemark et al., 2013). Combining dynamic causal connectivity analysis (Friston et al., 2013) and anxiety induction in a simple odor detection task, we demonstrate that an induced anxious state can reorganize the olfactory sensory circuitry, incorporating the amygdala as an integral step. That is, following anxiety induction, initially insignificant efferents from the anterior piriform cortex (APC) to the amygdala become important; also, efferents from the amygdala to PPC are further strengthened (Figure 2E). Notably, this circuitry reorganization is accompanied by a negative shift in perceived odor valence. Conceivably, by (almost invariably) inducing anxious/negative affective states, aversive conditioning can similarly enhance amygdala discharges to the PPC, promoting plastic changes in this area.

In consequence, long-lasting plasticity would arise in the sensory cortex, which then selectively updates neuronal ensemble response patterns to the CS, substantiating the long-term memory of acquired threat value in the CS. Notably, Weinberger’s lab was the first to show that the primary auditory cortex is the locus of long-term plasticity due to auditory fear conditioning, supporting altered sensory encoding of auditory CS (Weinberger, 2007, 2011; Weinberger and Bieszczad, 2011). To date, there has been considerable evidence of olfactory cortical plasticity as a result of olfactory aversive conditioning.

OLFACTORY CORTICAL PLASTICITY INDUCED BY OLFACTORY AVERSIVE CONDITIONING

The olfactory cortex consists of the anterior olfactory nucleus, olfactory tubercle, cortical nucleus of the amygdala, piriform cortex and entorhinal cortex (Carmichael et al., 1994; Shipley and Ennis, 1996; Haberly, 1998). The piriform cortex, divided into anterior and posterior piriform cortices (APC and PPC), is the largest subarea of the olfactory cortex. As described in excellent recent reviews (Gottfried, 2010; Mori and Sakano, 2011; Wilson and Sullivan, 2011), the APC serves as a primary olfactory cortex influenced strongly by bulbar mitral cell afferents and thus maintains considerable fidelity to the molecular properties of an odorant, whereas the PPC anatomically and functionally resembles a higher-level association cortex, supporting higher-order olfactory perception (e.g., odor quality encoding and categorization). Therefore, the PPC is postulated as the primary locus of olfactory threat AARs and threat encoding in the current model (Figure 1).

Indeed, akin to the associative and malleable nature of PPC, computational modeling and in vitro physiological studies suggest that long-term potentiation is more readily induced in the PPC (than APC), enabling long-term memory storage, whereas the APC is more associated with sensory processing and simple forms of short-term memory (Lynch and Granger, 1989; Jung et al., 1990). Empirical data of olfactory aversive conditioning largely concur with this view. After olfactory conditioning, PPC (but not APC) in the trained animals exhibits stronger local field potentials (Litaudon et al., 1997; Sevelinges et al., 2004) or BDNF expression (Jones et al., 2007) compared to the baseline. Hegoburu et al. (2009) also demonstrate increases in GABA and glutamate concentrations in the PPC of trained rats. To note, this PPC plasticity persists up to 30 min into odor-shock conditioning, contrasting with transient concentration increases of these amino acids in the basolateral amygdala (observed in the same study). These distinct plasticity time courses may correspond to the differential functions of these two areas: the amygdala is critical for initial learning while the PPC is important for long-term memory of learning. It is also worth noting that CS-odor evoked response potentiation has been observed in the APC of awake rats using single-unit recording (Barnes et al., 2011; Chen et al., 2011). This positive finding could be ascribed to the efficacy of the methodology, but as PPC activity was not assessed in these studies, it is unclear whether PPC plasticity coexisted or even mediated the APC changes. Finally, recent evidence also suggests that fear learning can induce plasticity in even lower levels of the olfactory hierarchy (i.e., olfactory receptor neurons and the olfactory bulb; Jones et al., 2008; Kass et al., 2013).

Concerning human evidence, our aforementioned conditioning study also shows that the PPC (but not APC) exhibits significant CS-evoked response changes, paralleling the rodent findings (Figure 2C, Li et al., 2008a); this superior plasticity in PPC further accords with a previous fMRI study in the lab involving simple perceptual learning, where the PPC but not APC demonstrates response enhancement following prolonged odor exposure (Li et al., 2006; Figure 2D). Critically, our conditioning study reveals that behavioral and PPC neuronal ensemble responses to two initially indistinguishable odor enantiomers (mirror-image molecules) become distinct after one of them is paired with electric shock (Figures 2B and C, Li et al., 2008a). This divergence contrasts with convergent response augmentation in the amygdala to both the CS odor and its non-conditioned enantiomer counterpart (Figure 2A). These findings thus emphasize that PPC plasticity is selective to the CS odor, independent of amygdala plasticity that is generalized to similar cues. This specialized PPC plasticity could thus underpin sensory cortical representation of the acquired threat value in the CS odor, supporting discrimination perception of the CS odor versus its counterpart following conditioning.

Finally, direct evidence for long-term storage of aversive conditioning in the olfactory cortex has emerged from a series of experiments conducted by Sacco and Sacchetti (2010). The authors demonstrate long-term memory of shock conditioning: trained rats exhibit strong freezing responses to the CS one month post-training. Nevertheless, lesioning CS-specific secondary sensory (visual, auditory and olfactory) cortex (including the PPC), one month post-training, largely abolishes the conditioned responses. Notably, lesions to the secondary sensory cortex do not impair new fear learning or recent (e.g., 1 day post-training) fear memories; this coincides with previous research which induced sensory cortical lesions either before or shortly after conditioning and failed to find impairment in fearing learning or recent fear memory (Romanski and LeDoux, 1992; Rosen et al., 1992; Falls and Davis, 1993; Campeau and Davis, 1995), suggesting that the sensory cortex is not critical for acquisition and consolidation of fear memory. Nonetheless, this recent study evinces that the secondary sensory cortex is essential for long-term storage and retrieval of acquired threat value in the CS. That is, as time elapses after initial learning, the CS will need to activate the threat AAR stored in the secondary (associative) sensory cortex to elicit conditioned responses.

CONCLUSION

This review integrates mnemonic theories of olfaction and evidence of olfactory aversive associative learning, promoting a sensory cortical model of threat perception. The amygdala may mediate olfactory associative learning and transfer this learning to the olfactory cortex. The consequent long-term plasticity in the olfactory associative cortex (PPC) may serve to support olfactory threat AARs (representing acquired threat value in conditioned odors). These threat AARs can independently enable sensory cortical encoding of threat and trigger various responses via projections to associative neural networks. This sensory pathway may specialize in mandatory, reflexive, and sensory-specific forms of threat encoding, whereas the amygdala, especially via interaction with sensory and ventral prefrontal cortices, may chiefly be responsible for flexible, context-relevant, and amodal (abstract) threat processing (Krusemark and Li, 2013). In addition, these parallel systems may confer further ecological advantage by integrating a fine-tuned sensory module for specific threat identification and a broadly-tuned amygdala module for sensitive threat detection (Li et al., 2008a). By elucidating threat encoding in the sensory cortex, this proposed model may provide new insights into the pathophysiology of emotional disorders, pointing to a concrete clinical intervention target in the sensory cortex.

LIMITATIONS AND FUTURE DIRECTIONS

Compared to rich data in auditory fear learning (Weinberger, 2004, 2007), a limitation of the olfactory fear learning literature is the limited evidence of long-term plasticity/memory storage in the sensory cortex, which is a critical neural basis of the neurosensory account of threat perception. That is, very few studies in this literature have assessed olfactory sensory cortical plasticity after a prolonged delay (e.g., 2 weeks or 1 month after initial learning), and there is virtually no human evidence. Therefore, future research is warranted to isolate evidence of long-term memory of acquired threat in the sensory cortex, especially in human subjects.

Another notable limitation concerns the spatial resolution of the fMRI methodology, a first-line non-invasive method in probing neural activities in humans. With millions of neurons in each fMRI voxel (a volume of few cube millimeters; Logothetis, 2008), patterns of voxel-wise fMRI signal intensity would reflect large-scale configurations of neural ensemble activity as opposed to single-unit neuronal response patterns. Also, various methods have been applied in pattern-based fMRI analysis, warranting comparisons and cross-validations of those findings in the future. That said, it is also worth noting that given the considerable level of redundant coding in neuronal populations in the sensory cortex (e.g., the often correlated firing of neighboring neurons; Smith and Kohn, 2008; Luczak et al., 2009), these large-scale patterns observed in humans could still provide useful insights into sensory coding, especially when integrated with animal electrophysiological data.

ACKNOWLEDGMENTS

The author would like to thank N. Weinberger for excellent input and the members of the Li Laboratory for comments and assistance. The author is supported by R01MH093413.

REFERENCES

Adolphs, R. (2013). The biology of fear. Curr. Biol. 23, R79–R93. doi: 10.1016/j.cub.2012.11.055

Barnes, D. C., Chapuis, J., Chaudhury, D., and Wilson, D. A. (2011). Odor fear conditioning modifies piriform cortex local field potentials both during conditioning and during post-conditioning sleep. PLoS One 6:e18130. doi: 10.1371/journal.pone.0018130

Barrett, L. F., and Bar, M. (2009). See it with feeling: affective predictions during object perception. Philos. Trans. R. Soc. Lond. B Biol. Sci. 364, 1325–1334. doi: 10.1098/rstb.2008.0312

Barsalou, L. W. (2008). Grounded cognition. Annu. Rev. Psychol. 59, 617–645. doi: 10.1146/annurev.psych.59.103006.093639

Büchel, C., Morris, J., Dolan, R. J., and Friston, K. J. (1998). Brain systems mediating aversive conditioning: an event-related fMRI study. Neuron 20, 947–957. doi: 10.1016/s0896-6273(00)80476-6

Campeau, S., and Davis, M. (1995). Involvement of subcortical and cortical afferents to the lateral nucleus of the amygdala in fear conditioning measured with fear-potentiated startle in rats trained concurrently with auditory and visual conditioned stimuli. J. Neurosci. 15, 2312–2327.

Carmichael, S. T., Clugnet, M. C., and Price, J. L. (1994). Central olfactory connections in the macaque monkey. J. Comp. Neurol. 346, 403–434. doi: 10.1002/cne.903460306

Chen, C. F., Barnes, D. C., and Wilson, D. A. (2011). Generalized vs. stimulus-specific learned fear differentially modifies stimulus encoding in primary sensory cortex of awake rats. J. Neurophysiol. 106, 3136–3144. doi: 10.1152/jn.00721.2011

Chen, D., and Dalton, P. (2005). The effect of emotion and personality on olfactory perception. Chem. Senses 30, 345–351. doi: 10.1093/chemse/bji029

Clark, D. A., and Beck, A. T. (2010). Cognitive theory and therapy of anxiety and depression: convergence with neurobiological findings. Trends Cogn. Sci. 14, 418–424. doi: 10.1016/j.tics.2010.06.007

Cohen, M. R., and Maunsell, J. H. (2011). Using neuronal populations to study the mechanisms underlying spatial and feature attention. Neuron 70, 1192–1204. doi: 10.1016/j.neuron.2011.04.029

Cousens, G., and Otto, T. (1998). Both pre- and posttraining excitotoxic lesions of the basolateral amygdala abolish the expression of olfactory and contextual fear conditioning. Behav. Neurosci. 112, 1092–1103. doi: 10.1037/0735-7044.112.5.1092

Damasio, A. R. (1989). Time-locked multiregional retroactivation: a systems-level proposal for the neural substrates of recall and recognition. Cognition 33, 25–62. doi: 10.1016/0010-0277(89)90005-x

Davis, M. (1992). The role of the amygdala in fear and anxiety. Annu. Rev. Neurosci. 15, 353–375. doi: 10.1146/annurev.ne.15.030192.002033

Falls, W. A., and Davis, M. (1993). Visual cortex ablations do not prevent extinction of fear-potentiated startle using a visual conditioned stimulus. Behav. Neural Biol. 60, 259–270. doi: 10.1016/0163-1047(93)90504-b

Feinstein, J. S., Buzza, C., Hurlemann, R., Follmer, R. L., Dahdaleh, N. S., Coryell, W. H., et al. (2013). Fear and panic in humans with bilateral amygdala damage. Nat. Neurosci. 16, 270–272. doi: 10.1038/nn.3323

Forscher, E. C., and Li, W. (2012). Hemispheric asymmetry and visuo-olfactory integration in perceiving subthreshold (micro) fearful expressions. J. Neurosci. 32, 2159–2165. doi: 10.1523/JNEUROSCI.5094-11.2012

Friston, K., Moran, R., and Seth, A. K. (2013). Analysing connectivity with granger causality and dynamic causal modelling. Curr. Opin. Neurobiol. 23, 172–178. doi: 10.1016/j.conb.2012.11.010

Gluck, M. A., and Granger, R. (1993). Computational models of the neural bases of learning and memory. Annu. Rev. Neurosci. 16, 667–706. doi: 10.1146/annurev.ne.16.030193.003315

Gluck, M. A., and Myers, C. E. (1993). Hippocampal mediation of stimulus representation: a computational theory. Hippocampus 3, 491–516. doi: 10.1002/hipo.450030410

Goldstone, R. L. (1998). Perceptual learning. Annu. Rev. Psychol. 49, 585–612. doi: 10.1146/annurev.psych.49.1.585

Gottfried, J. A. (2010). Central mechanisms of odour object perception. Nat. Rev. Neurosci. 11, 628–641. doi: 10.1038/nrn2883

Haberly, L. B. (1998). Olfactory Cortex. New York: Oxford University Press.

Haberly, L. B., and Bower, J. M. (1989). Olfactory cortex: model circuit for study of associative memory? Trends Neurosci. 12, 258–264. doi: 10.1016/0166-2236(89)90025-8

Harris, K. D., and Mrsic-Flogel, T. D. (2013). Cortical connectivity and sensory coding. Nature 503, 51–58. doi: 10.1038/nature12654

Hegoburu, C., Sevelinges, Y., Thevenet, M., Gervais, R., Parrot, S., and Mouly, A. M. (2009). Differential dynamics of amino acid release in the amygdala and olfactory cortex during odor fear acquisition as revealed with simultaneous high temporal resolution microdialysis. Learn. Mem. 16, 687–697. doi: 10.1101/lm.1584209

Herz, R. S. (2005). Odor-associative learning and emotion: effects on perception and behavior. Chem. Senses 30(Suppl. 1), i250–i251. doi: 10.1093/chemse/bjh209

Herz, R. S., Beland, S. L., and Hellerstein, M. (2004). Changing odor hedonic perception through emotional associations in humans. Int. J. Comp. Psychol. 17, 315–339.

James, W. (1890). The Principles of Psychology. New York: Holt.

Jones, S. V., Choi, D. C., Davis, M., and Ressler, K. J. (2008). Learning-dependent structural plasticity in the adult olfactory pathway. J. Neurosci. 28, 13106–13111. doi: 10.1523/JNEUROSCI.4465-08.2008

Jones, S. V., Stanek-Rattiner, L., Davis, M., and Ressler, K. J. (2007). Differential regional expression of brain-derived neurotrophic factor following olfactory fear learning. Learn. Mem. 14, 816–820. doi: 10.1101/lm.781507

Jung, M. W., Larson, J., and Lynch, G. (1990). Long-term potentiation of monosynaptic EPSPs in rat piriform cortex in vitro. Synapse 6, 279–283. doi: 10.1002/syn.890060307

Kass, M. D., Rosenthal, M. C., Pottackal, J., and McGann, J. P. (2013). Fear learning enhances neural responses to threat-predictive stimuli. Science 342, 1389–1392. doi: 10.1126/science.1244916

Kilpatrick, L., and Cahill, L. (2003). Modulation of memory consolidation for olfactory learning by reversible inactivation of the basolateral amygdala. Behav. Neurosci. 117, 184–188. doi: 10.1037/0735-7044.117.1.184

Krolak-Salmon, P., Hénaff, M. A., Vighetto, A., Bertrand, O., and Mauguière, F. (2004). Early amygdala reaction to fear spreading in occipital, temporal, and frontal cortex: a depth electrode ERP study in human. Neuron 42, 665–676. doi: 10.1016/s0896-6273(04)00264-8

Krusemark, E. A., and Li, W. (2011). Do all threats work the same way? Divergent effects of fear and disgust on sensory perception and attention. J. Neurosci. 31, 3429–3434. doi: 10.1523/JNEUROSCI.4394-10.2011

Krusemark, E. A., and Li, W. (2013). From early sensory specialization to later perceptual generalization: dynamic temporal progression in perceiving individual threats. J. Neurosci. 33, 587–594. doi: 10.1523/JNEUROSCI.1379-12.2013

Krusemark, E. A., Novak, L. R., Gitelman, D. R., and Li, W. (2013). When the sense of smell meets emotion: anxiety-state-dependent olfactory processing and neural circuitry adaptation. J. Neurosci. 33, 15324–15332. doi: 10.1523/JNEUROSCI.1835-13.2013

LaBar, K. S., Gatenby, C., Gore, J. C., LeDoux, J. E., and Phelps, E. A. (1998). Human amygdala activation during conditioned fear acquisition and extinction: a mixed trial fMRI study. Neuron 20, 937–945. doi: 10.1016/s0896-6273(00)80475-4

LeDoux, J. E. (2000). Emotion circuits in the brain. Annu. Rev. Neurosci. 23, 155–184. doi: 10.1146/annurev.neuro.23.1.155

LeDoux, J. E. (2012). Rethinking the emotional brain. Neuron 73, 653–676. doi: 10.1016/j.neuron.2012.02.018

Li, W., Howard, J. D., Parrish, T. B., and Gottfried, J. A. (2008a). Aversive learning enhances perceptual and cortical discrimination of indiscriminable odor cues. Science 319, 1842–1845. doi: 10.1126/science.1152837

Li, W., Luxenberg, E., Parish, T., and Gottfried, J. A. (2006). Learning to smell the roses: experience-dependent neural plasticity in human piriform and orbitofrontal cortices. Neuron 52, 1097–1108. doi: 10.1016/j.neuron.2006.10.026

Li, W., Zinbarg, R. E., Boehm, S. G., and Paller, K. A. (2008b). Neural and behavioral evidence for affective priming from unconsciously perceived emotional facial expressions and the influence of trait anxiety. J. Cogn. Neurosci. 20, 95–107. doi: 10.1162/jocn.2008.20006

Li, W., Zinbarg, R. E., and Paller, K. A. (2007). Trait anxiety modulates supraliminal and subliminal threat: brain potential evidence for early and late processing influences. Cogn. Affect. Behav. Neurosci. 7, 25–36. doi: 10.3758/cabn.7.1.25

Litaudon, P., Mouly, A. M., Sullivan, R., Gervais, R., and Cattarelli, M. (1997). Learning-induced changes in rat piriform cortex activity mapped using multisite recording with voltage sensitive dye. Eur. J. Neurosci. 9, 1593–1602. doi: 10.1111/j.1460-9568.1997.tb01517.x

Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature 453, 869–878. doi: 10.1038/nature06976

Luczak, A., Barthó, P., and Harris, K. D. (2009). Spontaneous events outline the realm of possible sensory responses in neocortical populations. Neuron 62, 413–425. doi: 10.1016/j.neuron.2009.03.014

Luskin, M. B., and Price, J. L. (1983). The topographic organization of associational fibers of the olfactory system in the rat, including centrifugal fibers to the olfactory bulb. J. Comp. Neurol. 216, 264–291. doi: 10.1002/cne.902160305

Lynch, G., and Granger, R. (1989). Simulation and analysis of a simple cortical network. Psychol. Learn. Motiv. 23, 205–241. doi: 10.1016/s0079-7421(08)60112-7

Maren, S., and Quirk, G. J. (2004). Neuronal signalling of fear memory. Nat. Rev. Neurosci. 5, 844–852. doi: 10.1038/nrn1535

McGaugh, J. L. (2004). The amygdala modulates the consolidation of memories of emotionally arousing experiences. Annu. Rev. Neurosci. 27, 1–28. doi: 10.1146/annurev.neuro.27.070203.144157

McGaugh, J. L., Mcintyre, C. K., and Power, A. E. (2002). Amygdala modulation of memory consolidation: interaction with other brain systems. Neurobiol. Learn. Mem. 78, 539–552. doi: 10.1006/nlme.2002.4082

Miranda, M. A., Ferry, B., and Ferreira, G. (2007). Basolateral amygdala noradrenergic activity is involved in the acquisition of conditioned odor aversion in the rat. Neurobiol. Learn. Mem. 88, 260–263. doi: 10.1016/j.nlm.2007.04.008

Mishkin, M. (1982). A memory system in the monkey. Philos. Trans. R. Soc. Lond. B Biol. Sci. 298, 83–95. doi: 10.1098/rstb.1982.0074

Moessnang, C., Pauly, K., Kellermann, T., Kramer, J., Finkelmeyer, A., Hummel, T., et al. (2013). The scent of salience—is there olfactory-trigeminal conditioning in humans? Neuroimage 77, 93–104. doi: 10.1016/j.neuroimage.2013.03.049

Mori, K., and Sakano, H. (2011). How is the olfactory map formed and interpreted in the mammalian brain? Annu. Rev. Neurosci. 34, 467–499. doi: 10.1146/annurev-neuro-112210-112917

Mouly, A., and Sullivan, R. (2010). “Memory and plasticity in the olfactory system: from infancy to adulthood,” in The Neurobiology of Olfaction, ed A. Menini (Boca Raton, FL: CRC Press), 367–394.

Myers, K. M., and Davis, M. (2007). Mechanisms of fear extinction. Mol. Psychiatry 12, 120–150. doi: 10.1038/sj.mp.4001939

Ohman, A., and Mineka, S. (2001). Fears, phobias, and preparedness: toward an evolved module of fear and fear learning. Psychol. Rev. 108, 483–522. doi: 10.1037//0033-295x.108.3.483

Oya, H., Kawasaki, H., Howard, M. A., 3rd, and Adolphs, R. (2002). Electrophysiological responses in the human amygdala discriminate emotion categories of complex visual stimuli. J. Neurosci. 22, 9502–9512.

Paré, D., Collins, D. R., and Pelletier, J. G. (2002). Amygdala oscillations and the consolidation of emotional memories. Trends Cogn. Sci. 6, 306–314. doi: 10.1016/s1364-6613(02)01924-1

Pavlov, I. P. (1927/1960). Conditional Reflexes. New York: Dover Publications (the 1960 edition is an unaltered republication of the 1927 translation by Oxford University Press).

Pessoa, L., and Adolphs, R. (2010). Emotion processing and the amygdala: from a ‘low road’ to ‘many roads’ of evaluating biological significance. Nat. Rev. Neurosci. 11, 773–783. doi: 10.1038/nrn2920

Phelps, E. A., and LeDoux, J. E. (2005). Contributions of the amygdala to emotion processing: from animal models to human behavior. Neuron 48, 175–187. doi: 10.1016/j.neuron.2005.09.025

Pollatos, O., Kopietz, R., Linn, J., Albrecht, J., Sakar, V., Anzinger, A., et al. (2007). Emotional stimulation alters olfactory sensitivity and odor judgment. Chem. Senses 32, 583–589. doi: 10.1093/chemse/bjm027

Proffitt, D. R. (2006). Distance perception. Curr. Dir. Psychol. Sci. 15, 131–135. doi: 10.1111/j.0963-7214.2006.00422.x

Rattiner, L. M., Davis, M., French, C. T., and Ressler, K. J. (2004). Brain-derived neurotrophic factor and tyrosine kinase receptor B involvement in amygdala-dependent fear conditioning. J. Neurosci. 24, 4796–4806. doi: 10.1523/JNEUROSCI.5654-03.2004

Rauch, S. L., Shin, L. M., and Phelps, E. A. (2006). Neurocircuitry models of posttraumatic stress disorder and extinction: human neuroimaging research–past, present and future. Biol. Psychiatry 60, 376–382. doi: 10.1016/j.biopsych.2006.06.004

Romanski, L. M., and LeDoux, J. E. (1992). Equipotentiality of thalamoamygdala and thalamocorticoamygdala circuits in auditory fear conditioning. J. Neurosci. 12, 4501–4509.

Rosen, J. B., Hitchcock, J. M., Miserendino, M. J., Falls, W. A., Campeau, S., and Davis, M. (1992). Lesions of the perirhinal cortex but not of the frontal, medial prefrontal, visual, or insular cortex block fear-potentiated startle using a visual conditioned stimulus. J. Neurosci. 12, 4624–4633.

Rosenkranz, J. A., and Grace, A. A. (2002). Dopamine-mediated modulation of odour-evoked amygdala potentials during pavlovian conditioning. Nature 417, 282–287. doi: 10.1038/417282a

Sacco, T., and Sacchetti, B. (2010). Role of secondary sensory cortices in emotional memory storage and retrieval in rats. Science 329, 649–656. doi: 10.1126/science.1183165

Savander, V., LeDoux, J. E., and Pitkanen, A. (1996). Topographic projections from the periamygdaloid cortex to select subregions of the lateral nucleus of the amygdala in the rat. Neurosci. Lett. 211, 167–170. doi: 10.1016/0304-3940(96)12750-6

Schiffman, S. S. (1974). Physicochemical correlates of olfactory quality. Science 185, 112–117. doi: 10.1126/science.185.4146.112

Sehlmeyer, C., Schöning, S., Zwitserlood, P., Pfleiderer, B., Kircher, T., Arolt, V., et al. (2009). Human fear conditioning and extinction in neuroimaging: a systematic review. PLoS One 4:e5865. doi: 10.1371/journal.pone.0005865

Sevelinges, Y., Desgranges, B., and Ferreira, G. (2009). The basolateral amygdala is necessary for the encoding and the expression of odor memory. Learn. Mem. 16, 235–242. doi: 10.1101/lm.1247609

Sevelinges, Y., Gervais, R., Messaoudi, B., Granjon, L., and Mouly, A. M. (2004). Oflactory fear conditioning induces field potential potentiation in rat olfactory cortex and amygdala. Learn. Mem. 11, 761–769. doi: 10.1101/lm.83604

Shipley, M. T., and Ennis, M. (1996). Functional organization of olfactory system. J. Neurobiol. 30, 123–176. doi: 10.1002/(SICI)1097-4695(199605)30:1<123::AID-NEU11>3.0.CO;2-N

Smith, M. A., and Kohn, A. (2008). Spatial and temporal scales of neuronal correlation in primary visual cortex. J. Neurosci. 28, 12591–12603. doi: 10.1523/JNEUROSCI.2929-08.2008

Squire, L. R. (1987). Memory and Brain. New York: Oxford University Press.

Squire, L. R., and Wixted, J. T. (2011). The cognitive neuroscience of human memory since H.M. Annu. Rev. Neurosci. 34, 259–288. doi: 10.1146/annurev-neuro-061010-113720

Stevenson, R. J., and Boakes, R. A. (2003). A mnemonic theory of odor perception. Psychol. Rev. 110, 340–364. doi: 10.1037/0033-295x.110.2.340

Tsuchiya, N., Moradi, F., Felsen, C., Yamazaki, M., and Adolphs, R. (2009). Intact rapid detection of fearful faces in the absence of the amygdala. Nat. Neurosci. 12, 1224–1225. doi: 10.1038/nn.2380

Vuilleumier, P., and Pourtois, G. (2007). Distributed and interactive brain mechanisms during emotion face perception: evidence from functional neuroimaging. Neuropsychologia 45, 174–194. doi: 10.1016/j.neuropsychologia.2006.06.003

Walker, D. L., Paschall, G. Y., and Davis, M. (2005). Glutamate receptor antagonist infusions into the basolateral and medial amygdala reveal differential contributions to olfactory vs. context fear conditioning and expression. Learn. Mem. 12, 120–129. doi: 10.1101/lm.87105

Wallace, K. J., and Rosen, J. B. (2001). Neurotoxic lesions of the lateral nucleus of the amygdala decrease conditioned fear but not unconditioned fear of a predator odor: comparison with electrolytic lesions. J. Neurosci. 21, 3619–3627.

Weinberger, N. M. (2004). Specific long-term memory traces in primary auditory cortex. Nat. Rev. Neurosci. 5, 279–290. doi: 10.1038/nrn1366

Weinberger, N. M. (2007). Associative representational plasticity in the auditory cortex: a synthesis of two disciplines. Learn. Mem. 14, 1–16. doi: 10.1101/lm.421807

Weinberger, N. M. (2011). The medial geniculate, not the amygdala, as the root of auditory fear conditioning. Hear. Res. 274, 61–74. doi: 10.1016/j.heares.2010.03.093

Weinberger, N. M., and Bieszczad, K. M. (2011). “Introduction: from traditional fixed cortical sensationism to contemporary plasticity of primary sensory cortical representations,” in Neurobiology of Sensation and Reward, ed J. A. Gottfried (Boca Raton, FL: CRC Press), 3–13.

Wilson, D. A., and Stevenson, R. J. (2003). The fundamental role of memory in olfactory perception. Trends Neurosci. 26, 243–247. doi: 10.1016/s0166-2236(03)00076-6

Wilson, D. A., and Sullivan, R. M. (2011). Cortical processing of odor objects. Neuron 72, 506–519. doi: 10.1016/j.neuron.2011.10.027

Yeshurun, Y., and Sobel, N. (2010). An odor is not worth a thousand words: from multidimensional odors to unidimensional odor objects. Annu. Rev. Psychol. 61, 219–241, C1–C5. doi: 10.1146/annurev.psych.60.110707.163639

Conflict of Interest Statement: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 07 January 2014; accepted: 09 March 2014; published online: 07 April 2014.

Citation: Li W (2014) Learning to smell danger: acquired associative representation of threat in the olfactory cortex. Front. Behav. Neurosci. 8:98. doi: 10.3389/fnbeh.2014.00098

Copyright © 2014 Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 06 May 2014
doi: 10.3389/fnbeh.2014.00158

Sweet reward increases implicit discrimination of similar odors

Eva Pool1,2*, Sylvain Delplanque1,2, Christelle Porcherot[3], Tatiana Jenkins[2], Isabelle Cayeux[3] and David Sander1,2

1Swiss Center for Affective Sciences, University of Geneva-CISA, Geneva, Switzerland

2Laboratory for the Study of Emotion Elicitation and Expression, Department of Psychology, FPSE, University of Geneva, Geneva, Switzerland

3Firmenich, SA, Geneva, Switzerland

* Correspondence: Eva Pool, Swiss Center for Affective Sciences, University of Geneva-CISA, Case Postale 60, 1211 Geneva 20, Switzerland e-mail: eva.pool@unige.ch

Edited by:
Nadine Ravel, Center for Research in Neuroscience of LYON (CRNL), France

Reviewed by:
Emma Robinson, Bristol University, UK
Tao Jiang, Lyon Neuroscience Research Center CRNL, France

Stimuli associated with emotional events signal the presence of potentially relevant situations, thus learning to rapidly identify this kind of stimuli can be highly beneficial. It has been demonstrated that individuals acquire a better perceptual representation of stimuli associated with negative and threatening emotional events. Here we investigated whether the same process occurs for stimuli associated with positive and rewarding emotional events. We used an appetitive Pavlovian conditioning paradigm during which one of two perceptually non-distinguishable odors was associated with a rewarding taste (i.e., chocolate). We investigated whether appetitive conditioning could improve the recognition of the odor associated with the reward, rendering it discriminable from its similar version that was never associated with the reward. Results revealed a dissociation between explicit perception and physiological reactions. Although participants were not able to explicitly perceive a difference, they reacted faster, inhaled more and had higher skin conductance responses when confronted with the reward-associated odor compared to its similar version that was never associated with the reward. Our findings have demonstrated that positive emotional associations can improve the implicit perceptual representation of odors, by triggering different physiological responses to odors that do not seem to be otherwise distinguishable.

Keywords: reward, odor discrimination, emotional learning, perceptual representation, incentive salience, taste-odor conditioning

INTRODUCTION

Understanding how organisms deal with their limited attentional resources in an environment composed of a virtually infinite number of stimuli has always been of main interest for cognitive sciences (Posner, 1980). For instance, if two persons are chatting and watching their children on a playground, they will not be able to represent every action their children make, because a part of their resources is invested in the conversation. Nonetheless, some particular actions such as the children crying or calling for them will be represented in spite of the conversation. Why is the processing of those particular stimuli privileged? It has been proposed that emotional stimuli may have a facilitated access to an organism’s attentional resources (Vuilleumier, 2005).

A large amount of evidence has shown that emotional stimuli are difficult to ignore (e.g., Segerstrom, 2001; Compton et al., 2003); that our gaze is rapidly oriented toward them (e.g., Nummenmaa et al., 2009; Theeuwes and Belopolsky, 2012); that disengaging attention from them is hard (e.g., Fox et al., 2001; Yiend and Mathews, 2001; di Pellegrino et al., 2011); and that in a complex environment with competing stimuli, they have a prioritized access to attentional resources (e.g., Ohman et al., 2001; Anderson, 2005; Hodsoll et al., 2011). These effects of emotional stimuli on cognitive processing have been shown to be at least mediated by an enhancement of the neuronal activity linked to sensorial processing. Indeed, neuroscientific evidence strongly suggests that sensorial information is processed more efficiently if it has emotional content (see Phelps and Ledoux, 2005; Vuilleumier, 2005, for a review). More particularly, the activity linked to sensory processing at very early stages is amplified during the processing of emotional stimuli, as demonstrated with electroencephalography, (e.g., Pourtois et al., 2004; Brosch et al., 2008; Hickey et al., 2010) and functional imagery (e.g., Vuilleumier et al., 2001; Grandjean et al., 2005; Mohanty et al., 2008).

The degree of specificity of this emotional facilitation remains to be fully understood. Is the perceptual enhancement selective for a particular emotional stimulus or does it generalize to neutral stimuli with similar perceptual characteristics? If it is specific, the discrimination between an emotional stimulus and a perceptually similar but neutral stimulus should be easier compared to the discrimination between two neutral stimuli. Li et al. (2008) investigated these questions for the processing of negative threatening stimuli by using a triangular test to measure the discriminability of similar odors (i.e., enantiomers) combined with an aversive emotional learning paradigm. The triangular test is a forced choice procedure used to investigate perceptual discriminability, in which a stimulus is presented twice with a third different but similar stimulus that has to be identified (Laska and Teubner, 1999). Through this procedure Li et al. (2008) demonstrated that after aversive emotional learning, during which one of the two odors was associated with an unpleasant electric shock, two odors that were initially indistinguishable became discriminable. The enhancement of the sensorial abilities through aversive emotional learning seems to thus be highly selective, when the learning context requires high discrimination abilities. This mechanism could be highly adaptive, because it allows the organism to discriminate stimuli with an emotional meaning from other less meaningful but similar stimuli and thereby preventing the organism to over-react when it is not necessary. To the best of our knowledge, this mechanism has never been tested for positive emotional stimuli. Positive and negative emotional stimuli should have the same influence on the sensorial processing, because the privileged status of the emotional stimuli would depend on the affective relevance of the stimulus for the concerns of the individual perceiving it, rather than on a distinct valence specific mechanism (Frijda, 1988; Sander et al., 2005). If an individual learns to associate an intrinsically neutral stimulus to an emotional event, either a threat or a reward, the stimulus that was initially neutral but acquired affective relevance through the learning experience also gains a privileged access to the individual resources (for aversive learning see Alpers et al., 2005; Koster et al., 2005; for appetitive learning see Seitz et al., 2009; Austin and Duka, 2010; Hickey et al., 2010; Anderson et al., 2011; Notebaert et al., 2011; Pool et al., 2014).

Based on this assumption, we investigated whether (i) discrimination capabilities could be enhanced through an appetitive emotional learning using positive rewarding stimuli and, if any, (ii) the degree of the selectivity of this enhancement at an implicit and an explicit level. To do so, we adapted and modified the experimental procedure used by Li et al. (2008), by combining a triangular test in which participants were asked to distinguish between two similar odors with an appetitive emotional learning paradigm. We selected two pairs of indistinguishable odors as the conditioned stimuli in a Pavlovian conditioning task. For the first pair of odors, one of them was associated with a rewarding piece of chocolate (Positive Conditioned Stimulus; CS+) whereas its similar version was not (CS+ similar). For the second pair, neither odor was associated with the pleasant reward (Negative Conditioned Stimulus; CS- and CS-similar). If positive and negative stimuli possess similar reinforcing capabilities then the odor that acquired affective relevance by being associated with a reward should be better discriminated from the similar odor compared to the odors that have never been associated with the reward. During the triangular test, two different measures of odor discrimination were used. The first was an explicit and classical measure consisting in the accuracy of the behavioral choices (Laska and Teubner, 1999; Li et al., 2008), the second was an implicit measure consisting in the inspiration volume (Bensafi et al., 2003, 2007; Frank et al., 2003; Mainland and Sobel, 2006). The inspiration volume was used as an implicit measure for two reasons. First, it is modulated by the pleasantness of the odor (Bensafi et al., 2003; Frank et al., 2003); since in appetitive conditioning the value of the reward is transferred to the CS, the odor associated with the reward should become more pleasant (De Houwer et al., 2001) and consequently, the inspiration volume could be modulated. Second, the inspiration volume is not influenced by any conscious strategy (Bensafi et al., 2007). We predicted that the odor that had acquired affective relevance by being associated with the reward would be discriminated from its pair: (a) explicitly as reflected by a higher accuracy in the behavioral choices during the triangular test after conditioning and (b) implicitly as reflected by a bigger inspiration volume for the reward-associated odor than for its similar version.

MATERIALS AND METHODS

PARTICIPANTS

Eighteen participants who liked chocolate and were not dieting were recruited on the premises of the University of Geneva. They received 20 Swiss francs for their participation. Two participants were later excluded due to a technical problem during the physiological recordings. The 16 participants included in the analysis (5 males; 26 ± 3.22 years old) had no problems with odor perception and were not wearing any fragrance. Participants were asked to refrain from eating 4 h before the experimental session to increase their motivation to eat chocolate.

MATERIALS

Stimuli

Two different pairs of odors were used (Firmenich, SA, Geneva, Switzerland). Each pair was composed by an odor that was qualitatively coherent in a feeding context and its similar version. This latter was created by mixing the original odor with a small percentage of Hedion® (methyl dihydrojasmonate), a fresh jasmin like odor. More specifically, first pair was composed of a lemon odor (100%) and a lemon-Hedion blend odor (98% and 2% respectively), the second of a strawberry odor (100%) and a strawberry-Hedion blend odor (97% and 3% respectively). These odors were selected based on a previous pilot study (N = 20) that revealed that when participants were asked to distinguish an odor mixed with Hedion from its pure version, their performances were not significantly better than chance, t(19) = −0.85, p = 0.430. The pure Hedion was also used to control for its perceptibility. Odors were administered through a computer-controlled olfactometer with an air flow fixed at 1.5 L/min that delivered the olfactory stimulation rapidly, without thermal and tactile confounds (Pool et al., 2014) via a nasal cannula.

The rewarding chocolate consisted of a small 0.5 g piece of chocolate (dark or milk, according to the participants’ preferences) to prevent that satiation processes modified the chocolate’s rewarding value during the conditioning procedure (Small et al., 2001).

Physiological recordings

Respiration and electrodermal activities were recorded using the MP150 Biopac Systems (Santa Barbara, CA) with a 1000 Hz sampling rate. The respiratory activity was recorded through a 2.5 mm tube (interior diameter) positioned at the entrance of the participants’ right nostril, on the nasal cannula used to deliver the odorants, and connected to a differential pressure transducer (TSD160A; ± 2.5 cm H_2O sensitivity range) to continuously recorded variations in the nostril airflow. The signal was first low-pass filtered at 1 Hz and the duration of the inspiration was calculated as the length of the depression (in ms) between two consecutive crossing of the zero values after the stimulus onset. The integral of flow variations and its maximal value within this duration were also calculated for each trial.

Electrodermal activity was measured with Beckman Ag–AgCl electrodes (8-mm diameter active area) filled with a skin conductance paste (Biopac) attached to the palmar side of the middle phalanges of the second and third fingers of the participants’ nondominant hand. Specific skin conductance responses (SCRs) were measured in microSiemens and analyzed offline (Bandpass filter: 0.05–5 Hz). They were scored as changes in conductance starting in the 1- to 5-s interval after the beginning of the stimulus. SCRs were square root transformed to normalize the data (Dawson et al., 2000).

PROCEDURE

Before the experiment started, physiological sensors were positioned on the participants. They also indicated whether they preferred dark (N = 9) or milk chocolate (N = 7) so that the reward used in the Pavlovian conditioning could be consequently adapted. Subsequently, they completed a control triangular test to measure their Hedion perception. They then completed the experimental procedure consisting in four triangular tests, two before and two after the Pavlovian conditioning. At the end of the experiment, they answered questions concerning the manipulation check. The entire experimental session took around 75 min.

Pavlovian conditioning

Each pair of odors was assigned to the Pavlovian role of the “CS+” or “CS−”. For the CS+ pair, one of the two odors was associated with a rewarding taste of chocolate (CS+) whereas the other one was not (similar CS+). For the “CS−”, neither of the two odors was associated with a reward (CS−; similar CS−). The assignment of the odors to the Pavlovian roles was counterbalanced across participants. For the sample included in the analysis, all odors were used as CS+ for at least 3 participants: the lemon (N = 5), the lemon-Hedion (N = 5), the strawberry (N = 3) and the strawberry-Hedion (N = 3).

There were 18 trials for each odor for a total of 72 trials. Every trial began with a 3 s countdown followed by an inspiration cue that request the participant to breath in evenly and trigger the release of the odor for 2.5 s. A gray patch then appeared and participants were asked to press as quickly as possible the “x” key to remove the patch and reveal a picture of chocolate for the CS+ and a red cross for all the other stimuli (i.e., the similar CS+, the CS− and the similar CS−). They were also explicitly asked to guess whether a particular odor could predict the rewarding chocolate delivery. When the chocolate picture was displayed on the screen, participants ate a small piece of chocolate and drank a sip of water (Prévost et al., 2012). To underline that the reward delivery depended on the odor and not on their action, participants were told that the key-pressing task was a measure of their sustained attention, independent of the odor-reward contingencies (Talmi et al., 2008). They were also informed that not responding during the 1-s interval after the gray patch onset would trigger the next screen anyway (Talmi et al., 2008). Every trial ended with an inter-trial interval of 4–6 s for the non-rewarded trials and of 2–4 s for the rewarded trials (see Figure 1). The Pavlovian conditioning procedure lasted around 30 min.

FIGURE 1. Pavlovian conditioning paradigm used in the present experiment. Participants were exposed to two pairs of odors, each composed of one odor and its similar version which was created by blending it with Hedion (CS+; similar CS+ and CS−; similar CS−). They smelled the odor and pressed on a keyboard to remove the gray patch and discovered whether the odor was associated with the rewarding chocolate or not. The CS+ was associated with the reward whereas the similar CS+, the CS− and the similar CS− were not.

After the conditioning task, participants took a small break (1–2 min) and then they evaluated on a visual analog scale presented on the screen, the subjective pleasantness (from “extremely unpleasant” to “extremely pleasant”), intensity (from “not perceived” to “extremely strong”) and familiarity (from “not familiar at all” to “extremely familiar”) of each odor and of the Hedion odor alone (e.g., Delplanque et al., 2008). The odor evaluation procedure lasted about 5 min.

Triangular test

We created a computerized version of the triangular discrimination test designed by Laska and Teubner (1999). Three pictures of bottles associated with three different odors were presented on the screen. Participants were told that two of the odors were identical and one they had to identify was different.

Each trial began with a screen displaying the first bottle pointed by an arrow for 2–5 s, then a 3 s countdown began followed by an inspiration cue and the odor was delivered for 1 s. Afterwards, the bottle remained on the screen for 8–5 s (so that the inter-stimulus interval was 10 s in total). Then, the second and the third bottle appeared and the same procedure was repeated. Once the three odors had been smelled, participants were instructed to use the mouse to determine the bottle containing the odd odor by clicking on the corresponding picture of bottle. During the inter-trial interval participants were asked to wait for 15 s. All possible combinations of apparition of the three bottles (and odors; i.e., 6 in total) were presented to each and randomly across participants. Separate triangular tests were administered for each pair of odors (lemon and lemon-Hedion blend; strawberry and strawberry-Hedion blend) used in the conditioning. To accomplish a triangular test, participants took around 6 min.

At the beginning of the experiment, participants accomplished a control triangular test in which they were asked to discriminate Hedion from odorless air.

Manipulation check

Participants were asked to eat a piece of the chocolate that was used during the conditioning and to evaluate, on a visual analog scale presented on the screen, its subjective pleasantness (from “extremely unpleasant” to “extremely pleasant”), intensity (from “extremely weak” to “extremely strong”) and familiarity (from “never tasted before this experiment” to “extremely familiar”). Subsequently, they answered two questions about chocolate: one about motivation (i.e., “On a scale from 1 to 10, how much would you say that you sometimes crave chocolate?”) and the other about hedonic pleasure (i.e., “On a scale from 1 to 10, how much would you say that you like chocolate?”; see Pool et al., 2014). This last part of the experiment took around 5 min.

RESULTS

DATA ANALYSIS

For the analysis of the Pavlovian conditioning and the triangular tests we used a repeated measures analysis of variance (ANOVA) and planned contrasts according to the hypothesis that was tested. Effect sizes are measured as eta squared (η^2) for the repeated measure ANOVA and as Cohen’s d (d) for the planned contrasts.

MANIPULATION CHECK

Participants evaluated the piece of chocolate’s taste used as reward as being pleasant (M = 93.16, SD = 8.77 out of 100); intense (M = 76.01; SD = 19.11 out of 100); and familiar (M = 94.53, SD = 6.41 out of 100), showing that the chocolate used as reward was indeed perceived as a pleasurable experience. Moreover, they reported a mean of 9.41 (SD = 1.01) out of 10 for the likeability item and a mean of 8.54 (SD = 1.29) out of 10 for the craving item on the questions about chocolate, showing that they associated chocolate with hedonic pleasure and motivation (Berridge and Robinson, 2003).

HEDION PERCEPTIBILITY

In the control triangular test, participants discriminated the Hedion odor from the odorless air with an accuracy of 71.87% (±23.36%) which was significantly better than the chance level of 33%, t(15) = 7.59, p < 0.001, d = 2.6. For the odor evaluation, participants reported a mean of 44.59 (±24.46; out of 100) for the intensity scale, a mean of 59.25 (±16.81; out of 100) for the pleasantness scale and a mean of 53.87 (±19.72; out of 100) for the familiarity scale.

PAVLOVIAN CONDITIONING

To assess the success of the Pavlovian conditioning, we used the reaction time of the key-pressing task and the SCRs as implicit indexes and the likeability rating of the odors used as CSs after conditioning as an explicit index.

The reaction times of three participants could not be recorded due to technical problems. All responses that were more than three standard deviations from the mean (<1% of the trials), or absent (<3% of the trials) were removed. A planned contrast revealed a specific effect of conditioning: participants were faster when the CS+ odor (M = 384.08, SD = 25.77) was delivered than when the similar CS+, the CS− and the similar CS− odors were delivered (M = 405.66, SD = 21.53), t(12) = 2.44, p = 0.031, d = 0.30. To test whether this difference could be accounted for in terms of reward learning, we conducted two other planned contrasts that revealed that this difference was significant in the second part of the conditioning, t(12) = 2.62, p = 0.022, d = 0.32, but not in the first, t(12) = 0.16, p = 0.860.

The likeability analysis revealed a general effect of conditioning: participants did not evaluate the CS+ odor as significantly more likeable than the similar CS+, the CS− and the similar CS−, t(15) = 1.19, p = 0.252; but they globally evaluated the CS+ and the similar CS+ as being more likeable than the CS− and the similar CS−, t(15) = 2.45, p = 0.026, d = 0.47 (see Figure 2A).

FIGURE 2. (A) Means of the likeability ratings for the four odors used as conditioned stimuli (CS) at the end of the Pavlovian conditioning. (B) Means of the amplitude of the skin conductance responses (SCR) during the perception of the four odors used as CS at the end of the Pavlovian conditioning during the rating phase. Error bars (± 1 SEM) are adapted for within design (Cousineau, 2005).

Autonomic responses recorded during the same phase were specifically affected by the conditioning: a planned contrast revealed that the amplitude of the SCR was bigger for the CS+ than for the similar CS+, the CS− and the similar CS−, t(12) = 2.41, p = 0.032, d = 0.21[1] (see Figure 2B).

TRIANGULAR TEST

Similarly to the conditioning, results on the triangular test showed specific effects only for the implicit measure, but not for the explicit one.

To test our hypothesis for the explicit measure, we applied a 2 (Pair: CS+ or CS− pair) × 2 (Session: pre- or post-conditioning) repeated measures ANOVA on the accuracy of behavioral choices of the triangular tests. The analysis did not reveal any significant effect (all ps > 0.05), t-test showed that the performance of the participants were not significantly different from the chance level of 33%, neither before nor after conditioning (all ps > 0.05; see Figure 3A).

FIGURE 3. (A) Means of the accuracy of the behavioral choice during the triangular test in which participants were asked to discriminate two similar odors. In the positive conditioned pair (CS+), one odor was associated with the reward (CS+) whereas the other was not (similar CS+). In the negative conditioned pair, neither of the two odors (CS− and CS− similar) was associated with the reward. (B) Means of the integral of the volume of the inspiration during the triangular tests. Error bars (±1 SEM) are adapted for within design (Cousineau, 2005).

To test our hypothesis for the implicit measures, we applied a 2 (Odor: CS+ or similar CS+) × 2 (Session: pre- or post-conditioning) repeated measures ANOVA on the integral of inspiration flow during the triangular tests. The analysis revealed a significant interaction, F(15,1) = 5.41, p = 0.034, η^2 = 0.26 showing that after conditioning participants inhaled more for the CS+ than for the similar CS+, t(15) = 2.49, p = 0.027, d = 0.28, this difference was not present before conditioning, t(15) = 0.69, p = 0.490. The same analysis on the CS− and the similar CS− did not reveal any significant effect (all ps > 0.05; See Figure 3B).

Since the ANOVAs revealed that conditioning increased the inspiration flow for the CS+ compared with similar CS+ but not for CS− and its similar version, we used planned comparisons to compare the inspiration across the fours odors after conditioning. Participants inhaled more when the CS+ was perceived than when the similar CS+, the CS− and the similar CS− were perceived t(15) = 2.23, p = 0.041, d = 0.25;[2] This difference was not present before conditioning (p = 0.511).

The same analysis conducted on the SCR amplitude did not reveal any significant effect (all p > 0.05).

DISCUSSION

The objective of this experiment was twofold: first to investigate whether perceptual discrimination of odors could be enhanced through appetitive emotional learning, and second, the degree of selectivity of this enhancement at an implicit and explicit level. To do so, an olfactory stimulus was associated with a sweet reward (i.e., chocolate) and we tested the capacity of the participants to discriminate it from another perceptually similar but non-conditioned stimulus. Before conditioning, the conditioned odor and its similar version were not discriminated in the triangular test, neither on the basis of accuracy of choices, nor for strength of the inspiration. After conditioning, we did not find any statistical evidence showing that participants were able to discriminate the odors in the triangular test, nonetheless their inspirations were more intense for the reward-associated odor compared to its similar non-conditioned version. A similar pattern was shown for the conditioning indexes: participants globally evaluated the reward-associated odor and its similar version more pleasurable compared with the evidently perceptually different non-conditioned odors, without differentiating the reward-associated odor from its similar non-conditioned version. However, participants’ responses to the reward-associated odor were differentiated from its similar non-conditioned version and the evidently perceptually different non-conditioned odors based on the implicit indexes of conditioning (i.e., the SCR’s amplitude after conditioning and the reaction times during conditioning). In sum, the explicit behavioral measure demonstrated that the conditioning effect of the CS+ is generalized to its similar version, whereas the physiological and implicit behavioral measure showed a specific effect of conditioning which influences selectively the CS+ but not its similar version.

Consistent with Li et al. (2008), our findings showed that emotional learning can enhance the discrimination between similar stimuli at both behavioral (reaction time) and physiological levels (inspiration and skin conductance); however by contrast Li et al. (2008) found an increase of the accuracy of the behavioral choices in the triangular test induced by aversive conditioning, while participants’ explicit discrimination was not increased in our study after the appetitive conditioning. Many factors could explain this absence of effect (e.g., number of trials, number of participants, odor choice). Nonetheless, such a dissociation between the explicit and the implicit measures is congruent with several findings in the literature.

First, the dissociation between the auto-reported evaluation of the odors’ pleasantness and the volume of the inspiration during the odor perception supports the idea that the inspiration volume is modulated by the affective properties of the odors (Bensafi et al., 2003; Frank et al., 2003; Mainland and Sobel, 2006), independently of the conscious processing (Bensafi et al., 2007). The way in which the odor is inspired can in turn influence different aspect of the odor perception such as its intensity or its identity (Mainland and Sobel, 2006). We did not find evidence showing that the increased inspiration volume improved the odor identity discrimination. However, this is congruent with the literature showing that the improvement of the odor identity is not proportional to the inspiration volume, but rather depends on the interaction between the intrinsic properties of the odor (e.g., sorption rate) and other parameters of the inspiration (e.g., velocity, Sobel et al., 1999; Mainland and Sobel, 2006).

Second, the fact that even though participants did not seem to be able to explicitly discriminate the reward-associated odor, they responded with more motivated reactions when they perceived is consistent with studies showing that it is possible to elicit affective reactions even when participants are not able to consciously report them (Winkielman and Berridge, 2004; Winkielman et al., 2005). Several theories (Murphy and Zajonc, 1993; Winkielman and Berridge, 2004; Winkielman and Schooler, 2011) postulate that the processing of affective stimuli can involve differential physiological responses and subjective feelings accessible through verbal reports. Affective processing may occur rapidly and automatically without accessing conscious processing, thereby triggering physiological responses without a parallel conscious and verbally reportable experience (Murphy and Zajonc, 1993; Silvestrini and Gendolla, 2011; Bornemann et al., 2012).

There might by an adaptive value in these differential effects of conditioning on the implicit and explicit levels. The generalization of the conditioning effects to similar stimuli at an explicit level, might allow the organism to flexibly stay vigilant to all potential indicators of reward, whereas the specificity of the conditioning effects at an implicit level might allow the organism to economize resources by reacting with expensive emotional reactions, even when positive, only when it is strictly necessary.

Finally, the increase of physiological discrimination of similar odors by appetitive emotional learning using positive rewarding stimuli is in line with studies showing an increased efficiency of sensorial processing for positive emotional stimuli (e.g., Brosch et al., 2008; Hickey et al., 2010; Hietanen and Nummenmaa, 2011) and with the prediction of appraisal theories (Frijda, 1988; Sander et al., 2005). Indeed, appraisal theories propose that both negative and positive events influence the perceptual system because they are underlain by an affective relevance mechanism rather than distinct valence-specific mechanisms. Whereas neutral stimuli associated with a threat acquired affective relevance because they increase the chances of avoiding a danger, neutral stimuli associated with a reward acquired affective relevance because they increase the chances of satisfying a goal.

CONCLUSIONS

The findings of this experiment suggest that emotional learning using positive rewarding stimuli can increase implicit discrimination of perceptually similar odors. The ability to learn to precisely identify stimuli that are affectively relevant not only increases the chances to indeed react to emotional events, but it also reduces the chances of reacting with expensive emotional reactions when it is not necessary. For instance, if two persons are watching their children and chatting, they will intensively react specifically to the voice of their children calling them, and not to all the voices of other children calling their parents. Furthermore, our findings suggest reward-associated odors can trigger affective reactions, even when it seems that they cannot be consciously discriminated. This deserves to be further investigated since it opens an interesting window to the influence that an odor can have on our experience.

ACKNOWLEDGMENTS

This research was supported by the National Center of Competence in Research (NCCR) for the Affective Sciences, financed by a grant from the Swiss National Science Foundation (51NF40-104897), hosted by the University of Geneva, and was also supported by a research grant from Firmenich, SA, to David Sander and Patrik Vuilleumier. The authors thank Nadine Gaudreau and all the members of the Perception and Bioresponses Department of the Research and Development Division of Firmenich, SA, for their precious advice and technical competence.

FOOTNOTES

1^Three participants were excluded from the analysis for missing data (listwise analysis). The maximal peak of their skin conductance appeared before the 1 s delay after the stimulus on set on at least one of the four conditions (e.g., CS+, CS+ similar, CS−, CS− similar). Therefore we could not consider these responses as being triggered by the stimulus. To guarantee the comparability of the results of the skin conductance and the auto-reports of the perceived pleasantness, we ran supplementary analyses. An analysis including all participants with mean substitutions for missing data gave a similar result t(15) = 2.42, p = 0.028. Moreover, if we removed the three missing participants from the analysis of the auto reports of pleasantness, the main results did not change t(12) = 2.75, p = 0.017.

2^The inspiration before and after for the CS+ was not tested, because sniffing can vary throughout time for several factors (e.g., habituation, see Wirth et al., 1998; or familiarity, see Delplanque et al., 2008). The differential measure (CS+; CS+ similar) is the most appropriate and precise one to test our hypothesis, because it is based on a subtraction that is free of biases related to the time course.

REFERENCES

Alpers, G. W., Ruhleder, M., Walz, N., Muhlberger, A., and Pauli, P. (2005). Binocular rivalry between emotional and neutral stimuli: a validation using fear conditioning and EEG. Int. J. Psychophysiol. 57, 25–32. doi: 10.1016/j.ijpsycho.2005.01.008

Anderson, A. K. (2005). Affective influences on the attentional dynamics supporting awareness. J. Exp. Psychol. Gen. 134, 258–281. doi: 10.1037/0096-3445.134.2.258

Anderson, B. A., Laurent, P. A., and Yantis, S. (2011). Value-driven attentional capture. Proc. Natl. Acad. Sci. U S A 108, 10367–10371. doi: 10.1073/pnas.1104047108

Austin, A. J., and Duka, T. (2010). Mechanisms of attention for appetitive and aversive outcomes in Pavlovian conditioning. Behav. Brain Res. 213, 19–26. doi: 10.1016/j.bbr.2010.04.019

Bensafi, M., Porter, J., Pouliot, S., Mainland, J., Johnson, B., Zelano, C., et al. (2003). Olfactomotor activity during imagery mimics that during perception. Nat. Neurosci. 6, 1142–1144. doi: 10.1038/nn1145

Bensafi, M., Sobel, N., and Khan, R. M. (2007). Hedonic-specific activity in piriform cortex during odor imagery mimics that during odor perception. J. Neurophysiol. 98, 3254–3262. doi: 10.1152/jn.00349.2007

Berridge, K. C., and Robinson, T. E. (2003). Parsing reward. Trends Neurosci. 26, 507–513. doi: 10.1016/s0166-2236(03)00233-9

Bornemann, B., Winkielman, P., and Van Der Meer, E. (2012). Can you feel what you do not see? Using internal feedback to detect briefly presented emotional stimuli. Int. J. Psychophysiol. 85, 116–124. doi: 10.1016/j.ijpsycho.2011.04.007

Brosch, T., Sander, D., Pourtois, G., and Scherer, K. R. (2008). Beyond fear: rapid spatial orienting toward positive emotional stimuli. Psychol. Sci. 19, 362–370. doi: 10.1111/j.1467-9280.2008.02094.x

Compton, R. J., Banich, M. T., Mohanty, A., Milham, M. P., Herrington, J., Miller, G. A., et al. (2003). Paying attention to emotion: an fMRI investigation of cognitive and emotional Stroop tasks. Cogn. Affect. Behav. Neurosci. 3, 81–96. doi: 10.3758/cabn.3.2.81

Cousineau, D. (2005). Confidence intervals in within-subject designs: a simpler solution to Loftus and Masson’s method. Tutor. Quant. Methods Psychol. 1, 42–45.

Dawson, M. E., Schell, A. M., and Filion, D. L. (2000). “The electrodermal system,” in Handbook of Psychophysiology, eds J. T. Cacioppo, L. G. Tassinary and G. G. Berntson (Cambridge: Cambridge University Press), 200–223.

De Houwer, J., Thomas, S., and Baeyens, F. (2001). Associative learning of likes and dislikes: a review of 25 years of research on human evaluative conditioning. Psychol. Bull. 127, 853–869. doi: 10.1037//0033-2909.127.6.853

Delplanque, S., Grandjean, D., Chrea, C., Aymard, L., Cayeux, I., Le Calve, B., et al. (2008). Emotional processing of odors: evidence for a nonlinear relation between pleasantness and familiarity evaluations. Chem. Senses 33, 469–479. doi: 10.1093/chemse/bjn014

di Pellegrino, G., Magarelli, S., and Mengarelli, F. (2011). Food pleasantness affects visual selective attention. Q. J. Exp. Psychol. (Hove) 64, 560–571. doi: 10.1080/17470218.2010.504031

Fox, E., Russo, R., Bowles, R., and Dutton, K. (2001). Do threatening stimuli draw or hold visual attention in subclinical anxiety? J. Exp. Psychol. Gen. 130, 681–700. doi: 10.1037/0096-3445.130.4.681

Frank, R. A., Dulay, M. F., and Gesteland, R. C. (2003). Assessment of the Sniff Magnitude Test as a clinical test of olfactory function. Physiol. Behav. 78, 195–204. doi: 10.1016/s0031-9384(02)00965-4

Frijda, N. H. (1988). The laws of emotion. Am. Psychol. 43, 349–358. doi: 10.1037//0003-066x.43.5.349

Grandjean, D., Sander, D., Pourtois, G., Schwartz, S., Seghier, M. L., Scherer, K. R., et al. (2005). The voices of wrath: brain responses to angry prosody in meaningless speech. Nat. Neurosci. 8, 145–146. doi: 10.1038/nn1392

Hickey, C., Chelazzi, L., and Theeuwes, J. (2010). Reward changes salience in human vision via the anterior cingulate. J. Neurosci. 30, 11096–11103. doi: 10.1523/jneurosci.1026-10.2010

Hietanen, J. K., and Nummenmaa, L. (2011). The naked truth: the face and body sensitive N170 response is enhanced for nude bodies. PLoS One 6:e24408. doi: 10.1371/journal.pone.0024408

Hodsoll, S., Viding, E., and Lavie, N. (2011). Attentional capture by irrelevant emotional distractor faces. Emotion 11, 346–353. doi: 10.1037/a0022771

Koster, E. H. W., Crombez, G., Van Damme, S., Verschuere, B., and De Houwer, J. (2005). Signals for threat modulate attentional capture and holding: fear-conditioning and extinction during the exogenous cueing task. Cogn. Emot. 19, 771–780. doi: 10.1080/02699930441000418

Laska, M., and Teubner, P. (1999). Olfactory discrimination ability of human subjects for ten pairs of enantiomers. Chem. Senses 24, 161–170. doi: 10.1093/chemse/24.2.161

Li, W., Howard, J. D., Parrish, T. B., and Gottfried, J. A. (2008). Aversive learning enhances perceptual and cortical discrimination of indiscriminable odor cues. Science 319, 1842–1845. doi: 10.1126/science.1152837

Mainland, J., and Sobel, N. (2006). The sniff is part of the olfactory percept. Chem. Senses 31, 181–196. doi: 10.1093/chemse/bjj012

Mohanty, A., Gitelman, D. R., Small, D. M., and Mesulam, M. M. (2008). The spatial attention network interacts with limbic and monoaminergic systems to modulate motivation-induced attention shifts. Cereb. Cortex 18, 2604–2613. doi: 10.1093/cercor/bhn021

Murphy, S. T., and Zajonc, R. B. (1993). Affect, cognition and awareness: affective priming with optimal and suboptimal stimulus exposures. J. Pers. Soc. Psychol. 64, 723–739. doi: 10.1037/0022-3514.64.5.723

Notebaert, L., Crombez, G., Van Damme, S., De Houwer, J., and Theeuwes, J. (2011). Signals of threat do not capture, but prioritize, attention: a conditioning approach. Emotion 11, 81–89. doi: 10.1037/a0021286

Nummenmaa, L., Hyona, J., and Calvo, M. G. (2009). Emotional scene content drives the saccade generation system reflexively. J. Exp. Psychol. Hum. Percept. Perform. 35, 305–323. doi: 10.1037/a0013626

Ohman, A., Flykt, A., and Esteves, F. (2001). Emotion drives attention: detecting the snake in the grass. J. Exp. Psychol. Gen. 130, 466–478. doi: 10.1037/0096-3445.130.3.466

Phelps, E. A., and Ledoux, J. E. (2005). Contributions of the amygdala to emotion processing: from animal models to human behavior. Neuron 48, 175–187. doi: 10.1016/j.neuron.2005.09.025

Pool, E., Brosch, T., Delplanque, S., and Sander, D. (2014). Where is the chocolate? Rapid spatial orienting toward stimuli associated with primary rewards. Cognition 130, 348–359. doi: 10.1016/j.cognition.2013.12.002

Posner, M. I. (1980). Orienting of attention. Q. J. Exp. Psychol. 32, 3–25. doi: 10.1080/00335558008248231

Pourtois, G., Grandjean, D., Sander, D., and Vuilleumier, P. (2004). Electrophysiological correlates of rapid spatial orienting towards fearful faces. Cereb. Cortex 14, 619–633. doi: 10.1093/cercor/bhh023

Prévost, C., Liljeholm, M., Tyszka, J. M., and O’Doherty, J. P. (2012). Neural correlates of specific and general Pavlovian-to-Instrumental Transfer within human amygdalar subregions: a high-resolution fMRI study. J. Neurosci. 32, 8383–8390. doi: 10.1523/jneurosci.6237-11.2012

Sander, D., Grandjean, D., and Scherer, K. R. (2005). A systems approach to appraisal mechanisms in emotion. Neural Netw. 18, 317–352. doi: 10.1016/j.neunet.2005.03.001

Segerstrom, S. C. (2001). Optimism and attentional bias for negative and positive stimuli. Pers. Soc. Psychol. Bull. 27, 1334–1343. doi: 10.1177/01461672012710009

Seitz, A. R., Kim, D., and Watanabe, T. (2009). Rewards evoke learning of unconsciously processed visual stimuli in adult humans. Neuron 61, 700–707. doi: 10.1016/j.neuron.2009.01.016

Silvestrini, N., and Gendolla, G. H. E. (2011). Masked affective stimuli moderate task difficulty effects on effort-related cardiovascular response. Psychophysiology 48, 1157–1164. doi: 10.1111/j.1469-8986.2011.01181.x

Small, D. M., Zatorre, R. J., Dagher, A., Evans, A. C., and Jones-Gotman, M. (2001). Changes in brain activity related to eating chocolate: from pleasure to aversion. Brain 124, 1720–1733. doi: 10.1093/brain/124.9.1720

Sobel, N., Khan, R. M., Saltman, A., Sullivan, E. V., and Gabrieli, J. D. E. (1999). Olfaction: the world smells different to each nostril. Nature 402, 35–35. doi: 10.1038/46944

Talmi, D., Seymour, B., Dayan, P., and Dolan, R. J. (2008). Human pavlovian-instrumental transfer. J. Neurosci. 28, 360–368. doi: 10.1523/jneurosci.4028-07.2008

Theeuwes, J., and Belopolsky, A. V. (2012). Reward grabs the eye: oculomotor capture by rewarding stimuli. Vision Res. 74, 80–85. doi: 10.1016/j.visres.2012.07.024

Vuilleumier, P. (2005). How brains beware: neural mechanisms of emotional attention. Trends Cogn. Sci. 9, 585–594. doi: 10.1016/j.tics.2005.10.011

Vuilleumier, P., Armony, J. L., Driver, J., and Dolan, R. J. (2001). Effects of attention and emotion on face processing in the human brain: an event-related fMRI study. Neuron 30, 829–841. doi: 10.1016/s0896-6273(01)00328-2

Winkielman, P., and Berridge, K. C. (2004). Unconscious emotion. Curr. Dir. Psychol. Sci. 13, 120–123. doi: 10.1111/j.0963-7214.2004.00288.x

Winkielman, P., and Schooler, J. W. (2011). Splitting consciousness: unconscious, conscious and metaconscious processes in social cognition. Eur. Rev. Soc. Psychol. 22, 1–35. doi: 10.1080/10463283.2011.576580

Winkielman, P., Berridge, K. C., and Wilbarger, J. L. (2005). Unconscious affective reactions to masked happy versus angry faces influence consumption behavior and judgments of value. Pers. Soc. Psychol. Bull. 31, 121–135. doi: 10.1177/0146167204271309

Wirth, S., Ferry, B., and Di Scala, G. (1998). Facilitation of olfactory recognition by lateral entorhinal cortex lesion in rats. Behav. Brain Res. 91, 49–59. doi: 10.1016/s0166-4328(97)00102-2

Yiend, J., and Mathews, A. (2001). Anxiety and attention to threatening pictures. Q. J. Exp. Psychol. A 54, 665–681. doi: 10.1080/02724980042000462

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 30 January 2014; Accepted: 16 April 2014; Published online: 06 May 2014.

Citation: Pool E, Delplanque S, Porcherot C, Jenkins T, Cayeux I and Sander D (2014) Sweet reward increases implicit discrimination of similar odors. Front. Behav. Neurosci. 8:158. doi: 10.3389/fnbeh.2014.00158

Copyright © 2014 Pool, Delplanque, Porcherot, Jenkins, Cayeux and Sander. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 03 July 2014
doi: 10.3389/fnbeh.2014.00229

Olfactory preference conditioning changes the reward value of reinforced and non-reinforced odors

Nicolas Torquet[1][*], Pascaline Aimé[2], Belkacem Messaoudi[3], Samuel Garcia[3], Elodie Ey[4], Rémi Gervais[3], A. Karyn Julliard[3] and Nadine Ravel[3][*]

1Neurophysiology and Behavior Laboratory, Neuroscience Paris-Seine – IBPS, CNRS UMR 8246 – UPMC UM CR18 – INSERM U1130, Paris, France

2Department of Pathology and Cell Biology, Columbia University Medical Center, New York, USA

3Equipe Olfaction: du Codage à la Mémoire, Centre de Recherche en Neurosciences de Lyon, CNRS UMR 5292 - INSERM U1028, Université Lyon 1, Lyon, France

4Human Genetics and Cognitive Functions, Neuroscience Department, Institut Pasteur, CNRS UMR 3571 Genes, Synapses and Cognition, Paris, France

* Correspondence: Nicolas Torquet, Neurophysiology and Behavior Laboratory, Neuroscience Paris-Seine - IBPS, CNRS UMR 8246 - UPMC UM CR18 - INSERM U1130, Université P. et M. Curie, Boite 16, 9 quai St. Bernard, 75005 Paris, France e-mail: nicolas.torquet@upmc.fr;
Nadine Ravel, Centre de Recherche en Neurosciences de Lyon, CNRS UMR 5292 - INSERM U1028 - Université Lyon1, Equipe “Olfaction : Du codage á la mémoire” Site de Gerland - 50 Avenue Tony Garnier, F-69366 Lyon Cedex 07, France e-mail: nravel@olfac.univ-lyon1.fr

Edited by:
Regina M. Sullivan, Nathan Kline Institute, USA

Reviewed by:
Daniel W. Wesson, Case Western Reserve University, USA
Anthony Sclafani, Brooklyn College of The City University of New York, USA

Olfaction is determinant for the organization of rodent behavior. In a feeding context, rodents must quickly discriminate whether a nutrient can be ingested or whether it represents a potential danger to them. To understand the learning processes that support food choice, aversive olfactory learning and flavor appetitive learning have been extensively studied. In contrast, little is currently known about olfactory appetitive learning and its mechanisms. We designed a new paradigm to study conditioned olfactory preference in rats. After 8 days of exposure to a pair of odors (one paired with sucrose and the other with water), rats developed a strong and stable preference for the odor associated with the sucrose solution. A series of experiments were conducted to further analyze changes in reward value induced by this paradigm for both stimuli. As expected, the reward value of the reinforced odor changed positively. Interestingly, the reward value of the alternative odor decreased. This devaluation had an impact on further odor comparisons that the animal had to make. This result suggests that appetitive conditioning involving a comparison between two odors not only leads to a change in the reward value of the reinforced odor, but also induces a stable devaluation of the non-reinforced stimulus.

Keywords: olfaction, odor, behavior, rat, olfactory preference, conditioning, discrimination

INTRODUCTION

Rodents are macrosmatic animals with olfactory abilities allowing them to detect and discriminate a wide range of odors and accomplish complex olfactory-mediated cognitive tasks (Slotnick, 2001). By relying on olfaction, rodents can learn to discriminate distant appetitive and edible items from aversive and potentially hazardous items. Although some odors or tastes can generate innate aversion as well as preference behaviors (Apfelbach et al., 2005; Kobayakawa et al., 2007; Ventura and Worobey, 2013), most food choices result from an associative process between food sensory characteristics and the post-ingestive consequences. This process most often leads to switch the initial neutral reward value of a specific food toward either a preference or an aversion (Slotnick, 2001; Gautam and Verhagen, 2010).

When consuming, we are exposed to two types of chemosensory stimuli: first, the odor, when items are at distance, and then the flavor (a combination of retronasally transmitted olfactory stimulation and taste) when items are in the mouth (Pierce and Halpern, 1996). These odor-taste interactions have been described as taste-odor synesthesia resulting from pairing of the two chemosensory modalities via oral and retronasal stimulation during food ingestion (Verhagen and Engelen, 2006). Indeed, in humans, food-related odorants are often reported and described as tastes (vanilla for example reported as sweet) and a novel odor can acquire a taste even after a single pairing (for a review see Stevenson and Tomiczek, 2007). From experimental work in rodent, it has become clear that taste-like qualities of odors are learned and are not innate (Gautam and Verhagen, 2010).

A large number of studies propose an unraveling of the contribution of each type of chemosensory stimulus in the learning process, most of them using aversive conditioning paradigms (Palmerino et al., 1980; Batsell et al., 1999; Batsell and Blankenship, 2002; Miranda, 2012). In such protocols, the presentation of a solution characterized by a specific odor or taste can be associated with a gastric malaise induced by an intraperitoneal injection of lithium chloride and leads to a strong further avoidance of this solution (Garcia et al., 1968). Interestingly, whereas a pure taste aversion could be induced, even when the malaise is delayed from the ingestion, odor aversion alone is more difficult to obtain and less resistant to an increase of ingestion-malaise delay (Palmerino et al., 1980). However, when the odor is combined with a specific taste during the initial experience (taste potentiated odor aversion, TPOA) or ingested without any gustatory impact, a very robust aversive behavior is obtained even when the odor is presented alone during the test (Rusiniak et al., 1979; Palmerino et al., 1980; Miranda, 2012). As a consequence, further animal models using conditioned flavor aversion or preference were developed. These models showed that an odor present in a solution or an ingested aliment could become aversive (Slotnick et al., 1997; Chapuis et al., 2007) or attractive exactly as observed in taste models (Holder, 1991; Boakes et al., 2007).

The respective role of the individual components of a flavor, odor and taste, have been less explored regarding appetitive learning. To our knowledge, only two paradigms have clearly established a strict Conditioned Olfactory Preference (COP; Holder, 1991; Lucas and Sclafani, 1995). Compared to previous work in which odorants were consumed by the animal, in the Holder's paradigm, the odors used (almond or peppermint) were presented during 4 consecutive days in close proximity with the ingested solution (sucrose or saccharin) but the rats never made gustatory contact with them. A two-bottle test performed 24 h later revealed that animals preferred the odor previously paired with sucrose over saccharin, suggesting both a calorie-mediated and an appetitive taste-mediated preference (Holder, 1991). In this paradigm however, as each odor is paired with a sugar-sweetened solution, it is difficult to interpret the real change of the reward value for the odor paired with non-nutritive saccharin. Indeed previous studies have demonstrated that the animals preferred odor paired with the nutritive diet (Baker and Booth, 1989; Holder, 1991) and strong odor preferences could be obtained for odors paired with the post ingestive actions of nutrients without the presence of added taste cues (Lucas and Sclafani, 1995). In the present study, we therefore propose to investigate the influence of appetitive learning on the reward value of both the odor used as the conditioned stimulus (CS+), paired with sucrose, and a second odor (CS−) that is paired with the neutral solution, pure water. Additionally, we also investigate whether COP could be obtained from an initially preferred odor as well as from a less preferred one.

Compared to previous studies (Holder, 1991; Lucas and Sclafani, 1995), the present work provides new and additional informations on Conditioned Olfactory Preference. In experiment 1, we first evaluated the spontaneous behavioral preferences of several odors presented in pairs. We then induced a conditioned olfactory preference by coupling one of the odors with a sucrose solution, and a second one with pure water, which remains a positive stimulus for water-deprived animals but less appetitive. The protocol efficacy was measured immediately and 1 month later by testing how the conditioning procedure impacts the initial observed preference for the conditioning pair of odors. A series of additional experiments was performed to evaluate how the conditioning procedure also affected the spontaneous preference for the two tested odors compared to others not involved in the conditioning paradigm (Experiments 2 and 3). This allowed us to demonstrate that, as expected, the conditioned odor was reinforced (increased positive valence). Interestingly, we also reported that the reward value of the non-conditioned odor was depreciated compared to any non-training odor.

MATERIALS AND METHODS

ANIMALS

Experiments were carried out in accordance with the European Community Council Directive of November 24th, 1986 (86/609/EEC) for the care and use of laboratory animals. The experimental protocols were approved by the Lyon1 University Ethics Committee (Direction of Veterinary Service #693870202).

A total of forty male Wistar rats (275–300 g, Charles River, L'arbresle, France) were involved in this study. They were housed individually in Plexiglas chambers at constant temperature and relative humidity (22 ± 0.5°C and 50 ± 5%) and under a 12-h light: 12-h dark cycle (light on at 6.00 AM) at least 1 week before the beginning of the experiments.

A total of forty rats were included in the 3 experiments. Sixteen of them participated in experiments 1 and 2. In experiment 1, eight rats were assigned to either group 1 or group 2. In experiment 2, animals from both groups were equally distributed between group 3 and group 4. Twenty-four naïve animals participated in experiment 3.

ODORS

Odors were purchased from Sigma-Aldrich (France). According to their chemical properties, they were diluted either in mineral oil or in water. The dilution was also adjusted as a function of their vapor pressure to be judged as moderate and equal in intensity by the experimentalist. In the following, + and − symbols with brackets [] refer to the enantiomer structure of the odors while the + and − symbols outside brackets refers to the association made during conditioning with sucrose (+suc) or water (−) respectively. Geraniol, eugenol, and two enantiomers of carvone (carvone[−] and carvone[+]) were diluted to 10% in mineral oil, limonene[−] was diluted to 5% in mineral oil and iso-amylacetate was used at a dilution of 10% in water. Geraniol and eugenol were used for experiment 1, carvone[−], carvone[+] and limonene[−] for experiment 2 and the two enantiomers of carvone, limonene [−] and iso-amylacetate in experiment 3. 150 μL of each odor solution was distributed on a 3-cm cotton disc (3M™ T156 Oil Sorbent Sheets). The disc was placed into the 3.2-cm cap of a tube, face turned toward the cage and secured in place by a thin grid of metal. The metal grid, the disc and the 3-cm cap were punched with a 16 mm hole allowing them to be placed around the spout of the bottle of drinking solution. Therefore, the rats could never touch the disc, as described in the experiments of Holder (1991). Importantly, such a device also precludes the bottle solution to be polluted by the odor.

EXPERIMENTAL SET UP (FIGURE 1)

Behavioral tests were conducted in parallel for four rats in individual Plexiglas operant chambers (330 × 210 × 180 mm). The chambers were set side by side so that the animals could not see each other. Two plastic tubes were mounted on the opposite sides of the flat ceiling of each chamber. These tubes (made from 15 ml polypropylene conical centrifuge tubes, Falcon, France) were cut and fire polished to build a 0.4 mm spout which protruded approximately 5 mm into the chamber, allowing the rats to drink from the spout with ease by rising up. The amount of liquid consumed by the rat from each tube was measured by a device called licking box. This system has already been used to measure odor detection in different states of satiety and described in details elsewhere (Aimé et al., 2007). Each tube was connected to a custom-made capacitance circuit which allowed detecting, visualizing and recording of individual licks at each bottle across time during the experimental sessions. Generated files were then transferred into a database developed in our laboratory to be analyzed offline. In parallel, the global consumption of each rat from each bottle was also measured to check the accuracy of the licking system.

FIGURE 1. Experimental Set-up. (A) Experimental set up. When the rat drinks, its nose is in close contact with the odor to ensure a good association with the drinking solution. The odor is never ingested. (B) Detail of the bottle and the licking box system. The tube containing the solution is coupled to a lick detector. The odor is placed on a disk into a cap face turned toward the cage. A thin grid of metal between the cap and the grid of the cage prevents the rat to lick the disk. Each lick at the bottle's spout is detected via the licking box and sent to a computer to be recorded into a database. A homemade computer interface allows to display the recorded licks for each bottle.

GENERAL PROTOCOL

To motivate their drinking behavior, rats had been acclimated to a restricted access to water during 1 week before the beginning of habituation. During the overall course of the study, their daily access to water was restricted to 15 min in the experimental cage, supplemented by a 20 min period of free access each afternoon at 6:00 PM in their home cages.

Habituation

During 5 days, rats were placed in the experimental cages with two bottles of water for a 15 min session. This phase was devoted to train rats to drink from the device, and to avoid stress during the conditioning.

Pretest

To assess a possible spontaneous aversion or preference, each pair of odors to be used in the following experiment was presented to the animal. During this phase, for each 15 min session, two bottles of tap water were each associated with one odor of the pair and simultaneously presented. The same pair was tested on two consecutive days switching the location of the odors to rule out place preference.

Conditioning

During the 8 days of conditioning, rats were exposed during 15 min to two odors paired with two bottles containing different solutions. Odor A was paired with a bottle containing a solution of 3.4% sucrose (A+suc), while odor B was paired with a bottle of tap water (B−). The preference for specific sugar concentration varies according to the level of water deprivation imposed to the animals. We used a concentration of 3.4% of sucrose as validated by Holder (1991). In our water restriction condition, this concentration was still preferred by the rats over water. Throughout the study, the position of each bottle was switched each day to rule out place preference.

Final test

Two bottles containing tap water associated with odor A or B were presented for 15 min on two consecutive daily sessions switching their position. Results are presented as the mean intake over 2 days allowing for position switch.

The same protocol was used for the three experiments, with a variation in conditioning odors according to the experiment (see Table 1 for the details of each group). When additional tests were performed (experiments 2 and 3), two additional conditioning sessions were conducted between each test to avoid a possible extinction of preference induced by the repetition of odor presentation without sucrose-pairing.

Table 1. Summary of experiments.

STATISTICAL ANALYSIS

The coefficient of correlation between the number of licks and the global water consumption measured during experiment 1 indicated a good reliability of our licking system (r = 0.985). As a consequence, we chose the number of licks as the study variable. To normalize data, we expressed consumption from each bottle as a ratio of the number of licks for this bottle on the sum of the number of licks for the two bottles. This variable was then compared between subjects according to the different experimental groups and for a given subject as a function of experimental condition (pretest, test). Within group differences were then analyzed using the non-parametric Wilcoxon test (WT). The changes in bottle position were never found to induce a significant difference in liquid consumption, we therefore pooled the average number of licks on each bottle at the two positions.

RESULTS

EXPERIMENT 1

Specific methods

Group 1 (n = 8) was conditioned to geraniol [i.e., geraniol was paired with sucrose (G+suc) and eugenol with water (E−)] while group 2 (n = 8) was conditioned to eugenol [i.e., eugenol was paired with sucrose (E+suc) and geraniol with water (G−)]. Two tests were performed to estimate olfactory preference: one immediately after the end of the conditioning period and the other, 1 month later to investigate the long-term retention of conditioning (see Table 1).

Results (Figure 2)

Before conditioning, no significant preference between geraniol and eugenol was detected in any group (WT: group 1, p = 0.326; group 2, p = 0.552). However, as soon as the second odor-sucrose association was run, rats exhibited a clear preference for the odor paired with the sucrose solution. We observed that the odor was progressively used to rapidly locate the sucrose solution. After conditioning, both groups showed a preference for the bottle associated with the odor previously paired with sucrose (WT test: group 1, n = 8, p = 0.002; group 2, n = 8, p = 0.011, see Figure 2). When rats from both groups were tested again 1 month later, their preference for the odor previously paired with sucrose was still significant (WT: group1, n = 8 and group 2, n = 8, p = 0.001).

FIGURE 2. Experiment 1: Tests for conditioned odor preference induced in the two groups of rats after conditioning and one month later. (A) Learning curve and test followed by long-term memory test (LTM test) performances for the two groups pooled (n = 16). During the conditioning period, the CS+ odor was associated with the sucrose solution and the CS- with plain water. Before and after conditioning (pretest and tests), both odors were associated with plain water. (B) Odor preference for each group (left panel: group 1 conditioned for G+ over E−; right panel: group 2 conditioned for E+ over G−; n = 8 for each group). Data are presented as means of ratio (number of licks for the bottle on the sum of the number of licks for the two bottles) + s.e.m. over the two consecutive pretest/test days (Wilcoxon tests; *p < 0.05; **p < 0.01; ***p < 0.001).

Discussion

Experiment 1 confirmed the efficiency of our conditioning protocol. Although no spontaneous preference was observed for geraniol or eugenol, as expected, in both groups, a clear preference for the sucrose-paired odor developed after 8 days of conditioning. This olfactory preference was independent of the odor paired with sucrose since the two groups had a similar learning curve. Interestingly, this acquired preference was stable since, even 1 month after the last odor-sucrose exposure; the rats still preferred the odor previously paired with sucrose.

EXPERIMENT 2

Specific methods

Three pretests were conducted before conditioning to assess possible spontaneous preference. For that purpose, odors were presented in pairs both associated with water, on separate sessions and in the following order: carvone[+] (C[+]) vs. carvone[−] (C[−]), carvone[+] vs. limonene[−] (L[−]).

These pretests were followed by an 8-day period of conditioning. During this phase, carvone [−] was associated with sucrose (C[−]+suc) L[−] with water (L[−]−). After conditioning, we first presented C[−] vs. L[−] both associated with tap water to confirm the acquisition of odor preference. We then separated the sixteen rats into two groups (3 and 4) mixing animals from the two groups previously used (group 1 and group 2). In group 3, we tested C[+] vs. L[−] and then C[−] vs. C[+]. In group 4, we reversed the test order and first tested C[−] vs. C[+], and then C[+] vs. L[−] (see Table 1 for a summary of all performed tests).

Results (Figure 3)

Regarding the pretests performed before conditioning, no significant difference of intake between C[−] and C[+] (n = 16, WT, p = 0.940), C[+] and L[−] (n = 16, WT, p = 0.537) and C[−] and L[−] (n = 16, WT, p = 0.640) emerged from the pretests.

FIGURE 3. Odor preference measured by lick ratio in experiment 2. (A) For all rats, there is no preference between C[−] and L[−] before conditioning. After conditioning, animals preferred C[−]. (B) For group 3, there is no significant difference between C[+] and L[−] before conditioning. After conditioning, rats preferred C[+]. (C) For group 3, there is no preference before conditioning between C[−] and C[+]. After conditioning, animals preferred C[−]. (D) For group 4, there is no odor preference between C[−] and C[+] before conditioning. After conditioning, rats preferred C[−]. (E) Before conditioning, rats of group 4 have no preference between C[+] and L[−]. After conditioning, they preferred C[+]. Data are presented as means of ratio (number of licks for the bottle on the sum of the number of licks for the two bottles) + s.e.m over the two successive pretest/test days (Wilcoxon tests; *p < 0.05; **p < 0.01; ***p < 0.001).

After pairing sucrose to C[−], as expected, rats drank more from the bottle associated with this odor (n = 16, WT, p < 0.001) than L[−] even in the absence of sucrose. In the following tests, rats from group 3 preferred to drink from the bottle associated with C[+] rather than from the one associated with L[−] (n = 8, WT, p < 0.001). However, when C[+] was simultaneously presented with C[−] the rats exhibited a preference for the bottle odorized with the sucrose-paired odorant C[−] (n = 8, WT p = 0.009).

Rats from group 4 submitted to the same tests in a reverse order, drank more from the bottle associated with C[−] than from the one associated with C[+] (n = 8, WT p = 0.011). Subsequently, they also preferred C[+] to L[−] (n = 8, WT, p = 0.049).

Discussion

This second experiment confirmed the efficacy of the protocol to induce an odor preference with another pair of stimuli. This experiment also addressed the question of specificity for odor preference acquisition. This was assessed by adjusting carvone enantiomers discriminability. Before conditioning, both groups of rats exhibited no preference between these two odors. This result could be interpreted in two ways: either the animal is not able to discriminate these two odorant molecules or it is indeed able to discriminate them but has no preference. After conditioning, both groups exhibited a preference for the enantiomer previously paired with sucrose (C[−]) when simultaneously presented with L[−] or with C[+]. When a preference for C[+] to L[−] was observed, one could interpretate that the rats transferred the positive value acquired by one enantiomer to the other because they are perceived as similar. However, after conditioning, both groups exhibited a preference for C[−] vs. C[+], implying that the rats have the capacity to discriminate among the two carvone enantiomers. This change, compared to the pretest situation, could be interpreted either as a learning-induced improvement of enantiomer discrimination as suggested by others (Escanilla et al., 2008) and/or by a learning-induced change in motivation for choosing C[−]. A further hypothesis that fits with the previous two ideas, is that rats drinking more from the C[+] bottle when simultaneously presented with L[−], could be due to a generalized sugar association to “carvons” or simply because they avoided the odor that has never been paired with sucrose. This issue was addressed in experiment 3.

EXPERIMENT 3

Specific methods

This experiment was carried out on a group of 24 rats. Four pretests were performed to evaluate spontaneous preference for each odor presented by pairs before conditioning in the following order: carvone[−] (C[−]) vs. geraniol (G) for all rats (n = 24); limonene[−] (L[−]) vs. iso-amylacetate (I) for group 1 (n = 12) and eugenol (E) vs. carvone[+] (C[+]) for group 2 (n = 12); carvone[−] vs. limonene[−] for all rats (n = 24). The experiment included a conditioning phase. During this 8-day period, carvone[−] was associated with sucrose (C[−]+suc), and limonene[−] with water (L[−]). The acquired preference for C[−] vs. L[−] was then tested. On following sessions, group 1 was tested with L[−] vs. I and group 2 with C[+] vs. E. Finally, all rats were tested with C[−] vs. G (see Table 1).

Results (Figure 4)

Before conditioning C[−] and L[−] were equally approached (n = 24, WT, p = 0.082). Following conditioning, both groups acquired a clear preference for C[−] (n = 24, WT, p < 0.001).

FIGURE 4. Odor preference measured by lick ratio in experiment 3. (A) For all animals, there is no preference between C[−] and L[−] before conditioning. After conditioning, animals preferred C[−]. (B) Animals of group 1 preferred L[−] over I before conditioning. After conditioning, this spontaneous preference disappeared. (C) There is no preference for animals of group 2 between C[+] and E before and after conditioning. (D) All animals preferred G over C[−] before conditioning. After conditioning, preference changed and animals preferred C[−] over G. Data are presented as means of ratio (number of licks for the bottle on the sum of the number of licks for the two bottles) + s.e.m. over the two successive pretest/test days (Wilcoxon tests; *p < 0.05; **p < 0.01; ***p < 0.001).

During the pretest, rats from group 1 exhibited a spontaneous preference for L[−] compared to I (n = 12, WT, p = 0.009), which was abolished during conditioning (n = 12, WT, p = 0.909). Rats from group 2 showed a tendency to prefer E over C[+] during the pretest but this failed to reach significance (n = 12, WT, p = 0.076), which was not modified after conditioning (n = 12, WT, p = 0.056). Pre-tests revealed a significant spontaneous preference for G vs. C[−] (n = 24, WT, p < 0.001). Conditioning reversed this preference (n = 24, WT, p < 0.001).

Discussion

The aim of experiment 3 was to elucidate the nature of change in the reward value induced by an odor preference conditioning. Thus, after preference acquisition for C[−] compared to L[−], each odor of this pair was presented with a novel one to evaluate the new preferences.

Before learning, animals from group 1 preferred to drink from the bottle associated with L[−] when this odor was simultaneously presented with I. This preference disappeared after conditioning, which led us to the conclusion that, during preference conditioning, rats also learnt that L[−] had never been associated with sucrose. As a consequence, its natural reward value decreased.

Moreover, results obtained from group 2 suggested that learning-induced changes in the reward value are specific to odorants used during the conditioning phase. Indeed, pairing C[−] with sucrose had no effect on how C[+] was perceived compared to E. We therefore interpret that in experiment 2, the generalization from C[−] to C[+] might be the consequence of avoidance of L[−] that has never been paired with sucrose.

Rats spontaneously preferred G to C[−], but after conditioning, their preference was reversed. This clearly indicates that learning increases carvone reward value. Putting together this result with those of the tests with C[+] and E, we could conclude that the present olfactory preference conditioning procedure selectively increased C[−] reward value and, as a consequence, devaluated L[−]. This experiment also confirmed the ability of rats to distinguish these two enantiomers, even when they had never been presented simultaneously for direct olfactory comparison.

GENERAL DISCUSSION AND CONCLUSIONS

These series of experiments confirm the possibility to induce a strong and reliable conditioning olfactory preference by associating the consumption of a sucrose solution and the close delivery of an odor without any ingestion of it. Indeed, the odor concentrated around the water bottle's spout was perceived as a characteristic of the solution contained inside the bottle. Experiments 1 and 2 assessed, for the first time, a display of olfactory preference for two different odor pairs (eugenol, geraniol and carvone[−], limonene[−]). Conditioning led to a specific preference for the odor associated with a sucrose solution, and as shown in experiment 1, this preference was maintained for at least 1 month. Experiment 2 and 3 addressed the question of specificity for odor preference acquisition and elucidated the nature of change in the reward value induced by such an odor preference conditioning.

As mentioned in the introduction, few studies have explored the individual contribution of odor and taste in conditioned food preferences. In the experiment performed by Holder (1991), each odor was paired with a different sweet taste solution (either sucrose or saccharin) and the animals were continuously exposed during 4 days in their home cage and tested 48 h later, after a period of access to plain water. In our protocol, only one of the two odors was associated with a sugar solution while the other was paired with plain water. This presentation was performed in an experimental cage during 8 consecutive daily sessions of 15 min. Moreover, the animals were also allowed to drink water in their home cage for 20 min each day. The advantage of using such an exposure mode was to maximally control the experimental environment and to avoid any diffusion of the odorant and subsequent contextual learning. Despite these differences, in both experiments, a clear preference was obtained for the odor paired with sucrose compared to saccharin in Holder's study and water in our case. Sucrose solution has very often been used for appetitive conditioning (Holder, 1991; Boakes et al., 2007) since its sugar taste is highly and spontaneously preferred (Shepherd, 2006), with a non-negligible energy supply (Myers and Sclafani, 2001). Other protocols that have used saccharin to avoid energy supply (Baker and Booth, 1989; Myers and Sclafani, 2001); demonstrated that conditioned odor preferences were reinforced by the postingestive effects of caloric substances (Myers and Sclafani, 2001). Nevertheless this reinforcement has been suggested to be dependent on the feeding state of animals (Harris et al., 2000). We cannot rule out the possibility that the metabolic effect of sucrose has contributed to the success and stability of our conditioning procedure. However, the main objective of our study was not to address this specific issue but rather to increase as much as possible the rewarding value of the odor paired with sucrose.

In experiments 2 and 3, the strategy of the animal's choice was investigated. In experiment 2, the reinforced odor (C[−]+) was replaced by its enantiomer (C[+]) during the test and presented simultaneously with the non-reinforced odor (L[−]−). Rats seemed to consider the new odor as the previous reinforced one. This result could lead to two distinct interpretations, either the rats were unable to discriminate between the two enantiomers of carvone, or they were generalizing the olfactory preference to closely related odorants. However, when rats later had the choice between the two enantiomers, they showed that they could discriminate between them and correctly chose the reinforced odor (C[−]+). This result confirmed their ability to discriminate the enantiomers carvone[−]/carvone[+] when necessary and to generalize when appropriate. In this experiment, the order of the pair presentations was reversed for group 2. When the rats had to choose between the enantiomers (carvone[−]/carvone[+]) before choosing between C[+] and L[−]−. The group exhibited a good discrimination between the enantiomers but always chose C[+] vs. L[−]−. However, this preference was less marked, as if rats knew C[+] was not the previously reinforced odor. This last result triggered another question: why did the rats choose C[+] when compared with L[−]−? It could be due to a generalization of the acquired value of C[−]+ to “carvons,” or to a decrease in the reward value of the non-reinforced odor (L[−]−). Experiment 3 allowed us to answer this question and to validate the second hypothesis.

The goal of experiment 3 was indeed to test whether COP also modified the reward value of the non-reinforced odor. This was assessed by comparing how the conditioning procedure affected the preference between the odor paired with water in our paradigm and different odors never included in the conditioning procedure. Figure 5 summarizes the variations of reward value for the odors used in experiment 3, following pairing C[−] with sucrose and L[−] with water. The comparison of preference before (Figure 5A) and after (Figure 5B) the olfactory conditioning shows that, as expected, C[−] became preferred over L[−]. In addition, the initial preference for G vs. C[−] was reversed by the conditioning. This confirmed a development of preference for the odor paired with sucrose during conditioning, independently of the value of L[−]. In experiment 2, rats preferentially chose C[+] over L[−]. We first hypothesized that this could be explained by a generalization between the two enantiomers of carvone. However, the initial spontaneous preference for L[−] vs. I disappeared totally, suggesting a decrease in the reward value of L[−] through conditioning. For new pairs of odors like E vs. C[+], conditioning had no influence on the preference for one or the other odor. If there was a generalization between the two carvones, C[+] would have been preferred over E as was the case for C[−]+ compared to G. This comparison therefore weakens the hypothesis of a generalization between the two carvones and instead confirms a decrease in reward value for L[−]− after conditioning, leading to a preference for C[+] when the two odors were simultaneously proposed.

FIGURE 5. Variations of preference for 6 odors in experiment 3, suggesting changes in the reward value after conditioning. Each odor used in experiment 3 is represented on a portion of the circle. On each of these portions, a scale represents the proportion of licks. Each portion of circle (i.e., each odor) is connected to another one to compare the preference (indicated by the proportion of licks) between these two odors: the larger the link is, the more preferred the odor is. (A) Spontaneous odor preference before conditioning during the pretests. (B) Odor preference after conditioning with reinforcement on C[−] during the tests. Data are presented as means of ratio (number of licks for the bottle on the sum of the number of licks for the two bottles) over the two successive pretests/test days (Wilcoxon tests; *p < 0.05; **p < 0.01; ***p < 0.001).

In the present protocol, changes in reward value were measured by the comparison of the consumption of two solutions, each associated with a different odor. Conditioning resulted in a preference for one of these odors, but, interestingly, the non-reinforced odor was also avoided when challenged with another odor. It remains an open question as to whether this avoidance reflects a strategy (a kind of learning by exclusion within the comparison of the two odors) or whether it reflects a real change in reward value, with a depreciation of the value of this odor (i.e., this odor acquires a negative value). Indeed, two theories could explain this reduced consumption of water associated with L[−] after conditioning. The first one is the model-free theory. In this case, animals learn by trial-and-error and each new trial reinforces their preference for the sucrose-associated odor (Doll et al., 2012). According to this theory, after many comparisons between C[−]+ and L[−]−, rats consider C[−]+ as rewarding and they will actively seek it. This theory interprets the learnt avoidance of L[−]− as reflecting a negative value, in a kind of habit behavior acquired during conditioning. However, it seems unlikely that water alone decreases the value of the odor with which it is associated. The model-based theory, in contrast with the model-free learning, describes adaptive and dynamic value inference in learning tasks (using a “world model”) (Doll et al., 2012), where the animal learns the structure of the task. For instance, it infers that the structure of the test is similar to the conditioning, that is, the test always includes a non-reinforced odor and a reinforced one. In this perspective, L[−]− would not become a negative cue, and would not be avoided, but would signal the other odor to be potentially reinforced, explaining the preference. Furthermore, decisions would arise from a goal-directed control, in contrast to the habit behavior developed in the model-free theory. An interesting insight on these processes may come from the extinction of this preference and, more precisely, from the observation of potential changes in reward values over time, for the CS+ and CS−, respectively.

The present protocol offers a framework to explore in parallel the behavior and the neurobiological processes involved in olfactory appetitive learning. Indeed, in this paradigm the odors are always perceived in an orthonasal way and odor-taste integration might have recruited circuits distinct from those of flavor perception. Using an olfactory aversion paradigm, our group described two different circuits involved according to how the odor was presented (ortho vs retronasally) to the animal during conditioning (Chapuis et al., 2009). It would be interesting to test whether if it is also the case for appetitive learning. More generally, trying to understand the importance of smell to the perception of flavor and the formation of cognitive and emotional responses to food will contribute to the biomedical knowledge required to solve today's rising obesity and diabetes rates.

ACKNOWLEDGMENTS

The authors want to thank Jeremie Naudé and Lauriane Harrington for valuable comments on the manuscript. This work was funded by the French ANR “Programme national de Recherche en alimentation et nutrition humaine.” AROMALIM Project: “Représentations sensorielles de l'arôme des aliments et état nutritionnel: de la réception à la cognition.” Resp: Rémi Gervais (UMR 5020, Lyon) and supported by the LABEX Cortex.

REFERENCES

Aimé, P., Duchamp-Viret, P., Chaput, M., Savigner, A., Mahfouz, M., and Julliard, A. (2007). Fasting increases and satiation decreases olfactory detection for a neutral odor in rats. Behav. Brain Res. 179, 258–264. doi: 10.1016/j.bbr.2007.02.012

Apfelbach, R., Blanchard, C. D., Blanchard, R. J., Hayes, R. A., and McGregor, I. S. (2005). The effects of predator odors in mammalian prey species: a review of field and laboratory studies. Neurosci. Biobehav. Rev. 29, 1123–1144. doi: 10.1016/j.neubiorev.2005.05.005

Baker, B. J., and Booth, D. A. (1989). Preference conditioning by concurrent diets with delayed proportional reinforcement. Physiol. Behav. 46, 585–590. doi: 10.1016/0031-9384(89)90336-3

Batsell, W. R., and Blankenship, A. G. (2002). Beyond potentiation: synergistic conditioning in flavor-aversion learning. Brain Mind 3, 383–408. doi: 10.1023/A:1022915512811

Batsell, W. R., Caperton, J., and Paschall, G. (1999). Olfactory transmission of aversive information in rats. Psychol. Rec. 49, 459–474.

Boakes, R. A., Albertella, L., and Harris, J. A. (2007). Expression of flavor preference depends on type of test and on recent drinking history. J. Exp. Psychol. Anim. Behav. Process. 33, 327–338. doi: 10.1037/0097-7403.33.3.327

Chapuis, J., Garcia, S., Messaoudi, B., Thevenet, M., Ferreira, G., Gervais, R., et al. (2009). The way an odor is experienced during aversive conditioning determines the extent of the network recruited during retrieval: a multisite electrophysiological study in Rats. J. Neurosci. 29, 10287–10298. doi: 10.1523/JNEUROSCI.0505-09.2009

Chapuis, J., Messaoudi, B., Ferreira, G., and Ravel, N. (2007). Importance of retronasal and orthonasal olfaction for odor aversion memory in rats. Behav. Neurosci. 121, 1383–1392. doi: 10.1037/0735-7044.121.6.1383

Doll, B. B., Simon, D. A., and Daw, N. D. (2012). The ubiquity of model-based reinforcement learning. Curr. Opin. Neurobiol. 22, 1075–1081. doi: 10.1016/j.conb.2012.08.003

Escanilla, O., Mandairon, N., and Linster, C. (2008). Odor-reward learning and enrichment have similar effects on odor perception. Physiol. Behav. 94, 621–626. doi: 10.1016/j.physbeh.2008.03.008

Garcia, J., McGowan, B., Ervi, F., and Koelling, R. A. (1968). Cues: their relative effectiveness as a function. Science 160, 794–795. doi: 10.1126/science.160.3829.794

Gautam, S. H., and Verhagen, J. V. (2010). Evidence that the sweetness of odors depends on experience in rats. Chem. Senses 35, 767–776. doi: 10.1093/chemse/bjq075

Harris, J. A., Gorissen, M. C., Glynis, K. B., and Westbrook, R. F. (2000). Motivational state regulates the content of learned flavor preferences. J. Exp. Psychol. Anim. Behav. Process. 26, 15–30. doi: 10.1037/0097-7403.26.1.15

Holder, M. (1991). Conditioned preferences for the taste and odor components of flavors: blocking but not overshadowing. Appetite 17, 29–45. doi: 10.1016/0195-6663(91)90082-4

Kobayakawa, K., Kobayakawa, R., Matsumoto, H., Oka, Y., Imai, T, Ikawa, M., et al. (2007). Innate versus learned odour processing in the mouse olfactory bulb. Nature 450, 503–508. doi: 10.1038/nature06281

Lucas, F., and Sclafani, A. (1995). Carbohydrate-conditioned odor preferences in rats. Behav. Neurosci. 109, 446–454. doi: 10.1037/0735-7044.109.3.446

Miranda, M. I. (2012). Taste and odor recognition memory: the emotional flavor of life. Rev. Neurosci. 23, 481–499. doi: 10.1515/revneuro-2012-0064

Myers, K. P., and Sclafani, A. (2001). Conditioned enhancement of flavor evaluation reinforced by intragastric glucose: i. intake acceptance and preference analysis. Physiol. Behav. 74, 481–93. doi: 10.1016/S0031-9384(01)00595-9

Palmerino, C., Rusiniak, K., and Garcia, J. (1980). Flavor-illness aversions: the peculiar roles of odor and taste in memory for poison. Science 208, 753–755. doi: 10.1126/science.7367891

Pierce, J., and Halpern, B. P. (1996). Orthonasal and retronasal odorant identification based upon vapor phase input from common substances. Chem. Senses 21, 529–543. doi: 10.1093/chemse/21.5.529

Rusiniak, K., Hankins, W., and Garcia, J. B. L. (1979). Flavor-illness aversions: potentiation of odor by taste in rats. Behav. Neural Biol. 25, 1–17. doi: 10.1016/S0163-1047(79)90688-5

Shepherd, G. M. (2006). Smell images and the flavour system in the human brain. Nature 444, 316–321. doi: 10.1038/nature05405

Slotnick, B. (2001). Animal cognition and the rat olfactory system. Trends Cogn. Sci. 5, 216–222. doi: 10.1016/S1364-6613(00)01625-9

Slotnick, B. M., Westbrook, F., and Darling, F. M. C. (1997). What the rat's nose tells the rat's mouth: long delay aversion conditioning with aqueous odors and potentiation of taste by odors. Anim. Learn. Behav. 25, 357–369. doi: 10.3758/BF03199093

Stevenson, R., and Tomiczek, C. (2007). Olfactory-induced synesthesias: a review and model. Psychol. Bull. 133, 294–309. doi: 10.1037/0033-2909.133.2.294

Ventura, A. K., and Worobey, J. (2013). Early influences on the development of food preferences. Curr. Biol. 23, R401–R408. doi: 10.1016/j.cub.2013.02.037

Verhagen, J. V., and Engelen, L. (2006). The neurocognitive bases of human multimodal food perception: sensory integration. Neurosci. Biobehav. Rev. 30, 613–650. doi: 10.1016/j.neubiorev.2005.11.003

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 05 April 2014; Accepted: 06 June 2014; Published online: 03 July 2014.

Citation: Torquet N, Aimé P, Messaoudi B, Garcia S, Ey E, Gervais R, Julliard AK and Ravel N (2014) Olfactory preference conditioning changes the reward value of reinforced and non-reinforced odors. Front. Behav. Neurosci. 8:229. doi: 10.3389/fnbeh.2014.00229

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Torquet, Aimé, Messaoudi, Garcia, Ey, Gervais, Julliard and Ravel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 17 September 2014
doi: 10.3389/fnbeh.2014.00326

Modulation of olfactory sensitivity and glucose-sensing by the feeding state in obese Zucker rats

Pascaline Aimé[1][†], Brigitte Palouzier-Paulignan[1], Rita Salem[1], Dolly Al Koborssy[1], Samuel Garcia[1], Claude Duchamp[2], Caroline Romestaing[2] and A. Karyn Julliard[1]*

1Team “Olfaction: From Coding to Memory”, Lyon Neuroscience Center, INSERM U1028-CNRS 5292- Université Lyon1, Lyon, France

2Laboratoire d’Ecologie des Hydrosystèmes Naturels et Anthropisés, CNRS 5023, Villeurbanne, France

* Correspondence: A. Karyn Julliard, Team “Olfaction: From Coding to Memory”, Lyon Neuroscience Center, INSERM U1028-CNRS 5292- Université Lyon1, 50 Av. Tony Garnier F-69366 Lyon Cedex 07, France e-mail: karyn.julliard@univ-lyon1.fr

[†] Present address: Department of Pathology and Cell Biology, Columbia University Medical Center, New York, NY 10032, USA

Edited by:
Donald A. Wilson, New York University School of Medicine, USA

Reviewed by:
Daniel W. Wesson, Case Western Reserve University, USA
Edgar Soria, Institut National de la Santé et de la Recherche Médicale, France

The Zucker fa/fa rat has been widely used as an animal model to study obesity, since it recapitulates most of its behavioral and metabolic dysfunctions, such as hyperphagia, hyperglycemia and insulin resistance. Although it is well established that olfaction is under nutritional and hormonal influences, little is known about the impact of metabolic dysfunctions on olfactory performances and glucose-sensing in the olfactory system of the obese Zucker rat. In the present study, using a behavioral paradigm based on a conditioned olfactory aversion, we have shown that both obese and lean Zucker rats have a better olfactory sensitivity when they are fasted than when they are satiated. Interestingly, the obese Zucker rats displayed a higher olfactory sensitivity than their lean controls. By investigating the molecular mechanisms involved in glucose-sensing in the olfactory system, we demonstrated that sodium-coupled glucose transporters 1 (SGLT1) and insulin dependent glucose transporters 4 (GLUT4) are both expressed in the olfactory bulb (OB). By comparing the expression of GLUT4 and SGLT1 in OB of obese and lean Zucker rats, we found that only SGLT1 is regulated in genotype-dependent manner. Next, we used glucose oxidase biosensors to simultaneously measure in vivo the extracellular fluid glucose concentrations ([Gluc]ECF) in the OB and the cortex. Under metabolic steady state, we have determined that the OB contained twice the amount of glucose found in the cortex. In both regions, the [Gluc]ECF was 2 fold higher in obese rats compared to their lean controls. Under induced dynamic glycemia conditions, insulin injection produced a greater decrease of [Gluc]ECF in the OB than in the cortex. Glucose injection did not affect OB [Gluc]ECF in Zucker fa/fa rats. In conclusion, these results emphasize the importance of glucose for the OB network function and provide strong arguments towards establishing the OB glucose-sensing as a key factor for sensory olfactory processing.

Keywords: olfactory sensitivity, obesity, olfactory bulb, glucose-sensing, GLUT4, SGLT1, extracellular glucose concentration, feeding state

INTRODUCTION

The Zucker “fatty” rat is a model of genetic obesity. The mutation, named fatty or fa, is autosomic recessive, therefore while fa/fa rats become obese at 3–5 weeks of age, the heterozygous fa/+ rats remain phenotypically normal. The fa mutation affects the extracellular part of the leptin receptor which demonstrates a weaker affinity for leptin and an altered signal transduction (White and Martin, 1997; Yamashita et al., 1997). Leptin receptors are present in several hypothalamic nuclei playing an important role in the central regulation of energy balance (Tartaglia et al., 1995; Mercer et al., 1996; Fei et al., 1997; Shioda et al., 1998; Burguera et al., 2000). Because leptin cannot exert its anorectic action in Zucker fa/fa rats, these animals develop a marked hyperphagia during the first weeks of life (Vasselli et al., 1980). Consequently, the Zucker fa/fa rats exhibit a severe obese phenotype, highly similar to the human obese syndrome (Guerre-Millo, 1997; Beck, 2000). Zucker fa/fa rats display many metabolic and hormonal defects including hyperleptinemia, hyperinsulinemia, mild to moderate hyperglycemia, glucose intolerance as well as peripheral and central insulin resistance (Zucker and Antoniades, 1972; Bray, 1977; Martin et al., 1978; Ikeda et al., 1986; Pénicaud et al., 1987; Zarjevski et al., 1992; Apweiler and Freund, 1993; Guerre-Millo, 1997; Beck, 2000). As a consequence of the leptin receptor gene mutation, in the obese Zucker rats, intracerebroventricular (ICV), injections of insulin or of non-metabolized 2-deoxy-D-glucose do not change food intake in contrast these injections induce hypo- or hyper-phagia, respectively in lean Zucker rats (Ikeda et al., 1980, 1986). The absence of behavioral response to insulin and glucose ICV injections in obese Zucker rats is partly due to an impairment of hypothalamic glucose-sensing neurons. These neurons cannot adapt their firing to fluctuations of interstitial glucose concentration and cannot take part in the regulation of feeding behavior according to the metabolic needs of the organism (Rowe et al., 1996; Spanswick et al., 1997, 2000; Colombani et al., 2009). Among the molecules involved in glucose-sensing, the mRNA expression of glucokinase, a glucose metabolizing enzyme and of GLUT2, a glucose transporter is altered in the hypothalamus of fa/fa Zucker rats (Bogacka et al., 2004). Moreover, the regional brain glucose utilization is modified in obese Zuckers, compared to lean controls (Tsujii et al., 1988; Marfaing-Jallat et al., 1992; Doyle et al., 1993). All together these findings converge to the conclusion that, at least in brain areas well-known to be involved in the control of food intake, the neuronal glucose sensitivity is largely impaired in hyperphagic fa/fa Zucker rats.

Food consumption is regulated by sensory modalities among which olfaction takes an important part. In turn, olfactory sensitivity is modulated by feeding state (Aimé et al., 2007) because normal-weight Wistar rats demonstrate a better olfactory sensitivity when they are fasted than when they are satiated (Aimé et al., 2007). Moreover, ICV administrations of leptin or insulin, two anorectic hormones, decrease the olfactory sensitivity of fasted rats to the level of satiated ones (Aimé et al., 2007; Julliard et al., 2007). Conversely, central infusions of orexin A or ghrelin, two orexigenic peptides, increase the olfactory sensitivity of satiated rats to the level of fasted ones (Julliard et al., 2007; Tong et al., 2011). These converging evidences demonstrate that the olfactory bulb (OB), the first central structure responsible for the olfactory information processing, is targeted by signals (orexin A, ghrelin, leptin, insulin, NPY and CCK) involved in the regulation of energy balance (Palouzier-Paulignan et al., 2012). In addition to the hormones involved in food intake regulation, a large body of evidence indicates that the olfactory system is also sensitive to blood-borne nutrients. Two neuronal markers of glucose sensitivity, insulin-dependent glucose transporter type 4 (GLUT4) and sodium glucose co-transporter type 1 (SGLT1), are found in the olfactory system (El Messari et al., 1998). Recent patch clamp studies have shown that mitral cells change their firing rate in response to changes in glucose concentration, identifying these neurons as glucose sensors (Tucker et al., 2010, 2013) and also to changes in insulin concentration (Fadool et al., 2000; Kuczewski et al., 2014).

Given the extreme metabolic features of the obese Zucker fa/fa rats described above and the recent studies demonstrating a strong effect of the peripheral and central signals involved in the regulation of energy balance on olfactory processing and glucose sensitivity in the olfactory system, the present study was conducted to (i) test whether the obese Zucker fa/fa rats display a normal regulation of the olfactory sensitivity by the feeding states; (ii) compare the olfactory sensitivity of lean Zucker fa/+ rats and obese Zucker fa/fa rats; and (iii) investigate whether the glucose concentration, GLUT4 and SGLT1 expression in the OB might be altered in the genetically obese and moderately diabetic Zucker fa/fa rat.

MATERIALS AND METHODS

ANIMALS PREPARATION

Male lean (fa/+; n = 24) and obese (fa/fa; n = 24) Zucker rats, were purchased from Charles River Laboratories. The animals were handled (5 min/day) and weighed daily. On arrival, they were 7–8 weeks old and weighed 299 ± 7.2 g (fa/+) and 344 ± 11.8 g (fa/fa). Experiments were carried out in accordance with the European Community Council Directive of November 24th, 1986 (86/609/EEC) for the care and use of laboratory animals. The Animals were housed in groups (4 rats) in plexiglas chambers at constant ambient temperature and relative humidity (22 ± 0.5°C and 50 ± 5%). They were acclimated to a 12 h light/12 h dark inverted cycle, with the light turned on at Zeitgeber time zero (ZT0). Upon arrival, the rats were given ad libitum access to food (chow pellets, Harlan, France) and water. 2–3 weeks prior to experimental procedure, the rats were gradually habituated to a 20 h/day food restriction (FR) schedule in which they had access to food during dark phase only, from ZT16 to ZT20. Since daily fluctuations in glycemia and insulin levels are cued by food intake (Kaul and Berdanier, 1975; Sitren and Stevenson, 1978), a single daily meal was imposed to synchronize the circadian variations of glycemia and insulin secretion among the animal cohort. Animals were handled (5 min/day) and weighed daily to assess their adaptation to FR. The daily amount of food consumed was measured for lean and obese rats in order to avoid weight loss and allow stabilization or a small gain of body weight. The daily food distribution schedule was performed by an automatic food distribution system (homemade) piloted by a computer using Matlab software.

BEHAVIORAL PROCEDURE

The behavioral test was performed as previously described (Aimé et al., 2007, 2012; Julliard et al., 2007; Tong et al., 2011) with minor modifications, as follow. 12 Zucker fa/+ and 12 Zucker fa/fa rats were submitted to the behavioral procedure. The conditioned odor aversion (COA) protocol consisted in pairing the ingestion of an odorized drink with an intraperitoneal (i.p.) injection of lithium chloride (LiCl, Sigma-Aldrich). This procedure induces a robust aversion to the odor diluted in the drinking solution. The olfactory sensitivity is then tested by measuring the aversive behavioral response to a range of lower odor concentrations. In order to induce drink intake during behavioral test, in addition to the FR, rats were placed on a 23 h water restriction schedule that started 1 week prior behavioral procedure and was maintained throughout the behavioral protocol. Animals had access to water for 1 h, during the daily food distribution. Experiments consisted of two 5-min daily sessions occurring at 9 h intervals. The first session (S1) started at the beginning of the dark phase (ZT13; after 17 h of fasting) and the second (S2) started during the dark phase, during the postprandial phase (ZT22, 2 h after the end of the meal; Figure 1A). This paradigm allowed each animal to be successively tested in two steady physiological states, fasted (S1) and satiated (S2) each day of the behavioral test. In a first period, corresponding to the habituation, rats were trained for 2 days to lick pure water at the two drinking tubes of the experimental cage (not shown). The experimental set-up allowed the recording of licking behavior using a two-tube device described elsewhere (Aimé et al., 2007, 2012; Julliard et al., 2007). During the next 2 days (Figure 1B: D1 to D2, Aversion acquisition), rats only had access to water odorized with isoamyl-acetate (ISO, Sigma-Aldrich). An ISO consumption of more than 0.5 mL was paired 15 min later, with an i.p. injection of LiCl (10 mL/kg at 0.15 M) to induce gastric malaise and establish a COA to ISO. ISO was used at the dilution of 10−5, for which the solution has a strong odor but has been shown to be tasteless (Slotnick et al., 1997). Thus at this dilution, the rats can only identify ISO via the olfactory pathways. Depending on individuals, 1–4 conditioning sessions (S1 and S2 for each 2 days) were required for the animal to develop a strong aversion to ISO 10−5. On D3 (Figure 1B: Aversion test), the COA efficiency was tested by giving the animals the choice between pure water and water odorized with ISO 10−5. Then the animals were submitted to the olfactory detection test (Figure 1B: D4 to D8). For this, rats were offered a choice between pure water and water odorized with ISO at lower dilutions. At the beginning of each experimental session of the olfactory detection test, the animals were placed under the odorized water tube. Rat olfactory detection for ISO was thus assessed using a forced-choice task, and not by using a simple choice task, since the thirsty rats were forced to smell the odorized tube first. This procedure was chosen to avoid the possibility that the rats, highly motivated by thirst, would go by chance to the pure water tube first, drink only water, and not sample the ISO tube. The conditioned aversion was then tested using drinking solution with ISO dilutions ranging from 10−11 to 10−7. Each given odor concentration was presented to the same rat in fasted (ZT13) and later in satiated (ZT22: i.e., 2 h after the food intake end) status. On D9 (Figure 1B: Aversion re-test), COA stability was assessed by giving the rats the choice between the standard ISO solution (10−5) and pure water.

FIGURE 1. Schematic representation of daily (A) and overall course (B) of the behavioral experiments. (A) Lights off occurred at Zeitgeber time twelve (ZT12) and rats had access to food from ZT16 to ZT20 and to drinking water from ZT16 to ZT17. Two 5-min daily experimental sessions S1 and S2 were performed at ZT13 (before the meal: fasted) and ZT22 (2 h after food removal: satiated) respectively. (B) During each day of the behavioral experiment that ran from D1 to D9, all animals were successively tested in fasted (S1) and satiated (S2) states. During the aversion acquisition period (Avers. acq., from D1 to D2), rats had access to water odorized with ISO 10−5. ISO 10−5 consumption was paired with an i.p. injection of LiCl to induce a COA. On D3, corresponding to aversion test (Avers. test), the COA efficiency was tested by giving the animals the choice between water odorized with ISO 10−5 and pure water (10−5/W). During the olfactory detection test period (D4–D8), rats were offered a choice between pure water and water odorized with ISO at lower dilutions ranging from 10−11 to 10−7. On D9, (Avers. re-test) the COA stability was assessed by giving the rats the choice between ISO 10−5 and pure water (10−5/W).

For each concentration, an olfactory detection index was calculated. It corresponds to the proportion of the number of licks at the pure water tube normalized to the total number of licks (odorized + pure water) in the experimental device. When rats perceived ISO in the drinking solution (and consequently avoided it), they licked more pure water during the experimental session, resulting in a higher olfactory detection index. In addition, the number of licks during the first ISO consumption (first licking burst) in the experimental device was recorded. This data allowed us to accurately measure the delay necessary for the animals to be able to detect the aversive ISO diluted in the drinking solution. Data from rats starting the experimental session by drinking at the pure water tube (maximum 2 rats out of 12 per ISO dilution) was excluded from this analysis. In order to exclude a potential bias induced by the number of conditioning sessions on the strength of the COA, a Pearson correlation test was performed between the number of LiCl injections and the olfactory detection index for each animal, for each nutritional status and for each ISO concentration tested. No correlation was observed (P > 0.05) for both fa/fa and fa/+ rats. The number of lick during the first burst, the end of the licking burst and the total number of licks at each tube during the experimental sessions were assessed and analyzed using SciPy and MySql database software (Open Source Licenses). A licking burst was defined by a train of high frequency licks (7–10 Hz) and the end of licking burst was determined when the last lick was followed by a period of inactivity lasting at least 1 s.

PHYSIOLOGICAL MEASUREMENTS

To characterize the two metabolic steady states of the fasted and satiated rats, the concentrations of plasma glucose, plasma insulin, and OB insulin were measured. Peripheral blood glucose level was determined by sampling 5 μL of tail blood 1 h before and 2 h after food intake for fasted and satiated rats respectively. Glucose levels were monitored with a glucose meter (Accu-Chek®  Roche, Mannheim, Allemagne/Performa). Plasma and OB insulin levels were measured in fasted (ZT12; n = 6 for each genotype) or in satiated (ZT22; n = 6 for each genotype) state. At the end of the behavioral procedure, rats were deeply anesthetized using an i.p. injection of ketamine (Imalgene, 80 mg/kg) and xylazine (Rompun, 10 mg/kg), then rats were euthanized, and their OBs immediately frozen in liquid nitrogen. Trunk blood was collected in heparinized tubes, and the plasma fraction was separated by centrifugation at 2000 g for 5 min. Insulin was extracted from one OB per rat according to the procedure of Baskin et al. (1983) and the other OB was saved for Western blotting. To determine the influence of the yield of the insulin extraction procedure, samples with known amounts of insulin, were submitted to the same protocol. The mean extraction output was found to be ~40%. Plasma and OB insulin levels were determined using a solid-phase, two-site enzyme immunoassay following the manufacturer’s protocol (Mercodia Ultrasensitive Rat Insulin ELISA).

WESTERN BLOT ANALYSIS

One of two OB removed from rats previously used for behavioral procedure was homogenized in ice-cold homogenization buffer (Tris 5 mM, EGTA 2 mM, pH 7.4) supplemented with an anti-protease cocktail (SIGMA P8340, 10 μl per mg tissue). Western Blot analysis was performed with OB homogenates from rats euthanized either in fasted (ZT12; n = 4 for each genotype) or in satiated (ZT22; n = 4 for each genotype) state. Protein concentrations were determined by BCA assay according to the manufacturer’s recommendation (PIERCE #23225). 100 μg of proteins were loaded into 7.5% SDS-polyacrylamide gels (SDS-PAGE) and electrophoresed for 1.5 h at 110 V. Proteins were then electro-transferred for 1 h at 300 mA onto a PVDF membrane. Membranes were blocked with 5% milk in TBS containing 0.05% of Tween 20 and incubated overnight with polyclonal antibodies directed against GLUT4 (Millipore 07-1404; dilution 1:250), or SGLT1 (Santa Cruz sc-20584; dilution 1:1000). Equal loading was verified using Ponceau red stain, and by detection of the control protein β-actin (Sigma; diultion 1:8000). Membranes were washed in 0.05% Tween-PBS buffer and incubated with horseradish peroxidase-conjugated secondary antibody (dilution 1:10000). Signals were detected using the enhanced chemiluminescence detection system (Pierce #32106). Immunoblots were scanned using a desktop scanner (Epson Perfection V350) and Adobe Photoshop. Band intensities were determined using Scion Image (Scion Corporation, USA).

IMMUNOSTAINING

Animals were anesthetized (using the same protocol as described previously for ELISA and Western Blot analysis) and euthanized in the fasted (ZT12; n = 4 for each genotype) or in the satiated (ZT22; n = 4 for each genotype) state. Immunofluorescence was performed on fresh frozen brain samples by using a modification of a published method (Julliard and Hartmann, 1998). Brain cryosections were pre-incubated for 15 min with a blocking buffer containing 0.1 M phosphate buffer saline (PBS, pH = 7.4), 3% bovine serum albumin (BSA, Sigma-Aldrich) and 5% normal serum from the host species of the antibodies. The sections were then incubated overnight at 4°C with primary antibodies for SGLT-1 (R-16) (1:100; Santa Cruz Biochemicals, Santa Cruz, CA). A GLUT4 mouse antibody was used which recognizes an epitope in the cytoplasmic portion of GLUT4 [1F8] (1:100 abCam). The sections were washed with 0.1 M PBS/3% BSA and incubated for 1 h at room temperature with anti-rabbit IgG, anti-mouse IgG, or anti-goat secondary antisera coupled to Alexa 488 (1:100), Cy3 (1:200; Jackson Immunoresearch), or Cy5 (1/100; Jackson Immunoresearch) respectively. After the final wash with PBS, slides were mounted with Vectashield mounting medium containing DAPI for nuclear staining (Vector Laboratories). Images were acquired using a Zeiss Apotome epifluorescence microscope equipped with a digital camera and Axiovision software.

BIOPROBE MEASUREMENTS OF EXTRACELLULAR GLUCOSE

Surgical procedure

Eight Zucker fa/+ and eight Zucker fa/fa rats were used during measurements of extracellular glucose in the brain. Rats in the fasted (ZT12, n = 4 for each genotype) or satiated (ZT22, n = 4 for each genotype) state were anesthetized with urethane (1.5 mg/kg, i.p.). The anesthetized animals were placed in a stereotaxic apparatus and kept on a heating pad, and additional doses of urethane were supplied as needed. The surgical procedure consisted of drilling three burr holes into the skull to expose the lateral region of each OB and the somatosensory cortex as a control brain area. The first glucose-oxidase biosensor was implanted into the lateral part of one OB, within or close to the glomerular layer (GL; coordinates: AP +6.5 mm from Bregma, M/L −2.3 mm and D/V −1.0 mm from dura, Figure 2). The second glucose-oxidase biosensor was inserted in the controlateral somatosensory cortex (coordinates: AP −3.5 mm from Bregma, M/L +2.3 and D/V −2.3 mm from dura). A control BSA sensor was implanted in the other OB to measure nonspecific variations in oxidation current (coordinates similar to glucose biosensors implanted in OB, Figure 2). A reference electrode (Ag/AgCl) was placed into neck muscles during in vivo recordings.

FIGURE 2. Localization of glucose-oxidase biosensors (red) and control BSA sensor (green) electrodes in OB (A, C) and somatosensory cortex (B, C). At the OB level (A), two electrodes were implanted symmetrically (coordinates: AP +6.5 mm from Bregma, M/L −2.3 mm and D/V −1.0 mm from dura). A glucose-oxidase biosensor (Gluc, red line) was positioned in one bulb and a control BSA sensor (BSA, green line) in the other bulb. The latter was set measure nonspecific variations in oxidation current. In the somatosensory cortex (B), contralateral to the OB glucose sensor, the second glucose-oxidase biosensor was inserted (coordinates: AP −3.5 mm from Bregma, M/L +2.3 and D/V −2.3 mm from dura). The coronal drawings are from the Paxinos and Watson (2007) atlas, plates 1 and 27 (EPL: external plexiform layer, GCL: granular cell layer, GL: glomerular layer, MCL: mitral cell layer).

Preparation of electrochemical sensors

The glucose biosensor uses glucose oxidase and amperometric detection of hydrogen peroxide (Vasylieva et al., 2011). The tip of the probe was coated with glucose oxidase only on 100–150 μm in length (Vasylieva et al., 2011) to target mainly the GL, which thickness is ranging from 100 to 300 μm. Glucose oxidase metabolizes glucose in the extracellular fluid. An oxidation current is thus generated and measured using Neurolabscope software. Each biosensor was connected to a potentiostat, which sent readings of the current generated by glucose in extracellular fluid to a computer. Glucose biosensors have been shown previously to have a range of 0–10 mM with in vitro sensitivity of 1.6 ± 0.4 nA/mM (mean ± SEM). To confirm the accuracy of the biosensors, prior to implantation and immediately following testing, biosensor probe were placed in 0.1 M PBS, connected to the potentiostat, and readings were allowed to stabilize (generally stable within 15–30 min).

Selectivity and calibration of biosensors

Before the experiments all biosensors were tested for detection of serotonin (5-HT, 20 μM in PBS; Sigma) and H_2O_2 (1 μM in PBS). Only electrodes exhibiting less than 1.2 μA.mM−1 cm−2 response for 5-HT were included in the study. In vitro calibrations were performed in standard PBS (0.01 M, pH 7.4) and solutions were maintained at a temperature of 36.5°C, comparable to the brain of an anesthetized rat (Zhu et al., 2004). The reference electrode (Ag/AgCl) was placed directly in the solution. After a stable baseline reading, the glucose sensors were calibrated using glucose solutions at different concentrations (0; 0.5; 1; 1.25 and 1.5 mM) to establish the nA/mM ratio. The applied voltage for amperometric studies was +500 mV. Biosensors were calibrated before and after each real-time in vivo experiment to ensure the sensitivity remained stable. Quantitative assessments of brain glucose concentrations were obtained by subtracting the non-specific current of the control biosensor (BSA) from the output of the glucose biosensors.

Real-time measurements in vivo

To compare glucose level variations in the OB and the cortex to the peripheral glucose concentrations, 5 μl blood samples were collected from the femoral artery at the beginning of the surgery and then every 10 min thereafter. Glucose readings were performed with a glucose meter (Accu-Chek®  Roche, Mannheim, Allemagne/Performa).

Measurements of extracellular glucose in rat brains started 1 h after the implantation of the electrodes, to allow restoration of the blood-brain barrier. Fasted rats (n = 4 for each genotype) were anesthetized before food intake, and satiated rats (n = 4 for each genotype) after food intake.

Recordings started once the electrodes were implanted, and lasted for approximately 3 h. Currents obtained after the signal stabilization corresponded to the initial steady state of the animal, i.e., fasted or satiated. To evaluate possible fluctuations of central glucose level during dynamic glycemia conditions, an i.p. injection of glucose (Lavoisier, Paris, France 30%; i.p. 3 g/kg) to the fasted rats, or a subcutaneous injection of insulin (Sigma, Saint Quentin-Fallavier, France; 7.5 U/mL; subcutaneous, 25 U/kg) to the satiated rats, were given to measure the effects of acute modifications in peripheral glucose levels on central structures (OB and cortex). 1 h later, rats in the induced hyperglycemic state were injected with insulin and rats in the induced hypoglycemic state were injected with glucose. In 1 of 9 satiated rats glucose was monitored in the OB alone, and three rats received only insulin injection. In 1 of 8 fasted rats, glucose was monitored only in the OB, and three rats received only glucose injection. When recordings ended, rats were euthanized using sodium pentobarbital (i.p. 3 g/kg).

STATISTICAL ANALYSIS

Data are shown as mean values ± SEM. For olfactory detection all percentage measures were transformed using the arcsine square root transformation to normalize the data and stabilize variance (Sokal and Rohlf, 1981). For behavioral data, Western Blot data, and extracellular glucose measurements, statistical analysis were performed by either a Student’s t-test or one- or two-way repeated-measures ANOVA depending on the data set. A Student-Newman-Keuls (SNK) post hoc test was used to complete the analysis when appropriate (Statview software). Physiological parameters (glycemia, plasma and OB insulin level) were analyzed using a non-parametric Mann-Whitney test, or Wilcoxon test when data were paired (Statview Software).

RESULTS

EFFECT OF FOOD RESTRICTION ON BODY WEIGHT OF LEAN AND OBESE ZUCKER RATS

Before any other experiment, fa/+ and fa/fa Zucker rats were gradually habituated to a 20 h/day FR schedule (Figure 1A). In order to determine the effect of FR schedule on weight gain, body weight of the rats was measured, on their arrival, just before the FR beginning and after 3–4 weeks of FR corresponding to the end of the experimental procedures (Figure 3). On their arrival, the rats were 7–8 weeks old and the body weight of the two genotypes was already significantly different (299 ± 7.2 g vs. 344 ± 11.8 g in fa/+ vs. fa/fa respectively; P < 0.001 Mann Whitney test). At the end of the experimental procedures, the difference in the body weight between fa/+ and fa/fa and rats was larger (312 ± 7.1 g vs. 412 ± 7.1 g in fa/+ vs. fa/fa respectively; P < 0.0001 Mann Whitney test). While the fa/+ rats’ body weight remained stable during the FR, (306 ± 5.7 g vs. 312.3 ± 4.4 g before FR vs. after the experimental procedure, P = 0.1, paired t-test) the fa/fa rats’ body weight had continued to increase (372.9 ± 7.4 g vs. 412 ± 7.1 g before FR vs. after the experimental procedure, P < 0.0001 paired t-test).

FIGURE 3. Monitoring of body weight of fa/+ and fa/fa Zucker rats during the experiments. On their arrival, the body weight of the fa/+ and fa/fa Zucker rats (7–8 weeks old), was already significantly different (299 ± 7.2 g vs. 344 ± 11.8 g lean vs. obese respectively; ** P < 0.001). Between their arrival and before food restriction (FR, 10–11 weeks old) the body weight of the two Zucker rats genotype had increased (299 ± 7.2 g to 306 ± 5.7 g for lean; # P < 0.05 and 344 ± 11.8 g to 372.9 ± 7.4 g for obese; ### P < 0.001) and the body weight difference between lean and obese rats was more pronounced (*** P < 0.0001). After the experimental procedure (After Exp) corresponding to 2 weeks of FR, the body weight of fa/+ rats stayed significantly different compared to their body weight measured at their arrival (## P < 0.001) but it was not significantly different compared to those measured before FR (306 ± 5.7 g vs. 312.3 ± 4.4 g; P = 0.1). The fa/fa rats body weight had continued to increase (372.9 ± 7.4 g before FR vs. 412 ± 7.1 g after Exp.; &&& P < 0.0001). Values are shown as medians ± interquartile range, # compared to the body weight at the arrival of each genotype group the statistical analyses were performed using paired t-test, n = 16 per genotype.

EFFECT OF FEEDING STATE ON GLYCEMIA, INSULINEMIA, AND OB INSULIN LEVELS IN LEAN AND OBESE ZUCKER RATS

In order to characterize the physiological hallmarks of the two genotypes of Zucker rats and to evaluate the physiological effects of the feeding state, glycemia, as well as plasma and OB insulin levels were measured in both 20 h fasted and satiated fa/+ and fa/fa Zucker rats (Table 1). A two-way ANOVA with feeding state and genotype as factors revealed a significant effect of these two factors on all three measurements; glycemia (F(1,20) = 8.4, P < 0.01 and F(1,20) = 5.0, P < 0.05 respectively), plasma (F(1,20) = 14.4, P < 0.005 and F(1,20) = 247.3, P < 0.0001 respectively) and OB insulin levels (F(1,20) = 38.7, P < 0.0001 and F(1,20) = 320.7, P < 0.0001 respectively). For each genotype, the three measurements were significantly higher in satiated than in fasted rats (Table 1: Mann-Whitney tests).

Table 1. The effects of feeding state on glycemia, plasmatic and OB insulin levels in lean fa/+ and obese fa/fa Zucker rats.

By comparing the two genotypes, obese fa/fa rats were moderately hyperglycemic (only in satiated state), compared to lean fa/+ rats and their insulin levels (in plasma and in OB) were higher than in fa/+ regardless of the feeding state (Table 1: Mann-Whitney tests). The difference in insulin concentration between the two rat strains was smaller in the OB (3–3.87 fold more concentrated in satiated and fasted states, respectively) than in the plasma (5.5–10 fold more concentrated in satiated and fasted states, respectively).

EFFECT OF FEEDING STATE AND OF GENOTYPE ON OLFACTORY DETECTION

In order to compare the olfactory detection abilities of fa/+ and fa/fa rats in fasted and satiated states, a behavioral test based on COA was performed and olfactory detection indexes were measured for ISO dilutions ranging from 10−11 to 10−7 (Figures 4A,B). During the olfactory detection test (Figure 1B, from day 4 to day 8, D4 to D8), a two-way ANOVA with feeding state and odor as factors revealed significant effects of feeding state (fa/+ F(1,88) = 15.8, P < 0.01; fa/fa F(1,88) = 30.4, P < 0.0001) and odor (fa/+ F(4,88) = 27.1, P < 0.0001; fa/fa F(4,88) = 15.8, P < 0.0001) on olfactory detection indexes. Post hoc analyses (paired t-test) revealed that fa/+ rats have higher olfactory detection indexes in the fasted state compared to the satiated state, for ISO 10−8 (fasted: 62.6 ± 6.8%, satiated: 19.5 ± 3.9%, P < 0.0001) and ISO 10−7 (fasted: 95.3 ± 1.7%, satiated: 55.6 ± 9.0%, P < 0.0005). This effect was even more pronounced in fa/fa rats, which have higher olfactory detection indexes in the fasted state compared to the satiated state for a concentration as low as ISO 10−9 (fasted: 51.3 ± 9.0%; satiated: 17.0 ± 4.0%, P < 0.01) but also for ISO 10−8 (fasted: 58.2 ± 7.4%, satiated: 12.9 ± 3.2%, P < 0.0001) and ISO 10−7 (fasted: 89.8 ± 5.6%, satiated: 61.2 ± 9.2%, P < 0.005).

FIGURE 4. Modulation of olfactory abilities of fa/+ and fa/fa Zucker rats by feeding state. Each animal was successively tested in fasted (Fast., in blue) and satiated (Sat., in red) states. (A, B, D, E, F) Values are shown as medians ± interquartile range, n = 6 per group (satiated and fasted rats); per rat genotype (fa/+ and fa/fa rats); per ISO dilution. (A, B) Box graphs showing the olfactory detection index corresponding to the percentage of licks at the pure water tube normalized to the total number of licks in the experimental cage when rats were given the choice between pure water and odorized water at ISO 10−11, ISO 10−10, ISO 10−9, ISO 10−8, ISO 10−7. The mean olfactory detection indexes were significantly higher for fasted fa/+ rats compared to satiated fa/+ rats for ISO 10−8 and ISO 10−7. The mean olfactory detection indexes were significantly higher for fasted fa/fa rats compared to satiated fa/fa rats for ISO 10−9, ISO 10−8 and ISO 10−7. (C) Aversion test and aversion re-test. Bar graphs showing the olfactory detection indexes of fa/+ (gray bars) and fa/fa (white bars) rats for the aversion test (Avers. Test, D3) and re-test (Avers. re-test D9). The olfactory detection indexes for fa/+ and fa/fa rats were not significantly different and close to 100% before (at day 3) and after (at day 9) the olfactory detection test. (D–F) Box graphs showing the number of licks during the first ISO consumption in the experimental device. (D) In fasted state (Fast.), the mean number of licks during the first ISO consumption was significantly lower for fa/fa rats compared to fa/+ rats for ISO 10−11 and ISO 10−8. (E) In satiated state (Sat.), the mean number of licks during the first ISO consumption was significantly lower in fa/fa rats compared to fa/+rats for ISO 10−11 and ISO 10−9. (F) The mean number of licks during the first ISO consumption was significantly lower for fa/fa rats compared to fa/+ rats for ISO 10−11, ISO 10−10 and ISO 10−9 regardless to feeding state.

To ensure that the COA was robust and maintained throughout the olfactory detection test, the olfactory detection indexes were measured at ISO 10−5 for all the animals, before (Figure 1B, Aversion test, on D3) and after (Aversion re-test, on D9) the olfactory detection test. During the aversion test (D3), carried out after the conditioning, the animals licked almost exclusively from the pure-water tube, as demonstrated by olfactory indexes close to 100% (Figure 4C, fasted fa/+: 99.97 ± 0.03%, satiated fa/+: 99.98 ± 0.02%, fasted fa/fa: 99.85 ± 0.15%, satiated fa/fa: 99.80 ± 0.10%) showing that the COA was well established at the beginning of the olfactory detection test. On the last day of the behavioral experiment (Aversion re-test, D9), all the animals drank, again, almost exclusively at the pure-water tube (fasted fa/+: 99.90 ± 0.06%, satiated fa/+: 99.38 ± 0.23%, fasted fa/fa: 98.01 ± 1.80%, satiated fa/fa: 98.05 ± 0.90%), indicating that COA was maintained throughout the behavioral experiment.

In order to further compare the olfactory detection abilities of fa/+ and fa/fa rats, the number of licks were measured during the first consumption of each given ISO concentration in the experimental device (Figures 4D–F). This measurement shows the number of licks that were necessary for the animal to detect the aversive ISO odor diluted in the drinking solution. One should expect that the better the animal can detect the ISO, the lower the number of licks during the first consumption would be. A three-way ANOVA with genotype, feeding state and odor as factors revealed significant effects of these three factors on the number of licks during the first ISO consumption (genotype: F(1,132) = 4.3, P < 0.05, feeding state: F(1,132) = 15.5, P < 0.0005, odor: F(4,132) = 7.1, P < 0.0001). Fasted animals required fewer licks than satiated animals to detect and leave the aversive solution (post hoc analysis SNK), further demonstrating that animals have better olfactory sensitivity when fasted than satiated. In fasted states (Figure 4D), fa/fa rats required fewer licks than fa/+ rats to detect and leave the aversive solution at ISO 10−11 (fa/fa: 33.89 ± 7.6, fa/+: 220.3 ± 78.0, P < 0.05 t-test), and at ISO 10−8 (fa/fa 15.67.5 ± 7.3, fa/+: 80.17 ± 29.2, P < 0.05 t-test). In satiated state (Figure 4E) fa/fa leaved the aversive solution faster than fa/+ rats at ISO 10−10 (fa/fa: 138.25 ± 21.2, fa/+: 247.67 ± 44.3, P < 0.05 t-test) and also at ISO 10−9 (fa/fa: 142.41 ± 35.2, fa/+: 275.33 ± 40.0, P < 0.05 t-test). In addition when data obtained in fasted and in satiated states were pooled (Figure 4F), the lick number during the first burst was significantly lower for fa/fa rats compared to their lean counterparts at ISO 10−11, ISO 10−8 and ISO 10−9 (P < 0.001, P < 0.05, P < 0.05 respectively, t-test). Neither genotype (fa/+ vs. fa/fa for each feeding state: t-test) nor feeding state (fasted vs. satiated for each genotype: paired t-test) effect was observed on the lick number of the first burst at the pure water tube during the habituation period observed (P > 0.05, fasted rats: fa/+ 76.16 ± 21.5, fa/fa 55.16 ± 6.11; satiated rats: fa/+ 110.8 ± 17.1, fa/fa 78.25 ± 11.1). Altogether, these results indicated that fa/fa rats have a better olfactory sensitivity than fa/+ rats.

EFFECTS OF FEEDING STATE ON EXPRESSION OF GLUT4 AND SGLT1 IN OB OF THE TWO GENOTYPES

In order to analyze the localization of SGLT1 and GLUT4 within the different OB layers in Zucker fa/fa and fa/+ rats, an immunofluorescence experiment was performed. SGLT1 and GLUT4 were found in different layers of the OB. In both lean and obese Zucker rats, immunostaining of SGLT1, a transporter of non-metabolized glucose, revealed a layer-specific pattern (Figures 5A,B). The highest staining was observed in the inner part of EPL (iEPL) with a stronger expression in fa/fa rats (Figure 5A). In the two genotypes, the outer part of the EPL (oEPL) was unlabeled (Figures 5A,B). SGLT1 immunostaining was also detected in some glomeruli more particularly in fa/fa Zucker rats. In both lean and obese Zucker rats, the highest level of GLUT4 immunostaining was detected in the glomerular layer (GL) and in the mitral cell layer (MCL; Figures 5C,D) while the nerve layer appeared unstained. Within the glomerular neuropil, GLUT4 staining varied from strongly to unlabeled glomeruli. The pictures shown in the manuscript are representative of SGLT1 and GLUT4 immunostaining in the OB of both rat genotypes and were consistent across rats within each group. We could not see any difference in the localization of GLUT4 and SGLT1 in fasted rats compared to satiated rats, for both genotypes (data not shown).

FIGURE 5. SGLT1 and GLUT4 localization in OB layers of fa/+ and fa/fa Zucker rats. Representative SGLT1 (A,B) and GLUT4 (C,D) immunolocalization among OB layers of fa/fa (A,C) and fa/+ (B,D) Zucker rats merged with the nuclear marker DAPI. In fa/fa (A) and fa/+ (B) Zucker rats, SGLT1 was mainly found in the inner part of the EPL (iEPL) and the overall expression of SGLT1 is stronger in Zucker fa/fa rats. The outer part (oEPL) was unlabeled. In the GL, some (#) but not all glomeruli express SGLT1. The number of SGLT1-positive glomeruli is higher in Zucker fa/fa rats compared to Zucker fa/+ rats. Very slight SGLT1 immunostaining was also found in the GCL. In fa/fa (C) and fa/+ (D) Zucker rats, no difference of immunolocalization of GLUT4 was observed. GLUT4 was located in the glomerular layer. Glomeruli showed different level of GLUT4 expression (strongly labeled: #, not labeled: *). Some mitral cells (arrowhead) were intensely immunostained (EPL: external plexiform layer, GCL: granular cell layer, GL: glomerular layer, MCL: mitral cell layer).

The effects of feeding state and genotype on GLUT4 and SGLT1 protein levels in the OB were analyzed by Western blotting (Figure 6). Total tissue GLUT4 and SGLT1 were analyzed using the total protein recovery from the OBs of four satiated and four fasted rats of each genotype (Figure 6A). A two-way ANOVA on SGLT1 protein levels (Figure 6B), with feeding state and genotype as factors, revealed no effect of feeding state, but a significant effect of genotype (F(1,12) = 14.87, P < 0.005). Post hoc analyses (SNK) of these data confirmed a higher expression of SGLT1 in fa/fa Zucker rats compared to fa/+ rats. This higher expression is observed in the satiated state only, as revealed by Mann-Whitney test (P < 0.05). Neither feeding state nor genotype had an effect on GLUT4 levels (Figure 6C) (F(1,12) = 0.54, P = 0.47 and F(1,12) = 1.146, P = 0.3, respectively). Together these results indicated that SGLT1 protein levels were significantly increased in the OB of Zucker fa/fa rats compared to fa/+ rats.

FIGURE 6. Western blot analysis of SGLT1 and GLUT4 in OB homogenates from fasted (Fast.) or satiated (Sat.) fa/fa and fa/+ Zucker rats. (A) A representative blot. The intensity of each band was measured by densitometry and normalized to the housekeeping protein actin, as a loading control. (B) SGLT1 levels were significantly greater in satiated fa/fa rats compared to satiated fa/+ rats. (C) GLUT4 levels were not significantly different significantly regardless of the rat genotype and the feeding state. * P < 0.05, mean ± S.E.M., n = 4/feeding state/genotype.

REAL TIME IN VIVO MONITORING OF EXTRACELLULAR FLUID GLUCOSE IN OB AND CORTEX OF LEAN AND OBESE ZUCKER RATS

During steady states

Extracellular fluid glucose concentration ([Gluc]ECF) was measured simultaneously in the OB glomerular layer and the somatosensory cortex of lean or obese Zucker rats, in fasted or satiated state. 2 weeks prior to the experimental study, animals were habituated to a 2 h feeding/22 h starvation schedule. At the beginning of the experiment, the metabolic status of the rats was considered to be steady, since satiety or hunger had been maintained for several hours. Glycemia was monitored to check each metabolic (fasted or satiated) steady state. As shown in Table 1, glycemia was significantly higher in satiated than in fasted state and in fa/fa than in fa/+ rats. The effects of feeding state and genotype on [Gluc]ECF in OB and cortex were analyzed by using a three-way ANOVA with brain areas (OB and cortex), feeding state and genotype as factors. A significant effect on [Gluc]ECF was observed during the initial steady state for factors “brain areas” and “genotype” (F(1,24) = 13.6, P < 0.005; F(1,24) = 21.6, P < 0.0001 respectively, Figure 7). [Gluc]ECF was significantly higher in the OB than in the cortex, and in fa/fa than fa/+ rats (SNK post hoc tests). No significant effect of feeding state was observed on [Gluc]ECF despite differences in plasma glucose levels between satiated and fasted rats (Table 1). For each genotype, [Gluc]ECF was significantly higher in OB compared to cortex (Wilcoxon test, * P < 0.05 for each rat genotype; in mM: Fasted: fa/+ in the OB, 1.48 ± 0.35, in the cortex, 0.92 ± 0.33; fa/fa OB, 3.05 ± 0.5, cortex, 1.67 ± 0.23; Satiated: fa/+ OB, 1.48 ± 0.25; cortex, 0.55 ± 0.09; fa/fa OB, 2.35 ± 0.5; cortex 1.5 ± 0.15, Figure 7). In both OB and cortex [Gluc]ECF were higher in fa/fa compared to fa/+ (Mann Whitney test, € P < 0.05 for each area brain, Figure 7).

FIGURE 7. Genotype effect on the [Gluc]ECF at steady state in OB and cortex. [Gluc]ECF in OB (full bars) and in cortex (Ctx, hatched bars) of lean fa/+ (left) and obese fa/fa (right) in fasted (Fast., blue) and satiated (Sat., red) sates. A three-way ANOVA with brain areas (OB and cortex), feeding state and genotype as factors showed a significant effect of brain areas (not shown) and genotype (not shown). Both in OB and Ctx, [Gluc]ECF were higher in fa/fa compared to fa/+ (Mann Whitney tests, € P < 0.05 for each area brain). For each genotype, [Gluc]ECF was significantly higher in OB compared to Ctx (not shown). Values are expressed as the mean ± SEM, n = 4/feeding state/genotype.

During dynamic states following insulin and glucose injections

Real time in vivo measurements of [Gluc]ECF were recorded simultaneously from the OB and the somatosensory cortex of fa/+ and fa/fa Zucker rats either fasted (Figures 8A,C respectively) or satiated (Figures 8B,D respectively). For each genotype, the two groups of rats (fasted and satiated) received two injections: one of glucose (G-Inj) and one of insulin (I-Ins). Fasted rats (Figures 8A,C) first received a glucose injection (G-Inj1) followed by an insulin injection (I-Inj2). This order was reversed for satiated rats (Figures 8B,D, I-Inj1, G-Inj2). Glucose and insulin injections induced substantial fluctuations in extracellular glucose levels, especially in the OB of fa/+ rats. In fa/fa compared to fa/+ rats, glucose injection (G-Inj) induced less pronounced variations of [Gluc]ECF. and insulin injection (I-Inj) induced a slower decrease of [Gluc]ECF. For each feeding state (satiated and fasted) a mixed ANOVA was performed with an intra-subject factor, corresponding to the three conditions (steady-state, Inj1 and Inj2) and two inter-subject factors: genotype (lean and obese) and brain area (OB and Cortex). Both in fasted (Figure 8E) and satiated (Figure 8F) rats, the statistical analysis showed a significant difference between conditions (F(2,24) = 26.66, P < 0.0001; F(2,24) = 29.28, P < 0.0001, respectively), genotype (F(1,24) = 8.35, # P < 0.01 Figure 8E; F(1,24) = 22.48, ### P < 0.0005, respectively Figure 8F) and brain areas (F(1,24) = 11.91, P < 0.005; F(1,24) = 5.56, P < 0.05, respectively). In each feeding state (satiated and fasted), [Gluc]ECF was higher in fa/fa than in fa/+ rats, and in the OB than in the cortex (SNK post hoc tests). Moreover G-Inj increased [Gluc]ECF significantly above steady-state and I-Inj. levels, and I-Inj decreased [Gluc]ECF significantly below steady-state and G-Inj levels (SNK post hoc tests).

FIGURE 8. Genotype effect on the dynamic response of [Gluc]ECF in OB and cortex to glucose and insulin peripheral injections. Composite figures compiled from in vivo real time recordings of [Gluc]ECF, performed simultaneously in OB (full line) and cortex (Ctx, dotted line) of lean fa/+ (A, B) and obese fa/fa rats (C, D) in fasted (A, C: Fast., blue) and satiated (B, D Sat., red) states. In fasted rats, glucose was injected in first (G. Inj1), and insulin in second (I. Inj2). The order of injections was reversed in satiated rats (I-Inj1, G-Inj2). For fasted (E) and satiated (F) rats, mixed ANOVA revealed a significant effect of genotype (in fasted rat # P < 0.01; in satiated rats ### P < 0.0005), conditions (not shown) and brain areas (not shown). SS: steady State; G: glucose; I: insulin.

Next, we calculated the difference of [Gluc]ECF measurements between steady state and after the first injection (G-Inj1 or I-Inj1). This measurement corresponds to the fluctuations of [Gluc]ECF shown in Figure 9. In each group (satiated and fasted rats) a two way ANOVA was performed with brain areas (OB and cortex) and genotype (fa/+ and fa/fa) as factors. The ANOVA demonstrated a significant effect of brain areas in fasted (F(1,12) = 7.03, P < 0.01) and satiated (F(1,12) = 9.81, P < 0.01) rats. Greatest [Gluc]ECF fluctuations were observed in the OB compared to the cortex (SNK post hoc tests). The ANOVA also demonstrated a significant difference on [Gluc]ECF fluctuation between fa/+ and fa/fa rats, but only in fasted rats (Figure 9, F(1,12) = 5.1, # P < 0.05), the fluctuations being greatest in fa/+ than in fa/fa rats. Further analysis demonstrated that this was due to fluctuations in the OB, because glucose injections had induced smallest fluctuations of [Gluc]ECF in the OB of fa/fa rats than of fa/+ (Mann Whitney test ϕ P < 0.05). In satiated fa/+ rats only, insulin injection, had induced a greater [Gluc]ECF fluctuation in the OB than in the cortex (Figure 9, Mann Whitney tests * P < 0.05).

FIGURE 9. Genotype effect on [Gluc]ECF fluctuations induced by the first injection of either Glucose or Insulin in OB and cortex. Bars represent the difference calculated between [Gluc]ECF measured at steady state and after the first injection of glucose (blue) or insulin (red). [Gluc]ECF fluctuations were observed in OB (full bar) and cortex (Ctx, dotted bar) of lean fa/+ (left) and obese fa/fa rats (right). When glucose was injected in first in fasted rats (Fast.), a significant effect of the genotype on [Gluc]ECF fluctuation was observed (ANOVA test, # P < 0.05; NS P > 0.05), [Gluc]ECF fluctuation being smaller in the OB of fa/fa compared to fa/+ rats (Mann Whitney, ϕ P < 0.05). No difference was observed in the Ctx. When insulin was injected in first in satiated rats (Sat.), only in fa/+ rats [Gluc]ECF fluctuation was significantly higher in OB than in cortex (Mann Whitney, * P < 0.05; ns P > 0.05).

DISCUSSION

The present study provides new information on the impact of a metabolic disorder on olfactory abilities and glucose-sensing in the olfactory system. Indeed, obese fa/fa Zucker rats, displayed a higher olfactory sensitivity than lean fa/+ rats, regardless of their feeding status (fasted or satiated). In addition, the expression of glucose-sensing markers and glucose fluctuations in the OB were strikingly different between the two rat genotypes. First, SGLT1 expression was higher in the OB of obese rats compared with lean rats; while GLUT4 levels were similar for the two rat genotypes. Second, [Gluc]ECF measured at steady states (fasted or satiated) was higher in fa/fa rats than in fa/+ rats, with a consistently higher concentration measured in the OB compared to the cortex. Third, OB [Gluc]ECF was differentially affected in fa/fa and fa/+ rats when glycemia was dynamically modified by peripheral injections of glucose or insulin. Indeed, after glucose injection (acute hyperglycemia), obese rats showed a slight increase of OB [Gluc]ECF (≈0.2 mM), whereas lean rats demonstrated a much higher increase (0.9 mM). Insulin injection (acute hypoglycemia) decreased [Gluc]ECF similarly in the OB of the two rat strains. Differences observed in olfactory detection between obese and lean rats will be discussed in link with the modulation of glucose-sensing and of food intake.

OBESE fa/fa ZUCKER RATS HAVE HIGHER OB INSULIN CONCENTRATION AND HIGHER [GLUC]ECF THAN LEAN fa/+ RATS

In addition to peripheral hyperinsulinemia and hyperglycemia, obese Zucker fa/fa rats displayed higher OB insulin levels than their lean counterparts. This is consistent with previous studies that reported that insulin level is highly increased in the cerebrospinal fluid of fa/fa compared to fa/+ Zucker rats (Stein et al., 1983; Figlewicz et al., 1985). These results provide evidence that brain insulin is derived from circulating insulin and suggest that obese Zucker rats do not have any defect of insulin transport through the blood brain barrier (BBB). Although some early contradictory studies suggested that insulin binding was altered in the brain of obese fa/fa Zucker rats (Melnyk, 1987; Wilcox et al., 1989), subsequent reports convincingly demonstrated that insulin receptors (IRs) had similar expression, number, distribution, binding affinity and tyrosine kinase activity in the brain of lean and obese Zucker rats (Livingston et al., 1993; Amessou et al., 2010). Interestingly, rats carrying at least one copy of the fa allele (fa/fa and fa/+) present lower insulin concentration (Baskin et al., 1985) and lower insulin binding (Figlewicz et al., 1985) than non-mutated +/+ rats. Therefore, these deficits are independent of a severe obese phenotype because they are found in obese fa/fa and lean fa/+ rats. Altogether, these data indicate that changes in insulin signaling are unlikely to account for the differences observed in the present study.

In both OB and cortex, [Gluc]ECF was found to be higher in fa/fa than in fa/+ Zucker rats. This is consistent with the higher brain glucose uptake observed in obese rats compared to lean controls (Liistro et al., 2010). It is interesting to note that a higher central glucose availability is associated with a lower local cerebral glucose utilization (Doyle et al., 1993), especially in brain areas implicated in the neuroendocrine regulation of food intake and in odor-taste perception, such as the hypothalamus and the OB (Marfaing-Jallat et al., 1992). Moreover, in both rat genotypes, [Gluc]ECF at steady states was found to be higher in the OB than in the cortex and this is consistent with the compartmentalization of [Gluc]ECF according to the brain area studied and to the level of neural activity (McNay and Gold, 1999; McNay et al., 2001). Work from the group of Magistretti has demonstrated that neuronal activity and glucose metabolism are tied together (Magistretti et al., 1999). Indeed, OB presents a higher functional activity than the somatosensory cortex (Yang et al., 1998). Moreover, the OB glomerular layer not only has remarkably high glucose consumption rates (Nawroth et al., 2007) but also possesses very high capillary network density (Chaigneau et al., 2007) combined with a distinct microvasculature (Yang et al., 1998) and a high permeability of the BBB (Ueno et al., 1996). Together these hallmarks suggest that OB not only elicits high-energy demands but could also be a glucose-sensing brain area.

OBESE fa/fa ZUCKER RATS HAVE A HIGHER EXPRESSION OF GLUCOSE-SENSING MOLECULAR MARKERS IN THE OB THAN LEAN fa/+ RATS

In the OB of both rat genotypes, the molecular markers of glucose-sensing GLUT4 and SGLT1 were detected in different layers of the OB. GLUT4 immunostaining was observed in glomeruli and in some mitral cells, while SGLT1 was mainly observed in the iEPL. This distribution is similar to that shown by in situ hybridization in Allen Brain Atlas (Allen Institute for Brain Science, 2010). This distinct regional location of GLUT4 and SGLT1 support the idea that these two families of glucose transporters play different roles in neuronal network processing as it has been previously reported in other brain areas (Yu et al., 2010, 2013).

Obesity did not modulate GLUT4 expression in the OB, since obese fa/fa and lean fa/+ rats presented similar GLUT4 localization and level of expression. However, obesity changed SGLT1 expression, which was higher in satiated fa/fa than in satiated fa/+. Concerning GLUT4, our results are in agreement with previous work performed on hyperinsulinemic-hyperglycemic db/db mice, which showed that GLUT4 protein levels were unchanged in the cortex and the OB (Vannucci et al., 1998). Accordingly, GLUT4 mRNA levels did not change in the cortex of hyperinsulinemic fa/fa Zucker rats (Alquier et al., 2006). Winocur and collaborators suggested that the stability of GLUT4 expression is related to the insulin resistance of fa/fa Zucker rats (Winocur et al., 2005), because GLUT4 translocation to the plasma membrane can no longer be triggered by insulin signaling as it is in a normal metabolic context (McEwen and Reagan, 2004). Indeed, in the hippocampus of Zucker rats, GLUT4 association to the plasma membrane was significantly reduced although total GLUT4 protein expression was not affected (Winocur et al., 2005). In order to confirm this hypothesis, it will be interesting to further study and compare GLUT4 subcellular localization in the OB of the two genotypes of Zucker rats. Concerning SGLT1, the up-regulation observed in the OB of fa/fa rats is consistent with previous studies performed on peripheral tissues in obese, hyperinsulinemic and diabetic rodent models as well in human (Morton and Hanson, 1984; Ferraris and Vinnakota, 1995; Dyer et al., 2002; Osswald et al., 2005; Tabatabai et al., 2009). In central nervous system, during pathological conditions such as ischemia or epileptic seizure which induce an over-consumption of glucose (Poppe et al., 1997; Elfeber et al., 2004) SGLT1 was up-regulated (Yu et al., 2010, 2013). Up-regulation of SGLT1 is proposed to compensate for impairment in GLUTs function (Yu et al., 2013). Thus, in the context of insulin resistance, the deficiency in GLUT4 translocation could explain the SGLT1 up-regulation.

FASTED RATS HAVE A BETTER OLFACTORY SENSITIVITY THAN SATIATED RATS, REGARDLESS OF THE GENOTYPE

Olfactory sensitivity of both fa/+ and fa/fa Zucker rats is modulated by the feeding states. They have lower olfactory detection and detect the aversive odorant more quickly in the fasted state than in the satiated state, as we previously observed with normal-weight Wistar rats (Aimé et al., 2007). Olfactory sensitivity is modulated by a number of peripheral and central signals involved in the regulation of energy balance such as leptin, orexin A, ghrelin and insulin (Julliard et al., 2007; Tong et al., 2011; Aimé et al., 2012; Palouzier-Paulignan et al., 2012). The OB is the target of orexinergic fibers originating from the lateral hypothalamic nucleus (Peyron et al., 1998). In addition, the olfactory system expresses, among others, receptors for orexin A, ghrelin, leptin, insulin, NPY and CCK (for review, see Palouzier-Paulignan et al., 2012). Our results suggest that although many of these signals are altered in the obese Zucker fa/fa rats, the redundancy of orexigenic and anorectic molecules acting on the olfactory system can ultimately maintain the modulation of olfactory sensitivity by the feeding state. For instance, we found that the OB insulin content in both fa/fa and fa/+ Zucker was modulated by the feeding states, with satiated animals showing a higher OB insulin level than fasted animals. Interestingly, we have previously demonstrated in Wistar rats that such fluctuations of OB insulin levels are sufficient to decrease the olfactory sensitivity of fasted animals to the level of satiated ones (Aimé et al., 2012). Changes of insulin levels in OB and in plasma were correlated confirming that the OB is highly sensitive to fluctuations in circulating insulin levels. This result is consistent with the general agreement that OB is the brain region containing the highest level of IRs (Hill et al., 1986; Unger et al., 1989; Marks et al., 1990). This receptor allows pancreatic insulin to enter the brain across brain capillaries (Schwartz et al., 1990; Banks et al., 1997; Woods et al., 2003; Banks, 2004). At the entire brain level, the rate of insulin entrance is regulated by several physiological factors, including the feeding state (Woods et al., 2003; Banks, 2004). When animals are fasted, the ability of insulin to cross the BBB is reduced, leading to a positive correlation between blood and cerebrospinal fluid insulin levels (Strubbe et al., 1988). Although one report suggested that OB insulin binding is modulated by the feeding state and reduced by chronic fasting (Marks and Eastman, 1989), we have recently shown that the OB IR expression is not modulated by the feeding state (Aimé et al., 2012). Consistent with the latter, nutrient availability does not modulate IR levels in brain regions involved in energy homeostasis regulation (Bowlby et al., 1997). Together, these data indicate that insulin signaling in the OB is dependent on the fluctuations of peripheral insulin levels, and participate in the modulation of synaptic transmission of odor-related stimuli by the feeding states.

Interestingly, both genotypes showed similar OB [Gluc]ECF in steady fasted or satiated states although glycemia was much higher in the satiated state. This is consistent with our previous observation in Wistar rats (data not shown) and could be due to adjustments of the glucose transport capacity at the BBB in response to brain metabolic rate and glucose availability (for review see Bradbury, 1993; Leybaert, 2005; Banks, 2006). Three mechanisms have been proposed to explain the modulation of glucose transport across the BBB (see for review Leybaert, 2005) an increase in: (i) the number of GLUT1; (ii) the intrinsic activity of the GLUT1; and/or (iii) the glucose concentration gradient over the barrier, thereby stimulating the driving force for glucose movement. It is possible that in metabolic disorders, these three mechanisms are not altered all together or at the same time. In the present study, the OB [Gluc]ECF stability observed in fa/fa rats could be the result of the modulation of either GLUT1 expression or intrinsic activity at the BBB. In future experiments, it will be interesting to study more precisely these different mechanisms of glucose transport through the BBB during the different steps of feeding state in the OB of fa/fa and fa/+ Zucker rats.

OBESE fa/fa ZUCKER RATS HAVE A HIGHER OLFACTORY SENSITIVITY THAN LEAN fa/+ RATS

In the present study, Zucker fa/fa rats demonstrated a better olfactory sensitivity than Zucker fa/+ rats. This finding is supported by several recent studies. Thanos and collaborators demonstrated that fa/fa Zucker rats displayed altered brain metabolic responses to food olfactory stimuli in several brain regions (Thanos et al., 2008). The same group also demonstrated that olfactory cues for a high-fat food stimulus elicit heightened behavioral responses in the obese Zucker fa/fa rat compared to lean Zucker fa/+ rats (Thanos et al., 2013). Interestingly, obese ob/ob mice (unable to produce leptin) and db/db mice (carrying a random mutation in the leptin receptor gene) are able to locate a hidden food reward based on olfactory cues, much faster than control mice. Several metabolic dysfunctions could explain the heightened olfactory perception of obese rats. As previously proposed, leptin is likely to mediate these responses (Thanos et al., 2013). Indeed, fa/fa rats are insensitive to leptin, an anorectic signal known to decrease olfactory sensitivity (Julliard et al., 2007; Aimé et al., 2012). The fa mutation of the Zucker rat (fa/fa) affects the leptin receptor gene and prevents the long form of the receptor (Ob-Rb) from being expressed (Chua et al., 1996). The Ob-Rb form of the receptor is expressed in the brain (Mercer et al., 1996; Fei et al., 1997) and is present at different levels of the olfactory system, including the OB (Shioda et al., 1998; Getchell et al., 2006; Baly et al., 2007; Prud’homme et al., 2009). Previous reports have demonstrated that leptin is able to modulate olfactory behavior as well as neuronal activity (Getchell et al., 2006; Julliard et al., 2007; Prud’homme et al., 2009; Savigner et al., 2009). Indeed, ICV administration of leptin decreases the expression of the activation marker c-fos in mitral and granular OB neurons (Prud’homme et al., 2009). Consistent with these studies, in a previous report, we found that ICV administration of leptin decreases the olfactory sensitivity of fasted animals to the level of satiated ones (Julliard et al., 2007). Here, we demonstrated that Zucker fa/fa rat, expressing a non-functional form of the leptin receptor, display a better olfactory sensitivity than the Zucker fa/+ rats, consistent with the aforementioned reports showing that leptin is responsible for a decrease in olfactory abilities.

Other metabolic dysfunctions could contribute to increase the olfactory perception of obese rats. The level of orexigenic peptides such as neuropeptide Y, ghrelin and orexin A is upregulated in the hypothalamus of obese Zucker rats (Beck et al., 1990, 2003; Sanacora et al., 1990; Mondal et al., 1999) and ghrelin and orexin A are known to increase olfactory sensitivity (Julliard et al., 2007; Tong et al., 2011). Zucker fa/fa rats are also insensitive to the anorectic action of CCK (McLaughlin and Baile, 1980); and the expression level of proopiomelanocortin (POMC), the precursor of the anorectic neuroptide αMSH, is decreased in the arcuate nucleus of fa/fa rats (Yamamoto et al., 2002). Although peripheral insulin injection induced similar fluctuations of OB [Gluc]ECF in fa/fa and fa/+ Zucker rats, peripheral glucose injection affected OB [Gluc]ECF differentially in lean and obese Zucker rats. A peripheral glucose injection induced a greater increase of OB [Gluc]ECF in the OB of Zucker fa/+ rats, compared to Zucker fa/fa rats. This result is supported by a recent report that found no significant difference of brain glucose levels in obese Zucker rats after glucose injection compared to fasting brain glucose levels (Liistro et al., 2010). The brain of obese animals is chronically overexposed to glucose (even during fasting), as a consequence, it seems no more able to respond to physiologic glucose fluctuations. Importantly, the alteration of the molecular markers of glucose-sensing (up-regulation of SGLT1 expression and a probable alteration of GLUT4 trafficking) could also participate to the differences of olfactory detection observed between the two genotypes. Indeed, both SGLT1 and GLUT4 have been shown to be involved in neuronal glucose-sensing (McEwen and Reagan, 2004; O’Malley et al., 2006; Yu et al., 2013). This sensitivity for glucose is observed in specialized neurons located in numerous brain regions involved directly or indirectly in homeostasis control (for review see Routh, 2010; Levin et al., 2011), including the OB (Tucker et al., 2010, 2013). In the OB, the mitral cells have recently been demonstrated to be glucose sensitive (Tucker et al., 2010, 2013) therefore glucose signaling could participate to the modulation of the olfactory sensitivity. Glucose-sensing neurons adapt their mean firing rate to the local fluctuations of extracellular glucose levels (González et al., 2008; McCrimmon, 2008). It is well-known that glucose-sensing is altered in the hypothalamus of obese animals (Rowe et al., 1996; Spanswick et al., 1997, 2000), and alteration of glucose-sensing in the olfactory system could play a role in the modulation of olfactory sensitivity observed in fa/fa rats. Altogether, Zucker fa/fa rats overexpress orexigenic peptides known to increase olfactory sensitivity (Julliard et al., 2007; Tong et al., 2011) and are insensitive to anorectic signals known to decrease olfactory sensitivity (Julliard et al., 2007; Aimé et al., 2012) and they present an altered glucose-sensing in the OB. This imbalance in nutrient sensing, in orexigenic and anorectic signals is likely to account for the increase of olfactory sensitivity demonstrated here by the Zucker fa/fa rats. However in the present study, it remains difficult to decipher the respective role of obesity and of glucose-sensing alteration related to hyperglycemia on olfactory sensitivity. In order to go further, it will be intriguing to study olfactory sensitivity and expression of glucose-sensing genes on female Zucker rats because numerous gender-related metabolic differences exist in peripheral (Clark et al., 1983; Corsetti et al., 2000; Gustavsson et al., 2011) as well as in central tissues (Bogacka et al., 2004). Indeed, females develop obesity and insulin resistance but remain normoglycemic (Clark et al., 1983; Corsetti et al., 2000) and express differently glucose-sensing genes in hypothalamus compared to males (Bogacka et al., 2004).

CONCLUSION

The whole of these data indicate that obese Zucker fa/fa rats demonstrate marked glucose intolerance and an alteration in the expression of glucose-sensing markers in the OB. These newly found impairments, along with the well-described multiple metabolic dysfunctions of obese Zucker fa/fa rats modulate the processing of olfactory information and contribute to increase the olfactory sensitivity. Ultimately, changes in olfactory sensitivity participate in the hyperphagia and food-related disorder characteristic of the Zucker fa/fa rat.

AUTHORS CONTRIBUTIONS

All experiments were performed in the Centre de Recherche en Neurosciences de Lyon (CRNL). Inserm U1028-CNRS 5292- UCBL1, Team—Olfaction: From Coding to Memory, Lyon, France. Pascaline Aimé and A. Karyn Julliard were responsible for the conception and design of the experiments; Pascaline Aimé, Rita Salem, Caroline Romestaing, Dolly Al Koborssy and Claude Duchamp were responsible for the collection of the data; Pascaline Aimé, Brigitte Palouzier-Paulignan, Rita Salem and A. Karyn Julliard were responsible for data analysis and interpretation; and Samuel Garcia designed the software used in analysis. Pascaline Aimé, Brigitte Palouzier-Paulignan, Claude Duchamp and A. Karyn Julliard drafted the article and revised it critically for important intellectual content. All authors approved the final version of the manuscript.

ACKNOWLEDGMENTS

This work was supported by Agence Nationale de la Recherche Grant ANR-05-PNRA 1E07 Aromalim, by the Ministère de la recherche et des nouvelles technologies, by the Centre National de la Recherche (CNRS) and the Claude Bernard University of Lyon (University Lyon1). We would like to thank the staff of the Neurochem facility (Université Claude-Bernard-Lyon I, 69373 Lyon Cedex 08, France), Anne Meiller for the engineering of the glucose biosensors.

REFERENCES

Aimé, P., Duchamp-Viret, P., Chaput, M. A., Savigner, A., Mahfouz, M., and Julliard, A. K. (2007). Fasting increases and satiation decreases olfactory detection for a neutral odor in rats. Behav. Brain Res. 179, 258–264. doi: 10.1016/j.bbr.2007.02.012

Aimé, P., Hegoburu, C., Jaillard, T., Degletagne, C., Garcia, S., Messaoudi, B., et al. (2012). A physiological increase of insulin in the olfactory bulb decreases detection of a learned aversive odor and abolishes food odor-induced sniffing behavior in rats. PLoS One 7:e51227. doi: 10.1371/journal.pone.0051227

Allen Institute for Brain Science. (2010). Allen Brain Atlas. Seattle,WA, USA: Allen Institute for Brain Science Available online at: http://www.brain-map.org/

Alquier, T., Leloup, C., Lorsignol, A., and Pénicaud, L. (2006). Translocable glucose transporters in the brain: where are we in 2006? Diabetes 55, S131–S138. doi: 10.2337/db06-s021

Amessou, M., Tahiri, K., Chauvet, G., and Desbuquois, B. (2010). Age-related changes in insulin receptor mRNA and protein expression in genetically obese Zucker rats. Diabetes Metab. 36, 120–128. doi: 10.1016/j.diabet.2009.09.004

Apweiler, R., and Freund, P. (1993). Development of glucose intolerance in obese (fa/fa) Zucker rats. Horm. Metab. Res. 25, 521–524. doi: 10.1055/s-2007-1002165

Baly, C., Aioun, J., Badonnel, K., Lacroix, M. C., Durieux, D., Schlegel, C., et al. (2007). Leptin and its receptors are present in the rat olfactory mucosa and modulated by the nutritional status. Brain Res. 1129, 130–141. doi: 10.1016/j.brainres.2006.10.030

Banks, W. A. (2004). The source of cerebral insulin. Eur. J. Pharmacol. 490, 5–12. doi: 10.1016/j.ejphar.2004.02.040

Banks, W. A. (2006). Blood-brain barrier and energy balance. Obesity (Silver Spring) 14(Suppl. 5), 234S–237S. doi: 10.1038/oby.2006.315

Banks, W. A., Jaspan, J. B., and Kastin, A. J. (1997). Selective, physiological transport of insulin across the blood-brain barrier: novel demonstration by species-specific radioimmunoassays. Peptides 18, 1257–1262. doi: 10.1016/s0196-9781(97)00198-8

Baskin, D. G., Porte, D. Jr., Guest, K., and Dorsa, D. M. (1983). Regional concentrations of insulin in the rat brain. Endocrinology 112, 898–903. doi: 10.1210/endo-112-3-898

Baskin, D. G., Stein, L. J., Ikeda, H., Woods, S. C., Figlewicz, D. P., Porte, D. Jr., et al. (1985). Genetically obese Zucker rats have abnormally low brain insulin content. Life Sci. 36, 627–633. doi: 10.1016/0024-3205(85)90166-3

Beck, B. (2000). Neuropeptides and obesity. Nutrition 16, 916–923. doi: 10.1016/S0899-9007(00)00410-X

Beck, B., Burlet, A., Nicolas, J. P., and Burlet, C. (1990). Hypothalamic neuropeptide Y (NPY) in obese Zucker rats: implications in feeding and sexual behaviors. Physiol. Behav. 47, 449–453. doi: 10.1016/0031-9384(90)90107-f

Beck, B., Richy, S., and Stricker-Krongrad, A. (2003). Ghrelin and body weight regulation in the obese Zucker rat in relation to feeding state and dark/light cycle. Exp. Biol. Med. (Maywood) 228, 1124–1131.

Bogacka, I., Roane, D. S., Xi, X., Zhou, J., Li, B., Ryan, D. H., et al. (2004). Expression levels of genes likely involved in glucose-sensing in the obese Zucker rat brain. Nutr. Neurosci. 7, 67–74. doi: 10.1080/10284150410001710401

Bowlby, M. R., Fadool, D. A., Holmes, T. C., and Levitan, I. B. (1997). Modulation of the Kv1.3 potassium channel by receptor tyrosine kinases. J. Gen. Physiol. 110, 601–610. doi: 10.1085/jgp.110.5.601

Bradbury, M. W. (1993). The blood-brain barrier. Exp. Physiol. 78, 453–472.

Bray, G. A. (1977). The Zucker-fatty rat: a review. Fed. Proc. 36, 148–153.

Burguera, B., Couce, M. E., Long, J., Lamsam, J., Laakso, K., Jensen, M. D., et al. (2000). The long form of the leptin receptor (OB-Rb) is widely expressed in the human brain. Neuroendocrinology 71, 187–195. doi: 10.1159/000054536

Chaigneau, E., Tiret, P., Lecoq, J., Ducros, M., Knöpfel, T., and Charpak, S. (2007). The relationship between blood flow and neuronal activity in the rodent olfactory bulb. J. Neurosci. 27, 6452–6460. doi: 10.1523/jneurosci.3141-06.2007

Chua, S. C. Jr., Chung, W. K., Wu-Peng, X. S., Zhang, Y., Liu, S. M., Tartaglia, L., et al. (1996). Phenotypes of mouse diabetes and rat fatty due to mutations in the OB (leptin) receptor. Science 271, 994–996. doi: 10.1126/science.271.5251.994

Clark, J. B., Palmer, C. J., and Shaw, W. N. (1983). The diabetic Zucker fatty rat. Proc. Soc. Exp. Biol. Med. 173, 68–75. doi: 10.3181/00379727-173-41611

Colombani, A. L., Carneiro, L., Benani, A., Galinier, A., Jaillard, T., Duparc, T., et al. (2009). Enhanced hypothalamic glucose sensing in obesity: alteration of redox signaling. Diabetes 58, 2189–2197. doi: 10.2337/db09-0110

Corsetti, J. P., Sparks, J. D., Peterson, R. G., Smith, R. L., and Sparks, C. E. (2000). Effect of dietary fat on the development of non-insulin dependent diabetes mellitus in obese Zucker diabetic fatty male and female rats. Atherosclerosis 148, 231–241. doi: 10.1016/s0021-9150(99)00265-8

Doyle, P., Rohner-Jeanrenaud, F., and Jeanrenaud, B. (1993). Local cerebral glucose utilization in brains of lean and genetically obese (fa/fa) rats. Am. J. Physiol. 264, E29–E36.

Dyer, J., Wood, I. S., Palejwala, A., Ellis, A., and Shirazi-Beechey, S. P. (2002). Expression of monosaccharide transporters in intestine of diabetic humans. Am. J. Physiol. Gastrointest. Liver Physiol. 282, G241–G248. doi: 10.1152/ajpgi.00310.2001

Elfeber, K., Köhler, A., Lutzenburg, M., Osswald, C., Galla, H. J., Witte, O. W., et al. (2004). Localization of the Na+-D-glucose cotransporter SGLT1 in the blood-brain barrier. Histochem. Cell Biol. 121, 201–207. doi: 10.1007/s00418-004-0633-9

El Messari, S., Leloup, C., Quignon, M., Brisorgueil, M. J., Penicaud, L., and Arluison, M. (1998). Immunocytochemical localization of the insulin-responsive glucose transporter 4 (Glut4) in the rat central nervous system. J. Comp. Neurol. 399, 492–512. doi: 10.1002/(sici)1096-9861(19981005)399:4<492::aid-cne4>3.0.co;2-x

Fadool, D. A., Tucker, K., Phillips, J. J., and Simmen, J. A. (2000). Brain insulin receptor causes activity-dependent current suppression in the olfactory bulb through multiple phosphorylation of Kv1.3. J. Neurophysiol. 83, 2332–2348.

Fei, H., Okano, H. J., Li, C., Lee, G. H., Zhao, C., Darnell, R., et al. (1997). Anatomic localization of alternatively spliced leptin receptors (Ob-R) in mouse brain and other tissues. Proc. Natl. Acad. Sci. U S A 94, 7001–7005. doi: 10.1073/pnas.94.13.7001

Ferraris, R. P., and Vinnakota, R. R. (1995). Intestinal nutrient transport in genetically obese mice. Am. J. Clin. Nutr. 62, 540–546.

Figlewicz, D. P., Dorsa, D. M., Stein, L. J., Baskin, D. G., Paquette, T., Greenwood, M. R., et al. (1985). Brain and liver insulin binding is decreased in Zucker rats carrying the ‘fa’ gene. Endocrinology 117, 1537–1543. doi: 10.1210/endo-117-4-1537

Getchell, T. V., Kwong, K., Saunders, C. P., Stromberg, A. J., and Getchell, M. L. (2006). Leptin regulates olfactory-mediated behavior in ob/ob mice. Physiol. Behav. 87, 848–856. doi: 10.1016/j.physbeh.2005.11.016

González, J. A., Jensen, L. T., Fugger, L., and Burdakov, D. (2008). Metabolism-independent sugar sensing in central orexin neurons. Diabetes 57, 2569–2576. doi: 10.2337/db08-0548

Guerre-Millo, M. (1997). Regulation of ob gene and overexpression in obesity. Biomed. Pharmacother. 51, 318–323. doi: 10.1016/s0753-3322(97)88048-1

Gustavsson, C., Soga, T., Wahlström, E., Vesterlund, M., Azimi, A., Norstedt, G., et al. (2011). Sex-dependent hepatic transcripts and metabolites in the development of glucose intolerance and insulin resistance in Zucker diabetic fatty rats. J. Mol. Endocrinol. 47, 129–143. doi: 10.1530/jme-11-0007

Hill, J. M., Lesniak, M. A., Pert, C. B., and Roth, J. (1986). Autoradiographic localization of insulin receptors in rat brain: prominence in olfactory and limbic areas. Neuroscience 17, 1127–1138. doi: 10.1016/0306-4522(86)90082-5

Ikeda, H., Nishikawa, K., and Matsuo, T. (1980). Feeding responses of Zucker fatty rat to 2-deoxy-D-glucose, norepinephrine and insulin. Am. J. Physiol. 239, E379–E384.

Ikeda, H., West, D. B., Pustek, J. J., Figlewicz, D. P., Greenwood, M. R., Porte, D. Jr., et al. (1986). Intraventricular insulin reduces food intake and body weight of lean but not obese Zucker rats. Appetite 7, 381–386. doi: 10.1016/s0195-6663(86)80006-x

Julliard, A. K., Chaput, M. A., Apelbaum, A., Aimé, P., Mahfouz, M., and Duchamp-Viret, P. (2007). Changes in rat olfactory detection performance induced by orexin and leptin mimicking fasting and satiation. Behav. Brain Res. 183, 123–129. doi: 10.1016/j.bbr.2007.05.033

Julliard, A. K., and Hartmann, D. J. (1998). Spatiotemporal patterns of expression of extracellular matrix molecules in the developing and adult rat olfactory system. Neuroscience 84, 1135–1150. doi: 10.1016/s0306-4522(97)00544-7

Kaul, L., and Berdanier, C. D. (1975). Effect of meal-feeding on the daily variations of insulin, glucose and NADP-linked dehydrogenases in rats. J. Nutr. 105, 1132–1140.

Kuczewski, N., Fourcaud-Trocmé, N., Savigner, A., Thevenet, M., Aimé, P., Garcia, S., et al. (2014). Insulin modulates network activity in olfactory bulb slices: impact on odour processing. J. Physiol. 592, 2751–2769. doi: 10.1113/jphysiol.2013.269639

Levin, B. E., Magnan, C., Dunn-Meynell, A., and Le Foll, C. (2011). Metabolic sensing and the brain: who, what, where and how? Endocrinology 152, 2552–2557. doi: 10.1210/en.2011-0194

Leybaert, L. (2005). Neurobarrier coupling in the brain: a partner of neurovascular and neurometabolic coupling? J. Cereb. Blood Flow Metab. 25, 2–16. doi: 10.1038/sj.jcbfm.9600001

Liistro, T., Guiducci, L., Burchielli, S., Panetta, D., Belcari, N., Pardini, S., et al. (2010). Brain glucose overexposure and lack of acute metabolic flexibility in obesity and type 2 diabetes: a PET-[18F]FDG study in Zucker and ZDF rats. J. Cereb. Blood Flow Metab. 30, 895–899. doi: 10.1038/jcbfm.2010.27

Livingston, J. N., Unger, J. W., Moxley, R. T., and Moss, A. (1993). Phosphotyrosine-containing proteins in the CNS of obese Zucker rats are decreased in the absence of changes in the insulin receptor. Neuroendocrinology 57, 481–488. doi: 10.1159/000126395

Magistretti, P. J., Pellerin, L., Rothman, D. L., and Shulman, R. G. (1999). Energy on demand. Science 283, 496–497. doi: 10.1126/science.283.5401.496

Marfaing-Jallat, P., Levacher, C., Calando, Y., Picon, L., and Penicaud, L. (1992). Glucose utilization and insulin binding in discrete brain areas of obese rats. Physiol. Behav. 52, 713–716. doi: 10.1016/0031-9384(92)90402-n

Marks, J. L., and Eastman, C. J. (1989). Effect of starvation on insulin receptors in rat brain. Neuroscience 30, 551–556. doi: 10.1016/0306-4522(89)90272-8

Marks, J. L., Porte, D. Jr., Stahl, W. L., and Baskin, D. G. (1990). Localization of insulin receptor mRNA in rat brain by in situ hybridization. Endocrinology 127, 3234–3236. doi: 10.1210/endo-127-6-3234

Martin, R. J., Wangsness, P. J., and Gahagan, J. H. (1978). Diurnal changes in serum metabolites and hormones in lean and obese Zucker rats. Horm. Metab. Res. 10, 187–192. doi: 10.1055/s-0028-1093431

McCrimmon, R. (2008). The mechanisms that underlie glucose sensing during hypoglycaemia in diabetes. Diabet. Med. 25, 513–522. doi: 10.1111/j.1464-5491.2008.02376.x

McEwen, B. S., and Reagan, L. P. (2004). Glucose transporter expression in the central nervous system: relationship to synaptic function. Eur. J. Pharmacol. 490, 13–24. doi: 10.1016/j.ejphar.2004.02.041

McLaughlin, C. L., and Baile, C. A. (1980). Decreased sensitivity of Zucker obese rats to the putative satiety agent cholecystokinin. Physiol. Behav. 25, 543–548. doi: 10.1016/0031-9384(80)90119-5

McNay, E. C., and Gold, P. E. (1999). Extracellular glucose concentrations in the rat hippocampus measured by zero-net-flux: effects of microdialysis flow rate, strain and age. J. Neurochem. 72, 785–790. doi: 10.1046/j.1471-4159.1999.0720785.x

McNay, E. C., McCarty, R. C., and Gold, P. E. (2001). Fluctuations in brain glucose concentration during behavioral testing: dissociations between brain areas and between brain and blood. Neurobiol. Learn. Mem. 75, 325–337. doi: 10.1006/nlme.2000.3976

Melnyk, R. B. (1987). Decreased binding to hypothalamic insulin receptors in young genetically obese rats. Physiol. Behav. 40, 237–241. doi: 10.1016/0031-9384(87)90213-7

Mercer, J. G., Hoggard, N., Williams, L. M., Lawrence, C. B., Hannah, L. T., and Trayhurn, P. (1996). Localization of leptin receptor mRNA and the long form splice variant (Ob-Rb) in mouse hypothalamus and adjacent brain regions by in situ hybridization. FEBS Lett. 387, 113–116. doi: 10.1016/0014-5793(96)00473-5

Mondal, M. S., Nakazato, M., Date, Y., Murakami, N., Hanada, R., Sakata, T., et al. (1999). Characterization of orexin-A and orexin-B in the microdissected rat brain nuclei and their contents in two obese rat models. Neurosci. Lett. 273, 45–48. doi: 10.1016/s0304-3940(99)00624-2

Morton, A. P., and Hanson, P. J. (1984). Monosaccharide transport by the small intestine of lean and genetically obese (ob/ob) mice. Q. J. Exp. Physiol. 69, 117–126.

Nawroth, J. C., Greer, C. A., Chen, W. R., Laughlin, S. B., and Shepherd, G. M. (2007). An energy budget for the olfactory glomerulus. J. Neurosci. 27, 9790–9800. doi: 10.1523/jneurosci.1415-07.2007

O’Malley, D., Reimann, F., Simpson, A. K., and Gribble, F. M. (2006). Sodium-coupled glucose cotransporters contribute to hypothalamic glucose sensing. Diabetes 55, 3381–3386. doi: 10.2337/db06-0531

Osswald, C., Baumgarten, K., Stümpel, F., Gorboulev, V., Akimjanova, M., Knobeloch, K. P., et al. (2005). Mice without the regulator gene Rsc1A1 exhibit increased Na+-D-glucose cotransport in small intestine and develop obesity. Mol. Cell. Biol. 25, 78–87. doi: 10.1128/mcb.25.1.78-87.2005

Palouzier-Paulignan, B., Lacroix, M. C., Aimé, P., Baly, C., Caillol, M., Congar, P., et al. (2012). Olfaction under metabolic influences. Chem. Senses 37, 769–797. doi: 10.1093/chemse/bjs059

Paxinos, G., and Watson, C. (2007). The Rat Brain in Stereotaxic Coordinates. San Diego: Academic Press.

Pénicaud, L., Ferré, P., Terretaz, J., Kinebanyan, M. F., Leturque, A., Doré, E., et al. (1987). Development of obesity in Zucker rats. Early insulin resistance in muscles but normal sensitivity in white adipose tissue. Diabetes 36, 626–631. doi: 10.2337/diabetes.36.5.626

Peyron, C., Tighe, D. K., van den Pol, A. N., de Lecea, L., Heller, H. C., Sutcliffe, J. G., et al. (1998). Neurons containing hypocretin (orexin) project to multiple neuronal systems. J. Neurosci. 18, 9996–10015.

Poppe, R., Karbach, U., Gambaryan, S., Wiesinger, H., Lutzenburg, M., Kraemer, M., et al. (1997). Expression of the Na+-D-glucose cotransporter SGLT1 in neurons. J. Neurochem. 69, 84–94. doi: 10.1046/j.1471-4159.1997.69010084.x

Prud’homme, M. J., Lacroix, M. C., Badonnel, K., Gougis, S., Baly, C., Salesse, R., et al. (2009). Nutritional status modulates behavioural and olfactory bulb Fos responses to isoamyl acetate or food odour in rats: roles of orexins and leptin. Neuroscience 162, 1287–1298. doi: 10.1016/j.neuroscience.2009.05.043

Routh, V. H. (2010). Glucose sensing neurons in the ventromedial hypothalamus. Sensors (Basel) 10, 9002–9025. doi: 10.3390/s101009002

Rowe, I. C., Boden, P. R., and Ashford, M. L. (1996). Potassium channel dysfunction in hypothalamic glucose-receptive neurones of obese Zucker rats. J. Physiol. 497(Pt. 2), 365–377.

Sanacora, G., Kershaw, M., Finkelstein, J. A., and White, J. D. (1990). Increased hypothalamic content of preproneuropeptide Y messenger ribonucleic acid in genetically obese Zucker rats and its regulation by food deprivation. Endocrinology 127, 730–737. doi: 10.1210/endo-127-2-730

Savigner, A., Duchamp-Viret, P., Grosmaitre, X., Chaput, M., Garcia, S., Ma, M., et al. (2009). Modulation of spontaneous and odorant-evoked activity of rat olfactory sensory neurons by two anorectic peptides, insulin and leptin. J. Neurophysiol. 101, 2898–2906. doi: 10.1152/jn.91169.2008

Schwartz, M. W., Sipols, A., Kahn, S. E., Lattemann, D. F., Taborsky, G. J. Jr., Bergman, R. N., et al. (1990). Kinetics and specificity of insulin uptake from plasma into cerebrospinal fluid. Am. J. Physiol. 259, E378–E383.

Shioda, S., Funahashi, H., Nakajo, S., Yada, T., Maruta, O., and Nakai, Y. (1998). Immunohistochemical localization of leptin receptor in the rat brain. Neurosci. Lett. 243, 41–44. doi: 10.1016/s0304-3940(98)00082-2

Sitren, H. S., and Stevenson, N. R. (1978). The effects of meal-feeding at different times of the day on daily changes in serum insulin, gastrin and liver enzymes in the rat. J. Nutr. 108, 1393–1401.

Slotnick, B. M., Westbrook, F., and Darling, F. M. C. (1997). What the rat’s nose tells the rat’s mouth: long delay aversion conditioning with aqueous odors and potentiation of tatse by odors. Anim. Learn. Behav. 25, 357–369. doi: 10.3758/BF03199093

Sokal, R., and Rohlf, F. (1981). Biometry: The Principals and Practice of Statistics in Biological Research. San Francisco: Freeman, W.H.

Spanswick, D., Smith, M. A., Groppi, V. E., Logan, S. D., and Ashford, M. L. (1997). Leptin inhibits hypothalamic neurons by activation of ATP-sensitive potassium channels. Nature 390, 521–525. doi: 10.1038/37379

Spanswick, D., Smith, M. A., Mirshamsi, S., Routh, V. H., and Ashford, M. L. (2000). Insulin activates ATP-sensitive K+ channels in hypothalamic neurons of lean, but not obese rats. Nat. Neurosci. 3, 757–758. doi: 10.1038/77660

Stein, L. J., Dorsa, D. M., Baskin, D. G., Figlewicz, D. P., Ikeda, H., Frankmann, S. P., et al. (1983). Immunoreactive insulin levels are elevated in the cerebrospinal fluid of genetically obese Zucker rats. Endocrinology 113, 2299–2301. doi: 10.1210/endo-113-6-2299

Strubbe, J. H., Porte, D. Jr., and Woods, S. C. (1988). Insulin responses and glucose levels in plasma and cerebrospinal fluid during fasting and refeeding in the rat. Physiol. Behav. 44, 205–208. doi: 10.1016/0031-9384(88)90139-4

Tabatabai, N. M., Sharma, M., Blumenthal, S. S., and Petering, D. H. (2009). Enhanced expressions of sodium-glucose cotransporters in the kidneys of diabetic Zucker rats. Diabetes Res. Clin. Pract. 83, e27–e30. doi: 10.1016/j.diabres.2008.11.003

Tartaglia, L. A., Dembski, M., Weng, X., Deng, N., Culpepper, J., Devos, R., et al. (1995). Identification and expression cloning of a leptin receptor, OB-R. Cell 83, 1263–1271. doi: 10.1016/0092-8674(95)90151-5

Thanos, P. K., Michaelides, M., Gispert, J. D., Pascau, J., Soto-Montenegro, M. L., Desco, M., et al. (2008). Differences in response to food stimuli in a rat model of obesity: in-vivo assessment of brain glucose metabolism. Int. J. Obes. (Lond). 32, 1171–1179. doi: 10.1038/ijo.2008.50

Thanos, P. K., Robison, L. S., Robinson, J. K., Michaelides, M., Wang, G. J., and Volkow, N. D. (2013). Obese rats with deficient leptin signaling exhibit heightened sensitivity to olfactory food cues. Synapse 67, 171–178. doi: 10.1002/syn.21627

Tong, J., Mannea, E., Aimé, P., Pfluger, P. T., Yi, C. X., Castaneda, T. R., et al. (2011). Ghrelin enhances olfactory sensitivity and exploratory sniffing in rodents and humans. J. Neurosci. 31, 5841–5846. doi: 10.1523/JNEUROSCI.5680-10.2011

Tsujii, S., Nakai, Y., Takahashi, H., Usui, T., Koh, T., Yonekura, Y., et al. (1988). Effect of food deprivation on regional brain glucose utilization in lean and fatty Zucker rats. Brain Res. 475, 371–375. doi: 10.1016/0006-8993(88)90628-2

Tucker, K., Cavallin, M. A., Jean-Baptiste, P., Biju, K. C., Overton, J. M., Pedarzani, P., et al. (2010). The olfactory bulb: a metabolic sensor of brain insulin and glucose concentrations via a voltage-gated potassium channel. Results Probl. Cell Differ. 52, 147–157. doi: 10.1007/978-3-642-14426-4_12

Tucker, K., Cho, S., Thiebaud, N., Henderson, M. X., and Fadool, D. A. (2013). Glucose sensitivity of mouse olfactory bulb neurons is conveyed by a voltage-gated potassium channel. J. Physiol. 591, 2541–2561. doi: 10.1113/jphysiol.2013.254086

Ueno, M., Dobrogowska, D. H., and Vorbrodt, A. W. (1996). Immunocytochemical evaluation of the blood-brain barrier to endogenous albumin in the olfactory bulb and pons of senescence-accelerated mice (SAM). Histochem. Cell Biol. 105, 203–212. doi: 10.1007/bf01462293

Unger, J., McNeill, T. H., Moxley, R. T. 3rd, White, M., Moss, A., and Livingston, J. N. (1989). Distribution of insulin receptor-like immunoreactivity in the rat forebrain. Neuroscience 31, 143–157. doi: 10.1016/0306-4522(89)90036-5

Vannucci, S. J., Koehler-Stec, E. M., Li, K., Reynolds, T. H., Clark, R., and Simpson, I. A. (1998). GLUT4 glucose transporter expression in rodent brain: effect of diabetes. Brain Res. 797, 1–11. doi: 10.1016/s0006-8993(98)00103-6

Vasselli, J. R., Cleary, M. P., Jen, K. L., and Greenwood, M. R. (1980). Development of food motivated behavior in free feeding and food restricted Zucker fatty (fa/fa) rats. Physiol. Behav. 25, 565–573. doi: 10.1016/0031-9384(80)90123-7

Vasylieva, N., Barnych, B., Meiller, A., Maucler, C., Pollegioni, L., Lin, J. S., et al. (2011). Covalent enzyme immobilization by poly(ethylene glycol) diglycidyl ether (PEGDE) for microelectrode biosensor preparation. Biosens. Bioelectron. 26, 3993–4000. doi: 10.1016/j.bios.2011.03.012

White, B. D., and Martin, R. J. (1997). Evidence for a central mechanism of obesity in the Zucker rat: role of neuropeptide Y and leptin. Proc. Soc. Exp. Biol. Med. 214, 222–232. doi: 10.3181/00379727-214-44090

Wilcox, B. J., Corp, E. S., Dorsa, D. M., Figlewicz, D. P., Greenwood, M. R., Woods, S. C., et al. (1989). Insulin binding in the hypothalamus of lean and genetically obese Zucker rats. Peptides 10, 1159–1164. doi: 10.1016/0196-9781(89)90008-9

Winocur, G., Greenwood, C. E., Piroli, G. G., Grillo, C. A., Reznikov, L. R., Reagan, L. P., et al. (2005). Memory impairment in obese Zucker rats: an investigation of cognitive function in an animal model of insulin resistance and obesity. Behav. Neurosci. 119, 1389–1395. doi: 10.1037/0735-7044.119.5.1389

Woods, S. C., Seeley, R. J., Baskin, D. G., and Schwartz, M. W. (2003). Insulin and the blood-brain barrier. Curr. Pharm. Des. 9, 795–800. doi: 10.2174/1381612033455323

Yamamoto, Y., Ueta, Y., Yamashita, H., Asayama, K., and Shirahata, A. (2002). Expressions of the prepro-orexin and orexin type 2 receptor genes in obese rat. Peptides 23, 1689–1696. doi: 10.1016/s0196-9781(02)00111-0

Yamashita, T., Murakami, T., Iida, M., Kuwajima, M., and Shima, K. (1997). Leptin receptor of Zucker fatty rat performs reduced signal transduction. Diabetes 46, 1077–1080. doi: 10.2337/diabetes.46.6.1077

Yang, X., Renken, R., Hyder, F., Siddeek, M., Greer, C. A., Shepherd, G. M., et al. (1998). Dynamic mapping at the laminar level of odor-elicited responses in rat olfactory bulb by functional MRI. Proc. Natl. Acad. Sci. U S A 95, 7715–7720. doi: 10.1073/pnas.95.13.7715

Yu, A. S., Hirayama, B. A., Timbol, G., Liu, J., Basarah, E., Kepe, V., et al. (2010). Functional expression of SGLTs in rat brain. Am. J. Physiol. Cell Physiol. 299, C1277–C1284. doi: 10.1152/ajpcell.00296.2010

Yu, A. S., Hirayama, B. A., Timbol, G., Liu, J., Diez-Sampedro, A., Kepe, V., et al. (2013). Regional distribution of SGLT activity in rat brain in vivo. Am. J. Physiol. Cell Physiol. 304, C240–C247. doi: 10.1152/ajpcell.00317.2012

Zarjevski, N., Doyle, P., and Jeanrenaud, B. (1992). Muscle insulin resistance may not be a primary etiological factor in the genetically obese fa/fa rat. Endocrinology 130, 1564–1570. doi: 10.1210/en.130.3.1564

Zhu, M., Nehra, D., Ackerman, J. J., and Yablonskiy, D. A. (2004). On the role of anesthesia on the body/brain temperature differential in rats. J. Therm. Biol. 29, 599–603. doi: 10.1016/j.jtherbio.2004.08.029

Zucker, L. M., and Antoniades, H. N. (1972). Insulin and obesity in the Zucker genetically obese rat “fatty”. Endocrinology 90, 1320–1330. doi: 10.1210/endo-90-5-1320

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 15 April 2014; Accepted: 01 September 2014; Published online: 17 September 2014.

Citation: Aimé P, Palouzier-Paulignan B, Salem R, Al Koborssy D, Garcia S, Duchamp C, Romestaing C and Julliard AK (2014) Modulation of olfactory sensitivity and glucose-sensing by the feeding state in obese Zucker rats. Front. Behav. Neurosci. 8:326. doi: 10.3389/fnbeh.2014.00326

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Aimé, Palouzier-Paulignan, Salem, Al Koborssy, Garcia, Duchamp, Romestaing and Julliard. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

HYPOTHESIS AND THEORY ARTICLE
published: 06 May 2014
doi: 10.3389/fnbeh.2014.00164

The orexinergic system influences conditioned odor aversion learning in the rat: a theory on the processes and hypothesis on the circuit involved

Barbara Ferry*

Centre of Research in Neuroscience Lyon, CNRS UMR 5292 - INSERM U1028 UCBL1, Lyon, France

* Correspondence: Barbara Ferry, Centre of Research in Neuroscience Lyon, CNRS UMR 5292 - INSERM U1028 UCBL1, 50 Avenue Tony Garnier, 69366 Lyon, France e-mail: bferry@olfac.univ-lyon1.fr

Edited by:
Regina M. Sullivan, Nathan Kline Institute and NYU School of Medicine, USA

Reviewed by:
Guillaume Ferreira, Institut National de la Recherche Agronomique (INRA), France
Gina Lorena Quirarte, Universidad Nacional Autonoma de Mexico, Mexico
Markus Fendt, Otto-von-Guericke University Magdeburg, Germany

A large variety of behaviors that are essential for animal survival depend on the perception and processing of surrounding smells present in the natural environment. In particular, food-search behavior, which is conditioned by hunger, is directly driven by the perception of odors associated with food, and feeding status modulates olfactory sensitivity. The orexinergic hypothalamic peptide orexin A (OXA), one of the central and peripheral hormones that triggers food intake, has been shown to increase olfactory sensitivity in various experimental conditions including the conditioned odor aversion learning paradigm (COA). COA is an associative task that corresponds to the association between an olfactory conditioned stimulus (CS) and a delayed gastric malaise. Previous studies have shown that this association is formed only if the delay separating the CS presentation from the malaise is short, suggesting that the memory trace of the odor is relatively unstable. To test the selectivity of the OXA system in olfactory sensitivity, a recent study compared the effects of fasting and of central infusion of OXA during the acquisition of COA. Results showed that the increased olfactory sensitivity induced by fasting and by OXA infusion was accompanied by enhanced COA learning performances. In reference to the duration of action of OXA, the present work details the results obtained during the successive COA extinction tests and suggests a hypothesis concerning the role of the OXA component of fasting on the memory processes underlying CS-malaise association during COA. Moreover, referring to previous data in the literature we suggest a functional circuit model where fasting modulates olfactory memory processes through direct and/or indirect activation of particular OXA brain targets including the olfactory bulb, the locus coeruleus (LC) and the amygdala.

Keywords: associative learning, olfactory memory, orexin, fasting, rat

INTRODUCTION

A large variety of behaviors that are essential for animal survival depend on the sensory perception and processing of odors present in the natural environment. In particular, food-search behavior, which is conditioned by hunger, is directly driven by the perception of odors associated with food (Le Magnen, 1959) and several studies have demonstrated that odor processing is influenced by the nutritional status of the animal. For example, olfactory system activity was shown to be directly modulated according to hunger and satiation status (Pager et al., 1972; Pager, 1974, 1978; Royet et al., 1983; Apelbaum et al., 2005). Moreover, fasting enhanced odor detection in rats whereas satiety reduced detection of odors in general (Aimé et al., 2007), and more precisely of one odorant specifically associated with the food type involved in the satiation (O’Doherty et al., 2000; Mulligan et al., 2002). Some data suggest that the central nervous system (CNS) regulates food-search behavior by modulating the detection threshold of the food odorant itself through centrifugal innervations (Doucette et al., 2007; Doucette and Restrepo, 2008; Fletcher and Chen, 2010). A large body of data indicates that the hypothalamus plays an important role in this process. Firstly, anatomic characterization of the lateral hypothalamus (LH) has shown the existence of a functional loop between structures involved in the first level of odor detection and the hypothalamus (Peyron et al., 1998; Sakurai, 2005; Swanson et al., 2005; Hahn and Swanson, 2010). Secondly, the crucial role of the basal hypothalamus, and in particular of orexigenic (appetite-stimulating) and anorexigenic (appetite-inhibiting) neurochemicals, in appetite regulation and energy balance has long been established (see Rodgers et al., 2002 for review). Thirdly, among the multitude of neurochemicals found in the hypothalamus, the most recently discovered orexigenic peptides (orexin A, OXA and orexin B, OXB) have been shown to be strongly involved in the regulation of feeding and energy metabolism (see Willie et al., 2001 for review) and the OXA has been involved in olfactory sensitivity. In particular, our team showed that intracerebroventricular (icv) infusion of OXA in rat increased olfactory detection performance in the same way as physiologically induced fasting (Aimé et al., 2007; Julliard et al., 2007). Fourthly, lateral hypothalamic orexin neurons project directly to the olfactory bulb (OB; Peyron et al., 1998; Nambu et al., 1999; Caillol et al., 2003; Shibata et al., 2008) and the two classes of receptors for OXA and for OXB have been found in OB neurons (Caillol et al., 2003; Hardy et al., 2005). In addition, central infusion of OXA increased OB Fos responses to food odor in both fasted and satiated animals (Prud’homme et al., 2009). All these results indicate that the orexin system is involved in the control of feeding behavior by modulating olfactory sensitivity.

It is, however, very unlikely that olfactory sensitivity is completely dissociated from olfactory memory. Rusiniak et al. (1982) and Slotnick et al. (1997) showed that the more intense the odor, the stronger the memory of its association with a reinforcement. Interestingly, other than its projection on the primary olfactory centers, the hypothalamic OXA neurons project to various structures involved in olfactory associative learning (see Rodgers et al., 2002 for review). Moreover, the OXA system was shown to be involved in the memory processes underlying various kinds of learning (Jaeger et al., 2002; Telegdy and Adamik, 2002; Mair and Hembrook, 2008; Di Sebastiano et al., 2011; Sears et al., 2013; Soya et al., 2013). In addition, Touzani and Sclafani (2002) have shown that the lesion of the LH induced a deficit in conditioned flavor aversion paradigms. Therefore, it can be suggested that the OXA system, may influence odor memory formation, directly, by modulating olfactory sensitivity, and indirectly, by activating particular hypothalamus target regions through the modulation of olfactory sensitivity.

In a natural environment, the relevance of the odor coming from a food source encountered by an animal during food-search is a crucial key, determining approach and ingestion of the food. Whether the odor of the food is new to the animal or has previously acquired a hedonic valence during a first intake experience will condition either approach or avoidance. Acquisition of hedonic valence by a food item has been shown to result from conditioned learning during which the sensory stimuli characterizing a particular food (odor and taste) become associated with the positive (energy input) or negative (gastric malaise, poisoning) consequences of the ingestion of the food, so that processing the odor and taste stimuli will cue the appropriate approach or avoidance responses (Rescorla, 1988; Holland, 1990; Mackintosh, 1991). These kinds of association have been experimentally studied for years (Slotnick and Katz, 1974; Nigrosh et al., 1975; Slotnick, 1984) and conditioned food aversion paradigms, such as conditioned taste or odor/taste-potentiated odor aversion learning, have provided fundamental insights into the mechanisms and CNS structures involved in food-reward/food-poisoning associations (see Miranda, 2012 for review).

One such paradigm, conditioned odor aversion (COA), is the avoidance of an odorized-tasteless solution (conditioned stimulus, CS) the ingestion of which precedes toxicosis (unconditioned stimulus, US). During COA acquisition, the presentation of the CS is separated from the US administration by a temporal gap that can be of various amplitudes depending on whether the CS is mixed (proximal presentation; Slotnick et al., 1997; Chapuis et al., 2007) or presented close to the solution (Palmerino et al., 1980; Rusiniak et al., 1982; Ferry et al., 1996, 2006). Thus COA is a trace conditioning that has been suggested to result from the association of the memory trace of the CS and the delayed US (see Bures and Buresova, 1990; Roldan and Bures, 1994).

Taking these data together with the fact that (i) the amygdala has been widely involved in the processes underlying the association between the CS and the delayed US during COA (see Miranda, 2012 for review), (ii) OXA neurons project to the amygdala and (iii) hypothalamic OXA neurons are activated by cues associated with consummatory rewards such as food (Harris et al., 2005), it is suggested that the OXA system may play a role in the learning and memory processes linked to higher cognitive aspects of feeding.

In order to test this hypothesis, a recent study has aimed to describe the role of the central OXA system in COA learning (Ferry and Duchamp-Viret, 2014). The results showed that fasting and icv infusion of OXA before the acquisition significantly enhanced COA performances. Moreover in that study, data obtained during the elevated plus maze task showed enhanced anxiety in Fasted but not in OXA infused animals suggesting that the enhancing effect of fasting on COA performances is likely mediated, at least in part, by the OXA component of fasting. In order to further precise which process the OXA system is involved in, the present experiment extends our previous one (Ferry and Duchamp-Viret, 2014) and includes a comparison between the effect of icv OXA infusion and fasting on the extinction of COA learning.

MATERIAL AND METHODS

The description of the material and methods has been simplified and subjects, surgery and microinfusion procedure are detailed in a previous article (Ferry and Duchamp-Viret, 2014).

Briefly, three groups of animals were acclimated for 7 days to a 23 h 45 min water deprivation schedule. Groups OXA and artificial CSF (aCSF) were constituted by animals implanted in the lateral ventricle that were microinfused with OXA (10 µg dissolved in 3 µl of sterile CSF, Sigma) or aCSF (3 µl, Harvard Apparatus) 20 min before the acquisition of the COA task (on Day 8). A third experimental group (Fasted) was constituted by animals placed in a 24-h food-deprivation schedule before acquisition of COA. Acquisition of COA consisted by the presentation of an olfactory CS (scented water corresponding to isoamylacetate, Sigma-Aldrich France, mixed with tap water at a final concentration of 10−6) followed 20 min after by an injection of 0.15 M lithium chloride-inducing gastric malaise (LiCl, 10 ml/kg; i.p., US). Conditioned aversion to the odor was tested 48 h later (on Day 11) and COA extinction learning took place from Days 12 to 15. All the testing and extinction sessions were conducted under a food-satiated condition in order to prevent any effect of fasting on the olfactory sensitivity between groups (Aimé et al., 2007; Julliard et al., 2007) and consisted of 15-min presentation of the CS (one bottle test). This procedure is based on previous studies (Julliard et al., 2007 for the OXA infusion and fasting Ferry et al., 2007 for the COA procedure). In order to focus our study on the role of OXA system in memory processes underlying COA learning, it is important to note that animals in our procedure received one OXA infusion before the CS-US pairing and not during the test.

RESULTS

Figure 1 illustrates the mean scented water intake measured during the acquisition (Day 8), the test (Day 11) and the four successive COA extinction sessions (Days 11 to 15) for the various groups. For comparison purpose, the last water-drinking habituation session (Day 7) has also been represented. As shown by this figure, performances obtained during the test and the number of extinction sessions differed between groups, depending on the treatment.

FIGURE 1. Effects of icv infusion of orexin (10 µg/3 µl, OXA group), icv infusion of artificial CSF (3 µl, aCSF group) and food-deprivation (Fasted group) on COA acquisition and extinction. The curves represent mean odorized-solution intakes (± SEM) for each group from Day 8 (acquisition) to Day 15. The mean water intake measured on the last day of habituation (Day 7) is also represented for each group for purposes of comparison. The Fasted group was food-deprived only during acquisition. All animals were habituated and tested while food satiated. *, ** and ***: P < 0.05, P < 0.01 and p < 0.001 between OXA and aCSF group. and P < 0.01 and 0.001 between Fasted and aCSF group. • P < 0.05 between OXA and Fasted group.

A two-factor ANOVA with repeated measures with Treatment (aCSF, OXA and Fasted) as between-subjects variable and Session of extinction (Day 11 to Day 15) as within-subject variable revealed significant Treatment (F(2,25) = 8.96, P < 0.001) and Session effects (F(4,100) = 103.85, P < 0.001) and a significant Treatment × Session interaction (F(8,100) = 5.40, P < 0.001).

Between-group analysis confirmed that the OXA and Fasted groups developed a stronger COA that extinguished more slowly than the aCSF group. One-way ANOVA on the mean scented water intake confirmed these observations, with inter-group differences at Day 11, Day 12, Day 13 and Day 14 (F(2,25) = 12.86, P < 0.001; (F(2,25) = 8.51, P < 0.01; F(2,25) = 9.95, P < 0.001 and F(2,25) = 6.83, P < 0.01 respectively). Post-hoc Bonferroni pairwise comparisons revealed significant differences between aCSF and both OXA and Fasted groups at Day 11, Day 12 and Day 13 (from P < 0.001 to P < 0.05), while the OXA group differed from the aCSF and Fasted groups at Day 14 (P < 0.01 and P < 0.05 respectively). One-way ANOVA within-group comparisons showed a significant effect of factor Session on COA performances in each of the groups (F(4,40) = 9.99; P < 0.001; F(4.45) = 12.54; P < 0.001 and F(4,40) = 15.85; P < 0.001 for aCSF, OXA and fasted groups respectively). In the aCSF group the extinction curve increased rapidly between D11 and D15. Post-hoc Bonferroni’s tests confirmed this observation and showed only a significant difference in the variables obtained in D11 and D12 compared to those obtained in D14 and D15 (P < 0.05 to 0.001). In the OXA and Fasted group, extinction curves reached a ceiling more slowly than the aCSF group. Post-hoc Bonferroni tests confirmed this observation and showed a significant difference in the means solution intake in D11 and D12 compared to D14 and D15 (P < 0.05 to 0.001) and a significant difference in the same variable between D13 and D15 (P < 0.001 and P < 0.01) in the OXA and Fasted groups respectively).

These statistical analyses show that scented water (CS) intake increased significantly in each group throughout the sessions, but it has to be borne in mind that the curves combine two distinct if related phenomena: extinction of COA learning and extinction of CS neophobia.

Concerning the neophobia, the figure shows a decrease in the mean solution intake between D7 and D8 in the three experimental groups and the decrease appears to be stronger in the Fasted group. A two-factor ANOVA with repeated measures with Treatment (aCSF, OXA and Fasted) as between-subjects variable and Session (Day 7 versus Day 8) as within-subject variable confirmed this description and revealed significant Treatment (F(2,25) = 6.24, P < 0.01) and Session effects (F(1,25) = 395.3, P < 0.001) and a significant Treatment × Session interaction (F(2,25) = 27.1, P < 0.001). Pairwise intergroup comparisons (one-way ANOVA) indicated no difference between groups on D7 and a significant decrease in mean scented water intake in Fasted group compared to aCSF and OXA groups (one-way ANOVA (2,25) = 28.92; P < 0.001, post-hoc Bonferroni P < 0.001).

Experimental COA extinction reflects not loss of the original memory trace but rather new learning whereby the CS comes to predict no US. It has been suggested that this learning inhibits the previously acquired conditioned response to the olfactory CS (Rescorla and Heth, 1975; Robbins, 1990).

Therefore, and in order to establish the real number of sessions needed to extinguish COA in each group taking the neophobia effect into account, additional analyses were performed considering the mean scented water intake measured during the acquisition in the variables.

One-way ANOVAs performed on these variables revealed a significant effect of factor Session in the aCSF and OXA groups (F(5,48) = 8.17; P < 0.001; F(5,54) = 14.1; P < 0.001 respectively). In the aCSF group, post-hoc Bonferroni analyses revealed that only mean scented water intake at Day 11 significantly differed from Day 8 (P < 0.01), suggesting that COA extinguished in a single session in this group. In the OXA group, post-hoc Bonferroni analyses revealed significant differences in the data obtained at Day 8 versus Day 11, Day 12 and Day 13 (P < 0.001, P < 0.001 and P < 0.05 respectively), suggesting that COA extinguished over three sessions in this group. Concerning the Fasted group, the fact that animals were conditioned and tested under two different conditions (food-deprived versus food-satiated), rendered the comparison between D8 and the following extinction sessions irrelevant due to the fact that the high level of CS neophobia displayed by the Fasted group on D8 may be attributed to stress induced by the fasting condition (see Section Discussion). Therefore, the data obtained in the Fasted group from Days 11 to 15 were compared to those of the aCSF group on Day 8. Here, one-way ANOVA revealed an effect of the session (F(5,48) = 16.2; P < 0.001) and post-hoc Bonferroni tests revealed a significant difference between data measured at D8 versus D11, D12 and D13 (P < 0.001, P < 0.001 and P < 0.05 respectively), suggesting that COA extinguished over three sessions in this group.

Taken together, these data show that COA extinction was significantly faster in the aCSF than in the OXA and Fasted groups. Moreover, the fact that the OXA group differed from the aCSF and Fasted groups at Day 14 might suggest that the process of COA extinction differed between OXA and fasted groups.

DISCUSSION

One of the main results represented on Figure 1 was that fasting and central OXA infusion induced similar COA enhancement compared to the Control group. The fact that all animals were satiated during the test rules out the possibility that factors such as olfactory hypersensitivity or stress induced by food-deprivation may have influenced the process of COA extinction and/or retrieval.

As shown in Figure 1, the Fasted group displayed a decrease in mean scented solution intake between the last water-drinking habituation session (Day 7) and the acquisition (Day 8), suggesting that, despite the use of a very low concentration of isoamylacetate solution (ISO, 10−6), the acute 24-h food and water deprivation schedule enhanced neophobia for a novel olfactory stimulus.

Neural processing of olfactory information is closely linked to the physiological and nutritional status of the organism, and fasting has been shown to increase OB reactivity (Pager et al., 1972; Apelbaum et al., 2005) and olfactory detection in rats (Aimé et al., 2007). Therefore, the strong neophobia toward the scented water observed in the Fasted group during acquisition may have resulted from the enhanced olfactory sensitivity induced by starvation. Although this starvation effect on olfactory sensitivity has been suggested to be mediated by activation of the central OXA system (Julliard et al., 2007), the very slight difference observed at acquisition (Day 8) between the OXA and aCSF groups suggests that the neophobia toward the CS displayed by the Fasted group at acquisition cannot have been simply and exclusively due to OXA-induced enhancement of olfactory detection under fasting. In this respect, data have shown that short-term (24-h) food deprivation induced a significant increase in serum corticosterone (Das et al., 2005; Johansson et al., 2008; Nowland et al., 2011) and anxiety levels (Ferry and Duchamp-Viret, 2014). Therefore, it can be assumed that the strong neophobia observed in the Fasted group at COA acquisition may have resulted, at least in part, from the combination of enhanced olfactory detection and increased anxiety induced by fasting. Moreover, the fact that OXA infusion did not induce any anxiety (Ferry and Duchamp-Viret, 2014) suggests the strong neophobia observed in the Fasted group at Day 8 was unlikely to have been mediated by central release of OXA.

The results presented in Figure 1 show that COA extinction differed between groups: while the aCSF group needed one session to extinguish COA, the Fasted and OXA groups needed three. The extinction phenomenon reflects the inhibition of the conditioned response by new learning of CS-no US (Rescorla and Heth, 1975) and it has been suggested that resistance to extinction of a CS-US association is directly dependent on the strength of the CS-US memory trace (Eisenberg et al., 2003). The present results suggest that the fasting and OXA infusion conditions both enhanced CS-US association strength, and the similarity between the Fasted and OXA groups in terms of extinction suggests that the enhancing effect of fasting on CS-US association strength is mediated, at least in part, by a central release of OXA.

Now COA is a trace conditioning that results from several processes that follow one another over time: during acquisition, CS and US processing is followed by association of the two stimuli; then, the CS-US association is consolidated and finally retrieved during the test when the CS is presented for the second time. Some studies have shown that behavioral effects of icv OXA infusion, such as feeding and drinking behavior or olfactory hypersensitivity, persist for at least 3 h (Sakurai et al., 1998; Edwards et al., 1999; Kunii et al., 1999; Julliard et al., 2007). Therefore, the effects of fasting and OXA infusion on COA observed in the present study may have resulted from changes in the processes of acquisition and/or consolidation taking place on Day 8.

HYPOTHESIS ON THE PROCESSES BY WHICH FASTING AND OXA MAY HAVE INFLUENCED COA LEARNING

CS-US Acquisition

Olfactory hypersensitivity and olfactory memory trace duration. As discussed in the Introduction, the acquisition of COA reflects the association between the memory trace of the olfactory CS and the delayed visceral US (see Bures and Buresova, 1990; Roldan and Bures, 1994). Several studies have shown that, when ingested, a tasteless olfactory stimulus (such as ISO) acquires a strong aversive value, even with CS-US intervals equivalent to those generally used for tastes (Rusiniak et al., 1982; Bouton et al., 1986; Slotnick et al., 1997; Chapuis et al., 2007). At a concentration of 10−4 in tap water, ISO, used as the CS for COA, has been shown to be resistant to a relatively long interval (up to 30 min) before delivery of the US (Chapuis et al., 2007; Ferry et al., 2007; Miranda et al., 2007). Interestingly, results obtained in aCSF group showed that ISO mixed in tap water at a concentration of 10−6 was able to induce a mild COA that extinguished in one session when the time interval (ISI) separating the CS from the US was about 20 min. Therefore, it is suggested that, in our conditions, the CS trace decayed over the 20-min ISI, at the end of which it is weakly associated to the US.

The effectiveness of an olfactory CS in inducing strong COA when paired with a delayed illness has been shown to be directly related to the intensity of the CS used during acquisition (Rusiniak et al., 1982; Slotnick et al., 1997). Thus, in the light of the increased olfactory sensitivity previously reported with 24-h fasting and icv OXA infusion (Julliard et al., 2007), it is possible that the strong COA obtained in the Fasted and OXA groups was directly linked to an enhanced memory for the CS resulting from the change in olfactory perception. Interestingly, a similar cause-effect relationship was described in a previous experiment by our team, in which entorhinal cortex lesion enhanced COA learning performances, an effect that was accompanied by olfactory hypersensitivity (Ferry et al., 1996). Therefore, by enhancing olfactory sensitivity, fasting and OXA infusion may have enhanced the duration of the olfactory trace by enhancing CS salience, thus rendering possible its association to the delayed toxicosis.

Higher US processing. In the same vein, Garcia and Koelling (1967) demonstrated that the strength of a conditioned response to an ingested CS previously paired with a gastric malaise US varies directly with the intensity of the US. Concerning the COA, similar results have been described and the strength of COA varied directly with the intensity of the US used (Rusiniak et al., 1982; Bouton et al., 1986; Slotnick et al., 1997; Chapuis et al., 2007; Ferry et al., 2007). It could therefore be argued that the enhanced COA observed in the fasted group resulted from the visceral discomfort induced by injecting the LiCl used in the COA protocol, being more intense in a 24-h fasted animal, enhancing the strength of the CS-US association. However, some studies have shown that the orexinergic system is independent of the system that deals with visceral processing of the LiCl-induced intoxication in conditioned aversive learning (Touzani and Sclafani, 2002; Di Sebastiano et al., 2011). Therefore, any indirect effect of fasting or OXA infusion on COA strength via a change in US processing may be discounted.

Regardless of this, Winsky-Sommerer et al. (2004) have shown that OXA system is activated by the corticotropin-releasing factor (CRF) that is released in condition of acute stress. Considering these data, it could be suggested that the stress induced by the US administration may have influenced the release of OXA through the activation of the CRF system in both groups of animals. If the fasting condition also enhanced the OXA level by the same route, it could be argued that the similarity in the COA performances obtained in Fasted and OXA groups was due to similar enhanced level of OXA with the different experimental conditions. If so, this would suggest that the stress induced by the US LiCl may have influenced the CS-US association or the processes underlying the CS-US memory. Even though this hypothesis could explain the similarity in the COA performances between Fasted and satiated OXA groups, however future studies will aim at verifying whether an acute restraint stress can be physiologically compared to this induced by a LiCl-induced intoxication and also whether the administration of the US in our conditions can induce OXA release.

CS-US consolidation

Finally, the temporal evolution of CS-US consolidation remains largely unknown; however, Dudai (1996) and Dudai and Morris (2000) proposed that consolidation involves two types of processes: synaptic consolidation, accomplished within the first minutes to hours after the CS-US association has been acquired; and system consolidation, involving reorganization of the brain circuits encoding the memory, which takes weeks, months or even years to be accomplished. Considering the duration of OXA action (at least 3 h, Sakurai et al., 1998; Edwards et al., 1999; Kunii et al., 1999; Julliard et al., 2007), it may be suggested that the enhanced COA observed in the OXA group was mediated by enhanced synaptic consolidation of the memory processes. According to this view, recent data have shown that central OXA system is involved in the acquisition and in the consolidation of fear conditioned learning (Soya et al., 2013). In order to test the involvement of the OXA system in COA consolidation process, it could be of interest to test the effect of selective blockade of OXA receptors at various times during COA learning. In this way, we assume that if the OXA system is selectively involved in the CS-US acquisition process, infusion of the antagonist before the CS presentation would disrupt both short (3–4 h after acquisition) and long term (24 h) memories. In contrast, if the OXA system is involved in consolidation, the pharmacological blockade of OXA receptors would impair selectively long-term memory leaving short-term memory intact (Sears et al., 2013).

As a first conclusion, the present results show that physiological or OXA-induced fasting affected COA through changes in memory processes occurring during the acquisition of a CS-US association and/or during the synaptic consolidation of this association. The olfactory hypersensitivity induced by fasting may influence acquisition of the CS-US association by enhancing the formation and maintenance of the CS memory trace.

HYPOTHESIS ON THE NEUROBIOLOGICAL SUBSTRATE INVOLVED IN THE EFFECTS OF FASTING AND OXA ON COA

The neurobiological substrate through which OXA affects the memory processes underlying COA learning remains to be elucidated. However, some reports open up a number of possible hypotheses according to which OXA release during fasting may enhance learning performance through a direct or indirect influence on particular hypothalamic projection targets.

Olfactory bulb (OB) and locus coeruleus (LC)

As previously mentioned, olfactory sensitivity cannot be dissociated from olfactory memory, and some data suggest that the indirect effects of fasting and OXA on the olfactory memory trace formation through increased olfactory sensitivity may involve the olfactory bulb (OB). As previously mentioned, olfactory sensitivity cannot be dissociated from olfactory memory, and some data suggest that the indirect effects of fasting and OXA on the olfactory memory trace formation through increased olfactory sensitivity may involve the OB. Firstly, in addition to its well documented role in detection and discrimination, the OB is involved in memory processes underlying various kinds of olfactory learning in adult rats (see Mandairon and Linster, 2009 for review). Moreover, the OB receives direct OXA innervation from the LH (de Lecea et al., 1998; Peyron et al., 1998; Sakurai et al., 1998; Shibata et al., 2008) and an increase in OB electrophysiological response induced by fasting and OXA has been described (Pager et al., 1972; Gervais and Pager, 1982; Apelbaum and Chaput, 2003; Apelbaum et al., 2005; Hardy et al., 2005). In addition, Prud’homme et al. (2009) found that OXA antagonist treatment blocked the enhancement of OB Fos responses to a food odor. Secondly, some evidence suggests that the OXA neurons terminating in the locus coeruleus (LC) may provide a second indirect pathway for orexinergic modulation of olfactory processing: direct OXA fibers innervate the LC (Horvath et al., 1999) and activation of OXA receptors in the LC increases cell firing of intrinsic noradrenergic (NA) neurons (Trivedi et al., 1998; Hagan et al., 1999). In addition, the LC projects over 40% of its neurons directly into the OB (McLean et al., 1989) and this large NA input has been shown to modulate OB excitability, olfactory perception and olfactory learning and memory abilities (see Devore and Linster, 2012 for review).

These data suggest that the fasting-induced increase in olfactory sensitivity observed in the present and other studies probably involved the OXA system in the OB. Moreover, and in the light of the work by Escanilla et al. (2012), it may be suggested that this increased olfactory sensitivity resulted directly from OXA system activation (Hardy et al., 2005) in the OB and/or indirectly through the effect of LC-OXA system activation on NA release in the OB. Finally, it is possible that activation of both systems may simultaneously influence the strength of olfactory memory through enhanced olfactory processing. In order to test this hypothesis, future studies will aim at verifying whether the presentation of a new olfactory stimulus in fasted animals can be correlated to changes in NA release in the OB.

Amygdala and locus coeruleus (LC)

Orexinergic innervation of the extended amygdala (including the basolateral amygdala, BLA) was clearly described by Schmitt et al. (2012). OXA administered into the LH significantly elevated cFos-immunoreactivity in the amygdala (Mullett et al., 2000) and fasting increased OXA mRNA levels in the amygdala (Lu et al., 2000). OXA applied in acute rat brain slices activated neurons in the amygdala (Bisetti et al., 2006). Moreover, the amygdala receives OB and visceral inputs (Saper and Loewy, 1980; Inui et al., 2006), and may be a nodal point at which olfactory and neuroendocrine stimuli are integrated to modulate feeding behavior (King, 2006).

Otherwise, a large amount of data indicates that the amygdala, and more precisely the BLA, is involved in the acquisition of COA (Sevelinges et al., 2009) and more precisely in the processes underlying the formation of the olfactory memory trace and its maintenance across the ISI during COA (Ferry et al., 1995; Ferry and Di Scala, 1997, 2000). Although a direct effect of starvation-induced OXA release in the amygdala on COA cannot be excluded, to our knowledge, the involvement of the OXA system in the amygdala has never been demonstrated in learning and memory.

On the other hand, some data indicate that the enhancing effect of starvation on COA memory processes could be indirectly mediated by activation of the NA system in the amygdala. The LC projects strongly onto the amygdala (Fallon et al., 1978) and the BLA β-adrenergic system is involved in the memory processes underlying the association between odor and delayed US during COA (Miranda et al., 2007). Given the direct action of LH orexinergic neurons on the LC (Horvath et al., 1999; van den Pol et al., 2002), activation of the LC-amygdala NA system during processing of the new odor CS may be potentiated by fasting-induced OXA release. Possibly, the strength of the olfactory memory trace, and/or its association to the US, was influenced by activation of this pathway in the Fasted and OXA groups.

Although the results shown on Figure 1 do not identify particular structures receiving OXA projections as being involved in the memory processes underlying COA acquisition, all the above-mentioned data lead us to propose a model according to which OXA release during fasting enhances learning performance through a direct or indirect influence on the circuit involved in COA. Our model shown on Figure 2 comprises some of the structures involved in this circuit including the LC, amygdala and OB, which receive LH-OXA projections. According to this model, the activation of the LH-OXA system induced by fasting reinforces the role of each structure in the circuit by enhancing the neural processes underlying attention and olfactory memory through direct and/or indirect influences.

FIGURE 2. Representation of a hypothetic model according to which OXA terminating in regions such as the OB, LC and amygdala may constitute a pathway for orexinergic modulation of the olfactory memory trace formation underlying COA. The left panel represents the sequence of events that may take place during presentation of a new olfactory CS in the satiated condition. 1a) Olfactory CS induces activation of the OB. 1b) The novelty of the olfactory CS induces LC activation, which results in NA release in the OB and BLA. 2) The olfactory information is transmitted to the BLA where the odor trace is formed pending its association with the US. In the case of a short ISI, this sequence of events results in normal COA. The right panel represents the sequence of events that may take place during presentation of a new olfactory CS in the fasting condition. Fasting induces release of OXA in the OB, BLA and LC, preparing the system to respond to any food-odor event. 2a) Olfactory CS induces activation of the OB, potentiated by activation of the OXA system and LC-mediated NA system in the OB. In the fasting condition, CS leads to improved olfactory detection and processing. 2b) The novelty of the olfactory CS induces LC activation, resulting in enhanced NA release in the OB and BLA. 3) The enhanced olfactory information is transmitted to the BLA. Combined with OXA system activation, LC-mediated NA release in the BLA and potentiated OB activation, the olfactory memory trace strengthened or lengthened, and can thus be associated to a delayed US. This sequence of events results in COA when a long ISI is used.

This model is consistent with the idea of Cleland and Linster (2005) according to which the multiple feedback and feed-forward interactions between olfactory and non-olfactory areas contribute to complex processes, such as filtering and constructing olfactory representations, and compare these representations to those previously acquired in order to cue an appropriate response to relevant stimuli. The present model also supports the hypothesis that centrifugal modulatory inputs influence olfactory processing and learning mechanisms within the OB (Sullivan et al., 2000; Linster and Cleland, 2002; Yuan et al., 2003), leading us to extend these influences to other key structures involved in attention and olfactory learning and memory (Aston-Jones and Cohen, 2005; Miranda, 2012).

Of course the list of structures included in this model is not exhaustive; involvement of other feedback and feed-forward interactions between these structures and others (e.g., piriform cortex, entorhinal cortex, orbitofrontal cortex, hippocampus, etc.) will have to be considered in order to achieve a more realistic idea of the circuit actually involved in food conditioned learning (see Ferry et al., 2006, 2007; Chapuis et al., 2009; Sahay et al., 2011; Wilson and Sullivan, 2011; Chapuis et al., 2013).

CONCLUSION

Feeding behavior is part of a complex integrated adaptive system, governed by the brain, in which the processing of metabolic signals reflecting the animal’s nutritional state (gastrointestinal distention, blood glucose, feeding peptides such as OXA, etc.) and of olfactory signals indicative of food determines the appropriate response to a food source. However, the differentiation between palatable and unpalatable items that conditions ingestive behavior often depends on previous experience during which the odor of the food acquired (or did not, in the case of a new odor) a hedonic valence after feeding, through CS-US associative learning. By showing that OXA system influenced the processes underlying the CS-US association, or/and consolidation of this association during COA, the present study introduces a new mechanism by which the LH-OXA system may influence the processes that enable animals to learn to select food available in the environment and to adapt their behavior to previous experience through a modulation of complex neural circuit activity. Finally, the OXA system represents a critical link between peripheral energy balance and CNS mechanisms that coordinate olfactory processing and memory, especially in the physiological state of fasting.

CONFLICT OF INTEREST STATEMENT

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

ACKNOWLEDGMENTS

This work was supported by the LABEX CORTEX (ANR-11-LABX-0042) of Université de Lyon, within the program “Investissements d’Avenir” (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR).

REFERENCES

Aimé, P., Duchamp-Viret, P., Chaput, M. A., Savigner, A., Mahfouz, M., and Julliard, A. K. (2007). Fasting increases and satiation decreases olfactory detection for a neutral odor in rats. Behav. Brain Res. 179, 258–264. doi: 10.1016/j.bbr.2007.02.012

Apelbaum, A. F., and Chaput, M. A. (2003). Rats habituated to chronic feeding restriction show a smaller increase in olfactory bulb reactivity compared to newly fasted rats. Chem. Senses 28, 389–395. doi: 10.1093/chemse/28.5.389

Apelbaum, A., Perrut, A., and Chaput, M. (2005). Orexin A effects on the olfactory bulb spontaneous activity and odor responsiveness in freely breathing rats. Regul. Pept. 129, 49–61. doi: 10.1016/j.regpep.2005.01.003

Aston-Jones, G., and Cohen, J. D. (2005). An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. Annu. Rev. Neurosci. 28, 403–450. doi: 10.1146/annurev.neuro.28.061604.135709

Bisetti, A., Cvetkovic, V., Serafin, M., Bayer, L., Machard, D., Jones, B. E., et al. (2006). Excitatory action of hypocretin/orexin on neurons of the central medial amygdala. Neuroscience 142, 999–1004. doi: 10.1016/j.neuroscience.2006.07.018

Bouton, M. E., Jones, D. E., McPhillips, S. A., and Swartzentruber, D. (1986). Potentiation and overshadowing in odor-aversion learning: role of method of odor presentation, the distal–proximal cue distinction and the conditionability of odor. Learn. Motiv. 17, 115–138. doi: 10.1016/0023-9690(86)90006-8

Bures, J., and Buresova, O. (1990). Reversible lesions allow reinterpretation of system level studies of brain mechanisms of behavior. Concepts Neurosci. 1, 69–89.

Caillol, M., Aïoun, J., Baly, C., Persuy, M. A., and Salesse, R. (2003). Localization of orexins and their receptors in the rat olfactory system: possible modulation of olfactory perception by a neuropeptide synthetized centrally or locally. Brain Res. 960, 48–61. doi: 10.1016/s0006-8993(02)03755-1

Chapuis, J., Messaoudi, B., Ferreira, G., and Ravel, N. (2007). Importance of retronasal and orthonasal olfaction for odor aversion memory in rats. Behav Neurosci. 121, 1383–1392. doi: 10.1037/0735-7044.121.6.1383

Chapuis, J., Garcia, S., Messaoudi, B., Thevenet, M., Ferreira, G., Gervais, R., et al. (2009). The way an odor is experienced during aversive conditioning determines the extent of the network recruited during retrieval: a multisite electrophysiological study in rats. J. Neurosci. 29, 10287–10298. doi: 10.1523/JNEUROSCI.0505-09.2009

Chapuis, J., Cohen, Y., He, X., Zhang, Z., Jin, S., Xu, F., et al. (2013). Lateral entorhinal modulation of piriform cortical activity and fine odor discrimination. J. Neurosci. 33, 13449–13459. doi: 10.1523/jneurosci.1387-13.2013

Cleland, T. A., and Linster, C. (2005). Computation in the olfactory system. Chem. Senses 30, 801–813. doi: 10.1093/chemse/bji072

Das, A., Rai, D., Dikshit, M., Palit, G., and Nath, C. (2005). Nature of stress: differential effects on brain acetylcholinesterase activity and memory in rats. Life Sci. 77, 2299–2311. doi: 10.1016/j.lfs.2005.02.020

de Lecea, L., Kilduff, T. S., Peyron, C., Gao, X., Foye, P. E., Danielson, P. E., et al. (1998). The hypocretins: hypothalamus-specific peptides with neuroexcitatory activity. PNAS 95, 322–327. doi: 10.1073/pnas.95.1.322

Devore, S., and Linster, C. (2012). Noradrenergic and cholinergic modulation of olfactory bulb sensory processing. Front. Behav. Neurosci. 6:52. doi: 10.3389/fnbeh.2012.00052

Di Sebastiano, A. R., Wilson-Pérez, H. E., Lehman, M. N., and Coolen, L. M. (2011). Lesions of orexin neurons block conditioned place preference for sexual behavior in male rats. Horm. Behav. 59, 1–8. doi: 10.1016/j.yhbeh.2010.09.006

Doucette, W., and Restrepo, D. (2008). Profound context-dependent plasticity of mitral cell responses in olfactory bulb. PLoS Biol. 6:e258. doi: 10.1371/journal.pbio.0060258

Doucette, W., Milder, J., and Restrepo, D. (2007). Adrenergic modulation of olfactory bulb circuitry affects odor discrimination. Learn. Mem. 14, 539–547. doi: 10.1101/lm.606407

Dudai, Y. (1996). Consolidation: fragility on the road to the engram. Neuron 17, 367–370. doi: 10.1016/s0896-6273(00)80168-3

Dudai, Y., and Morris, R. G. M. (2000). “To consolidate or not to consolidate: what are the questions?,” in Brain, Perception, Memory. Advances in Cognitive Sciences, eds J. J. Bolhuis (Oxford: Oxford University Press), 149–162.

Edwards, C. M., Abusnana, S., Sunter, D., Murphy, K. G., Ghatei, M. A., and Bloom, S. R. (1999). The effect of the orexins on food intake: comparison with neuropeptide Y, melanin-concentrating hormone and galanin. J. Endocrinol. 160, R7–R12. doi: 10.1677/joe.0.160r007

Eisenberg, M., Kobilo, T., Berman, D. E., and Dudai, Y. (2003). Stability of retrieved memory: inverse correlation with trace dominance. Science 301, 1102–1104. doi: 10.1126/science.1086881

Escanilla, O., Alperin, S., Youssef, M., Ennis, M., and Linster, C. (2012). Noradrenergic but not cholinergic modulation of olfactory bulb during processing of near threshold concentration stimuli. Behav. Neurosci. 126, 720–728. doi: 10.1037/a0030006

Fallon, J. H., Koziell, D. A., and Moore, R. Y. (1978). Catecholamine innervation of the basal forebrain. II. Amygdala, suprarhinal cortex and entorhinal cortex. J. Comp. Neurol. 180, 509–532. doi: 10.1002/cne.901800308

Ferry, B., and Duchamp-Viret, P. (2014). The orexin component of fasting triggers memory processes underlying food selection in the rat. Learn. Mem. 21, 185–189. doi: 10.1101/lm.033688.113

Ferry, B., Sandner, G., and Di Scala, G. (1995). Neuroanatomical and functional specificity of basolateral amygdaloid nucleus in taste potentiated odor aversion. Neurobiol. Learn. Mem. 64, 169–180. doi: 10.1006/nlme.1995.1056

Ferry, B., Herbeaux, K., Traissard, N., Cosquer, B., Galani, R., and Cassel, J. C. (2007). Immunotoxic cholinergic lesions in the basal forebrain reverse the effects of entorhinal cortex lesions on conditioned odor aversion in the rat. Neurobiol. Learn. Mem. 88, 114–126. doi: 10.1016/j.nlm.2007.01.007

Ferry, B., Ferreira, G., Traissard, N., and Majchrzak, M. (2006). Selective involvement of the lateral entorhinal cortex in the control of the olfactory memory trace during conditioned odor aversion in the rat. Behav. Neurosci. 120, 1180–1186. doi: 10.1037/0735-7044.120.5.1180

Ferry, B., and Di Scala, G. (1997). Bicuculline administration into basolateral amygdala facilitates trace conditioning of odor aversion in the rat. Neurobiol. Learn. Mem. 67, 80–83. doi: 10.1006/nlme.1996.3743

Ferry, B., and Di Scala, G. (2000). Basolateral amygdala NMDA receptors are selectively involved in the acquisition of taste potentiated odor aversion in the rat. Behav. Neurosci. 114, 1005–1010. doi: 10.1037/0735-7044.114.5.1005

Ferry, B., Oberling, P., Jarrard, L. E., and Di Scala, G. (1996). Facilitation of conditioned odor aversion by entorhinal cortex lesions in the rat. Behav. Neurosci. 110, 443–450. doi: 10.1037/0735-7044.110.3.443

Fletcher, M. L., and Chen, W. R. (2010). Neural correlates of olfactory learning: critical role of centrifugal neuromodulation. Learn. Mem. 17, 561–570. doi: 10.1101/lm.941510

Garcia, J., and Koelling, R. A. (1967). A comparison of aversions induced by X rays, toxins and drugs in the rat. Radiat. Res. Suppl. 7, 439–450. doi: 10.2307/3583736

Gervais, R., and Pager, J. (1982). Functional changes in waking and sleeping rats after lesions in the olfactory pathways. Physiol. Behav. 29, 7–15. doi: 10.1016/0031-9384(82)90358-4

Hagan, J. J., Leslie, R. A., Patel, S., Evans, M. L., Wattam, T. A., Holmes, S., et al. (1999). Orexin A activates locus coeruleus cell firing and increases arousal in the rat. Proc. Natl. Acad. Sci. U S A 96, 10911–10916. doi: 10.1073/pnas.96.19.10911

Hahn, J. D., and Swanson, L. W. (2010). Distinct patterns of neuronal inputs and outputs of the juxtaparaventricular and suprafornical regions of the lateral hypothalamic area in the male rat. Brain Res. 64, 14–103. doi: 10.1016/j.brainresrev.2010.02.002

Hardy, A. B., Aioun, J., Baly, C., Julliard, K. A., Caillol, M., Salesse, R., et al. (2005). Orexin A modulates mitral cell activity in the rat olfactory bulb: patch-clamp study on slices and immunocytochemical localization of orexin receptors. Endocrinol. 146, 4042–4053. doi: 10.1210/en.2005-0020

Harris, G. C., Wimmer, M., and Aston-Jones, G. (2005). A role for lateral hypothalamic orexin neurons in reward seeking. Nature 437, 556–559. doi: 10.1038/nature04071

Holland, P. (1990). Event representation in pavlovian conditioning: image and action. Cognition 37, 105–131. doi: 10.1016/0010-0277(90)90020-k

Horvath, T. L., Peyron, C., Diano, S., Ivanov, A., Aston-Jones, G., Kilduff, T. S., et al. (1999). Hypocretin (orexin) activation and synaptic innervation of the locus coeruleus noradrenergic system. J. Comp. Neurol. 415, 145–159. doi: 10.1002/(sici)1096-9861(19991213)415:2<145::aid-cne1>3.0.co;2-2

Inui, T., Shimura, T., and Yamamoto, T. (2006). Effects of brain lesions on taste-potentiated odor aversion in rats. Behav. Neurosci. 120, 590–599. doi: 10.1037/0735-7044.120.3.590

Jaeger, L. B., Farr, S. A., Banks, W. A., and Morley, J. E. (2002). Effects of orexin-A on memory processing. Peptides 23, 1683–1688. doi: 10.1016/s0196-9781(02)00110-9

Johansson, A., Fredriksson, R., Winnergren, S., Hulting, A. L., Schiöth, H. B., and Lindblom, J. (2008). The relative impact of chronic food restriction and acute food deprivation on plasma hormone levels and hypothalamic neuropeptide expression. Peptides 29, 1588–1595. doi: 10.1016/j.peptides.2008.04.018

Julliard, A. K., Chaput, M. A., Apelbaum, A., Aimé, P., Mahfouz, M., and Duchamp-Viret, P. (2007). Changes in rat olfactory detection performance induced by orexin and leptin mimicking fasting and satiation. Behav. Brain Res. 183, 123–129. doi: 10.1016/j.bbr.2007.05.033

King, B. M. (2006). Amygdaloid lesion-induced obesity: relation to sexual behavior, olfaction and the ventromedial hypothalamus. Am. J. Physiol. Regul. Integr. Comp. Physiol. 291, R1201–R1214. doi: 10.1152/ajpregu.00199.2006

Kunii, K., Yamanaka, A., Nambu, T., Matsuzaki, I., Goto, K., and Sakurai, T. (1999). Orexins/hypocretins regulate drinking behaviour. Brain Res. 842, 256–261. doi: 10.1016/s0006-8993(99)01884-3

Le Magnen, J. (1959). The role of olfacto-gustatory stimuli in the regulation of the alimentary behavior of the mammal. J. Psychol. Norm. Pathol. (Paris) 56, 137–160.

Linster, C., and Cleland, T. A. (2002). Cholinergic modulation of sensory representations in the olfactory bulb. Neural Netw. 15, 709–717. doi: 10.1016/s0893-6080(02)00061-8

Lu, X. Y., Bagnol, D., Burke, S., Akil, H., and Watson, S. J. (2000). Differential distribution and regulation of OX1 and OX2 orexin/hypocretin receptor messenger RNA in the brain upon fasting. Horm. Behav. 37, 335–344. doi: 10.1006/hbeh.2000.1584

Mackintosh, N. J. (1991). “Simple conditioning,” in Perspectives on Cognitive Neuroscience, eds R. G. Lister and H. J. Weingartner (London: Oxford University Press), 65–75.

Mair, R. G., and Hembrook, J. R. (2008). Memory enhancement with event-related stimulation of the rostral intralaminar thalamic nuclei. J. Neurosci. 28, 14293–14300. doi: 10.1523/jneurosci.3301-08.2008

Mandairon, N., and Linster, C. (2009). Odor perception and olfactory bulb plasticity in adult mammals. J. Neurophysiol. 101, 2204–2209. doi: 10.1152/jn.00076.2009

McLean, J. H., Shipley, M. T., Nickell, W. T., Aston-Jones, G., and Reyher, C. K. (1989). Chemoanatomical organization of the noradrenergic input from locus coeruleus to the olfactory bulb of the adult rat. J. Comp. Neurol. 285, 339–349. doi: 10.1002/cne.902850305

Miranda, M. I. (2012). Taste and odor recognition memory: the emotional flavor of life. Rev. Neurosci. 23, 481–499. doi: 10.1515/revneuro-2012-0064

Miranda, M. A., Ferry, B., and Ferreira, G. (2007). Basolateral amygdala noradrenergic activity is involved in the acquisition of conditioned odor aversion in the rat. Neurobiol. Learn. Mem. 88, 260–263. doi: 10.1016/j.nlm.2007.04.008

Mullett, M. A., Billington, C. J., Levine, A. S., and Kotz, C. M. (2000). Hypocretin I in the lateral hypothalamus activates key feeding-regulatory brain sites. Neuroreport 11, 103–108. doi: 10.1097/00001756-200001170-00021

Mulligan, C., Moreau, K., Brandolini, M., Livingstone, B., Beaufrère, B., and Boirie, Y. (2002). Alterations of sensory perceptions in healthy elderly subjects during fasting and refeeding. A pilot study. Gerontology 48, 39–43. doi: 10.1159/000048923

Nambu, T., Sakurai, T., Mizukami, K., Hosoya, Y., Yanagisawa, M., and Goto, K. (1999). Distribution of orexin neurons in the adult rat brain. Brain Res. 827, 243–260. doi: 10.1016/s0006-8993(99)01336-0

Nigrosh, B. J., Slotnick, B. M., and Nervin, J. A. (1975). Olfactory discrimination, reversal learning, and stimulus control in rats. J. Comp. Physiol. Psychol. 89, 285–294. doi: 10.1037/h0076821

Nowland, M. H., Hugunin, K. M., and Rogers, K. L. (2011). Effects of short-term fasting in male Sprague-Dawley rats. Comp. Med. 61, 138–144.

O’Doherty, J., Rolls, E. T., Francis, S., Bowtell, R., McGlone, F., Kobal, G., et al. (2000). Sensory-specific satiety-related olfactory activation of the human orbitofrontal cortex. Neuroreport 11, 893–897. doi: 10.1097/00001756-200003200-00046

Pager, J. (1974). A selective modulation of the olfactory bulb electrical activity in relation to the learning of palatability in hungry and satiated rats. Physiol. Behav. 12, 189–195. doi: 10.1016/0031-9384(74)90172-3

Pager, J., Giachetti, I., Holley, A., and Le Magnen, J. (1972). A selective control of olfactory bulb electrical activity in relation to food deprivation and satiety in rats. Physiol. Behav. 9, 573–579. doi: 10.1016/0031-9384(72)90014-5

Pager, J. (1978). Ascending olfactory information and centrifugal influx contributing to a nutritional modulation of the rat mitral cell responses. Brain Res. 140, 251–269. doi: 10.1016/0006-8993(78)90459-6

Palmerino, C. C., Rusiniak, K. W., and Garcia, J. (1980). Flavor–illness aversions: the peculiar roles of odor and taste in memory for poison. Science 208, 753–755. doi: 10.1126/science.7367891

Peyron, C., Tighe, D., van Den Pol, A. N., De Lecea, L., Heller, H. C., Sutcliffe, J. G., et al. (1998). Neurons containing hypocretin (orexin) project to multiple neuronal systems. J. Neurosci. 18, 9996–10015.

Prud’homme, M. J., Lacroix, M. C., Badonnel, K., Gougis, S., Baly, C., Salesse, R., et al. (2009). Nutritional status modulates behavioural and olfactory bulb Fos responses to isoamyl acetate or food odour in rats: roles of orexins and leptin. Neuroscience 162, 1287–1298. doi: 10.1016/j.neuroscience.2009.05.043

Rescorla, R. A., and Heth, C. D. (1975). Reinstatement of fear to an extinguished conditioned stimulus. J. Exp. Psychol. Anim. Behav. Process 1, 88–96. doi: 10.1037/0097-7403.1.1.88

Rescorla, R. A. (1988). Behavioral studies of Pavlovian conditioning. Annu. Rev. Neurosci. 11, 329–352. doi: 10.1146/annurev.neuro.11.1.329

Robbins, S. J. (1990). Mechanisms underlying spontaneous recovery in autoshaping. J. Exp. Psychol. Anim. Behav. Process 16, 235–249. doi: 10.1037/0097-7403.16.3.235

Rodgers, R. J., Ishii, Y., Halford, J. C., and Blundell, J. E. (2002). Orexins and appetite regulation. Neuropeptides 36, 303–325. doi: 10.1016/s0143-4179(02)00085-9

Roldan, G., and Bures, J. (1994). Tetrodotoxin blockade of amygdala overlapping with poisoning impairs acquisition of conditioned taste aversion in rats. Behav. Brain Res. 65, 213–219. doi: 10.1016/0166-4328(94)90107-4

Royet, J. P., Gervais, R., and Araneda, S. (1983). Effect of local 6-OHDA and 5, 6-DHT injections into the rat olfactory bulb on neophobia and learned aversion to a novel food. Behav. Brain Res. 10, 297–309. doi: 10.1016/0166-4328(83)90036-0

Rusiniak, K. W., Palmerino, C. C., Rice, A. G., Forthman, D. L., and Garcia, J. (1982). Flavor-illness aversions: potentiation of odor by taste with toxin but not shock in rats. J. Comp. Physiol. Psychol. 96, 527–539. doi: 10.1037/h0077902

Sahay, A., Wilson, D. A., and Hen, R. (2011). Pattern separation: a common function for new neurons in hippocampus and olfactory bulb. Neuron 70, 582–588. doi: 10.1016/j.neuron.2011.05.012

Sakurai, T., Amemiya, A., Ishii, M., Matsuzaki, I., Chemelli, R. M., Tanaka, H., et al. (1998). Orexins and orexin receptors: a family of hypothalamic neuropeptides and G protein-coupled receptors that regulate feeding behavior. Cell 92, 573–585. doi: 10.1016/s0092-8674(00)80949-6

Sakurai, T. (2005). Roles of orexin/hypocretin in regulation of sleep/wakefulness and energy homeostasis. Sleep Med. Rev. 9, 231–241. doi: 10.1016/j.smrv.2004.07.007

Saper, C. B., and Loewy, A. D. (1980). Efferent connections of the parabrachial nucleus in the rat. Brain Res. 197, 291–317. doi: 10.1016/0006-8993(80)91117-8

Schmitt, O., Usunoff, K. G., Lazarov, N. E., Itzev, D. E., Eipert, P., Rolfs, A., et al. (2012). Orexinergic innervation of the extended amygdala and basal ganglia in the rat. Brain Struct. Funct. 217, 233–256. doi: 10.1007/s00429-011-0343-8

Sears, R. M., Fink, A. E., Wigestrand, M. B., Farb, C. R., de Lecea, L., and Ledoux, J. E. (2013). Orexin/hypocretin system modulates amygdala-dependent threat learning through the locus coeruleus. Proc. Natl. Acad. Sci. U S A 110, 20260–20265. doi: 10.1073/pnas.1320325110

Sevelinges, Y., Desgranges, B., and Ferreira, G. (2009). The basolateral amygdala is necessary for the encoding and the expression of odor memory. Learn. Mem. 16, 235–242. doi: 10.1101/lm.1247609

Shibata, M., Mondal, M. S., Date, Y., Nakazato, M., Suzuki, H., and Ueta, Y. (2008). Distribution of orexins-containing fibers and contents of orexins in the rat olfactory bulb. Neurosci. Res. 61, 99–105. doi: 10.1016/j.neures.2008.01.017

Slotnick, B. M., Westbrook, F., and Darling, F. M. C. (1997). What the rat’s nose tells the rat’s mouth: long delay aversion conditioning with aqueous odors and potentiation of taste by odors. Anim. Learn. Behav. 25, 357–369. doi: 10.3758/bf03199093

Slotnick, B. M. (1984). Olfactory stimulus control in the rat. Chem. Senses 9, 157–165. doi: 10.1093/chemse/9.2.157

Slotnick, B. M., and Katz, H. M. (1974). Olfactory learning set formation in rats. Science 185, 796–798. doi: 10.1126/science.185.4153.796

Soya, S., Shoji, H., Hasegawa, E., Hondo, M., Miyakawa, T., Yanagisawa, M., et al. (2013). Orexin receptor-1 in the locus coeruleus plays an important role in cue-dependent fear memory consolidation. J. Neurosci. 33, 14549–14557. doi: 10.1523/jneurosci.1130-13.2013

Sullivan, R. M., Stackenwalt, G., Nasr, F., Lemon, C., and Wilson, D. A. (2000). Association of an odor with activation of olfactory bulb noradrenergic beta-receptors or locus coeruleus stimulation is sufficient to produce learned approach responses to that odor in neonatal rats. Behav. Neurosci. 114, 957–962. doi: 10.1037/0735-7044.114.5.957

Swanson, L. W., Sanchez-Watt, G., and Watt, A. G. (2005). Comparison of melanin-concentrating hormone and hypocretin/orexin mRNA expression patterns in a new parceling scheme of the lateral hypothalamic zone. Neurosci. Lett. 387, 80–84. doi: 10.1016/j.neulet.2005.06.066

Telegdy, G., and Adamik, A. (2002). The action of orexin A on passive avoidance learning. Regul. Pept. 104, 105–110. doi: 10.1016/s0167-0115(01)00341-x

Touzani, K., and Sclafani, A. (2002). Lateral hypothalamic lesions impair flavour-nutrient and flavour-toxin trace learning in rats. Eur. J. Neurosci. 16, 2425–2433. doi: 10.1046/j.1460-9568.2002.02404.x

Trivedi, P., Yu, H., MacNeil, D. J., Van der Ploeg, L. H., and Guan, X. M. (1998). Distribution of orexin receptor mRNA in the rat brain. FEBS Lett. 438, 71–75. doi: 10.1016/s0014-5793(98)01266-6

van den Pol, A. N., Ghosh, P. K., Liu, R. J., Li, Y., Aghajanian, G. K., and Gao, X. B. (2002). Hypocretin (orexin) enhances neuron activity and cell synchrony in developing mouse GFP-expressing locus coeruleus. J. Physiol. 541, 169–185. doi: 10.1113/jphysiol.2002.017426

Willie, J. T., Chemelli, R. M., Sinton, C. M., and Yanagisawa, M. (2001). To eat or to sleep? Orexin in the regulation of feeding and wakefulness. Annu. Rev. Neurosci. 24, 429–458. doi: 10.1146/annurev.neuro.24.1.429

Wilson, D. A., and Sullivan, R. M. (2011). Cortical processing of odor objects. Neuron 72, 506–519. doi: 10.1016/j.neuron.2011.10.027

Winsky-Sommerer, R., Yamanaka, A., Diano, S., Borok, E., Roberts, A. J., Sakurai, T., et al. (2004). Interaction between the corticotropin-releasing factor system and hypocretins (orexins): a novel circuit mediating stress response. J. Neurosci. 24, 11439–11448. doi: 10.1523/jneurosci.3459-04.2004

Yuan, Q., Harley, C. W., and McLean, J. H. (2003). Mitral cell beta1 and 5-HT2A receptor colocalization and cAMP coregulation: a new model of norepinephrine-induced learning in the olfactory bulb. Learn. Mem. 10, 5–15. doi: 10.1101/lm.54803

Conflict of Interest Statement: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 05 February 2014; Accepted: 18 April 2014; Published online: 06 May 2014.

Citation: Ferry B (2014) The orexinergic system influences conditioned odor aversion learning in the rat: a theory on the processes and hypothesis on the circuit involved. Front. Behav. Neurosci. 8:164. doi: 10.3389/fnbeh.2014.00164

Copyright © 2014 Ferry. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 14 March 2014
doi: 10.3389/fnbeh.2014.00081

Olfactory tubercle stimulation alters odor preference behavior and recruits forebrain reward and motivational centers

Brynn J. FitzGerald[1], Kara Richardson[1] and Daniel W. Wesson1,2*

1Department of Neurosciences, Case Western Reserve University, Cleveland, OH, USA

2Department of Biology, Case Western Reserve University, Cleveland, OH, USA

* Correspondence: Daniel W. Wesson, Department of Neurosciences, Case Western Reserve University, 2109 Adelbert Avenue, Cleveland, OH 44106, USA e-mail: dww53@case.edu

Edited by:
Anne-Marie Mouly, Centre de Recherche en Neurosciences de Lyon, France

Reviewed by:
Carmen Agustín-Pavón, Imperial College London, UK
Satoshi Ikemoto, National Institutes of Health, USA

Rodents show robust behavioral responses to odors, including strong preferences or aversions for certain odors. The neural mechanisms underlying the effects of odors on these behaviors in animals are not well understood. Here, we provide an initial proof-of-concept study into the role of the olfactory tubercle (OT), a structure with known anatomical connectivity with both brain reward and olfactory structures, in regulating odor-motivated behaviors. We implanted c57bl/6 male mice with an ipsilateral bipolar electrode into the OT to administer electric current and thereby yield gross activation of the OT. We confirmed that electrical stimulation of the OT was rewarding, with mice frequently self-administering stimulation on a fixed ratio schedule. In a separate experiment, mice were presented with either fox urine or peanut odors in a three-chamber preference test. In absence of OT stimulation, significant preference for the peanut odor chamber was observed which was abolished in the presence of OT stimulation. Perhaps providing a foundation for this modulation in behavior, we found that OT stimulation significantly increased the number of c-Fos positive neurons in not only the OT, but also in forebrain structures essential to motivated behaviors, including the nucleus accumbens and lateral septum. The present results support the notion that the OT is integral to the display of motivated behavior and possesses the capacity to modulate odor hedonics either by directly altering odor processing or perhaps by indirect actions on brain reward and motivation structures.

Keywords: olfaction, olfactory cortex, self-stimulation, ventral striatum

INTRODUCTION

Odors have long been known to possess degrees of attractiveness or aversion (Locke and Grimm, 1949). These hedonics can either be innate from birth or in other cases, conditioned through learning. In both cases, perception of odors on the ends of the hedonic spectrum may elicit robust behavioral reactions. For instance, the bold odor of decaying meat elicits quite a repulsive reaction in humans whereas contrastingly, the sweet smell of freshly baked bread is in most cases pleasant. The neural mechanisms underlying these hedonic-driven behavioral responses are becoming increasingly known (e.g., Sullivan and Leon, 1987; Mennella and Garcia, 2000; Rolls et al., 2003; Stevenson and Repacholi, 2003; Sullivan, 2003; Bensafi et al., 2007; Grabenhorst et al., 2007; Baum, 2009; Doucette et al., 2011; Ferrero et al., 2011; Bensafi et al., 2012; Kass et al., 2013), yet major questions still remain.

Rodents are an excellent model for studying the neurobiological mechanisms of odor hedonics. From birth, and during early postnatal life, rats and mice display robust behavioral responses to odors, especially maternal odors to aid in maternal localization and feeding (Blass and Teicher, 1980; Sullivan, 2003; Logan et al., 2012). Most commonly studied in adult rodents, fearful responses (aversion, freezing/immobility, threat assessment) are reliably observed in response to predator odors (Blanchard et al., 2001; Wallace and Rosen, 2001; Takahashi et al., 2005; Ferrero et al., 2011). Thus rodents must possess a highly sophisticated system for the detection and response to odors.

The control of odor hedonic-driven behaviors likely requires not only a fully functional olfactory system to detect and discriminate the stimulus over background stimuli (for review see Wilson and Mainen, 2006; Wilson and Sullivan, 2011), but also the relay of this information into emotional and reward-related brain structures. The olfactory tubercle (OT) is an olfactory structure residing in the ventral striatum with large amounts of known anatomical connectivity into brain reward structures (Ikemoto, 2007; Wesson and Wilson, 2011). Due to this, we previously predicted that the OT serves a major role in regulating odor hedonics (Wesson and Wilson, 2011). Possible evidence for the regulation of rodent behavioral responses to odors by the OT was provided in a recent study by Agustín-Pavón et al. (2014). In the mentioned study, the authors created lesions containing a portion of the OT and observed that female mice with lesions displayed less attraction to male odors (Agustín-Pavón et al., 2014). This finding raises the interesting possibility that the OT regulates odor-hedonic behaviors either by means of its intrinsic rewarding properties (e.g., Prado-Alcalá and Wise, 1984; Ikemoto, 2003) and/or connectivity with reward and motivated behavior centers.

In the present study, we sought to further explore the role of the OT in odor-guided behaviors and the brain reward system in mice. To manipulate OT activity, we employed focal micro-stimulation of the OT using bipolar electrodes—a well-established method to probe principles of both olfactory and reward system activity (Freeman, 1960; Phillips and Mogenson, 1969; Prado-Alcalá and Wise, 1984; Mouly et al., 1985; Mouly and Holley, 1986; Wilson and Sullivan, 1990; Carlezon and Chartoff, 2007). As predicted based upon previous results in rats (Prado-Alcalá and Wise, 1984; Ikemoto, 2003), we found that mice self-administered current stimulation into the OT. Further, persistent automatic stimulation of the OT altered behavior in a three-chamber preference test. Finally, in separate groups of mice, we explored the recruitment of brain reward centers using the immediate early gene c-Fos. We found that OT stimulation not only recruited OT neurons focally, but also those of structures known connected to the OT—providing initial mechanistic insights into the likely importance of the OT to odor hedonics.

MATERIALS AND METHODS

EXPERIMENTAL SUBJECTS

Adult male c57bl/6 mice (2–4 months of age), bred and maintained within the Case Western Reserve University School of Medicine animal facility were used. Food and water were available ad libitum except during behavioral testing. All experiments were conducted in accordance with the guidelines of the National Institutes of Health and were approved by the Case Western Reserve University’s Institutional Animal Care Committee.

CHRONIC STIMULATING ELECTRODE IMPLANTATION SURGERY

A first cohort of mice (n = 8) were initially anesthetized with Isoflurane anesthesia (3.5–3% in 1 L/min O_2) before being transferred and mounted into a stereotaxic frame where Isoflurane was further provided (3–1%). Core body temperature was maintained at 38°C with a hot water-filled heating pad. Upon confirmation of anesthesia depth, the head was shaved, cleaned with betadine and 70% EtOH, and a single injection of lidocaine (0.1 ml of 1% in H_2O, S.C.) was administered within the future wound margin. A single incision was made from ~3 mm posterior of the nose along the midline to lambda and the skull surface cleaned with 3% H_2O_2. A single craniotomy (1 mm diameter) was created on the skull overlying the site of the OT for implantation of the stimulating electrode. The electrode consisted of 240 µm diameter stainless steel insulated wires (A-M Systems, Carlsborg, WA, USA) twisted together and connected by silver epoxy onto an Omnetics micro PS1 connector (Minneapolis, MN, USA). A micromanipulator was used to lower the bipolar electrode into the craniotomy and further into the site of the OT. The electrodes and plug were then cemented onto the skull by means of dental cement and the wound closed with Vetbond (3 M; St. Paul, MN, USA). Rimadyl (Carprofen, Pfizer animal health, 5 mg/kg, S.C.) was administered immediately following surgery and animals allowed to recover on the heating pad for >4 h. Rimadyl was administered daily for 5 days post-op. Food and water were available ad libitum except during behavioral procedures. All animals were singly-housed starting the day of implantation on a 12:12 h (light:dark) schedule with all behavioral procedures occurring during the light phase of the cycle (12:00:18:00 h). At least 5 days of recovery from surgery was allowed prior to any behavioral procedures. Following all behavioral procedures, mice were overdosed with urethane (3 mg/kg, I.P.) and transcardially perfused with 10 ml of 0.9% NaCl followed by 15 ml of 10% formalin and brains removed for histological verification of electrode sites.

ACUTE OLFACTORY TUBERCLE (OT) STIMULATION

A separate cohort of 21 mice were anesthetized via urethane injection (1.0 mg/kg, I.P.) and mounted on a stereotaxic frame upon a water-filled heating pad (38°C) for acute OT stimulation. The basic surgical methods follow as described above for the chronic stimulating electrode implantation surgery, but with a few notable differences described herein. A stimulating bipolar electrode (same as described above) was lowered into the site of the OT. The stimulating electrode connector was then connected by a headstage via a motorized commutator to a Cygnus Technology SIU-91 isolated current source (Delaware Water Gap, PA, USA). Stimulated mice received 5 trials of current delivery (200 s train of bimodal, rectangular pulses, 50 ms in pulse width (i.e., 10 Hz), 100 µA in amplitude) at a 1 min inter-stimulus interval. Sham mice simply remained on the heating pad with the electrode in their OT for the same duration of time as the stimulated mice. Following which, the electrode was gently raised out of the brain and the animals transferred onto a heating pad for 90 min prior to transcardial perfusion as described above.

SELF-ADMINISTRATION BEHAVIOR TESTING

The self-stimulation chamber was made of acrylonitrile butadiene styrene (ABS) plastic and consisted of a 150 × 150 mm (W × L) floor bordered by 225 mm tall walls and an open celling. One wall was removable to allow insertion and extraction of the mouse from the testing chamber. Above the chamber was a video camera for recording behavioral events as well as a motorized commutator (Tucker Davis Technologies, Alachua, FL, USA) to allow the mice to be freely mobile but still connected to the stimulation tether. In the center of the chamber floor was a 10 × 25 mm piezo electric foil (“touch pad”; Parallax, Inc., Rocklin, CA, USA) for reception of paw presses. All self-administration testing was performed in a dark room with illumination enough to see the subject’s behavior provided by a single dim red light.

Digitization of paw presses and triggering of stimulation via the paw presses occurred by means of a Tucker Davis Technologies recording amplifier (RZ5) running custom code. A threshold was set for triggering of stimulation based upon an average touch pad voltage while mice freely explored the self-stimulation chamber during acclimation. During self-stimulation testing therefore, touch pad contact that crossed the threshold triggered the delivery of current stimulation (0.5 s train of bimodal, rectangular pulses, 50 ms in pulse width (i.e., 10 Hz), 100 µA in amplitude).

On 2 consecutive days mice were connected to the stimulation tether and allowed to freely explore the self-stimulation chamber (with stimulation triggering off) for 30 min of acclimation to the testing apparatus. Following, over the course of the next days, mice were again connected to the stimulation tether and allowed to explore the self-stimulation chamber for behavioral testing wherein contact with the touch pad triggered stimulation. Stimulation occurred on a fixed ratio 1 schedule. Importantly, touch pad contact must have been released in order for the mouse to receive the next stimulation upon contact. Mice were allowed access to stimulation in the self-stimulation chamber for up to 60 min each day throughout which the number of presses and the time of each press event were recorded.

THREE-CHAMBER ODOR PREFERENCE BEHAVIOR TESTING

For a test of odor preferences, a 600 × 300 × 300 mm (length × width × height) clear acrylic chamber was divided into three equal zones with black markings. Above the chamber was a video camera for recording behavioral events as well as a motorized commutator (Tucker Davis Technologies) to allow the mice to be freely mobile but still connected to the stimulation tether. All testing was performed in a dark room with illumination enough to see the subject’s behavior provided by a single dim red light.

A single perforated dark plastic stimulus container (20 mm diameter × 20 mm tall) was placed on each end of the preference chamber for all testing. These stimulus containers were designed to allowing olfactory inspection of their contents but no distinct visual, somatosensory, or gustatory cues. On the first day, the mice were connected to the stimulation tether and allowed to freely explore the preference chamber (with stimulation off) for 30 min of acclimation to the testing chamber and clean odor-less stimulus containers. On the second and third days, the mice were again connected to the stimulation tether but this time the stimulus containers contained either a 1 × 10−3 dilution of fox urine[1] placed on a cotton ball (100 µl fluid) or 3 g of crushed peanut. We predicted that these two different stimuli would elicit unique investigation behaviors (time spent/investigation) related to their emotional values (Takahashi et al., 2005) and thereby would provide a test as to whether or not stimulation of the OT would impact odor hedonic-related behaviors. The side of the preference chamber containing each stimulus was counterbalanced across all mice. On 1 day per mouse (counterbalanced across mice), current stimulation was provided throughout the entire duration of a daily session (continuous bimodal train, rectangular pulses, 50 ms in pulse width (i.e., 10 Hz), 100 µA in amplitude). On each day the testing lasted 500 s, throughout which the amount of time spent in each zone of the preference chamber and the number of zone crosses were recorded onto video. Videos were scored off-line by a single experimenter (K.R.) manually tallying zone crosses (defined by contact of all four paws across the divider line) and cumulative time based upon the video stopwatch.

c-Fos IMMUNOHISTOCHEMISTRY AND QUANTIFICATION

Alternate 40 µm coronal brain sections were acquired from mice which received the acute OT stimulation paradigm or sham controls using a sliding microtome. ≥5 sections/mouse spanning regions ~0.8–0.4 mm anterior to bregma were collected and left floating in 0.03% sodium azide in Tris-buffered saline (TBS, pH 7.4) until staining. c-Fos immunohistochemistry followed the methods of Kang et al. (2011b). First, the brain slices were rinsed in TBS and then quenched in a solution of 0.3% hydrogen peroxide in methanol. They were rinsed again in TBS, and then in 0.1% TX-100 in TBS. Following the rinses, the sections were blocked in 5.0% NDS in 0.1% TX-100 (Jackson ImmunoResearch, West Grove, PA, USA) for an hour and then incubated overnight at 4°C in the anti-c-Fos primary antibody (1:1000, Calbiochem, EMD Millipore, Billerica, MA, USA). A subset of slices in each run were used as a primary antibody control to ensure specificity of staining. The next day, the sections were rinsed in a diluting buffer, incubated in a secondary antibody (1:600, Jackson ImmunoResearch), rinsed again in 0.1% TX-100 in TBS, and incubated in an Avidin/Biotinylated enzyme Complex kit (Vector Laboratories, Inc., Burlingame, CA, USA). Finally, the sections were rinsed with 0.1% TX-100 in TBS, and incubated in a peroxidase substrate kit with diaminobenzidine (Vector Laboratories) and rinsed with ddH_2O. Sections were then transferred onto slides and, after drying, cover slipped with Permount (Fisher Scientific, Pittsburgh, PA, USA).

a priori regions for analysis included the OT, ventral pallidum (VP), nucleus accumbens (NAc), lateral septum (LS), and caudate putamen (CPu). These regions were identified using known cytoarchitectural features (Paxinos and Franklin, 2000) and imaged at 20x magnification using a Leica microscope and a 3MP camera. Equal size (200 µm^2) bounding boxes were overlaid upon the digital images for cell counting. The location of the bounding boxes were held constant across mice. Any cell bodies which touched the bounding box were excluded from counts. c-Fos+ cell bodies were manually identified and counted by a single observed based upon density of the 3,3′-diaminobenzidine (DAB) reaction versus background. Sections containing significant damage within the bounding box from the stimulating electrode were excluded from analysis and replaced with non-damaged sections. All steps including sectioning, staining, imaging, and quantification were completed in a group-counterbalanced order by a single experimenter blind to the experimental group of the tissue (B.F.).

ELECTRODE PLACEMENT VERIFICATION

All stimulation sites were verified by post-mortem histological examinations of slide-mounted 40 µm coronal brain sections stained with a 0.1% cresyl violet solution or in other cases, DAPI (4′,6-diamidino-2-phenylindole, Life Technologies, City State, USA). We defined an OT stimulation site as successful when the wires terminated within either layers i, ii, and/or iii of the OT (Figure 1). Electrode tip locations were verified by multiple-observers (B.F. and D.W.) with reference to a mouse brain atlas (Paxinos and Franklin, 2000). Data associated with sites outside of the OT were entirely excluded from this study.

FIGURE 1. Stimulation electrode locations from both chronic and acute stimulation experiments. Mouse coronal stereotaxic panels displaying the location of stimulating electrode tips in the olfactory tubercle (OT) from mice self-administering stimulation (circles) and those which received acute stimulation (squares). Panels adapted from Paxinos and Franklin (2000).

DATA ANALYSIS

Behavioral data (time spent in preference zones, # touch pad presses, # zone crossings) were compared between conditions (stimulation on vs. stimulation off) with an ANOVA. The total number of c-Fos+ neurons was compared between conditions (stimulated vs. sham) and hemispheres (ipsilateral vs. contralateral) within brain regions specified also using an ANOVA. Data were analyzed in Origin 8.5 (Northampton, MA) with a significance level of p < 0.05. Values are reported as mean ± SEM unless otherwise noted.

RESULTS

We first asked whether OT electrical stimulation is rewarding in mice. To address this, we allowed a cohort of eight mice chronically implanted with bipolar stimulating electrodes into the OT to freely explore the self-stimulation chamber for 2 days wherein contact with a touch pad triggered OT stimulation (see Section Materials and Methods). Following, on the third day, mice were again placed into the self-stimulation chamber and numbers of touch pad presses recorded. Stimulation was allowed ad libitum over two blocks of 15 min, separated by a single block of 15 min wherein touch pad contact did not trigger stimulation. During the first 15 min block, mice readily pressed the touch pad (Figure 2A). The touch pad contacts were not resultant from random contact by the mice since turning off the stimulus in the middle 15 min block entailed a significant decrease in touch pad presses in all but one mouse (7/8, 87.5%) (Figures 2A, B) (F(1,12) = 19.576, p = 0.0008). Reinstatement of the ad libitum reward delivery significantly restored touch pad presses in the final 15 min block (Figures 2A, B) (F(1,12) = 13.786, p = 0.003). These data demonstrate that electrical stimulation of the OT is rewarding in mice.

FIGURE 2. Self-delivery of OT stimulation in mice. (A) Mice (n = 8) were allowed to deliver current stimulation pulses for an initial block of 15 min, followed by a blank of 15 min wherein touch pad press did not delivery stimulation, and later a restoration block wherein again they were allowed to deliver current stimulation. Mice pressed more when stimulation was available (gray shaded regions) vs. when it was not, except for one mouse who strikingly increased the number of touch pad presses even though stimulation was not delivered (black dashed line). Red line = population mean, including outlier (dashed line). (B) Mean number of reward administrations from (A) in each 15 min block in the self-stimulation task, but with the outlier (dashed line) subject removed from the mean. p-values = ANOVA followed by Fisher’s PLSD.

We next investigated the influence of OT stimulation on odor preference behavior in the three-chamber preference test in the same mice. Notably, one mouse (Figure 2A, dashed line) displayed highly aberrant behavior from the group in the self-stimulation testing and was thus excluded from this preference experiment. A separate mouse did not explore the preference chamber and instead stayed immobile in a single zone—qualifying exclusion. Across the remaining six mice, all spent statistically similar time in both end zones in the presence of blank odor vials (F(1,10) = 0.035, p = 0.855) (data not shown). This baseline data verifies that mice did not have a preference towards simply being on one side of the chamber vs. the other. Next, over 2 days, mice were tested for preference behavior among odorized zones, with OT stimulation being provided consecutively on one of those days. Mice received the stimulation on counterbalanced days (1/2 mice received it on day 1 and 1/2 on day 2), and thus in this design effects of OT stimulation on preference behavior at the population level can be considered independent of learning. No differences between these two groups were observed and thus their data were pooled together (p = 0.446, Kolmogorov-Smirnov test). We found that in the absence of OT stimulation, mice spent significantly greater time in the peanut zone of the chamber vs. either the neutral (F(1,10) = 11.901, p = 0.0062) or the fox urine zones (F(1,10) = 11.882, p = 0.0063) (Figure 3). OT stimulation strikingly abolished this difference, with statistically indistinguishable time spent in both the fox and peanut zones (F(1,10) = 0.004, p = 0.949) (Figure 3). No effect of OT stimulation was observed on the number of side crossings in the preference apparatus (F(1,10) = 0.235, p = 0.639) (data not shown), suggesting that the effect of OT stimulation on the display of behavior in the odor preference task was independent of stimulation influencing gross locomotor activity.

FIGURE 3. OT stimulation alters odor-guided behaviors. (A) Mice were allowed to explore a three-chamber odor preference chamber for 10 min with one side containing crushed peanuts (3 g) and the other fox urine (1:10 dilution) both contained within a perforated dark plastic container. On counterbalanced days, current stimulation was delivered throughout the entire duration of the session. On the day with stimulation off, mice spent significantly greater time in the peanut zone, whereas this was abolished with OT stimulation on. n = 6 mice. n.s. = not significant. p-values = ANOVA followed by Fisher’s PLSD.

Based upon the above behavioral findings demonstrating that OT stimulation influences odor-driven behavioral responses, we next sought to test the mechanisms whereby OT stimulation may alter hedonic-related behaviors. We predicted based upon known anatomical connectivity between the OT and brain reward structures (Ikemoto, 2007), that OT stimulation recruits forebrain structures necessary for reward (Koob and Le Moal, 2001; Berridge, 2003; Ikemoto, 2007). Therefore, a separate cohort of anesthetized mice received OT stimulation (n = 13) or sham OT stimulation (n = 8) in a paradigm mimicking that received while awake (see Section Materials and Methods) and later, their brains probed by means of immunohistochemistry for levels of c-Fos expression (Sagar et al., 1988; Figure 4A). The use of anesthetized mice for this analysis was advantageous to ensure changes in c-Fos expression were directly due to OT stimulation, vs. extraneous influences of OT stimulation upon behavior.

FIGURE 4. OT stimulation recruits forebrain structures involved in motivated behaviors. (A) Photomicrograph from an OT tissue sample stained for c-Fos (left) and the same micrograph following detection of c-Fos+ neurons. As illustrated, only neurons with intense enough reaction/staining were applied towards c-Fos counts (green dots). Scale bar = 20 µm. (B) OT stimulation elicits a significant increase in c-Fos+ neurons in the ipsilateral OT. c-Fos+ neuron counts were quantified within a 200 µm^2 bounding box. (C) Effects of OT stimulation on the number of c-Fos+ neurons in forebrain reward and motivated behavior centers, including the NAc (nucleus accumbens), CPu (caudate putamen), LS (lateral septum), and VP (ventral pallidum). n = 13 mice stimulated, 8 mice sham for all structures, ≥5 sections/structure. c-Fos+ neuron counts were quantified within a 200 µm^2 bounding box. p-values = ANOVA followed by Fisher’s PLSD.

Confirming the physiological potency of the stimulation paradigm, we found that OT stimulated mice had a significantly greater number of c-Fos+ neurons in their ipsilateral OT than sham treated mice (F(1,19) = 4.679, p = 0.0435) (Figure 4B). No group effect was observed when comparing between contralateral OT hemispheres (F(1,19) = 1.157, p = 0.296) (Figure 4B). Looking beyond the OT, we found that mice receiving OT stimulation had a significantly greater number of c-Fos+ neurons compared to sham mice in their LS (F(1,19) = 4.852, p = 0.040) and NAc (F(1,19) = 4.828, p = 0.0406), but not their CPu (F(1,19) = 0.032, p = 0.861) nor VP (F(1,19) = 1.793, p = 0.196) (ipsilateral vs. ipsilateral) (Figure 4C). Across all of these structures, only in the CPu did the number of c-Fos+ neurons in the contralateral hemisphere significantly differ between stimulated and sham groups (F(1,19) = 6.09, p = 0.023) (Figure 4C). No group effect of stimulation was observed between the contralateral hemispheres in the LS (F(1,19) = 0.943, p = 0.344), NAc (F(1,19) = 0.931, p = 0.347), or the VP (F(1,19) = 2.179, p = 0.156). In all structures analyzed, the number of c-Fos+ neurons was statistically similar comparing between the ipsilateral to contralateral hemispheres (p > 0.05). For a reason we are unaware of, perhaps related to damage of near-by electrode insertion, sham mice displayed significant increases in the number of c-Fos+ neurons between their contralateral and ipsilateral hemispheres in both the CPu (F(1,14) = 15.067, p = 0.0017) and NAc (F(1,14) = 12.684, p = 0.0031). Taken together, these results suggest that the influence of OT stimulation on motivated and odor-driven behaviors may occur via connectivity between the OT and the NAc, LS, and possibly CPu.

DISCUSSION

In this study we provide an initial proof-of-concept exploration into the role of the OT, a structure with known anatomical connectivity with both brain reward and olfactory structures, in regulating odor-motivated behaviors and their possible mechanisms. We confirm that electrical stimulation of the OT was rewarding (Prado-Alcalá and Wise, 1984), in this case in mice, and possessed the capacity to alter odor-directed preference behaviors. In separate experiments we also found that OT stimulation significantly increased the number of c-Fos positive neurons in not only the OT, but also in forebrain structures essential to motivated behaviors, including the NAc and LS. The present results support the notion that the OT is integral to motivated behaviors and likely involved in odor hedonics.

INFLUENCE OF THE OLFACTORY TUBERCLE (OT) ON REWARD AND ODOR-GUIDED BEHAVIORS

In the present study we found that mice self-administered electrical current into the OT. This finding is analogous and complimentary to a much earlier finding by Prado-Alcalá and Wise (1984) also one by Ikemoto demonstrating that rats readily self-administer cocaine into the OT and further that cocaine infusions into the OT are sufficient for the development of a conditioned place preference (Ikemoto, 2003). Our self-administration experiments in the present study were not designed to be interpreted as novel in theory by any means but instead to reinforce the concept that the OT is instrumental in driving reward-related behaviors (Koob et al., 1978; Prado-Alcalá and Wise, 1984; Alheid and Heimer, 1988; Heimer, 2003; Ikemoto, 2003). Reward system projections into the OT include the rostral linear nucleus of the ventral tegmental area (Del-Fava et al., 2007), the medial forebrain bundle (Gaykema et al., 1990), the NAc (Zahm and Heimer, 1993), and the substantia nigra (Fallon et al., 1978). Thus, possibly due to both intrinsic (DAergic receptor expression (Li and Kuzhikandathil, 2012)) and extrinsic factors (inter-network connectivity), the OT appears capable of eliciting motivated behaviors. Exploring possible mechanisms of connectivity between the OT and the LS and CPu, as suggested herein, will be important in furthering our understanding of the OTs’ rewarding properties.

In addition to being connected with reward-related structures, the OT also receives dense innervation from secondary neurons in the olfactory bulb (e.g., White, 1965; Scott et al., 1980; Schwob and Price, 1984; Nagayama et al., 2010; Kang et al., 2011a) and consequently represents and processes odors in a manner similar to the primary (piriform) olfactory cortex (Payton et al., 2012). A recent study reported that lesions of the anteriomedial ventral striato-pallidum complex (including the OT) altered preferences of female mice for male sociosexual odors (Agustín-Pavón et al., 2014). This finding posed the important question as to whether or not manipulation of OT activity alone might be sufficient to alter odor guided behavior? Our present results support and extend the previous finding by Agustín-Pavón et al. (2014) by demonstrating that electrical manipulation of OT activity, specifically, possesses the capacity to modulate odor-guided behaviors. The use of electrical stimulation to modulate and explore principles of olfactory system function and olfactory behaviors is well established (Mouly et al., 1985; Mouly and Holley, 1986; Wilson and Sullivan, 1990) and thus we employed that method herein for bulk modulation of OT activity. Using this, we found that stimulation of the OT abolished preference for peanut odor vs. fox urine odor. Notably, using this gross odor preference behavior to test the OTs involvement in olfaction is a considerably insensitive assay and thus additional studies will be needed employing more precise olfactory read-outs to explore unique and specific aspects of olfactory behavior under control by the OT. Indeed, as they stand, our results demonstrate the role for the OT in modulating odor-guided behaviors, but not specifically modulating olfactory perception. That said, our results from the odor preference assay do support the idea that OT local and/or inter-regional processing modulates odor-guided behaviors if not at least in an indirect manner. This indirect mechanism could be by shaping motivation and/or the reward features of the odors (e.g., via modulation of structures identified in Figure 4). Directly, OT stimulation might perturb basic aspects of odor processing known to occur in the OT (Wesson and Wilson, 2010; Payton et al., 2012; Carlson et al., 2014) and thus alter odor perception more specifically. We predict that the effects observed in the present paper stem form a combination of both direct and indirect impacts of OT activity on behavior.

POSSIBLE NEURAL SUBSTRATES WHEREBY OLFACTORY TUBERCLE (OT) AFFECTS BEHAVIOR

In the present study, we found that moderate electrical stimulation of the OT resulted in significant recruitment of neurons within brain structures believed essential to motivated behaviors (Koob and Le Moal, 2001; Berridge, 2003; Ikemoto, 2007), including the NAc and LS. Notably, this list is not exhaustive in that we did not analyze every possible motivated behavior center, but instead approached this study with an a priori set of forebrain regions. Likely analyzing additional brain structures, especially the ventral tegmental area and orbitofrontal cortex, would provide additional evidence for the functional interconnectedness of the OT (Barbas, 1993; Illig, 2005; Del-Fava et al., 2007). Additionally, the medial vs. lateral aspects of the OT are hypothesized differentially involved in olfactory and reward-related behaviors (Josephson et al., 1997; Ikemoto, 2007) and thus, it is possible that our grouping of OT stimulation sites spanning the entire OT overlooked more subtle aspects of neuronal output which otherwise might be observed if one were to employ regionally-restricted stimulation.

It is interesting to speculate upon why OT stimulation did not more strongly activate structures like the VP which also hold strong interconnectedness with the OT (Millhouse, 1986). While the number of c-Fos+ neurons was increased in this structure, this was not significant. It is possible that in the majority of animals bulk OT stimulation was insufficient to recruit distinct sub-populations of neurons innervating the VP and thus VP activation was minimal. Indeed, populations of VP projecting neurons from the OT are GABAergic (Meyer et al., 1989; Hsieh and Puche, 2013) and thus the effects of OT stimulation on the VP would be inhibitory. While the present study yields novel evidence upon the effects of gross activation of the OT, employing more precise stimulation methods, including genetically guided cell-specific methods, will be needed to more closely address mechanisms of OT connectivity.

ACKNOWLEDGMENTS

This work was supported by NSF grant IOS-1121471 to Daniel W. Wesson and a Battelle Science award from Denison University to Brynn J. FitzGerald.

FOOTNOTES

1^Predatorpee.com

REFERENCES

Agustín-Pavón, C., Martínez-García, F., and Lanuza, E. (2014). Focal lesions within the ventral striato-pallidum abolish attraction for male chemosignals in female mice. Behav. Brain Res. 259, 292–296. doi: 10.1016/j.bbr.2013.11.020

Alheid, G. F., and Heimer, L. (1988). New perspectives in basal forebrain organization of special relevance for neuropsychiatric disorders: the striatopallidal, amygdaloid and corticopetal components of substantia innominata. Neuroscience 27, 1–39. doi: 10.1016/0306-4522(88)90217-5

Barbas, H. (1993). Organization of cortical afferent input to orbitofrontal areas in the rhesus monkey. Neuroscience 56, 841–864. doi: 10.1016/0306-4522(93)90132-y

Baum, M. J. (2009). Sexual differentiation of pheromone processing: links to male-typical mating behavior and partner preference. Horm. Behav. 55, 579–588. doi: 10.1016/j.yhbeh.2009.02.008

Bensafi, M., Iannilli, E., Poncelet, J., Seo, H.-S., Gerber, J., Rouby, C., et al. (2012). Dissociated representations of pleasant and unpleasant olfacto-trigeminal mixtures: an fMRI study. PLoS One 7:e38358. doi: 10.1371/journal.pone.0038358

Bensafi, M., Sobel, N., and Khan, R. M. (2007). Hedonic-specific activity in piriform cortex during odor imagery mimics that during odor perception. J. Neurophysiol. 98, 3254–3262. doi: 10.1152/jn.00349.2007

Berridge, K. C. (2003). Pleasures of the brain. Brain Cogn. 52, 106–128. doi: 10.1016/S0278-2626(03)00014-9

Blanchard, R. J., Yang, M., Li, C.-I., Gervacio, A., and Blanchard, D. C. (2001). Cue and context conditioning of defensive behaviors to cat odor stimuli. Neurosci. Biobehav. Rev. 25, 587–595. doi: 10.1016/s0149-7634(01)00043-4

Blass, E. M., and Teicher, M. H. (1980). Suckling. Science 210, 15–22.

Carlezon, W. A., and Chartoff, E. H. (2007). Intracranial self-stimulation (ICSS) in rodents to study the neurobiology of motivation. Nat. Protoc. 2, 2987–2995. doi: 10.1038/nprot.2007.441

Carlson, K. S., Dillione, M., and Wesson, D. W. (2014). Odor- and state-dependent olfactory tubercle local field potential dynamics in awake rats. J. Neurophysiol. doi: 10.1152/jn.00829.2013. [Epub ahead of print].

Del-Fava, F., Hasue, R. H., Ferreira, J. G. P., and Shammah-Lagnado, S. J. (2007). Efferent connections of the rostral linear nucleus of the ventral tegmental area in the rat. Neuroscience 145, 1059–1076. doi: 10.1016/j.neuroscience.2006.12.039

Doucette, W., Gire, D. H., Whitesell, J., Carmean, V., Lucero, M. T., and Restrepo, D. (2011). Associative cortex features in the first olfactory brain relay station. Neuron 69, 1176–1187. doi: 10.1016/j.neuron.2011.02.024

Fallon, J. H., Riley, J. N., Sipe, J. C., and Moore, R. Y. (1978). The islands of Calleja: organization and connections. J. Comp. Neurol. 181, 375–395. doi: 10.1002/cne.901810209

Ferrero, D. M., Lemon, J. K., Fluegge, D., Pashkovski, S. L., Korzan, W. J., Datta, S. R., et al. (2011). Detection and avoidance of a carnivore odor by prey. Proc. Natl. Acad. Sci. U S A 108, 11235–11240. doi: 10.1073/pnas.1103317108

Freeman, W. J. (1960). Repetitive electrical stimulation of prepyriform cortex in cat. J. Neurophysiol. 23, 383–396.

Gaykema, R. P., Luiten, P., Nyakas, C., and Traber, J. (1990). Cortical projection patterns of the medial septum-diagonal band complex. J. Comp. Neurol. 293, 103–124. doi: 10.1002/cne.902930109

Grabenhorst, F., Rolls, E. T., Margot, C., da Silva, M. A., and Velazco, M. I. (2007). How pleasant and unpleasant stimuli combine in different brain regions: odor mixtures. J. Neurosci. 27, 13532–13540. doi: 10.1523/jneurosci.3337-07.2007

Heimer, L. (2003). A new neuroanatomical framework for neuropsychiatric disorders and drug abuse. Am. J. Psychiatry 160, 1726–1739. doi: 10.1176/appi.ajp.160.10.1726

Hsieh, Y.-C., and Puche, A. C. (2013). Development of the Islands of Calleja. Brain Res. 1490, 52–60. doi: 10.1016/j.brainres.2012.10.051

Ikemoto, S. (2003). Involvement of the olfactory tubercle in cocaine reward: intracranial self-administration studies. J. Neurosci. 23, 9305–9311.

Ikemoto, S. (2007). Dopamine reward circuitry: two projection systems from the ventral midbrain to the nucleus accumbens-olfactory tubercle complex. Brain Res. Rev. 56, 27–78. doi: 10.1016/j.brainresrev.2007.05.004

Illig, K. R. (2005). Projections from orbitofrontal cortex to anterior piriform cortex in the rat suggest a role in olfactory information processing. J. Comp. Neurol. 488, 224–231. doi: 10.1002/cne.20595

Josephson, E., Padgett, M. L., and Buxton, D. F. (1997). The lateral and medial compartments of the olfactory tubercle and their relation to olfactory-related input as determined by artificial neural network analysis. Brain Res. 744, 253–271. doi: 10.1016/s0006-8993(96)01086-4

Kang, N., Baum, M. J., and Cherry, J. A. (2011a). Different profiles of main and accessory olfactory bulb mitral/tufted cell projections revealed in mice using an anterograde tracer and a whole-mount, flattened cortex preparation. Chem. Senses 36, 251–260. doi: 10.1093/chemse/bjq120

Kang, N., McCarthy, E. A., Cherry, J. A., and Baum, M. J. (2011b). A sex comparison of the anatomy and function of the main olfactory bulb-medial amygdala projection in mice. Neuroscience 172, 196–204. doi: 10.1016/j.neuroscience.2010.11.003

Kass, M. D., Rosenthal, M. C., Pottackal, J., and McGann, J. P. (2013). Fear learning enhances neural responses to threat-predictive sensory stimuli. Science 342, 1389–1392. doi: 10.1126/science.1244916

Koob, G. F., and Le Moal, M. (2001). Drug addiction, dysregulation of reward, and allostasis. Neuropsychopharmacology 24, 97–129. doi: 10.1016/s0893-133x(00)00195-0

Koob, G. F., Riley, S. J., Smith, S. C., and Robbins, T. W. (1978). Effects of 6-hydroxydopamine lesions of the nucleus accumbens septi and olfactory tubercle on feeding, locomotor activity, and amphetamine anorexia in the rat. J. Comp. Physiol. Psychol. 92, 917–927. doi: 10.1037/h0077542

Li, Y., and Kuzhikandathil, E. V. (2012). Molecular characterization of individual D3 dopamine receptor-expressing cells isolated from multiple brain regions of a novel mouse model. Brain Struct. Funct. 217, 809–833. doi: 10.1007/s00429-012-0383-8

Locke, B., and Grimm, C. H. (1949). Odor selection, preferences and identification. J. Appl. Psychol. 33, 167–174. doi: 10.1037/h0062514

Logan, D. W., Brunet, L. J., Webb, W. R., Cutforth, T., Ngai, J., and Stowers, L. (2012). Learned recognition of maternal signature odors mediates the first suckling episode in mice. Curr. Biol. 22, 1998–2007. doi: 10.1016/j.cub.2012.08.041

Mennella, J. A., and Garcia, P. L. (2000). Children’s hedonic response to the smell of alcohol, effects of parental drinking habits. Alcohol. Clin. Exp. Res. 24, 1167–1171. doi: 10.1111/j.1530-0277.2000.tb02079.x

Meyer, G., Gonzalez Hernandez, T., Carrillo Padilla, F., and Ferres Torres, R. (1989). Aggregations of granule cells in the basal forebrain (islands of Calleja): Golgi and cytoarchitectonic study in different mammals, including man. J. Comp. Neurol. 284, 405–428. doi: 10.1002/cne.902840308

Millhouse, O. E. (1986). Pallidal neurons in the rat. J. Comp. Neurol. 254, 209–227. doi: 10.1002/cne.902540206

Mouly, A. M., and Holley, A. (1986). Perceptive properties of the multi-site electrical microstimulation of the olfactory bulb in the rat. Behav. Brain Res. 21, 1–12. doi: 10.1016/0166-4328(86)90054-9

Mouly, A. M., Vigouroux, M., and Holley, A. (1985). On the ability of rats to discriminate between microstimulations of the olfactory bulb in different locations. Behav. Brain Res. 17, 45–58. doi: 10.1016/0166-4328(85)90006-3

Nagayama, S., Enerva, A., Fletcher, M. L., Masurkar, A. V., Igarashi, K. M., Mori, K., et al. (2010). Differential axonal projection of mitral and tufted cells in the mouse main olfactory system. Front. Neural Circuits 4, 1–8. doi: 10.3389/fncir.2010.00120

Paxinos, G., and Franklin, K. (2000). The Mouse Brain in Stereotaxic Coordinates. 2nd Edn. San Diego: Academic Press.

Payton, C. A., Wilson, D. A., and Wesson, D. W. (2012). Parallel odor processing by two anatomically distinct olfactory bulb target structures. PLoS One 7:e34926. doi: 10.1371/journal.pone.0034926

Phillips, A. G., and Mogenson, G. J. (1969). Self-stimulation of the olfactory bulb. Physiol. Behav. 4, 195–197. doi: 10.1016/0031-9384(69)90080-8

Prado-Alcalá, R., and Wise, R. A. (1984). Brain stimulation reward and dopamine terminal fields. I. Caudate-putamen, nucleus accumbens and amygdala. Brain Res. 297, 265–273. doi: 10.1016/0006-8993(84)90567-5

Rolls, E. T., Kringelbach, M. L., and de Araujo, I. E. (2003). Different representations of pleasant and unpleasant odours in the human brain. Eur. J. Neurosci. 18, 695–703. doi: 10.1046/j.1460-9568.2003.02779.x

Sagar, S. M., Sharp, F. R., and Curran, T. (1988). Expression of c-fos protein in brain: metabolic mapping at the cellular level. Science 240, 1328–1331. doi: 10.1126/science.3131879

Schwob, J. E., and Price, J. L. (1984). The development of axonal connections in the central olfactory system of rats. J. Comp. Neurol. 223, 177–202. doi: 10.1002/cne.902230204

Scott, J. W., McBride, R. L., and Schneider, S. P. (1980). The organization of projections from the olfactory bulb to the piriform cortex and olfactory tubercle in the rat. J. Comp. Neurol. 194, 519–534. doi: 10.1002/cne.901940304

Stevenson, R. J., and Repacholi, B. M. (2003). Age related changes in children’s hedonic response to male body odor. Dev. Psychol. 39, 670–679. doi: 10.1037/0012-1649.39.4.670

Sullivan, R. M. (2003). Developing a sense of safety: the neurobiology of neonatal attachment. Ann. N Y Acad. Sci. 1008, 122–131. doi: 10.1196/annals.1301.013

Sullivan, R. M., and Leon, M. (1987). One-trial olfactory learning enhances olfactory bulb responses to an appetitive conditioned odor in 7-day-old rats. Brain Res. 432, 307–311. doi: 10.1016/0165-3806(87)90056-3

Takahashi, L. K., Nakashima, B. R., Hong, H., and Watanabe, K. (2005). The smell of danger: a behavioral and neural analysis of predator odor-induced fear. Neurosci. Biobehav. Rev. 29, 1157–1167. doi: 10.1016/j.neubiorev.2005.04.008

Wallace, K. J., and Rosen, J. B. (2001). Neurotoxic lesions of the lateral nucleus of the amygdala decrease conditioned fear but not unconditioned fear of a predator odor: comparison with electrolytic lesions. J. Neurosci. 21, 3619–3627.

Wesson, D. W., and Wilson, D. A. (2010). Smelling sounds: olfactory-auditory sensory convergence in the olfactory tubercle. J. Neurosci. 30, 3013–3021. doi: 10.1523/jneurosci.6003-09.2010

Wesson, D. W., and Wilson, D. A. (2011). Sniffing out the contributions of the olfactory tubercle to the sense of smell: hedonics, sensory integration and more? Neurosci. Biobehav. Rev. 35, 655–668. doi: 10.1016/j.neubiorev.2010.08.004

White, L. E. (1965). Olfactory bulb projections of the rat. Anat. Rec. 152, 465–479. doi: 10.1002/ar.1091520406

Wilson, R. I., and Mainen, Z. F. (2006). Early events in olfactory processing. Annu. Rev. Neurosci. 29, 163–201. doi: 10.1146/annurev.neuro.29.051605.112950

Wilson, D. A., and Sullivan, R. M. (1990). Olfactory associative conditioning in infant rats with brain stimulation as reward. I. Neurobehavioral consequences. Brain Res. Dev. Brain Res. 53, 215–221. doi: 10.1016/0165-3806(90)90009-n

Wilson, D. A., and Sullivan, R. M. (2011). Cortical processing of odor objects. Neuron 72, 506–519. doi: 10.1016/j.neuron.2011.10.027

Zahm, D. S., and Heimer, L. (1993). Specificity in the efferent projections of the nucleus accumbens in the rat: comparison of the rostral pole projection patterns with those of the core and shell. J. Comp. Neurol. 327, 220–232. doi: 10.1002/cne.903270205

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 30 January 2014; Accepted: 24 February 2014; Published online: 14 March 2014.

Citation: FitzGerald BJ, Richardson K and Wesson DW (2014) Olfactory tubercle stimulation alters odor preference behavior and recruits forebrain reward and motivational centers. Front. Behav. Neurosci. 8:81. doi: 10.3389/fnbeh.2014.00081

Copyright © 2014 FitzGerald, Richardson and Wesson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 18 February 2014
doi: 10.3389/fnbeh.2014.00053

Interactions with the young down-regulate adult olfactory neurogenesis and enhance the maturation of olfactory neuroblasts in sheep mothers

Maïna Brus1,2,3,4, Maryse Meurisse1,2,3,4, Matthieu Keller1,2,3,4 and Frédéric Lévy1,2,3,4*

1INRA, UMR 85, Physiologie de la Reproduction et des Comportements, Nouzilly, France

2CNRS, UMR 7247, Nouzilly, France

3Université François Rabelais, Tours, France

4IFCE, Nouzilly, France

* Correspondence: Frédéric Lévy, INRA, UMR 85, Physiologie de la Reproduction et des Comportements, F-37380 Nouzilly, France e-mail: frederic.levy@tours.inra.fr

Edited by:
Anne-Marie Mouly, Centre de Recherche en Neurosciences de Lyon, France

Reviewed by:
Muriel Koehl, French Institute of Health and Medical Research, France
Grace Schenatto Pereira, Universidade Federal de Minas Gerais, Brazil

New neurons are continuously added in the dentate gyrus (DG) and the olfactory bulb of mammalian brain. While numerous environmental factors controlling survival of newborn neurons have been extensively studied, regulation by social interactions is less documented. We addressed this question by investigating the influence of parturition and interactions with the young on neurogenesis in sheep mothers. Using Bromodeoxyuridine, a marker of cell division, in combination with markers of neuronal maturation, the percentage of neuroblasts and new mature neurons in the olfactory bulb and the DG was compared between groups of parturient ewes which could interact or not with their lamb, and virgins. In addition, a morphological analysis was performed by measuring the dendritic arbor of neuroblasts in both structures. We showed that the postpartum period was associated with a decrease in olfactory and hippocampal adult neurogenesis. In the olfactory bulb, the suppressive effect on neuroblasts was dependent on interactions with the young whereas in the DG the decrease in new mature neurons was associated with parturition. In addition, dendritic length and number of nodes of neuroblasts were significantly enhanced by interactions with the lamb in the olfactory bulb but not in the DG. Because interactions with the young involved learning of the olfactory signature of the lamb, we hypothesize that this learning is associated with a down-regulation in olfactory neurogenesis and an enhancement of olfactory neuroblast maturation. Our assumption is that fewer new neurons decrease cell competition in the olfactory bulb and enhance maturation of those new neurons selected to participate in the learning of the young odor.

Keywords: olfactory bulb, hippocampus, maternal behavior, olfactory learning, brain plasticity

INTRODUCTION

In most mammals, newborn neurons are continuously provided in two main structures of the brain throughout life, the dentate gyrus (DG) of the hippocampus and the olfactory bulb (Ming and Song, 2005; Curtis et al., 2007; Brus et al., 2013). In the DG, stem cells reside in the subgranular zone (SGZ) and give rise to neuroblasts which become new granule neurons in the overlying granule cell layer (GCL; Ming and Song, 2005). In the olfactory system, neural stem cells function as primary precursors in the subventricular zone (SVZ) located on the wall of the lateral ventricles. These cells produce transient amplifying cells which rapidly divide to produce neuroblasts. The neuroblasts migrate toward the olfactory bulb along the rostral migratory stream. After reaching the olfactory bulb, the new cells migrate radially and mature into granular interneurons for the majority of them (Rochefort and Lledo, 2005).

In rodents, a growing body of literature aiming to block neurogenesis by using various methodologies shows the role of hippocampal neurogenesis in some forms of hippocampus–dependent learning tasks despite some inconsistent results (Deng et al., 2010; Arruda-Carvalho et al., 2011; Gu et al., 2012). Similarly, olfactory neurogenesis is involved in the memory processing of olfactory cues (Lazarini and Lledo, 2011). Blocking neurogenesis impaired olfactory perceptual learning (Moreno et al., 2009), short-term and long-term memory (Breton-Provencher et al., 2009; Lazarini et al., 2009; Sultan et al., 2010). In addition it has been recently demonstrated that immediate activation of newborn olfactory neurons, by using an optogenetic approach, enhances discrimination learning and memory when the task is difficult (Alonso et al., 2012). A common role in pattern separation has been proposed for newborn hippocampal and olfactory neurons and adult neurogenesis could constitute an adaptive mechanism to optimally encode contextual or olfactory information (for review, see Sahay et al., 2011).

While the implication of adult neurogenesis has been demonstrated in spatial and olfactory learning, a better understanding of the role of adult born neurons in an ethological context has begun to emerge. Social environment can modify hippocampal neurogenesis. Decrease in cell proliferation induced by social isolation rearing could be reversed by subsequent group rearing (Lu et al., 2003). Exposure to chronic social stress dramatically decreases cell proliferation in the DG of rats, mice and tree shrews (Gould et al., 1997; Czeh et al., 2002; Mitra et al., 2006). In a socio-sexual context, exposure to the male urine compounds that are involved in mate recognition increased the survival of granule cells in the accessory olfactory bulb, the main olfactory bulb (MOB), and the DG of female mice (Mak et al., 2007; Oboti et al., 2011). Moreover, suppression of neurogenesis by an anti-mitotic agent prevented mate recognition (Oboti et al., 2011) and the display of preference for dominant male in female mice (Mak et al., 2007). Another approach to assess the role of adult neurogenesis consists in evaluating whether newly-generated neurons might functionally integrate the olfactory network which process olfactory information. In male hamsters, double immunohistochemistry labeling for Fos, a marker of cell activation, and neuronal nuclei (NeuN), a marker of post-mitotic neurons, shows that olfactory bulb cells born in adulthood are activated by socio-sexual stimuli such as estrous female or aggressive male (Huang and Bittman, 2002).

A link between adult neurogenesis and parenting has not been clearly established yet (Lévy et al., 2011). Some studies indicate a regulation of neurogenesis by parturition and the onset of motherhood. In all of rodent species studied so far parturition and the early postpartum period are accompanied by a significant decrease in cell proliferation in the hippocampus. In primiparous mother rats, this was reported at postpartum day 1, 2 and 8 (Darnaudery et al., 2007; Leuner et al., 2007; Pawluski and Galea, 2007) although no effect was observed later, at postpartum day 28 and after weaning (Leuner et al., 2007). Parturition and early postpartum period do not stimulate cell proliferation in the SVZ of mice but an increase is observed at 7 days postpartum (Shingo et al., 2003). Surprisingly, whether cell survival in the DG or in the MOB is altered during parturition and early postpartum period at the onset of maternal behavior is not known in rodents. Rather, cell survival in the DG of rats was assessed either at postpartum day 14 (Darnaudery et al., 2007) or 21 (Pawluski and Galea, 2007) and both studies report a significant decrease when compared to virgins. A few studies have investigated the importance of stimuli provided by neonatal pups but outside the context of parturition. Nulliparous rats exposed to pups show increased cell proliferation in the DG when compared to nulliparous females regardless of their parental response (Pawluski and Galea, 2007). Likewise, virgin female prairie voles exposed to pups exhibit increased hippocampal cell proliferation (Ruscio et al., 2008). Although an increase in cell survival in the DG was reported in virgin females 21 days after pup-exposure (Pawluski and Galea, 2007), the influence on survival of the newborn neurons either in the DG or in the MOB at the time of pup-exposure is not known. The consequences of neurogenesis ablation on the onset of maternal behavior have been investigated in mice. Irradiation of the SVZ induces minor disturbances of maternal behavior (Feierstein et al., 2010b). However, infusion of an anti-mitotic agent which transiently impairs both hippocampal and olfactory neurogenesis has been shown to affect maternal behavior but only when animals are tested in an anxiogenic environment (Larsen and Grattan, 2010), whereas genetic manipulations inducing profound and long-term alterations of neurogenesis impair nursing behavior in the home cage (Sakamoto et al., 2011).

In sheep, a down-regulation of cell proliferation has been observed in mothers in contact with their lambs for 2 days both in the DG and the SVZ (Brus et al., 2010). However there has been no report examining a change in survival of newly-born neurons in the DG or the MOB that could occur during the early postpartum period. In addition, no study has disentangled the influence of parturition and the first interactions with the young on cell survival and this could improve our understanding of the contribution of neurogenesis to maternal behavior. In this context, maternal behavior in sheep constitutes an interesting model in which endocrine changes occurring at parturition and olfaction play a central role (Lévy et al., 1995; Lévy and Keller, 2008). In addition, not only infantile odors become very potent stimuli allowing the development of maternal care but they also provide a basis for individual recognition of the offspring. Ewes develop discriminative maternal care, called maternal selectivity, favoring their own young at suckling while rejecting any alien young. This recognition is based on the learning of olfactory characteristics of the lamb and takes place within the first hours after parturition (Lévy et al., 2004; Lévy and Keller, 2009). Some of the learning mechanisms reside in extensive neurochemical changes occurring in the MOB at parturition (Lévy et al., 1993; Lévy and Keller, 2009). In addition to these neurochemical changes, olfactory neurogenesis could provide another mechanism through which olfaction can contribute to the onset of maternal behavior and associated learning.

The aim of the present study was to evaluate the influence of parturition and learning of the lamb odor on the survival of newborn neurons. Bromodeoxyuridine (BrdU), a marker of cell division, was used in combination with two markers of neuronal maturation (doublecortin (DCX), an early maturation marker and NeuN), to compare both hippocampal and olfactory neurogenesis between virgins and parturient ewes which could interact or not with their lamb. In addition, because learning accelerates the maturation of the dendritic trees of newborn neurons in the DG (Tronel et al., 2010; Lemaire et al., 2012), and motherhood is accompanied by changes in the morphology of newborn neurons in the MOB (Kopel et al., 2012), we assessed the influence of lamb olfactory learning on this maturation by measuring the dendritic length and the number of nodes of new neuroblasts.

MATERIALS AND METHODS

ANIMALS

Experiment was conducted on 17 Ile de France ewes, of 1.5–2 years of age, from the INRA research center in Nouzilly (Indre et Loire, France) approved by local authority (agreement number E37-175-2). Animals were permanently housed indoors, with free access to water and were fed with lucerne, maize, straw and a supplement of vitamins and minerals. Animal care and experimental treatments complied with the guidelines of the French Ministry of Agriculture for animal experimentation and European regulations on animal experimentation (86/609/EEC) and were performed in accordance with the local animal regulation (authorization No. 006352 of the French Ministry of Agriculture in accordance with EEC directive). Ewes were sacrificed by a licensed butcher in an official slaughterhouse (authorization No. A37801 E37-175-2 agreement UEPAO). All efforts were made to minimize the number of animals (5–6 animals per group).

BROMODEOXYURIDINE INJECTIONS AND TISSUE PREPARATION

Four months before sacrifice, ewes were housed in an individual pen (2 × 1 m) and received four intravenous injections of BrdU, (1 injection/day, 20 mg/Kg in 0.9% saline; Sigma-Aldrich, France), a thymidine analogue incorporated into the DNA during the S-phase of the mitotic division. Doses of BrdU and timing between injections and sacrifice were based on a previous study reporting that maturation of adult-born cells both is much longer in sheep than that of rodents and that the highest proportion of new mature neurons is found at 4 months after BrdU injections (Brus et al., 2013).

GROUPS

Three groups of ewes were constituted (Figure 1). In parturient groups, mating was synchronized by the use of vaginal sponges containing 45 mg of fluorogestone acetate for 14 days followed by an intra-muscular injection of pregnant-mare-stimulating gonadotropins to induce ovulation. Just after parturition, mothers were either left 48 h with their lambs in their individual pen in the same barn (“With Lamb” group, n = 5), or were separated from them for 48 h (“No Lamb” group, n = 6). After being separated from their lamb immediately after birth, ewes of the “No Lamb” group were placed in a different barn to avoid any contact with lambs and ewes were housed together in a large pen to avoid stress induced by separation from the young. All the ewes had never given birth before the study. Lambing occurred within a period of gestation of 149 ± 4 days. Against all expectations, at birth the lambs displayed low vigor preventing them from feeding normally, probably due to BrdU injections in early pregnancy. Thus, in the “With lamb” group, adoptions have been performed with newborn lambs provided by the flock of the research center. It has been well established in previous studies that adoption, when performed at birth, are without any consequences on the quality of the mother-young relationship in comparison to normal mother-young lambing (Keverne et al., 1983; Kendrick et al., 1991; Lévy et al., 2010). In this group, maternal behavior was observed for 10 min at 0, 6 and 24 h after parturition to completely ensure that maternal care was normally provided to lambs. At 2 days postpartum just before sacrifice, selectivity was tested by presenting an alien lamb to the mother and rejection and acceptance behaviors were recorded for 3 min. The alien lamb was then taken away and the ewe was observed with her own lamb for an additional 3 min (Keller et al., 2004, 2005). These tests indicated that all the ewes of the “With Lamb” group were maternal and selective. The “Virgin” group (n = 6) was composed of nulliparous anoestrus ewes of similar age than the two parturient groups and housed together.

FIGURE 1. Time table and protocol design. All ewes received four intravenous injections of BrdU (20 mg/kg, 1 injection/day) 4 months before sacrifice. In the two parturient groups (“With Lamb” and “No Lamb”), BrdU injections were performed 1 month after fecundation. In the ”With Lamb” group (n = 5), ewes could interact with her lamb and maternal behavior was observed at 0, 6 and 24 h after parturition for 10 min each and selectivity test was performed at 2 days postpartum just before sacrifice. In the “No Lamb” group (n = 6), lambs were removed just after delivery. Virgin group (n = 6) is composed of nulliparous ewes.

BRAIN PERFUSION AND IMMUNOHISTOCHEMISTRY

Two days after lambing, ewes were anesthetized with thiopental and decapitated by a licensed butcher in an official slaughterhouse. Brains were immediately perfused via carotid arteries with 2 L of 1% sodium nitrite in phosphate buffer saline, followed by 4 L of ice-cold 4% paraformaldehyde solution in 0.1M phosphate buffer (pH 7.4). The brain was dissected out, cut into blocks and post-fixed in the same fixative for 48 h. The tissues were then stored in 30% sucrose for 2 days until sectioned. Frontal sections were cut at a thickness of 30 µm using microtome or cryostat and stored at −20°C in cryoprotectant.

To reveal BrdU positive cells in the MOB and the DG, a peroxydase single-immunolabeling was used as described previously (Brus et al., 2013). To characterize the populations of BrdU positive cells which could be affected by parturition and interaction with the young, double immunolabeling was performed against the BrdU and two markers of neuronal maturation, NeuN for mature neurons (Valley et al., 2009) and the DCX for neuroblasts (Gleeson et al., 1999; Brown et al., 2003). Sections of the MOB and the hippocampus were treated with a solution of Tris Buffer Saline 0.025M (TBS, pH=7.4)-Triton 0.3%-Azide 0.1%-Bovine Serum Albumin (BSA) 0.1% (TBSTA-BSA) for 1 h. After one rinse in TBS, sections were treated with 2N HCl in TBS for 30 min at room temperature. After three rinses in TBS, sections were incubated overnight in primary rat anti-BrdU (1:300; AbCys AbC117-7513, Paris, France) and primary mouse anti-NeuN (1:1000, Chemicon MAB377, Millipore, St. Quentin-en-Yvelines, France) or primary goat anti-DCX (1:100, Santa Cruz Biotechnology, Tebu-Bio, Le Perray en Yvelines, France) in TBSTA-BSA. The following day, sections were rinsed three times in TBS, and were incubated in two secondary antibodies simultaneously for 1 h 30 min in TBS 0.025M, pH7.4—Saponine 0.3%—BSA 0.1% (TBS-Saponine-BSA), except for BrdU/DCX for which secondary antibodies were incubated in TBS-rabbit serum 1%-saponine 0.3%. Secondary antibodies used for BrdU/NeuN immunolabeling were a donkey anti-rat CY3 (1:300, Immunotech, Jackson ImmunoResearch, United Kingdom), and a goat anti-mouse 488 (1:300, AlexaFluor A11029, Molecular Probes, Eugene, Oregon, USA); for BrdU/DCX, we used a rabbit anti-rat 488 (1:300, AlexaFluor, Molecular Probes, Eugene, Oregon, USA) and a Donkey anti-goat CY3 (1:300, Immunotech, Jackson ImmunoResearch, UK). After four rinses in TBS, sections were immersed in a Hoechst bath for 2 min (Hoechst 33258, 2 µg/ml in water, Invitrogen, USA), rinsed in two baths of water and one bath of TBS (5 min each), then cover-slipped under fluoromount-G (SouthernBiotech, Birmingham, AL, USA) and stored at 4°C in dark.

Because in a previous study we observed cell proliferation within the MOB (Brus et al., 2010), we evaluated the influence of parturition and interactions with the young on cell proliferation in this olfactory structure as well as in the DG. To this end, single-immunolabeling was performed against the Ki67 marker, an endogenous marker of cell division which is expressed at all the phases of the cellular cycle (Kee et al., 2007). Sections were treated with TBSTA-BSA for 1 h and were incubated overnight in primary antibody rabbit anti-Ki67 (1:500, Abcam ab15580-25, Cambridge, UK) in TBSTA-BSA at room temperature. The following day, sections were rinsed four times in TBS and were incubated in secondary antibody sheep anti-rabbit (1:400, produced by the INRA center of Nouzilly) in TBS-BSA 0.1% during 3 h at 4°C. After four rinses in TBS 0.1%, sections were incubated with rabbit peroxydase anti peroxydase (PAP, 1:80000, Dako Z0113, Trappes, France) overnight at 4°C. The last day, after two rinses in TBS and two rinses in Tris-HCl (0.05M, pH 7.6) sections were reacted for peroxydase detection in a solution of 3,3′-Diaminobenzidine tetrahydrochloride (DAB, 0.15 mg/mL; Sigma) containing 0.001% H_2O_2 and 0.018% nickel ammonium sulfate for 7 min.

QUANTIFICATION

The number of BrdU+ (10 sections/animal, 800 µm between sections) and Ki67+ cells (6 sections/animal, 950 µm between sections) was assessed by counting peroxydase/DAB-stained frontal sections of the MOB and the DG through different levels along the rostrocaudal axis, using a light microscope (Axioskope 2, Zeiss, Germany) on a magnification of x20 and cell count analysis software (computerized image analysis Mercator, Explora Nova, La Rochelle, France). The counter was blind to the experimental group. Areas of the MOB (granular and periventricular layers) and the DG (GCL and SGZ) were measured with this system through an x2.5 objective (MOB) and an x10 objective (DG). Cell densities were then calculated by dividing the numbers of BrdU+ or Ki67+ cells by the layer area. The Ki67+ cell density corresponds to the mean of cell density measured in the granular and the periventricular layers for the MOB and in the GCL and the SGZ for the DG. Densities of BrdU+ cells and proportions of new neurons and neuroblasts were counted only in the target structures of newly-born cells integration, the granular layer of the MOB and the GCL of the DG.

To determine the percentage of BrdU+/NeuN+ cells and BrdU+/DCX+ cells in the MOB and in the DG, approximately 100 cells per ewe were observed for the three groups (around 500–600 cells per group). Each BrdU+ cell was analyzed in its entire z-axis, with 0.5 µm step intervals, through an x40 oil immersion objective, using a confocal laser-scanning microscope (LSM700, Zeiss, Germany) equipped of excitation wavelengths 488 and 555. Cells rotated in orthogonal planes to verify double labeling with NeuN or DCX. For each selected cell that showed co-localization of BrdU with NeuN or DCX, an image was collected with the software Zen (Carl Zeiss, Germany). All images shown correspond to one focal plane (0.5 µm) and were imported into Gimp Pack Mode 2.6 software to adjust brightness and contrasts.

To determine the development of the newborn neuroblasts in the different groups, the number of nodes and the length of the dendritic arbor were measured in 16–18 BrdU+/DCX+ cells per ewe in the GCL of the MOB and of the DG. Each BrdU+/DCX+ cell was analyzed with confocal laser scanning microscope (LSM 700, Zeiss, Germany), in its entire Z-axis with 0.5 µm step interval, using x63 oil immersion objective to measure the length between the cell body to the end of the longest process. Total length of the dendritic tree was obtained by summing the length of all processes of each BrdU+/DCX+ cell. The number of nodes was obtained by counting the number of occurrences of branch points in the dendritic arbor. Interestingly, these newborn cells seemed to be at an early stage of maturation as most of them displayed only one process. Thus, the population of BrdU+/DCX+ was separated in two categories depending on the number of nodes and the percentage of cells with no nodes (one process, less mature) or with one or more nodes (two or more processes, more mature) was calculated (Figures 4B–E). Ambiguous cases were further analyzed using a semiautomatic neuron tracing system Imaris (Bitplane, USA).

FIGURE 4. Effects of parturition and/or interactions with the young on the dendritic tree of neuroblasts. (A, D) Length of dendritic processes of BrdU+/DCX+ cells (16–18 cells/animal) in the granular layer of the MOB (A) and in the granular cell layer of the DG (D) in the “Virgin” (n = 6), the “No Lamb” (n = 6) and the “With Lamb” (n = 5) groups. (B, E) Percentage of BrdU+/DCX+ cells which displayed one or more nodes in each group in the granular layer of the MOB (B) and in the granular cell layer of the DG (E). In the MOB, the “With Lamb” group showed highest length of dendritic trees and more nodes on BrdU+/DCX+ cells than in the two other groups (A–B). In the DG no significant difference was found (D–E). Data are represented as median and interquartile ranges. * p ≤ 0.05, ** p < 0.01. •: extreme value. (C) Representative illustration of dendritic lengths of DCX+/BrdU+ cells measured in the granular layer of the MOB in the “Virgin”, the “No Lamb” and the “With Lamb” groups. Scales bars: 10 µm.

STATISTICAL ANALYSIS

As densities of BrdU+ and Ki67+ cells, proportions of BrdU+/NeuN+ and BrdU+/DCX+ cells, and dendritic lengths of BrdU+/DCX+ cells were not normally distributed, the data were analyzed with nonparametric tests (Siegel, 1956). Inter-group comparisons were analyzed using two-tailed Kruskal-Wallis and Mann-Whitney tests. As the percentage of BrdU+/DCX+ cells which displayed one or more nodes was normally distributed, the data were analyzed with a one-way analysis of variance (ANOVA), and significance was probed by the Newman-Keuls test. Statistical analyses were performed using the statistical package SPSS 10 (Chicago, IL, USA) and the level of statistical significance was set at p ≤ 0.05. All data were represented as median and interquartile ranges except the number of nodes which were represented as mean ± SEM. Because we found that cell proliferation is down-regulated in postpartum ewes (Brus et al., 2010), we predicted that density of Ki67+ cells will be lower in parturient groups and therefore we used one-tailed Kruskal-Wallis and Mann-Whitney tests for this variable.

RESULTS

SURVIVAL OF NEUROBLASTS AND NEW MATURE NEURONS IN THE MAIN OLFACTORY BULB (MOB) AND THE DENTATE GYRUS (DG)

In the granular layer of the MOB and in the GCL of the DG, the densities of BrdU+ cells did not significantly differ between groups (MOB: H = 2.27, p = 0.3; DG: H = 4.48, p = 0.1; Figures 2A, B).

FIGURE 2. Effects of parturition and/or interactions with the young on cell survival. Number of BrdU+ cells per millimeter^2 (median and interquartile ranges) in the granular layer of the MOB (A) and in the DG (B). There is no significant difference between groups for both regions.

The proportion of newborn neurons was measured in each group by using a double immunofluorescent labeling for BrdU and NeuN, a marker of post-mitotic neurons, and DCX, a marker of neuroblasts (Figures 3E–H). In the granular layer of the MOB, only the proportion of BrdU+/DCX+ cells significantly differed between groups (H = 8.27, p = 0.02; Figure 3A). This proportion was significantly lower in the “With Lamb” group compared to the “Virgin” or the “No Lamb” groups (“With Lamb” vs. “Virgin” or “No Lamb” groups: U = 2.47, p = 0.01; Figure 3A). The proportion of new post-mitotic neurons (BrdU/NeuN+ cells) did not differ between groups (H = 1.23, p = 0.5; Figure 3B).

Contrary to the MOB, only the proportion of BrdU+/NeuN+ cells significantly differed between groups in the GCL of the DG (H = 11.07, p = 0.004; Figure 3D). The proportion of new mature neurons was significantly lower in the two parturient groups compared to the Virgin group (“With Lamb” vs. “Virgin” groups: U = 0, p = 0.004; “No Lamb” vs. “Virgin” groups: U = 0, p = 0.002). However, the proportion of BrdU+/DCX+ cells did not significantly differ between groups (H = 0.73, p = 0.7; Figure 3C).

FIGURE 3. Effects of parturition and/or interactions with the young on the proportion of neuroblasts and mature neurons. Percentage of neuroblasts (BrdU+/DCX+) and new mature neurons (BrdU+/NeuN+) in the granular layer of the MOB (A–B) and in the granular cell layer of the DG (C–D), in the “Virgin” (n = 6), the “No Lamb” (n = 6) and the “With Lamb” (n = 5) groups. Data are expressed as percentage of BrdU+ cells colabeled with NeuN (B–D) and DCX (A–C). In the MOB, only the proportion of BrdU+/DCX+ cells in the “With Lamb” group significantly differed from the two other groups (A). In the DG, only the proportion of BrdU+/NeuN+ cells in both parturient groups is significantly lower than the “Virgin” group (D). (E–H) High magnification of confocal images of fluorescent immunolabeling depicting colocalization of BrdU+/NeuN+ (F, H) and BrdU+/DCX+ (E, G) in the granular layer of the MOB (E–F), in the granular cell layer of the DG (G–H). Cell nuclei are labeling with Hoechst. Scales bars: 10 µm. MOB: main olfactory bulb, DG: dentate gyrus, BrdU: bromodeoxyuridine, DCX: doublecortin, NeuN: neuronal nuclei. * p ≤ 0.05, ** p < 0.01.

DENDRITIC LENGTHS OF NEWBORN NEURONS IN THE MAIN OLFACTORY BULB (MOB) AND THE DENTATE GYRUS (DG)

To assess the influence of interactions with the young on the development of the dendritic arbor of new neurons, we measured dendritic lengths and number of nodes of BrdU+/DCX+ cells in the granular layer of the MOB and in the GCL of the DG (Figure 4).

In the MOB, dendritic lengths and number of nodes significantly differed between groups (dendritic length: H = 6.01, p = 0.049; nodes: F = 6.58, p = 0.01). The highest dendritic lengths were found in the “With Lamb” group and significantly differed from the “No Lamb” (U = 3, p = 0.03; Figure 4A) and showed a tendency to differ from the “Virgin” group due to an animal showing an extreme value (U = 5, p = 0.08; Figure 4A). The percentage of BrdU+/DCX+ cells possessing nodes was significantly greater in the “With Lamb” group than in the “Virgin” or “No Lamb” groups (“With Lamb” vs. “Virgin” groups: p = 0.04; “With Lamb” vs. “No Lamb” groups: p = 0.007; Figure 4B).

By contrast in the DG, no difference in dendritic lengths and in the number of nodes were found between groups (dendritic length: H = 1.35, p = 0.5; nodes; F = 1.04, p = 0.38; Figures 4D, E). Finally, the diameter of BrdU+/DCX+ cell bodies did not differ between groups in both structures (MOB; F = 2.33, p = 0.13; DG: F = 1.56, p = 0.24).

CELL PROLIFERATION IN THE MAIN OLFACTORY BULB (MOB) AND THE DENTATE GYRUS (DG)

To evaluate the influence of parturition and interactions with the young on cell proliferation, a single immunolabeling was performed against the Ki67 protein, an endogen marker of cell division, in the MOB and the DG (Figure 5). In both the MOB and the DG, the density of Ki67+ cells significantly differed between groups (MOB: H = 6.13, p = 0.025, Figure 5A; DG: H = 7.59, p = 0.01, Figure 5B). For both structures, in the two parturient groups, the density of Ki67+ cells in the MOB was significantly lower compared to the “Virgin” group (MOB: “With Lamb” vs. “Virgin” groups: U = 2, p = 0.01; “No Lamb” vs. “Virgin” groups: U = 7, p = 0.04; DG “With Lamb” and the “Virgin” groups: U = 1, p = 0.01; “No Lamb” group vs. “Virgin” groups: U = 8, p = 0.05).

FIGURE 5. Effects of parturition and/or interactions with the young on cell proliferation. Densities of Ki67+ cells in the MOB (A) and in the DG (B) in the “Virgin”, “No Lamb” and “With Lamb” groups, represented as median and interquartile ranges. In both structures, the density of Ki67+ cells was significantly lower in the two parturient groups compared to “Virgin” group. * p ≤ 0.05, ** p < 0.01.

DISCUSSION

Two main results have been obtained in this study. First, interactions with the young and associated learning, but not parturition, reduce the survival of neuroblasts in the MOB, whereas in the DG, parturition induces a decrease in the number of new neurons. These results suggest that learning of the olfactory signature of the lamb, which occurred during the first mother/young interactions, is specifically associated with a down-regulation in olfactory neurogenesis. Secondly, a positive effect on the development of the dendritic length of new neuroblasts has been specifically observed in the MOB of mothers interacting with their lamb suggesting that olfactory learning accelerates the maturation of adult-born neurons in the MOB but not in the DG.

In the MOB, while the number of BrdU+ cells was similar between parturient groups and the “Virgin” group, the population of neuroblasts was decreased by interactions with the young. This reduction is probably not a consequence of a decrease in new cell production during gestation since a positive, rather than a negative, influence of pregnancy on cell proliferation has been described in the SVZ of rodents (Shingo et al., 2003; Furuta and Bridges, 2005; Larsen and Grattan, 2010). In addition, parturient ewes separated from their lambs did not show any change of cell survival in comparison to virgins indicating that parturition could not account for this down-regulation. However, the suppressive effect of interactions with the lamb for 2 days on the survival of neuroblasts could be a consequence of various behaviors involved in maternal care. For instance licking the newborn lamb depends on olfactory attraction to amniotic fluids (Lévy et al., 1983). However since licking behavior occurs at parturition and only last for 2 h it is unlikely involved in the decrease in neuroblasts population observed after 2 days of mother-young contact. Somatosensory stimulation associated with suckling could also account for this suppressive effect. However there is so far no evidence in the literature that suckling can have a direct effect on olfactory neurogenesis. In this respect, it would be of interest to assess olfactory neurogenesis in mothers whose udders are covered to prevent lambs from suckling. In rodents, restriction of suckling and tactile stimulation in lactating mothers in the presence of pups modulate neurochemical activity of the MOB, suggesting the importance of olfactory interactions with the young (Munaro, 1990). In sheep, during the early postpartum period various neurochemical and electrophysiological changes in the functional circuitry of the MOB underlie the formation of olfactory memory for lamb (Sanchez-Andrade et al., 2005; Lévy and Keller, 2008). Learning of the lamb odor could also be accompanied by changes in olfactory neurogenesis. Numerous studies report that bulbar neurogenesis varies following olfactory learning and that these changes could affect or not mature new neurons (see review: Feierstein et al., 2010a). However, whether these changes concern neuroblasts is not known. Here we show that neuroblasts but not more mature neurons are sensitive to social interactions. The decrease in the number of BrdU+/DCX+ cells cannot be explained by an enhancement of neuronal maturation since we did not observe an increase in BrdU+/NeuN+ cells in ewes interacting with their lambs. This decrease rather suggests that neuroblasts die during the 2 days of lamb exposure. Studies of adult-born neurons showed that olfactory learning has a complex effect on neuronal turnover, increasing or decreasing the survival of newborn olfactory cells as a function of their age (Mandairon et al., 2006; Mouret et al., 2008). Further studies using different timing of BrdU injections are needed to examine whether newborn cells of different age and maturation could have been differently affected.

Together with a reduced number of neuroblasts, a decrease in cell proliferation in the MOB is observed in both groups of parturient ewes in comparison to virgins, as previously shown (Brus et al., 2010). The functional significance of this reduced proliferation and maturation processes in the MOB remains unknown. It may accentuate a survival-promoting effect by eliminating newborn neurons that are not functional and would favor the maturation and integration of new neurons which participate in learning by reducing cell competition. Computational studies support this hypothesis by showing that the rate of cell proliferation and cell survival contribute to the stability of the neural activity in the network (Lehmann et al., 2005; Butz et al., 2006). This assumption is supported by our results showing that dendritic maturation of neuroblasts is only enhanced in mothers interacting with their lamb in the MOB.

Two main non-exclusive mechanisms could be considered to account for this accelerated maturation. Firstly, nursing and suckling could influence the morphology of new neurons as several studies report an effect of lactation on dendritic arbor in the mother brain. For instance, enhanced spine density has been reported in the hippocampus of lactating rats (Kinsley et al., 2006). Similarly lactating females have longer basal dendritic length compared to diestrous females in the medial preoptic area, a key region for maternal behavior (Keyser-Marcus et al., 2001). However, a negative effect has been reported on oxytocin neurons having less dendritic branches and a fewer total dendritic length in lactating rats compared to virgin rats (Armstrong and Stern, 1998; Stern and Armstrong, 1998). Secondly and more interestingly, learning the familiar lamb odor could induce this increased maturation as a positive influence of olfactory stimulation. The effect of odor learning on dendritic trees of newborn neurons has already been reported. Sensory deprivation drastically decreases the number, the dendritic length and the spine density of newborn granule cells in the MOB (Rochefort and Lledo, 2005) and odor enrichment induces structural synaptic plasticity of adult-born granule neurons (Livneh and Mizrahi, 2011). Since evidence of an influence of learning but not of suckling on the maturation of new adult neurons has been reported in the literature, we hypothesized that learning of the olfactory signature of the lamb is involved in this maturation process. Further studies need to be done to establish whether suckling and/or learning of the lamb odor are responsible for this increased maturation of neuroblasts and whether this could also affect new mature neurons.

In the DG, while no change in the number of BrdU+ cells was found between groups, survival of new mature neurons was reduced by parturition itself, since parturient ewes isolated from their lamb showed a decrease in BrdU+/NeuN+ cells in comparison to virgins. The effect of pregnancy on the decrease in cell production is unlikely since no changes in hippocampal neurogenesis have been reported either during early or late pregnancy in rodents (see review: Lévy et al., 2011). However we cannot exclude a species dependent regulation of neurogenesis during pregnancy. These effects could also be due to the stress induced by removal of the lamb. This is however unlikely since we observed a similar decrease in the survival of mature neurons in mothers staying with their lambs. The inhibition of neurogenesis could be the consequence of hormonal changes, mainly steroids and glucocorticoids occurring at parturition. Oestradiol induced a decrease in survival of newborn neurons (Barker and Galea, 2008) and applications of corticosterone suppress cell survival in the DG of the hippocampus (Wong and Herbert, 2004, 2006). Interestingly, oestradiol and corticosterone were found to affect neurogenesis and some interactions exist between both hormones. For example, adrenal steroids mediate the suppression of cell proliferation induced by oestradiol in ovariectomized females (Ormerod and Galea, 2001). The interplay between estradiol and cortisol at parturition may also mediate levels of cell survival in the DG and further studies are needed to better determine this complex influence on neurogenesis. In addition, our results suggest a hormonal influence depending on cell type since no change in the proportion of neuroblasts was found between groups in the DG. Recent studies report that high corticosterone differently affects population of new cells according to their maturity stage (Gonzalez-Perez et al., 2011; Lussier et al., 2013). Different effects on different cell lineages may represent adaptative actions of glucocorticoids, which provide a compensatory mechanism to protect some types of cells from death in the hippocampus. Such a mechanism would prevent from a dysfunction of the hippocampus during the postpartum period and would allow a better spatial memory in lactating females (Kinsley et al., 1999).

In conclusion, this study supports the hypothesis that the maternal brain undergoes neuronal changes, such as adult neurogenesis, which could constitute an adaptive response to motherhood by favoring learning ability. More specifically, interactions with the young are associated with a down-regulation in olfactory neurogenesis and an enhancement of neuroblast maturation. Our hypothesis is that fewer new neurons decrease cell competition in the olfactory bulb and enhance maturation of those new neurons selected to participate in the learning of the lamb odor. Further experiments will aim at understanding how these adult born neurons could be integrated to the neural network involved in maternal behavior.

AUTHOR CONTRIBUTIONS

Maïna Brus, Maryse Meurisse, Matthieu Keller and Frédéric Lévy designed and performed research, analyzed data and wrote the paper.

ACKNOWLEDGMENTS

The authors would like to acknowledge the financial support of the “Programme transversal INRA/Institut Pasteur No. 319”, the ANR programme blanc (2012-2015) PLASTMATBEHAV and the INRA/Région for the grant to M. Brus. We particularly thank (1) N. Jouaneau and M. Chauvet for histological preparation; (2) J. Cognié for veterinary assistance; (3) C. Moussu, E. Archer and F. Cornilleau for their assistance at lambing; (4) J.-P. Dubois and A. Arnould for sacrifice of animals; (5) D. Capo and its staff for animal breeding; (6) G. Le Pape for statistical analysis; (7) L. Szymanski for English corrections; and (8) the cellular imaging platform (PIC) of UMR PRC, Nouzilly.

REFERENCES

Alonso, M., Lepousez, G., Sebastien, W., Bardy, C., Gabellec, M. M., Torquet, N., et al. (2012). Activation of adult-born neurons facilitates learning and memory. Nat. Neurosci. 15, 897–904. doi: 10.1038/nn.3108

Armstrong, W. E., and Stern, J. W. (1998). Phenotypic and state-dependent expression of the electrical and morphological properties of oxytocin and vasopressin neurones. Prog. Brain. Res. 119, 101–113. doi: 10.1016/s0079-6123(08)61564-2

Arruda-Carvalho, M., Sakaguchi, M., Akers, K. G., Josselyn, S. A., and Frankland, P. W. (2011). Posttraining ablation of adult-generated neurons degrades previously acquired memories. J. Neurosci. 31, 15113–15127. doi: 10.1523/jneurosci.3432-11.2011

Barker, J. M., and Galea, L. A. (2008). Repeated estradiol administration alters different aspects of neurogenesis and cell death in the hippocampus of female, but not male, rats. Neuroscience 152, 888–902. doi: 10.1016/j.neuroscience.2007.10.071

Breton-Provencher, V., Lemasson, M., Peralta, M. R. 3rd., and Saghatelyan, A. (2009). Interneurons produced in adulthood are required for the normal functioning of the olfactory bulb network and for the execution of selected olfactory behaviors. J. Neurosci. 29, 15245–15257. doi: 10.1523/jneurosci.3606-09.2009

Brown, J. P., Couillard-Despres, S., Cooper-Kuhn, C. M., Winkler, J., Aigner, L., and Kuhn, H. G. (2003). Transient expression of doublecortin during adult neurogenesis. J. Comp. Neurol. 467, 1–10. doi: 10.1002/cne.10874

Brus, M., Meurisse, M., Franceschini, I., Keller, M., and Levy, F. (2010). Evidence for cell proliferation in the sheep brain and its down-regulation by parturition and interactions with the young. Horm. Behav. 58, 737–746. doi: 10.1016/j.yhbeh.2010.07.006

Brus, M., Meurisse, M., Gheusi, G., Keller, M., Lledo, P. M., and Levy, F. (2013). Dynamics of olfactory and hippocampal neurogenesis in adult sheep. J. Comp. Neurol. 521, 169–188. doi: 10.1002/cne.23169

Butz, M., Lehmann, K., Dammasch, I. E., and Teuchert-Noodt, G. (2006). A theoretical network model to analyse neurogenesis and synaptogenesis in the dentate gyrus. Neural. Netw. 19, 1490–1505. doi: 10.1016/j.neunet.2006.07.007

Curtis, M. A., Kam, M., Nannmark, U., Anderson, M. F., Axell, M. Z., Wikkelso, C., et al. (2007). Human neuroblasts migrate to the olfactory bulb via a lateral ventricular extension. Science 315, 1243–1249. doi: 10.1126/science.1136281

Czeh, B., Welt, T., Fischer, A. K., Erhardt, A., Schmitt, W., Muller, M. B., et al. (2002). Chronic psychosocial stress and concomitant repetitive transcranial magnetic stimulation: effects on stress hormone levels and adult hippocampal neurogenesis. Biol. Psychiatry 52, 1057–1065. doi: 10.1016/s0006-3223(02)01457-9

Darnaudery, M., Perez-Martin, M., Del Favero, F., Gomez-Roldan, C., Garcia-Segura, L. M., and Maccari, S. (2007). Early motherhood in rats is associated with a modification of hippocampal function. Psychoneuroendocrinology 32, 803–812. doi: 10.1016/j.psyneuen.2007.05.012

Deng, W., Aimone, J. B., and Gage, F. H. (2010). New neurons and new memories: how does adult hippocampal neurogenesis affect learning and memory? Nat. Rev. Neurosci. 11, 339–350. doi: 10.1038/nrn2822

Feierstein, C. E., Lazarini, F. O., Wagner, S., Gabellec, M.-M., De Chaumont, F., Olivo-Marin, J.-C., et al. (2010b). Disruption of adult neurogenesis in the olfactory bulb affects social interaction but not maternal behavior. Front. Behav. Neurosci. 4:12. doi: 10.3389/fnbeh.2010.00176

Feierstein, C. E., Lazarini, F., Wagner, S., Gabellec, M. M., De Chaumont, F., Olivo-Marin, J. C., et al. (2010a). Disruption of adult neurogenesis in the olfactory bulb affects social interaction but not maternal behavior. Front. Behav. Neurosci. 4:176. doi: 10.3389/fnbeh.2010.00176

Furuta, M., and Bridges, R. S. (2005). Gestation-induced cell proliferation in the rat brain. Brain Res. Dev. Brain Res. 156, 61–66. doi: 10.1016/j.devbrainres.2005.01.008

Gleeson, J. G., Lin, P. T., Flanagan, L. A., and Walsh, C. A. (1999). Doublecortin is a microtubule-associated protein and is expressed widely by migrating neurons. Neuron 23, 257–271. doi: 10.1016/s0896-6273(00)80778-3

Gonzalez-Perez, O., Chavez-Casillas, O., Jauregui-Huerta, F., Lopez-Virgen, V., Guzman-Muniz, J., Moy-Lopez, N., et al. (2011). Stress by noise produces differential effects on the proliferation rate of radial astrocytes and survival of neuroblasts in the adult subgranular zone. Neurosci. Res. 70, 243–250. doi: 10.1016/j.neures.2011.03.013

Gould, E., Mcewen, B. S., Tanapat, P., Galea, L. A., and Fuchs, E. (1997). Neurogenesis in the dentate gyrus of the adult tree shrew is regulated by psychosocial stress and NMDA receptor activation. J. Neurosci. 17, 2492–2498.

Gu, Y., Arruda-Carvalho, M., Wang, J., Janoschka, S. R., Josselyn, S. A., Frankland, P. W., et al. (2012). Optical controlling reveals time-dependent roles for adult-born dentate granule cells. Nat. Neurosci. 15, 1700–1706. doi: 10.1038/nn.3260

Huang, L., and Bittman, E. L. (2002). Olfactory bulb cells generated in adult male golden hamsters are specifically activated by exposure to estrous females. Horm. Behav. 41, 343–350. doi: 10.1006/hbeh.2002.1767

Kee, N., Teixeira, C. M., Wang, A. H., and Frankland, P. W. (2007). Preferential incorporation of adult-generated granule cells into spatial memory networks in the dentate gyrus. Nat. Neurosci. 10, 355–362. doi: 10.1038/nn1847

Keller, M., Meurisse, M., and Levy, F. (2005). Mapping of brain networks involved in consolidation of lamb recognition memory. Neuroscience 133, 359–369. doi: 10.1016/j.neuroscience.2005.02.027

Keller, M., Perrin, G., Meurisse, M., Ferreira, G., and Levy, F. (2004). Cortical and medial amygdala are both involved in the formation of olfactory offspring memory in sheep. Eur. J. Neurosci. 20, 3433–3441. doi: 10.1111/j.1460-9568.2004.03812.x

Kendrick, K. M., Lévy, F., and Keverne, E. B. (1991). Importance of vaginocervical stimulation for the formation of maternal bonding in primiparous and multiparous parturient ewes. Physiol. Behav. 50, 595–600. doi: 10.1016/0031-9384(91)90551-x

Keverne, E. B., Lévy, F., Poindron, P., and Lindsay, D. R. (1983). Vaginal stimulation: an important determinant of maternal bonding in sheep. Science 219, 81–83. doi: 10.1126/science.6849123

Keyser-Marcus, L., Stafisso-Sandoz, G., Gerecke, K., Jasnow, A., Nightingale, L., Lambert, K. G., et al. (2001). Alterations of medial preoptic area neurons following pregnancy and pregnancy-like steroidal treatment in the rat. Brain Res. Bull. 55, 737–745. doi: 10.1016/s0361-9230(01)00554-8

Kinsley, C. H., Madonia, L., Gifford, G. W., Tureski, K., Griffin, G. R., Lowry, C., et al. (1999). Motherhood improves learning and memory. Nature 402, 137–138.

Kinsley, C. H., Trainer, R., Stafisso-Sandoz, G., Quadros, P., Marcus, L. K., Hearon, C., et al. (2006). Motherhood and the hormones of pregnancy modify concentrations of hippocampal neuronal dendritic spines. Horm. Behav. 49, 131–142. doi: 10.1016/j.yhbeh.2005.09.001

Kopel, H., Schechtman, E., Groysman, M., and Mizrahi, A. (2012). Enhanced synaptic integration of adult-born neurons in the olfactory bulb of lactating mothers. J. Neurosci. 32, 7519–7527. doi: 10.1523/jneurosci.6354-11.2012

Larsen, C. M., and Grattan, D. R. (2010). Prolactin-induced mitogenesis in the subventricular zone of the maternal brain during early pregnancy is essential for normal postpartum behavioral responses in the mother. Endocrinology 151, 3805–3814. doi: 10.1210/en.2009-1385

Lazarini, F., and Lledo, P.-M. (2011). Is adult neurogenesis essential for olfaction? Trends Neurosci. 34, 20–30. doi: 10.1016/j.tins.2010.09.006

Lazarini, F., Mouthon, M. A., Gheusi, G., De Chaumont, F., Olivo-Marin, J. C., Lamarque, S., et al. (2009). Cellular and behavioral effects of cranial irradiation of the subventricular zone in adult mice. PLoS One 4:e7017. doi: 10.1371/journal.pone.0007017

Lehmann, K., Butz, M., and Teuchert-Noodt, G. (2005). Offer and demand: proliferation and survival of neurons in the dentate gyrus. Eur. J. Neurosci. 21, 3205–3216. doi: 10.1111/j.1460-9568.2005.04156.x

Lemaire, V., Tronel, S., Montaron, M. F., Fabre, A., Dugast, E., and Abrous, D. N. (2012). Long-lasting plasticity of hippocampal adult-born neurons. J. Neurosci. 32, 3101–3108. doi: 10.1523/jneurosci.4731-11.2012

Leuner, B., Mirescu, C., Noiman, L., and Gould, E. (2007). Maternal experience inhibits the production of immature neurons in the hippocampus during the postpartum period through elevations in adrenal steroids. Hippocampus 17, 434–442. doi: 10.1002/hipo.20278

Lévy, F., and Keller, M. (2008). “Neurobiology of maternal behavior in sheep,” in Advances in the Study of Behavior, eds H. J. Brockmann, T. J. Roper, M. Naguib, K. E. Wynne-Edwards, C. Barnard and J. C. Mitani (San Diego, California, USA: Elsevier Inc., Academic Press), 399–437.

Lévy, F., and Keller, M. (2009). Olfactory mediation of maternal behavior in selected mammalian species. Behav. Brain. Res. 200, 336–345. doi: 10.1016/j.bbr.2008.12.017

Lévy, F., Gheusi, G., and Keller, M. (2011). Plasticity of the parental brain: a case for neurogenesis. J. Neuroendocrinol. 23, 984–993. doi: 10.1111/j.1365-2826.2011.02203.x

Lévy, F., Guevara-Guzman, R., Hinton, M. R., Kendrick, K. M., and Keverne, E. B. (1993). Effects of parturition and maternal experience on noradrenaline and acetylcholine release in the olfactory bulb of sheep. Behav. Neurosci. 107, 662–668. doi: 10.1037/0735-7044.107.4.662

Lévy, F., Keller, M., and Poindron, P. (2004). Olfactory regulation of maternal behavior in mammals. Horm. Behav. 46, 284–302. doi: 10.1016/j.yhbeh.2004.02.005

Lévy, F., Keller, M., Cornilleau, F., Moussu, C., and Ferreira, G. (2010). Vaginocervical stimulation of ewes induces the rapid formation of a new bond with an alien young without interfering with a previous bond. Dev. Psychobiol. 52, 537–544. doi: 10.1002/dev.20459

Lévy, F., Locatelli, A., Piketty, V., Tillet, Y., and Poindron, P. (1995). Involvement of the main but not the accessory olfactory system in maternal behavior of primiparous and multiparous ewes. Physiol. Behav. 57, 97–104. doi: 10.1016/0031-9384(94)00200-o

Lévy, F., Poindron, P., and Le Neindre, P. (1983). Attraction and repulsion by amniotic fluids and their olfactory control in the ewe around parturition. Physiol. Behav. 31, 687–692. doi: 10.1016/s0031-9384(83)80004-3

Livneh, Y., and Mizrahi, A. (2011). Experience-dependent plasticity of mature adult-born neurons. Nat. Neurosci. 15, 26–28. doi: 10.1038/nn.2980

Lu, L., Bao, G., Chen, H., Xia, P., Fan, X., Zhang, J., et al. (2003). Modification of hippocampal neurogenesis and neuroplasticity by social environments. Exp. Neurol. 183, 600–609. doi: 10.1016/s0014-4886(03)00248-6

Lussier, A. L., Lebedeva, K., Fenton, E. Y., Guskjolen, A., Caruncho, H. J., and Kalynchuk, L. E. (2013). The progressive development of depression-like behavior in corticosterone-treated rats is paralleled by slowed granule cell maturation and decreased reelin expression in the adult dentate gyrus. Neuropharmacology 71, 174–183. doi: 10.1016/j.neuropharm.2013.04.012

Mak, G. K., Enwere, E. K., Gregg, C., Pakarainen, T., Poutanen, M., Huhtaniemi, I., et al. (2007). Male pheromone-stimulated neurogenesis in the adult female brain: possible role in mating behavior. Nat. Neurosci. 10, 1003–1011. doi: 10.1038/nn1928

Mandairon, N., Sacquet, J., Garcia, S., Ravel, N., Jourdan, F., and Didier, A. (2006). Neurogenic correlates of an olfactory discrimination task in the adult olfactory bulb. Eur. J. Neurosci. 24, 3578–3588. doi: 10.1111/j.1460-9568.2006.05235.x

Ming, G. L., and Song, H. (2005). Adult neurogenesis in the mammalian central nervous system. Annu. Rev. Neurosci. 28, 223–250. doi: 10.1146/annurev.neuro.28.051804.101459

Mitra, R., Sundlass, K., Parker, K. J., Schatzberg, A. F., and Lyons, D. M. (2006). Social stress-related behavior affects hippocampal cell proliferation in mice. Physiol. Behav. 89, 123–127. doi: 10.1016/j.physbeh.2006.05.047

Moreno, M. M., Linster, C., Escanilla, O., Sacquet, J., Didier, A., and Mandairon, N. (2009). Olfactory perceptual learning requires adult neurogenesis. Proc. Natl. Acad. Sci. U S A 106, 17980–17985. doi: 10.1073/pnas.0907063106

Mouret, A. L., Gheusi, G., Gabellec, M.-M., De Chaumont, F., Olivo-Marin, J.-C., and Lledo, P.-M. (2008). Learning and survival of newly generated neurons: when time matters. J. Neurosci. 28, 11511–11516. doi: 10.1523/jneurosci.2954-08.2008

Munaro, N. I. (1990). Maternal behavior: glutamic acid decarboxylase activity in the olfactory bulb of the rat. Pharmacol. Biochem. Behav. 36, 81–84. doi: 10.1016/0091-3057(90)90129-6

Oboti, L., Schellino, R., Giachino, C., Chamero, P., Pyrski, M., Leinders-Zufall, T., et al. (2011). Newborn interneurons in the accessory olfactory bulb promote mate recognition in female mice. Front. Neurosci. 5:113. doi: 10.3389/fnins.2011.00113

Ormerod, B., and Galea, L. (2001). Reproductive status influences cell proliferation and cell survival in the dentate gyrus of adult female meadow voles: a possible regulatory role for estradiol. Neuroscience 102, 369–379. doi: 10.1016/s0306-4522(00)00474-7

Pawluski, J. L., and Galea, L. A. (2007). Reproductive experience alters hippocampal neurogenesis during the postpartum period in the dam. Neuroscience 149, 53–67. doi: 10.1016/j.neuroscience.2007.07.031

Rochefort, C., and Lledo, P. M. (2005). Short-term survival of newborn neurons in the adult olfactory bulb after exposure to a complex odor environment. Eur. J. Neurosci. 22, 2863–2870. doi: 10.1111/j.1460-9568.2005.04486.x

Ruscio, M. G., Sweeny, T. D., Hazelton, J. L., Suppatkul, P., Boothe, E., and Carter, C. S. (2008). Pup exposure elicits hippocampal cell proliferation in the prairie vole. Behav. Brain. Res. 187, 9–16. doi: 10.1016/j.bbr.2007.08.028

Sahay, A., Wilson, D. A., and Hen, R. (2011). Pattern separation: a common function for new neurons in hippocampus and olfactory bulb. Neuron 70, 582–588. doi: 10.1016/j.neuron.2011.05.012

Sakamoto, M., Imayoshi, I., Ohtsuka, T., Yamaguchi, M., Mori, K., and Kageyama, R. (2011). Continuous neurogenesis in the adult forebrain is required for innate olfactory responses. Proc. Natl. Acad. Sci. U S A 108, 8479–8484. doi: 10.1073/pnas.1018782108

Sanchez-Andrade, G., James, B. M., and Kendrick, K. M. (2005). Neural encoding of olfactory recognition memory. J. Reprod. Dev. 51, 547–558. doi: 10.1262/jrd.17031

Shingo, T., Gregg, C., Enwere, E., Fujikawa, H., Hassam, R., Geary, C., et al. (2003). Pregnancy-stimulated neurogenesis in the adult female forebrain mediated by prolactin. Science 299, 117–120. doi: 10.1126/science.1076647

Siegel, S. (1956). Nonparametric Statistics for the Behavioral Sciences. Tokyo: McGraw-Hill.

Stern, J. E., and Armstrong, W. E. (1998). Reorganization of the dendritic trees of oxytocin and vasopressin neurons of the rat supraoptic nucleus during lactation. J. Neurosci. 18, 841–853.

Sultan, S., Mandairon, N., Kermen, F., Garcia, S., Sacquet, J., and Didier, A. (2010). Learning-dependent neurogenesis in the olfactory bulb determines long-term olfactory memory. FASEB J. 24, 2355–2363. doi: 10.1096/fj.09-151456

Tronel, S., Fabre, A., Charrier, V., Oliet, S. H., Gage, F. H., and Abrous, D. N. (2010). Spatial learning sculpts the dendritic arbor of adult-born hippocampal neurons. Proc. Natl. Acad. Sci. U S A 107, 7963–7968. doi: 10.1073/pnas.0914613107

Valley, M. T., Mullen, T. R., Schultz, L. C., Sagdullaev, B. T., and Firestein, S. (2009). Ablation of mouse adult neurogenesis alters olfactory bulb structure and olfactory fear conditioning. Front. Neurosci. 3:51. doi: 10.3389/neuro.22.003.2009

Wong, E. Y., and Herbert, J. (2004). The corticoid environment: a determining factor for neural progenitors’ survival in the adult hippocampus. Eur. J. Neurosci. 20, 2491–2498. doi: 10.1111/j.1460-9568.2004.03717.x

Wong, E. Y., and Herbert, J. (2006). Raised circulating corticosterone inhibits neuronal differentiation of progenitor cells in the adult hippocampus. Neuroscience 137, 83–92. doi: 10.1016/j.neuroscience.2005.08.073

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 29 November 2013; Accepted: 03 February 2014; Published online: 18 February 2014.

Citation: Brus M, Meurisse M, Keller M and Lévy F (2014) Interactions with the young down-regulate adult olfactory neurogenesis and enhance the maturation of olfactory neuroblasts in sheep mothers. Front. Behav. Neurosci. 8:53. doi: 10.3389/fnbeh.2014.00053

Copyright © 2014 Brus, Meurisse, Keller and Lévy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 16 June 2014
doi: 10.3389/fnbeh.2014.00211

Differential memory persistence of odor mixture and components in newborn rabbits: competition between the whole and its parts

Gérard Coureaud[1]*, Thierry Thomas-Danguin[1], Frédérique Datiche[1], Donald A. Wilson[2] and Guillaume Ferreira3,4

1Centre des Sciences du Goût et de l’Alimentation (CSGA), UMR 6265 CNRS, UMR 1324 INRA, Université de Bourgogne, Dijon, France

2Department of Child and Adolescent Psychiatry, New York University Langone School of Medicine, New York, NY, USA

3Nutrition and Integrative Neurobiology Group, INRA UMR 1286, Bordeaux, France

4Université de Bordeaux, Bordeaux, France

* Correspondence: Gérard Coureaud, Centre des Sciences du Goût et de l’Alimentation (CSGA), UMR 6265 CNRS, UMR 1324 INRA, Université de Bourgogne, 9E Boulevard Jeanne d’Arc, 21000 Dijon, France e-mail: gerard.coureaud@u-bourgogne.fr

Edited by:
Anne-Marie Mouly, Centre de Recherche en Neurosciences de Lyon, France

Reviewed by:
Christiane Linster, Cornell University, USA
Mouna Maroun, University of Haifa, Israel

Interacting with the mother during the daily nursing, newborn rabbits experience her body odor cues. In particular, the mammary pheromone (MP) contained in rabbit milk triggers the typical behavior which helps to localize and seize the nipples. It also promotes the very rapid appetitive learning of simple or complex stimuli (odorants or mixtures) through associative conditioning. We previously showed that 24 h after MP-induced conditioning to odorants A (ethyl isobutyrate) or B (ethyl maltol), newborn rabbits perceive the AB mixture in a weak configural way, i.e., they perceive the odor of the AB configuration in addition to the odors of the elements. Moreover, after conditioning to the mixture, elimination of the memories of A and B does not affect the memory of AB, suggesting independent elemental and configural memories of the mixture. Here, we evaluated whether configural memory persistence differs from elemental one. First, whereas 1 or 3-day-old pups conditioned to A or B maintained their responsiveness to the conditioned odorant for 4 days, those conditioned to AB did not respond to the mixture after the same retention period. Second, the pups conditioned to AB still responded to A and B 4 days after conditioning, which indicates stronger retention of the elements than of the configuration when all information are learned together. Third, we determined whether the memory of the elements competes with the memory of the configuration: after conditioning to AB, when the memories of A and B were erased using pharmacological treatment, the memory of the mixture was extended to day 5. Thus, newborn rabbits have access to both elemental and configural information in certain odor mixtures, and competition between these distinct representations of the mixture influences the persistence of their memories. Such effects certainly occur in the natural context of mother-pup interactions and may contribute to early acquisition of knowledge about the surroundings.

Keywords: Oryctolagus cuniculus, newborn, odor mixture, configural perception, stimulus representation, retention, memory persistence

INTRODUCTION

In some cases, mixtures of volatile molecules are perceived as a collection of independent, identifiable elements; the perception is then elemental (e.g., Laing and Francis, 1989; Laska and Hudson, 1993; Linster and Cleland, 2004). However, some mixtures induce a configural processing. Then, the mixture gives rise to either a unique and novel odor quality, different from the odor qualities of the elements (robust configural perception; e.g., Smith, 1996; Jinks and Laing, 1999; Kay et al., 2005), or to a novel quality perceived in addition to the qualities of the odorants (weak configural perception; Rescorla, 1973; Kay et al., 2005). Configural odor processing has been described with different mixtures and different approaches in a variety of species from invertebrates to vertebrates, including humans (e.g., Derby et al., 1996; Linster and Smith, 1999; Valentincic et al., 2000; Wise and Cain, 2000; Deisig et al., 2001; Wiltrout et al., 2003; Mandairon et al., 2006; Riffell et al., 2009; Gottfried, 2010; Wilson and Sullivan, 2011). For instance, data in human adults revealed that a mixture of two odorants (AB), one smelling like strawberry (A: ethyl isobutyrate) and the other like caramel (B: ethyl maltol), generates the configural perception of a pineapple odor at a specific ratio of A/B (30/70 v/v; Le Berre et al., 2008, 2010; Barkat et al., 2012). Interestingly, recent results in a newborn mammal, the newborn rabbit, showed similar configural processing abilities with the same AB mixture, at the same ratio. Indeed, after single appetitive conditioning to odorant A or to odorant B by pairing with the mammary pheromone (MP) (naturally contained in rabbit milk and experimentally used as unconditioned stimulus in associative conditioning procedure; Coureaud et al., 2006, 2010), rabbit pups respond to the conditioned element and respectively to the AC or BC mixtures (C: guaïacol). However, they do not respond to the AB mixture. These results suggest that they perceive AB differently from the odors of A and of B, while they perceive AC or BC as the sum of their component odors (Coureaud et al., 2008, 2009a, 2011a). Furthermore, after single conditioning to the AB mixture, rabbit pups respond to the components A and B in addition to AB, indicating that the AB mixture is perceived in a weak configural way (Coureaud et al., 2008). Very recently, combining behavioral approaches with pharmacological tools, we provided another confirmation of this neonatal weak configural perception of AB and demonstrated that rabbit pups memorize the AB configuration independently (at least in part) from the representations of each element. After conditioning to AB, amnesia of A and B did not propagate to AB: pups that did not respond to either A or B still responded to AB (Coureaud et al., in press). Thus, after AB conditioning, distinct elemental and configural memories of the mixture are created.

In the previous studies in newborn rabbits, memories of the elements A and B or of the AB configuration were evaluated 24 or 48 h after conditioning, which are the optimal delays for responsiveness to a stimulus conditioned by single pairing with the MP (Coureaud et al., 2006). However, to date, whether memory persistence of the configural odor of the AB mixture differs from that of the odorants A and B (also detected in the mixture) had never been evaluated; here, we hypothesized that they could be distinguished. This issue constituted the first and main goal of our study. Besides understanding early memory and perception in the rabbit, this evaluation could more broadly help provide additional and original information on the general topic of odor object perception in mammals. Indeed, as said above, a perceptual match has been evidenced for the AB mixture between rabbit pups and human adults, which suggests a relative conservation in the processing of certain odor mixtures across species.

Our second goal aimed to assess whether memory retention of the configural AB odor and of the elemental odors of A and B depends on the age of the pups at conditioning. Indeed, it is known in newborn mammals (especially in altricial mammals, which develop very rapidly) that the meaning acquired by a conditioned odorant can change from one day to another (e.g., Barr et al., 2009; Sullivan and Holman, 2010). In previous experiments, conditioning was mainly performed in 2-day-old rabbit neonates. In the present study, conditioning was therefore conducted either 1 or 3 days after birth to determine the influence of development on the perception and memory of a complex odor stimulus.

These two issues were evaluated by taking advantage of the configural nature of the previously described AB mixture. To date, this is the only mixture which has been extensively characterized regarding its perceptual properties (weak configural perception) in the rabbit both in terms of behavior (Coureaud et al., 2008, 2009a, 2011a; Sinding et al., 2011), memory (Coureaud et al., in press) and brain processing (Schneider et al., in preparation). To that goal, we conditioned either 1- or 3-day-old pups to simple odorants A or B, or to their binary mixture, and assessed the retention of the conditioned stimuli from 24 to 96 h after conditioning using independent groups for each stimulus and each retention interval (Experiment 1). Pups conditioned to the AB mixture were also tested for their responsiveness to A and to B with the aim to evaluate, by means of a within-subject analysis, whether responsiveness to AB differed over time from that of the single odorants (Experiment 2). Finally, as differences appeared in the responsiveness to the mixture and to its odorants in the first two experiments, we determined whether competition occurred between the memory of AB and the respective memories of A and B created during conditioning to the whole mixture (Experiment 3). Indeed, interference and competition between different associative memories have been classically reported (e.g., Eisenberg et al., 2003; Suzuki et al., 2004; Bradfield and Balleine, 2013) and might contribute to the present results. Therefore, we either prevented the formation of the configural memory by successive conditioning to the single odorants only, or erased the elemental memory after initial conditioning to the AB mixture followed by separate reactivations of odorants A and B and amnesia-inducing anisomycin (AN) injection (Coureaud et al., 2009b, 2011b, 2013). We then evaluated whether these two procedures influenced the responsiveness to the AB mixture after a long retention interval.

MATERIALS AND METHODS

ANIMALS AND HOUSING CONDITIONS

Males and females New-Zealand rabbits Oryctolagus cuniculus (Charles River strain, L’Arbresle, France) from the Centre de Zootechnie of the University of Burgundy (Dijon) were kept in individual cages. A nest box (0.39 × 0.25 × 0.32 m) was added on the outside of the pregnant females’ cages 2 days before delivery (day of delivery was day 0; d0). To equalize pups’ nursing experience, all females had access to their nest between 11:30–11:45 a.m. This procedure allowed females to follow the brief (3–4 min) daily nursing of the species (Zarrow et al., 1965). Animals were kept under a constant 12:12 light:dark cycle (light on at 7:00 a.m.) with ambient air temperature maintained at 21–22°C. Water and pelleted food (Lapin Elevage 110, Safe, France) were provided ad libitum. In the study, 524 newborns (from 106 litters) were used.

The study was carried out under the local, institutional and national rules (French Ministries of Agriculture, and of Research and Technology) regarding the care and experimental use of the animals. All experiments were conducted in accordance with ethical rules enforced by French law, and were approved by the Ethical Committee for Animal Experimentation (no. 2406).

ODORANTS

The stimuli consisted of 2-methylbut-2-enal (the Mammary Pheromone, MP, CAS# 497-03-0), ethyl isobutyrate (odorant A, CAS# 97-62-1), ethyl maltol (odorant B, CAS# 4940-11-8) for pure components, and of the AB mixture. This mixture included 0.3 × 10−5 and 0.7 × 10−5 g/ml of components A/B; the 30/70 v/v ratio elicits the configural perception of a pineapple odor in human adults due to blending properties (Le Berre et al., 2008, 2010; Barkat et al., 2012), and weak configural perception in newborn rabbits (Coureaud et al., 2008, 2009a, 2011a; Sinding et al., 2011).

The MP allowed inducing the learning by the pups of odorant A, odorant B, or the AB mixture through associative conditioning (see below). It was used at 10−5 g/ml, a concentration known to be highly efficient to promote conditioning (Coureaud et al., 2006). Thus, the A-MP and B-MP blends were prepared at a final concentration of 10−5 g/ml of each constituent. The AB-MP blend included 1 × 10−5 g/ml of MP and 0.3 and 0.7 × 10−5 g/ml of odorants A and B.

Single odorants A and B were also used in the reactivation procedure (Experiment 3b) at a concentration of 10−5 g/ml.

In all experiments, we deliberately kept constant the overall concentration of the different stimuli (single odorants or AB mixture and blends; 10−5 g/ml) and maintained this constancy between conditioning and testing to avoid direct influence of the concentration on our results and to focus on the influence of complexity (single odorant vs. mixture).

All the odorants were purchased from Sigma-Aldrich (Saint-Quentin Fallavier, France) and all the final solutions were prepared in a solvent composed of 0.1% of ethanol (anhydrous, Carlo Erba, Val de Reuil, France) and 99.9% of MilliQ water (Millipore, Molsheim, France).

ODOR CONDITIONING, REACTIVATION AND PHARMACOLOGICAL TREATMENT

Conditioning sessions were run on day 1 or 3 after birth in an experimental room close to the breeding room. The pups from an individual litter were transferred by groups of 5 (usual case) or 4 (Experiment 3b) into a box maintained at room temperature. The MP-induced conditioning was run following a procedure previously described (e.g., Coureaud et al., 2006, 2008, 2009a,b, 2013; Sinding et al., 2011; Charra et al., 2013). This procedure offered the advantage of being extremely rapid (single trial) and appetitive (thus avoiding the possible modulation of responsiveness due to the negative emotional state that may occurred after aversive conditioning).

In most cases, just before the conditioning session, 4 ml of the A-MP (Experiment 1), B-MP (Experiment 1) or AB-MP (Experiments 1, 2 and 3b) blends were pipetted on a pad (19 × 14 cm, 100% cotton) then held 2 cm above the pups for 5 min. The conditioning occurred 1 h before the daily nursing (10:30 a.m.) to equalize the pups’ motivational state and limit the impact of satiation on behavioral responses (Montigny et al., 2006). Two minutes after the end of the conditioning, the pups were individually marked with ink and returned to their nest. The box containing the pups was rinsed with alcohol and with distilled water after each conditioning session.

In one group (Experiment 3a), the procedure was the same except that pups were conditioned successively to odorants A and B: they were exposed to the A-MP blend for 2.5 min (for the half of the pups, randomly chosen, and B-MP for the other half), then transferred to a second box in which they remained non stimulated for 1 min before being exposed to the B-MP (or A-MP) blend for 2.5 min.

Finally, in two other groups (Experiment 3b), 24 h after the conditioning to the AB mixture, the memory of pups was reactivated by exposure to odorant A then odorant B in half of the pups (or conversely in the other half) following the same procedure than above (2.5 min per odorant, delay inter-stimulation: 1 min). Immediately after reactivation, anisomycin (AN; Sigma-Aldrich) was injected to the half of the pups (42 mg/kg, i.p.) after dilution in 0.9% NaCl solution and adjustment to pH 7.2 with 1N HCl. The AN was used after reactivation performed at 24 h as we have previously demonstrated the effectiveness of this procedure in erasing memory of odor element(s) in newborn rabbits (Coureaud et al., 2009b, 2011b, 2013, in press). Control for the effect of AN injection was carried out with the other half of animals, which received saline 0.9%. As in other studies with newborn or adult mammals (e.g., Davis and Squire, 1984; Gruest et al., 2004; Desgranges et al., 2008) we considered that AN in newborn rabbits may induce a real amnesia and not a perturbation in responsiveness due to an aversive effect. Pups were returned to the nest just after AN or saline injection.

BEHAVIORAL ASSAY

The behavioral assay occurred 24, 48, 72 or 96 h after the conditioning, i.e., on days 2, 3, 4 or 5 when the conditioning occurred on day 1, and on days 4, 5, 6 or 7 when it happened on day 3. The assay was run in the experimental room previously used for conditioning and reactivation, and happened also 1 h before the daily nursing to limit the impact of satiation on motivation and behavioral responsiveness (Montigny et al., 2006). It consisted of an oral activation test during which a pup was immobilized in one gloved hand of the experimenter, its head being left free. The odor stimulus was presented for 10 s with a glass rod 0.5 cm in front of the nares (e.g., Coureaud et al., 2006, 2008, 2011a, 2013). A test was positive when the stimulus elicited head-searching movements (vigorous, low amplitude horizontal and vertical scanning movements displayed after stretching towards the rod) usually followed by grasping movements (labial seizing of the rod extremity). Non-responding pups displayed no response except sniffing. Pups were tested in groups of 4 or 5 (same groups than during the conditioning), and only once, on a given day (i.e., different groups were tested on different days).

In Experiment 1, the pups were tested for their responsiveness to one stimulus only, except those which were conditioned to the AB mixture. The latter were indeed tested to AB but also (these results are the results of Experiment 2) to odorants A and B. In Experiment 3, the pups were tested to A, B and AB. Successive testing involved the presentation of a first stimulus to a pup, then a second stimulus to another pup, and so on with an inter-trial interval of 60 s. The order of stimuli presentation was systematically counterbalanced from one to another pup. If a pup responded to a stimulus, its nose was softly dried before the next stimulation. The pups were immediately reintroduced in their nest after testing.

STATISTICS

Each group was composed of 18–20 pups except the groups of Experiment 3b (with saline and AN injection; n = 11–12 pups). The proportions of pups responding during the behavioral assay were compared using either the χ^2-test of Pearson (with Yates correction when necessary) when the groups were independent (i.e., distinct groups tested for their response to a same stimulus on different days, or to different stimuli on a given day), or the Cochran’s Q-test when the groups were dependent (i.e., pups from a same group tested for their response to the three stimuli). When the Cochran’s Q-test was significant, proportions of responding pups were compared 2 × 2 by the χ^2-test of McNemar. Degrees of freedom are indicated when >1. Data were considered as significant when the two-tailed test ended with p < 0.05.

RESULTS

EXPERIMENT 1. RETENTION OF A, B AND AB AFTER THEIR RESPECTIVE LEARNING ON DAY 1 OR DAY 3

To compare the retention of the odors of odorant A, odorant B and of the AB configuration, four groups of pups were conditioned to A on day 1, four other groups to B and four other groups to AB (n = 20 pups/group, one pup died in one of the groups; all groups were independent). Each group was tested for its behavioral responsiveness to the conditioned stimulus at one single time point only: 24, 48, 72 or 96 h after the conditioning. The same experiment was conducted with 16 other independent groups of pups conditioned on day 3 instead of day 1 (n = 20/group, one pup died in two groups and two pups in another group).

After conditioning on day 1 (Figure 1, left column), the responsiveness of pups to the conditioned stimulus decreased over time whatever the nature of the stimulus (i.e., in pups conditioned to A, or B or AB; χ^2 > 19.37, df = 3, p < 0.001 in the three situations). In pups conditioned to A, the responsiveness to the odorant was maximal and similar 24 and 48 h after the conditioning (>85%; χ^2 < 1, p > 0.05), lower at 72 h (55%; χ^2 > 4.3, p < 0.05 compared to 24 and 48 h) and 96 h (35%; χ^2 > 8.4, p < 0.01 compared to 24 and 48 h), and not different between 72 and 96 h (χ^2 = 1.6, p > 0.05). Regarding the pups conditioned to B, the pattern of responsiveness was nearly the same. The responsiveness was maximal and equivalent at 24 and 48 h (>85%; χ^2 = 1.4, p > 0.05), lower at 72 h (60%; comparison with 24 h: χ^2 = 7.6, p < 0.01, with 48 h: χ^2 = 3.1, p = 0.07) and 96 h (42.1%; comparison with 24 and 48 h: χ^2 > 6.03, p < 0.01), and similar between 72 and 96 h (χ^2 = 1.2, p > 0.05). After conditioning to the AB mixture, the pups highly and similarly responded to AB 24 and 48 h later (95%) and less at 72 h (65%; comparison with 24 and 48 h: χ^2 = 3.9, p < 0.05). At 96 h, only 5% of the pups responded, a level which was lower compared to 24, 48 and 72 h (χ^2 > 18.6, p < 0.001). Interestingly, whereas the responsiveness of the three categories of pups (conditioned to A, B and AB) did not differ at 24, 48 and 72 h (between categories comparisons: χ^2 < 1.29, df = 2, p > 0.05), at 96 h the pups conditioned to AB responded less to this stimulus than the pups conditioned to A or B (χ^2 = 7.74, df = 2, p < 0.05; comparisons AB vs. A or B: χ^2 > 3.91, p < 0.05; comparison A vs. B: χ^2 < 0.5, p > 0.05).

FIGURE 1. Proportions of rabbit pups responding in an oral activation test to odorant A (ethyl isobutyrate; black bars), odorant B (ethyl maltol; white bars) or the AB mixture (gray bars) 24, 48, 72 and 96 h after conditioning to one of the stimuli. Conditioning occurred on day 1 or day 3 by pairing with the mammary pheromone (MP). Independent groups of pups (n = 18 to 20/group) were tested on each day. Distinct letters indicate significant differences in responsiveness over time between pups conditioned to the same stimulus, and distinct symbols indicate differences on a given period between pups conditioned to different stimuli.

After conditioning on day 3 (Figure 1, right column), the retention of the acquired stimulus changed over time independently of its nature (χ^2 > 8.28, df = 3, p < 0.05 in the three situations). In pups conditioned to A or B, the responsiveness was maximal and close at 24 and 48 h (>75%; χ^2 < 0.9, p > 0.05 for A or B comparisons), lower at 72 h (around 60%) compared to 24 h (χ^2 > 4.8, p < 0.05 for A or B), and lower at 96 h compared to 24 and 48 h for A (45%; χ^2 > 5.2, p < 0.05) or compared to 24 h for B (50%; χ^2 = 7.4, p < 0.001). The responsiveness was similar between 96 and 72 h both in pups conditioned to A and in pups conditioned to B (χ^2 < 0.6, p > 0.05). In pups conditioned to AB, the responsiveness to AB was strong and equivalent at 24 and 48 h (>70%; χ^2 = 1.4, p < 0.05), but it became extremely weak as soon as 72 h (<10.5%; comparisons between 72 or 96 vs. 24 or 48 h: χ^2 > 11.8, p < 0.001). While the responsiveness to the conditioned stimulus was equivalent between pups conditioned to A, B or AB at 24 and 48 h (χ^2 < 0.5, df = 2, p > 0.05), it was lower in pups conditioned to AB than to A or to B at 72 and 96 h (χ^2 > 8.91, df = 2, p > 0.05; A or B vs. AB: χ^2 > 4.88, p < 0.05; A vs. B: χ^2 < 0.5, p > 0.05).

Thus, whatever the information that was learned (A, B, or AB) on either day 1 or 3, its retention by the pups decreased over the 4 post-conditioning days. However, the retention of the AB mixture or of its odorants was not the same: all the pups stopped responding to the mixture 72 and/or 96 h after conditioning while a significant proportion of them (>35%) were still responding to the odorant they have previously learned. Besides the time of memory testing, the animal’s age at conditioning also influenced the retention of AB memory: pups conditioned to AB at day 3 stopped responding earlier than those conditioned at day 1 (72 h vs. 96 h). This effect of age was not observed for A and B memories when comparing conditioning at day 1 and 3.

EXPERIMENT 2. RETENTION OF A, B AND AB AFTER LEARNING OF AB ON DAY 1 OR 3

Results of Experiment 1 showed clear differences in the retention of the odors of A, B and AB when each odorant or the mixture were learned separately by different pups, suggesting that the memory retention of the binary mixture is weaker compared to the retention of its components. Here, we assessed whether similar results could be obtained at the individual level (within-subject design), namely after conditioning to AB (during which acquisition of the A and B elements and of the AB configuration happens; Coureaud et al., 2008, 2009a, 2011a; Sinding et al., 2011) and during successive testing to the three stimuli. To that goal, the pups conditioned to the AB mixture on day 1 or 3 in Experiment 1 (4 independent groups/day) were tested for their response to A and to B, in addition to AB, at one single time point only, i.e., either at 24, 48, 72 or 96 h. According to the results of Experiment 1, it was expected that the retention of the configuration would be shorter than the retention of each element that compose the mixture.

After conditioning to AB on day 1 (Figure 2, upper part), the pups strongly and similarly responded to AB, A and B at 24 and 48 h (>95%; Q < 1.1, df = 2, p > 0.05). At 72 h, the responsiveness decreased for AB (Exp. 1) but also for A and for B (responsiveness to A or B at 72 vs. 24 or 48 h: χ^2 > 3.9, p < 0.05); it remained however similar between AB, A and B (65–75%; Q = 1.2, df = 2, p > 0.05). At 96 h, the responsiveness to A and to B was still around 60 and 75% (thus lower than at 24 and 48 h for A: χ^2 = 7.6, p < 0.01, equivalent for B: χ^2 = 3.1, p = 0.07, and similar for A and for B compared to 72 h: χ^2 < 1.02, p > 0.05). Strikingly, in the same animals, while responsiveness to A and B was relatively maintained, the responsiveness to AB was dramatically reduced (5%; Q = 23.3, df = 2, p < 0.001; AB vs. A or B: McNemar χ^2 > 9.09, p < 0.01; A vs. B: McNemar χ^2 = 1.3, p > 0.05).

FIGURE 2. Proportions of rabbit pups responding in an oral activation test to odorant A (ethyl isobutyrate), odorant B (ethyl maltol) and the AB mixture 24, 48, 72 and 96 h after conditioning to that mixture. Conditioning occurred on day 1 or 3 by pairing with the MP. Independent groups of pups (n = 19 or 20/group) were tested on each day. Distinct letters indicate significant differences in responsiveness of the same pups to the three stimuli presented at a given time, and distinct symbols indicate differences in responsiveness to a same stimulus of different pups conditioned on days 1 or 3 and tested during the same postnatal day (dotted bars correspond to results already presented in Figure 1).

Similar results were observed after conditioning on day 3 (Figure 2, lower part), though with a slightly different time course. After conditioning to AB on day 3, the responsiveness of pups was high and similar to AB, A and B 24 h later (>90%; Q = 3, df = 2, p > 0.05) and 48 h later (70–85%; Q = 3, df = 2, p > 0.05). At 72 and 96 h, the responsiveness to A and B remained around 50 and 60% (lower than at 24 h, χ^2 > 5.6, p < 0.01, but not different than at 48 h, χ^2 < 2.1, p > 0.05), but the responsiveness to AB was weaker (<10.5%; for the two periods: Q > 14.88, df = 2, p < 0.001; AB vs. A or B: McNemar χ^2 > 5.14, p < 0.05; A vs. B: McNemar χ^2 < 1.3, p > 0.05).

Thus, in line with the data from Experiment 1 obtained with independent groups of pups, the responsiveness of newborn rabbits to the mixture and to its components decreased over time after conditioning to the mixture and individual testing to the three A, B and AB stimuli: it disappeared earlier for the mixture than for the odorants, and earlier after conditioning on day 3 than on 1.

Moreover, on postnatal day 5, the proportion of pups still responding to AB was higher in pups conditioned on day 3 than in pups conditioned on day 1 (70 vs. 5%; χ^2 = 15.3, p < 0.001) while the responsiveness to A or to B was similar (60–85%; χ^2 < 0.5, p > 0.05). This suggests that the difference observed for AB was the consequence of memory processes and not of pure developmental effects.

EXPERIMENT 3. COMPETITION BETWEEN THE MEMORIES OF THE ELEMENTS AND OF THE CONFIGURATION

According to the results of Experiments 1 and 2, newborn rabbits have a distinct memory and retention of the AB mixture and of its components. After acquisition of the AB mixture, this difference in terms of retention could be due to a competition between the memory of each odorant and the memory of the AB configuration (since the pups perceived both the odorants and the configuration in the weak configurally processed AB mixture). To evaluate this hypothesis, two procedures were followed.

The first procedure (Experiment 3a) attempted to prevent the creation of a memory for the configuration. In a previous paper (Coureaud et al., 2008), we showed that after successive conditioning to odorant A then odorant B on day 1, the pups responded 24 h later to the AB mixture (they did not display such a response after conditioning to a single odorant). Here, this paradigm was repeated but with a test of responsiveness 96 h later. This group (n = 20 pups, two of them died, results concerned 18 pups) was compared to the group conditioned to the whole mixture on day 1 and tested on day 5 (+96 h) in Experiment 2 (i.e., a group that learned the AB configuration). Whereas the conditioning to the AB mixture was followed by an absence of responsiveness to AB but not to A and B at 96 h (Figure 3A, same results as in Figure 2), the successive learning of A and B induced a high and similar level of responsiveness to both the mixture and the individual odorants (66.6–72.2%; Q = 1, df = 2, p > 0.05) (Figure 3B). The responsiveness to AB was therefore higher in the situation of successive learning of the elements than after learning of the mixture (72.2 vs. 5%; χ^2 = 15.6, p < 0.001). The responsiveness to the odorants was similar in the two experimental conditions (60–75%; χ^2 < 0.5, p > 0.05). Therefore, an absence of perception of the AB configuration during conditioning, due to separate and successive conditioning to odorants A and B, was followed by the maintenance of responsiveness to the AB mixture 4 days later.

FIGURE 3. Proportions of rabbit pups responding in an oral activation test to odorant A (ethyl isobutyrate), odorant B (ethyl maltol) and the AB mixture 96 h after conditioning to AB on day 1 (Figures 3A,C,D, n = 20, 12 and 11 pups, respectively), or after successive conditioning to A then to B on day 1 (Figure 3B, n = 18 pups). Conditioning was performed by pairing with the MP. In Figures 3C,D, the pups were separately exposed to odorants A and B the day after the conditioning and immediately injected either with saline (NaCl) or anisomycin (AN). Distinct letters indicate significant differences in responsiveness to the three stimuli in the same pups, and distinct symbols indicate differences in responsiveness to the same stimulus in pups differently conditioned (3A vs. 3B) or differently treated the day after conditioning (3C vs. 3D) (dotted bars correspond to results already presented in Figure 2).

The second procedure (Experiment 3b) attempted to promote the creation of memories to both the configuration and the elements after AB conditioning, and to make the pups rapidly amnesic of the individual odorants’ odor. We hypothesized that in this situation, the responsiveness to AB could be maintained 4 days after the conditioning because of the absence of competition between the configural and elemental memories. Thus, two independent groups of newborns (n = 12/group, 4 pups/litter, 2 pups/litter/group) were conditioned on day 1 to the AB mixture, and reactivated the day after (day 2) by separated exposure to each individual odorant (A then B for half of the pups in each group, B then A for the other half). Immediately after memory reactivation, the first group was injected with saline, while the other group received an AN injection inducing amnesia. The pups were all tested for their responses to AB, A and B on day 5, 96 h after the conditioning (72 h after the reactivation). Blocking memory for the individual odorants induced major differences in mixture memory between the groups. The pattern of response in the saline-treated group (Figure 3C) was the same as in pups from previous experiments which were not manipulated on day 2 (Figure 3A): they did not respond to AB but still displayed a high and similar level of responsiveness to A and to B (8.3 vs. 75.0 vs. 66.6%, respectively; Q = 12.6, df = 2, p < 0.01; AB vs. A or B: McNemar χ^2 > 5.1, p < 0.05; A vs. B: McNemar χ^2 < 0.5, p > 0.05). In contrast, newborns treated with AN (1 pup died, n = 11 for this group in the results) showed a completely reversed pattern of responsiveness to the mixture compared to the elements (Q = 10.3, df = 2, p < 0.01) (Figure 3D): they responded strongly to the mixture but very weakly to the individual odorants (63.6 vs. 9.1 vs. 9.1%; AB vs. A or B: McNemar χ^2 = 4.16, p < 0.05). Comparison of the responsiveness between the two groups clearly showed that AN-treated pups responded more to AB and less to A and to B than saline-treated neonates (χ^2 > 5.49, p < 0.05).

Thus, when rabbit pups forgot the odors of the individual elements initially acquired during the conditioning to the AB mixture, their responsiveness to the mixture was extended over time. This result strongly supports our hypothesis that the learning of the mixture induced a competition between the memory of the AB configuration and the memory of the elements A and B.

DISCUSSION

The present results, obtained in rabbit pups using the AB weak configural mixture, demonstrate that the memory of odor elements is more robust and lasts longer than the configural memory of the mixture, and in fact can interfere with the maintenance of the configural memory. In the absence of elemental odor memory, the duration of the configural memory of the AB mixture is significantly enhanced. Although further work is required to determine whether these results generalize to other mixtures (weak configurally, configurally or, in contrast, elementally perceived mixtures), the present findings have important implications for understanding the mechanisms of odor mixture (and odor object) perception and the organization of odor mixture memory.

Advantage of elemental over configural memory of the AB mixture was first observed in the duration of memory following conditioning to the elements alone or to the mixture (Experiment 1). For example, conditioning to either element (A or B) alone induced a memory of that element that extended for at least 96 h. In contrast, conditioning to the binary mixture induced memory that extended no more than 72 h. Interestingly, the duration of memory for the mixture was age-dependent, while the duration of elemental memory was not. Animals conditioned to the mixture on day 1 reactivated memory for the mixture for 72 h, while animals conditioned on day 3 displayed mixture memory for no more than 48 h. Additional work is required to determine the mechanisms of this age-dependent variation in the duration of configural memory. Regarding general memory mechanisms, one may hypothesize that the information is more easily processed when the animal is exposed to a single odorant compared to a mixture of odorants. To be encoded correctly, a weak configural mixture could require a higher arousal level than individual odorants perceived separately, since the animals might have to share attentional level between the different stimuli of the mixture to engage associative learning for all stimuli (Sharot and Phelps, 2004). As a result, after only one conditioning session, the memory trace of a complex odor stimulus might be rather fragile and its decay might be faster over time in comparison with more simple stimuli. Here, the present conditioning was appetitive. One may therefore wonder whether similar results would be obtained with aversive conditioning, using odor-malaise association for instance (see Gruest et al., 2004), since a stronger arousal is then supposed to occur during acquisition. It would be of interest to evaluate in future experiments whether this other kind of conditioning induces longer lasting configural memory of the AB mixture.

Advantage of elemental over configural memory of odor mixture was also found after associative conditioning with the AB mixture, and within-subject testing to both the mixture and its elements (Experiments 2 and 3). After conditioning to this mixture known to be perceived configurally in humans (Le Berre et al., 2008; Barkat et al., 2012), rabbit pups expressed a weak configural memory of the mixture, meaning that they expressed both a memory for the mixture’s configuration and for the individual elements. In particular, they did not respond to AB at 96 h or 72 h (after conditioning on day 1 or 3, respectively) but still responded to A and to B at these retention times (Experiment 2). In terms of odor mixture processing, these results may seem surprising. Indeed, if the mixture is perceived in a weak configural way during the retention test, rabbit pups should perceive in the mixture both the elements they have previously learned and the AB configuration. Moreover, when pups have learned independently enough elements forming a mixture, they can respond (generalize) to the mixture even if the mixture is perceived in part configurally (Experiment 3a showing responsiveness to AB after successive learning of A and B; see also Coureaud et al., 2008). Therefore, after conditioning to AB on day 1 and because the pups responded to A and to B at 96 h, a response to the mixture could be expected. To explain the lack of response to AB at 96 h, we hypothesize that the AB mixture is, initially, weakly configurally perceived during the conditioning with the MP, but that a particular associative strength is then given to the configuration compared to the elements. As a consequence, the pups would perceive the mixture more as the configuration when they are exposed again to the mixture several days after conditioning. This hypothesis is supported by theoretical considerations suggesting that during conditioning to complex stimulus, a specific value is assigned to the configural representation (“unique cue”) independently of that given to each element (Rescorla, 1973; Rescorla et al., 1985). Alternatively, one may assume that during the conditioning, the presence of the MP leads rabbit neonates to perceive the AB mixture less configurally (i.e., weak configurally) compared to its processing in the absence of unconditioned stimulus (robust configural perception).

Importantly, the present results suggest that the long-term memory of the elemental and configural representations of the AB mixture compete. That is, with intact elemental memory, configural memory of the mixture degrades significantly faster than memory of the elements (Experiment 2). In contrast, if elemental memory is disrupted by selective reconsolidation blockade, configural memory of the mixture is maintained significantly longer (Experiment 3b). This indicates that removing a potential source of interference facilitates memory performance for the AB configural information. The retrieved memory of the AB mixture can thus be regarded as the sum of conflicting processes involving, in our case, the elemental memory and the configural memory. The outcome seems dependent on the dominance of one of the memories at the time of retrieval, here the elemental memory, a result that contributes to unravel aspects of memory organization (e.g., Eisenberg et al., 2003; Suzuki et al., 2004; Bradfield and Balleine, 2013). The competition would rely on the retention time (Experiments 1 and 2) and the simultaneous (but not successive; Experiment 3a) exposure to odorants forming a weak configurally perceived mixture. This interference/competition may happen theoretically and first during consolidation of configural and elemental memories of the mixture. However, this assumption has certainly to be ruled out, since we have previously shown in newborn rabbits that consolidation of odor stimuli learned by association with the MP happened in the first 4 h after conditioning (Coureaud et al., 2009b); the consolidation phase is thus terminated well before the erasure of A and B memories performed here (i.e., 24 h after conditioning; Experiment 3b). Therefore, in the present paradigm, interference/competition between configural and elemental memories of the mixture certainly occurs during maintenance and/or recall of the memories rather than during consolidation phase, with elemental memory the stronger of the two. Even if the most probable explanation for the absence of response to AB at 96 h is the absence of recognition of the AB configuration, one can not exclude the possibility that rabbit pups still find familiarity in AB but without any motivational significance.

The present results do not allow direct determination of the mechanisms of elemental and configural memory interaction, although hypotheses based on the known neurobiology of odor mixture processing can be developed. In rodents, odor mixtures are processed differently by the olfactory bulb and primary olfactory (piriform) cortex (Wilson and Sullivan, 2011). For example, the activity of olfactory bulb mitral/tufted cell neural ensembles is strongly consistent with pattern separation processing–responding uniquely to even minor changes in mixture elements (Barnes et al., 2008; Chapuis and Wilson, 2011; Sahay et al., 2011). Thus, it has been hypothesized that olfactory bulb neural ensembles respond to odor mixture features or components, rather than to the mixture configuration. In strong contrast, piriform cortical neural ensembles respond in a manner consistent with configural processing. In fact, even at the single-unit level of the piriform cortex, individual cells can distinguish between mixtures and their components in cross-adaptation procedures (Wilson, 2003). Mitral/tufted cells, on the other hand, show strong cross-adaptation between mixtures and their components, again consistent with an elemental or feature-dependent process within the olfactory bulb (Wilson, 2003). After having reported in newborn rabbits mapping of activations induced by the MP or by a MP-learned odorant in the olfactory bulb and central regions, including the piriform cortex (Charra et al., 2012, 2013) we are currently assessing the brain activity mapping of the AB mixture (Schneider et al., in preparation).

Odor memory is dependent on plasticity within both the olfactory bulb and piriform cortex (Wilson et al., 2004), even in neonates (Moriceau and Sullivan, 2005; Charra et al., 2013; Fontaine et al., 2013). In the context of the present results, and waiting for further results obtained with the AB mixture as with other odor mixtures, we hypothesize that the more stable elemental memory of an odor mixture may mainly depend on olfactory bulb plasticity, while configural memory of the same mixture may mainly depend on piriform cortical processes (in accordance with the presumed role of piriform cortex as an associative cortex; Johnson et al., 2000). These cortical processes may be less robust during early development than memory-dependent events in the olfactory bulb, resulting in the distinction between these two memory forms in the present study. The less robust configural memory might depend on differences in the developmental program of both the olfactory bulb and piriform cortex: functional odor maps in the glomerular layer seems rather well defined after birth (Guthrie and Gall, 1995) whereas the maturation of inhibitory processes and the intracortical associational fibers throughout the 3-layered piriform cortex (Schwob and Price, 1984) might follow a slightly different time curve (Garske et al., 2013). One may also suggest that this distinction does not rely on robustness of cortical processing only, but on changes during animal development in the dialogue between sensory/mnesic regions of the brain (olfactory bulb/piriform cortex/amygdala/hippocampus), in accordance with the behavioral and adaptive needs of the animal (Schacher and Hu, 2014). Future work could combine the powerful reconsolidation-mediated dissection of memory utilized here, with neurophysiological techniques to explore neurobiological underpinnings of these basic memory and perceptual processes.

Finally, the present findings are further evidence that while odor mixtures are perceived sometimes configurally, and conscious analysis of odor mixtures is notoriously difficult (e.g., Jinks and Laing, 1999), information about the underlying individual components can remain intact in both neonatal rabbits (shown here) and adult humans (Grabenhorst et al., 2007). In the case of newborn rabbits, this may be especially important given the life and death importance of odor recognition to interact with the mother, attach to the nipples, survive and grow up. Having access to both the elements and the configuration may help ensure successful recognition, improved discrimination between odorous substrates or conspecifics in the surroundings, adaptation to the actual environment or anticipation of social and feeding changes that will come later in development.

ACKNOWLEDGMENTS

We sincerely thank Valérie Saint-Giorgio, Nicolas Malaty, Florent Costilhes, Jérôme Antoine and all the Centre de Zootechnie from the University of Burgundy for their cooperation. The work was supported by French ANR-2010-JCJC-1410-1 MEMOLAP to Gérard Coureaud, Thierry Thomas-Danguin, Frédérique Datiche and Guillaume Ferreira.

REFERENCES

Barkat, S., Le Berre, E., Coureaud, G., Sicard, G., and Thomas-Danguin, T. (2012). Perceptual blending in odor mixtures depends on the nature of odorants and human olfactory expertise. Chem. Senses 37, 159–166. doi: 10.1093/chemse/bjr086

Barnes, D. C., Hofacer, R. D., Zaman, A. R., Rennaker, R. L., and Wilson, D. A. (2008). Olfactory perceptual stability and discrimination. Nat. Neurosci. 11, 1378–1380. doi: 10.1038/nn.2217

Barr, G. A., Moriceau, S., Shionoya, K., Muzny, K., Gao, P., Wang, S., et al. (2009). Transitions in infant learning are modulated by dopamine in the amygdala. Nat. Neurosci. 12, 1367–1369. doi: 10.1038/nn.2403

Bradfield, L. A., and Balleine, B. W. (2013). Hierarchical and binary associations compete for behavioral control during instrumental biconditional discrimination. J. Exp. Psychol. Anim. Behav. Process. 39, 2–13. doi: 10.1037/a0030941

Chapuis, J., and Wilson, D. A. (2011). Bidirectional plasticity of cortical pattern recognition and behavioral sensory acuity. Nat. Neurosci. 15, 155–161. doi: 10.1038/nn.2966

Charra, R., Datiche, F., Castanho, A., Gigot, V., Schaal, B., and Coureaud, G. (2012). Brain processing of the mammary pheromone in newborn rabbits. Behav. Brain Res. 226, 179–188. doi: 10.1016/j.bbr.2011.09.008

Charra, R., Datiche, F., Gigot, V., Schaal, B., and Coureaud, G. (2013). Pheromone-induced odour learning modifies Fos expression in the newborn rabbit brain. Behav. Brain Res. 237, 129–140. doi: 10.1016/j.bbr.2012.09.017

Coureaud, G., Charra, R., Datiche, F., Sinding, C., Thomas-Danguin, T., Languille, S., et al. (2010). A pheromone to behave, a pheromone to learn: the rabbit mammary pheromone. J. Comp. Physiol. A Neuroethol. Sens. Neural Behav. Physiol. 196, 779–790. doi: 10.1007/s00359-010-0548-y

Coureaud, G., Gibaud, D., Le Berre, E., Schaal, B., and Thomas-Danguin, T. (2011a). Proportion of odorants impacts the configural versus elemental perception of a blending mixture in newborn rabbits. Chem. Senses 36, 693–700. doi: 10.1093/chemse/bjr049

Coureaud, G., Hamdani, Y., Schaal, B., and Thomas-Danguin, T. (2009a). Elemental and configural processing of odour mixtures in the newborn rabbit. J. Exp. Biol. 212, 2525–2531. doi: 10.1242/jeb.032235

Coureaud, G., Hars, B., Languille, S., and Schaal, B. (2009b). Pheromone-induced olfactory memory in newborn rabbits: involvement of consolidation and reconsolidation processes. Learn. Mem. 16, 470–473. doi: 10.1101/lm.1434009

Coureaud, G., Languille, S., Joly, V., Schaal, B., and Hars, B. (2011b). Independence of first- and second-order memories in newborn rabbits. Learn. Mem. 18, 401–404. doi: 10.1101/lm.2145111

Coureaud, G., Moncomble, A.-S., Montigny, D., Dewas, M., Perrier, G., and Schaal, B. (2006). A pheromone that rapidly promotes learning in the newborn. Curr. Biol. 16, 1956–1961. doi: 10.1016/j.cub.2006.08.030

Coureaud, G., Thomas-Danguin, T., Le Berre, E., and Schaal, B. (2008). Perception of odor blending mixtures in the newborn rabbit. Physiol. Behav. 95, 194–199. doi: 10.1016/j.physbeh.2008.05.018

Coureaud, G., Thomas-Danguin, T., Wilson, D. A., and Ferreira, G. (in press). Neonatal representation of odour objects: distinct memories of the whole and its parts. Proc. R. Soc. B.

Coureaud, G., Tourat, A., and Ferreira, G. (2013). Sensory preconditioning in newborn rabbits: from common to distinct odor memories. Learn. Mem. 20, 453–458. doi: 10.1101/lm.030965.113

Davis, H. P., and Squire, L. R. (1984). Protein synthesis and memory: a review. Psychol. Bull. 96, 518–559. doi: 10.1037//0033-2909.96.3.518

Deisig, N., Lachnit, H., Giurfa, M., and Hellstern, F. (2001). Configural olfactory learning in honeybees: negative and positive patterning discrimination. Learn. Mem. 8, 70–78. doi: 10.1101/lm.8.2.70

Derby, C., Huston, M., Livermore, B., and Lynn, W. (1996). Generalization among related complex odorant mixtures and their components: analysis of olfactory perception in the spiny lobster. Physiol. Behav. 60, 87–95. doi: 10.1016/0031-9384(95)02237-6

Desgranges, B., Lévy, F., and Ferreira, G. (2008). Anisomycin infusion in amygdala impairs consolidation of odor aversion memory. Brain Res. 1236, 166–175. doi: 10.1016/j.brainres.2008.07.123

Eisenberg, M., Kobilo, T., Berman, D. E., and Dudai, Y. (2003). Stability of retrieved memory: inverse correlation with trace dominance. Science 301, 1102–1104. doi: 10.1126/science.1086881

Fontaine, C. J., Harley, C. W., and Yuan, Q. (2013). Lateralized odor preference training in rat pups reveals an enhanced network response in anterior piriform cortex to olfactory input that parallels extended memory. J. Neurosci. 33, 15126–15131. doi: 10.1523/JNEUROSCI.2503-13.2013

Garske, A. K., Lawyer, C. R., Peterson, B. M., and Illig, K. R. (2013). Adolescent changes in dopamine D1 receptor expression in orbitofrontal cortex and piriform cortex accompany an associative learning deficit. PLoS One 8:e56191. doi: 10.1371/journal.pone.0056191

Gottfried, J. A. (2010). Central mechanisms of odour object perception. Nat. Rev. Neurosci. 11, 628–641. doi: 10.1038/nrn2883

Grabenhorst, F., Rolls, E. T., Margot, C., da Silva, M. A., and Velazco, M. I. (2007). How pleasant and unpleasant stimuli combine in different brain regions: odor mixtures. J. Neurosci. 27, 13532–13540. doi: 10.1523/jneurosci.3337-07.2007

Gruest, N., Richer, P., and Hars, B. (2004). Memory consolidation and reconsolidation in the rat pup require protein synthesis. J. Neurosci. 24, 10488–10492. doi: 10.1523/jneurosci.2984-04.2004

Guthrie, K. M., and Gall, C. M. (1995). Functional mapping of odor-activated neurons in the olfactory bulb. Chem. Senses 20, 271–282. doi: 10.1093/chemse/20.2.271

Jinks, A., and Laing, D. G. (1999). A limit in the processing of components in odour mixtures. Perception 28, 395–404. doi: 10.1068/p2898

Johnson, D. M., Illig, K. R., Behan, M., and Haberly, L. B. (2000). New features of connectivity in piriform cortex visualized by intracellular injection of pyramidal cells suggest that “primary” olfactory cortex functions like “association” cortex in other sensory systems. J. Neurosci. 20, 6974–6982.

Kay, L. M., Crk, T., and Thorngate, J. (2005). A redefinition of odor mixture quality. Behav. Neurosci. 119, 726–733. doi: 10.1037/0735-7044.119.3.726

Laing, D. G., and Francis, G. W. (1989). The capacity of humans to identify odors in mixtures. Physiol. Behav. 46, 809–814. doi: 10.1016/0031-9384(89)90041-3

Laska, M., and Hudson, R. (1993). Discriminating parts from the whole: determinants of odor mixture perception in squirrel monkeys, Saimiri sciureus. J. Comp. Physiol. A 173, 249–256. doi: 10.1007/bf00192984

Le Berre, E., Jarmuzek, E., Béno, N., Etiévant, P., Prescott, J., and Thomas-Danguin, T. (2010). Learning influences the perception of odor mixtures. Chem. Percept. 3, 156–166. doi: 10.1007/s12078-010-9076-y

Le Berre, E., Thomas-Danguin, T., Beno, N., Coureaud, G., Etievant, P., and Prescott, J. (2008). Perceptual processing strategy and exposure influence the perception of odor mixtures. Chem. Senses 33, 193–199. doi: 10.1093/chemse/bjm080

Linster, C., and Cleland, T. A. (2004). Configurational and elemental odor mixture perception can arise from local inhibition. J. Comput. Neurosci. 16, 39–47. doi: 10.1023/b:jcns.0000004840.87570.2e

Linster, C., and Smith, B. H. (1999). Generalization between binary odor mixtures and their components in the rat. Physiol. Behav. 66, 701–707. doi: 10.1016/s0031-9384(99)00007-4

Mandairon, N., Sacquet, J., Garcia, S., Ravel, N., Jourdan, F., and Didier, A. (2006). Neurogenic correlates of an olfactory discrimination task in the adult olfactory bulb. Eur. J. Neurosci. 24, 3578–3588. doi: 10.1111/j.1460-9568.2006.05235.x

Montigny, D., Coureaud, G., and Schaal, B. (2006). Rabbit pup response to the mammary pheromone: from automatism to prandial control. Physiol. Behav. 89, 742–749. doi: 10.1016/j.physbeh.2006.08.022

Moriceau, S., and Sullivan, R. M. (2005). Neurobiology of infant attachment. Dev. Psychobiol. 47, 230–242. doi: 10.1002/dev.20093

Rescorla, R. A. (1973). Evidence for unique stimulus account of configural conditioning. J. Comp. Physiol. Psychol. 85, 331–338. doi: 10.1037/h0035046

Rescorla, R. A., Gruau, J. W., and Durlach, P. J. (1985). Analysis of the unique cue in configural discriminations. J. Exp. Psychol. Anim. Behav. Process. 11, 356–366. doi: 10.1037//0097-7403.11.3.356

Riffell, J. A., Lei, H., and Hildebrand, J. G. (2009). Neural correlates of behavior in the moth Manduca sexta in response to complex odors. Proc. Natl. Acad. Sci. U S A 106, 19219–19226. doi: 10.1073/pnas.0910592106

Sahay, A., Wilson, D. A., and Hen, R. (2011). Pattern separation: a common function for new neurons in hippocampus and olfactory bulb. Neuron 70, 582–588. doi: 10.1016/j.neuron.2011.05.012

Schacher, S., and Hu, J. Y. (2014). The less things change, the more they are different: contributions of long-term synaptic plasticity and homeostasis to memory. Learn. Mem. 21, 128–134. doi: 10.1101/lm.027326.112

Schwob, J. E., and Price, J. L. (1984). The development of lamination of afferent fibers to the olfactory cortex in rats, with additional observations in the adult. J. Comp. Neurol. 223, 203–222. doi: 10.1002/cne.902230205

Sharot, T., and Phelps, E. A. (2004). How arousal modulates memory: disentangling the effects of attention and retention. Cogn. Affect. Behav. Neurosci. 4, 294–306. doi: 10.3758/cabn.4.3.294

Sinding, C., Thomas-Danguin, T., Crepeaux, G., Schaal, B., and Coureaud, G. (2011). Experience influences elemental and configural perception of certain binary odour mixtures in newborn rabbits. J. Exp. Biol. 214, 4171–4178. doi: 10.1242/jeb.063610

Smith, B. H. (1996). The role of attention in learning about odorants. Biol. Bull. 191, 76–83. doi: 10.2307/1543065

Sullivan, R. M., and Holman, P. J. (2010). Transitions in sensitive period attachment learning in infancy: the role of corticosterone. Neurosci. Biobehav. Rev. 34, 835–844. doi: 10.1016/j.neubiorev.2009.11.010

Suzuki, A., Josselyn, S. A., Frankland, P. W., Masushige, S., Silva, A. J., and Kida, S. (2004). Memory reconsolidation and extinction have distinct temporal and biochemical signatures. J. Neurosci. 24, 4787–4795. doi: 10.1523/jneurosci.5491-03.2004

Valentincic, T., Kralj, J., Stenovec, M., Koce, A., and Caprio, J. (2000). The behavioral detection of binary mixtures of amino acids and their individual components by catfish. J. Exp. Biol. 203, 3307–3317. doi: 10.1111/j.1095.8649.2011.02976.x

Wilson, D. A., and Sullivan, R. M. (2011). Cortical processing of odor objects. Neuron 72, 506–519. doi: 10.1016/j.neuron.2011.10.027

Wilson, D. A. (2003). Rapid, experience-induced enhancement in odorant discrimination by anterior piriform cortex neurons. J. Neurophysiol. 90, 65–72. doi: 10.1152/jn.00133.2003

Wilson, D. A., Best, A. R., and Sullivan, R. M. (2004). Plasticity in the olfactory system: lessons for the neurobiology of memory. Neuroscientist 10, 513–524. doi: 10.1177/1073858404267048

Wiltrout, C., Dogras, S., and Linster, C. (2003). Configurational and nonconfigurational interactions between odorants in binary mixtures. Behav. Neurosci. 117, 236–245. doi: 10.1037/0735-7044.117.2.236

Wise, P. M., and Cain, W. S. (2000). Latency and accuracy of discriminations of odor quality between binary mixtures and their components. Chem. Senses 25, 247–265. doi: 10.1093/chemse/25.3.247

Zarrow, M. X., Denenberg, V. H., and Anderson, C. O. (1965). Rabbit: frequency of suckling in the pup. Science 150, 1835–1836. doi: 10.1126/science.150.3705.1835

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 10 April 2014; Accepted: 26 May 2014; Published online: 16 June 2014.

Citation: Coureaud G, Thomas-Danguin T, Datiche F, Wilson DA and Ferreira G (2014) Differential memory persistence of odor mixture and components in newborn rabbits: competition between the whole and its parts. Front. Behav. Neurosci. 8:211. doi: 10.3389/fnbeh.2014.00211

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Coureaud, Thomas-Danguin, Datiche, Wilson and Ferreira. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 05 June 2014
doi: 10.3389/fnbeh.2014.00193

Olfactory bulb encoding during learning under anesthesia

Alister U. Nicol[1]*†, Gabriela Sanchez-Andrade[2]†, Paloma Collado[3], Anne Segonds-Pichon[4] and Keith M. Kendrick[5]*†

1Sub-department of Animal Behaviour, University of Cambridge, Cambridge, UK

2Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK

3Department of Psychobiology, Universidad Nacional Educación a Distancia (UNED), Madrid, Spain

4Bioinformatics Group, The Babraham Institute, Cambridge, UK

5Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China

* Correspondence: Alister U. Nicol, Sub-department of Animal Behaviour, University of Cambridge, Madingley, Cambridge CB23 8AA, UK e-mail: aun10@cam.ac.uk
Keith M. Kendrick, Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, 4, North Jianshe Road, Chengdu, Sichuan 610054, China e-mail: k.kendrick.uestc@gmail.com

[†]These authors have contributed equally to this work (i.e., joint first authors).

Edited by:
Anne-Marie Mouly, Centre de Recherche en Neurosciences de Lyon, France

Reviewed by:
Donald A. Wilson, New York University School of Medicine, USA
Matthieu Keller, Centre National de la Recherche Scientifique, France

Neural plasticity changes within the olfactory bulb are important for olfactory learning, although how neural encoding changes support new associations with specific odors and whether they can be investigated under anesthesia, remain unclear. Using the social transmission of food preference olfactory learning paradigm in mice in conjunction with in vivo microdialysis sampling we have shown firstly that a learned preference for a scented food odor smelled on the breath of a demonstrator animal occurs under isofluorane anesthesia. Furthermore, subsequent exposure to this cued odor under anesthesia promotes the same pattern of increased release of glutamate and gamma-aminobutyric acid (GABA) in the olfactory bulb as previously found in conscious animals following olfactory learning, and evoked GABA release was positively correlated with the amount of scented food eaten. In a second experiment, multiarray (24 electrodes) electrophysiological recordings were made from olfactory bulb mitral cells under isofluorane anesthesia before, during and after a novel scented food odor was paired with carbon disulfide. Results showed significant increases in overall firing frequency to the cued-odor during and after learning and decreases in response to an uncued odor. Analysis of patterns of changes in individual neurons revealed that a substantial proportion (>50%) of them significantly changed their response profiles during and after learning with most of those previously inhibited becoming excited. A large number of cells exhibiting no response to the odors prior to learning were either excited or inhibited afterwards. With the uncued odor many previously responsive cells became unresponsive or inhibited. Learning associated changes only occurred in the posterior part of the olfactory bulb. Thus olfactory learning under anesthesia promotes extensive, but spatially distinct, changes in mitral cell networks to both cued and uncued odors as well as in evoked glutamate and GABA release.

Keywords: anesthesia, microdialysis, mitral cells, multiarray electrophysiology, neurotransmitters, olfactory bulb, olfactory learning, social transmission of food preference

INTRODUCTION

A number of studies have provided evidence for the occurrence of associative learning under general anesthesia. Initial findings using auditory fear conditioning paradigms suggested that learning under anesthesia only occurred if epinephrine was given during the conditioning procedure (Weinberger et al., 1984; Gold et al., 1985). However, subsequent studies using both auditory and olfactory conditioning paradigms have provided evidence for learning under anesthesia without the necessity for epinephrine treatment. For both auditory and olfactory conditioning under anesthesia evidence for altered responses of neurons in the medial prefrontal cortex (Rosenkranz et al., 2003; Laviolette et al., 2005) and lateral amygdala has been reported (Rosenkranz and Grace, 2002; Rosenkranz et al., 2003; Fenton et al., 2013). All these studies have used paradigms where a CS+ is associated with foot-shock and effects of associations with positive reinforcers have not been investigated. Additionally there is evidence from in vivo neurotransmitter release, and localized pharmacological intervention and electrophysiological recording studies in both sheep (Kendrick et al., 1992, 1997) and mice (Wilson et al., 1987; Brennan et al., 1998), that plasticity changes occurring within primary sensory cortex, notably the olfactory bulb, are important for learning. However, whether similar changes occur in the olfactory bulb during learning under anesthesia is unknown.

Odor learning in mammals, under various paradigms, has been shown to be supported, to a considerable extent, by biochemical and physiological changes occurring in the mitral cell layer of the olfactory bulb. Learning-related elevations in extracellular levels of glutamate and gamma-aminobutyric acid (GABA), and an increase in the ratio of GABA relative to glutamate have been found in both sheep (Kendrick et al., 1992) and mice (Brennan et al., 1998) following olfactory learning. This suggests a mechanism involving reciprocal increases in both excitation and inhibition, where the relative impact on inhibitory activity is greater. Other reported extracellular changes include increased noradrenaline, nitric oxide and aspartate (Kendrick et al., 1997; Brennan et al., 1998). In the accessory olfactory bulb (AOB), an area associated with pheromonal perception and learning, neurochemical (Brennan et al., 1995) and electrophysiological (Binns and Brennan, 2005) effects consistent with such a mechanism have also been reported in relation to the Bruce effect in mice, whereby exposure to the odor of an unfamiliar male causes termination of pregnancy. Local field potential recordings in the AOB suggest selective inhibition of the familiar pheromone in the underlying recognition system. In the main olfactory system however odor learning can be associated with both increased and decreased responses of mitral cells (Wilson et al., 1987; Kendrick et al., 1992).

One of the most robust models of olfactory learning in rodents is the social transmission of food preference. Rodents such as mice and rats are generally neophobic with regard to novel foods, preferring to eat food items which are familiar to them. However, following social interaction with a conspecific “demonstrator” which has previously consumed a novel food, otherwise naïve “observer” animals subsequently show a preference for the same novel food by consuming more of that food than an alternative novel one (Galef and Wigmore, 1983; Valsecchi and Galef, 1989). Indeed, the acquired attractiveness of the novel food may be such that the consumption of this food initially exceeds the preceding consumption of the animals’ normal daily diet (Galef and Whiskin, 2000). The social transfer of food preference does not require direct physical contact between the demonstrator and observer animals since it is mediated by carbon disulfide (CS_2; Galef et al., 1988), a metabolic by-product carried in the exhaled breath of rodents. Effective training of the observer may be accomplished using an anesthetized demonstrator (Galef and Wigmore, 1983; Valsecchi and Galef, 1989), or even replacing the demonstrator with an artificial surrogate, such as a wad of cotton wool carrying a novel food odor and a few drops of CS_2 (Galef et al., 1988).

We have previously provided behavioral evidence that social transmission of food preference can occur under anesthesia in mice, using anesthetized demonstrators (Burne et al., 2010), although whether this involves neurochemical and electrophysiological changes in the olfactory bulb similar to those seen following learning in awake animals is unknown. In another study on responses of single neurons in the olfactory bulb of anesthetized mice test odors were mixed with the anesthetic gas and produced reliable mitral cell responses (Lin et al., 2005). In the present study we therefore used this same approach to investigate firstly in vivo neurochemical changes occurring post-learning in anesthetized mice in response to odors smelled on the breath of a demonstrator mouse. During the learning phase of the experiment both observer and demonstrator mice were anesthetized. In a second experiment multielectrode array (24 electrode) electrophysiological recordings were used to monitor the olfactory responses of neurons in the mitral cell layer of the olfactory bulb in isofluorane anesthetized mice both during and shortly after learning using a paradigm where animals were exposed to a novel (CS+) odor with CS_2.

EXPERIMENTAL PROCEDURES

ANIMALS

All procedures were conducted under licence in accordance with UK Home Office regulations (Animals (Scientific Procedures) Act, 1986).[1]

The subject animals were adult male mice (129svC57B6) which were bred in-house (Babraham Institute) and maintained to an age of 3–6 months before any experimental procedures. Mice were housed in same-sex groups of 2–5 animals in standard M3 cages, under temperature-controlled conditions in a 12 h light—12 h dark cycle (lights on at 07:00 h). All animals were handled and weighed on each of the 2 days before the start of experimentation.

PREPARATION OF FOOD ODORS

Scented foods were prepared by introducing an additive to the normal diet in powdered form. The additives used were cumin (0.2%), ginger (1%), coriander (1.5%) and cocoa (2.0%), each obtained from a local retailer and stored in airtight containers. Odor samples were prepared by inserting a non-airtight capsule containing 2–3 g of powdered food into a polyvinyl fluoride gas sampling bag (Adtech Polymer Engineering, UK). All air was removed from the bag, which was then refilled with 1 L of nitrogen gas (odorless). Preparation of scented foods and food odors was done in a room separate from areas where experimental procedures were performed. Odor bags containing CS_2 (∼800 ppm) were prepared by placing a 1 cm square of filter paper into an evacuated odor bag, adding 0.2 µL of CS_2 using a 10 µl precision syringe (Hamilton Bonaduz AG, Switzerland) through a sealed injection site, then refilling the bag with 1 L of nitrogen gas. Samples of N_2 gas were also prepared.

BEVAVIOR

Restricted feeding

Prior to training, mice were submitted to a restricted feeding regime (see Figure 1 and legend). During the restricted feeding regime, animals exhibiting weight loss exceeding 15% of initial weight were returned to normal feeding and excluded from further procedures.

FIGURE 1. Training and testing of mice in social transmission of food preference paradigms. Behavioral procedures were conducted over a 5 day period. In the 3 days preceding training and testing, mice were submitted to a restricted feeding regime in which they were allowed free access to food in a fixed 2 h period each day. The food provided in this period was the normal daily diet in powdered form rather than the pellets to which they were accustomed. Access to drinking water was unrestricted during the restricted feeding regime. In days 2 and 3 of this regime the animals spent 30 min of the feeding period individually in the test arena (a 33 × 33 × 25 cm high white plastic container). In this period of habituation to the test environment, ∼2 g of the powdered food was provided in a feed cup—a plastic cylinder (4 cm diameter × 5 cm high) fixed in the center of a plastic dish (6 cm diameter × 2 cm high) to catch any spillage (Figure 1). On day 4, Demonstrator mice were “loaded” by being given unrestricted access to either a flavored food or the normal food, both in powdered form. Observer mice were anesthetized, and exposed to the training odor by one of two procedures; either (1) carried on the breath of an anesthetized demonstrator, or (2) introduced to the anesthetic gas with or without CS_2. On day 5, mice were given a 30 min simultaneous choice test to determine their food preference. Mice which consumed none of either food in the preference test were excluded from further analysis.

Training procedure 1—social transfer of food preference

On day 4 of restricted feeding, demonstrator mice were transferred individually to a clean cage which was the same as the home-cage but with no sawdust, and allowed to feed for 2 h ad libitum from a dish (6 cm diameter × 2 cm high) containing 2–3 g of food. The food presented in this loading period was either the normal powdered diet, or food scented with either cumin or cocoa. Mice eating less than 0.1 g in this period were not used as demonstrators and were excluded from further experimental procedures. After loading, the demonstrator was anesthetized (25% Hypnorm, 25% Hypnovel, i.p., 0.1 ml/g body weight). Observer mice were anesthetized (1.5% isofluorane in O_2 delivered at 150 ml/min via a mask over the nose) and, when fully anesthetized, placed individually in close nose to nose proximity, but without actual direct physical contact, with the demonstrator mouse for 1 min. There were two groups of trained observers: for one group the training food (i.e., the food used to load the demonstrator) was cocoa-scented (cocoa-trained, n = 10 mice), for another group the training food was cumin-scented (cumin-trained, n = 7). For a third group the demonstrator had been fed the normal powdered diet (control, n = 11). Observers remained anesthetized for 1–2 min after removal from isofluorane. During this recovery period they were placed in a standard cage and on a heated plate to keep them warm. They were then returned to their home-cage where they were allowed 2 h access to plain powdered food (∼2 g per mouse). On day 5, observers in each of the three groups were tested for their preference between cumin-scented and cocoa-scented food.

Training procedure 2—simulated social transfer of food preference

Preliminary behavioral experiments were conducted in order to establish the naïve preference in this paradigm between a number of different combinations of odor pairs. From these tests ginger was chosen as the training odor and coriander as an alternative (untrained) one because there was a stable apparently innate preference for coriander over ginger. For the task, observer mice (n = 37) were anesthetized (as in Training Procedure 1) and then transferred from the mask carrying the isofluorane and O_2 anesthetic gas to one carrying the anesthetic gas with an odor introduced. Odors were added to the anesthetic gas by first drawing off a sample of the odor from an odor bag into a 50 ml syringe, then delivering this into the anesthetic gas using a syringe pump (Harvard Apparatus, UK—model 22). Three groups of mice were used: group 1 (Ginger + CS_2, n = 14) received 30 ml/min ginger food odor with 15 ml/min CS_2, group 2 (Ginger, n = 10) received 30 ml/min ginger food odor, and group 3 (CS_2, N = 13) received 15 ml/min CS_2. These odors were mixed with 120 ml/min of the anesthetic gas, and any shortfall beneath 150 ml/min was made up with N_2 gas. Odor exposure was for 1 min. After this, they were placed in a heated cage to recover from anesthesia and then returned to their home-cage where they were allowed to feed for 2 h (as in Training Procedure 1). On day 5, observers in each of the three groups were tested for their preference between ginger-scented and coriander-scented food.

Food preference testing

Observer mice were tested individually under red light for food preference. On day 5 of restricted feeding, each mouse was placed in the center of the test arena in which there were two food cups in diagonally opposite corners (Figure 1), each containing a different scented food. Mice were left undisturbed for 30 min, monitored and recorded via an overhead video camera. Tests were conducted during the 2 h period when the mice had been fed on the preceding days of restricted feeding. At the end of the test the food remaining in each cup, and the amount displaced into the outer dish, was weighed to determine the amount of each food consumed.

For each food type the amount consumed in the test was expressed as a percentage of the total food consumption during the test. Training Procedure 1: Data were divided in to three groups depending on the food the demonstrator had eaten, cocoa-trained, cumin-trained and plain-trained. To establish whether there was a training related food preference, a Kruskall Wallis test followed by Dunn’s multiple comparisons test was used to compare the cocoa-scented food consumption.

IN VIVO NEUROCHEMISTRY

Preparation for in vivo microdialysis sampling

Animals included in the microdialysis study were those used in the behavioral study under Training Procedure 1. Immediately after completion of the food preference test (Figure 2A), mice were anesthetized with an i.p. injection (0.1 ml/g body weight) of Avertin (5 g 1,2,2,2 -tribromoethanol, 3 ml 2-methylbutan-2-ol, 20 ml ethanol, 222 ml saline). Animals were then placed in a stereotaxic frame (Kopf Instruments, California) where they were fixed using ear bars and a bite bar. Anesthesia was maintained with 1.5% isofluorane in oxygen (150–200 ml/min) delivered through a diffuser (Univentor Ltd, Malta) through a mask over the nose. Body temperature was maintained using a homeothermic heated blanket system and a rectal probe (Harvard Instruments, UK). A microdialysis probe (MAB 4.15.2 Cu, Microbiotech, Sweden) was inserted unilaterally into the external plexiform layer of the left olfactory bulb through an incision in the scalp and a small craniotomy (AP 3.9 mm, LR 5.25 mm, DV 5.25; Figure 2B). Coordinates were calculated from the bregma and dural surface (dorso-ventral coordinates) according the mouse brain atlas of Paxinos and Franklin (2001) and were sufficiently posterior to avoid any possibility of sampling from the AOB.

FIGURE 2. Changes in in vivo glutamate and GABA following olfactory learning. (A) Food preference test of observer mice where they were given a choice between cocoa- or cumin-scented foods, 24 h after exposure to the demonstrator’s breath when both observer and demonstrator were anesthetized (Training procedure 1). Histograms show relative consumption of cocoa-scented food (% from total intake). Relative consumption of the two foods was significantly different across the groups (p = 0.0007). While plain food-trained mice (controls, whose demonstrator had been given normal plain food) showed no significant preference for either food, cocoa-trained mice ate almost exclusively cocoa-scented food and cumin-trained animals ate nearly none (p-values given in the Figure are relative to cumin-trained mice). (B) Immediately after being tested for food preference, mice were used for microdialysis experiment. A microdialysis probe was placed in the posteromedial region of the olfactory bulb and 15 min microdialysis samples (25 µl) were taken. Odors were presented, via an odor containing bag, on samples 5 and 10. (C) In test mice (both cocoa- and cumin-trained animals) the concentrations of both glutamate and GABA sampled during exposure to the cued food odor were significantly increased relative to baseline (dotted line), while there was no such increase when exposed to the uncued odor. In the control (plain-trained) mice on the other hand there were no differences in concentrations of either transmitter during exposure to the two odors. (D) For test mice, the amount of cued food consumed during the preference test was positively correlated with concentrations of GABA collected during exposure to the cued but not uncued odor during microdialysis sampling (right panel). In the control mice, there was no such relationship between release of either transmitter during exposure to a food odor, and the quantity of that food consumed in the preference test (left panel).

Sampling protocol and analysis

Dialysis probes were perfused with a Krebs-Ringer solution (pH 7.4, 138 mM NaCl, 1.5 mM CaCl_2, 11 mM NaHCO_3, 5 mM KCl, 1 mM MgCl_2, 1 mM NaH_2CO_3) at 1.5 µl/min, using a syringe pump (CMA-10; CMA Microdialysis, Sweden), throughout the course of the experiment. Sampling commenced 90 min after probe implantation. Samples (25 µl) were collected at 15 min intervals (Figure 2B) into tubes containing 2 µl acetic acid, and frozen (−20°C) immediately for further analysis by high performance liquid chromatography (HPLC). During the 5th and 10th of these intervals, the normal air supply to the mask was switched from the normal gas supply (isofluorane in O_2 and N_2) to a balanced supply carrying a food odor introduced from an odor bag (see above), cumin in one interval, cocoa in the other. The order of presentation of the two food odors was randomized between animals, and independent of the food consumed by the demonstrator animal. The odor, carried in N_2, was diffused through a hypodermic needle (25 G, 0.5 × 16 mm) into a gas supply carrying isofluorane in O_2 using a syringe pump (Harvard Apparatus, UK—Model 33 Twin Syringe Pump).

At the end of the experiment, mice were killed by cervical dislocation, brains were removed and probe placement confirmed. Concentrations of the amino acids glutamate, gamma-aminobutyric acid (GABA) and aspartate were quantified by HPLC with fluorescence detection following derivatization with ophthaldialdehyde (Sigma) performed with an autosampler (Gilson 231), as previously described (Kendrick et al., 1996).

All data were collected using Chromeleon 6.5 software (Dionex, Sunnyvale, USA). Stable, baseline concentrations of extracellular amino acids were taken from the average of the two samples preceding the odor challenge. Changes from baseline (100%) in response to the odor challenges were analyzed using a Wilcoxon matched-pairs signed rank test. Correlation between learning and changes in neurotransmitter release was assessed using a Spearman correlation test. The amount of cued food eaten was correlated with the percentage change in glutamate and GABA release in response to the cued food odor.

ELECTROPHYSIOLOGICAL RECORDING

Preparation for recording

After initial induction of anesthesia (25% hypnorm, 25% hypnovel, i.p., 0.01 ml/g body weight), mice were placed on a heated blanket (33.4°C) in a stereotaxic frame, and their heads fixed with ear bars and a bite bar. Anesthesia was maintained with 1.5% isofluorane in air (30% Nitrogen, 70% Oxygen) delivered through a mask over the nose. Breathing was monitored using a thermistor located in the mask (see Figure 3A).

FIGURE 3. Changes in neuronal activity during olfactory learning under anesthesia. (A) Neuronal activity was sampled from neurons across a 6 × 4 array of tungsten microelectrodes (tip separation 350 µ) advanced into the olfactory bulb from the dorsal surface. Spikes from individual neurons were sorted from multineuronal activity at multiple channels across the array. The averaged waveform is shown for spikes generated by a single neuron at the position highlighted on the array. Odors were presented for 10 s, by switching the normal anesthetic gas supply with one carrying an odor. (B) The times of occurrence of spikes generated by the neuron depicted in (A) are shown during presentations of the coriander food odor. The histogram shows the average response across 10 presentations of this odor. (C) Each odor was presented 10 times before training, and 10 times after training. Training comprised 10 presentations of the ginger food odor combined with CS_2. The response during training was elevated relative to the response to ginger alone before training. This enhanced response achieved significance after training when ginger was presented in the absence of CS_2. The response to coriander fell from pre- to post-training. (D) In the pilot behavioral simulation of social transfer of odor preference, mice were anesthetized and presented with odors carried in the anesthetic gas; ginger, CS_2, or ginger and CS_2 combined. When tested for preference between coriander and ginger flavored foods, mice which had been exposed to the ginger odor or CS_2 separately preferred coriander over ginger. In those exposed to ginger and CS_2 combined, this preference was overcome, and the mice consumed similar amounts of both foods.

The left olfactory bulb was exposed through a dorsal craniotomy large enough to accommodate the electrode array (approx. 2 mm AP × 1.5 mm ML), and the dura retracted. Exposed tissue was bathed in sterile saline delivered at 33.4°C. A 6 × 4 electrode array (sharpened tungsten microelectrodes, 300–500 kΩ, tip separation 350 µ) was positioned at the surface of the bulb, and advanced until spontaneous extracellular neuronal activity (action potentials, “spikes”) were detected across a substantial portion of the array, ranging from 3–15 electrodes per mouse (average = 8 electrodes). The objective was to make recordings from the dorsal mitral cell layer across the majority of the main olfactory bulb. Once this condition was satisfied, the preparation was left to stabilize for a period of no less than 2 h. The placement of recording arrays was sufficiently posterior to avoid any possibility of recordings being made from the AOB.

Recording protocols and analysis

Neuronal activity was sampled using a 64 channel Plexon Multichannel Acquisition Processor (MAP, Plexon Inc., USA). The signal from each electrode was sampled at 30 kHz. Extracellularly recorded action potentials (spikes) were isolated from the continuous signal when this signal exceeded a negative triggering threshold set at an estimated 5× background (∼−25 µV). Experimental markers and ventilation were recorded using a Power1401 laboratory interface and Spike2 software (Cambridge Electronic Design Ltd., UK). This system was also used for experimental control.

An experimental trial comprised a pre-stimulus period of at least 10 s, during which ventilatory and neuronal activity were visually monitored for stability, followed by a 10 s odor presentation (see Figure 3B). Using the animals’ monitored ventilation, the onset of odor presentation was automated to a point mid-way through exhalation, odor onset occurring at the first such point after the 10 s stable pre-stimulus period. Odor delivery was as described above in neurochemistry methods (sampling), with the exception that the odors used here were ginger and coriander, or ginger paired with CS_2 during the training phase. Neuronal activity sampled during inhalation and exhalation was considered separately in subsequent analyses.

Recordings were made during three experimental phases: (1) pre-training; (2) training; and (3) post-training. In the pre-training phase, mice were presented with the three food odors: normal, ginger and coriander. These were presented in blocks of 5 trials, in balanced order, a total of 10 trials per odor. Training comprised 10 trials in which the training odor (ginger) was presented in combination with CS_2. The post-training phase replicated the pre-training phase, using the same presentation order as used in pre-training recordings. Throughout the recordings, an inter-trial interval of ≥5 min was used. For some of the animals, a final block of trials was conducted in which CS_2 alone was presented. On completion of the electrophysiological recordings, animals received a lethal dose of pentobarbital (Dolethal, 0.5 ml i.p.).

Initial processing of the data to extract the times and waveforms of spikes in relation to stimulus and ventilatory information was performed using Spike2 software. For the majority of electrodes where spikes were detected, these represented the activities of multiple neurons. The activities of individual neurons were sorted from multineuronal activity using a machine learning algorithm applied to principal components extracted from the spike waveforms using principal components analysis, and waveform properties detected by curve fitting (Horton et al., 2007). This spike-sorting technique was realized in custom-written software running in MatLab. For each trial, the activity of each neuron was partitioned into data sampled during inhalation or during exhalation, and data sampled pre-stimulus presentation, or during stimulus presentation.

On the basis of data sampled during inhalation in the pre-stimulus period, the activity of each neuron was categorized as parametric or non-parametric. In each trial, the ratio of mean to standard deviation was calculated for the firing rate of each neuron. The average of this ratio was then calculated across the full set of trials for each stimulus condition. If this average was ≥2, the neuron was defined as “parametric” under the given condition, meaning that the distribution of the spikes across the time windows was considered normal. If the average was <2, the neuron was defined as “non-parametric”. This property was taken into account in subsequent analyses.

Other studies mainly report weaker neuronal responses to sensory stimuli in anesthetized animals when compared to awake animals (see Brown and Horn, 1994), and rely on cumulative change in activity across multiple recordings (e.g., McLennan and Horn, 1992), although one study on the mouse olfactory bulb has reported stronger responses to odor stimuli under ketamine anesthesia (Rinberg et al., 2006). Here we have adopted a similar approach by using change in activity across multiple neurons, but also address the issue of weak single neuron responses by employing Cohen’s D as a measure of response strength. Using this approach, single neurons were classified as weakly, moderately or strongly responsive.

Neuronal responses were quantified using the squared log ratio of the firing rate during stimulus presentation relative to the firing rate before. This was calculated for each neuron across the 10 trials (considered as technical replicates). Only the neurons for which data were obtained under the full series of seven conditions (plain, ginger and coriander before and after training, and training with ginger and CS_2) were considered in analyses. Initially focusing on the neurons identified as parametric (see above), two-way repeated measures ANOVA was applied, with odor as the within group factor and subject as the between group factor.

RESULTS

NEUROTRANSMITTER CHANGES IN RESPONSE TO LEARNED ODORS

Prior to preparation for neurochemical analysis by in vivo microdialysis, mice were anesthetized and trained by social transfer of food preference from an anesthetized conspecific (Training Procedure 1, Figure 1). Twenty-four hours after recovery from anesthetic, they were tested for food preference (Figure 2A). The total amount of food consumed in the preference test by observer mice in each group was not significantly influenced by the food used to load the demonstrator (0.47 sg plain-trained, 0.27 g cocoa-trained and 0.25 g cumin-trained—p > 0.05). However, the relative consumption of each food by the observers in the preference test varied significantly according to the odor presented in the demonstrator’s exhaled breath when both animals were anesthetized (Kruskal-Wallis, = 142.67, p = 0.0007). The trained observers consumed more of the training food than the alternative food, cocoa-trained groups ate significantly more of the cocoa-scented food than the cumin-trained group (Dunn’s Multiple Comparison Test, p < 0.001). Mice in the control group (exposed to the breath of demonstrators fed with plain food) showed no significant preference for either scented food, although they ate significantly more cocoa-scented food than cumin-trained animals (Dunn’s Multiple Comparison Test, p < 0.05). On completion of these procedures, animals were again anesthetized and prepared for in vivo microdialysis (Figure 2B).

The full microdialysis sampling protocol was successfully completed in 13 trained and 9 control mice. Four trained and two control animals were either excluded either because the full sampling protocol could not be completed due to technical problems or, in the trained group, did not show clear evidence of a learned preference. Figure 2B shows the typical medial posterior olfactory bulb placements of microdialysis probes. Glutamate and GABA concentrations were measured in 15 min microdialysis samples collected during both baseline conditions and exposure to the different odors (i.e., cocoa-scented and cumin-scented food odors; see protocol in Figure 2B). Changes in transmitter concentrations in samples taken during odor presentations were calculated relative to the two preceding 15 min baseline sample periods.

In mice trained by exposure to demonstrators that had eaten a scented food (cumin or cocoa), glutamate and GABA concentrations were only significantly increased in microdialysis samples taken during presentation of these same odors (Wilcoxon test: Glutamate p = 0.0237; GABA p = 0.0266—Figure 2C). There was no significant change in the ratio of glutamate to GABA during presentation of the cued odor (p > 0.1) although the magnitude of evoked GABA release tended to be higher than that of glutamate. There were no significant changes in the concentrations of either glutamate or GABA, or in the glutamate/GABA ratio in samples collected during exposure to the alternative control odor not present on the demonstrator’s breath. There were no changes in glutamate or GABA concentrations during presentation of either odor to control mice which had been exposed to demonstrator mice that had eaten only plain unscented food (Figure 2C).

The increased concentrations of GABA during presentation of the cued food were significantly positively correlated with the amount of that food consumed in the preference test (Spearman rank test: r = 0.57, p = 0.042; Figure 2D). There was no significant correlation between either glutamate or GABA concentrations and the total amount of food consumed in the preference test. Furthermore, in the untrained controls the combined consumption of both foods in the preference test was not significantly correlated with release of either transmitter (Figure 2D).

MULTIARRAY ELECTROPHYSIOLOGICAL CHANGES IN RESPONSE TO LEARNED ODORS

Behavior

In the electrophysiological studies ginger was used as the training odor and coriander as the alternative test odor. These were delivered from odor bags rather than anesthetized demonstrators (see Training Procedure 2). Mice in three groups were each exposed to different odors while anesthetized: (1) ginger + food odor; (2) CS_2; and (3) ginger + food odor paired with CS_2. While the total amount of food consumed in the preference test did not vary across the three groups the relative consumption of coriander-scented food and ginger-scented food in the preference test reflected odor exposure under anesthesia. In both control groups, mice exposed under anesthesia to either ginger or CS_2, food consumption indicated a clear preference for coriander over ginger (Figure 3D; paired t-tests, Ginger: t_9 = 3.46, p = 0.007 and CS_2: t_12 = 3.61, p = 0.004). Mice which had been exposed under anesthesia to Ginger and CS_2 combined showed no such preference since they consumed similar quantities of both foods (Figure 3D). A comparison of the Ginger and CS_2 alone groups combined with the Ginger +CS_2 group also showed a significant difference in terms of the relative consumption of ginger-scented food (t_33 = 3.295, p = 0.002). Thus, in these mice the innate preference for coriander over ginger seen in controls was overcome.

Electrophysiological recordings

Electrophysiological recordings were made from a separate cohort of mice to those used in the behavioral tests described above. The mice were anesthetized throughout all electrophysiological recording procedures and their breathing pattern was not influenced differentially by the odors used. While the first breathing cycle (inhale and exhale) after odor presentation was significantly faster than the average pre-stimulus breathing rate, this did not vary across the different odors and returned to normal in the second and subsequent breathing cycles during stimulus presentation.

In seven mice, neuronal activity was detected at a total of 50 electrodes (3–15 active channels per animal). From these spike trains, the activity of 260 individual neurons was discriminated by spike sorting (Horton et al., 2007).

For each experimental condition, neurons were identified as parametric or non-parametric on the basis of their background activity (prestimulus activity) during inhalation. Neurons classified in this way did not necessarily remain in the same category across all conditions.

Analyses of odor-evoked changes in neuronal activity were initially performed on activity sampled during inhalation amongst neurons classified as parametric. Activity recorded during odor presentation was compared to that in the pre-stimulus period across presentations of each odor.

Changes in multiunit activity

In the pre-training period (i.e., before presentation of the training odor combined with CS_2), a similar increase in multiunit activity was evoked by presentation of each food odor in the parametric neurons (mean ± sem respectively plain 5.2% ± 2.4, ginger 3.5% ± 2.1; coriander 5.4% ± 2.7—Figure 3C). During training, the response to the ginger food odor combined with CS_2 showed a trend towards being greater than that evoked by presentation of the ginger food odor alone (paired t-test, t_84 = 1.81, p = 0.074, 10.4% ± 2.7—Figure 3C). After training, the response to the ginger food odor alone (13.03% ± 2.7—Figure 3C) was significantly greater (t-test vs. ginger pretraining, t_84 = 2.58, p = 0.011) than that evoked by presentation of this odor combined with CS_2 during training, and significantly greater than the responses to the coriander food odor (paired t-test, t_84 = 3.47, p = 0.001), which was not the case before training (paired t-test p = 0.577). For all but one of the mice, there was a consistent increase in the size of the response to the ginger odor in multiunit activity amongst parametric neurons; in the remaining mouse the size of this response did not increase. Significant variability between mice was related to the size of the training-related increase in the response to the ginger food odor. After completion of the post-training tests for responsiveness to the three food odors, some animals (n = 3) were also tested with CS_2 alone. The multiunit response to this odor was similar to the pre-training responses to food odors confirming that CS_2 odor alone did not evoke a differential response compared to the other odors used.

In the multiunit activity sampled during exhalation from parametric neurons, there was a significantly larger increase in activity in response to presentation of the ginger food odor combined with CS_2 during training than in response to any of the three food odors in the pre-training recordings. However, none of these responses to the food odors was altered after training.

The general profile of responsiveness in the multiunit activity amongst the non-parametric neurons was similar to that seen in the parametric ones (data not shown).

Changes in single neuron activity

The responses of single neurons in these recordings were generally weak, and few reached significance using conventional methods (e.g., t-tests). Using Cohen’s D as a measure of response strength yielded a larger sample of responsive neurons, thereby improving the power of subsequent analyses investigating the changing profiles of neuronal responsiveness to the different odors.

The proportion of single neurons showing increased activity in response to the ginger food odor after training showed a trend towards significance (29/85, 34% before and 39/85, 46%—McNemar test, p = 0.123) as well as during the training period when ginger was presented with CS_2 (40/85, 47%, p = 0.1). This increase was largely confined to neurons producing excitatory responses (increase in firing rate) with the proportion of neurons producing inhibitory responses remaining stable. The proportion of neurons responding to plain food odor did increase significantly from pre-training (33/85, 39%) to post-training (47/85, 55%, p = 0.005). Again this increase was confined to the neurons producing excitatory responses, but here was also accompanied by a decrease in the proportion of neurons producing inhibitory responses. The proportion OF neurons responding to the coriander food odor increased slightly, but not significantly, between the pre-training (34/85, 40%) and post-training (41/85, 48%, p = 0.349) periods, although that of neurons with an excitatory response to coriander declined, but not significantly, from pre-training (25/85, 29%) to post-training (19/85, 22%, p = 0.391). Among the 85 recorded individual neurons, 6 changed their response to coriander from excitatory to inhibitory after training, whereas 13 changed their response to ginger from inhibitory to excitatory. This difference in the pre vs. post-training pattern of response to the two odors was significant (McNemar test, df = 84,p = 0.016).

Localization of learning-evoked electrophysiological changes

Preliminary analyses of the spatial distribution of responsiveness and learning-related effects were made by comparing effects across the X (anteroposterior, 6 columns of electrodes) and Y (mediolateral, 4 rows) dimensions of the electrode array (see Figure 4).

FIGURE 4. Spatial distribution of olfactory bulb neural responses during learning. (A) In order to establish the spatial distribution of olfactory bulb mitral cell responses and learning-related changes, the microelectrode array was partitioned mediolaterally and anteroposteriorly (photograph and schematic illustrate the localization of the 24-electrode arrays). There was no significant variation in multiunit responses, or in learning-related changes, across the mediolateral dimension of the array. Histograms in (B) show that the increase (mean ± sem) in the size of the multiunit responses to the combined presentation of ginger and CS_2, and the learning-related increase in the size of the multineuron response to ginger was restricted to the posterior half of the array.

In the pre-training period, there were no significant differences in either dimension in the change in multiunit activity evoked by presentation of any of the three odors (ginger, coriander, or plain food). In the post-training period, there was also no significant difference in the distribution of learning-related change in responsiveness in the Y dimension of the array (i.e., medial vs. lateral). However, the increased multiunit response to ginger following training exposure to ginger combined with CS_2 (see Figure 3) was restricted to the posterior part of the array (Figure 4). Only in this part of the array were the changes in multiunit activity evoked by presentation of ginger and CS_2 during training, and by ginger alone after training, significantly greater than the multiunit response to ginger before training (paired t-tests, t_30 = 2.5, p = 0.018 and t_30 = 2.89, p = 0.007 respectively). Also, only in the posterior part of the array was the multiunit response to ginger significantly greater than that to coriander after training (t_30 = 2.62, p = 0.014). Furthermore, after training there were significantly more excitatory than inhibitory responses to ginger in the posterior part of the array (23% before vs. 37% after, p < 0.05). There was no change in responsiveness to either of the other stimuli. In the anterior part of the array, there was no training-related change in the size of the response to any stimulus, and no significant difference between excitatory and inhibitory responses either before or after training.

DISCUSSION

Overall our results have provided both neurochemical and electrophysiological evidence in vivo that similar plasticity changes occur in mouse olfactory bulb encoding associated with learning under anesthesia as previously reported in olfactory learning contexts in conscious animals (Wilson et al., 1987; Kendrick et al., 1992, 1997; Brennan et al., 1998). This provides yet further evidence that the mammalian brain can undergo plasticity changes and learning in unconscious as in conscious states and also demonstrates similarities in altered olfactory bulb encoding associated with olfactory learning in both cases. Our findings also establish this anesthetized learning model as potentially useful for more detailed investigation of the precise neural encoding changes associated with learning which can be more difficult to achieve using awake, behaving animals.

Our present results provide support for our previous findings that learning in the social transfer of food preference paradigm can occur under anesthesia (Burne et al., 2010). Our previous study showed however that food preference transferred in this way is influenced by different types of anesthetic agents which may depend upon which transmitter receptors are targeted by them (Burne et al., 2010). Specifically, those acting by blocking NMDA receptor activity (e.g., Vetalar—Ketamine hydrochloride) appear to prevent acquisition of preference for a novel food, whereas a GABA receptor agonist (Sagatal), produce an aversion rather than a preference for the odor presented during anesthesia. Indeed, a previous study has reported increased responsivity to odors in olfactory bulb mitral cells under ketamine anesthesia in mice, compared to conscious recordings (Rinberg et al., 2006), possibly suggesting reduced selectivity and increased noise in mitral cell networks might contribute to its blockade of learning effects. In the present work, we therefore used isofluorane which is a membrane stabilizer that does not act specifically on receptor mechanisms, and leaves cortical neurons responsive to synaptic input (Orth et al., 2006).

Our observed in vivo increases in release of both glutamate and GABA in the olfactory bulb in response to learned odors in anesthetized mice is similar to that observed in conscious sheep (Kendrick et al., 1992, 1997) and mice (Brennan et al., 1995) in other olfactory learning paradigms. Importantly, in the current study strength of learning, as indexed by the amount of cued odor food eaten, was positively correlated with the magnitude of GABA concentration changes. Overall this provides further support for a common mechanism whereby learned odors evoke both a greater excitatory mitral cell (glutamate) and greater inhibitory granule cell (GABA) responses in the olfactory bulb. Unlike studies in conscious mice (Brennan et al., 1995) and sheep (Kendrick et al., 1992) there was no significant increase in the ratio of glutamate to GABA associated with olfactory learning in the current study, although there was a trend in this direction with the change in learned odor-evoked GABA release being slightly greater than for glutamate. This might reflect an impact of anesthesia in weakening learning-induced facilitation of mitral to granule cell communication via enhanced dendrodendritic synaptic sensitivity and/or the different learning paradigm used compared with other studies.

The findings from multi-array electrophysiological recordings provide further evidence that learned associations with complex odorants result in an altered response profile across an extensive population of mitral cell neurons. Before training by exposure to ginger and CS_2 together, responses (both in multiunit and single neuron activity) to each food odor (plain, ginger, coriander) were similar, and also similar to those for CS_2 presented alone, and no differential effects of odors were seen on breathing rate. Thus, CS_2 itself did not evoke greater changes than other odors and its presentation was only accompanied by an elevated response when it was paired with the ginger food odor. After training, increased responsiveness was maintained only for the ginger food odor and this elevated response to the training odor was rapid, occurring shortly after its combination with CS_2. In agreement with single neuron recordings from the olfactory bulb of the conscious maternal sheep which had learned to recognize the odors of their lamb (Kendrick et al., 1992), the cued odor primarily evoked an increase in the proportion of neurons exhibiting excitatory responses. While we cannot rule out the possibility that some of the neurons recorded by the array were inhibitory granule cells, the electrodes are likely to have been primarily mitral cells. This assumption is based on (a) the depth of penetration of the microelectrode array; and (b) the large signal to noise ratio of the spikes detected which is characteristic of the mitral cell rather than the granule cell layer; and (c) since granule cells do not have an axon they are less likely to generate “spikes” which can be recorded extracellularly. As such the activity detected represents the output from the granule cell layer, where the learning processes are thought to take place in the network between inhibitory granule cells and mitral cell dendrites (Brennan and Keverne, 1997; Brennan and Kendrick, 2006; Sanchez-Andrade and Kendrick, 2010). Thus it would appear likely that learning primarily increases responses of glutamatergic mitral cell neurons to the learned odor, which is consistent with increased in vivo glutamate release.

Despite evidence for enhanced release of GABA in the olfactory bulb during exposure to the learned odor this does not appear to be associated with extensive reductions in mitral cell responses to learned odors in the olfactory bulb of the anesthetized mouse, in the current study, or in the conscious sheep (Kendrick et al., 1992). This contrasts with conscious recordings from the mouse AOB in the pregnancy block model where mating produced a reduction in mitral cell responses to the learned odors from the urine of a male (Binns and Brennan, 2005). However, this may reflect the fact that in the latter model it is proposed that pregnancy block does not occur in response to the familiar male because his odors fail to induce increases in mitral cell responses which would promote changes in the hypothalamo-pituitary axis and subsequent pregnancy termination (see Brennan and Kendrick, 2006). For the main olfactory system on the other hand it is assumed that learned odors with positive associations can more effectively promote behavioral and endocrine changes through an enhanced response in mitral cells having a greater impact on the relevant downstream projection region (see Brennan and Kendrick, 2006; Sanchez-Andrade and Kendrick, 2010). Possibly the parallel enhancement of inhibitory GABA release in this case is more important for helping to minimize interference from other, potentially competing odors, by reducing responses of other adjacent mitral cells. Indeed, the finding in the current study of a trend towards an increased proportion of mitral cells exhibiting reduced responses to the uncued odor supports such a possibility, and similar evidence for reduced mitral cell responses to other odors has also been observed in the sheep olfactory bulb (Kendrick et al., 1992). In such a system, increased lateral inhibition through elevated GABA release could also contribute to facilitated responses to the cued odor. Interestingly, similar evidence for increased inhibitory responses to unlearned stimuli has been reported in the chick brain following visual imprinting (Nicol et al., 1995; also Nicol et al., submitted). Enhanced inhibition of mitral cells via granule cells is associated with improved discrimination between odors (Abraham et al., 2010), and thus learning-associated changes in odor-evoked GABA release may reflect an improved ability to discriminate learned odors from other potentially competing ones.

In the context of the current study the dual influence of training in enhancing responses to the cued (ginger) odor but reducing them to an uncued (coriander) one suggests that the changes in responsiveness to ginger food odor were not simply due to sensitization following exposure, as similar exposure to each food odor induced contrasting results. Further, short-term changes in responsiveness were not apparent in pre-training exposures to any food odor and no significant changes were observed in responses across the 10 trials with each odor.

The complex odors used in our study influenced the activity of a large proportion of mitral cells recorded by the recording arrays in line with a recent study showing dense representation of natural odorants in the mouse olfactory bulb (Vincis et al., 2012). On the other hand learning associated changes were localized to mitral cell populations in the dorsal posterior rather than anterior part of the olfactory bulb suggesting a degree of spatial localization, at least in terms of neural plasticity changes. In vivo neurochemical changes associated with learning were also found in posterior regions of the olfactory bulb although we did not investigate whether they were absent in more anterior regions. Possibly our electrophysiological findings may reflect differential localization of processing for innate compared with learned odors reported previously in the mouse olfactory bulb (Kobayakawa et al., 2007). This is in accordance with findings that the necklace glomeruli receiving input from the specialized guanylyl cyclase GC-D sensory neurons in the posterior region of the olfactory bulb responds to social signals like CS_2 and mediate social transmission of food preference in mice (Munger et al., 2010). Perhaps more anterior populations of dorsal mitral cells are innately tuned to other classes of key biological odorants which exhibit less plasticity in response to new learned associations. Clearly such a possibility requires further investigation.

A limitation of our study is that the large differences in scale for olfactory bulb activity changes made using microdialysis sampling and recording microelectrodes make it difficult to draw anything other than tentative conclusions linking neurotransmitter changes with altered mitral cell responses. It is also important to emphasize the temporal differences in the two different sampling approaches as well as the fact that in the microdialysis experiment changes were sampled in the olfactory bulb 24 h after learning whereas for the recording experiments changes were restricted to a relatively short period before, during and immediately after learning. Thus the pattern of neurochemical changes measured would therefore have reflected post-consolidation learning whereas the electrophysiological changes are associated with initial acquisition of learning and may have been further modified post- consolidation. However, other studies of consolidation in visual recognition memory suggest that neurons recruited during in acquisition are likely to be those maintained post-consolidation (Horn et al., 2001; Jackson et al., 2008). Recordings were also restricted to the dorsal mitral cell layer and other patterns of response might have occurred in lateral and ventral layers.

Our findings provide further evidence that the mammalian brain is capable of supporting some forms of learning under anesthesia, although to the best of our knowledge this is the first study to show detailed neurochemical and neurophysiological plasticity changes occurring in a primary sensory processing region. A converging body of evidence has also implicated sleep as being crucial in memory formation (Ellenbogen et al., 2006; Arzi et al., 2012), and specifically in memory consolidation (Walker and Stickgold, 2004; Rasch et al., 2007; Jackson et al., 2008), and thus learning-related changes during unconscious states would appear to be both important and complimentary to those occurring during conscious ones. While we have observed broadly similar patterns of neural plasticity changes in the olfactory bulb under anesthesia to those known to occur under conditions of conscious learning more detailed comparisons of learning using the same paradigm under conscious and unconscious conditions are required before we can establish whether certain differences exist. In particular factors such as the duration and specificity of memory and neural plasticity changes need further investigation. A further interesting question is whether the established role of neurogenesis in some learning paradigms involving both the hippocampus and olfactory bulb (Lazarini and Lledo, 2011) can also occur under anesthesia or other unconscious states.

CONCLUSIONS

Overall we have provided the first in vivo neurochemical and neurophysiological evidence for olfactory learning under anesthesia in mice being supported by broadly similar changes in olfactory bulb encoding as shown previously in conscious paradigms and involving potentiated glutamate and GABA release from mitral and granule cell synapses. Observed changes support the presence of dense, but also spatially restricted representation of changes with the olfactory bulb associated with learned associations with complex odorants. The pattern of changes found reveal enhanced excitatory responses in mitral cell populations but also corresponding reductions in responses in those tuned to other, potentially interfering odors.

AUTHOR CONTRIBUTIONS

Alister U. Nicol, Gabriela Sanchez-Andrade, and Keith M. Kendrick conceived and designed the study; Alister U. Nicol, Gabriela Sanchez-Andrade and Paloma Collado carried out the experimental work; Alister U. Nicol, Gabriela Sanchez-Andrade, Anne Segonds-Pichon and Keith M. Kendrick analyzed and interpreted the data; Alister U. Nicol, Gabriela Sanchez-Andrade, Anne Segonds-Pichon, Paloma Collado and Keith M. Kendrick drafted and approved the final version of the paper.

ACKNOWLEDGMENTS

This work was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) UK. Paloma Collado was supported by grant: MEC: PR2005-0081.

FOOTNOTES

1^http://www.legislation.gov.uk/ukpga/1986/14/enacted

REFERENCES

Abraham, N. M., Egger, V., Shimsek, D. R., Renden, R., Fukunga, I., Sprengel, R., et al. (2010). Synaptic inhibition in the olfactory bulb accelerates odor discrimination in mice. Neuron 65, 399–411. doi: 10.1016/j.neuron.2010.01.009

Arzi, A., Shedlesky, L., Ben-Shaul, M., Nasser, K., Oksenberg, A., Hairston, I., et al. (2012). Humans can learn new information during sleep. Nat. Neurosci. 15, 1460–1465. doi: 10.1038/nn.3193

Binns, K. E., and Brennan, P. A. (2005). Changes in electrophysiological activity in the accessory olfactory bulb and medial amygdala associated with mate recognition in mice. Eur. J. Neurosci. 21, 2529–2537. doi: 10.1111/j.1460-9568.2005.04090.x

Brennan, P. A., and Keverne, E. B. (1997). Neural mechanisms of mammalian olfactory learning. Prog. Neurobiol. 51, 457–481. doi: 10.1016/s0301-0082(96)00069-x

Brennan, P. A., and Kendrick, K. M. (2006). Mammalian social odours: attraction and individual recognition. Philos. Trans. R. Soc. Lond. B Biol. Sci. 361, 2061–2078. doi: 10.1098/rstb.2006.1931

Brennan, P. A., Kendrick, K. M., and Keverne, E. B. (1995). Neurotransmitter release in the accessory olfactory-bulb during and after the formation of an olfactory memory in mice. Neuroscience 69, 1075–1086. doi: 10.1016/0306-4522(95)00309-7

Brennan, P. A., Schellinck, H. M., de la Riva, C., Kendrick, K. M., and Keverne, E. B. (1998). Changes in neurotransmitter release in the main olfactory bulb following an olfactory conditioning procedure in mice. Neuroscience 87, 583–590. doi: 10.1016/s0306-4522(98)00182-1

Brown, M. W., and Horn, G. (1994). Learning-related alterations in the visual responsiveness of neurons in a memory system of the chick brain. Eur. J. Neurosci. 6, 1479–1490. doi: 10.1111/j.1460-9568.1994.tb01009.x

Burne, T. H. J., Johnston, A. N. B., Wilkinson, L. S., and Kendrick, K. M. (2010). Effects of anesthetic agents on socially transmitted olfactory memories in mice. Neurobiol. Learn. Mem. 93, 268–274. doi: 10.1016/j.nlm.2009.10.007

Ellenbogen, J. M., Hulbert, J. C., Stickgold, R., and Dinges, D. F. (2006). Interfering with theories of sleep and memory: sleep, declarative memory and associative interference. Curr. Biol. 16, 1290–1294. doi: 10.1016/j.cub.2006.05.024

Fenton, G. E., Spicer, C. H., Halliday, D. M., Mason, R., and Stevenson, C. W. (2013). Basolateral amygdala activity during the retrieval of associative learning under anaesthesia. Neuroscience 233, 146–156. doi: 10.1016/j.neuroscience.2012.12.039

Galef, B. G., Mason, J. R., Preti, G., and Bean, N. J. (1988). Carbon-disulphide: a semiochemical mediating socially-induced diet choice in rats. Physiol. Behav. 42, 119–124. doi: 10.1016/0031-9384(88)90285-5

Galef, B. G., and Whiskin, E. E. (2000). Social influences on the amount of food eaten by Norway rats. Appetite 34, 327–332. doi: 10.1006/appe.2000.0319

Galef, B. G., and Wigmore, S. W. (1983). Transfer of information concerning distant foods – a laboratory investigation of the information-centre hypothesis. Anim. Behav. 31, 748–758. doi: 10.1016/s0003-3472(83)80232-2

Gold, P. E., Weinberger, N. M., and Sternberg, D. B. (1985). Epinephrine-induced learning under anaesthesia: retention performance at several training-testing intervals. Behav. Neurosci. 99, 1019–1022. doi: 10.1037//0735-7044.99.5.1019

Horn, G., Nicol, A. U., and Brown, M. W. (2001). Tracking memory’s trace. Proc. Natl. Acad. Sci. U S A 98, 5282–5287. doi: 10.1073/pnas.091094798

Horton, P. M., Nicol, A. U., Kendrick, K. M., and Feng, J. F. (2007). Spike sorting based upon machine learning algorithms (SOMA). J. Neurosci. Methods 160, 52–68. doi: 10.1016/j.jneumeth.2006.08.013

Jackson, C., McCabe, B. J., Nicol, A. U., Grout, A. S., Brown, M. W., and Horn, G. (2008). Dynamics of a memory trace: effects of sleep on consolidation. Curr. Biol. 18, 393–400. doi: 10.1016/j.cub.2008.01.062

Kendrick, K. M., Guevara-Guzman, R., de la Riva, C., Christensen, J., Ostergaard, K., and Emson, P. C. (1996). NMDA and kainate-evoked release of nitric oxide and classical transmitters in the rat striatum: in vivo evidence that nitric oxide may play a neuroprotective role. Eur. J. Neurosci. 8, 2619–2634. doi: 10.1111/j.1460-9568.1996.tb01557.x

Kendrick, K. M., Guevara-Guzman, R., Zorrilla, J., Hinton, M. R., Broad, K. D., Mimmack, M., et al. (1997). Formation of olfactory memories mediated by nitric oxide. Nature 388, 670–674. doi: 10.1038/41765

Kendrick, K. M., Lévy, F., and Keverne, E. B. (1992). Changes in the sensory processing of olfactory signals induced by birth in sheep. Science 256, 833–836. doi: 10.1126/science.1589766

Kobayakawa, K., Kobayakawa, R., Matsumoto, H., Oka, Y., Imai, T., Ikawa, M., et al. (2007). Innate versus learned odour processing in the mouse olfactory bulb. Nature 450, 503–508. doi: 10.1038/nature06281

Laviolette, S. R., Lipski, W. J., and Grace, A. A. (2005). A subpopulation of neurons in the medial prefrontal cortex encodes emotional learning with burst and frequency codes through a dopamine D_4 receptor-dependent basolateral amygdala input. J. Neurosci. 25, 6066–6075. doi: 10.1523/jneurosci.1168-05.2005

Lazarini, F., and Lledo, P.-M. (2011). Is adult neurogenesis essential for olfaction. Trends Neurosci. 34, 20–30. doi: 10.1016/j.tins.2010.09.006

Lin, D. Y., Zhang, S. Z., Block, E., and Katz, L. C. (2005). Encoding social signals in the mouse main olfactory bulb. Nature 434, 470–477. doi: 10.1038/nature03414

McLennan, J. G., and Horn, G. (1992). Learning-dependent changes in the responses to visual stimuli of neurons in a recognition memory system. Eur. J. Neurosci. 4, 1112–1122. doi: 10.1111/j.1460-9568.1992.tb00138.x

Munger, S. D., Leinders-Zufall, T., McDougall, L. M., Cockerham, R. E., Schmid, A., Wandernoth, P., et al. (2010). An olfactory subsystem that detects carbon disulfide and mediates food-related social learning. Curr. Biol. 20, 1438–1444. doi: 10.1016/j.cub.2010.06.021

Nicol, A. U., Brown, M. W., and Horn, G. (1995). Neurophysiological investigations of a recognition memory system for imprinting in the domestic chick. Eur. J. Neurosci. 7, 766–776. doi: 10.1111/j.1460-9568.1995.tb00680.x

Orth, M., Bravo, E., Barter, L., Carstens, E., and Antognini, J. F. (2006). The differential effects of halothane and isoflurane on electroencephalographic responses to electrical microstimulation of the reticular formation. Anesth. Analg. 102, 1709–1714. doi: 10.1213/01.ane.0000205752.00303.94

Paxinos, G., and Franklin, K. B. J. (2001). The Mouse Brain in Stereotaxic Coordinates. 2nd Edn. New York: Academic Press.

Rasch, B., Buechel, C., Gais, S., and Born, J. (2007). Odor cues during slow-wave sleep prompt declarative memory consolidation. Science 315, 1426–1429. doi: 10.1126/science.1138581

Rinberg, D., Koulakov, A., and Gelperin, A. (2006). Sparse odor coding in awake behaving mice. J. Neurosci. 26, 8857–8865. doi: 10.1523/jneurosci.0884-06.2006

Rosenkranz, J. A., and Grace, A. R. (2002). Dopamine-mediated modulation of odour-evoked amygdala potentials during Pavlovian conditioning. Nature 417, 282–287. doi: 10.1038/417282a

Rosenkranz, J. A., Moore, H., and Grace, A. A. (2003). The prefrontal cortex regulates lateral amygdala neuronal plasticity and responses to previously conditioned stimuli. J. Neurosci. 23, 11054–11064.

Sanchez-Andrade, G., and Kendrick, K. M. (2010). The main olfactory system and social learning in mammals. Behav. Brain Res. 200, 323–335. doi: 10.1016/j.bbr.2008.12.021

Valsecchi, P., and Galef, B. G. (1989). Social influences on the food preferences of house mice (Mus musculus). Int. J. Comp. Psychol. 2, 245–256.

Vincis, R., Gschwend, O., Bhaukaurally, K., Beroud, J., and Carleton, A. (2012). Dense representation of natural odorants in the mouse olfactory bulb. Nat. Neurosci. 15, 537–539. doi: 10.1038/nn.3057

Walker, M. P., and Stickgold, R. (2004). Sleep-dependent learning and memory consolidation. Neuron 44, 121–133. doi: 10.1016/j.neuron.2004.08.031

Weinberger, N. M., Gold, P. E., and Sternberg, D. B. (1984). Epinephrine enables Pavlovian fear conditioning under anaesthesia. Science 223, 605–607. doi: 10.1126/science.6695173

Wilson, D. A., Sullivan, R. M., and Leon, M. (1987). Single-unit analysis of postnatal olfactory learning: modified olfactory bulb output response patterns to learned attractive odors. J. Neurosci. 7, 3154–3162.

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 30 March 2014; Accepted: 09 May 2014; Published online: 05 June 2014.

Citation: Nicol AU, Sanchez-Andrade G, Collado P, Segonds-Pichon A and Kendrick KM (2014) Olfactory bulb encoding during learning under anesthesia. Front. Behav. Neurosci. 8:193. doi: 10.3389/fnbeh.2014.00193

Copyright © 2014 Nicol, Sanchez-Andrade, Collado, Segonds-Pichon and Kendrick. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

REVIEW ARTICLE
published: 11 March 2014
doi: 10.3389/fnbeh.2014.00072

Olfactory systems and neural circuits that modulate predator odor fear

Lorey K. Takahashi[*]

Department of Psychology, University of Hawaii at Manoa, Honolulu, HI, USA

* Correspondence: Lorey K. Takahashi, Department of Psychology, University of Hawaii at Manoa, 2530 Dole St., Sakamaki C400, Honolulu, HI 96822, USA e-mail: lkt@hawaii.edu

Edited by:
Regina M. Sullivan, NYU School of Medicine, USA

Reviewed by:
Jeffrey B. Rosen, University of Delaware, USA
Gordon A. Barr, Children's Hospital of Philadelphia, USA

When prey animals detect the odor of a predator a constellation of fear-related autonomic, endocrine, and behavioral responses rapidly occur to facilitate survival. How olfactory sensory systems process predator odor and channel that information to specific brain circuits is a fundamental issue that is not clearly understood. However, research in the last 15 years has begun to identify some of the essential features of the sensory detection systems and brain structures that underlie predator odor fear. For instance, the main (MOS) and accessory olfactory systems (AOS) detect predator odors and different types of predator odors are sensed by specific receptors located in either the MOS or AOS. However, complex predator chemosignals may be processed by both the MOS and AOS, which complicate our understanding of the specific neural circuits connected directly and indirectly from the MOS and AOS to activate the physiological and behavioral components of unconditioned and conditioned fear. Studies indicate that brain structures including the dorsal periaqueductal gray (DPAG), paraventricular nucleus (PVN) of the hypothalamus, and the medial amygdala (MeA) appear to be broadly involved in predator odor induced autonomic activity and hypothalamic-pituitary-adrenal (HPA) stress hormone secretion. The MeA also plays a key role in predator odor unconditioned fear behavior and retrieval of contextual fear memory associated with prior predator odor experiences. Other neural structures including the bed nucleus of the stria terminalis and the ventral hippocampus (VHC) appear prominently involved in predator odor fear behavior. The basolateral amygdala (BLA), medial hypothalamic nuclei, and medial prefrontal cortex (mPFC) are also activated by some but not all predator odors. Future research that characterizes how distinct predator odors are uniquely processed in olfactory systems and neural circuits will provide significant insights into the differences of how diverse predator odors activate fear.

Keywords: predator odor, fear, main and accessory olfactory systems, amygdala, hippocampus, medial hypothalamus, medial prefrontal cortex

BACKGROUND

Fear and anxiety are activated by threat and the ability to regulate their responses is essential to adaptation and survival. Moreover, an extensive body of work indicates that abnormalities in the detection of threat may lead to pathological fear and anxiety (Lang et al., 2000; Charney, 2004; Green and Phillips, 2004; Blanchard et al., 2011; Britton et al., 2011). Thus, the biology of fear has attracted considerable attention in relation to the causal and modulatory factors linked to normal and exaggerated fear and anxiety states.

Animal models of fear that involve exposing prey to predator odor offer fundamental insights into the biology of threat detection and behavioral expression. Many species depend on olfactory sensory systems to detect a predator and engage in anti-predator behavior. Chemosensory cues or predator odors may at times be the only information available to prey that are hiding, under cover, or unable to visually detect the source of threat. Wide-ranging field and laboratory studies have discussed the diverse behavioral repertoire prey animals display when predator chemosensory cues are detected (Kats and Dill, 1998; Apfelbach et al., 2005) and the decision-making consequences prey exhibit under of risk of predation (Lima, 1998; Bytheway et al., 2013). In addition, burgeoning research is focusing on the neurobiology of predator-induced stress. In particular, exposing mice and rats to predator odor stimuli, which induces long-lasting behavioral and physiological effects, are increasingly becoming a useful, animal model that may offer insights into the pathophysiology of humans undergoing uncontrollable stress and anxiety as in post-traumatic stress disorder (Mackenzie et al., 2010; Clinchy et al., 2011; Corley et al., 2012; Matar et al., 2013).

This review highlights current research on the olfactory and neural systems that activate fear elicited by predator odors. The review begins with an overview of the olfactory systems that detect odors derived especially from predator fur, urine, and synthesized chemosignals from predators. Interconnections from olfactory systems to brain circuits activated by predator odor will then be discussed in relation to autonomic, endocrine, and fear-related unconditioned and conditioned fear.

DIVERSE OLFACTORY SYSTEMS DETECT PREDATOR ODORS

The olfactory system consists of several subsystems (Breer et al., 2006; Munger et al., 2009) that include the main olfactory system (MOS), the accessory olfactory system (AOS), the septal organ of Masera, the Grueneberg ganglion (GG), and the trigeminal system. Research on predator odor detection has focused almost entirely on the two well-known MOS and AOS. A long-standing view is the MOS serves as a broadly tuned odor sensor that responds to a multitude of volatile, airborne chemicals that convey information such as the location of food and the whereabouts of predators and prey (Firestein, 2001). In contrast, the AOS mediates innate responses to non-volatile, fluid-phase chemical cues or pheromones (Meredith, 1991; Breer et al., 2006), which are chemicals released from one organism that influence the behavior or physiology of another organism of the same species (Karlson and Luscher, 1959). Pheromones likely evolved to facilitate intraspecific communication such as the reproductive and social status of a conspecific (Dulac and Torello, 2003). However, the role of the AOS has now broadened to include not only intraspecific communication but also interspecific signaling. That is, the AOS also detects the odor of non-conspecifics such as predators (Ben-Shaul et al., 2010; Papes et al., 2010) and these interspecific signaling odors are referred to as kairomones (Dicke and Grostal, 2001). Thus, both the MOS and AOS have the potential to detect predator odor and activate unconditioned fear behavior. This review section discusses our expanding knowledge of the different olfactory subsystems, especially in laboratory mice and rats, in processing predator odors.

THE MAIN OLFACTORY SYSTEM

The MOS consists of olfactory sensory neurons in the main olfactory epithelium (MOE) that express odorant receptors (ORs) and trace amine associated receptors (TAARs) (Buck and Axel, 1991; Liberles and Buck, 2006) and project to the MOB for further olfactory information processing. The MOB then projects directly or indirectly to brain regions that modulate physiological and behavioral functions.

THE MOS OF THE RAT AND MOUSE APPEARS ESPECIALLY SENSITIVE IN PROCESSING THE PREDATOR ODOR TRIMETHYLTHIAZOLINE

Although only a few studies have investigated the role of the rat MOS in detecting predator odors, an important observation is the MOS does not process all types of predator odors. For example, exposure to cat odor obtained from the body or fur induces only a modest increase in Fos expression in the glomerular cell layer in the MOB (McGregor et al., 2004). In contrast, 2,4,5 dihydro 2,5 trimethylthiazoline (TMT), a highly volatile synthesize compound from red fox anal secretions induces a pronounced increase in Fos expression in both the granular layer and dorsal lateral portion of the MOB glomerular layer (Illig and Haberly, 2003; Day et al., 2004; Staples et al., 2008). An early study also reported that rats exposed to the odor of fox feces exhibited elevations in olfactory bulb mitral cell multiunit responses that were accompanied by increased vigilance, as indicated by heightened EEG and neck muscle EMG recordings and freezing behavior (Catterelli and Chanel, 1979). A role of the MOB in processing TMT is further supported in a study showing that olfactory bulb ablation effectively reduces freezing to TMT (Ayers et al., 2013).

Consistent with the limited work in rats, a number of studies in mice have further supported a role of the MOS in detecting TMT. Studies showed that intranasal perfusion of zinc sulfate, which induces transient anosmia of the MOS (McBride et al., 2003), reduces freezing and avoidance to TMT (Hacquemand et al., 2010; Galliot et al., 2012). Using genetic methods, a study reported that mutant mice with dorsal epithelium zone depletion of olfactory neurons exhibited deficits in avoidance behavior when exposed to TMT (Kobayakawa et al., 2007). Another study in mice showed that altered olfactory sensory neuron projections from the dorsal region of the olfactory epithelium to the dorsal olfactory bulb impaired avoidance behavior to TMT (Cho et al., 2011). These results suggest that a circuit in the MOS from the dorsal olfactory epithelium region to the dorsal olfactory bulb play a critical role in processing TMT.

In addition to TMT, the urine of predators is detected by the mouse MOS. One study identified an involvement of TAARs expressed in neurons of the MOE in detecting predator urine odors (Dewan et al., 2013). More specifically, this study found that genetic deletion of the olfactory TAAR gene family abolished the aversion of mice to low concentrations of volatile amines and to the odor of predator urine. Of interest, analysis of predator urine identified the volatile amine β-phenyethylamine (PEA), which is recognized by TAAR4 (Ferrero et al., 2011). Of particular relevance, mice lacking the olfactory TAAR4 exhibit deficits in their aversion to PEA (Ferrero et al., 2011; Dewan et al., 2013). These studies demonstrate predator urine contains a volatile compound, i.e., PEA, detected by a specific receptor in the MOE that induces predator fear-related behavior.

THE ACCESSORY OLFACTORY SYSTEM

The AOS consists of the vomeronasal organ (VNO), a chemoreceptive structure situated at the base of the nasal septum, which houses the microvillar vomeronasal sensory neurons (VSNs). Pheromones are detected by three classes of VSN receptors including vomeronasal receptor type 1 (V1R) and type 2 (V2R) and formyl peptide receptors (Munger et al., 2009; Liberles, 2014). The VNO sends projections to the accessory olfactory bulb (AOB), a forebrain region that serves as the first processing center of vomeronasal information. The AOB then sends information directly or indirectly to a number of brain sites involved in diverse functions.

THE RAT AND MOUSE AOS MAY BE NECESSARY BUT NOT SUFFICIENT IN MEDIATING PREDATOR ODOR PROCESSING

TMT and cat odor are prominently used to investigate the roles of the MOS and AOS in predator odor detection. As previously indicated, rats exposed to TMT, but not cat odor, showed a significant increase in Fos expression in the glomerular cell layer in the MOB. However, in striking contrast, exposure to cat odor, but not TMT, induces a robust increase in Fos expression in the glomerular, mitral, and granule cell layers of the posterior AOB (Staples et al., 2008). These results suggest that TMT is detected primarily by the MOS whereas cat odor is detected by AOS. Cat odor derived from the fur/body appears to act as a kairomone processed by the AOS.

To further assess the potentially independent role of the MOS and AOS in processing predator odor, an interesting study (Masini et al., 2010) exposed rats after destruction occurring in either the olfactory epithelium or VNO to ferret odor collected on a small towel. The study showed that rats rendered anosmic with zinc sulfate or with ablation of the VNO continued to show elevations in stress-induced corticosterone secretion when exposed to ferret odor. Only after joint destruction of the MOB and VNO did significant deficits occur in ferret odor-induced secretion of corticosterone. These results suggest the MOS and AOS have overlapping roles in modulating the ferret odor-induced increase in stress hormone secretion. Due to the reported role of the rat AOB in detecting cat odor as indicated by increased Fos expression (McGregor et al., 2004), removal of the VNO in rats would be useful to determine whether the remaining functional MOS may similarly modulate the processing of fear-related behavior induced by cat fur/body odors.

Several lines of evidence in mice also support an involvement of the VNO chemosensory system in detecting predator odor and activating fear. First, mice exposed to the urine of predators, (e.g., bobcat, fox, rat) exhibit a robust increase in electrical activity in the AOB (Ben-Shaul et al., 2010). Second, removal of the VNO or genetically impairing activation of the VNO increases the time mice spent investigating a collar worn by a cat (Samuelsen and Meredith, 2009) or the bedding of a rat (Isogai et al., 2011). Third, VN1 and VN2 receptors appear to have unique roles in detecting and identifying different types of potential predators, e.g., mammalian, reptiles, or predatory birds (Isogai et al., 2011).

The VNO role in predator odor processing was further investigated in mice lacking the TrpC2, which is the primary signal transduction channel of VNO sensory neurons. This study showed that TrpC2−/− mutants exhibited deficits in avoidance and risk assessment behavior when exposed to cat odor (neck swab), snake (shed skin), and rat urine (Papes et al., 2010). Furthermore, consistent with the research implicating the olfactory dorsal epithelium in processing TMT-induced fear in mice (Kobayakawa et al., 2007), TrpC2−/− mutants exposed to TMT displayed fear behavior. Thus, in the mouse and to some extent in the rat, the MOS and AOS specifically detect different types of predator odors to facilitate fear.

Of relevance in identifying specific kairomone chemosignals that activate the VNO to induce fear, investigators isolated in cat saliva the protein Feld4, a cat homolog belonging to the mouse and rat major urinary protein (MUP) family (Smith et al., 2004). The MUP family also consists of closely related proteins excreted by rodent exocrine glands (Cavaggioni and Mucignat-Caretta, 2000). Mice exposed to Feld4 exhibit an increase in both c-Fos expression in AOB and defensive behavior (Papes et al., 2010). In addition, exposure to the recombinant Feld4 ligand facilitated fear behavior and stress hormone secretion in wild type but not TrpC2−/− mice with deficits in VNO signaling. The MUP ligand, cat Feld4, appears to be a chemosensory signal detected by the VNO that triggers fear in the mouse. The potential presence of Feld4 on cat fur/body during cat grooming may be the basis of the chemosensory signal that activates fear in the mouse. Generalization of the fear-eliciting effects of Feld4 from the mouse to the rat and other prey species requires further investigations.

THE MOUSE GRUENEBERG GANGLION DETECTS A VOLATILE THAT SIGNALS DANGER

In mice, the GG is a small olfactory structure located on the tip of the nose that detects odors via V2Rs, several TAARs, and ORs (Fleischer and Breer, 2010). GG neurons send axonal projections to dorsal regions of the caudal MOB (Munger et al., 2009). The mouse GG is reported to detect the intraspecific predator odor TMT and conspecific alarm pheromones or odors released when mice are killed, injured, or threatened (Brechbühl et al., 2008, 2013). Threat-induced activation of both conspecific alarm pheromones and TMT via the GG may be linked to the detection of sulfur-containing related volatiles generated by meat digestion of potential predators (Nolte et al., 1994; Brechbühl et al., 2013). These results indicate a dual role of the GG in detecting a volatile produced by both conspecifics and interspecifics that signals danger.

To determine the specific involvement of GG in activating threat, investigators found that sectioning GG axonal projections, which are sent to the MOB (Fuss et al., 2005; Koos and Fraser, 2005), reduced freezing but not risk assessment behavior when mice were exposed to volatile alarm pheromones and TMT (Brechbühl et al., 2013). The researchers suggest that although GG function is impaired, these odorants are still detected, likely by receptors in the MOE (Kobayakawa et al., 2007), which send axonal projections to MOB regions distinct from GG projections (Mamasuew et al., 2011), to maintain activation of some features of fear-related behavior.

THE POTENTIAL ROLE OF THE SEPTAL ORGAN OF MASERA IN MEDIATING PREDATOR ODOR HAS NOT BEEN DETERMINED

Situated bilaterally in the septal wall between the caudal end of the VNO and the rostrally located MOE lie the septal organ of Masera (SO) (Ma, 2007). The location of the SO in the nasal cavity air path of many rodent species was hypothesized to serve as a general odor detector that alerts the individual (Giannetti et al., 1995) or plays a role in assessing food or social odors (Breer et al., 2006). Of potential interest, in mice, the SO innervates glomeruli in the olfactory bulb from the MOE (Lèvai and Strotmann, 2003), which may have relevance in detecting predator odors. An early study investigated the potential arousing or alerting function of the SO by exposing intact and SO lesioned rats to TMT and food odors as well as non-biological odors such as eucalyptol (Giannetti et al., 1995). Results indicated that both biologically relevant and meaningless odors induced similar awakening and habituation responses in intact and SO lesioned groups, which suggest the SO does not have a unique alerting functional role.

THE TRIGEMINAL SYSTEM

The trigeminal system is essential in protecting against toxic or irritating odors by triggering reflexes such as apnea and sneezing. In mice and rats, noxious, pungent odors activate the trigeminal system via a large population of chemosensory cells that reach the surface of the nasal epithelium to form synaptic contacts with trigeminal afferent nerve fibers (Munger et al., 2009).

THE TRIGEMINAL SYSTEM IS ACTIVATED BY HIGH DOSES OF TMT BUT MAY NOT BE INVOLVED IN MEDIATING UNCONDITIONED FEAR BEHAVIOR

Some investigators have suggested that TMT should be classified as a general noxious odorant that activates the trigeminal system to trigger avoidance behavior (McGregor et al., 2002; Apfelbach et al., 2005; Fortes-Marco et al., 2013). Evidence suggesting a noxious, irritating role of TMT is based on work showing that exposing rats to low concentrations of TMT (35 μmol) is capable of activating the external lateral parabrachial nucleus of the trigeminal sensorial system (Day et al., 2004). In addition, 75 μmol or more of TMT stimulates stress hormone ACTH and corticosterone secretion. TMT was also reported to induce nausea in humans (Fendt et al., 2005a). Although many studies were conducted with application of large TMT doses, when low doses are used, investigators observed a pattern of murine behavior akin to unconditioned fear-related responses such as freezing and avoidance (Wallace and Rosen, 2000; Blanchard et al., 2003b; Endres et al., 2005; Hacquemand et al., 2013).

To date, only one study has specifically manipulated the trigeminal system to determine its role in mediating the effects of TMT (Ayers et al., 2013). In this study, rats with trigeminal nerve transection were exposed to TMT (300 μmol) or the noxious irritant butyric acid (900 μmol). Results indicated that although trigeminal deafferentation effectively impaired freezing to butyric acid, TMT exposed rats were spared and exhibited freezing. On the basis of these observations, TMT odors appear to activate fear via an intact MOS and not through the trigeminal system.

SUMMARY OF THE ROLE OF OLFACTORY SYSTEMS IN MODULATING PREDATOR ODOR FEAR

In recent years, a number of investigators have begun to unravel the complex olfactory systems that process predator odors. A general conclusion of these studies is that different olfactory subsystems appear to have distinct roles in detecting different predator odors (see summary Table 1). For example, receptor systems in the MOS play a key role in mediating the sensory detection of TMT and compounds found in predator urine that activate fear. On the other hand, the AOS appears especially sensitive in processing cat odor or a potential kairomone compound found in cat saliva. However, the majority of studies that yielded insights into the specific role of olfactory subsystems were obtained almost exclusively in the mouse and broad generalization of these novel observations to other small prey species remains to be determined.

Table 1. Roles of the olfactory systems in modulating predator odor fear.

Another conclusion is that different olfactory subsystems may have complex overlapping effects in processing predator odors. Studies from the extensive sociosexual literature demonstrate that both the MOS and AOS are capable of detecting and processing both volatile and non-volatile chemosignals (Restrepo et al., 2004; Shepherd, 2006; Spehr et al., 2006; Martinez-Marcos, 2009) but differ in their signaling properties (Xu et al., 2005). Furthermore, although isolated compounds may be specifically detected by one olfactory subsystem, in natural predator odor exposure conditions complex chemosensory compounds may be present and processed by several olfactory subsystems to facilitate unconditioned fear. For example, studies showed the volatile predator urine amine PEA is detected by TAAR4 found in MOE neurons (Ferrero et al., 2011), whereas the nonvolatile kairomone MUP ligand cat Feld4 is detected by VNO neurons (Papes et al., 2010). However, the natural predator scent from the fur/body of cats and ferrets may contain a complex mixture of volatile and non-volatile sensory cues detected by the rodent MOS or AOS (see Masini et al., 2010) to induce unconditioned fear.

Similar overlapping olfactory subsystems may apply to the GG and MOS, where both olfactory subsystems express TARRs, and disruption in GG axonal projections to the MOB do not incur pronounced deficits in predator odor TMT-induced fear. Expression of V2Rs in the GG may play yet another complex role in detecting kairomone-like molecules. Thus, the complex predator body odor fur may be detected by multiple olfactory subsystems to facilitate broad activation of brain circuits that modulate unconditioned fear.

PREDATOR ODOR ACTIVATES BRAIN CIRCUITS THAT MODULATE AUTONOMIC, ENDOCRINE, AND FEAR-RELATED RESPONSES

This section of the review highlights some of the key brain regions linked to olfactory systems, which modulate three major components—autonomic, endocrine, and behavior—of predator odor fear or threat.

EXPOSURE TO PREDATOR ODORS ACTIVATE THE AUTONOMIC NERVOUS SYSTEM

Exposure to threat activates the autonomic nervous system (ANS) to rapidly trigger physiological responses that facilitate immediate survival in a dangerous situation (Ulrich-Lai and Herman, 2009). Secretion of the catecholamine epinephrine and norepinephrine from the sympatho-adrenomedullary system increases heart rate, vasoconstriction, and energy mobilization to support the classic “flight or fight” response. In addition, activation of peripheral β-adrenoceptors on vagal afferents that terminate in the nucleus of the solitary tract located in the brainstem may influence locus coeruleus pathways that secrete NE throughout the brain to further support physiological, behavioral and cognitive functions (Jöels et al., 2006). Here, the few studies that examined the effects of predator odor on ANS activity are discussed (Table 2).

Table 2. Effects of predator odor on autonomic and endocrine functions.

One study reported that urethane-anesthetized rats exposed to either TMT or the pungent non-predatory odor butyric acid showed a dramatic increase in adrenal sympathetic nerve activity only to TMT (Horii et al., 2010), which likely increased epinephrine section and elevated blood pressure and heart rate. The authors also showed that live rats exposed to TMT displayed an increase in freezing and a reduction in exploratory behavior. In a subsequent study (Horii et al., 2013), this research group demonstrated that rats exposed to TMT exhibited elevations not only in adrenal sympathetic nerve activity but also in body temperature, an indication of increased metabolism and another measure of elevated autonomic activity (Bouwknecht et al., 2007).

Cardiovascular functions were also studied in rats exposed to cat odor emitted from a collar worn by a cat (Dielenberg and McGregor, 2001). In this study, cat odor exposure induced a sustained increase in blood pressure but not heart rate. These autonomic responses were accompanied by a reduction in exploration, avoidance of the cat collar and heightened head out and risk-assessment activity.

Information is scarce on the role of specific neural sites underlying predator odor-induced autonomic activity. However, one brain region of interest is the dorsal periaqueductal gray (DPAG), which was implicated a number of years ago to modulate cardiovascular functions (Carobrez et al., 1983; Schenberg et al., 1983; Depaulis et al., 1992). Previous studies reported that rats exposed to cat odor exhibit an increase in Fos-positive cells in the DPAG (Dielenberg and McGregor, 2001; McGregor et al., 2004). To investigate the role of the DPAG in cat odor-induced ANS activation, rats with DPAG lesions were implanted with telemetric probes to measure heart rate and blood pressure (Dielenberg et al., 2004). When exposed to cat odor, DPAG lesioned rats showed a reduction in heart rate and locomotor activity, but no significant decrease in the cat odor-induced rise in blood pressure. The results indicate the DPAG modulates some components of the ANS activated by cat odor.

Although TMT exposure increases sympathetic nerve activity (Horii et al., 2010, 2013), the specific role of the DPAG on ANS functions are not known. Unlike cat odor showing increased Fos expression in the DPAG, exposure to TMT is not accompanied by increases in c-fos mRNA (Day et al., 2004), albeit recent functional magnetic resonance imaging revealed a TMT-induced increase in neural activity in the DPAG (Kessler et al., 2012). Perhaps the DPAG modulates ANS functions activated by TMT as reported with cat odor (Dielenberg et al., 2004).

EXPOSURE TO PREDATOR ODORS RAPIDLY ACTIVATES THE NEUROENDOCRINE STRESS SYSTEM

In addition to facilitating of the ANS, a number of studies demonstrated that predator odor activates the hypothalamic-pituitary-adrenal system (HPA) in mice and rats (see Table 2). For example, exposure to TMT (Morrow et al., 2000; Day et al., 2004; Kobayakawa et al., 2007), cat odor (File et al., 1993; Cohen et al., 2006; Muñoz-Abellán et al., 2011), ferret odor (Masini et al., 2005) or 2-propylthietane, the main constituent of weasel anal gland secretion (Perrot-Sinal et al., 1999) increases adrenocorticotropin (ACTH) or corticosterone secretion.

The hypothalamic paraventricular nucleus (PVN) plays an important integrative role in stress by sending neuronal projections to the median eminence to regulate pituitary-adrenal hormone secretion (Sawchenko et al., 1996; Herman et al., 2002). The PVN also sends projections to brainstem sites including the parabrachial nucleus, the dorsal motor nucleus of the vagus nerve and the nucleus of the solitary tract to regulate autonomic activity (Swanson and Kuypers, 1980).

In relation to predator odor-induced HPA activation, the PVN receives information from the medial amygdala (MeA), a recipient of both direct and indirect MOS and direct AOS projections (Petrovich et al., 2001; Meredith and Westberry, 2004; Pro-Sistiaga et al., 2007). Rats with fiber-sparing lesions of the MeA exhibit significant deficits in ACTH and corticosterone secretion when exposed to ferret odor (Masini et al., 2009). However, the role of the MeA in facilitating HPA hormone secretion is not specific to predator odor. Impairment of the MeA is also reported to attenuate stress hormone secretion induced by restraint stress (Dayas et al., 1999).

Another study found that mice with dorsal epithelium zone depletion of olfactory neurons impaired ACTH secretion when exposed to TMT (Kobayakawa et al., 2007). This impairment in TMT detection and processing may disrupt a MOS-MeA-PVN circuit responsible for facilitating HPA hormone secretion. The study further indicated that dorsal epithelium zone depleted mice showed reductions in TMT-induced Zif268-positive cells in the bed nucleus of the stria terminalis (BST), anterior division, dorsal medial nucleus (BSTdm), which may reflect a disruption in a BST to PVN circuit that facilitates stress hormone secretion (Kobayakawa et al., 2007). The BST also receives direct projections from the AOB and indirect projections from the MeA (Scalia and Winans, 1975; Canteras et al., 1995; Fan and Luo, 2009) and impairments in potential odor processing in the BST may compromise activation of both HPA and ANS functions (Ulrich-Lai and Herman, 2009).

THE AMYGDALA, ESPECIALLY THE MEDIAL AMYGDALA, PLAYS AN ESSENTIAL ROLE IN MODULATING PREDATOR ODOR UNCONDITIONED AND CONDITIONED FEAR

A number of reviews have discussed the neural basis of fear (e.g., LeDoux, 2000; Rosen and Donley, 2006; Pessoa and Adolphs, 2010; Gross and Canteras, 2012). A common theme of these reviews emphasizes the importance of the amygdala in threat detection, the elicitation of fear behavior, and its role in modulating fear learning and memory.

As indicated previously, the MeA receives both direct and indirect projections from olfactory systems and modulates HPA stress hormone secretion induced by predator odor. MeA cells appear sensitive to cat odor as indicated in a study showing impairments in facilitating field excitatory post synaptic potentials in the MeA of rats after exposure to cat odor (Collins, 2011). Behavioral studies demonstrate in rats that fiber-sparing lesions or temporary inactivation the MeA dramatically impair unconditioned freezing when exposed to either cat odor (Li et al., 2004; Blanchard et al., 2005) or TMT (Fendt et al., 2003). Moreover, MeA lesioned rats approached and contacted the cloth that contained cat odor and additional studies indicated MeA lesions did not produce a general increase in locomotor activity or a major deficit in olfactory detection (Li et al., 2004).

The role of the MeA was also studied in relation to predator odor contextual fear consolidation and retrieval (Takahashi et al., 2007). Rats with MeA inactivation immediately after exposure to predator odor exhibited no deficits in the consolidation of contextual fear-related behavior. However, rats with acute inactivation of the MeA immediately prior to retrieval of contextual fear showed increased approach behavior to the apparatus sector that previously contained the cat odor cloth. Thus, the MeA may have a dual role in detecting predator odor, which activates unconditioned fear, and in recalling a previous location associated with predator odor. Of note, a study showed that MeA lesioned rats are capable of freezing to an auditory stimulus paired with footshock (Nader et al., 2001) and suggests that impairment of the MeA does not produce global deficits in learned fear behavior. Rather, the MeA lesion-induced deficit in conditioned fear behavior appears specific to impairments in predator odor-context associations.

The basolateral amygdala (BLA), which is widely implicated in fear conditioning using footshock as the unconditioned stimulus paired with auditory (Fanselow and Poulos, 2005), contextual (Maren et al., 2013), or olfactory cues (Otto et al., 2000; Mouly and Sullivan, 2010), has been the focus of studies that determined whether the BLA is also involve in associating the unconditioned predator odor stimulus with the test context. Using TMT, studies in rats demonstrated that BLA inactivation or fiber-sparing lesions did not induce robust impairments in unconditioned freezing (Wallace and Rosen, 2001; Müller and Fendt, 2006) and produced only mild deficits in conditioned freezing to the context (Wallace and Rosen, 2001). However, other investigators that exposed BLA lesioned rats to cat odor reported deficits in both unconditioned freezing and approach to the cat odor (Vazdarjanova et al., 2001; Takahashi et al., 2007). In addition, temporary inactivation of the BLA immediately after exposure to cat odor impaired contextual avoidance behavior when rats were tested the next day (Takahashi et al., 2007). Thus, the BLA, which is broadly involved in emotional memory consolidation (McGaugh, 2000), is further implicated in the consolidation of cat odor-induced contextual fear.

Unlike the MeA, the BLA in rats does not receive direct projections for either the MOS or AOS (McDonald, 1998; Pitkänen, 2000). Different olfactory detection systems accompanied by downstream indirect projections to the BLA may contribute to the reported differences between TMT and cat odor in unconditioned and conditioned fear. Of possible relevance, cat and ferret fur/body odor, but not TMT, increases c-Fos expression in the BLA (Day et al., 2004; Masini et al., 2005; Staples et al., 2008), which suggests predator odor differences in activating the BLA. Nonetheless, the temporal pattern of c-Fos expression is known to vary (Redburn and Leah, 1997), and a time-course study may reveal temporal increases in c-Fos expression activated by TMT. Furthermore, although the BLA may not modulate an increase in unconditioned fear elicited by TMT, the BLA may play an alternative role in modulating the arousing effects of TMT on dorsolateral striatal-dependent response learning (Leong and Packard, 2014).

The central nucleus of the amygdala (CeA) is another target of interest in predator odor studies due to the broad role of the CeA in modulating autonomic, endocrine, and anxiety and fear behavior (Davis, 2000; LeDoux, 2000). However, studies in rats involving fiber-sparring lesions or temporary inactivation of the CeA demonstrate that exposure to either TMT (Fendt et al., 2003) or cat odor (Li et al., 2004) are ineffective in attenuating unconditioned fear behavior. Thus, not all nuclei in the amygdala are involved in modulating predator odor-induced fear behavior.

THE BED NUCLEUS OF THE STRIA TERMINALS MODULATES PREDATOR ODOR FEAR INDUCED BY OLFACTORY INFORMATION PROCESSED BY THE MAIN OLFACTORY SYSTEM

Another major direct and indirect projection from olfactory systems is the BST as previously discussed. Studies revealed that inactivation of the BST, especially the ventral BST, reduces freezing when rats are exposed to TMT (Fendt et al., 2003, 2005b), or cat urine (Xu et al., 2012). Notably, the MOS plays a key role in processing both TMT (Kobayakawa et al., 2007) and predator urine (Ferrero et al., 2011; Dewan et al., 2013). Whether kairomone odors derived from predators such as from fur/body odors of the cat or ferret facilitate unconditioned fear behavior via the BST is not known, albeit exposure to both cat odor (Dielenberg and McGregor, 2001) and TMT (Day et al., 2004; Asok et al., 2013) appears to activate the BST as suggested by increased Fos expression.

THE VENTRAL HIPPOCAMPUS MODULATES PREDATOR ODOR UNCONDITIONED AND CONDITIONED FEAR

The hippocampus plays a prominent role in emotional behavior, especially in processing contextual fear information (Kim and Fanselow, 1992; Phillips and LeDoux, 1992; Zhang et al., 2001). The ventral hippocampus (VHC) has attracted attention in odor studies due to dense reciprocal connections to the MeA and to other amygdalar nuclei such as the cortical nucleus that receives input from the MOS (Scalia and Winans, 1975; McDonald, 1998). The VHC also projects to the AOB and the piriform cortex, a major target of the MOB (Shipley and Adamek, 1984; Illig and Haberly, 2003). In rats, both the olfactory bulb and hippocampal dentate gyrus respond to weasel gland secretion 2-propylthietane and TMT by exhibiting fast wave bursts (Heale et al., 1994). TMT exposure also increases c-fos mRNA in the hippocampal dentate gyrus (Day et al., 2004).

Concerning predator odor unconditioned fear behavior, rats with VHC, but not dorsal (DHC), hippocampal lesions exhibited deficits in freezing and crouching when exposed to cat odor (Pentkowski et al., 2006). When tested the next day for contextual fear, VHC lesioned rats continued to show deficits in freezing. Another study in mice exposed to coyote urine showed that VHC lesions impaired avoidance and risk assessment behavior (Wang et al., 2013). In addition, VHC lesioned mice exhibited less freezing than control mice in the contextual fear test. These investigators further reported that exposure to coyote urine activates place cells in the CA1 region of the DHC and modify their firing patterns to stabilize a spatial representation of the fear eliciting encounter (Wang et al., 2012). Together, these studies in the rat and mouse using cat odor and predator urine suggest the VHC plays a role in predator odor processing of unconditioned and contextual fear and the DHC may also contribute to processing contextual fear. However, additional research is required to determine the extent to which the MOS and AOS connected to the hippocampus have overlapping or distinct roles in modulating unconditioned and conditioned predator fear behavior.

EXPOSURE TO SPECIFIC PREDATOR ODORS MAY REQUIRE MEDIAL HYPOTHALAMIC NUCLEI TO ACTIVATE FEAR BEHAVIOR

Another major projection from the MeA, especially from the posteroventral MeA region, and from the BST is to medial hypothalamic nuclei, which broadly regulate reproductive, ingestive, and defensive behavior (Risold et al., 1997). Distinct medial hypothalamic nuclei consisting of the anterior hypothalamic nucleus, dorsomedial part of the ventromedial nucleus, and dorsal premammillary nucleus (PMd) are hypothesized to underlie a medial hypothalamic defensive system (Canteras, 2002).

The PMd stands out in predator studies due to robust increases in Fos expression in rats exposed to cat or cat odor (Canteras et al., 1997; Dielenberg and McGregor, 2001) and dense connections to the PAG (Cezario et al., 2008), which is involved in ANS functions and behavioral expression. Behavioral studies show that PMd lesions significantly impair cat odor-induced fear behavior (Blanchard et al., 2003a; Canteras et al., 2008). Furthermore, β-adrenoreceptor blockade in the PMd of rats prior to exposure to cat odor or prior to the context associated with cat odor effectively reduces freezing (Do Monte et al., 2008). These studies demonstrate an important role of the PMd in modulating the occurrence of predator odor unconditioned and conditioned fear.

However, some predator odors do not require the PMd to facilitate fear behavior. A study in rats showed that electrolytic lesions that damage both cells and fibers of passage through the PMd did not impair freezing to TMT (Pagani and Rosen, 2009). Furthermore, fiber-sparring lesions of the anterior and ventromedial hypothalamus failed to disrupt TMT-induced unconditioned freezing. The authors suggest the BST sends projections that pass through the anterior and ventromedial hypothalamic nuclei and circumvent the PMd to terminate in the PAG to modulate freezing expression.

This neural pathway from the BST to the PAG that circumvent connections to nuclei in the medial hypothalamus currently appears specifically activated by TMT. A study comparing the effects of cat odor and TMT on Fos expression found that exposure to cat odor, but not TMT, induced significant Fos expression in both the anterior and dorsal ventromedial hypothalamus as well and the PMd (Staples et al., 2008). This result suggests the medial hypothalamic defensive system is not broadly activated by all predator odors to modulate fear behavior.

THE MEDIAL PREFRONTAL CORTEX MODULATES UNCONDITIONED FEAR ELICITED BY PREDATOR ODORS

The medial prefrontal cortex (mPFC) is connected to a number of brain structures including the amygdala, hypothalamus, and periaqueductal gray (Gabbott et al., 2005; Price, 2005) that are involved in predator odor fear. In addition, the mPFC is critically involved in fear extinction (Sotres-Bayon et al., 2006; Peters et al., 2009; Marek et al., 2013).

Recent studies implicated the mPFC, consisting of the prelimbic and infralimbic cortex, in predator odor-induced unconditioned fear but the precise role of the mPFC in modulating predator odor fear is not clear. One study reported that temporary inactivation of the prelimbic region increased freezing in rats exposed to TMT (Fitzpatrick et al., 2011). However, another study showed that in 38–42 days old adolescent rats, inactivation of the prelimbic cortex impaired freezing induced by cat odor (Chan et al., 2011). Although both studies reported no significant effects of infralimbic cortex inactivation on freezing, the seemingly opposite effects of prelimbic cortex inactivation on predator odor unconditioned freezing induced by TMT and cat odor is puzzling.

In addition to the predator odor-induced behavioral differences involving the mPFC, studies indicate that exposure to cat odor activates c-Fos expression (Staples et al., 2008; Chan et al., 2011) in the mPFC, whereas no significant increases in mPFC c-Fos were found after exposure to TMT (Day et al., 2004; Staples et al., 2008; Asok et al., 2013). The mPFC of rats also showed elevations in expression of ΔFosB several days after exposure to cat odor (Mackenzie et al., 2010). In this study, expression of ΔFosB in the mPFC was associated with long-term effects of predator odor on conditioned fear. Another study measured egr-1, a gene transcription factor linked to learning and memory synaptic plasticity (Alberini, 2009). Of interest, rats exposed to TMT showed no significant increase in egr-1 mRNA in the mPFC (Asok et al., 2013). Research will be required to determine how distinct predator odors such as TMT and cat odor are first processed in olfactory systems that project directly to brain structures for further processing before interacting with the mPFC to modulate unconditioned and conditioned fear behavior.

SUMMARY OF BRAIN CIRCUITS THAT MODULATE PREDATOR ODOR FEAR

Several distinct and overlapping brain regions have been identified that play key roles in predator odor activation of autonomic, endocrine, and fear behavior responses. For example, the MeA, which receive direct projections from MOS and AOS, appears to have a necessary role in general predator odor activation of both HPA hormone secretion and unconditioned fear behavior (see Tables 2, 3). In addition, the MeA and BLA are involved in the retrieval and consolidation, respectively, of predator odor contextual fear. The VHC, another key brain center that interacts with olfactory projection targets such as the MeA and piriform cortex, also appears to have a general role in modulating predator odor unconditioned and contextual fear behavior.

Table 3. Brain structures that modulate predator odor unconditioned and conditioned fear.

A number of investigators reported that exposure to cat odor induces robust contextual (Blanchard et al., 2001; Dielenberg and McGregor, 2001; Takahashi et al., 2005; Canteras et al., 2008) or auditory (Takahashi et al., 2008) fear conditioning in comparison to the less intense fear conditioning observed with TMT (Blanchard et al., 2003b; Fortes-Marco et al., 2013; Staples et al., 2008; see also ferret odor-induced conditioning in Masini et al., 2006). Notwithstanding the ability of TMT to induced contextual fear conditioning in rats tested under specific environmental conditions such as variations in test cage size (Rosen et al., 2008) or multiple pairings of TMT and the context (Endres and Fendt, 2007), behavioral testing with different predator odors are identifying some neural sites that may account for inconsistencies in displays of predator odor unconditioned and conditioned fear. For example, cat odor, but not TMT, appears to involve the BLA. Although the precise basis underlying this difference is not clear, the possibility exist that vomeronasal signal processing in the BLA may be necessary for predator odor fear conditioning. A study reported that non-volatile pheromones are especially attractive to female mice and the BLA stood out as a distinct region for vomeronal-olfactory associative learning (Moncho-Bogani et al., 2005). Furthermore, another study reported that darcin, an involatile protein sex pheromone in male mouse urine, rapidly induced conditioned preference in female mice that spent time in the site were darcin was previously detected (Roberts et al., 2012). Perhaps exposure to a predator odor kairomone with similar darcin-like properties and/or the MUP ligand, cat Feld4, will trigger rapid and robust fear conditioning in the BLA. Furthermore, predator odor fear conditioning may be enhanced by simultaneous activation of the MeA, VH, medial hypothalamic nuclei, and mPFC. That is, these nuclei are sensitive to the kairomone cat odor (see Table 3) and may require activation in conjunction with the BLA when exposed to predator odor to facilitate unconditioned and conditioned fear.

In stark contrast to the extensively studied olfactory connected neural systems in reproductive behavior or the neural systems in emotional learning and memory, based largely on work involving the application of the unconditioned footshock stimulus, knowledge on biology of predator odor fear is limited. Future predator odor research should address not only the brain circuits that modulate unconditioned and conditioned fear but also the olfactory sensory structures such as the accessory and main olfactory bulbs that are implicated in important developmental, social, and reproductive learning and memory processing (Brennan and Keverne, 1997; Landers and Sullivan, 2012). A recent study in mice reported that fear learning to threat-predictive odors involved changes in synaptic output of olfactory sensory neurons, which suggests that emotional information can be encoded at the level of primary sensory processing (Kass et al., 2013). Thus, characterizing the processing of different predator odors occurring in olfactory structures in conjunction with the complex interconnected neural circuits that mediate both the physiology and behavior of fear will be required to provide a complete picture of how predator chemosignals are uniquely processed to facilitate adaptive fear-related defensive behavior.

REFERENCES

Alberini, C. M. (2009). Transcription factors in long-term memory and synaptic plasticity. Physiol. Rev. 89, 121–145. doi: 10.1152/physrev.00017.2008

Apfelbach, R., Blanchard, C. D., Blanchard, R. J., Hayes, R. A., and McGregor, I. S. (2005). The effects of predator odors in mammalian prey species: a review of field and laboratory review of field and laboratory studies. Neurosci. Biobehav. Rev. 29, 1123–1144. doi: 10.1016/j.neubiorev.2005.05.005

Asok, A., Ayers, L. W., Awoyemi, B., Schulkin, J., and Rosen, J. B. (2013). Immediate early gene and neuropeptide expression following exposure to the predator odor 2,5-dihydro-2,4,5-trimethylthiazoline (TMT). Behav. Brain Res. 248, 85–93. doi: 10.1016/j.bbr.2013.03.047

Ayers, L. W., Asok, A., Heyward, F. D., and Rosen, J. B. (2013). Freezing to the predator odor 2,4,5 dihydro 2,5 trimethylthiazoline (TMT) is disrupted by olfactory bulb removal but not trigeminal deafferentation. Behav. Brain Res. 253, 54–59. doi: 10.1016/j.bbr.2013.06.034

Ben-Shaul, Y., Katz, L. C., Mooney, R., and Dulac, C. (2010). In vivo vomeronasal stimulation reveals sensory encoding of conspecific and allospecific cues by the mouse accessory olfactory bulb. Proc. Natl. Acad. Sci. U.S.A. 107, 5172–5177. doi: 10.1073/pnas.0915147107

Blanchard, D. C., Canteras, N. S., Markham, C. M., Pentkowski, N. S., and Blanchard, R. J. (2005). Lesions of structures showing FOS expression to cat presentation: effects on responsivity to a Cat, Cat odor, and nonpredator threat. Neurosci. Biobehav. Rev. 29, 1243–1253. doi: 10.1016/j.neubiorev.2005.04.019

Blanchard, D. C., Griebel, G., Pobbe, R., and Blanchard, R. J. (2011). Risk assessment as an evolved threat detection and analysis process. Neurosci. Biobehav. Rev. 35, 991–998. doi: 10.1016/j.neubiorev.2010.10.016

Blanchard, D. C., Li, C. I., Hubbard, D., Markham, C. M., Yang, M., Takahashi, L. K., et al. (2003a). Dorsal premammillary nucleus differentially modulates defensive behaviors induced by different threat stimuli in rats. Neurosci. Lett. 345, 145–148. doi: 10.1016/S0304-3940(03)00415-4

Blanchard, D. C., Markham, C., Yang, M., Hubbard, D., Madarang, E., and Blanchard, R. J. (2003b). Failure to produce conditioning with low-dose trimethylthiazoline or cat feces as unconditioned stimuli. Behav. Neurosci. 117, 360–368. doi: 10.1037/0735-7044.117.2.360

Blanchard, R. J., Yang, M., Li, C.-I., Garvacio, A., and Blanchard, D. C. (2001). Cue and context conditioning of defensive behaviors to cat odor stimuli. Neurosci. Biobehav. Rev. 26, 587–595. doi: 10.1016/S0149-7634(01)00043-4

Bouwknecht, J. A., Olivier, B., and Paylor, R. E. (2007). The stress-induced hyperthermia paradigm as a physiological animal model for anxiety: A review of pharmacological and genetic studies in the mouse. Neurosci. Biobehav. Rev. 31, 41–59. doi: 10.1016/j.neubiorev.2006.02.002

Brechbühl, J., Klaey, M., and Broillet, M. C. (2008). Grueneberg ganglion cells mediate alarm pheromone detection in mice. Science 321, 1092–1095. doi: 10.1126/science.1160770

Brechbühl, J., Moine, F., Klaey, M., Nenniger-Tosato, M., Hurni, N., Sporkert, F., et al. (2013). Mouse alarm pheromone shares structural similarity with predator scents. Proc. Natl. Acad. Sci. U.S.A. 110, 4762–4767. doi: 10.1073/pnas.1214249110

Breer, H., Fleischer, J., and Strotmann, J. (2006). The sense of smell: multiple olfactory subsystems. Cell. Mol. Life Sci. 63, 1465–1475. doi: 10.1007/s00018-006-6108-5

Brennan, P. A., and Keverne, E. B. (1997). Neural mechanisms of mammalian olfactory learning. Prog. Neurobiol. 51, 457–481. doi: 10.1016/S0301-0082(96)00069-X

Britton, J. C., Lissek, S., Grillon, C., Norcross, M. A., and Pine, D. S. (2011). Development of anxiety: the role of threat appraisal and fear learning. Depress. Anxiety 28, 5–17. doi: 10.1002/da.20733

Buck, L., and Axel, R. (1991). A novel multigene family may encode odorant receptors: a molecular basis for odor recognition. Cell 65, 175–187. doi: 10.1016/0092-8674(91)90418-X

Bytheway, J. P., Carthey, A. J. R., and Banks, P. B. (2013). Risk vs. reward: how predators and prey respond to aging olfactory cues. Behav. Ecol. Sociobiol. 67, 715–725. doi: 10.1007/s00265-013-1494-9

Canteras, N. S. (2002). The medial hypothalamic defensive system: hodological organization and functional implications. Pharm. Biochem. Behav. 71, 481–491. doi: 10.1016/S0091-3057(01)00685-2

Canteras, N. S., Chiavegatto, S., Valle, L. E., and Swanson, L. W. (1997). Severe reduction of rat defensive behavior to a predator by discrete hypothalamic chemical lesions. Brain Res. Bull. 44, 297–305. doi: 10.1016/S0361-9230(97)00141-X

Canteras, N. S, Kroon, J. A. V., Do-Monte, F. H. M., Pavesi, E., and Carobrez, A. P. (2008). Sensing danger through the olfactory system: the role of the hypothalamic dorsal premammillary nucleus. Neurosci. Biobehav. Rev. 32, 1228–1235. doi: 10.1016/j.neubiorev.2008.05.009

Canteras, N. S., Simerly, R. B., and Swanson, L. W. (1995). Organization of projections from the medial nucleus of the amygdala: a PHAL study in the rat. J. Comp. Neurol. 360, 213–245. doi: 10.1002/cne.903600203

Carobrez, A. P., Schenberg, L. C., and Graeff, F. G. (1983). Neuroeffector mechanisms of the defense reaction in the rat. Physiol. Behav. 31, 439–444. doi: 10.1016/0031-9384(83)90063-X

Catterelli, M., and Chanel, J. (1979). Influence of some biologically meaningful odorants on the vigilance states of the rat. Physiol. Behav. 23, 831–838. doi: 10.1016/0031-9384(79)90186-0

Cavaggioni, A., and Mucignat-Caretta, C. (2000). Major urinary proteins, α(2U)-globulins and aphrodisin. Biochim. Biophys. Acta 1482, 218–228. doi: 10.1016/S0167-4838(00)00149-7

Cezario, A. F., Ribeiro-Barbosa, E. R., Baldo, M. V. C., and Canteras, N. S. (2008). Hypothalamic sites responding to predator threats – the role of the dorsal premammillary nucleus in unconditioned and conditioned antipredatory defensive behavior. Eur. J. Neurosci. 28, 1003–1015. doi: 10.1111/j.1460-9568.2008.06392.x

Chan, T., Kyere, K., Davis, B. R., Shemyakin, A., Kabitzke, P. A., Shair, H. N., et al. (2011). The role of the medial prefrontal cortex in innate fear regulation in infants, juveniles, and adolescents. J. Neurosci. 31, 4991–4999. doi: 10.1523/JNEUROSCI.5216-10.2011

Charney, D. S. (2004). Psychobiological mechanisms of resilience and vulnerability: implications for successful adaptation to extreme stress. Am. J. Psychiatry 161, 195–216. doi: 10.1176/appi.ajp.161.2.195

Cho, J. H., Prince, J. E. A., Cutforth, T., and Cloutier, J.-F. (2011). The pattern of glomerular map formation defines responsiveness to aversive odorants in mice. J. Neurosci. 31, 7920–7926. doi: 10.1523/JNEUROSCI.2460-10.2011

Clinchy, M., Schulkin, J., Zanette, L. Y., Sheriff, M. J., McGowan, P. O., and Boonstra, R. (2011) The neurological ecology of fear: insights neuroscientists and ecologists have to offer one another. Front. Behav. Neurosci. 4:21. doi: 10.3389/fnbeh.2011.00021

Cohen, H., Zohar, J., Gidron, Y., Matar, M. A., Belkind, D., Loewenthal, U., et al. (2006). Blunted HPA axis response to stress influences susceptibility to posttraumatic stress response in rats. Biol. Psychiatry 59, 1208–1218. doi: 10.1016/j.biopsych.2005.12.003

Collins, D. R. (2011). Synthetic versus natural cat odorant effects on rodent behavior and medial amygdala plasticity. Behav. Neurosci. 125, 124–129. doi: 10.1037/a0021955

Corley, M. J., Caruso, M. J., and Takahashi, L. K. (2012). Stress-induced enhancement of fear conditioning and sensitization facilitates extinction-resistant and habituation-resistant fear behaviors in a novel animal model of posttraumatic stress disorder. Physiol. Behav. 105, 408–416. doi: 10.1016/j.physbeh.2011.08.037

Davis, M. (2000). “The role of the amygdala in conditioned and unconditioned fear and anxiety,” in The Amygdala: a Functional Analysis, 2nd Edn., ed J. P. Aggleton (New York: Oxford University Press), 213–287.

Day, H. E. W., Masini, C. V., and Campeau, S. (2004). The pattern of brain c-fos mRNA induced by a component of fox odor, 2,5-dihydro-2,4,5-trimethylthiazoline (TMT), in rats, suggests both systemic and processive stress characteristics. Brain Res. 1025, 139–151. doi: 10.1016/j.brainres.2004.07.079

Dayas, C. V., Buller, K. M., and Day, T. A. (1999). Neuroendocrine responses to an emotional stressor: evidence for involvement of the medial but not the central amygdala. Eur. J. Neurosci. 11, 2312–2322. doi: 10.1046/j.1460-9568.1999.00645.x

Depaulis, A., Keay, K. A., and Bandler, R. (1992). Longitudinal neuronal organization of defensive reactions in the midbrain periaqueductal gray region of the rat. Exp. Brain Res. 90, 307–318. doi: 10.1007/BF00227243

Dewan, A., Pacifico, R., Zhan, R., Rinberg, D., and Bozza, T. (2013). Non-redundant coding of aversive odours in the main olfactory pathway. Nature 497, 486–489. doi: 10.1038/nature12114

Dicke, M., and Grostal, P. (2001). Chemical detection of natural enemies by arthroponds: An ecological perspective. Annu. Rev. Ecol. Syst. 32, 1–23. doi: 10.1146/annurev.ecolsys.32.081501.113951

Dielenberg, R. A., Leman, S., and Carrive, P. (2004). Effect of dorsal periaqueductal gray lesions on cardiovascular and behavioral responses to cat odor exposure in rats. Behav. Brain Res. 153, 487–496. doi: 10.1016/j.bbr.2004.01.015

Dielenberg, R. A., and McGregor, I. S. (2001). Defensive behavior in rats towards predatory odors: a review. Neurosci. Biobehav. Rev. 25, 597–609. doi: 10.1016/S0149-7634(01)00044-6

Do Monte, F. H. M., Canteras, N. S., Fernandes, D., Assreuy, J., and Carobrez, A. P. (2008). New perspectives on β-adrenergic mediation of innate and learned fear responses to predator odor. J. Neurosci. 28, 13296–13302. doi: 10.1523/JNEUROSCI.2843-08.2008

Dulac, C., and Torello, A. T. (2003). Molecular detection of pheromone signals in mammals: from genes to behaviour. Nat. Rev. Neurosci. 4, 551–562. doi: 10.1038/nrn1140

Endres, T., Apfelbach, R., and Fendt, M. (2005). Behavioral changes induced in rats by exposure to trimethylthiazoline, a component of fox odor. Behav. Neurosci. 119, 1004–1010. doi: 10.1037/0735-7044.119.4.1004

Endres, T., and Fendt, M. (2007). Conditioned behavioral responses to a context paired with the predator odor trimethylthiazoline. Behav. Neurosci. 121, 594–601. doi: 10.1037/0735-7044.121.3.594

Fan, S., and Luo, M. (2009). The organization of feedback projections in a pathway important for processing pheromonal signals. Neuroscience 161, 489–500. doi: 10.1016/j.neuroscience.2009.03.065

Fanselow, M. S., and Poulos, A. M. (2005). The neuroscience of mammalian associative learning. Annu. Rev. Psychol. 56, 207–234. doi: 10.1146/annurev.psych.56.091103.070213

Fendt, M., Endres, T., and Apfelbach, R (2003). Temporary inactivation of the bed nucleus of the stria terminalis but not of the amygdala blocks freezing induced by trimethylthiazoline, a component of fox feces. J. Neurosci. 23, 23–27.

Fendt, M., Endres, T., Lowry, C. A., Apfelbach, R., and McGregor, I. S. (2005a). TMT-induced autonomic and behavioral changes and the neural basis of its processing. Neurosci. Biobehav. Rev. 29, 1145–1156. doi: 10.1016/j.neubiorev.2005.04.018

Fendt, M., Siegl, S., and Steiniger-Brach, B. (2005b). Noradrenaline transmission within the ventral bed nucleus of the stria terminalis is critical for fear behavior induced by trimethylthiazoline, a component of fox odor. J. Neurosci. 25, 5998–6004. doi: 10.1523/JNEUROSCI.1028-05.2005

Ferrero, D. M., Lemona, J. K., Flueggeb, D., Pashkovskic, S. L., Korzana, W. J., Dattac, S. R., et al. (2011). Detection and avoidance of a carnivore odor by prey. Proc. Natl. Acad. Sci. U.S.A. 108, 11235–11240. doi: 10.1073/pnas.1103317108

File, S. E., Zangrossi, H. Jr., Sanders, F. L., and Mabbutt, P. S. (1993). Dissociation between behavioral and corticosterone responses on repeated exposures to cat odor. Physiol. Behav. 54, 1109–1111. doi: 10.1016/0031-9384(93)90333-B

Firestein, S. (2001). How the olfactory system makes sense of scents. Nature 413, 211–218. doi: 10.1038/35093026

Fitzpatrick, C. J., Knox, D., and Liberzon, I. (2011). Inactivation of the prelimbic cortex enhances freezing induced by trimethylthiazoline, a component of fox feces. Behav. Brain Res. 221, 320–323. doi: 10.1016/j.bbr.2011.03.024

Fleischer, J., and Breer, H. (2010). The Grueneberg ganglion: a novel sensory system in the nose. Histol. Histopathol. 25, 909–915.

Fortes-Marco, L., Lanuza, E., and Martinez-Garcia, F. (2013). Of pheromones and kairomones: what receptors mediate innate emotional responses? Anat. Rec. 296, 1346–1363. doi: 10.1002/ar.22745

Funk, D., and Amir, S. (2000). Circadian modulation of Fos responses to odor of the red fox, a rodent predator, in the rat olfactory system. Brain Res. 866, 262–267. doi: 10.1016/S0006-8993(00)02249-6

Fuss, S. H., Omura, M., and Mombaerts, P. (2005). The Grueneberg ganglion of the mouse projects axons to glomeruli in the olfactory bulb. Eur. J. Neurosci. 22, 2649–2664. doi: 10.1111/j.1460-9568.2005.04468.x

Gabbott, P. L. A., Warner, T. A., Jays, P. R. L., Salway, P., and Busby, S. J. (2005). Prefrontal cortex in the rat: projections to subcortical autonomic, motor, and limbic centers. J. Comp. Neurol. 492, 145–177. doi: 10.1002/cne.20738

Galliot, E., Laurent, L., Hacquemand, R., Pourié, G., and Millot, J.-L. (2012). Fear-like behavioral responses in mice in different odorant environments: Trigeminal versus olfactory mediation under low doses. Behav. Proc. 90, 161–166. doi: 10.1016/j.beproc.2012.01.002

Giannetti, N., Saucier, D., and Astic, L. (1995). Analysis of the possible altering function of the septal organ in rats: a lesional and behavioral study. Physiol. Behav. 58, 837–845. doi: 10.1016/0031-9384(95)00127-5

Green, M. J., and Phillips, M. L. (2004). Social threat perception and the evolution of paranoia. Neurosci. Biobehav. Rev. 28, 333–342. doi: 10.1016/j.neubiorev.2004.03.006

Gross, C. T., and Canteras, N. S. (2012). The many paths to fear. Nat. Rev. Neurosci. 13, 651–658. doi: 10.1038/nrn3301

Hacquemand, R., Choffat, N., Jacquot, L., and Brand, G. (2013). Comparison between low doses of TMT and cat odor exposure in anxiety- and fear-related behaviors in mice. Behav. Brain Res. 238, 227–231. doi: 10.1016/j.bbr.2012.10.014

Hacquemand, R., Jacquot, L., and Brand, G. (2010). Comparative fear-related behaviors to predator odors (TMT and natural fox feces) before and after intranasal ZnSO4 treatment in mice. Front. Behav. Neurosci. 4: 188. doi: 10.3389/fnbeh.2010.00188

Heale, V. R., Vanderwolf, C. H., and Kavaliers, M. (1994). Components of weasel and fox odors elicit fast wave bursts in the dentate gyrus of rats. Behav. Brain Res. 63, 159–165. doi: 10.1016/0166-4328(94)90087-6

Herman, J. P., Cullinan, W. E., Ziegler, D. R., and Tasker, J. G. (2002). Role of the paraventricular nucleus microenvironment in stress integration. Eur. J. Neurosci. 16, 381–385. doi: 10.1046/j.1460-9568.2002.02133.x

Horii, Y., Nagai, K., and Nakashima, T. (2013). Order of exposure to pleasant and unpleasant odors affects autonomic nervous system response. Behav. Brain Res. 243, 109–117. doi: 10.1016/j.bbr.2012.12.042

Horii, Y., Nikaido, Y., Nagai, K., and Nakashima, T. (2010). Exposure to TMT odor affects adrenal sympathetic nerve activity and behavioral consequences in rats. Behav. Brain Res. 214, 317–322. doi: 10.1016/j.bbr.2010.05.047

Illig, K. R., and Haberly, L. B. (2003). Odor-evoked activity is spatially distributed in piriform cortex. J. Comp. Neurol. 457, 361–373. doi: 10.1002/cne.10557

Isogai, Y., Si, S., Pont-Lezica, L., Tan, T., Kapoor, V., Murthy, V. N., et al. (2011). Molecular organization of vomeronasal chemoreception. Nature 478, 241–245. doi: 10.1038/nature10437

Jöels, M., Pu, Z., Wiegert, O., Oitzl, M. S., and Krugers, H. J. (2006). Learning under stress: how does it work? Trends Cogn. Sci. 10, 152–158. doi: 10.1016/j.tics.2006.02.002

Karlson, P., and Luscher, M. (1959). “Pheromones”: a new term for a class of biologically active substances. Nature 183, 55–56. doi: 10.1038/183055a0

Kass, M. D., Rosenthal, M. C., Pottackal, J., and McGann, J. P. (2013). Fear learning enhances neural responses to threat-predictive sensory stimuli. Science 342, 1389–1392. doi: 10.1126/science.1244916

Kats, L. B., and Dill, L. M. (1998) The scent of death: chemosensory assessment of predation risk by prey animals. Ecoscience 5, 361–364.

Kessler, M. S., Debilly, S., Schöppenthau, S., Bielser, T., Bruns, A., Künnecke, B., et al. (2012). fMRI fingerprint of unconditioned fear-like behavior in rats exposed to trimethylthiazoline. Eur. Neuropsychopharmcol. 22, 222–230. doi: 10.1016/j.euroneuro.2011.07.011

Kim, J. J., and Fanselow, M. S. (1992). Modality-specific retrograde amnesia of fear. Science 256, 675–677. doi: 10.1126/science.1585183

Kobayakawa, K., Kobayakawa, R., Matsumoto, H., Oka, Y., Imai, T., Ikawa, M., et al. (2007). Innate versus learned odour processing in the mouse olfactory bulb. Nature 450, 503–510. doi: 10.1038/nature06281

Koos, D. S., and Fraser, S. E. (2005). The Grueneberg ganglion projects to the olfactory bulb. Neuroreport 16, 1929–1932. doi: 10.1097/01.wnr.0000186597.72081.10

Landers, M. S., and Sullivan, R. M. (2012). The development and neurobiology of infant attachment and fear. Dev. Neurosci. 34, 101–114. doi: 10.1159/000336732

Lang, P. J, Davis, M., and Öhman, A. (2000). Fear and anxiety: animal models and human cognitive psychophysiology. J. Affect. Disord. 61, 137–159. doi: 10.1016/S0165-0327(00)00343-8

LeDoux, J. E. (2000). Emotion circuits in the brain. Annu. Rev. Neurosci. 23, 155–184. doi: 10.1146/annurev.neuro.23.1.155

Leong, K.-C., and Packard, M. G. (2014). Exposure to predator odor influences the relative use of multiple memory systems: role of basolateral amygdala. Neurobiol. Learn. Mem. 109, 56–61. doi: 10.1016/j.nlm.2013.11.015

Lèvai, O., and Strotmann, J. (2003). Projection pattern of nerve fibers from the septal organ: DiI-tracing studies with transgenic OMP mice. Histochem. Cell. Biol. 120, 483–492. doi: 10.1007/s00418-003-0594-4

Li, C. I., Maglinao, T. L., and Takahashi, L. K. (2004). Medial amygdala modulation of predator odor-induced unconditioned fear in the rat. Behav. Neurosci. 118, 324–332. doi: 10.1037/0735-7044.118.2.324

Liberles, S. D. (2014). Mammalian pheromones. Annu. Rev. Physiol. 76, 151–175. doi: 10.1146/annurev-physiol-021113-170334

Liberles, S. D., and Buck, L. B. (2006). A second class of chemosensory receptors in the olfactory epithelium. Nature 442, 645–650. doi: 10.1038/nature05066

Lima, S. L. (1998) Stress and decision making under the risk of predation: recent developments from behavioral, reproductive, and ecological perspectives. Adv. Study Behav. 27, 215–290. doi: 10.1016/S0065-3454(08)60366-6

Ma, M. (2007). Encoding olfactory signals via multiple chemosensory systems. Crit. Rev. Biochem. Mol. Biol. 42, 463–480. doi: 10.1080/10409230701693359

Mackenzie, L., Nalivaiko, E., Beig, M. I., Day, T. A., and Walker, F. R. (2010). Ability of predator odour exposure to elicit conditioned versus sensitized post traumatic stress disorder-like behaviours, and forebrain FosB expression, in rats. Neuroscience 169, 733–742. doi: 10.1016/j.neuroscience.2010.05.005

Mamasuew, K., Hofmann, N., Breer, H., and Fleischer, J. (2011). Grueneberg ganglion neurons are activated by a defined set of odorants. Chem. Senses 36, 271–282. doi: 10.1093/chemse/bjq124

Marek, R., Strobel, C., Bredy, T. W., and Sah, P. (2013). The amygdala and medial prefrontal cortex: partners in the fear circuit. J. Physiol. 591, 2381–2391. doi: 10.1113/jphysiol.2012.248575

Maren, S., Phan, K. L., and Liberzon, I. (2013). The contextual brain: implications for fear conditioning, extinction and psychopathology. Nat. Rev. Neurosci. 14, 417–428. doi: 10.1038/nrn3492

Martinez-Marcos, A. (2009). On the organization of olfactory and vomeronasal cortices. Prog. Neurobiol. 87, 21–30. doi: 10.1016/j.pneurobio.2008.09.010

Masini, C. V., Garcia, R. J., Sasse, S. K., Nyhuis, T. J., Day, H. E. W., and Campeau, S. (2010). Accessory and main olfactory systems influences on predator odor-induced behavioral and endocrine stress responses in rats. Behav. Brain Res. 207, 70–77. doi: 10.1016/j.bbr.2009.09.038

Masini, C. V., Sasse, S. K., Garcia, R. J., Nyhuis, T. J., Day, H. E. W., and Campeau, S. (2009). Disruption of neuroendocrine stress responses to acute ferret odor by medial, but not central amygdala lesions in rats. Brain Res. 1288, 79–87. doi: 10.1016/j.brainres.2009.07.011

Masini, C. V., Sauer, S., and Campeau, S. (2005). Ferret odor as a processive stress model in rats: neurochemical, behavioral, and endocrine evidence. Behav. Neurosci. 119, 280–292. doi: 10.1037/0735-7044.119.1.280

Masini, C. V., Sauer, S., White, J., Day, H. E. W., and Campeau, S. (2006). Non-associative defensive responses of rats to ferret odor. Physiol. Behav. 87, 72–81. doi: 10.1016/j.physbeh.2005.08.044

Matar, M. A., Zohar, J., and Cohen, H. (2013). Translationally relevant modeling of PTSD in rodents. Cell. Tissue Res. 354, 127–139. doi: 10.1007/s00441-013-1687-6

McBride, K., Slotnick, B., and Margolis, F. L. (2003). Does intranasal application of zinc sulfate produce anosmia in mouse? An olfactometric and anatomical study. Chem. Senses 28, 659–670. doi: 10.1093/chemse/bjg053

McDonald, A. J. (1998). Cortical pathways to the mammalian amygdale. Prog. Neurobiol. 55, 257–332. doi: 10.1016/S0301-0082(98)00003-3

McGaugh, J. L. (2000). Memory – a century of consolidation. Science 287, 249–251. doi: 10.1126/science.287.5451.248

McGregor, I. S., Hargreaves, G. A., Apfelbach, R., and Hunt, G. E. (2004). Neural correlates of cat odor-induced anxiety in rats: region-specific effects of the benzodiazepine midazolam. J. Neurosci. 24, 4134–4144. doi: 10.1523/JNEUROSCI.0187-04.2004

McGregor, I. S., Schrama, L., Ambermoon, P., and Dielenberg, R. A. (2002). Not all ‘predator odours’ are equal: cat odour but not 2,4,5 trimethylthiazoline (TMT; fox odour) elicits specific defensive behaviours in rats. Behav. Brain Res. 129, 1–16. doi: 10.1016/S0166-4328(01)00324-2

Meredith, M. (1991). Sensory processing in the main and accessory olfactory systems: comparisons and contrasts. J. Steroid Biochem. Mol. Biol. 39, 601–614. doi: 10.1016/0960-0760(91)90258-7

Meredith, M., and Westberry, J. M. (2004). Distinctive responses in the medial amygdala to same-species and different-species pheromones. J. Neurosci. 24, 5719–5725. doi: 10.1523/JNEUROSCI.1139-04.2004

Moncho-Bogani, J., Martinez-Garcia, F., Novejarque, A., and Lanuza, E. (2005). Attraction to sexual pheromones and associated odorants in female mice involves activation of the reward system and basolateral amygdala. Eur. J. Neurosci. 21, 2186–2198. doi: 10.1111/j.1460-9568.2005.04036.x

Morrow, B. A., Redmond, A. J., Roth, R. H., and Elsworth, J. D. (2000). The predator odor, TMT, displays a unique, stress-like pattern of dopaminergic and endocrinological activation in the rat. Brain Res. 864, 146–151. doi: 10.1016/S0006-8993(00)02174-0

Mouly, A.-M., and Sullivan, R. (2010). “Memory and plasticity in the olfactory system: from infancy to adulthood,” in The Neurobiology of Olfaction, ed A. Menini (Boca Raton, FL: CRC Press), 367–394.

Müller, M., and Fendt, M. (2006). Temporary inactivation of the medial and basolateral amygdala differentially affects TMT-induced fear behavior in rats. Behav. Brain Res. 167, 57–62. doi: 10.1016/j.bbr.2005.08.016

Munger, S. D., Leinders-Zufall, T., and Zufall, F. (2009). Subsystem organization of the mammalian sense of smell. Annu. Rev. Physiol. 71, 115–140. doi: 10.1146/annurev.physiol.70.113006.100608

Muñoz-Abellán, C., Rabasa, C., Daviu, N., Nadal, R., and Armario, A. (2011). Behavioral and endocrine consequences of simultaneous exposure to two different stressors in rats: interaction or independence? PLoS ONE 6: e21426. doi: 10.1371/journal.pone.0021426

Nader, K., Majidishad, P., Amorapanth, P., and LeDoux, J. E. (2001). Damage to the lateral and central, but not other, amygdaloid nuclei prevents the acquisition of auditory fear conditioning. Learn. Mem. 8, 156–163. doi: 10.1101/lm.38101

Nolte, D. L., Mason, J. R., Epple, G., Aronov, E., and Campbell, D. L. (1994). Why are predator urines aversive to prey? J. Chem. Ecol. 20, 1505–1516. doi: 10.1007/BF02059876

Otto, T., Cousens, G., and Herzog, C. (2000). Behavioral and neuropsychological foundations of olfactory fear conditioning. Behav. Brain Res. 110, 119–128. doi: 10.1016/S0166-4328(99)00190-4

Pagani, J. H., and Rosen, J. B. (2009). The medial hypothalamic defensive circuit and 2,5-dihydro-2,4,5-trimethylthiazoline (TMT) induced fear: comparison of electrolytic and neurotoxic lesions. Brain Res. 1286, 133–146. doi: 10.1016/j.brainres.2009.06.062

Papes, F., Logan, D. W., and Stowers, L. (2010). The vomeronasal organ mediates interspecies defensive behaviors through detection of protein pheromone homologs. Cell 141, 692–703. doi: 10.1016/j.cell.2010.03.037

Pentkowski, N. S., Blanchard, D. C., Lever, C., Litvin, Y., and Blanchard, R. J. (2006). Effects of lesions to the dorsal and ventral hippocampus on defensive behaviors in rats. Eur. J. Neurosci. 23, 2185–2196. doi: 10.1111/j.1460-9568.2006.04754.x

Perrot-Sinal, T. S., Ossenkopp, K. P., and Kavaliers, M. (1999). Brief predator odour exposure activates the HPA axis independent of locomotor changes. Neuroreport 10, 775–780. doi: 10.1097/00001756-199903170-00021

Pessoa, L., and Adolphs, R. (2010). Emotion processing and the amygdala: from a ‘low road’ to ‘many roads’ of evaluating biological significance. Nat. Rev. Neurosci. 11, 773–782. doi: 10.1038/nrn2920

Peters, J., Kalivas, P. J., and Quirk, G. J. (2009). Extinction circuits for fear and addiction overlap in prefrontal cortex. Learn. Mem. 16, 279–288. doi: 10.1101/lm.1041309

Petrovich, G. D., Canteras, N. S., and Swanson, L. W. (2001). Combinatorial amygdalar inputs to hippocampal domains and hypothalamic behavior systems. Brain Res. Rev. 38, 247–289. doi: 10.1016/S0165-0173(01)00080-7

Phillips, R. G., and LeDoux, J. E. (1992). Differential contribution of amygdala and hippocampus to cued and contextual fear conditioning. Behav. Neurosci. 106, 274–285. doi: 10.1037/0735-7044.106.2.274

Pitkänen, A. (2000). “Connectivity of the rat amygdaloid complex,” in The Amygdala: a Functional Analysis, 2nd Edn., ed J. P. Aggleton (New York, NY: Oxford University Press), 13–116.

Price, J. L. (2005). Free will versus survival: brain systems that underlie intrinsic constraints on behavior. J. Comp. Neurol. 493, 132–139. doi: 10.1002/cne.20750

Pro-Sistiaga, P., Mohedana-Moriano, A., Ubeda-Bañon, I., Arroyo-Jimenez, M. D. M., Marcos, P., Artacho-Pérula, E., et al. (2007). Convergence of olfactory and vomeronasal projections in the rat basal telencephalon. J. Comp. Neurol. 504, 346–362. doi: 10.1002/cne.21455

Redburn, J. L., and Leah, J. D. (1997). Accelerated breakdown and enhanced expression of c-Fos in the rat brain after noxious stimulation. Neurosci. Lett. 237, 97–100. doi: 10.1016/S0304-3940(97)00820-3

Restrepo, D., Arellano, J., Oliva, A. M., Schaefer, M. L., and Lin, W. (2004). Emerging views on the distinct but related roles of the main and accessory olfactory systems in responsiveness to chemosensory signals in mice. Horm. Behav. 46, 247–256. doi: 10.1016/j.yhbeh.2004.02.009

Risold, P. Y., Thompson, R. H., and Swanson, L. W. (1997). The structural organization of connections between hypothalamus and cerebral cortex. Brain Res. Rev. 24, 197–254. doi: 10.1016/S0165-0173(97)00007-6

Roberts, S. A., Davidson, A. J., McLean, L., Beynon, R. J., and Hurst, J. L. (2012). Pheromonal induction of spatial learning in mice. Science 338, 1462–1465. doi: 10.1126/science.1225638

Rosen, J. B., and Donley, M. P. (2006). Animal studies of amygdala function in fear and uncertainty: Relevance to human research. Biol. Psychol. 73, 49–60. doi: 10.1016/j.biopsycho.2006.01.007

Rosen, J. B., Pagani, J. H., Rolla, K. L. G., and Davis, C. (2008). Analysis of behavioral constraints and the neuroanatomy of fear to the predator odor trimethylthiazoline: a model for animal phobias. Neurosci. Biobehav. Rev. 32, 1267–1276. doi: 10.1016/j.neubiorev.2008.05.006

Samuelsen, C. L., and Meredith, M. (2009). The vomeronasal organ is required for the male mouse medial amygdala response to chemical-communication signals, as assessed by immediate early gene expression. Neuroscience 164, 1468–1476. doi: 10.1016/j.neuroscience.2009.09.030

Sawchenko, P. E., Brown, E. R., Chan, R. K., Ericsson, A., Li, H. Y., Roland, B. L., et al. (1996). The paraventricular nucleus of the hypothalamus and the functional neuroanatomy of visceromotor responses to stress. Prog. Brain Res. 107, 201–222. doi: 10.1016/S0079-6123(08)61866-X

Scalia, F., and Winans, S. S. (1975). The differential projections of the olfactory bulb and accessory olfactory bulb in mammals. J. Comp. Neurol. 161, 31–55. doi: 10.1002/cne.901610105

Schenberg, L. C., de Aguiar, J. C., and Graeff, F. G. (1983). GABA modulation of the defense reaction induced by brain electrical stimulation. Physiol. Behav. 31, 429–437. doi: 10.1016/0031-9384(83)90062-8

Shepherd, G. M. (2006). Smells, brain and hormones. Nature 439, 149–151. doi: 10.1038/439149a

Shipley, M. T., and Adamek, G. D. (1984). The connections of the mouse olfactory bulb: a study using orthograde and retrograde transport of wheat germ agglutinin conjugated to horseradish peroxidase. Brain Res. Bull. 12, 669–688. doi: 10.1016/0361-9230(84)90148-5

Smith, W., Butler, A. J., Hazell, L. A., Chapman, M. D., Pomes, A., Nickels, D. G., et al. (2004). Feld 4, a cat lipocalin allergen. Clin. Exp. Allergy 34, 1732–1738. doi: 10.1111/j.1365-2222.2004.02090.x

Sotres-Bayon, F., Cain, C. K., and LeDoux, J. E. (2006). Brain mechanisms of fear extinction: historical perspectives on the contribution of prefrontal cortex. Biol. Psychiatry 60, 329–336. doi: 10.1016/j.biopsych.2005.10.012

Spehr, M., Spehr, J., Ukhanov, K., Kelliher, K. R., Leinders-Zufall, T., and Zufall, F. (2006). Parallel processing of social signals by the mammalian main and accessory olfactory systems. Cell. Mol. Life Sci. 63, 1476–1484. doi: 10.1007/s00018-006-6109-4

Staples, L. G., McGregor, I. S., Apfelbach, R., and Hunt, G. E. (2008). Cat odor, but not trimethylthiazoline (fox odor), activates accessory olfactory and defense-related brain regions in rats. Neuroscience 151, 937–947. doi: 10.1016/j.neuroscience.2007.11.039

Swanson, L. W., and Kuypers, H. G. (1980). The paraventricular nucleus of the hypothalamus: cytoarchitectonic subdivisions and organization of projections to the pituitary, dorsal vagal complex, and spinal cord as demonstrated by retrograde fluorescence double-labeling methods. J. Comp. Neurol. 194, 555–570. doi: 10.1002/cne.901940306

Takahashi, L. K., Chan, M. M., and Pilar, M. L. (2008). Predator odor fear conditioning: current perspectives and new directions. Neurosci. Biobehav. Rev. 32, 1218–1227. doi: 10.1016/j.neubiorev.2008.06.001

Takahashi, L. K., Hubbard, D. T., Lee, I., Dar, Y., and Sipes, S. M. (2007). Predator odor-induced conditioned fear involves the basolateral and medial amygdala. Behav. Neurosci. 121, 100–110. doi: 10.1037/0735-7044.121.1.100

Takahashi, L. K., Nakashima, B. R., Hong, H., and Watanabe, K. (2005). The smell of danger: a behavioral and neural analysis of predator odor-induced fear. Neurosci. Biobehav. Rev. 29, 1157–1167. doi: 10.1016/j.neubiorev.2005.04.008

Ulrich-Lai, Y. M., and Herman, J. P. (2009). Neural regulation of endocrine and autonomic stress responses. Nat. Rev. Neurosci. 10, 307–409. doi: 10.1038/nrn2647

Vazdarjanova, A., Cahill, L., and McGaugh, J. L. (2001). Disrupting basolateral amygdala function impairs unconditioned freezing and avoidance in rats. Eur. J. Neurosci. 14, 709–718. doi: 10.1046/j.0953-816x.2001.01696.x

Wallace, K. J., and Rosen, J. B. (2000). Predator odor as an unconditioned fear stimulus in rats: elicitation of freezing by trimethylthiazoline, a component of fox feces. Behav. Neurosci. 114, 912–922. doi: 10.1037/0735-7044.114.5.912

Wallace, K. J., and Rosen, J. B. (2001). Neurotoxic lesions of the lateral nucleus of the amygdala decrease conditioned fear, but not unconditioned fear of a predator odor: comparison to electrolytic lesions. J. Neurosci. 21, 3619–3627.

Wang, M. E., Fraize, N. P., Yin, L., Yuan, R. K., Petsagourakis, D., Wann, E. G., et al. (2013). Differential roles of the dorsal and ventral hippocampus in predator odor contextual fear conditioning. Hippocampus 23, 415–466. doi: 10.1002/hipo.22105

Wang, M. E., Wann, E. G., Yuan, R. K., Ramos Álvarez, M. M., Stead, S. M., and Muzzio, I. A. (2012). Long-term stabilization of place cell remapping produced by a fearful experience. J. Neurosci. 32, 15802–15814. doi: 10.1523/JNEUROSCI.0480-12.2012

Xu, F., Schaefer, M., Kida, I., Schafer, J., Liu, N., Rothman, D. L., et al. (2005). Simultaneous activation of mouse main and accessory olfactory bulbs by odors or pheromones. J. Comp. Neurol. 489, 491–500. doi: 10.1002/cne.20652

Xu, H.-Y., Liu, Y.-J., Xu, M.-Y., Zhang, Y.-H., Zhang, J.-X., and Wu, Y.-J. (2012). Inactivation of the bed nucleus of the stria terminalis suppresses the innate fear responses of rats induced by the odor of cat urine. Neuroscience 221, 21–27. doi: 10.1016/j.neuroscience.2012.06.056

Zhang, W.-N., Bast, T., and Feldon, J. (2001). The ventral hippocampus and fear conditioning in rats: different anterograde amnesias of fear after infusion of N-methyl-D-aspartate or its noncompetitive antagonist MK-801 into the ventral hippocampus. Behav. Brain Res. 126, 159–174. doi: 10.1016/S0166-4328(01)00256-X

Conflict of Interest Statement: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 29 January 2014; Accepted: 20 February 2014; Published online: 11 March 2014.

Citation: Takahashi LK (2014) Olfactory systems and neural circuits that modulate predator odor fear. Front. Behav. Neurosci. 8:72. doi: 10.3389/fnbeh.2014.00072

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Takahashi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 08 April 2014
doi: 10.3389/fnbeh.2014.00116

Chronic exposure to a predator or its scent does not inhibit male–male competition in male mice lacking brain serotonin

Ying Huo1,2, Qi Fang1,2, Yao-Long Shi1,2, Yao-Hua Zhang[1][*] and Jian-Xu Zhang[1][*]

1State Key Laboratory of Integrated Management of Pest Insects and Rodents in Agriculture, Institute of Zoology, Chinese Academy of Sciences, Beijing, China

2Department of College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China

* Correspondence: Yao-Hua Zhang and Jian-Xu Zhang, State Key Laboratory of Integrated Management of Pest Insects and Rodents in Agriculture, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China e-mail: zhangyh@ioz.ac.cn; zhangjx@ioz.ac.cn

Edited by:
Donald A. Wilson, New York University School of Medicine, USA

Reviewed by:
Daniel W. Wesson, Case Western Reserve University, USA
Matthieu Keller, Centre National de la Recherche Scientifique, France

Although it is well-known that defective signaling of the 5-HT system in the brain and stressful stimuli can cause psychological disorders, their combined effects on male–male aggression and sexual attractiveness remain unknown. Our research aimed at examining such effects using tryptophan hydroxylase 2 (Tph2) knockout male mice vs. a rat- or rat scent-based chronic stress model. Tph2+/+ and Tph2−/− male mice were placed individually into the rat home cage (rat), a cage containing soiled rat bedding (rat scent) or a cage containing fresh bedding (control) for 5 h every other day for 56 consecutive days. In Tph2+/+ male mice, rat-exposure decreased male–male aggression and sexual attractiveness of urine odor relative to either rat scent-exposure or control; and rat scent-exposure decreased aggression rather than sexual attractiveness of urine odor compared with control. However, such dose-dependent and long-lasting behavioral inhibitory effects vanished in Tph2−/− male mice. RT-PCR assay further revealed that putative regulatory genes, such as AR, ERα and GluR4 in the prefrontal cortex, and TrkB-Tc and 5-HTR1A in the hippocampus, were down-regulated at the mRNA level in either rat- or rat scent-exposed Tph2+/+ male mice, but partially in the Tph2−/− ones. Hence, we suggest that the dose-dependent and long-lasting inhibitory effects of chronic predator exposure on male–male aggression, sexual attractiveness of urine odor, and mRNA expression of central regulatory genes might be mediated through the 5-HT system in the brain of male mice.

Keywords: predator, aggression, sexual attractiveness, central regulatory genes, serotonin, disinhibition

INTRODUCTION

Rodents rely heavily on chemical senses to detect potential predators and minimize predation risk in natural habitats (Herman and Valone, 2000; Dielenberg and McGregor, 2001; Zhang et al., 2008). The presence of predators or their chemical cues often have non-lethal, but negative, effects on the behavioral, and neurophysiological states of rodents (Dielenberg and McGregor, 2001; Zhang et al., 2003; Adamec et al., 2004; Apfelbach et al., 2005). For example, chronic exposure of rodents to a predator or its chemical cues can enhance anxiety-like behaviors and decrease aggression levels (Francis, 1988; Blanchard et al., 2001; Zhang et al., 2003). Rodents vs. predator-based paradigms have been used extensively to study innate fear- and stress-related psychiatric diseases, such as post-traumatic stress disorder (PTSD) (Hendrie et al., 1996; Adamec et al., 2006a).

Predator and predator scent are evidenced to be graded stress to rodents (Adamec et al., 2008). Unlike the strong stress resulting from close contact with the predator, mild predator scent presumably contains only the pheromones comprised of both volatile and non-volatile molecules from urine and sebaceous gland secretion, and odors emitted from the non-present predator (Kelliher et al., 1999). Previous studies have found that the effect of predator scent-exposure on rodent anxiety and risk assessment fall between controls and those exposed to a predator (Adamec et al., 2004, 2006b). Characterized by mouse-killing behavior (muricide), the rat is often regarded as a mouse predator (Molina et al., 1987; Beekman et al., 2005). Hence, rat- and rat scent-exposure are graded predator stress to mice.

Generally, stress would increase hypothalamic–pituitary–adrenal (HPA) functioning and provoke hyperactivity of the neuropeptide-secreting systems, which eventually lead to the release of stress hormones (Creel, 2001; Beekman et al., 2005). The elevated stress hormones alter metabolic pathways, which exert profound and diffusive effects, such as on reproduction competition ability, including aggressive and defensive levels, pheromone production, sexual attractiveness of urine odor, and related modulations of the neural systems (Creel, 2001; Sands and Creel, 2004; de Kloet et al., 2005; Zhang et al., 2008). To rodents, the released stress hormones induced by predator stimuli would lead to a state that presents reduced aggression levels, as well as less attractiveness to female conspecifics, which might weaken male–male competition, resulting in a reduction of reproductive success (Francis, 1988; Creel, 2001; Zhang et al., 2003).

As one of the most important neurotransmitters, serotonin (5-HT) exerts an extremely wide-ranging influence on rodents, including reproductive activity, aggression, sensory processing, stress adaption, behavioral disinhibition, cognition, memory, and emotion (Canli and Lesch, 2007; Tops et al., 2009; Liu et al., 2011; Kane et al., 2012; Sachs et al., 2013). Tryptophan hydroxylase 2 (Tph2) is required for the synthesis of central 5-HT. Tph2 knockout mice were recently generated and have been used as a model animal to study 5-HT functions in the brain. Tph2−/− mice are characterized by only minute amounts of brain 5-HT, but normal serotonergic neurons (Liu et al., 2011; Gutknecht et al., 2012). Tph2−/− male mice display social impairments, communication deficits, and behavioral disinhibition indicated by impulsivity (Liu et al., 2011; Angoa-Pérez et al., 2012; Kane et al., 2012). They also show increased aggression in the resident–intruder paradigm and decreased anxiety-like behaviors in the elevated plus-maze (Mosienko et al., 2012). In spite of the 50% decrease in Tph2 transcriptional activity, Tph2+/− mice only have a 10% reduction of brain 5-HT, which is insufficient to differentiate them from the Tph2+/+ ones on aggression and anxiety-like behaviors (Gutknecht et al., 2012; Mosienko et al., 2012). Currently, little information is available about the modulation effect of 5-HT on mouse urine pheromone; however, our lab has found that Tph2 knockout would increase the absolute levels of 2-heptanone and E-5-hepten-2-one; whereas, it decreases (s)-2-s-butyl-4,5-dihydrothiazole in male urine, indicative of the possible role of central 5-HT in the regulation of pheromone composition (unpublished data).

Previous studies have suggested that the effect of predator threat on rodents may be influenced by the central 5-HT system (Adamec et al., 2006a). In this context, the impulsive Tph2−/− male mice characterized by behavioral disinhibition can be expected to show less stress responses through modulations in the neural circuitry, which may play a causative role in the alterations of aggression levels and urine metabolites (Angoa-Pérez et al., 2012; Mosienko et al., 2012).

Many brain regions are known to play a role in the central processing of stressful stimuli from predators, such as the hippocampus, prefrontal cortex, lateral septum (LAS), and central amygdala (Hayley et al., 2001; Beekman et al., 2005; Joels et al., 2007). Stress hormones feed back to the brain and bind to two types of nuclear receptors serving as transcriptional regulators of brain region-specific susceptible effectors, such as 5-HT receptors, brain-derived neurotrophic factor (BDNF), BDNF receptor TrkB, AMPA (α-amino-3-hydroxy-5-methyl-4 isoxazole propionic acid) glutamate receptor (GluR) subunit, and the receptors of sex steroids that underlie stress-induced responses and behavioral adaptation, all of which function through the 5-HT system in the brain (Nibuya et al., 1999; Shutoh et al., 2000; Nelson and Chiavegatto, 2001; de Kloet et al., 2005; Lund et al., 2006; Nelson and Trainor, 2007). Sex steroid receptors in many brain regions have been suggested to play the modulation role of regulating neuroendocrine stress response and stress-related behaviors such as aggression via effects downstream on sites 5-HTR1A and 5-HTR1B (Simon et al., 1998; Handa and Weiser, 2013). Generally, androgen receptor (AR) and estrogen receptor α (ERα) are positively correlated with aggression levels (Nelson and Chiavegatto, 2001; Li et al., 2004; Scordalakes and Rissman, 2004). 5-HT also can change the expression of AMPA receptor subunits to alter the glutamatergic system activities and, thus, other neuronal functions (Shutoh et al., 2000). For example, GluR4 in the medial prefrontal cortex are in bidirectional control of social dominance hierarchy (Wang et al., 2011). Through 5-HT receptors, particularly 1A and 2A, stress decreases BDNF and increases full-length TrkB (TrkB-FL) expression, respectively, in the hippocampus to modulate learning, adaptive processes, inhibition of aggression, and anxiety- and depression-like behaviors in rodents (Nibuya et al., 1999; Vaidya et al., 2001; Pizarro et al., 2004; Kozlovsky et al., 2007; Martinowich and Lu, 2007; Ito et al., 2011). Unlike TrkB-FL acting as a reverse retrieve for BDNF, TrkB-Tc is speculated to be a negative regulator of TrkB-FL (Eide et al., 1996; Nibuya et al., 1999). Furthermore, 5-HTR1A and 5-HTR1B in the brain, especially in the hippocampus, are closely related to anxiety, aggression, and behavioral disinhibition (de Boer and Koolhaas, 2005; Ambar and Chiavegatto, 2009; Wang et al., 2009).

In rodents, even though there is little known about the central regulation of the urine metabolites, stress would inhibit aggression primarily via inhibitory inputs from the frontal cortex and hippocampus; whereas, the brain areas of medial amygdala (MEA), LAS, bed nucleus of the stria terminalis (BNST), and anterior hypothalamic area (AHA) have been evidenced to prompt the periaquaductal gray (PAG) into promoting aggression (Nelson and Trainor, 2007). In spite of many molecules, such as neurotransmitters, hormones, cytokines, enzymes, growth factors, and signaling molecular affect aggression, 5-HT remains the primary molecular determinant that others may act through the 5-HT signaling system (Nelson and Chiavegatto, 2001).

5-HIAA, the 5-HT metabolite, was found to be elevated only in the hippocampus and prefrontal cortex of both Balb/c and C57BL/6J mice when they were killed 20 min after rat exposure, which might suggest that the hippocampus and prefrontal cortex appear to be regulation sites of the central serotonin system on the effects of predator threat, especially on aggressive behaviors (Hayley et al., 2001). Accordingly, in the current study, prefrontal cortex-specific AR, ERα, and GluR4, and hippocampus-specific AR, ERα, BDNF, TrkB-Tc, 5-HTR1A, and 5-HTR1B that might participate in the stress-induced central responses which function through the 5-HT signaling system in the brain would be selected as the candidate neural substrates.

Previous studies have largely focused on the negative impacts of chronic predator exposure and the modulation roles of the 5-HT system (Apfelbach et al., 2005; Zhou et al., 2008). However, whether the impacts of chronic predator exposure on male–male competition are influenced in the absence of brain 5-HT and possible neural substrates remain unknown. In this study, we used Tph2+/+ or Tph2−/− male mice vs. a rat- or rat scent-based paradigms to obtain insight into the differentiation between the two genotypes on the aggressive and defensive levels in staged male–male encounters and sexual attractiveness of urine odor after chronic predator exposure. Then, the mRNA expression of candidate genes in the prefrontal cortex and hippocampus were investigated to explore the possible neural substrates.

MATERIALS AND METHODS

EXPERIMENTAL ANIMALS

Twenty-four male Sprague–Dawley (SD) rats at 8 weeks of age were purchased from the Vital River Laboratories, Beijing, China, and acclimated for 4 weeks prior to use. The rats were housed individually in plastic cages (37 × 26 × 17 cm).

The Tph2 line was born in our laboratory, while the parental lines were a kind gift from Dr. Yi Rao's laboratory (Peking University, Beijing, China). Mice were weaned at 21 days of age. Then, the male mice were housed in groups with their brothers up to 8 weeks old. After this, male mice were kept singly in plastic cages (27 × 12 × 17 cm). Female mice were always housed in groups of 4 per cage (27 × 12 × 17 cm).

The housing room was under a reversed 14L: 10D light/dark photoperiod (lights on at 7:00 pm), and the temperature was maintained at 23 ± 2°C. Food (standard mouse chow) and water were provided ad libitum. The animal maintenance and handling complied with the Institutional Guidelines for Animal Use and Care at the Institute of Zoology, Chinese Academy of Sciences. Ethics approval was obtained from the Institutional Ethics Committee of the Institute of Zoology, Chinese Academy of Sciences (approval number IOZ12017).

The Tph2 line was maintained using crossing heterozygotes that the littermates contained wild-type, heterozygotes, and homozygous knockout mice. Genomic DNA was isolated from mouse tails using the phenol/chloroform extracting method at the age of 3 weeks for mouse genotyping. The primers for genotyping were: GCAGCCAGTAGACGTCTCTTAC; GGGCATCTCAGGACGTAGTAG; and GGGCCTGCCGATAGTAACAC. The thermal cycling conditions were as follows: 94°C for 5 min followed by 35 cycles of 94°C for 1 min, 62°C for 30 s, and 72°C for 1 min 30 s; then 72°C for 10 min (Liu et al., 2011).

PROCEDURES OF RAT- AND RAT SCENT- EXPOSURE

Twenty-four Tph2+/+ or Tph2−/− male mice aged 12 weeks were randomly assigned into 3 groups of 8 mice each: rat group (each mouse was put into the rat home cage (37 × 26 × 17 cm) where the rat was individually housed and the bedding (250 g) was changed every week, and no barriers existed between the mouse and rat during rat exposure); rat scent group (each mouse was put into a cage (37 × 26 × 17 cm) containing 250 g soiled rat bedding that was used for 1 week by one rat); control group (each mouse was put into a cage (37 × 26 × 17 cm) that contained 250 g fresh bedding). Each Tph2+/+ or Tph2−/− male mouse was regularly put into the corresponding standard plastic cage from 9:00 am to 2:00 pm, every other day for 56 consecutive days in a fixed donor-acceptor manner. Such treatments were alternately imposed on Tph2+/+ or Tph2−/− male mice. In these rat-exposure pairings, the responses of rats to the mice ranged from watching, sniffing, and chasing with attacks. One assaultive rat exhibiting muricide was excluded.

Additionally, another 8 raw Tph2+/+ male mice and 6 raw Tph2−/− male mice without any treatment (including exposure procedures and the following urine collection and behavioral tests presented below) were maintained in parallel for the comparison of gene expression between the two genotypes in the raw state.

URINE COLLECTION

On days 57–59, the next 3 days after completing the 56 consecutive treatment days, the urine of all of the male mice subjected to the exposure procedures were collected in the dark phase, as we previously described (Zhang et al., 2007, 2008). In brief, each donor mouse was placed in a clean mouse cage (27 × 12 × 17 cm) with a wire grid 1 cm above the bottom. Once the animal urinated, the urine was immediately absorbed and transferred to an eppendorf tube in ice, using a disposable glass capillary (i.d. 1.8 mm, 15 cm long). Urine that was deposited with or next to feces was not collected. Urine was individually sealed and kept at −20°C until use.

ENCOUNTERING TEST

On days 60–62, the next 3 days after urine collection, male–male encountering tests were performed between male mice subjected to different exposure procedures within the same genotype, as previously described (Clancy et al., 1984; Zhang et al., 2008). Weight-matched male mice from different groups within the same genotype were paired and simultaneously placed into a clean mouse cage (27 × 12 × 17 cm) for 10 min continuous recording after initial aggressive behavior or defensive behavior. Such records were conducted by hand on a data sheet with a pre-calibrated time scale in units of 10 s using a stopwatch. A behavior pattern that lasted 10 s or less was treated as one unit. Each mouse was used only once a day. Aggressive behaviors included tail rattles, sideway postures, pushing, chasing, and biting; defensive behaviors included fleeing and upright postures.

BINARY TEST OF URINARY ATTRACTIVENESS

Binary choice tests via two capillaries were conducted to explore urinary attractiveness, as we previously described (Zhang et al., 2007, 2008). In brief, 14–15 adult female Tph2+/+ mice in estrus (6–9 months of age) were used as test recipient subjects, each of which was used only once per day. The estrous status of female mice was determined by microscopic examination of vaginal epithelium. Tph2+/+ female mice were given 1 day of rest between each test. Urine samples from different groups were randomly paired and presented to female mice by two identical disposable glass capillaries (internal diameter = 1.8 mm, length = 15 cm), which contained 2 μ L urine about 1 cm from the sample-containing end, and the other end sealed by odorless gum. The sample-containing end was presented to the test mice in their home cages simultaneously and kept approximately 2 cm apart from each other. We recorded investigating behavior (sniff within 1 cm of the tips or lick the end of the capillaries) for 3 min after the initial sniffing response. The durations that the test mouse spent investigating each odor were recorded using two hand-held stopwatches.

TISSUE SAMPLING

On day 70, after 1 week of rest after the encountering tests, all of the male mice, including the ones subjected to the exposure procedure, urine collection and behavioral tests, and the raw ones without any treatment were directly and quickly (within 3 s) decapitated using a pair of sterile operating scissors. The prefrontal cortex and hippocampus were dissected in a mouse brain matrix on ice for the following real-time PCR analysis. The whole process was conducted in a separate dim room in the dark phase. The tissues were frozen in liquid nitrogen immediately and stored at −80°C until use.

REAL-TIME PCR

Total RNA of the prefrontal cortex and unilateral hippocampus were isolated using Trizol (Invitrogen), and cDNA was reverse-transcribed from total RNA (2 μ g) using PrimeScript® RT reagent Kit With gDNA Eraser (Perfect Real Time) (Takara), following the manufacturer's instructions. Real-time PCR was performed using the RealMasterMix (SYBR Green) (Tiangen). Specific primers were designed for genes, while the housekeeping gene β-actin was chosen as a control for normalizing the relative mRNA level (the primer sequences are available upon request). Twenty μL reaction agents were carried out in the Mx3005P quantitative PCR system (Stratagene, La Jolla, CA, USA) comprised of 9 μL of 2.5 × RealMasterMix/20 × SYBR solution, 1 μL of template cDNA, 0.5 μM of each primer, and 9 μL sterile water. Negative controls containing no template were also performed for each primer pair. The thermal cycling conditions were as follows: 95°C for 2 min followed by 40 cycles of 95°C for 20 s, 60°C for 20 s, and 68°C for 40 s. The melting curve analysis was performed to eliminate the presence of unspecific products by a high-resolution data collection during an incremental temperature change from 55 to 95°C with a ramp rate of 0.2°C/s. The data derived from the Mx3005P quantitative software were calculated using the 2−△CT formula (Livak and Schmittgen, 2001; Wang et al., 2006).

STATISTICAL ANALYSIS

Behavioral data were analyzed by the Wilcoxon matched-pairs signed-rank test because of the non-normal distribution characteristics (the normality was checked using the Kolmogorov–Smirnov test). The comparisons of gene expression among the three groups of Tph2+/+ male mice or the Tph2−/− ones that were subjected to exposure procedures, and the following urine collection and behavioral tests were analyzed by One-Way analysis of variance (ANOVA) with least significant difference (LSD) tests. The comparisons of gene expression between the raw Tph2+/+ male mice and the raw Tph2−/− ones were analyzed by independent-samples t-test. All statistical analyses were conducted using SPSS 16.0 software (SPSS Inc., Chicago, IL, USA) with the critical value of α = 0.05.

RESULTS

RAT- AND RAT SCENT-EXPOSURE INHIBITED MALE–MALE AGGRESSION IN TPH2+/+ MALE MICE, BUT NOT IN THE TPH2−/− ONES

In staged dyadic encounters between Tph2+/+ male mice, both the control [Figure 1A, Wilcoxon rank sum test, median 13.00 (interquartile range 6.000–18.00) vs. median 2.000 (interquartile range 0.000–12.00), Z = −2.201, N = 7, P = 0.028] and rat scent-exposed ones [Figure 1C, Wilcoxon rank sum test, median 16.00 (interquartile range 4.000–25.00) vs. median 0.000 (interquartile range 0.000–12.00), Z = −2.028, N = 7, P = 0.043] showed higher levels of aggression than the rat-exposed ones; and the control ones showed higher levels of aggression than the rat scent-exposed ones [Figure 1B, Wilcoxon rank sum test, median 12.50 (interquartile range 6.000–22.00) vs. median 6.500 (interquartile range 0.000–9.000), Z = −2.106, N = 8, P = 0.035].

FIGURE 1. Comparison of behavioral patterns between groups in the encountering test. Aggression and defence of Tph2+/+ rat vs. Tph2+/+ control (A), Tph2+/+ rat scent vs. Tph2+/+ control (B), Tph2+/+ rat vs. Tph2+/+ rat scent (C), Tph2−/− rat vs. Tph2−/− control (D), Tph2−/− rat scent vs. Tph2−/− control (E), Tph2−/− rat vs. Tph2−/− rat scent (F). Mean ± SE, nRat = 7, nRat scent = 8, nControl = 8, *p < 0.05, Wilcoxon matched-pairs signed-rank test.

Correspondingly, rat-exposed Tph2+/+ males [Figure 1A, Wilcoxon rank sum test, median 13.00 (interquartile range 0.000–17.00) vs. median 0.000 (interquartile range 0.000–6.000), Z = −1.997, N = 7, P = 0.046] and the rat scent-exposed ones [Figure 1B, Wilcoxon rank sum test, median 4.500 (interquartile range 0.000–21.00) vs. median 0.000 (interquartile range 0.000–1.000), Z = −2.023, N = 8, P = 0.043] showed more defensive behaviors than the control ones; and the rat-exposed ones showed more defensive behaviors than the rat scent-exposed ones [Figure 1C, Wilcoxon rank sum test, median 12.00 (interquartile range 0.000–19.00) vs. median 0.000 (interquartile range 0.000–3.000), Z = −2.120, N = 7, P = 0.034].

However, such treatments did not differentiate aggressive and defensive behaviors in dyadic interactions between Tph2−/− male mice (Figures 1D–F).

RAT-EXPOSURE DECREASED SEXUAL ATTRACTIVENESS OF URINE ODOR IN TPH2+/+ MALE MICE, BUT NOT IN THE TPH2−/− ONES

Two-choice tests unraveled that the urine odor from either rat scent-exposed Tph2+/+ males [Figure 2A, Wilcoxon rank sum test, median 3.390 (interquartile range 0.000–15.76) vs. median 1.280 (interquartile range 0.000–12.25), Z = −2.073, N = 15, P = 0.038] or control Tph2+/+ males [Figure 2A, Wilcoxon rank sum test, median 4.780 (interquartile range 0.400–44.87) vs. median 3.495 (interquartile range 0.000–30.79), Z = −2.103, N = 14, P = 0.035] induced higher attraction of Tph2+/+ females than that from rat-exposed Tph2+/+ males. However, Tph2+/+ females showed no olfactory preferences between rat scent-exposed and control Tph2+/+ males [Figure 2A, Wilcoxon rank sum test, median 3.140 (interquartile range 0.290–24.94) vs. median 3.585 (interquartile range 0.000–52.18), Z = −0.722, N = 14, P = 0.470].

FIGURE 2. Duration of the investigation (mean ± SE, nRat = 7, nRat scent = 8, nControl = 8) of Tph2+/+ female mice to male urine of different treatments during a 3-min choice test. (A) Comparison of investigation time of Tph2+/+ female mice to the urine from two of the rat-exposed, rat scent-exposed and control Tph2+/+ male mice. (B) Comparison of investigation time of Tph2+/+ female mice to the urine from two of the rat-exposed, rat scent-exposed and control Tph2−/− male mice. *p < 0.05, Wilcoxon matched-pairs signed-rank test.

Even so, two-choice tests revealed that the urine odor between any two groups of Tph2−/− males induced no apparent differences in attraction of Tph2+/+ females (Figure 2B).

BOTH RAT- AND RAT SCENT-EXPOSURE DOWN-REGULATED THE GENE EXPRESSION IN THE PREFRONTAL CORTEX OF TPH2+/+ MALE MICE, BUT NOT IN THE TPH2−/− ONES

In the prefrontal cortex of Tph2+/+ male mice, the expression of AR [Figure 3A, One-Way ANOVA, F(2, 9) = 6.913, P = 0.015], ERα [Figure 3A, One-Way ANOVA, F(2, 9) = 55.31, P = 0.000] and GluR4 [Figure 3A, One-Way ANOVA, F(2, 9) = 4.594, P = 0.042] apparently differed among the 3 groups. Compared with the control ones, Tph2+/+ AR (Figure 3A, LSD post-hoc t-tests, rat-exposure: P = 0.007; rat scent-exposure: P = 0.016), ERα (Figure 3A, LSD post-hoc t-tests, rat-exposure: P = 0.000; rat scent-exposure: P = 0.000) and GluR4 (Figure 3A, LSD post-hoc t-tests, rat-exposure: P = 0.016; rat scent-exposure: P = 0.073, marginal significance) expression were down-regulated.

FIGURE 3. Comparison of the relative expression of genes in the prefrontal cortex of different groups. (A) Expression patterns of AR, ERα and GluR4 in the prefrontal cortex of the rat-exposed, rat scent-exposed and control Tph2+/+ male mice (mean ± SE, nRat = 4, nRat scent = 4, nControl = 4). In order to obtain a large amount for each Tph2+/+ biological replicate, the prefrontal cortex from two male mice in the same group were ground together for RNA extraction. (B) Expression patterns of AR, ERα and GluR4 in the prefrontal cortex of the rat-exposed, rat scent-exposed and control Tph2−/− male mice (mean ± SE, nRat = 7, nRat scent = 8, nControl = 8). The data were analyzed by One-Way ANOVA, followed by LSD post-hoc t-tests (*P < 0.05; **p < 0.01).

However, unlike Tph2+/+ male mice, the Tph2−/− ones up-regulated AR expression [Figure 3B, One-Way ANOVA, F(2, 20) = 42.51, P = 0.000] in the rat- (Figure 3B, LSD post-hoc t-tests, P = 0.000) and rat scent-exposed male mice (Figure 3B, LSD post-hoc t-tests, P = 0.000) as compared with the control ones. In addition, AR was up-regulated in the rat-exposed Tph2−/− male mice more than the rat scent-exposed ones (Figure 3B, LSD post-hoc t-tests, P = 0.027). The expression of other genes, including ERα and GluR4 did not differ among the 3 groups of Tph2−/− male mice (Figure 3B).

Additionally, the expression of AR, ERα, and GluR4 in the prefrontal cortex did not differ between the raw Tph2+/+ male mice and the raw Tph2−/− ones (Figure 4).

FIGURE 4. Comparison of the relative expression of genes in the prefrontal cortex between the raw Tph2+/+ male mice and the raw Tph2−/− ones (mean ± SE, for the raw Tph2+/+ male mice, n = 8; for the raw Tph2−/− male mice, n = 6). The data were analyzed by independent-samples t-test, no significant differences were showed between groups.

BOTH RAT- AND RAT SCENT-EXPOSURE DOWN-REGULATED HIPPOCAMPAL GENE EXPRESSION MORE IN TPH2+/+ MALE MICE THAN IN THE TPH2−/− ONES

In the hippocampus of Tph2+/+ male mice, the AR [Figure 5A, One-Way ANOVA, F(2, 9) = 12.62, P = 0.002], ERα [Figure 5A, One-Way ANOVA, F(2, 9) = 46.55, P = 0.000], TrkB-Tc [Figure 5B, One-Way ANOVA, F(2, 9) = 75.98, P = 0.000] and 5-HTR1A [Figure 5C, One-Way ANOVA, F(2, 9) = 5.875, P = 0.023] expression were significantly different among the 3 groups. As compared with the control ones, Tph2+/+ hippocampal AR (Figure 5A, LSD post-hoc t-tests, rat group: P = 0.003; rat-scent group: P = 0.001), ERα (Figure 5A, LSD post-hoc t-tests, rat group: P = 0.000; rat-scent group: P = 0.000), TrkB-Tc (Figure 5B, LSD post-hoc t-tests, rat group: P = 0.000; rat-scent group: P = 0.000), and 5-HTR1A (Figure 5C, LSD post-hoc t-tests, rat group: P = 0.033; rat-scent group: P = 0.010) expression were down-regulated.

FIGURE 5. Comparison of the relative expression of genes in the hippocampus of different groups. Expression patterns of AR and ERα (A), BDNF and TrkB-Tc (B), and 5-HTR1A and 5-HTR1B (C) in the hippocampus of the rat-exposed, rat scent-exposed and control Tph2+/+ male mice. Expression patterns of AR and ERα (D), BDNF and TrkB-Tc (E), and 5-HTR1A and 5-HTR1B (F) in the hippocampus of the rat-exposed, rat scent-exposed and control Tph2−/− male mice. Mean ± SE, nRat = 4, nRat scent = 4, nControl = 4. In order to obtain a large amount for each biological replicate, the hippocampi from two male mice in the same group were ground together for RNA extraction. The data were analyzed by One-Way ANOVA, followed by LSD post-hoc t-tests (*P < 0.05; **p < 0.01).

In the hippocampus of Tph2−/− male mice, the AR [Figure 5D, One-Way ANOVA, F(2, 9) = 14.43, P = 0.002], ERα [Figure 5D, One-Way ANOVA, F(2, 9) = 147.0, P = 0.000] and BDNF [Figure 5E, One-Way ANOVA, F(2, 9) = 4.142, P = 0.053, marginal significance] expression also presented significant differentiation among the 3 groups. As compared with the control ones, Tph2−/− hippocampal ERα was both down-regulated in the mice exposed to either rat (Figure 5D, LSD post-hoc t-tests, P = 0.000) or rat scent (Figure 5D, LSD post-hoc t-tests, P = 0.000). However, Tph2−/− hippocampal AR of rat scent-exposed mice (Figure 5D, LSD post-hoc t-tests, P = 0.001) and BDNF of rat-exposed mice (Figure 5E, LSD post-hoc t-tests, P = 0.019) were only down-regulated relative to the control ones. Additionally, the strong rat-exposure stress increased Tph2−/− hippocampal AR expression than the milder rat scent-exposure stress (Figure 5D, LSD post-hoc t-tests, P = 0.004).

Tph2+/+ BDNF, Tph2+/+ 5-HTR1B, Tph2−/− TrkB-Tc, Tph2−/− 5-HTR1A, and Tph2−/− 5-HTR1B expression in the hippocampus were not changed by rat- or rat scent- exposure (Figures 5B,C,E,F).

However, compared with the raw Tph2+/+ males, hippocampal ERα (Figure 6A, independent-samples t-test, t = 4.464, df = 12, P = 0.001) and 5-HTR1A (Figure 6C, independent-samples t-test, t = 2.888, df = 12, P = 0.014) expression of the raw Tph2−/− ones decreased; whereas, the BDNF (Figure 6B, independent-samples t-test, t = −3.571, df = 12, P = 0.004) expression increased. The expression of other genes, including AR, TrkB-Tc, and 5-HTR1B in the hippocampus did not differ between the two genotypes in the raw state (Figures 6A–C).

FIGURE 6. Comparison of the relative expression of AR and ERα (A), BDNF and TrkB-Tc (B), and 5-HTR1A and 5-HTR1B (C) in the hippocampus between the raw Tph2+/+ male mice and the raw Tph2−/− ones (mean ± SE, for the raw Tph2+/+ male mice, n = 8; for the raw Tph2−/− male mice, n = 6). The data were analyzed by independent-samples t-test (*P < 0.05; **p < 0.01).

DISCUSSION

Our data showed that chronic exposure to rat or rat scent both could produce long-lasting inhibitory effects on Tph2+/+ male mice, reflected in the down-regulated male–male aggression, sexual attractiveness of urine odor, and mRNA expression of central regulatory genes. However, such inhibitory effects were reduced or abolished in Tph2−/− male mice.

One of the primary responses to stress is an increase in the concentration of circulating adrenal glucocorticooids (GC). Instead of GC elevations lasting for hours, chronic exposure to a predator or its scent causes GC levels to remain high for more than a few days, causing a broad and long-lasting negative effects (Creel, 2001). Such impacts have been widely proven in rats and mice, focusing on anxiogenic effects as measured in the EPM, light/dark box, and acoustic startle tests (Dielenberg and McGregor, 2001; Adamec et al., 2006a). For example, the anxiety state of rats exposed to a cat lasted 21 days or more, similar to long-lasting effects on acoustic startle responses of CD-1 mice exposed to rat odor (Adamec and Shallow, 1993; Hebb et al., 2003a).

It is a general belief that psychological stressors, such as the threat of a predator, would negatively affect social and sexual behaviors, such as aggression levels and sexual attractiveness (Francis, 1988; Blanchard et al., 2001; Zhang et al., 2003; Apfelbach et al., 2005). Receptive females expend much more effort during reproduction than do males; hence, they prefer to select high-quality males, in order to increase the probability of reproductive success and offspring survival (Huck and Banks, 1982). Both aggression levels and urinary attractiveness to female conspecifics are representatively manifested in the ability of male mice in male–male competition. In our current study, the descending degree in aggression levels of Tph2+/+ males, dependent upon stimulation intensity, strongly supported the dose-dependent negative influences and predicted competitive ability. Furthermore, such inhibition lasted for a minimum of 6 days, which is consistent with the proven long-lasting effects on anxiogenic states, as stated above.

However, rat scent-exposure did not reduce the urinary attractiveness of Tph2+/+ male mice. Such an anti-intuitive result might be ascribed to the insufficient intensity of the rat scent used, since the intensity of predator stress has been suggested as an important factor in the severity of long-lasting effects (Hebb et al., 2003b). For instance, we previously reported that male mice exposed to a low dose of cat urine showed higher sexual attractiveness to female conspecifics than the control ones (Zhang et al., 2008). Even so, the results that urine odor from both rat scent-exposed and control Tph2+/+ males were more attractive to females than the rat-exposed ones still demonstrated the dose-dependent inhibitory effects of chronic predator exposure on male–male competition.

Accordingly, male–male aggression and sexual attractiveness of urine odor of Tph2+/+ male mice were long-lastingly inhibited by graded stress from rat and rat scent, depending on the stimulation intensity in the current study, which is consistent with and expands on previously published reports.

The pharmacological challenge of neuronal 5-HT, such as treatment with 5-HTR1A and 5-HTR1B agonists, depletion using p-Chlorophenylalanine (PCPA; irreversible inhibitor of the 5-HT synthesizing enzyme tryptophan hydroxylase), or intracerebral injection of the 5-HT neurotoxic agent 5,7-dihydroxytryptamine (5,7-DHT), also induces multiple alterations, such as aggression levels and hormone release (Willoughby et al., 1982; Chiavegatto et al., 2001; de Boer and Koolhaas, 2005). Even so, these manipulations have some limitations to consider, including time (stages of pre- or post-natal development), target tissue (brain or periphery), route, effective time, and complex mechanisms regulating 5-HT neuron firing, presynaptic release, and post-synaptic receptor expression (Miyazaki et al., 2011; van Kleef et al., 2012). However, genetic disruption in the Tph2 affects the central 5-HT system throughout the lifespan, leading to possible alterations in the formation of the neural circuitry mediating emotion and stress adaption during a critical period of brain development (Adamec et al., 2006a; Liu et al., 2011).

Accordingly, in the current study, the predator or its scent exposure did not alter aggression and urinary attractiveness of Tph2−/− male mice, suggesting that brain 5HT deficiency might prevent the inhibitory effects by affecting sensory processing and, thus, induce behavioral disinhibition (Raleigh et al., 1991; Nelson and Chiavegatto, 2001; Freichel et al., 2004; Canli and Lesch, 2007; Tops et al., 2009; Liu et al., 2011; Kane et al., 2012). Moreover, the reduced expression of ERα and 5-HTR1A accompanied by increased BDNF in the hippocampus of the raw Tph2−/− male mice, which may suggest that deficiency of brain 5-HT synthesis likely extends beyond the serotonin system itself (Murphy et al., 2003; Adamec et al., 2006a).

The expression of the prefrontal cortex- and hippocampus-specific genes, which were selectively used in the current study, can reasonably reflect stress-induced central responses and elucidate the mechanisms underlying behavioral alterations. In the current study, the down-regulation of AR, ERα and GluR4 in the prefrontal cortex, and TrkB-Tc and 5-HTR1A in the hippocampus might result from stress responses and, in turn, provide the neural substrates for the alterations of male–male aggression and sexual attractiveness in the Tph2+/+ male mice exposed to a rat or rat scent. The decreased expression of GluR4 in the prefrontal cortex and 5-HTR1A in the hippocampus of stressed Tph2+/+ male mice was consistent with previous literature (Wang et al., 2009, 2011). Despite being in line with the expected low expression in the hippocampus of stressed Tph2+/+ TrkB-Tc, the results of the indifferent Tph2+/+ BDNF expression were surprising (Nibuya et al., 1999; Vaidya et al., 2001; Pizarro et al., 2004; Kozlovsky et al., 2007). Such bifurcations originated mostly from differences in genetic background, as previous studies on the impacts of predator stress on hippocampal BDNF expression focusing on rats other than the C57BL/6J × 129S5/S mice used in our study. Furthermore, the decreased hippocampal BDNF expression of C57BL/6 mice followed acute social stress other than chronic predator stress in our study. In this respect, it is necessary to note, instead of decreased hippocampal BDNF mRNA expression in rat vs. predator-based models, the reduced hippocampal TrkB-Tc mRNA expression might play an important role in modulation of stress response in mice vs. predator-based paradigms, according to our results. AR and ERα have been suggested to be positively correlated with aggression level, as stated above (Li et al., 2004; Scordalakes and Rissman, 2004). In this research, AR and ERα in the prefrontal cortex, rather than in the hippocampus, might contribute to central regulation and related behavioral alterations after predator stress, thus confirming and extending the aforementioned work.

In addition, although rat- and rat-scent exposure could have different effects on male–male competition of Tph2+/+ male mice, their differences in threat intensity were not sufficient to differentiate the mRNA expression of the examined genes.

Tph2−/− male mice showed different mRNA expression patterns from the Tph2+/+ ones. Specifically, rat- and/or rat scent- exposure up-regulated prefrontal AR expression, but down-regulated hippocampal AR, ERα, and BDNF expression, suggesting that 5-HT might mediate central regulation of responses to predation risk. However, such alterations of gene expression appeared to exert no impacts on male–male aggression and sexual attractiveness in Tph2−/− male mice, likely reflecting that these genes were not able to work through the defective 5-HT system (Nelson and Chiavegatto, 2001).

In conclusion, the current results are consistent with previous work that exposure to a predator or predator scent causes dose-dependent and long-lasting negative impacts on rodents. However, we particulalrly relate predator-based chronic stress to brian 5-HT-deficent male mice and male–male competition ability for potential female mates, as indicated by male–male aggression and urinary attractiveness to female conspecifics. For the first time, inhibitory effects on these behavioral measures are found to be abolished in Tph2−/− male mice. We also demonstrate that AR, ERα and GluR4 expression in the prefrontal cortex, and TrkB-Tc and 5-HTR1A expression in the hippocampus are changed in response to predation risk. In turn, they may regulate the alterations of male–male aggression and urinary attractiveness to female conspecifics through the 5-HT system in the brain of male mice.

ACKNOWLEDGMENTS

We thank Yi Rao's laboratory (Peking University, Beijing) and Yan Liu (Peking University, Beijing) for the kind gift of the Tph2 line mice. We also thank Jin-Hua Zhang for animal breeding and behavioral tests, and Jin-Long Han for his assistance in early PCR assay. This work was supported primarily by grants from the National Basic Research Program of China [973 Program, No. 2010CB833900], the China National Science Foundation [31272322], the Chinese Academy of Sciences [KSCX2-EW-N-5], and the State Key Laboratory of Integrated Management of Pest Insects and Rodents in Agriculture (Chinese IPM1208).

REFERENCES

Adamec, R., Burton, P., Blundell, J., Murphy, D. L., and Holmes, A. (2006a). Vulnerability to mild predator stress in serotonin transporter knockout mice. Behav. Brain Res. 170, 126–140. doi: 10.1016/j.bbr.2006.02.012

Adamec, R., Head, D., Blundell, J., Burton, P., and Berton, O. (2006b). Lasting anxiogenic effects of feline predator stress in mice: sex differences in vulnerability to stress and predicting severity of anxiogenic response from the stress experience. Physiol. Behav. 88, 12–29. doi: 10.1016/j.physbeh.2006.03.005

Adamec, R., Holmes, A., and Blundell, J. (2008). Vulnerability to lasting anxiogenic effects of brief exposure to predator stimuli: sex, serotonin and other factors—Relevance to PTSD. Neurosci. Biobehav. Rev. 32, 1287–1292. doi: 10.1016/j.neubiorev.2008.05.005

Adamec, R., Walling, S., and Burton, P. (2004). Long-lasting, selective, anxiogenic effects of feline predator stress in mice. Physiol. Behav. 83, 401–410. doi: 10.1016/j.physbeh.2004.08.029

Adamec, R. E., and Shallow, T. (1993). Lasting effects on rodent anxiety of a single exposure to a cat. Physiol. Behav. 54, 101–109. doi: 10.1016/0031-9384(93)90050-P

Ambar, G., and Chiavegatto, S. (2009). Anabolic−androgenic steroid treatment induces behavioral disinhibition and downregulation of serotonin receptor messenger RNA in the prefrontal cortex and amygdala of male mice. Genes Brain Behav. 8, 161–173. doi: 10.1111/j.1601-183X.2008.00458.x

Angoa-Pérez, M., Kane, M. J., Briggs, D. I., Sykes, C. E., Shah, M. M., Francescutti, D. M., et al. (2012). Genetic depletion of brain 5HT reveals a common molecular pathway mediating compulsivity and impulsivity. J. Neurochem. 121, 974–984. doi: 10.1111/j.1471-4159.2012.07739.x

Apfelbach, R., Blanchard, C. D., Blanchard, R. J., Hayes, R. A., and McGregor, I. S. (2005). The effects of predator odors in mammalian prey species: a review of field and laboratory studies. Neurosci. Biobehav. Rev. 29, 1123–1144. doi: 10.1016/j.neubiorev.2005.05.005

Beekman, M., Flachskamm, C., and Linthorst, A. C. (2005). Effects of exposure to a predator on behaviour and serotonergic neurotransmission in different brain regions of C57bl/6N mice. Eur. J. Neurosci. 21, 2825–2836. doi: 10.1111/j.1460-9568.2005.04107.x

Blanchard, R. J., McKittrick, C. R., and Blanchard, D. C. (2001). Animal models of social stress: effects on behavior and brain neurochemical systems. Physiol. Behav. 73, 261–271. doi: 10.1016/S0031-9384(01)00449-8

Canli, T., and Lesch, K.-P. (2007). Long story short: the serotonin transporter in emotion regulation and social cognition. Nat. Neurosci. 10, 1103–1109. doi: 10.1038/nn1964

Chiavegatto, S., Dawson, V. L., Mamounas, L. A., Koliatsos, V. E., Dawson, T. M., and Nelson, R. J. (2001). Brain serotonin dysfunction accounts for aggression in male mice lacking neuronal nitric oxide synthase. Proc. Natl. Acad. Sci. U.S.A. 98, 1277–1281. doi: 10.1073/pnas.98.3.1277

Clancy, A. N., Coquelin, A., Macrides, F., Gorski, R. A., and Noble, E. (1984). Sexual behavior and aggression in male mice: involvement of the vomeronasal system. J. Neurosci. 4, 2222–2229.

Creel, S. (2001). Social dominance and stress hormones. Trends Ecol. Evol. 16, 491–497. doi: 10.1016/S0169-5347(01)02227-3

de Boer, S. F., and Koolhaas, J. M. (2005). 5-HT1A and 5-HT 1B receptor agonists and aggression: a pharmacological challenge of the serotonin deficiency hypothesis. Eur. J. Pharmacol. 526, 125–139. doi: 10.1016/j.ejphar.2005.09.065

de Kloet, E. R., Joëls, M., and Holsboer, F. (2005). Stress and the brain: from adaptation to disease. Nat. Rev. Neurosci. 6, 463–475. doi: 10.1038/nrn1683

Dielenberg, R. A., and McGregor, I. S. (2001). Defensive behavior in rats towards predatory odors: a review. Neurosci. Biobehav. Rev. 25, 597–609. doi: 10.1016/S0149-7634(01)00044-6

Eide, F. F., Vining, E. R., Eide, B. L., Zang, K., Wang, X.-Y., and Reichardt, L. F. (1996). Naturally occurring truncated trkB receptors have dominant inhibitory effects on brain-derived neurotrophic factor signaling. J. Neurosci. 16, 3123–3129.

Francis, R. C. (1988). On the relationship between aggression and social dominance. Ethology 78, 223–237. doi: 10.1111/j.1439-0310.1988.tb00233.x

Freichel, M., Vennekens, R., Olausson, J., Hoffmann, M., Müller, C., Stolz, S., et al. (2004). Functional role of TRPC proteins in vivo: lessons from TRPC-deficient mouse models. Biochem. Biophys. Res. Commun. 322, 1352–1358. doi: 10.1016/j.bbrc.2004.08.041

Gutknecht, L., Araragi, N., Merker, S., Waider, J., Sommerlandt, F. M., Mlinar, B., et al. (2012). Impacts of brain serotonin deficiency following Tph2 inactivation on development and raphe neuron serotonergic specification. PLoS ONE 7:e43157. doi: 10.1371/journal.pone.0043157

Handa, R. J., and Weiser, M. J. (2013). Gonadal steroid hormones and the hypothalamo–pituitary–adrenal axis. Front. Neuroendocrinol. 35, 197–220. doi: 10.1016/j.yfrne.2013.11.001

Hayley, S., Borowski, T., Merali, Z., and Anisman, H. (2001). Central monoamine activity in genetically distinct strains of mice following a psychogenic stressor: effects of predator exposure. Brain Res. 892, 293–300. doi: 10.1016/S0006-8993(00)03262-5

Hebb, A., Zacharko, R., Dominguez, H., Laforest, S., Gauthier, M., Levac, C., et al. (2003a). Changes in brain cholecystokinin and anxiety-like behavior following exposure of mice to predator odor. Neuroscience 116, 539–551. doi: 10.1016/S0306-4522(02)00710-8

Hebb, A. L., Zacharko, R. M., Gauthier, M., and Drolet, G. (2003b). Exposure of mice to a predator odor increases acoustic startle but does not disrupt the rewarding properties of VTA intracranial self-stimulation. Brain Res. 982, 195–210. doi: 10.1016/S0006-8993(03)03008-7

Hendrie, C., Weiss, S., and Eilam, D. (1996). Exploration and predation models of anxiety: evidence from laboratory and wild species. Pharmacol. Biochem. Behav. 54, 13–20. doi: 10.1016/0091-3057(95)02176-0

Herman, C. S., and Valone, T. J. (2000). The effect of mammalian predator scent on the foraging behavior of Dipodomys merriami. Oikos 91, 139–145. doi: 10.1034/j.1600-0706.2000.910113.x

Huck, U. W., and Banks, E. M. (1982). Male dominance status, female choice and mating success in the brown lemming, Lemmus trimucronatus. Anim. Behav. 30, 665–675. doi: 10.1016/S0003-3472(82)80136-X

Ito, W., Chehab, M., Thakur, S., Li, J., and Morozov, A. (2011). BDNF−restricted knockout mice as an animal model for aggression. Genes Brain Behav. 10, 365–374. doi: 10.1111/j.1601-183X.2010.00676.x

Joels, M., Krugers, H., and Karst, H. (2007). Stress-induced changes in hippocampal function. Prog. Brain Res. 167, 3–15. doi: 10.1016/S0079-6123(07)67001-0

Kane, M. J., Angoa-Peréz, M., Briggs, D. I., Sykes, C. E., Francescutti, D. M., Rosenberg, D. R., et al. (2012). Mice genetically depleted of brain serotonin display social impairments, communication deficits and repetitive behaviors: possible relevance to autism. PLoS ONE 7:e48975. doi: 10.1371/journal.pone.0048975

Kelliher, K., Liu, Y., Baum, M., and Sachs, B. (1999). Neuronal Fos activation in olfactory bulb and forebrain of male rats having erections in the presence of inaccessible estrous females. Neuroscience 92, 1025–1033. doi: 10.1016/S0306-4522(99)00050-0

Kozlovsky, N., Matar, M. A., Kaplan, Z., Kotler, M., Zohar, J., and Cohen, H. (2007). Long-term down-regulation of BDNF mRNA in rat hippocampal CA1 subregion correlates with PTSD-like behavioural stress response. Int. J. Neuropsychopharmacol. 10, 741. doi: 10.1017/S1461145707007560

Li, R., Wheeler, T., Dai, H., Frolov, A., Thompson, T., and Ayala, G. (2004). High level of androgen receptor is associated with aggressive clinicopathologic features and decreased biochemical recurrence-free survival in prostate: cancer patients treated with radical prostatectomy. Am. J. Surg. Pathol. 28, 928–934. doi: 10.1097/00000478-200407000-00013

Liu, Y., Si, Y., Kim, J. Y., Chen, Z. F., and Rao, Y. (2011). Molecular regulation of sexual preference revealed by genetic studies of 5-HT in the brains of male mice. Nature 472, 95–99. doi: 10.1038/nature09822

Livak, K. J., and Schmittgen, T. D. (2001). Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT Method. Methods 25, 402–408. doi: 10.1006/meth.2001.1262

Lund, T. D., Hinds, L. R., and Handa, R. J. (2006). The androgen 5α-dihydrotestosterone and its metabolite 5α-androstan-3β, 17β-diol inhibit the hypothalamo–pituitary–adrenal response to stress by acting through estrogen receptor β-expressing neurons in the hypothalamus. J. Neurosci. 26, 1448–1456. doi: 10.1523/JNEUROSCI.3777-05.2006

Martinowich, K., and Lu, B. (2007). Interaction between BDNF and serotonin: role in mood disorders. Neuropsychopharmacology 33, 73–83. doi: 10.1038/sj.npp.1301571

Miyazaki, K., Miyazaki, K. W., and Doya, K. (2011). Activation of dorsal raphe serotonin neurons underlies waiting for delayed rewards. J. Neurosci. 31, 469–479. doi: 10.1523/JNEUROSCI.3714-10.2011

Molina, V., Ciesielski, L., Gobaille, S., Isel, F., and Mandel, P. (1987). Inhibition of mouse killing behavior by serotonin-mimetic drugs: effects of partial alterations of serotonin neurotransmission. Pharmacol. Biochem. Behav. 27, 123–131. doi: 10.1016/0091-3057(87)90486-2

Mosienko, V., Bert, B., Beis, D., Matthes, S., Fink, H., Bader, M., et al. (2012). Exaggerated aggression and decreased anxiety in mice deficient in brain serotonin. Transl. Psychiatry 2, e122. doi: 10.1038/tp.2012.44

Murphy, D., Uhl, G., Holmes, A., Ren−Patterson, R., Hall, F., Sora, I., et al. (2003). Experimental gene interaction studies with SERT mutant mice as models for human polygenic and epistatic traits and disorders. Genes Brain Behav. 2, 350–364. doi: 10.1046/j.1601-1848.2003.00049.x

Nelson, R. J., and Chiavegatto, S. (2001). Molecular basis of aggression. Trends Neurosci. 24, 713–719. doi: 10.1016/S0166-2236(00)01996-2

Nelson, R. J., and Trainor, B. C. (2007). Neural mechanisms of aggression. Nat. Rev. Neurosci. 8, 536–546. doi: 10.1038/nrn2174

Nibuya, M., Takahashi, M., Russell, D. S., and Duman, R. S. (1999). Repeated stress increases catalytic TrkB mRNA in rat hippocampus. Neurosci. Lett. 267, 81–84. doi: 10.1016/S0304-3940(99)00335-3

Pizarro, J. M., Lumley, L. A., Medina, W., Robison, C. L., Chang, W. E., Alagappan, A., et al. (2004). Acute social defeat reduces neurotrophin expression in brain cortical and subcortical areas in mice. Brain Res. 1025, 10–20. doi: 10.1016/j.brainres.2004.06.085

Raleigh, M. J., McGuire, M. T., Brammer, G. L., Pollack, D. B., and Yuwiler, A. (1991). Serotonergic mechanisms promote dominance acquisition in adult male vervet monkeys. Brain Res. 559, 181–190. doi: 10.1016/0006-8993(91)90001-C

Sachs, B. D., Rodriguiz, R. M., Siesser, W. B., Kenan, A., Royer, E. L., Jacobsen, J., et al. (2013). The effects of brain serotonin deficiency on behavioural disinhibition and anxiety-like behaviour following mild early life stress. Int. J. Neuropsychopharmacol. 16, 2081–2094. doi: 10.1017/S1461145713000321

Sands, J., and Creel, S. (2004). Social dominance, aggression and faecal glucocorticoid levels in a wild population of wolves, Canis lupus. Anim. Behav. 67, 387–396. doi: 10.1016/j.anbehav.2003.03.019

Scordalakes, E., and Rissman, E. (2004). Aggression and arginine vasopressin immunoreactivity regulation by androgen receptor and estrogen receptor α. Genes Brain Behav. 3, 20–26. doi: 10.1111/j.1601-183X.2004.00036.x

Shutoh, F., Hamada, S., Shibata, M., Narita, M., Shiga, T., Azmitia, E. C., et al. (2000). Long term depletion of serotonin leads to selective changes in glutamate receptor subunits. Neurosci. Res. 38, 365–371. doi: 10.1016/S0168-0102(00)00184-X

Simon, N. G., Cologer-Clifford, A., Lu, S.-F., McKenna, S. E., and Hu, S. (1998). Testosterone and its metabolites modulate 5HT1A and 5HT1B agonist effects on intermale aggression. Neurosci. Biobehav. Rev. 23, 325–336. doi: 10.1016/S0149-7634(98)00034-7

Tops, M., Russo, S., Boksem, M. A., and Tucker, D. M. (2009). Serotonin: modulator of a drive to withdraw. Brain Cogn. 71, 427–436. doi: 10.1016/j.bandc.2009.03.009

Vaidya, V., Castro, M., Pei, Q., Sprakes, M., and Grahame-Smith, D. (2001). Influence of thyroid hormone on 5-HT1A and 5-HT2A receptor-mediated regulation of hippocampal BDNF mRNA expression. Neuropharmacology 40, 48–56. doi: 10.1016/S0028-3908(00)00094-0

van Kleef, E. S., Gaspar, P., and Bonnin, A. (2012). Insights into the complex influence of 5−HT signaling on thalamocortical axonal system development. Eur. J. Neurosci. 35, 1563–1572. doi: 10.1111/j.1460-9568.2012.8096.x

Wang, F., Zhu, J., Zhu, H., Zhang, Q., Lin, Z., and Hu, H. (2011). Bidirectional control of social hierarchy by synaptic efficacy in medial prefrontal cortex. Science 334, 693–697. doi: 10.1126/science.1209951

Wang, H.-S., Zhou, C.-S., Guo, W., and Kang, L. (2006). Thermoperiodic acclimations enhance cold hardiness of the eggs of the migratory locust. Cryobiology 53, 206–217. doi: 10.1016/j.cryobiol.2006.06.003

Wang, S.-H., Zhang, Z.-J., Guo, Y.-J., Teng, G.-J., and Chen, B.-A. (2009). Decreased expression of serotonin 1A receptor in the dentate gyrus in association with chronic mild stress: a rat model of post-stroke depression. Psychiatry Res. 170, 245–251. doi: 10.1016/j.psychres.2008.07.006

Willoughby, J. O., Menadue, M., and Jervouis, P. (1982). Function of serotonin in physiologic secretion of growth hormone and prolactin: action of 5, 7-dihydroxytryptamine, fenfluramine and p-chlorophenylalanine. Brain Res. 249, 291–299. doi: 10.1016/0006-8993(82)90063-4

Zhang, J.-X., Cao, C., Gao, H., Yang, Z.-S., Sun, L., Zhang, Z.-B., et al. (2003). Effects of weasel odor on behavior and physiology of two hamster species. Physiol. Behav. 79, 549–552. doi: 10.1016/S0031-9384(03)00123-9

Zhang, J.-X., Sun, L., Bruce, K. E., and Novotny, M. V. (2008). Chronic exposure of cat odor enhances aggression, urinary attractiveness and sex pheromones of mice. J. Ethol. 26, 279–286. doi: 10.1007/s10164-007-0060-1

Zhang, J.-X., Sun, L., and Novotny, M. (2007). Mice respond differently to urine and its major volatile constituents from male and female ferrets. J. Chem. Ecol. 33, 603–612. doi: 10.1007/s10886-006-9220-y

Zhou, J., Li, L., Tang, S., Cao, X., Li, Z., Li, W., et al. (2008). Effects of serotonin depletion on the hippocampal GR/MR and BDNF expression during the stress adaptation. Behav. Brain Res. 195, 129–138. doi: 10.1016/j.bbr.2008.06.009

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 17 November 2013; Accepted: 19 March 2014; Published online: 08 April 2014.

Citation: Huo Y, Fang Q, Shi Y-L, Zhang Y-H and Zhang J-X (2014) Chronic exposure to a predator or its scent does not inhibit male–male competition in male mice lacking brain serotonin. Front. Behav. Neurosci. 8:116. doi: 10.3389/fnbeh.2014.00116

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Huo, Fang, Shi, Zhang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 09 December 2013
doi: 10.3389/fnbeh.2013.00193

Mouse Grueneberg ganglion neurons share molecular and functional features with C. elegans amphid neurons

Julien Brechbühl†, Fabian Moine† and Marie-Christine Broillet[*]

Department of Pharmacology and Toxicology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland

* Correspondence: Marie-Christine Broillet, Department of Pharmacology and Toxicology, Faculty of Biology and Medicine, University of Lausanne, Bugnon 27, CH-1005 Lausanne, Switzerland e-mail: mbroille@unil.ch

†These authors have contributed equally to this work.

Edited by:
Anne-Marie Mouly, Centre de Recherche en Neurosciences de Lyon, France

Reviewed by:
Daniel W. Wesson, Case Western Reserve University, USA
Joerg Fleischer, Universität of Hohenheim, Germany

The mouse Grueneberg ganglion (GG) is an olfactory subsystem located at the tip of the nose close to the entry of the naris. It comprises neurons that are both sensitive to cold temperature and play an important role in the detection of alarm pheromones (APs). This chemical modality may be essential for species survival. Interestingly, GG neurons display an atypical mammalian olfactory morphology with neurons bearing deeply invaginated cilia mostly covered by ensheathing glial cells. We had previously noticed their morphological resemblance with the chemosensory amphid neurons found in the anterior region of the head of Caenorhabditis elegans (C. elegans). We demonstrate here further molecular and functional similarities. Thus, we found an orthologous expression of molecular signaling elements that was furthermore restricted to similar specific subcellular localizations. Calcium imaging also revealed a ligand selectivity for the methylated thiazole odorants that amphid neurons are known to detect. Cellular responses from GG neurons evoked by chemical or temperature stimuli were also partially cGMP-dependent. In addition, we found that, although behaviors depending on temperature sensing in the mouse, such as huddling and thermotaxis did not implicate the GG, the thermosensitivity modulated the chemosensitivity at the level of single GG neurons. Thus, the striking similarities with the chemosensory amphid neurons of C. elegans conferred to the mouse GG neurons unique multimodal sensory properties.

Keywords: olfactory, amphid neurons, Grueneberg ganglion, behavior, calcium imaging, temperature sensing, alarm pheromone

INTRODUCTION

Organisms have evolved specialized populations of sensory receptor neurons to detect the chemical information that is present in their environment (Ache and Young, 2005). In the model organism Caenorhabditis elegans (C. elegans), chemo- and thermo-sensing are performed by 12 pairs of ciliated sensory neurons, the amphid neurons, that are found in the anterior region of the animal head (Mori and Ohshima, 1995; Bargmann, 2006). Amphid neurons include three principal olfactory classes named amphid wing neurons of type A (AWA), B (AWB), and C (AWC) (Figure 1A). They play a fundamental role in mate identification, in food finding and in noxious conditions avoidance by odortaxis (Bargmann, 2006). They also participate in the development of the organism by navigating through spatial thermal gradients by thermotaxis (Mori and Ohshima, 1995). In the mouse, the olfactory sensory neurons are dispatched in four distinct subsystems: the main olfactory epithelium (MOE), the septal organ of Masera (SO), the vomeronasal organ (VNO) and the most recently described Grueneberg ganglion (GG) (Gruneberg, 1973) (Figure 1B). These subsystems are implicated in the detection of molecules carrying chemical messages, such as odorants and pheromones that play a role in behaviors ranging from food finding to social communication (Munger et al., 2009). We have identified one of these subsystems, the GG, as a chemodetector of alarm pheromones (APs) (Brechbühl et al., 2008). APs signal injury, distress or the presence of predators (Kiyokawa et al., 2004). A wide variety of changes in behaviors can be observed in the presence of APs such as a decreased exploratory activity or increase in vigilance and freezing behaviors (Zalaquett and Thiessen, 1991; Kiyokawa et al., 2006). We have recently isolated and identified the chemical structure of one mouse AP, 2-sec-butyl-4,5-dihydrothiazole (SBT) (Brechbühl et al., 2013). This volatile APs is produced by both male and female mice under different alarm conditions and resembles the sulfur-containing volatiles present in predator scents. In addition to its reported chemosensory modality (Brechbühl et al., 2008, 2013; Mamasuew et al., 2011a; Hanke et al., 2013), the mouse GG has also been implicated in sensing cold temperatures (Mamasuew et al., 2008; Schmid et al., 2010).

FIGURE 1. Mouse GG neurons express in conserved subcellular localizations a set of signaling proteins related to those present in C. elegans chemosensory amphid neurons. (A,B) Schematic comparison of the main signaling proteins expressed in C. elegans amphid AWC neurons (A) and in the mouse GC-G positive GG neurons (B). C. elegans amphid neurons are indicated (in green), in particular the AWA, AWB, and AWC neurons (in red). The mouse olfactory subsystems (in green) are indicated as follows: GG, Grueneberg ganglion (in red); VNO, vomeronasal organ; SO, septal organ; MOE, main olfactory epithelium. The signaling elements are indicated as follows: cGMP, cyclic guanosine monophosphate; cGKII, cGMP-dependent protein kinase of type 2; CNGA3, cyclic nucleotide-gated channels 3; DAF-11/ODR-1, potential receptor-like transmembranous guanylyl cyclase; egl-4, cGMP-dependent protein kinase; GC-G, particulate guanylyl cyclase G; GPCRs, G protein coupled receptors; PDE, phosphodiesterase; PDE2A, phosphodiesterase 2A; TAX-2/4, cyclic nucleotide-gated (CNG)-like channels. (C) Immunohistochemistry experiments on coronal slices of the GG of OMP-GFP mice for homologous signaling proteins found in C. elegans amphid neurons. GC-G was found to be expressed in the cilia. CNGA3 was found to be expressed principally in cilia and axons; some somatic expression could also be observed. PDE2A was found in soma and in axons. cGKII was found in soma. The specific subcellular localizations are shown in high power views (white dashed rectangles). White arrowheads indicate ciliary processes, black arrowheads indicate soma and white arrows indicate axons. A minimum of 2 animals (from P0–P29) and 6 slices were used for each antibody staining tested. Nuclei are shown in blue (DAPI counterstain). Scale bars, 20 μm.

We noticed the morphological resemblance between the mouse GG neurons and the olfactory AWA, AWB, and AWC amphid neurons from the nematode C. elegans (Brechbühl et al., 2008). Indeed, mouse GG neurons display an atypical olfactory morphology with neurons that bear deeply invaginated cilia and that are mostly covered by glial cells (Gruneberg, 1973; Tachibana et al., 1990; Brechbühl et al., 2008; Liu et al., 2009). They have a rostral localization in a water permeant epithelium and they lack direct contact with the nasal cavity (Gruneberg, 1973; Fuss et al., 2005; Koos and Fraser, 2005; Fleischer et al., 2006a; Roppolo et al., 2006; Storan and Key, 2006; Brechbühl et al., 2008; Liu et al., 2009). In C. elegans, pairs of either AWA, AWB, or AWC neurons detect volatile odorants. They are found under a water permeant cuticle and are wrapped by a single ensheathing glial cell that also surrounds the modified cilia (Bargmann et al., 1993; Bargmann, 2006; Inglis et al., 2007; Bacaj et al., 2008).

These morphological similarities might also indicate molecular similarities between mouse GG neurons and C. elegans amphid neurons. Indeed, recent studies have revealed that canonical and non-canonical signaling elements are expressed in GG neurons (Fleischer et al., 2006b, 2007, 2009; Pyrski et al., 2007; Brechbühl et al., 2008; Liu et al., 2009, 2012). They could, as in C. elegans, be potentially implicated in parallel and/or convergent G protein-coupled receptors (GPCRs)- and cyclic guanosine monophosphate (cGMP)-dependent signaling pathways for thermo- and chemodetection (Mamasuew et al., 2010, 2011a,b; Schmid et al., 2010; Hanke et al., 2013).

Here, we investigated the conserved multisensory modalities of mouse GG neurons. We found striking similarities between mouse GG neurons and nematode amphid neurons, especially the AWC class. We found that 2,4,5-trimethylthiazole, a known AWC ligand, was able to initiate neuronal responses in mouse GG neurons in a cGMP-dependent manner. AWC neurons are also known to act as thermosensors and we found here that the temperature can modulate the chemosensitivity of GG neurons. Thus, GG neurons, through their position at the tip of the nose, are able to integrate multiple sensory inputs thereby allowing an animal to assess additional aspects of its olfactory environment.

EXPERIMENTAL PROCEDURES

ANIMALS AND TISSUE PREPARATION

OMP-GFP mice (Potter et al., 2001) from pups to adult stages were used for all experimental investigations. This particular gene-targeted mouse strain expresses the green fluorescent protein (GFP) as a histological reporter under the control of the olfactory marker protein (OMP) promoter (Mombaerts et al., 1996; Potter et al., 2001). OMP is a marker specific for mature olfactory sensory neurons (Margolis, 1972). Animal care was in accordance with the Swiss legislation and the veterinary authority. Mice were killed by CO_2 or cervical dislocation. Nasal cavities were prepared in ice-cold artificial cerebrospinal fluid (ACSF), containing 118 mM NaCl, 25 mM NaHCO_3, 10 mM D-glucose, 2 mM KCl, 2 mM MgCl_2, 1.2 mM NaH_2PO_4, and 2 mM CaCl_2 (pH 7.4) saturated with oxycarbon gas [95% O_2: 5% CO_2; (vol/vol)] under a fluorescence-equipped dissecting microscope (M165 FC; Leica). For specific experiments, ACSF calcium free solution was also used and it was composed of NaCl 118 mM, NaHCO_3 25 mM, D-Glucose 10 mM, KCl 2 mM, MgCl_2 2 mM, NaH_2PO_4 1.2 mM, EDTA 10 mM and EGTA 10 mM saturated with oxycarbon gas.

IMMUNOHISTOCHEMISTRY

Protocol for floating immunohistochemistry was adapted from (Brechbühl et al., 2011, 2013). Briefly, the tip of the nose was carefully dissected in PBS (138 mM NaCl, 2.7 mM KCl, 0.9 mM KH_2PO_4, and 0.8 mM Na_2HPO_4, pH 7.6) before being fixed in 4% paraformaldehyde (PAF 4%, in PBS pH 7.4; 158127, Sigma) at 4°C for 3 h. Fixed tissue preparations were embedded in 5% agar (A7002, Sigma) prepared in PBS. Agar blocks were transferred on ice for 30 s for solidification. The agar blocks were fixed vertically with cyanacrylat glue (Roti coll 1, Carl Roth) onto the holder of the vibroslicer (VT1200S, Leica). 80 μm coronal sections were cut in PBS and were selected with a fluorescence-equipped dissecting microscope (M165 FC; Leica). Slices were blocked overnight at 4°C in a PBS solution containing 10% NGS (normal goat serum, Jackson ImmunoResearch) and Triton X-100 0.5%. Primary antibodies were applied to the slices for 16 h at RT in a PBS solution containing NGS 5% and Triton X-100 0.25%. Slices were washed in NGS 2% and were incubated in the dark with the secondary antibody in a PBS solution containing NGS 2% for 1 h at RT. Slices were finally washed and mounted in Vectashield (H-1200, Vector Labs) with DAPI mounting medium. The primary antibodies used for the detection of signaling proteins were the particulate guanylyl cyclase G [GC-G (PGCG-701AP); 1:300, Rabbit, FabGennix], the cyclic nucleotide-gated channel type 3 [CNGA3 (LS-C14509); 1:300, Rabbit, Lifespan Bioscience], the phosphodiesterase 2A [PDE2A (PD2A-101AP); 1:500, Rabbit, FabGennix] and the cGMP-dependent protein kinase type II [cGKII (H-120, sc-25430); 1:50, Rabbit, Santa Cruz biotechnology]. The secondary antibody used was coupled to Cy3 [Cy3-conjugated AffiniPure anti-Rabbit (111-165-144); 1:200, Goat, Jackson ImmunoResearch]. Control experiments were performed by omitting primary antibodies. Observations and acquisitions were made by confocal microscopy (SP5, Leica) under objectives of 40–100×. Post-analysis and reconstructions were made with Imaris (Bitplane IMARIS 6.3).

PROTEIN SEQUENCE ANALYSES

The protein sequences were obtained from the National Centre for Biotechnology Information (NCBI) database. We chose the following C. elegans sequences DAF-11 (gi: 198447220); TAX-4 (gi: 13548488); pde-2 (gi: 71989276); egl-4 (gi: 71989393) and obtained, respectively, the following mouse homologous sequences after BLASTP algorithm: GC-G (gi: 124487301); CNGA3 (gi: 530537234); PDE2A (gi: 344217717); cGKII (gi: 188219585). Scores of identity and similarity were used to evaluate the sequence homologies.

CALCIUM IMAGING

Calcium imaging experiments were performed on acute tissue slices of mouse GG (Brechbühl et al., 2008, 2013). Briefly, pups and adult mice were killed and dissected in fresh ACSF solution at 4°C. The tip of the mouse nose was included in a block of low melting 5% agar at 41°C and directly placed on ice for solidification. Blocks were fixed vertically with cyanacrylat glue (Roti coll 1, Carl Roth) on the object holder and coronal slices from 60–80 μm were cut at 4°C with a vibroslicer (VT1200S, Leica). Multiple slices could be obtained from one mouse GG. Slices were selected for their GFP expression with a fluorescent stereomicroscope (M165 FC, Leica). Selected slices were loaded with Fura-2 acetoxymethyl ester (AM) (5 μ M; TEFLabs) and pluronic acid (0.1%; Pluronic F-127, Invitrogen) in an incubator for 45 min (37°C, 5% CO_2). Slices were placed in the bath chamber (RC-26, Warner Instruments) and immobilized with a slice anchor. Observations were made under an inverted fluorescence microscope (Axiovert 135, Zeiss) with a 25 or 63x objective and Cool SNA-HQ camera. A bipolar temperature controller (SC-20/CL-100, Warner instruments) was used to control the bath temperature. RT (room temperature) corresponded to 23–25°C. The software MetaFluor (MetaFluor, Visitron Systems) was used to monitor intracellular calcium and to acquire images (Brechbühl et al., 2011).

CHEMOSTIMULATION

Odorant and drugs were obtained from Sigma-Aldrich and were prepared freshly before each experiment by direct dilution in ACSF. For pyrazine, a stock solution was prepared in alcohol (w:v; 1:2) before final dilution (Bargmann et al., 1993; Tonkin et al., 2002). The osmolarity of solutions was between 285 and 300 Osm/L. These chemical cues are known to reversibly mediate intracellular calcium increases in C. elegans when used at 1:1,000–1:1,000,000 dilutions (Lans et al., 2004; Chalasani et al., 2007). For this reason, for each tested cue, a range of concentrations from 100–1 μ M was used in our experiments. A short exposure to an extracellular potassium concentration of 20 mM was used as a viability test and standard reference. The percentages of responses were standardized by comparing the calcium increases observed with KCl vs. the ones observed with the tested cue. Fura-2AM ratio (F340/380 nm) observed during the perfusion of ACSF was considered as baseline activity; it corresponded to ~5% of a KCl response. Calcium increases twice larger than this baseline activity (10% of the KCl response) were considered as responses (Brechbühl et al., 2013). 8-bromoguanosine 3′,5′-cyclic monophosphate (8-Br; 500 μM) was used to mimic the intracellular source of cGMP in GG (Schmid et al., 2010). To inhibit CNGA3 channels, the CNG inhibitor L-cis diltiazem hydrochloride (Dilt) was used (100–500 μM) and continuously perfused on tissue slices during a minimum of 5 min before perfusing any tested cues for inhibition tests (Frings et al., 1992). In calcium free experiments, the ACSF calcium free solution was perfused during a minimum 5 min before any cues were perfused.

THERMOTAXIS AND HUDDLING

A total of 31 pups from OMP-GFP mice from 5 different litters were used to study thermotaxis and huddling behaviors. As previously described (Roppolo et al., 2006; Brechbühl et al., 2008), surgical ablation of the GG was performed in 25 P0 mice by cutting the GG axon bundles. Untreated mice did not undergo the surgical procedure (n = 6). In order to assess the effective surgical procedure, mice were phenotyped at P15 (after the behavioral sessions), using a stereomicroscope (MZ16FA, Leica). GFP fluorescence of the GG and, as control, the MOE was observed. Mice were considered as axotomized mice (Axo; n = 18/25) in case of total absence of GFP fluorescence at the normal localization of the GG, the other mice were considered as sham control mice (Ctrl; n = 7/25). Between behavioral tests, pups were returned to their mothers and littermates. By precaution, adult male mice were kept separate from the litters. Because no differences were measured between untreated and Ctrl mice, only results from Ctrl and Axo mice are presented for clarity purposes. The thermotaxis protocol was adapted from (Pacheco-Cobos et al., 2003; Serra and Nowak, 2008). Untreated, Ctrl and Axo mice were placed at P5, P9, and P12 in a Plexiglas arena (13 × 13 cm) in which a thermal gradient (37–0°C) was generated by controlling the temperatures of the walls. To prevent place preference, a four-session test design was performed for each pup, where the temperatures of the walls changed clockwise. Between each session, the Plexiglas arena was cleaned with alcohol and water. Sessions of 3 min were recorded with a standard HD camera and/or a thermal camera (ThermaCAM™ E45, FLIR Systems). Post-analysis was performed with computer assistance, the position of the body center (green dots) as well as the tip of the nose (red dots, corresponding to the GG region) were reported as a function of time (t = 0s, t = 30s, t = 60s, t = 120s) and the first contact of the nose with the hottest wall was measured. To determine the huddling behavior, six pups (at P5, P9, and P12) were placed in the center of a Plexiglas arena (13 × 13 cm) at RT (23–25°C). Sessions of 3 min were recorded with a standard HD camera and/or a thermal camera (ThermaCAM™ E45, FLIR Systems). Active behaviors (climbing, contact maintenance) as well as body temperature were measured.

STATISTICS

For statistical comparisons, open source statistical package R version 3.0.2 was used. Normality and homogeneity were evaluated by the Shapiro test. Monofactorial comparisons were done with student's t-tests or Wilcoxon w-tests. Multifactorial comparisons were done by ANOVA. Values are expressed as mean ± s.e.m. Significance levels are indicated as follows: *p < 0.05; **p < 0.01; ***p < 0.001; ns for non-significant.

RESULTS

CONSERVED MOLECULAR SIGNALING IN MOUSE GG NEURONS

The amphid AWA, AWB, and AWC neurons of C. elegans express multiple molecular signaling proteins that are directly related to their functional roles (Bargmann, 2006). For example, in a single AWC neuron, one can find, in specific subcellular localizations, both canonical and non-canonical GPCRs (Sengupta et al., 1996; Battu et al., 2003; Alcedo and Kenyon, 2004), potential receptor-like transmembranous guanylyl cyclase DAF-11/ODR-1 (Vowels and Thomas, 1994; Birnby et al., 2000), downstream elements Gi-like proteins (Roayaie et al., 1998; Jansen et al., 1999), phosphodiesterases (PDE) (Bargmann, 2006; O'halloran et al., 2012), cGMP-dependent protein kinase (egl-4) (L'etoile et al., 2002; O'halloran et al., 2009; Lee et al., 2010) and cyclic nucleotide-gated (CNG)-like channels TAX-2/4 (L'etoile and Bargmann, 2000; Kaupp and Seifert, 2002; Bargmann, 2006). Interestingly, similar signaling proteins are also present in mouse GG neurons (Fleischer et al., 2006b, 2007, 2009; Brechbühl et al., 2008; Liu et al., 2009) (Figures 1A,B). In a first approach, we focused on the cGMP-dependent proteins and verified by immunohistochemistry their neuronal expression and localization in mouse GG neurons (Figure 1C). GG tissue slices were obtained from OMP-GFP transgenic mice (Mombaerts et al., 1996; Potter et al., 2001). We looked for the presence of the particulate guanylyl cyclase G (GC-G), a potential transmembrane receptor (Fleischer et al., 2009; Liu et al., 2009), the cyclic nucleotide-gated channels 3 (CNGA3) (Liu et al., 2009) as well as downstream regulatory elements such as the phosphodiesterase 2A (PDE2A) (Fleischer et al., 2009; Liu et al., 2009; Matsuo et al., 2012) and the cGMP-dependent protein kinase of type 2 (cGKII) (Liu et al., 2009). We found GC-G exclusively in ciliary structures but CNGA3 in cilia, cell bodies and axons. The downstream element PDE2A was expressed in cell bodies and axons but cGKII was located exclusively in the cell bodies. These specific subcellular localizations were conserved among GG neurons and mice (>90 neurons were checked in each GG slice corresponding to >1000 neurons observed for the expression of each investigated proteins). Moreover, these expression patterns were identical to those found in amphid neurons (Coburn and Bargmann, 1996; Mccleskey, 1997; Coburn et al., 1998; Dwyer et al., 1998; Bargmann, 2006). We next evaluated, by BLAST algorithm, the identity and similarity of these GG-expressed proteins with the ones of amphid neurons. We found that the mouse GC-G shares 29% identity and 65% similarity with the C. elegans DAF-11, CNGA3 and TAX-4 share 44% identity and 91% similarity, PDE2A and pde-2 share 38% identity and 58% similarity and cGKII and egl-4 share 47% identity and 96% similarity. The similar expression patterns as well as their high similarity scores suggest that these molecular signaling elements might be an orthologous set of proteins (Altenhoff and Dessimoz, 2009).

CONSERVED CHEMOSENSITIVITY OF MOUSE GG NEURONS

In amphid neurons, these cGMP-related proteins participate in chemosensing (Bargmann, 2006). Indeed, volatile water soluble cues such as pyrazine for AWA neurons, 2-nonanone for AWB neurons, thiazole and 2,4,5-trimethylthiazole for AWC neurons (Bargmann, 2006) are known to reversibly induce intracellular calcium increases. This neuronal stimulation is partially mediated by the second messenger cGMP (Coburn et al., 1998; Bargmann, 2006). To verify if these known ligands of amphid neurons could also activate GG neurons, we performed calcium imaging experiments on GG coronal slices from OMP-GFP mice (Brechbühl et al., 2008). Tissue slices were incubated in Fura-2AM, a ratiometric calcium-sensitive dye. GG cells were identified by the intrinsic green fluorescence of GFP in their cell bodies and by their specific morphology (Figure 2A). The uptake of the dye was confirmed by fluorescence observations (Figure 2B). Chemical stimuli were delivered at room temperature in oxycarbonated ACSF continuously perfused on the tissue slices in the imaging chamber and the neuronal viability was evaluated by a brief stimulation of KCl (Figures 2B,C). We found that stimulation with these ligands of amphid neurons evoked calcium transients of different amplitudes in most mouse GG neurons. Interestingly, the AWA and AWC ligand 2,4,5-trimethylthiazole induced the largest responses (mT; 66.2 ± 3.6%; n = 66 responding neurons/66 tested neurons) (Figure 2D). Smaller responses were observed with thiazole (Th; 35.2 ± 3.9%; n = 14/16) and pyrazine (Py; 26.7 ± 2.0%; n = 32/34). Single GG neurons could be stimulated by all tested AWA and AWC ligands (Figure 2C; n = 14/14). On the other hand, in responding neurons no activation was observed with the AWB ligand 2-nonanone (No; 4.2 ± 1.3%; n = 0/18), thus conferring to GG neurons a selectivity for related-chemical structures (Brechbühl et al., 2013). Calcium increases due to mT were rapidly reversible and reproducible (Figure 2E). No adaptation was observed in the presence of the stimulus for a period of 10 min (Figure 2E). Responses occurred over a broad range of concentrations (tested from 100–1 μ M) (Figure 2F). Recently, dependence of GG chemosensitivity on cGMP signaling has been shown using animals lacking cGMP-associated signaling proteins (Mamasuew et al., 2011b; Hanke et al., 2013). The calcium transients we observed could be mimicked by perfusion of the cGMP membrane-permeable analog 8-bromoguanosine 3′, 5′-cyclic monophosphate (8-Br; 500 μM; n = 88/95) (Zufall and Munger, 2010) (Figure 2G). Thus, in addition to previous reports, we showed that the cyclic nucleotide-gated channel blocker L-cis Diltiazem (Dilt; 500 μM) was able to inhibit completely the calcium transients generated by 8-Br (Figure 2G; n = 11/11) but only partially those generated by mT (58%) (Figure 2H; n = 6/6). The presence of extracellular calcium was necessary to observe an 8-Br or mT response (Figures 2G,H). It therefore appears that mouse GG neurons also partially display a cGMP-dependent chemosensitivity resembling the one of AWC amphid neurons (Coburn et al., 1998; Fujiwara et al., 2002; Bargmann, 2006; Tsunozaki et al., 2008).

FIGURE 2. GG neurons respond to ligands of amphid neurons. (A) GG coronal slice from an OMP-GFP mouse where GG neurons can be observed with their intrinsic GFP fluorescence and Hoffman modulation view (Hv). (B) Fluorescence of Fura-2AM into GG cells observed at 380 nm in color encoded map for unbound Fura before and at the peak of an intracellular calcium increase induced by 2,4,5-trimethylthiazole (mT). (C–H) Chemostimulations of GG neurons realized at RT. (C) Representative calcium transients induced in the same GG neuron by the successive perfusion of amphid AWA, AWB, and AWC ligands (100 μ M). (D) Histogram summarizing Fura-2 ratio peak Ca2+ responses to stimulation with the different activators (100 μ M) normalized to KCl responses (20 mM). Perfusion of mT (n number of responding neurons/number of total neurons = 66/66), thiazole (Th; n = 14/16), pyrazine (Py; n = 32/34) but not 2-nonanone (No; n = 0/18) increased the intracellular calcium concentration. Color bars indicate the AWA (in black), AWB (in red) or AWC (in green) ligand's relationship. 2–9 mice (P1–P26) were used for each tested chemical. Values are expressed as mean ± s.e.m. (E) mT responses were rapidly reversible and reproducible (n number of tested neurons = 16). No adaptation was observed in the presence of mT for a period of 10 min (n = 15). (F) The calcium increases generated by 2,4,5-trimethylthiazole (mT) were observed over a broad concentration range (from 100–1 μ M; n = 10). (G) Representative calcium transients induced in GG neurons by perfusion of a cGMP membrane-permeable analog, the 8-bromoguanosine 3′, 5′-cyclic monophosphate (8-Br; 500 μ M), and inhibited by increasing concentrations of the cyclic nucleotide-gated channel blocker L-cis diltiazem (Dilt, n = 11). A 8-Br response was not observed in calcium-free medium (n = 48). (H) mT responses could be partially inhibited by L-cis diltiazem [Dilt 250 μ M (26%, n = 11); 500 μ M (58%, n = 6)]. Fluorescence intensity Fura-2 ratio = F340/F380 is indicated by arbitrary units (a.u.). Perfusion times are indicated by horizontal bars. Traces illustrated in (C), (E), (F), (G), and (H) are representative responses observed in single GG neurons for each panel. Scale bars, 20 μm in (A) and (B).

TEMPERATURE-DEPENDENT CHEMOSENSITIVITY OF GG NEURONS

Interestingly, the chemosensitive amphid neurons, especially the AWC neurons, are also sensitive to temperature variations, which influence animal behavior (Biron et al., 2008; Kuhara et al., 2008). Indeed, genetic or laser deletion of AWC neurons, or members of their signaling pathway, demonstrated their contribution to the animal thermotactic behavior (Biron et al., 2008). Mouse GG neurons are also known to be sensitive to temperature changes (Mamasuew et al., 2008; Schmid et al., 2010) and in mice, two essential behaviors depend on temperature sensing, thermotaxis and huddling (Pacheco-Cobos et al., 2003; Alberts, 2007). We therefore tested the potential role of the GG in these two behaviors, comparing sham control mice (Ctrl) with GG axotomized mice (Axo) (Roppolo et al., 2006; Brechbühl et al., 2008, 2013). We focused on mouse pups, as the maintenance of body temperature within narrow limits is one of their most basic homeostatic needs (Pacheco-Cobos et al., 2003). Contrary to their homoiothermic mother, mouse pups are poikilothermic, which means that their body temperature varies with the temperature of their surroundings (Alberts, 2007). When mouse pups are separated from their mother, their internal temperature drops. Thus, when pups are placed in a thermal gradient, they naturally search by active movements the warmest region (Pacheco-Cobos et al., 2003). To evaluate the potential implication of the GG in mouse thermotaxis, we used a behavioral arena where a thermal gradient was generated and we filmed the pups performance with a combination of a normal and a thermal camera (Figure 3A). Performance was assessed in Ctrl (n = 7) and Axo (n = 18) animals of different ages by placing the pups in the middle of the arena and subsequently measuring the time of the first contact with the 37°C wall at different ages (Figure 3B). As expected, age was a critical factor for the mice performance (ANOVA: ***), but phenotype was not relevant (ANOVA: ns). Indeed, no significant differences were observed between Ctrl and Axo pups at P5 (Ctrl: 36.7 ± 5.5 s; Axo: 43.7 ± 3.9 s; w-test: ns), P9 (Ctrl: 18.2 ± 3.7 s; Axo: 11.1 ± 1.2 s; w-test: ns) nor at P12 (Ctrl: 11.5 ± 2.3 s; Axo: 15.7 ± 1.7 s; w-test: ns). Temporal localizations of the tip of the nose (corresponding to the GG location) as well as the body center were plotted (Figure 3C and supplementary Movies 1, 2), which demonstrated that both phenotypes were equally efficient in thermotaxis. Indeed, after 30 s the majority of the pups had found their final position. Furthermore, for both Ctrl and Axo mice, the tip of the nose was the body part that was found physically closest to the 37°C wall.

FIGURE 3. A functional GG is not necessary for thermotaxis and huddling behaviors. (A–C) Thermotaxis analysis. (A) Schematic representation of the arena visualized with a thermal camera. The pup is placed in the middle of the arena and, during a session of 3 min, localization is recorded. Red dot represents the GG localization and, the green dot, the body center. (B) The time necessary for the tip of the nose (red dot) to be in contact with the 37°C wall is measured, for P5, P9, and P12. Experiments were done with sham control (Ctrl; n = 7) pups and GG axotomized (Axo; n = 18) pups; each pup has been used in 4 sessions. (C) Merge view of the localization of the tip of the nose (red dots) and body center (green dots) after 0, 30, 60, and 120 s for P5, P9, and P12. For clarity, only the 4 sessions of 6 different pups (at the three tested ages) per phenotype were plotted (C). (D–F) Huddling analysis. (D) Thermal view of 6 pups per phenotype at P5, after 40, 80, and 120 s. (E) Normal view at P9 after 40 s. (F) Phenotyping was done at the end of the thermotaxis and huddling behavioral sessions, at P15. The Ctrl (presence of GG) and Axo (no GG) mice were grouped for post-analysis. Scale bars, 500 μm in (F). Values are expressed as mean ± s.e.m.; w-test, *P < 0.05; **P < 0.01; ***P < 0.001; ns, not significant.

In addition to thermotaxis, mouse pups have to develop efficient huddling behaviors (Alberts, 2007). Huddles of pups are aggregations established by the tendency of pups to approach one another and then actively maintain contact. By huddling, mouse pups are able to preserve their body heat and thus reduce their metabolic expenditure in relation to the ambient temperature, saving their energy for growth (Alberts, 2007). Pups within a huddle are observed to root and burrow between other bodies, climb on one another, crawl around the periphery and then re-enter the huddle (Schank and Alberts, 1997). A multitude of sensory cues, including tactile, thermal and olfactory stimuli, govern the behavior of pups (Alberts, 1978). We evaluated the huddling behaviors of Ctrl (n = 6) and Axo (n = 6) mice pups (at P5 and P9) placed in the middle of an arena at room temperature and recorded for 3 min (Figures 3D,E). Both phenotypes were active and typical features of huddling as “climbing” and “contact maintenance” were observed. In addition, the body temperatures as well as the size of the “mice aggregates” were similar between both groups (supplementary Movies 3, 4). Thus, the absence of a functional GG did not seem to affect the natural huddling behavior of mice pups. The evaluation of the phenotype was done by stereomicroscopy observations after the thermotaxis and huddling behavioral sessions at P15 (Figure 3F). Based on these results, these two behaviors are not governed by thermal sensors expressed in GG neurons.

At the single amphid neuron level, experiments have shown the modulation of neuronal responses by temperature (Biron et al., 2008; Kuhara et al., 2008). Moreover, it has been reported that cool temperature enhances the number of odorant-responsive GG neurons (Mamasuew et al., 2011a). We therefore tested the presence of a temperature-dependent chemosensitivity of GG neurons by calcium imaging on GG slices from adult and young OMP-GFP mice (Figures 4A,B). We first exposed GG neurons to continuous temperature variations of the bath perfusion (7–39°C) and observed a fine adjustment of the calcium level in the majority of GG neurons (Figure 4C; 36/48 cells). The recovery of the initial intracellular calcium level was only observed with a return to the basal temperature (Figure 4C). In an additional set of experiment, we observed that, as in amphid neurons, the coolness-evoked GG responses were also partially dependent on cGMP signaling. Indeed, L-cis Diltiazem (Dilt; 500 μM) was able to inhibit partially (69%) the calcium transients generated by a decrease in temperature from 25–7°C in responding neurons (n = 13/16) (Figure 4D; n = 13/13). The presence of extracellular calcium was necessary to observe the coolness-evoked response (Figure 4D; n = 13/13). We next evaluated the modulation of chemosensitivity of mouse GG neurons by temperature. We performed calcium imaging experiments and perfused mT at different temperatures (from 25–13°C). Interestingly, we observed in single neurons, increased mT responses with a decrease of the bath temperature (Figure 4E). This temperature modulation of the chemical response was significant when a 10°C difference was applied (Figure 4F). Thus, we here demonstrate that, not only the number of odorant-responsive GG neurons is increased by cool temperatures (Mamasuew et al., 2011a) but also the neuronal signal intensity.

FIGURE 4. The chemosensitivity of GG neurons is modulated by temperature variations. (A) GG coronal slice from an OMP-GFP mouse, GG neurons can be observed with their intrinsic GFP fluorescence and Hoffman modulation view (Hv). (B) Representative Fura-2AM loaded slice observed at 380 nm in color encoded map for unbound Fura during different combination of temperature and mT perfusion. (C–D) Fine adjustment of the calcium level in the majority of GG neurons by temperature variations (n number of responding neurons/number of total neurons = 36/48; 7 mice from P1–P19). Gray lines indicate bath temperature variations. (C) In perfusions of 10 min, a linear intracellular adaptation of the calcium level in a single GG neuron is observed during variations in bath temperature (n number of tested neurons = 23). (D) cGMP dependence of coolness-evoked GG responses. Calcium transients could be partially inhibited by L-cis diltiazem [Dilt 100 μ M (32%, n = 5); 500 μ M (69%, n = 16)] and totally in calcium-free medium (n = 16). (E) Representative calcium transients induced in GG neurons by perfusion of mT (100 μ M) at different temperatures. Perfusions of KCl (20 mM) were used as viability control. (F) Normalized increased response to mT in function of the temperature (n = 25; 3 mice from P4–P23). (G,H) The apparent temperature of the tip of the mouse nose (GG region; white arrowheads) is dependent on the ambient temperature. Thermal view of a female mouse head (P21). (G) The GG region is close to the ambient temperature (25 ± 3°C). (H) The same mouse is observed while sniffing a cold (upper panel, white square, 4°C) or a hot (lower panel, black square, 37°C) tube. The observed temperatures of the tip of the nose are 18 ± 2°C and 29 ± 3°C, respectively. Thermal gradient scales are indicated under the mouse head pictures. Fluorescence intensity Fura-2 ratio = F340/F380 is indicated by arbitrary units (a.u.). Perfusion times are indicated by horizontal bars. Traces illustrated in (C), (D), and (E) are representative responses observed in single GG neurons for each panel. Scale bars, 20 μm in (A) and (B). Values are expressed as mean ± s.e.m.;t-test, *P < 0.05; **P < 0.01; ***P < 0.001; ns, not significant.

Interestingly, thermal observations of the adult mouse head (Figures 4G,H) allowed us to estimate the temperature of the tip of the nose (corresponding to the GG localization). It appeared to vary with the environmental temperature, confirming the fact that GG neurons may be influenced by ambient temperature. Taken together, these results strongly support the notion of conserved multisensory modalities present in the mouse GG.

DISCUSSION

In C. elegans, the AWC class of amphid neurons are known to express different sets of proteins in parallel and convergent signaling pathways (Bargmann, 2006). These neurons possess canonical transduction cascade elements such as multi GPCRs odorant-like receptors, Gi-like proteins as well as non-canonical cGMP effectors such as membrane potential receptor guanylyl cyclases or downstream proteins like CNG channels and regulatory enzymes (Bargmann, 2006). In mouse GG neurons, we report here the expression of a set of homologous proteins that probably retained similar functions. GG neurons have indeed preserved their cGMP-dependent activities (Mamasuew et al., 2010, 2011b; Hanke et al., 2013) and are implicated both in thermo- and chemo- sensing suggesting an orthologous status for the involved signaling proteins (Altenhoff and Dessimoz, 2009). The relevance of cGMP involvement for both chemo and temperature sensing in GG neurons (Mamasuew et al., 2011b; Hanke et al., 2013) was verified in our study. We also found that extracellular calcium was necessary to record both types of cellular responses but, since L-cis Diltiazem is not only a selective inhibitor of cyclic-nucleotide gated channels (Kraus et al., 1998; Gomora and Enyeart, 1999; Takahira et al., 2005) we cannot rule out that ion channels other than CNGA3 are implicated in the observed neuronal activations as suggested previously (Schmid et al., 2010).

Multimodalities are known to be important for the animal to sense its olfactory environment (Ma, 2010). In addition to chemical stimulation, physical stimuli can elicit responses in the different mouse olfactory subsystems. In the MOE as well as in the SO, neurons respond to both odorants and pressure using the same signaling pathway (Grosmaitre et al., 2007). Moreover, TRPM5 positive neurons found in mouse nasal cavities respond to a large variety of irritant odorants and may also be able to detect differences in temperature (Talavera et al., 2005; Lin et al., 2008a,b; Tizzano et al., 2010). Similarly to these examples it appears that GG neurons are both chemo- and thermosensitive (Brechbühl et al., 2008, 2013; Mamasuew et al., 2008, 2011a,b; Schmid et al., 2010; Hanke et al., 2013). We found that mouse GG neurons were activated by ligands of AWC amphid neurons in particular by the 2,4,5-trimethylthiazole. Moreover, GG neurons, like AWC neurons, display a chemosensitivity, that also responds to temperature variations. Nevertheless, fundamental differences exist between the behavior of amphid and GG neurons that limit comparisons. Thus, the neuronal responses in GG neurons occur during exposure to chemical cues, but they were observed afterwards in absence of chemical cues for amphid neurons (Chalasani et al., 2007). In addition, methylated thiazole structures seem to be rather more attractive for the nematode (Bargmann, 2006), while they are repulsive for the mouse (Apfelbach et al., 2005). These differences might be explained by evolutionary adaptation such as the loss or gain of unknown cellular or molecular switches (Tsunozaki et al., 2008).

The localization of the GG, close to the entry of the naris, is influenced by changes in the environmental temperature. Nevertheless, we showed here that the absence of a functional GG does not interfere with two specific pup behaviors namely thermotaxis and huddling. However, at the single neuronal level, temperature can modulate the chemosensitivity of GG neurons; a mechanism that could be implicated in the fine adjustments necessary to modulate the sensitivity of the neurons exposed to the outside environment. Interestingly, this thermo-tuning of olfactory sensing is present throughout the animal kingdom such as in nematodes (Adachi et al., 2008) or in insects (Zeiner and Tichy, 2000; Riveron et al., 2009), indicating an inherited and conserved adaptation to the environmental pressure.

Danger cues such as APs and predator scents share a structural similarity that is detected by GG neurons (Brechbühl et al., 2013). The identified mouse APs, the 2-sec-butyl-4,5-dihydrothiazole as well as predator scents such as the fox 2,4,5-trimethylthiazoline and the bobcat 2,6-dimethylpyrazine or other pyrazine-related cues (Mamasuew et al., 2011a) are closely related to the methyl thiazole structure that activates C. elegans amphid neurons. Interestingly, most of these cues are natural products of bacterial metabolism (Brown, 1979; Schellinck and Brown, 2000; Apfelbach et al., 2005; Bargmann, 2006; Zhang, 2008). The detection of these products is known to be important for odortaxis in C. elegans (Bargmann, 2006). In rodents, these products are, for example, generated in the guts of predators and induce upon sensing immediate attention as well as avoidance and survival strategies (Apfelbach et al., 2005). Thus, we may speculate, that the ability of an organism to detect cues from similar origin (bacterial degradation) occurs in a cluster of specialized olfactory neurons (Enjin and Suh, 2013) that has been conserved throughout evolution.

AUTHOR CONTRIBUTIONS

Author contributions: Julien Brechbühl, Fabian Moine and Marie-Christine Broillet designed research; Julien Brechbühl, Fabian Moine, and Marie-Christine Broillet performed research; Julien Brechbühl, Fabian Moine and Marie-Christine Broillet analyzed data; and Julien Brechbühl and Marie-Christine Broillet wrote the paper.

ACKNOWLEDGMENTS

We thank I. Rodriguez for the OMP-GFP mice; the Cellular Imaging Facility of the University of Lausanne and its coordinator J.-Y. Chatton; V. Ackermann and S. Citherlet from the LESBAT of the EIVD for the thermal camera and practical advices; G. Luyet, N. Hurni and M. Nenniger Tosato for their excellent technical support; R. Stoop and I. Rodriguez for fruitful discussions on the manuscript. This work is supported by the Department of Pharmacology and Toxicology, University of Lausanne, and by Swiss National Foundation Grant FNS 3100A0-125192 (to Marie-Christine Broillet).

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://www.frontiersin.org/journal/10.3389/fnbeh.2013.00193/abstract

Movie 1 | Thermotaxis in a sham Ctrl mouse. A thermal gradient is generated in the behavioral arena. Here the Ctrl mouse pup (P5) is placed in the middle of the arena and the thermotaxis behavior is recorded with a thermal camera. Temperature is indicated by the pseudocolorized scale.

Movie 2 | Thermotaxis in an Axo mouse. A thermal gradient is generated in the behavioral arena. Here the Axo mouse pup (P5) is placed in the middle of the arena and the thermotaxis behavior is recorded with a thermal camera. Temperature is indicated by the pseudocolorized scale.

Movie 3 | Huddling in sham Ctrl mice. Here 6 Ctrl mouse pups (P5) are placed in the middle of an arena at RT. Huddling behaviors are recorded with a thermal camera. Temperature is indicated by the pseudocolorized scale.

Movie 4 | Huddling in Axo mice. Here 6 Axo mouse pups (P5) are placed in the middle of an arena at RT. Huddling behaviors are recorded with a thermal camera. Temperature is indicated by the pseudocolorized scale.

REFERENCES

Ache, B. W., and Young, J. M. (2005). Olfaction: diverse species, conserved principles. Neuron 48, 417–430. doi: 10.1016/j.neuron.2005.10.022

Adachi, R., Wakabayashi, T., Oda, N., and Shingai, R. (2008). Modulation of Caenorhabditis elegans chemotaxis by cultivation and assay temperatures. Neurosci. Res. 60, 300–306. doi: 10.1016/j.neures.2007.11.010

Alberts, J. R. (1978). Huddling by rat pups: multisensory control of contact behavior. J. Comp. Physiol. Psychol. 92, 220–230. doi: 10.1037/h0077458

Alberts, J. R. (2007). Huddling by rat pups: ontogeny of individual and group behavior. Dev. Psychobiol. 49, 22–32. doi: 10.1002/dev.20190

Alcedo, J., and Kenyon, C. (2004). Regulation of C. elegans longevity by specific gustatory and olfactory neurons. Neuron 41, 45–55. doi: 10.1016/S0896-6273(03)00816-X

Altenhoff, A. M., and Dessimoz, C. (2009). Phylogenetic and functional assessment of orthologs inference projects and methods. PLoS Comput. Biol. 5:e1000262. doi: 10.1371/journal.pcbi.1000262

Apfelbach, R., Blanchard, C. D., Blanchard, R. J., Hayes, R. A., and Mcgregor, I. S. (2005). The effects of predator odors in mammalian prey species: a review of field and laboratory studies. Neurosci. Biobehav. Rev. 29, 1123–1144. doi: 10.1016/j.neubiorev.2005.05.005

Bacaj, T., Tevlin, M., Lu, Y., and Shaham, S. (2008). Glia are essential for sensory organ function in C. elegans. Science 322, 744–747. doi: 10.1126/science.1163074

Bargmann, C. I. (2006). Chemosensation in C. elegans. WormBook, ed The C. elegans Research Community, 1–29. doi: 10.1895/wormbook.1.123.1

Bargmann, C. I., Hartwieg, E., and Horvitz, H. R. (1993). Odorant-selective genes and neurons mediate olfaction in C. elegans. Cell 74, 515–527. doi: 10.1016/0092-8674(93)80053-H

Battu, G., Hoier, E. F., and Hajnal, A. (2003). The C. elegans G-protein-coupled receptor SRA-13 inhibits RAS/MAPK signalling during olfaction and vulval development. Development 130, 2567–2577. doi: 10.1242/dev.00497

Birnby, D. A., Link, E. M., Vowels, J. J., Tian, H., Colacurcio, P. L., and Thomas, J. H. (2000). A transmembrane guanylyl cyclase (DAF-11) and Hsp90 (DAF-21) regulate a common set of chemosensory behaviors in Caenorhabditis elegans. Genetics 155, 85–104.

Biron, D., Wasserman, S., Thomas, J. H., Samuel, A. D., and Sengupta, P. (2008). An olfactory neuron responds stochastically to temperature and modulates Caenorhabditis elegans thermotactic behavior. Proc. Natl. Acad. Sci. U.S.A. 105, 11002–11007. doi: 10.1073/pnas.0805004105

Brechbühl, J., Klaey, M., and Broillet, M. C. (2008). Grueneberg ganglion cells mediate alarm pheromone detection in mice. Science 321, 1092–1095. doi: 10.1126/science.1160770

Brechbühl, J., Luyet, G., Moine, F., Rodriguez, I., and Broillet, M. C. (2011). Imaging pheromone sensing in a mouse vomeronasal acute tissue slice preparation. J. Vis. Exp. 58, e3311. doi: 10.3791/3311

Brechbühl, J., Moine, F., Klaey, M., Nenniger-Tosato, M., Hurni, N., Sporkert, F., et al. (2013). Mouse alarm pheromone shares structural similarity with predator scents. Proc. Natl. Acad. Sci. U.S.A. 110, 4762–4767. doi: 10.1073/pnas.1214249110

Brown, R. E. (1979). “Mammalian social odors: a critical review,” in Advances in the Study of Behavior, eds J. S. Rosenblatt, R. A. Hinde, C. Breer, and M.-C. Busnel (New York, NY: Academic Press), 103–162.

Chalasani, S. H., Chronis, N., Tsunozaki, M., Gray, J. M., Ramot, D., Goodman, M. B., et al. (2007). Dissecting a circuit for olfactory behaviour in Caenorhabditis elegans. Nature 450, 63–70. doi: 10.1038/nature06292

Coburn, C. M., and Bargmann, C. I. (1996). A putative cyclic nucleotide-gated channel is required for sensory development and function in C. elegans. Neuron 17, 695–706. doi: 10.1016/S0896-6273(00)80201-9

Coburn, C. M., Mori, I., Ohshima, Y., and Bargmann, C. I. (1998). A cyclic nucleotide-gated channel inhibits sensory axon outgrowth in larval and adult Caenorhabditis elegans: a distinct pathway for maintenance of sensory axon structure. Development 125, 249–258.

Dwyer, N. D., Troemel, E. R., Sengupta, P., and Bargmann, C. I. (1998). Odorant receptor localization to olfactory cilia is mediated by ODR-4, a novel membrane-associated protein. Cell 93, 455–466. doi: 10.1016/S0092-8674(00)81173-3

Enjin, A., and Suh, G. S. (2013). Neural mechanisms of alarm pheromone signaling. Mol. Cells 35, 177–181. doi: 10.1007/s10059-013-0056-3

Fleischer, J., Hass, N., Schwarzenbacher, K., Besser, S., and Breer, H. (2006a). A novel population of neuronal cells expressing the olfactory marker protein (OMP) in the anterior/dorsal region of the nasal cavity. Histochem. Cell Biol. 125, 337–349. doi: 10.1007/s00418-005-0077-x

Fleischer, J., Schwarzenbacher, K., Besser, S., Hass, N., and Breer, H. (2006b). Olfactory receptors and signalling elements in the Grueneberg ganglion. J. Neurochem. 98, 543–554. doi: 10.1111/j.1471-4159.2006.03894.x

Fleischer, J., Mamasuew, K., and Breer, H. (2009). Expression of cGMP signaling elements in the Grueneberg ganglion. Histochem. Cell Biol. 131, 75–88. doi: 10.1007/s00418-008-0514-8

Fleischer, J., Schwarzenbacher, K., and Breer, H. (2007). Expression of trace amine-associated receptors in the Grueneberg ganglion. Chem. Senses 32, 623–631. doi: 10.1093/chemse/bjm032

Frings, S., Lynch, J. W., and Lindemann, B. (1992). Properties of cyclic nucleotide-gated channels mediating olfactory transduction. Activation, selectivity, and blockage. J. Gen. Physiol. 100, 45–67. doi: 10.1085/jgp.100.1.45

Fujiwara, M., Sengupta, P., and Mcintire, S. L. (2002). Regulation of body size and behavioral state of C. elegans by sensory perception and the EGL-4 cGMP-dependent protein kinase. Neuron 36, 1091–1102. doi: 10.1016/S0896-6273(02)01093-0

Fuss, S. H., Omura, M., and Mombaerts, P. (2005). The Grueneberg ganglion of the mouse projects axons to glomeruli in the olfactory bulb. Eur. J. Neurosci. 22, 2649–2654. doi: 10.1111/j.1460-9568.2005.04468.x

Gomora, J. C., and Enyeart, J. J. (1999). Dual pharmacological properties of a cyclic AMP-sensitive potassium channel. J. Pharmacol. Exp. Ther. 290, 266–275.

Grosmaitre, X., Santarelli, L. C., Tan, J., Luo, M., and Ma, M. (2007). Dual functions of mammalian olfactory sensory neurons as odor detectors and mechanical sensors. Nat. Neurosci. 10, 348–354. doi: 10.1038/nn1856

Gruneberg, H. (1973). A ganglion probably belonging to the N. terminalis system in the nasal mucosa of the mouse. Z. Anat Entwicklungsgesch. 140, 39–52. doi: 10.1007/BF00520716

Hanke, W., Mamasuew, K., Biel, M., Yang, R. B., and Fleischer, J. (2013). Odorant-evoked electrical responses in Grueneberg ganglion neurons rely on cGMP-associated signaling proteins. Neurosci. Lett. 539, 38–42. doi: 10.1016/j.neulet.2013.01.032

Inglis, P. N., Ou, G., Leroux, M. R., and Scholey, J. M. (2007). The sensory cilia of Caenorhabditis elegans. WormBook, ed The C. elegans Research Community, 1–22. doi: 10.1895/wormbook.1.126.2

Jansen, G., Thijssen, K. L., Werner, P., Van Der Horst, M., Hazendonk, E., and Plasterk, R. H. (1999). The complete family of genes encoding G proteins of Caenorhabditis elegans. Nat. Genet. 21, 414–419. doi: 10.1038/7753

Kaupp, U. B., and Seifert, R. (2002). Cyclic nucleotide-gated ion channels. Physiol. Rev. 82, 769–824. doi: 10.1152/physrev.00008.2002

Kiyokawa, Y., Kikusui, T., Takeuchi, Y., and Mori, Y. (2004). Alarm pheromones with different functions are released from different regions of the body surface of male rats. Chem. Senses 29, 35–40. doi: 10.1093/chemse/bjh004

Kiyokawa, Y., Shimozuru, M., Kikusui, T., Takeuchi, Y., and Mori, Y. (2006). Alarm pheromone increases defensive and risk assessment behaviors in male rats. Physiol. Behav. 87, 383–387. doi: 10.1016/j.physbeh.2005.11.003

Koos, D. S., and Fraser, S. E. (2005). The Grueneberg ganglion projects to the olfactory bulb. Neuroreport 16, 1929–1932. doi: 10.1097/01.wnr.0000186597.72081.10

Kraus, R. L., Hering, S., Grabner, M., Ostler, D., and Striessnig, J. (1998). Molecular mechanism of diltiazem interaction with L-type Ca2+ channels. J. Biol. Chem. 273, 27205–27212. doi: 10.1074/jbc.273.42.27205

Kuhara, A., Okumura, M., Kimata, T., Tanizawa, Y., Takano, R., Kimura, K. D., et al. (2008). Temperature sensing by an olfactory neuron in a circuit controlling behavior of C. elegans. Science 320, 803–807. doi: 10.1126/science.1148922

Lans, H., Rademakers, S., and Jansen, G. (2004). A network of stimulatory and inhibitory Galpha-subunits regulates olfaction in Caenorhabditis elegans. Genetics 167, 1677–1687. doi: 10.1534/genetics.103.024786

Lee, J. I., O'halloran, D. M., Eastham-Anderson, J., Juang, B. T., Kaye, J. A., Scott Hamilton, O., et al. (2010). Nuclear entry of a cGMP-dependent kinase converts transient into long-lasting olfactory adaptation. Proc. Natl. Acad. Sci. U.S.A. 107, 6016–6021. doi: 10.1073/pnas.1000866107

L'etoile, N. D., and Bargmann, C. I. (2000). Olfaction and odor discrimination are mediated by the C. elegans guanylyl cyclase ODR-1. Neuron 25, 575–586. doi: 10.1016/S0896-6273(00)81061-2

L'etoile, N. D., Coburn, C. M., Eastham, J., Kistler, A., Gallegos, G., and Bargmann, C. I. (2002). The cyclic GMP-dependent protein kinase EGL-4 regulates olfactory adaptation in C. elegans. Neuron 36, 1079–1089. doi: 10.1016/S0896-6273(02)01066-8

Lin, W., Ezekwe, E. A. Jr., Zhao, Z., Liman, E. R., and Restrepo, D. (2008a). TRPM5-expressing microvillous cells in the main olfactory epithelium. BMC Neurosci. 9:114. doi: 10.1186/1471-2202-9-114

Lin, W., Ogura, T., Margolskee, R. F., Finger, T. E., and Restrepo, D. (2008b). TRPM5-expressing solitary chemosensory cells respond to odorous irritants. J. Neurophysiol. 99, 1451–1460. doi: 10.1152/jn.01195.2007

Liu, C. Y., Fraser, S. E., and Koos, D. S. (2009). Grueneberg ganglion olfactory subsystem employs a cGMP signaling pathway. J. Comp. Neurol. 516, 36–48. doi: 10.1002/cne.22096

Liu, C. Y., Xiao, C., Fraser, S. E., Lester, H. A., and Koos, D. S. (2012). Electrophysiological characterization of Grueneberg ganglion olfactory neurons: spontaneous firing, sodium conductance, and hyperpolarization-activated currents. J. Neurophysiol. 108, 1318–1334. doi: 10.1152/jn.00907.2011

Ma, M. (2010). “Multiple olfactory subsystems convey various sensory signals,” in The Neurobiology of Olfaction, ed A. Menini (Boca Raton, FL: Taylor and Francis Group, LLC), 225–240. doi: 10.1201/9781420071993-c9

Mamasuew, K., Breer, H., and Fleischer, J. (2008). Grueneberg ganglion neurons respond to cool ambient temperatures. Eur. J. Neurosci. 28, 1775–1785. doi: 10.1111/j.1460-9568.2008.06465.x

Mamasuew, K., Hofmann, N., Breer, H., and Fleischer, J. (2011a). Grueneberg ganglion neurons are activated by a defined set of odorants. Chem. Senses 36, 271–282. doi: 10.1093/chemse/bjq124

Mamasuew, K., Hofmann, N., Kretzschmann, V., Biel, M., Yang, R. B., Breer, H., et al. (2011b). Chemo- and thermosensory responsiveness of Grueneberg ganglion neurons relies on cyclic guanosine monophosphate signaling elements. Neurosignals 19, 198–209. doi: 10.1159/000329333

Mamasuew, K., Michalakis, S., Breer, H., Biel, M., and Fleischer, J. (2010). The cyclic nucleotide-gated ion channel CNGA3 contributes to coolness-induced responses of Grueneberg ganglion neurons. Cell. Mol. Life Sci. 67, 1859–1869. doi: 10.1007/s00018-010-0296-8

Margolis, F. L. (1972). A brain protein unique to the olfactory bulb. Proc. Natl. Acad. Sci. U.S.A. 69, 1221–1224. doi: 10.1073/pnas.69.5.1221

Matsuo, T., Rossier, D. A., Kan, C., and Rodriguez, I. (2012). The wiring of Grueneberg ganglion axons is dependent on neuropilin 1. Development 139, 2783–2791. doi: 10.1242/dev.077008

Mccleskey, E. W. (1997). Thermoreceptors: recent heat in thermosensation. Curr. Biol. 7, R679–681. doi: 10.1016/S0960-9822(06)00354-X

Mombaerts, P., Wang, F., Dulac, C., Chao, S. K., Nemes, A., Mendelsohn, M., et al. (1996). Visualizing an olfactory sensory map. Cell 87, 675–686. doi: 10.1016/S0092-8674(00)81387-2

Mori, I., and Ohshima, Y. (1995). Neural regulation of thermotaxis in Caenorhabditis elegans. Nature 376, 344–348. doi: 10.1038/376344a0

Munger, S. D., Leinders-Zufall, T., and Zufall, F. (2009). Subsystem organization of the mammalian sense of smell. Annu. Rev. Physiol. 71, 115–140. doi: 10.1146/annurev.physiol.70.113006.100608

O'halloran, D. M., Altshuler-Keylin, S., Lee, J. I., and L'etoile, N. D. (2009). Regulators of AWC-mediated olfactory plasticity in Caenorhabditis elegans. PLoS Genet. 5:e1000761. doi: 10.1371/journal.pgen.1000761

O'halloran, D. M., Hamilton, O. S., Lee, J. I., Gallegos, M., and L'etoile, N. D. (2012). Changes in cGMP levels affect the localization of EGL-4 in AWC in Caenorhabditis elegans. PLoS ONE 7:e31614. doi: 10.1371/journal.pone.0031614

Pacheco-Cobos, L., Rosetti, M., Distel, H., and Hudson, R. (2003). To stay or not to stay: the contribution of tactile and thermal cues to coming to rest in newborn rabbits. J. Comp. Physiol. A Neuroethol. Sens Neural Behav. Physiol. 189, 383–389. doi: 10.1007/s00359-003-0413-3

Potter, S. M., Zheng, C., Koos, D. S., Feinstein, P., Fraser, S. E., and Mombaerts, P. (2001). Structure and emergence of specific olfactory glomeruli in the mouse. J. Neurosci. 21, 9713–9723.

Pyrski, M., Koo, J. H., Polumuri, S. K., Ruknudin, A. M., Margolis, J. W., Schulze, D. H., et al. (2007). Sodium/calcium exchanger expression in the mouse and rat olfactory systems. J. Comp. Neurol. 501, 944–958. doi: 10.1002/cne.21290

Riveron, J., Boto, T., and Alcorta, E. (2009). The effect of environmental temperature on olfactory perception in Drosophila melanogaster. J. Insect Physiol. 55, 943–951. doi: 10.1016/j.jinsphys.2009.06.009

Roayaie, K., Crump, J. G., Sagasti, A., and Bargmann, C. I. (1998). The G alpha protein ODR-3 mediates olfactory and nociceptive function and controls cilium morphogenesis in C. elegans olfactory neurons. Neuron 20, 55–67. doi: 10.1016/S0896-6273(00)80434-1

Roppolo, D., Ribaud, V., Jungo, V. P., Luscher, C., and Rodriguez, I. (2006). Projection of the Gruneberg ganglion to the mouse olfactory bulb. Eur. J. Neurosci. 23, 2887–2894. doi: 10.1111/j.1460-9568.2006.04818.x

Schank, J. C., and Alberts, J. R. (1997). Self-organized huddles of rat pups modeled by simple rules of individual behavior. J. Theor. Biol. 189, 11–25. doi: 10.1006/jtbi.1997.0488

Schellinck, H. M., and Brown, R. E. (2000). Selective depletion of bacteria alters but does not eliminate odors of individuality in Rattus norvegicus. Physiol. Behav. 70, 261–270. doi: 10.1016/S0031-9384(00)00277-8

Schmid, A., Pyrski, M., Biel, M., Leinders-Zufall, T., and Zufall, F. (2010). Grueneberg ganglion neurons are finely tuned cold sensors. J. Neurosci. 30, 7563–7568. doi: 10.1523/JNEUROSCI.0608-10.2010

Sengupta, P., Chou, J. H., and Bargmann, C. I. (1996). odr-10 encodes a seven transmembrane domain olfactory receptor required for responses to the odorant diacetyl. Cell 84, 899–909. doi: 10.1016/S0092-8674(00)81068-5

Serra, J., and Nowak, R. (2008). Olfactory preference for own mother and litter in 1-day-old rabbits and its impairment by thermotaxis. Dev. Psychobiol. 50, 542–553. doi: 10.1002/dev.20327

Storan, M. J., and Key, B. (2006). Septal organ of Gruneberg is part of the olfactory system. J. Comp. Neurol. 494, 834–844. doi: 10.1002/cne.20858

Tachibana, T., Fujiwara, N., and Nawa, T. (1990). The ultrastructure of the ganglionated nerve plexus in the nasal vestibular mucosa of the musk shrew (Suncus murinus, insectivora). Arch. Histol. Cytol. 53, 147–156. doi: 10.1679/aohc.53.147

Takahira, M., Sakurai, M., Sakurada, N., and Sugiyama, K. (2005). Fenamates and diltiazem modulate lipid-sensitive mechano-gated 2P domain K(+) channels. Pflugers Arch. 451, 474–478. doi: 10.1007/s00424-005-1492-5

Talavera, K., Yasumatsu, K., Voets, T., Droogmans, G., Shigemura, N., Ninomiya, Y., et al. (2005). Heat activation of TRPM5 underlies thermal sensitivity of sweet taste. Nature 438, 1022–1025. doi: 10.1038/nature04248

Tizzano, M., Gulbransen, B. D., Vandenbeuch, A., Clapp, T. R., Herman, J. P., Sibhatu, H. M., et al. (2010). Nasal chemosensory cells use bitter taste signaling to detect irritants and bacterial signals. Proc. Natl. Acad. Sci. U.S.A. 107, 3210–3215. doi: 10.1073/pnas.0911934107

Tonkin, L. A., Saccomanno, L., Morse, D. P., Brodigan, T., Krause, M., and Bass, B. L. (2002). RNA editing by ADARs is important for normal behavior in Caenorhabditis elegans. EMBO J. 21, 6025–6035. doi: 10.1093/emboj/cdf607

Tsunozaki, M., Chalasani, S. H., and Bargmann, C. I. (2008). A behavioral switch: cGMP and PKC signaling in olfactory neurons reverses odor preference in C. elegans. Neuron 59, 959–971. doi: 10.1016/j.neuron.2008.07.038

Vowels, J. J., and Thomas, J. H. (1994). Multiple chemosensory defects in daf-11 and daf-21 mutants of Caenorhabditis elegans. Genetics 138, 303–316.

Zalaquett, C., and Thiessen, D. (1991). The effects of odors from stressed mice on conspecific behavior. Physiol. Behav. 50, 221–227. doi: 10.1016/0031-9384(91)90524-R

Zeiner, R., and Tichy, H. (2000). Integration of temperature and olfactory information in cockroach antennal lobe glomeruli. J. Comp. Physiol. A 186, 717–727. doi: 10.1007/s003590000125

Zhang, Y. (2008). Neuronal mechanisms of Caenorhabditis elegans and pathogenic bacteria interactions. Curr. Opin. Microbiol. 11, 257–261. doi: 10.1016/j.mib.2008.04.003

Zufall, F., and Munger, S. D. (2010). Receptor guanylyl cyclases in mammalian olfactory function. Mol. Cell Biochem 334, 191–197. doi: 10.1007/s11010-009-0325-9

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 10 October 2013; Accepted: 20 November 2013; Published online: 09 December 2013.

Citation: Brechbühl J, Moine F and Broillet M-C (2013) Mouse Grueneberg ganglion neurons share molecular and functional features with C. elegans amphid neurons. Front. Behav. Neurosci. 7:193. doi: 10.3389/fnbeh.2013.00193

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2013 Brechbühl, Moine and Broillet. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 05 May 2014
doi: 10.3389/fnbeh.2014.00145

Sniff adjustment in an odor discrimination task in the rat: analytical or synthetic strategy?

Emmanuelle Courtiol†, Laura Lefèvre, Samuel Garcia, Marc Thévenet, Belkacem Messaoudi and Nathalie Buonviso[*]

Centre de Recherche en Neurosciences de Lyon, Equipe Olfaction: du codage à la mémoire, CNRS UMR 5292—INSERM U1028—Université Lyon1, Lyon, France

* Correspondence: Nathalie Buonviso, Centre de Recherche en Neurosciences de Lyon, Equipe Olfaction: du codage à la mémoire, CNRS UMR 5292—INSERM U1028—Université Lyon1, 50 Avenue Tony Garnier, 69366 Lyon, Cedex 07, France e-mail: buonviso@olfac.univ-lyon1.fr

†Present address: Emmanuelle Courtiol, Emotional Brain Institute, New York University Langone Medical Center, Department of Child and Adolescent Psychiatry and the Nathan Kline Institute for Psychiatric Research, NY, USA

Edited by:
Regina M. Sullivan, Nathan Kline Institute and NYU School of Medicine, USA

Reviewed by:
Christiane Linster, Cornell University, USA
Daniel W. Wesson, Case Western Reserve University, USA

A growing body of evidence suggests that sniffing is not only the mode of delivery for odorant molecules but also contributes to olfactory perception. However, the precise role of sniffing variations remains unknown. The zonation hypothesis suggests that animals use sniffing variations to optimize the deposition of odorant molecules on the most receptive areas of the olfactory epithelium (OE). Sniffing would thus depend on the physicochemical properties of odorants, particularly their sorption. Rojas-Líbano and Kay (2012) tested this hypothesis and showed that rats used different sniff strategies when they had to target a high-sorption (HS) molecule or a low-sorption (LS) molecule in a binary mixture. Which sniffing strategy is used by rats when they are confronted to discrimination between two similarly sorbent odorants remains unanswered. Particularly, is sniffing adjusted independently for each odorant according to its sorption properties (analytical processing), or is sniffing adjusted based on the pairing context (synthetic processing)? We tested these hypotheses on rats performing a two-alternative choice discrimination of odorants with similar sorption properties. We recorded sniffing in a non-invasive manner using whole-body plethysmography during the behavioral task. We found that sniffing variations were not only a matter of odorant sorption properties and that the same odorant was sniffed differently depending on the odor pair in which it was presented. These results suggest that rather than being adjusted analytically, sniffing is instead adjusted synthetically and depends on the pair of odorants presented during the discrimination task. Our results show that sniffing is a specific sensorimotor act that depends on complex synthetic processes.

Keywords: sniffing, olfaction, rat, sorption properties, discrimination, olfactomotor act

INTRODUCTION

Sampling of sensory information is achieved through dedicated motor systems. In olfaction, sniffing allows a rhythmic sampling of the environment and constrains both the timing and the intensity of the input to the olfactory structures. A remarkable feature of sniffing is its highly dynamic nature; sniffing in rats varies both in frequency and flow rate (Youngentob et al., 1987). This characteristic raises the important question of what the implications of these variations are on olfactory processing. The zonation hypothesis, proposed by Schoenfeld and Cleland (2005, 2006), is based on the observation that the olfactory epithelium (OE) is activated by both imposed and inherent patterns (for review, see Scott, 2006). Imposed patterns are determined by a complex interplay between the sorption properties of odorant molecules and the rate of nasal airflow, which affects the deposition of molecules across the OE (Mozell, 1964a,b; Mozell and Jagodowicz, 1973). In contrast, inherent patterns are determined by populations of olfactory neurons with different receptive properties distributed in distinct regions of the OE (Moulton, 1976; Kent and Mozell, 1992; Vassar et al., 1993; Freitag et al., 1995; Yoshihara and Mori, 1997). Hence, modifications of sniffing parameters affect the deposition of odorant molecules and the activation of the OE (Ezeh et al., 1995; Scott-Johnson et al., 2000; Scott et al., 2006; Yang et al., 2007; Scott et al., 2014). This body of evidence led Schoenfeld and Cleland (2005, 2006) to propose that during odor discrimination, an animal may adapt its sniffing parameters to optimize the deposition of odorant molecules on the most receptive OE areas. Sniffing would thus depend on the physicochemical properties of the odorant molecules, particularly their sorption (which depends notably on their water solubility and volatility). Rojas-Líbano and Kay (2012) tested this hypothesis and showed that rats used different sniff strategies when targeting a high-sorption (HS) molecule or a low-sorption (LS) molecule in a binary mixture. However, the strategy used for sniff adjustment when animal is confronted to a choice discrimination between two similarly sorbent odorants is still unknown. Indeed, at least two alternative strategies might exist for performing a two-alternative choice odor discrimination: (1) sniffing is adjusted independently for each odorant according to its sorption properties (analytical processing), or (2) sniffing is not adjusted independently for each odorant but is instead adjusted based on the pairing context (synthetic processing). The goal of this report was to test these possible sniffing strategies in the rat. We developed a method to non-invasively record sniffing to maintain physiological sniffing dynamics (Teichner, 1966). We used whole-body plethysmography with a two-alternative choice odor discrimination (Uchida and Mainen, 2003). We showed that similar sorption properties did not inevitably endow molecules with the property to be similarly sniffed. Moreover, we showed that the same odorant was sniffed differently depending on the odor pair in which it was presented. These results suggest that sniffing is adjusted in a synthetic manner that is dependent on the context in which the odorant is presented.

MATERIALS AND METHODS

ANIMALS

Data were obtained from five male Long–Evans rats (Charles River, l'Arbresle, France) that weighed 250–300 g at the start of experimentation. Animals were housed individually at 23°C and were maintained under a 12 h light–dark cycle (lights on from 6:00 a.m. to 6:00 p.m.). Food was available ad libitum during the experiment. Rats were placed under water restriction, with access to water provided during the behavioral session and for 1 h after each session. Experiments were performed in strict accordance with the European Community Council directive of November 24, 1986 (86/609/EEC), and the guidelines of the French Ethical Committee and French Legislation.

SNIFF RECORDING

The recording apparatus consisted of a whole-body customized plethysmograph (diameter: 20 cm, height: 30 cm, EMKA Technologies, France; Figure 1A) placed in a homemade sound-attenuating cage (length: 60 cm, width: 60 cm, height: 70 cm). The apparatus was composed of two independent airtight chambers: the animal chamber and the reference chamber. The pressure changes that resulted from animal respiration were measured by a differential pressure transducer (Model dpt, EMKA Technologies) with one sensor in the animal chamber and another in the reference chamber. The measured signal was amplified, digitally sampled at 1 kHz and acquired with a PC using an acquisition card (MC-1608FS, Measurement Computing, USA).

FIGURE 1. Non-invasive sniff recording. (A) Schematic representation of the whole-body plethysmography. This setup allows us to access the respiratory signal of the animal in a non-invasive manner. The plethysmograph is equipped with three ports: an odor port (pink, central) connected to the olfactometer and two reward ports (black and white circles surrounded with blue lines) equidistant from the central odor port. (B) Example of one trial. The rat starts the trial by poking its nose into the central port; this motion triggers the delivery of an odorant for 3 s (green). Each odorant was associated with a reward port: odorant A/left port and odorant B/right port. If the rat makes the correct choice, water is available at the reward port for 6 s from the beginning of the trial. The first lick (light green) triggers the delivery of 60 μL of water (blue).

The whole-body plethysmograph was equipped with three ports (inner diameter: 2 cm, depth: 2.5 cm; Figure 1A). The odor port was elevated 8 cm from the floor and was between two reward ports that were located 6 cm to the left and right of the odor port. This central odor port was connected to a homemade olfactometer. Odorants were delivered at a constant flow of 400 mL/min. Constant deodorized air also flowed through the top of the plethysmograph at a rate of 1100 mL/min.

To maintain a constant flow through the plethysmograph and to leave the sniffing signal undisturbed, a ventilation pump was connected to the whole body plethysmograph to vacuum out the equivalent of the air pushed into the chamber at 1500 mL/min (1100 + 400 mL/min).

The reward ports contained a pipette connected to a water pump. Each port was equipped with a capacitive sensor that allowed nose poke detection in the odor port and lick detection in each of the reward ports.

BEHAVIORAL TRAINING

Task

We adapted a two-alternative choice odor discrimination task developed by Uchida and Mainen (2003). Rats started a trial by poking their nose into the central port. The nose poke triggered the delivery of an odorant for 3 s. Each odorant of an odor pair was associated with a reward port: odorant A/left port and odorant B/right port (Figure 1B). The rat had 6 s to reach a reward port and lick for water. If the rat made the correct choice, the first lick triggered the delivery of 60 μL of water for 2 s. The inter-trial interval was at least 7 s. Each day, the rats performed a session with an odor pair. Each session was composed of 60–100 trials.

Animals were considered successful at discriminating an odor pair when they achieved the training criterion of 70% correct trials for each reward port on two consecutive days.

Odorants

We used the following odorants at saturated vapor pressure (Sigma-Aldrich, Fluka): methyl benzoate (mbz), ethyl benzoate (etbz), both enantiomers of carvone (L-car/D-car), both enantiomers of limonene (L-lim/D-lim), isoamyl acetate (iso), heptanol (hept), cumene (cum), and cyclooctane (cyc). Odorants were classified as LS or HS, as in Rojas-Líbano and Kay (2012). The sorption coefficients for all molecules are listed in Table 1. The odor pairs used were (Table 1):

• Two pairs of enantiomers: LS/LS molecules L-lim/D-lim and HS/HS molecules L-car/D-car. Enantiomers are well suited to form similar odor pairs because they possess the same physicochemical properties (Table 1) and evoke closely related responses in the OE (Schoenfeld and Cleland, 2005, 2006; Scott, 2006).

• Two pairs of non-enantiomeric odorants with similar sorption properties and comparable vapor pressures: cum/cyc (LS/LS) and hept/mbz (HS/HS).

• One pair of odorants with similar sorption properties (HS/HS) but different vapor pressures: hept/iso.

• We also tested whether an odorant induced a specific sniffing pattern or whether the pattern varied based on the pair in which the odorant was presented. For this purpose, we used the same odorant in the following pairs: D-car in the L-car/D-car pair and in the D-car/D-lim pair; hept in the hept/iso pair and in the hept/mbz pair and D-lim in the L-lim/D-lim pair and in the D-car/D-lim pair.

Table 1. Physicochemical properties of the odorant molecules: vapor pressure (VP, in mm Hg at 25°C), Henry's law constant (Kaw1 from the group method and Kaw2 from the bond method; in atm-m^3/mole) and S (sorptiveness).

In the discrimination task, seven pairs of odorants were used. All rats first learned the rule of the task with the odor pair mbz/etbz. Data acquired with this pair were not included in the analysis. The other odor pairs were randomly presented once the rats reached the criterion performance.

DATA PROCESSING

The sampling duration (Sd), time of odorant delivery, number of licks on each reward port, time of water delivery, and respiratory signal were recorded and stored in an SQL database using OpenElectrophy (Garcia and Fourcaud-Trocmé, 2009).

Respiratory signal

Using the whole-body plethysmography setup, the natural respiratory signal was a periodic function showing alternating negative (inspiration) and positive (expiration) deflections (Figure 2A). A key aspect of respiratory signal analysis was the detection of these deflections to measure respiratory cycles, which was achieved using an algorithm described in Roux et al. (2006). The algorithm performed signal smoothing for noise reduction and detection of zero-crossing points to accurately define the inspiration and expiration phases. The inspiration phase started at the zero-crossing point of the falling phase and ended at the zero-crossing point of the rising phase. The expiration phase started at the zero-crossing point of the rising phase and ended at the zero-crossing point of the falling phase (Figure 2A). In addition, to eliminate detection artifacts, we determined a cut-off value for signal duration (rejection if value < median/4) and for signal amplitude (rejection if value < median/6).

FIGURE 2. Sniffing signal processing. (A) Top: Raw sniffing signal recorded by the plethysmograph. An algorithm was applied to detect the zero-crossing points. Bottom: The blue squares represent the detection of the beginning of the inspiratory phase, and the violet squares represent the beginning of the expiratory phase. (B1) Sampling duration and number of sniffs. Sampling duration (Sd) is defined as the time spent in the odor port. The number of sniffs (Ns) is defined as the number of sniffs occurring during the sampling period (pink square). (B2) A representative sniff cycle is shown to illustrate the parameters measured: inspiration duration (ID), expiration duration (ED), and inspiration peak flow rate (IPF).

Sniffing parameters

Before each session, the plethysmograph was calibrated by pushing 1 mL of air into the rat chamber. The resulting pressure variation in the experimental box was recorded, allowing measurement of the respiratory volume. Respiratory cycles were measured during the sampling period (Sd, Figure 2B1), and the number of sniffs (Ns, Figure 2B1) during this period was collected. Sniffs that were considered as occurring during the sampling period were cycles in which the inspiration was included in the sampling period (Figure 2B1). For each respiratory cycle, inspiration peak flow rate (IPF), inspiration duration (ID), and expiration duration (ED; Figure 2B2) were measured. To compare the values between rats and across time, we performed data normalization for each cycle by dividing the value of each parameter for each sniff by the mean value of each parameter over the whole session.

STATISTICS

Python scripts (scipy.stats) and Statview were used for all statistical analyses. We first measured the average duration of the sampling period for all the experimental sessions. We removed trials whose duration was greater than the mean sampling time plus 2 standard deviations.

The analysis focused on the respiratory signals of sessions in which the animals achieved ≥70% accuracy. Only periods resulting in a correct behavioral response were considered. To compare sniffing or sampling parameters between two odorants, we used a paired t-test, with the level of significance set at 0.05 (*p < 0.05, **p < 0.01, and ***p < 0.001).

VALIDATION

To validate our setup, we presented D-lim at two different concentrations. As demonstrated previously (Youngentob et al., 1987), we found that a decrease in odorant concentration led to an increase in IPF (data not shown). This control shows that our experimental setup allowed us to accurately measure sniffing and reproduce data obtained by others.

RESULTS

We measured sniffing in unrestrained animals performing a two-alternative choice odor discrimination task (Figure 1). Global sniffing strategies were measured according to the Sd (time spent in the odor port) and the Ns (number of sniffs occurring during the Sd, Figure 2B1). We also individually analyzed each sniff cycle during the Sd. For all pairs combined, animals sampled odorants with 3.3 ± 0.026 sniffs (mean ± s.e.m.). We therefore focused our analysis on the first three sniffs following the odor onset (first, second, and third sniffs). For these three cycles, we analyzed the normalized ID, the normalized IPF, and the normalized ED (Figure 2B2).

Sniffing parameters were compared between odorants presented in a pair of odorants with similar sorption properties. Pairs were either HS enantiomeric (L-car/D-car), LS enantiomeric (L-lim/D-lim), HS non-enantiomeric (hept/mbz and hept/iso, with comparable and different vapor pressures in the odor pair, respectively), or LS non-enantiomeric (cum/cyc) molecules. On the whole, five pairs of odorants with similar sorption properties were tested. To test the effect of the context, we additionally used the odor pair D-car/D-lim.

ODORANT SORPTION AND SNIFF ADJUSTMENT: AN ANALYTICAL STRATEGY IS NOT SUPPORTED

Pairs of enantiomeric odorants

For the two pairs of enantiomers we tested (LS/LS and HS/HS pairs), the global sampling parameters were similar; animals sniffed these enantiomers with a similar Sd and a similar Ns [Figure 3A, Sd: L-car/D-car pair t(194) = −0.323, p = 0.74; L-lim/D-lim pair t(210) = −0.085, p = 0.93, Ns: L-car/D-car pair t(194) < 0.001, p > 0.05; L-lim/D-lim pair t(210) = −0.592, p = 0.56]. Similarly, an analysis of the fine sniffing parameters revealed few or no significant differences between enantiomeric odorants, regardless of the sorption properties, as shown in Figure 3B. For the L-car/D-car pair (Figure 3B, left), a significant difference appeared only in the ID during the second cycle [t(187) = 2.994, p < 0.01] and in the IPF during the first cycle [t(194) = −2.044, p < 0.05]. We also observed few differences between L-lim and D-lim (Figure 3B, right) with a significant difference only in the ID during the second cycle [t(200) = 2.207, p < 0.05]. Thus, very similar molecules, such as the two pairs of enantiomers tested, induce similar sniffing strategies.

FIGURE 3. Enantiomers are sniffed similarly. (A) Global sampling parameters (mean ± s.e.m.), Sd (left) and Ns (right), for each odorant in each enantiomer odor pair: L-car (magenta)/D-car (red) n = 195; L-lim (cyan)/D-lim (dark blue) n = 211. (B) Modulation of sniff parameters in the first, second, and third cycles for (left to right) L-car/D-car and L-lim/D-lim. From top to bottom: mean (± s.e.m.) normalized ID, IPF, and ED. Same colors as in (A). The number of trials for each odorant and cycle in L-car/D-car pair is: ncycle 1 = 195, ncycle 2 = 188, and ncycle 3 = 175 and in L-lim/D-lim pair is: ncycle 1 = 211, ncycle 2 = 201, and ncycle 3 = 162. Data were analyzed using a paired t-test; *p < 0.05; and **p < 0.01.

Pairs of non-enantiomeric odorants

In a second step, we analyzed sniffing strategies when the animals had to discriminate between odorants that had similar sorption properties but were non-enantiomers (Figure 4). Here, we surprisingly observed more heterogeneous results; the odorants of the LS/LS pair (cum/cyc) were sampled similarly, but the odorants of the HS/HS pair (hept/mbz) were sampled differently. As shown in Figure 4, both global sampling parameters [Figure 4A, Sd: t(165) = −0.513, p = 0.6084; Ns: t(165) = −0.985, p = 0.3261] and fine parameters (Figure 4B, right) of individual sniffs were similar for cum and cyc. In contrast, both the Sd and Ns were significantly different between HS odorants hept and mbz [Figure 4A, Sd: t(167) = −4.295, p < 0.001; Ns: t(167) = −3.553, p < 0.001]. When we examined each cycle individually (Figure 4B, left), we observed only a few differences between hept and mbz with only one significant difference in the ID during the third cycle [t(143) = −2.691, p < 0.01]. Thus, for the two different pairs of odorant molecules used that were non-enantiomers, we did not observe a sorption-based rule; molecules could be sniffed either similarly or differently even if they were endowed with similar sorption properties. This finding suggests that sorption is not the only parameter involved in sniffing adjustment and is confirmed by results from another pair of odorants, hept/iso, which have similar sorption properties (HS/HS, see Table 1) but differ in their vapor pressure. The results presented in Figure 5 reveal that both global sampling parameters [Figure 5A, Sd: t(149) = 7.603, p < 0.001; Ns: t(149) = 10.049, p < 0.001] and fine parameters of individual sniffs [Figure 5B, IPF second cycle: t(136) = 3.551, p < 0.001; third cycle: t(64) = 6.962, p < 0.001; ED first cycle: t(149) = −2.553, p < 0.05; second cycle: t(136) = −7.941, p < 0.001; third cycle: t(64) = −7.952, p < 0.001] were significantly different between hept and iso. Thus, vapor pressure seems to enhance differences between sniffing strategies.

FIGURE 4. Odorants with similar sorption properties but which are not enantiomers can induce different sniffing strategies. (A) Global sampling parameters (mean ± s.e.m.), Sd (left), and Ns (right), for non-enantiomer odor pairs: hept (green)/mbz (orange) n = 168; cum (brown)/cyc (gray) n = 166. (B) Modulation of respiratory parameters in the first, second, and third cycles for (left to right) hept/mbz and cum/cyc. From top to bottom: mean (± s.e.m.) normalized ID, IPF, and ED. Same colors as in (A). The number of trials for each odorant and cycle in hept/mbz pair is: ncycle 1 = 168, ncycle 2 = 163, and ncycle 3 = 144 and in cum/cyc pair is: ncycle 1 = 166, ncycle 2 = 160, and ncycle 3 = 110. Data were analyzed using a paired t-test; **p < 0.01; and ***p < 0.001.

FIGURE 5. Sorption is not the only parameter that determines global sniffing variations. (A) Global sampling parameters (mean ± s.e.m.), Sd (left), and Ns (right), for non-enantiomer odor pair: hept (green)/iso (black) n = 150. (B) Modulation of respiratory parameters in the first, second, and third cycles for hept/iso. From top to bottom: mean (± s.e.m.) normalized ID, IPF, and ED. Same colors as in (A). The number of trials for each odorant and cycle in hept/iso pair is: ncycle 1 = 150, ncycle 2 = 137, ncycle 3 = 65. Data were analyzed using a paired t-test; *p < 0.05; and ***p < 0.001.

Taken together, these results show that odorants with similar sorption properties can be sniffed similarly (three pairs out of five) or differently (two pairs out of five). This finding suggests that sorption is not the only parameter involved in sniffing adjustment. Sniffing may not be restricted to analytical processing but instead may be adjusted synthetically, taking into account the pair in which the odorant is presented. We tested this possibility by analyzing sniffing strategies when the same molecule was presented in two different odor pairs.

THE SAME ODORANT INDUCES DIFFERENT SNIFFING STRATEGIES WHEN PRESENTED IN DIFFERENT ODOR PAIRS: A SYNTHETIC STRATEGY IS LIKELY

Three odorants, D-car, hept and D-lim, were presented in two different pairs (Figure 6), which allowed us to compare the sniffing pattern for the same odorant when it was presented in two different pairing contexts (see Materials and Methods). As shown in Figure 6A, except for D-car [Sd: t(205) = −1.424, p = 0.156; Ns: t(205) = −1.618, p = 0.107], the global sampling parameters were significantly affected by the pair in which the odorant was presented. Animals took more sniffs and remained longer in the odor port when hept was presented in the hept/iso pair than in the hept/mbz pair [Figure 6A, Sd: t(154) = 4.335, p < 0.001; Ns: t(154) = 3.104, p < 0.01]. Similarly, animals took more sniffs and remained longer in the odor port when D-lim was presented in the D-car/D-lim pair than in the L-lim/D-lim pair [Sd: t(205) = −2.191, p < 0.05; Ns: t(205) = −1.976, p < 0.05].

FIGURE 6. Sniffing variations depend on the pair in which an odorant is presented. (A) Global sampling parameters (mean ± s.e.m.), Sd (left), and Ns (right), for each odorant depending on the pair in which it was presented. D-car (red, n = 206) in the D-car/L-car pair (filled triangle) and in the D-car/D-lim pair (empty triangle); hept (green, n = 155) in the hept/iso pair (filled square) and the hept/mbz (empty square); D-lim (dark blue, n = 206) in the D-lim/L-lim pair (filled circle) and the D-car/D-lim pair (empty circle). (B) Modulation of respiratory parameters in the first, second, and third cycles for the same odorant presented in two different pairs. Left: D-car in the enantiomer pair or the D-car/D-lim pair; middle: hept in the hept/iso pair or the hept/mbz pair; right: D-lim in the enantiomer pair or the D-car/D-lim pair. From top to bottom: mean (±s.e.m.) of the normalized ID, IPF, and ED. The number of trials for each odorant and cycle is: D-car: ncycle 1 = 206, ncycle 2 = 197, and ncycle 3 = 181; hept: ncycle 1 = 155, ncycle 2 = 148, and ncycle 3 = 121; D-lim: ncycle 1 = 206, ncycle 2 = 200, and ncycle 3 = 157. Data were analyzed using a paired t-test; *p < 0.05; **p < 0.01; ***p < 0.001.

We further measured the respiratory cycle parameters. Here too, we observed significant variations in sniffing cycle parameters depending on the pair in which the odorant was presented. As shown in Figure 6B, D-car induced significant differences in the ID, IPF, and ED parameters when presented in different pairs. Animals sniffed longer (higher ID and ED) and with a lower IPF when D-car was presented in the L-car/D-car pair than in the D-car/D-lim pair [Figure 6B, left; ID: first cycle t(205) = 3.734, p < 0.001; second cycle t(196) = 3.164, p < 0.01; third cycle t(180) = 8.022, p < 0.001; IPF: first cycle t(205) = −3.440, p < 0.001; second cycle t(196) = −3.165, p < 0.01; ED: first cycle t(205) = 4.702, p < 0.001; third cycle t(180) = 5.322, p < 0.001]. Similarly, hept induced a significant difference in the ID and IPF of the three cycles and in the ED of the first cycle when presented in two different pairs [Figure 6B, middle; ID: first cycle t(154) = 3.132, p < 0.01; second cycle t(147) = 3.775, p < 0.001; third cycle t(120) = 3.641, p < 0.001; IPF: first cycle t(154) = 2.77, p < 0.01; second cycle t(147) = 2.687, p < 0.01; third cycle t(120) = 2.27, p < 0.05; ED: first cycle t(154) = 2.041, p < 0.05]. In this case, animals sniffed hept with a higher ID, ED, and IPF in the hept/iso pair than in the hept/mbz pair. For D-lim, the respiratory cycle variations were more modest, with significant differences only in the IPF of the third sniff [Figure 6B, right; t(156) = 3.836, p < 0.001] and in the ED of the first cycle [t(205) = 3.891, p < 0.001].

As a whole, these results show that the same odorant presented in two different odor pairs was sniffed differently by changing the global sampling and/or the individual sniff cycle features. This finding suggests that there is no absolute sniff pattern corresponding to one odorant or a category of odorants but rather a relative pattern based on the pairing context of the odorants.

DISCUSSION

By finely measuring the sniff parameters of rats performing a two-alternative odor choice discrimination task, we showed that molecules with similar sorption properties can be sniffed similarly (three out of five odor pairs tested) or differently (two out of five odor pairs tested) indicating that sniffing variations are not only governed by odorant sorption. Further, we provided new evidence that sniffing adjustment is a synthetic process dependent on the pair of odorants presented in the discrimination task.

RELATION BETWEEN SNIFFING PATTERN AND SORPTION PROPERTIES

Based on pioneering studies of OE function, the zonation hypothesis was proposed to explain the role of sniffing in olfaction (Schoenfeld and Cleland, 2005, 2006). This hypothesis proposes that sniffing optimizes the deposition of odorant molecules through the OE and is dependent on the sorption properties of the odorant molecules. Rojas-Líbano and Kay (2012) showed that odorants with different sorption properties induce different sniffing strategies. However, they did not show that odorants with similar sorption properties induce similar sniffing patterns. For the first time, we showed that enantiomers were sniffed similarly, at least true for the two odor pairs used (Figure 3), confirming the relationship between molecular properties and sniff parameters. However, we also demonstrated that such a strong relationship does not exist for non-enantiomeric odorants with similar sorption properties; some non-enantiomers were sniffed similarly, whereas others were sniffed differently (Figure 4). This observation may explain why significant sniffing variations between odorants differing in their sorption properties were not observed by Cenier et al. (2013). Several factors can account for the lack of a strict relationship between sorption properties and sniffing strategy in non-enantiomeric molecules. First, as shown recently by Scott et al. (2014), the electroolfactogram responses of medial and lateral recesses of the OE—which are anatomically optimized for odorants with different solubility—are differently affected by high nasal flow rates in active sniffing. Thus, using a larger set of odorants and/or odorants specifically activating the central zone may have led to different results. Second, physicochemical properties of the odorant other than sorption are likely components of sniff adjustment. For example, we showed that a difference in vapor pressure seems to enhance the difference of sniffing strategy between two odorants with similar sorption properties (Figure 5). Third, other factors related to the interaction between odorant molecules and their receptors may be involved, such as molecule/receptor affinity, molecule/odorant binding protein interactions (Pelosi, 2001), the number of receptors accessible to the molecule or enzymatic degradation of the odorant molecule (Thiebaud et al., 2013). Fourth, it is highly likely that sniffing is not adjusted in an absolute manner that is dependent on the properties of an individual odorant but instead takes into account the context (here, the pair of odorants) in which an odorant is presented.

SNIFFING AS A SYNTHETIC STRATEGY

We observed that a same odorant could induce different sniffing pattern depending on the odor pair in which it is presented. It seems that, in our experimental conditions, sniff modulation is a synthetic process taking into account not only the individual odorant properties but also the context in which an odorant is presented. This result was unexpected, and the physiological role of such a strategy is therefore of interest. We propose that a synthetic strategy may optimize sniffing to achieve the maximal decorrelation of OE activation between odorants. Indeed, in our task, the goal of the animal was to act rapidly and successfully to gain a reward. In such conditions, the aim is not to clearly identify the odorant but rather to find the most reliable clue to make the correct decision. Thus, the animal may adjust its sniffing to achieve the greatest difference between the two OE activation patterns and could thus adopt a type of intermediate sniffing pattern. This hypothesis could explain why the same odorant was sniffed differently when presented in two different pairs. We do not claim that the olfactory system does not use the sorption properties of the odorants. Rather, the system likely uses these properties in combination with other contextual information to quickly perform correct odorant discrimination.

Our observation that sniffing is synthetically modulated fits well with the concept that sniffing is modulated by higher functions such as emotion (Hegoburu et al., 2011), context (Wesson et al., 2008a), social behavior (Wesson, 2013), or attentional demand (Plailly et al., 2008). This concept implies that we could observe different results using other experimental conditions such as a different behavioral paradigm. Interestingly, the activity of the first olfactory brain relay, the olfactory bulb, is modulated by context and learning (Kay and Laurent, 1999; Doucette et al., 2011). The dependence of sniff modulation and olfactory bulb activity on contextual clues likely originates in the complex relationship among perception, motor, motivation and respiratory pathways (Clarke and Trowill, 1971; Ikemoto and Panksepp, 1994; Kepecs et al., 2007). For example, inputs from these centers may modify olfactory bulb activity as a function of attention, motivation and learning (Gray and Skinner, 1988). These centers may also act on the respiratory system and be part of the network involved in controlling olfactomotor action.

SNIFFING ADJUSTMENT: A RAPID AND FINE OLFACTOMOTOR ACT

The olfactomotor act could be defined as a modulation of sniffing by the olfactory system. Different authors have shown that odorant presentation can modify or trigger sniffing (Welker, 1964; Alberts and May, 1980) as well as induces a concomitant modification of the firing pattern of respiratory center neurons (du Pont, 1987). The effect on the sniff is extremely fast and appears in the 50 ms following the olfactory bulb activation by an odorant (Wesson et al., 2008a,b) or even earlier if the olfactory bulb is electrically stimulated (45 ms, Monod et al., 1989). In humans, Johnson et al. (2003) also showed that sniffing could be quickly adapted, i.e., within 160 ms, depending on odorant concentration. In accordance with these observations, we showed that sniffing can vary during the first cycle following odor onset (Figures 3–5). The animal thus has the possibility to adjust his sniffing very quickly. Moreover, when we analyzed the global sniffing parameters (Sd and Ns) and the fine parameters of the first, second, and third cycles (ID, IPF, and ED), we observed that all these parameters can be modulated individually or concomitantly. As a comparison, sampling frequency and flow rate can act either independently or synergistically on bulbar output to shape the neuronal message (Courtiol et al., 2011; Esclassan et al., 2012). The animal could use each parameter independently or combine some of them to achieve specific functions. Those various modulations reveal a high flexibility in sniff adjustment and the olfactory system may need to use all the possible adjustments to improve odor representation in the olfactory center and therefore odor discrimination.

CONCLUSIONS

In conclusion, we showed that rats use a synthetic sniffing strategy that considers the pair of odorants to be discriminated. The system likely uses the odorant properties in combination with other contextual information to quickly perform correct odorant discrimination. Our results provide an additional argument demonstrating that sniffing is a specific, quickly and finely adapted sensorimotor act. Future studies will investigate the extent to which sniffing variations help or are mandatory to perform correct olfactory discrimination.

REFERENCES

Alberts, J. R., and May, B. (1980). Development of nasal respiration and sniffing in the rat. Physiol. Behav. 24, 957–963. doi: 10.1016/0031-9384(80)90156-0

Cenier, T., McGann, J. P., Tsuno, Y., Verhagen, J. V., and Wachowiak, M. (2013). Testing the sorption hypothesis in olfaction: a limited role for sniff strength in shaping primary odor representations during behavior. J. Neurosci. 33, 79–92. doi: 10.1523/JNEUROSCI.4101-12.2013

Clarke, S., and Trowill, J. A. (1971). Sniffing and motivated behavior in the rat. Physiol. Behav. 6, 49–52. doi: 10.1016/0031-9384(71)90013-8

Courtiol, E., Hegoburu, C., Litaudon, P., Garcia, S., Fourcaud−Trocmé, N., and Buonviso, N. (2011). Individual and synergistic effects of sniffing frequency and flow rate on olfactory bulb activity. J. Neurophysiol. 106, 2813–2824. doi: 10.1152/jn.00672.2011

Doucette, W., Gire, D. H., Whitesell, J., Carmean, V., Lucero, M. T., and Restrepo, D. (2011). Associative cortex features in the first olfactory brain relay station. Neuron 69, 1176–1187. doi: 10.1016/j.neuron.2011.02.024

du Pont, J. S. (1987). Firing patterns of bulbar respiratory neurones during sniffing in the conscious, non−paralyzed rabbit. Brain Res. 414, 163–168. doi: 10.1016/0006-8993(87)91340-0

Esclassan, F., Courtiol, E., Thévenet, M., Garcia, S., Buonviso, N., and Litaudon, P. (2012). Faster, deeper, better: the impact of sniffing modulation on bulbar olfactory processing. PLoS ONE 7:e40927. doi: 10.1371/journal.pone.0040927

Ezeh, P. I., Davis, L. M., and Scott, J. W. (1995). Regional distribution of rat electroolfactogram. J. Neurophysiol. 73, 2207–2220.

Freitag, J., Krieger, J., Strotmann, J., and Breer, H. (1995). Two classes of olfactory receptors in Xenopus laevis. Neuron 15, 1383–1392. doi: 10.1016/0896-6273(95)90016-0

Garcia, S., and Fourcaud-Trocmé, N. (2009). OpenElectrophy: an electrophysiological data- and analysis-sharing framework. Front. Neuroinform. 3:14. doi: 10.3389/neuro.11.014.2009

Gray, C. M., and Skinner, J. E. (1988). Centrifugal regulation of neuronal activity in the olfactory bulb of waking rabbit as revealed by reversible cryogenic blockade. Exp. Brain Res. 69, 378–386. doi: 10.1007/BF00247583

Hegoburu, C., Shionoya, K., Garcia, S., Messaoudi, B., Thévenet, M., and Mouly, A. M. (2011). The RUB cage: respiration-ultrasonic vocalizations-behavior acquisition setup for assessing emotional memory in rats. Front. Behav. Neurosci. 5:25. doi: 10.3389/fnbeh.2011.00025

Ikemoto, S., and Panksepp, J. (1994). The relationship between self-stimulation and sniffing in rats: does a common brain system mediate these behaviors? Behav. Brain Res. 61, 143–162. doi: 10.1016/0166-4328(94)90155-4

Johnson, B. N., Mainland, J. D., and Sobel, N. (2003). Rapid olfactory processing implicates subcortical control of an olfactomotor system. J. Neurophysiol. 90, 1084–1094. doi: 10.1152/jn.00115.2003

Kay, L. M., and Laurent, G. (1999). Odor- and context-dependent modulation of mitral cell activity in behaving rats. Nat. Neurosci. 2, 1003–1009. doi: 10.1038/14801

Kent, P. F., and Mozell, M. M. (1992). The recording of odorant-induced mucosal activity patterns with a voltage-sensitive dye. J. Neurophysiol. 68, 1804–1819.

Kepecs, A., Uchida, N., and Mainen, Z. F. (2007). Rapid and precise control of sniffing during olfactory discrimination in rats. J. Neurophysiol. 98, 205–213. doi: 10.1152/jn.00071.2007

Monod, B., Mouly, A. M., Vigouroux, M., and Holley, A. (1989). An investigation of some temporal aspects of olfactory coding with the model of multi−site electrical stimulation of the olfactory bulb in the rat. Behav. Brain Res. 33, 51–63. doi: 10.1016/S0166-4328(89)80018-X

Moulton, D. G. (1976). Spatial patterning of response to odors in the peripheral olfactory system. Physiol. Rev. 56, 578–593.

Mozell, M. M. (1964a). Olfactory discrimination: electrophysiological spatiotemporal basis. Science 143, 1336–1337. doi: 10.1126/science.143.3612.1336

Mozell, M. M. (1964b). Evidence for sorption as a mechanism of the olfactory analysis of vapours. Nature 203, 1181–1182. doi: 10.1038/2031181a0

Mozell, M. M., and Jagodowicz, M. (1973). Chromatographic separation of odorants by the nose: retention times measured across in vivo olfactory mucosa. Science 181, 1247–1249. doi: 10.1126/science.181.4106.1247

Pelosi, P. (2001). The role of perireceptor events in vertebrate olfaction. Cell. Mol. Life Sci. 58, 503–509. doi: 10.1007/PL00000875

Plailly, J., Howard, J. D., Gitelman, D. R., and Gottfried, J. A. (2008). Attention to odor modulates thalamocortical connectivity in the human brain. J. Neurosci. 28, 5257–5267. doi: 10.1523/JNEUROSCI.5607-07.2008

Rojas-Líbano, D., and Kay, L. M. (2012). Interplay between sniffing and odorant sorptive properties in the rat. J. Neurosci. 32, 15577–15589. doi: 10.1523/JNEUROSCI.1464-12.2012

Roux, S. G., Garcia, S., Bertrand, B., Cenier, T., Vigouroux, M., Buonviso, N., et al. (2006). Respiratory cycle as time basis: an improved method for averaging olfactory neural events. J. Neurosci. Methods 152, 173–181. doi: 10.1016/j.jneumeth.2005.09.004

Schoenfeld, T. A., and Cleland, T. A. (2005). The anatomical logic of smell. Trends Neurosci. 28, 620–627. doi: 10.1016/j.tins.2005.09.005

Schoenfeld, T. A., and Cleland, T. A. (2006). Anatomical contributions to odorant sampling and representation in rodents: zoning in on sniffing behavior. Chem. Senses 31, 131–144. doi: 10.1093/chemse/bjj015

Scott, J. W. (2006). Sniffing and spatiotemporal coding in olfaction. Chem. Senses 31, 119–130. doi: 10.1093/chemse/bjj013

Scott, J. W., Acevedo, H. P., and Sherrill, L. (2006). Effects of concentration and sniff flow rate on the rat electroolfactogram. Chem. Senses 31, 581–593. doi: 10.1093/chemse/bjj063

Scott, J. W., Sherrill, L., Jiang, J., and Zhao, K. (2014). Tuning to odor solubility and sorption pattern in olfactory epithelial responses. J. Neurosci. 34, 2025–2036. doi: 10.1523/JNEUROSCI.3736-13.2014

Scott-Johnson, P. E., Blakley, D., and Scott, J. W. (2000). Effects of air flow on rat electroolfactogram. Chem. Senses 25, 761–768. doi: 10.1093/chemse/25.6.761

Teichner, W. H. (1966). A method for studying olfaction in the unrestrained rat. J. Psychol. 63, 291–297. doi: 10.1080/00223980.1966.10543043

Thiebaud, N., Veloso Da Silva, S., Jakob, I., Sicard, G., Chevalier, J., Ménétrier, F., et al. (2013). Odorant metabolism catalyzed by olfactory mucosal enzymes influences peripheral olfactory responses in rats. PLoS ONE 8:e59547. doi: 10.1371/journal.pone.0059547

Uchida, N., and Mainen, Z. F. (2003). Speed and accuracy of olfactory discrimination in the rat. Nat. Neurosci. 6, 1224–1229. doi: 10.1038/nn1142

Vassar, R., Ngai, J., and Axel, R. (1993). Spatial segregation of odorant receptor expression in the mammalian olfactory epithelium. Cell 74, 309–318. doi: 10.1016/0092-8674(93)90422-M

Welker, W. I. (1964). Analysis of sniffing in the albino rat. Behavior 22, 223–244. doi: 10.1163/156853964X00030

Wesson, D. W. (2013). Sniffing behavior communicates social hierarchy. Curr. Biol. 23, 575–580. doi: 10.1016/j.cub.2013.02.012

Wesson, D. W., Carey, R. M., Verhagen, J. V., and Wachowiak, M. (2008b). Rapid encoding and perception of novel odors in the rat. PLoS Biol. 6:e82. doi: 10.1371/journal.pbio.0060082

Wesson, D. W., Donahou, T. N., Johnson, M. O., and Wachowiak, M. (2008a). Sniffing behavior of mice during performance in odor-guided tasks. Chem. Senses 33, 581–596. doi: 10.1093/chemse/bjn029

Yang, G. C., Scherer, P. W., and Mozell, M. M. (2007). Modeling inspiratory and expiratory steady state velocity fields in the Sprague Dawley rat nasal cavity. Chem. Senses 32, 215–223. doi: 10.1093/chemse/bjl047

Yoshihara, Y., and Mori, K. (1997). Basic principles and molecular mechanisms of olfactory axon pathfinding. Cell Tissue Res. 290, 457–463. doi: 10.1007/s004410050953

Youngentob, S. L., Mozell, M. M., Sheehe, P. R., and Hornung, D. E. (1987). A quantitative analysis of sniffing strategies in rats performing odor detection tasks. Physiol. Behav. 41, 59–69. doi: 10.1016/0031-9384(87)90131-4

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 04 February 2014; Accepted: 10 April 2014; Published online: 05 May 2014.

Citation: Courtiol E, Lefèvre L, Garcia S, Thévenet M, Messaoudi B and Buonviso N (2014) Sniff adjustment in an odor discrimination task in the rat: analytical or synthetic strategy? Front. Behav. Neurosci. 8:145. doi: 10.3389/fnbeh.2014.00145

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Courtiol, Lefèvre, Garcia, Thévenet, Messaoudi and Buonviso. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 11 June 2014
doi: 10.3389/fnbeh.2014.00214

The olfactory bulb theta rhythm follows all frequencies of diaphragmatic respiration in the freely behaving rat

Daniel Rojas-Líbano1,2[†], Donald E. Frederick2,3, José I. Egaña[4] and Leslie M. Kay1,2,3[*]

1Committee on Neurobiology, The University of Chicago, Chicago, IL, USA

2Institute for Mind and Biology, The University of Chicago, Chicago, IL, USA

3Department of Psychology, The University of Chicago, Chicago, IL, USA

4Departamento de Anestesiología y Reanimación, Facultad de Medicina, Universidad de Chile, Santiago, Chile

* Correspondence: Leslie M. Kay, Institute for Mind and Biology, The University of Chicago, 940 E 57th St., Chicago, IL 60637, USA e-mail: lkay@uchicago.edu

[†]Present address: Daniel Rojas-Líbano, Laboratorio de Neurosistemas, Facultad de Medicina, Universidad de Chile, Santiago, Chile;
Facultad de Educación, Universidad Alberto Hurtado, Santiago, Chile

Edited by:
Donald A. Wilson, New York University School of Medicine, USA

Reviewed by:
Thomas A. Cleland, Cornell University, USA
Emmanuelle Courtiol, New York University Langone Medical Center, USA

Sensory-motor relationships are part of the normal operation of sensory systems. Sensing occurs in the context of active sensor movement, which in turn influences sensory processing. We address such a process in the rat olfactory system. Through recordings of the diaphragm electromyogram (EMG), we monitored the motor output of the respiratory circuit involved in sniffing behavior, simultaneously with the local field potential (LFP) of the olfactory bulb (OB) in rats moving freely in a familiar environment, where they display a wide range of respiratory frequencies. We show that the OB LFP represents the sniff cycle with high reliability at every sniff frequency and can therefore be used to study the neural representation of motor drive in a sensory cortex.

Keywords: local field potential, olfactory bulb, respiration, rat, theta rhythm

INTRODUCTION

Whenever the performance of a sensory system becomes crucial for an animal, it is always in the context of interaction with its immediate environment, such as when searching for food, hunting, escaping, mating, or simply exploring its surroundings. Animals execute these tasks by actively manipulating sensory stimuli through motor activities that sample physical features in time and space. Well known examples are eye movements and finger movements in primates, whisking in rodents, and sniffing in mammals. Active stimulus manipulation facilitates feature selection from complex stimuli during learning processes, ongoing adaptation to the task at hand and optimization of stimulus discrimination (Mitchinson et al., 2007; Rojas-Líbano and Kay, 2012). Neural circuits implicated in the different motor sampling modalities comprise almost all brain systems, display profuse interconnection with sensory areas and underlie higher-level cognitive functions (Liversedge and Findlay, 2000; Diamond et al., 2008; Wachowiak, 2011).

In olfaction, stimuli must be carried from external air to the sensory epithelium inside the nose. Mammals accomplish this through sniffing, a voluntary modification of regular breathing. Sniffing motor patterns have effects on the temporal structure of sensory input (Carey et al., 2009), subsequent brain processing (Carey and Wachowiak, 2011) and gain modulation of incoming sensory signals (Verhagen et al., 2007; Courtiol et al., 2011a; Rosero and Aylwin, 2011; Esclassan et al., 2012). However, there are no systematic studies of the basal conditions of the system and its internal neural representation, close to its ecological mode of operation. For example, rodents navigate through an environment constantly sensing and learning, mainly through whisking and sniffing. This allows them to gauge object distances, track entities of interest, and adapt their displacement accordingly and generally to learn about their immediate environments.

In mammals, it is hypothesized that air entry on each respiration cycle causes either a mechanical or odor signal, or both, detected by olfactory sensory neurons in the nose (Ueki and Domino, 1961; Onoda and Mori, 1980; Grosmaitre et al., 2007). This, in turn, produces a strong local field potential (LFP) oscillation in the olfactory bulb (OB) that represents the sniff cycle in voltage change over time. Early efforts by Walter Freeman combining modeling and recording hypothesized that the transient coordinated volley of impulses arriving to the OB with each inspiration would modify gain parameters of the OB neuronal network. This in turn would cause a transient increase in OB activity, reflected in LFP oscillations at the respiratory frequency, a phase-locked burst of gamma activity, and an increase in single neuron activities (Freeman, 1975). Empirical evidence for this relationship comes from anesthetized preparations (Adrian, 1950; Fontanini et al., 2003) or from short-time example traces in awake animals (Freeman, 1976; Courtiol et al., 2011b), and most of the time in the context of odorant stimulation. The normal activity range within which this respiratory-neural correlation is valid is therefore unknown, as is whether it holds in conditions of regular exploratory behavior, with non-specific odorant stimulation.

Given this lack of systematic evidence, we wanted to address at least two experimental questions. Does the strong coupling between respiration and the OB LFP hold for the continually-adapting sniffing activity displayed in free exploratory behavior? Does this relationship change with the animal's behavior?

We addressed these questions in freely behaving rats using a task-free paradigm to encourage the rats to use a wide range of exploratory respiratory behaviors. On the motor side, we monitored the diaphragm, the main muscle target of the respiration network. Simultaneously we monitored the sensory side by recording LFPs from the olfactory bulb. We found that the relationship between motor sampling behavior and sensory activity is consistent across all respiratory frequencies without specific odorant stimulation other than the normal olfactory environment. We also found that several features of respiration are present in and therefore can be reliably extracted from OB oscillations, and that different behavioral states modulate the relationship. Our data show an ongoing sensory-motor coupling across a range of normal behaviors. In a situation like this, when there are no clear-cut, experimenter-controlled stimulus delivery times, and the animal is moving freely, our results also illustrate the usefulness of investigating sensory responses using the temporal frame of the relevant motor sampling activity.

MATERIALS AND METHODS

SUBJECTS

Five adult male Sprague Dawley rats were used in the experiments (Harlan). Of these, two rats also participated in an olfactory discrimination protocol published elsewhere (Rojas-Líbano and Kay, 2012). All rats were housed individually in standard clear polycarbonate home cages with filter tops and maintained on a 14/10 h light/dark cycle [lights on at 8:00 AM central standard time (CST)]. All recording sessions were performed during the light phase, between 9:00 AM and 5:00 PM CST. All experimental procedures were done with approval and oversight by the University of Chicago Institutional Animal Care and Use Committee, according to Association for Assessment and Accreditation of Laboratory Animal Care guidelines.

ELECTRODE FABRICATION

LFP electrodes were made using 100 μm diameter, formvar-coated stainless steel wire (304 HFV, California Fine Wire, Grover Beach, CA). On each recording site were implanted two electrodes previously glued together longitudinally, with an inter-tip separation of ~0.5 mm. Impedance of LFP electrodes was in the range of 50–100 kΩ.

Ground and reference electrodes were made using 250 μm diameter, polyimide-coated stainless steel wire (304 H-ML, California Fine Wire, Grover Beach, CA). A ~10 cm long piece was cut and ~2–3 cm of insulation removed at one end, which was wrapped around a small stainless steel screw (MPX-080-2P, Size:#0−80 × 1/8", Pan head style, Small Parts Inc., Miami Lakes, FL). At the other end, ~0.5 cm of insulation was removed and the exposed wire was crimped to a gold connector.

EMG electrodes were made following the design of Shafford et al., designed originally for recording of the diaphragm EMG in awake rabbits (Shafford et al., 2006). A full description of the fabrication can be found elsewhere (Rojas-Líbano and Kay, 2012). Briefly, the EMG recording electrode was made using a piece of 250 μm diameter stainless steel wire forming a coil of length 1–2 mm. This piece was attached to a flexible, multistranded stainless steel wire (AS 155-30, Cooner Wire, Chatsworth, CA).

Prior to surgery, EMG electrodes and their wires were disinfected in a solution of benzalkonium chloride (diluted 1:750).

SURGERY

Rats were anesthetized initially using a Ketamine-Xylazine-Acepromazine cocktail injected subcutaneously. Anesthesia during surgery was maintained using Pentobarbital sodium solution administered intraperitoneally. Rodent aseptic surgery guidelines were followed for all surgical procedures (Cunliffe-Beamer, 1993). A midline incision was done to gain access to the abdominal cavity. EMG electrodes were held by the heat shrink tubing segment using a hemostat and inserted into the diaphragm, perpendicular to the diaphragm surface. Three EMG leads were implanted in each rat, at the right costal diaphragm. The flexible wire was passed through the abdominal muscles, 2–3 cm lateral to the midline incision, and knots were tied with the same wire cable on both sides of the abdominal muscle to prevent excess wire movement. The abdominal muscle incision was sutured using absorbable polyglycolic acid suture (Surgical Specialties Co, Reading, PA). Electrode wires were tunneled under the skin and exteriorized at the back of the head. The abdominal skin incision was sutured with non-absorbable polyamide suture (Henry Schein, Melville, NY). Placement of diaphragm electrodes took ~2.5 h from incision to suturing.

After placement of EMG electrodes, rats were placed in a stereotaxic frame. Electrodes were positioned using stereotaxic coordinates with respect to Bregma, in the OB (8.5 mm anterior, 1.5 mm lateral) and the PC (0.5 mm anterior, 3 mm lateral). The reference and ground electrodes were screwed into the skull. All gold connectors, from EMG, LFP and ground/reference electrodes, were inserted into a threaded round nine-pin socket (GS09SKT-220; Ginder Scientific) and fixed onto the rat's head with dental acrylic.

Postsurgical analgesia was provided via subcutaneous injection of buprenorphine (Buprenex®). Recording sessions were performed 15 days after surgery at the earliest.

EXPERIMENTAL PROTOCOLS AND DATA ACQUISITION

For the recording sessions, rats were connected to the recording system and then placed back in their home cages, using a modified cage top to pass the cable through it. The arrangement allowed the rat to move freely in the cage. Recordings lasted 10–25 min.

Electrophysiological data were acquired using a Cheetah-32 (Neuralynx, Bozeman, MT) amplifier system of 2.5 MΩ input impedance, and a unity gain headstage preamplifier (HS-18-CNR, Neuralynx). During acquisition, signals were amplified (2000×) and filtered online with an analog Butterworth filter (Rolloff 12 DB per Octave). LFP channels were filtered between 1 and 200 Hz or between 1 and 475 Hz. EMG raw channels were filtered between 1 and 3000 Hz and EMG bipolar channels were filtered between 300 and 3000 Hz. In some sessions, all channels were sampled at 1892 Hz. In other sessions, LFP channels were sampled at 2000 Hz and EMG channels at 8000 Hz.

DATA ANALYSIS

All data analysis was done offline using MATLAB®. Code was written to store and process the data. Neuralynx MATLAB function Nlx2MatCSC was used to import the data into MATLAB.

EMG data processing

In order to extract the respiratory parameters (i.e., inhalation duration, tidal volume, cycle duration, inhalation strength), the EMG signal was processed. Full details of this processing are described elsewhere (Rojas-Líbano and Kay, 2012). First, the signal was rectified by taking its absolute value. Second, a moving mean filter of the signal was calculated using a window of 40 ms, with a step size of 1 sample, to produce the rectified and smoothed EMG (rsEMG). Each point of the rsEMG was calculated according to the formula:

where x(t) is the original rectified EMG, 2w + 1 is the dimension of the window and y(t) is rsEMG, with t representing the index of the sample at any given time.

Inhalation-detection algorithm

An algorithm was written in MATLAB to automate the detection of inhalation episodes. The timing of inhalation (start and end points) is the only parameter required to calculate all the respiratory timing variables. The algorithm operated on the rsEMG signal and compared it with a moving threshold signal (moving-mean filter applied to rsEMG with window of 80 ms). In a first pass, any points on the rsEMG above the threshold were kept and the rest were made equal to zero. Then a collection of indices was obtained by searching for all the transitions from zero to positive (inhalation start), and from positive to zero (inhalation end). In a second pass, a minimum inhalation duration (30 ms) value was defined and all the inhalation events shorter than the minimum were removed. In a third and final pass, a minimum exhalation duration (10 ms) value was defined and all shorter events were removed. The collection of indices was used then in the original rsEMG signal to determine all respiratory variables. All inhalation episodes detected by the algorithm were visually inspected.

Spectral analysis

Spectral analysis was carried out with open-source Chronux algorithms (http://chronux.org). The multitaper method was used to estimate frequency spectra. Theory for multitaper estimates can be found in several articles and books (Thomson, 1982, 2001; Percival and Walden, 1993). In the multitaper procedure, a time-bandwidth NW product is defined, where N is the number of samples of the electrophysiological time series and W is the bandwidth of interest. Then multiple data tapers are selected and computed. The tapers correspond to a set of functions called discrete prolate spheroidal, or Slepian, sequences (Slepian, 1978), and usually the number of tapers K is selected to be not larger than 2NW − 1, with NW ≈ 4–6 a typical choice. Spectrum estimates are computed by taking the discrete Fourier transform of the time series multiplied by the selected tapers and averaging over tapers (Thomson, 1982).

Spectra estimates Ŝ were calculated as the average over K tapers as

where * means complex conjugate and Ṽ(n, k) is the discrete-time Fourier transform of the product between the time series x(t) and the k-th taper w(k)(t):

where f represents frequency in Hz, t represents time and T is the duration of the time series in seconds (i.e., T = N · tS, with tS being the sampling interval). Spectrograms were constructed by plotting spectral power during a series of overlapping constant-width time windows (overlap: 0.1 s; width: 0.4 s). Spectral coherence C between two signals (e.g., between rsEMG and LFP) was computed as

where Ŝ_12(f)corresponds to the cross-spectrum calculated from the fast-Fourier transforms (FFTs) of the time-tapered vector signals, and Ŝ_1(f), Ŝ_2(f) correspond to the individual power spectra of the two signals. Confidence limits (95%) were estimated for coherence magnitude by a jackknife procedure. Coherencegrams were constructed by plotting spectral coherence during a series of overlapping constant-width time windows (overlap: 2 s; width: 0.3 s).

LFP polarity and LFP phase calculations

Our LFP recordings from the OB were made with electrodes penetrating perpendicular to the dorsal surface of the bulb. In these conditions, electrode tips ended either dorsal (D) or ventral (V) to the mitral cell layer. Recordings in D and V positions have almost identical shape but inverted polarity, since the layered architecture of the bulb forms an electrical dipole with zero isopotential surface near the mitral cell layer (Freeman, 1972). For purposes of the analysis, all LFP recordings were transformed off-line to the same polarity, by multiplying the time series by −1 where appropriate. The polarity chosen was the polarity of D recordings, i.e., where the characteristic gamma bursts occur in the falling phase of the Theta-LFP, as shown in Figure 1A. In this orientation, the end or peak of inhalation is coincident with the theta positive peak, and exhalation occurs on the downward trajectory, ending with the theta trough.

FIGURE 1. Overview of signals recorded and EMG processing to obtain respiratory variables. (A) Drawing of a rat showing the two structures and signals recorded: the local field potential (LFP) from the olfactory bulb (OB) in the brain (top trace), and the electromyogram (EMG) from the diaphragm (bottom trace). Respiratory airways from the nose to the lungs are also depicted. Gray traces correspond to raw signals and black traces to low-pass filtered signals. (B) Example EMG traces showing signal processing, sequentially from top to bottom. The EMG raw signal is positively rectified and then is smoothed using a moving-mean filter to produce the rsEMG. (C) Enlarged image of square in bottom trace in (B), showing example of performance of the inhalation-detection algorithm.

After polarity correction, we first low-pass filtered the raw LFP at 15 Hz to calculate theta-LFP phase. Then we calculated the instantaneous phase of the filtered signal using the discrete Hilbert transform, from the MATLAB Signal Processing Toolbox. We considered as the start of the theta cycle the point of phase = −π of the instantaneous phase time series, i.e., the local minima of the theta-LFP, and the point of phase = π the end of the theta cycle (i.e., the next local minimum of the theta-LFP after cycle start).

LFP Phase-Amplitude coupling measure

We calculated a measure of phase-amplitude coupling to study the relationship between the phase of slow (1–15 Hz) oscillations and the amplitude of fast (40–100 Hz) oscillations in the OB LFP. Our measure was based on the methods described by Tort and colleagues in several articles (Tort et al., 2009, 2010). Briefly, we first filtered the raw LFP signal to obtain two time series containing separately the slow and fast oscillations. Then we calculated the instantaneous phase for the slow oscillation and the amplitude envelope for the fast oscillation by means of the Hilbert transform, obtaining two new time series containing the phase and amplitude measures. Out of these we calculated two estimates of phase-amplitude coupling. By binning the phase, we produced histograms of amplitude distribution over the phase. And by measuring the difference between this distribution from a uniform distribution, we also calculated a Modulation Index (MI) for the phase-amplitude coupling (Tort et al., 2010).

RESULTS

Relationships between respiratory behavior and what is termed the respiratory rhythm in the OB LFP have been studied only within a narrow range of frequencies. In order to study this relationship over a wide range of respiratory frequencies we used simple free behavior in the rats' home cage. Our previous research showed a tightly defined set of respiratory behaviors during odor discrimination (Rojas-Líbano and Kay, 2012), and we have observed that OB LFP properties are highly variable dependent on behavior (Kay, 2003). We recorded from chronically implanted rats during free behavior in their home cages, using a modified cage top to allow for recording. We simultaneously acquired the olfactory bulb LFP and the diaphragm EMG signals to observe the sensory and motor activities of the animals while they explored (Figure 1). Cages did not contain specific odorous stimuli, but only objects usually present: feces, bedding, and food pieces. In our experience, this odorous environment produces a full range of sniffing behaviors.

GENERAL DESCRIPTION OF RESPIRATORY PATTERNS

We used the processed diaphragm signal, i.e., the rsEMG, to extract the relevant respiratory variables (see Materials and Methods and Figure 1). Table 1 shows central tendency and variability measures for all rats and variables. We show a total of five rats with diaphragm (respiratory) data, one of which (rat 5) did not have LFP data and therefore is not shown in figures with LFP results (Figures 2–7). Rats displayed a continuously varying respiratory frequency, consistent with free behavior, ranging approximately from 2 to 12 Hz, and could switch between frequencies rapidly, in 1–2 respiratory cycles (see Figure 2). While the entire waking respiratory range is represented in the data, all rats spent the vast majority of their time during the recorded epochs engaging in sniffing behavior (5–10 Hz), which was the object of this study. Only one rat (rat 3) produced low respiratory frequencies for a large amount of the time, and this rat had a lower mean frequency and a much larger variance in frequency than the other four rats (see Table 1). As we have previously shown for odorant sampling periods (Rojas-Líbano and Kay, 2012), the diaphragm signal allows us to monitor sniffing activity, and we extend it now to the spontaneously exhibited respiratory range of freely behaving rats, without interfering with airflow at the nasal cavity.

Table 1. Means and standard deviations of respiratory parameters, and number of respiratory cycles for each rat, calculated from diaphragm rsEMG.

FIGURE 2. Respiratory frequency distributions and variability over time. (A) Respiratory frequency distributions for all rats. Numbers of respiratory cycles per rat are shown in Table 1. Rats 3 and 5 showed behavioral periods of quietness, almost to the point of falling asleep, especially Rat 3. In these periods, respiratory frequency decreased considerably. (B) Example of respiratory frequency over time during 70 s, for rat 5. Distribution fit is shown on the left. For each respiratory cycle the time assigned corresponds to time of inhalation start. (C) Enlarged image of section marked with a rectangle in (B), to show that changes in respiratory frequencies can occur rapidly (e.g., changes of 4–6 Hz in magnitude occur in 1–2 cycles).

RELATIONSHIP BETWEEN DIAPHRAGMATIC RESPIRATION AND THE OLFACTORY BULB LFP

Central representation of the respiratory event in cortical signals allows us to infer how much information about the motor drive is present in the sensory signal. In this case the motor activity (inhalation, exhalation) produces changes in the external immediate environment (airflow and pressure changes in nasal cavity), which result in activation of the sensory neurons and the corresponding brain structure. We therefore assess the strength of sensorimotor coupling between the OB, a cortical area that receives the sensory signal from the olfactory nerve, and the diaphragm, the muscle driven by the output of the neuronal respiratory center (Feldman et al., 2013). We first consider whether there is a general association at all respiratory frequencies associated with many types of behaviors between diaphragmatic EMG activity and the LFP of neuronal populations in the OB. In the frequency domain, we observe a strong relationship between the signals, with high spectral coherence precisely within the range of respiration exhibited by the rats (see Figure 3). For each rat we calculated the spectral frequency of maximum coherence (fmaxC) and the median respiratory frequency (medianRF), and the values obtained where (in Hz): Rat 1, fmaxC = 5.67, medianRF = 5.85; Rat2, fmaxC = 8.46, medianRF = 7.84; Rat 3, fmaxC = 8.26, medianRF = 7.8; Rat 4, fmaxC = 5.82, medianRF = 5.88. This coupling is sustained over time, as shown by spectral coherence in a time-frequency chart (Figure 3).

FIGURE 3. Spectral coherence between diaphragm EMG and olfactory bulb LFP. Top: example traces of filtered (1–15 Hz) OB LFP and rsEMG, from Rat 4. Middle: spectral coherence magnitude between rsEMG and low-pass filtered OB LFP for each of the four rats from which we collected both LFP and EMG signals. Dotted line marks the confidence level above which values are assumed to be significantly coherent. Note that coherent activity occurs precisely at the range of respiratory frequencies, which distribution is shown underneath in green. Bottom: time-frequency chart showing spectral coherence between rsEMG and LFP, for Rat 2. Coherence spectra were calculated using a 4-s moving window, stepped by 200 ms, using 7 tapers. Black dots correspond to instantaneous respiratory frequency calculated from the diaphragm rsEMG.

We also analyzed LFP epochs surrounding the diaphragmatic respiration cycle using the motor period as a temporal frame to study the sensory signal. Figure 4C shows these data in raster plots of LFP segments, sorted by respiratory cycle duration. This sorting, based exclusively on EMG data, also produced a sorting of the LFP epochs, reflecting that the temporal structure of respiratory cycles was conserved in the LFP across all cycle durations. Inspection of these rasters suggests a correlation between the respiratory cycle duration (vertical black curves in rasters) and LFP cycles starting at approximately 80 ms after inhalation onset or a constant delay between the start of respiration in the diaphragm and arrival of signal at the olfactory bulb (Figure 4B). We estimated this delay in all the rats by extracting the timepoints of the relevant LFP cycles. We used two approaches to extract cycles from the LFP time series. First, we calculated the maxima and minima from the low pass-filtered LFP (1–15 Hz) and defined one cycle to be the LFP epoch going from one minimum to the next. We also calculated the instantaneous phase of the LFP using the Hilbert transform, and defined one cycle to be the LFP epoch going between points of instantaneous phase = −π to phase = π. We obtained identical results with both techniques.

FIGURE 4. Cycle-by-cycle agreement between diaphragmatic respiration and OB LFP. (A) Filtered LFP (1–15 Hz) and rsEMG example traces from Rat 1. (B) Enlarged plot from section marked with a rectangle in (A), to show the delay between EMG cycle onset and LFP cycle onset, and the LFP cycle. (C) Raster plots of peri-inhalation LFP epochs, sorted by respiration cycle duration (shortest at bottom; white represents values above zero). Black thick lines mark the end of the respiratory cycle estimated from the EMG for each epoch. Note that the end of the EMG cycle precedes the end of the LFP cycle with a small apparent drift as cycle length increases. (D) Scatter plots of cycle durations. The durations of respiratory cycles, as determined from the rsEMG signal, were plotted against the duration of the corresponding LFP cycles. Blue dots correspond to the duration of the EMG-LFP delay and red dots to the duration of the complete LFP cycles. The lines are linear fits from the regressions described in the text. Although the correlation coefficient between delay and cycle duration is very small, the small apparent drift in delay from (C) is also present in the non-zero slope of the line fit.

All the rats displayed a relatively constant delay between inhalation onset at the diaphragm and the start of a new LFP theta cycle in the OB (Figure 4D, blue dots). Mean and standard deviations for the delay were: Rat 1, 78.5 ± 34.1 ms; Rat 2, 79.2 ± 15.3 ms; Rat 3, 82.6 ± 12.1 ms; Rat 4, 78.8 ± 15.2 ms; Sample mean, 79.8 ± 1.9 ms. Consistent with a delay independent of respiration duration, in the linear least-squares model (LFP cycle duration) = β_0 + β_1. (EMG cycle duration) we found low Pearson's product-moment correlation coefficients and regression coefficients when we tried to correlate the EMG cycle duration with the simultaneously measured (i.e., exactly corresponding segment in time) LFP cycle: Rat 1, β_1 = 0.077, β_0 = 0.066, r = 0.0679; Rat 2, β_1 = 0.069, β_0 = 0.07, r = 0.095; Rat 3, β_1 = 0.12, β_0 = 0.064, r = 0.29; Rat 4, β_1 = 0.094, β_0 = 0.065, r = 0.21. (see Figure 4D, blue-dots scatter plots).

Conversely, when we assessed the correlation between respiration duration of individual cycles in the diaphragm and the duration of the LFP cycle shifted in time (i.e., measured from the start of a new LFP cycle and not exactly simultaneous with EMG cycle), we found that data in all rats were consistent with a cycle-by-cycle representation of diaphragm respiration in the theta LFP cycles of the rats' OB. In this case the coefficients were: Rat 1, β_1 = 0.78, β_0 = 0.029, r = 0.64; Rat 2, β_1 = 0.76, β_0 = 0.032, r = 0.62; Rat 3, β_1 = 0.91, β_0 = 0.016, r = 0.86; Rat 4, β_1 = 0.93, β_0 = 0.011, r = 0.84. (see Figure 4D, red-dots scatter plots)

We also analyzed the low frequency (1–2 Hz) respiratory cycles from Rat 3. These slower cycles showed an EMG-LFP delay of about 200 ms, longer than the rest of the rats and himself at higher (4–12 Hz) respiratory frequencies (Figure 5). Regardless of this difference, it was clear from the data that the LFP oscillations were following the respiratory cycles at these low frequencies (Figure 5A) as well as they did at higher respiratory rates. The relationship between respiration and the LFP is also clear in inhalation-triggered LFP means, both in low (Figure 5B) and high (Figure 5C) respiratory frequencies.

FIGURE 5. LFP oscillations in low and high frequency respirations. (A) Raster plot of peri-inhalation LFP epochs for rat 3, for slow (<1.5 Hz) cycles, sorted by respiration cycle duration (shortest at bottom; bottom axis in seconds). Black line marks the ends of the respiratory cycles. Red color indicates values greater than zero. (B) Inhalation-triggered LFP mean for data in (A). (C) Inhalation-triggered LFP means for data of respiratory frequencies between (4–12 Hz), showing one trace per rat.

We analyzed a previously described relationship between low and high frequencies within the OB LFP. Gamma activity bursts (40–100 Hz) in the OB LFP are reported to occur phase-locked to theta oscillations (Freeman, 1976; Rojas-Líbano and Kay, 2008). This can be quantified using phase-amplitude coupling (PAC) measures such as the modulation index (Tort et al., 2010) between the user-defined low and high frequencies. We computed these coupling measures for the rats' LFP and found a consistent PAC for all the rats with LFP and EMG data (Figure 6). Gamma activity, which in our recordings was 80–100 Hz for the most part, was consistently modulated by theta phase, increasing in amplitude in the second half (from phase = 0 to phase = π) of each theta cycle.

FIGURE 6. Phase-Amplitude Coupling (PAC) measures for the OB LFP. Top: example trace showing raw LFP and its Gamma (40–100 Hz) and Theta (1–15 Hz) filtered versions, from Rat 1. Middle row: mean LFP Gamma amplitude distribution over LFP Theta phase bins. For clarity, two cycles are shown (1 cycle = 360°). Black horizontal line shows the expected amplitude values for a uniform distribution (i.e., no modulation). Bottom row: contour plots of Modulation Index (MI) for all pairs of Gamma and Theta LFP frequencies. The data are dominated in the gamma band by high frequency activity, so little coupling is shown for lower gamma frequencies. Respiration frequency distributions from EMG data are shown as the 25th and 75th quartiles (white vertical lines) and the median (white circle).

During the recording sessions, all rats alternated between two main behavioral modes: at first, they actively explored the cage, covering most of its area. Occasionally they stopped and sniffed with the tip of the nose close to the bedding, the walls or the air space above, and then resumed body movement. In the second behavioral mode, rats nose poked at a hole in one of cage walls and stayed still with the nose protruding a little to the outside of the cage, sniffing. The wall hole is the place where the rat normally finds the drinking tube of a water bottle, which was removed for the recording session. In general, rats spent 30% of the session in the nose-poke mode and the remaining 70% in the exploratory mode (see Figure 7A).

FIGURE 7. Behavioral modulations of the sensory-motor coupling. (A) Rats displayed two main behaviors: remaining still and poking the nose at a hole in the cage wall (“NOSEPOKE”) or actively exploring the cage (“EXPLORE”). (B) Respiratory frequency time series, calculated from diaphragm EMG; note the large variance in frequency during exploratory periods relative to nose-poke periods (marked by gray bars). (C) Raw LFP time series. Note the much larger amplitudes, which represent primarily theta activity at this time scale, during exploration as compared to nose-poke. (D) Time-frequency chart of raw LFP spectral power. During the large amplitude LFP periods (C) both gamma and theta power are dramatically increased relative to nose-poke. (E) Time-frequency chart of LFP-rsEMG spectral coherence. The magnitude of coherence does not change across frequencies in the two conditions. (F) Example traces of raw LFP and rsEMG before and after a transition between both behavioral modes.

Respiratory behavior was considerably modified as a function of the behavioral modes. This is illustrated in Figure 7. In the exploratory mode, respiratory frequency varied between 4 and 12 Hz. In contrast, during nose-poke behavior it remained between 5.5 and 6.5 Hz, with very little variation throughout the period (Figure 7B). LFP power decreased drastically during nose-poke, both in theta and gamma ranges (Figures 7C,D). Coupling between LFP and EMG, however, remained high in both behavioral modes (Figure 7E). It is interesting to note that the reduction in LFP power seen during nosepoke periods was not the result of reduced inhalation strength, as rsEMG amplitude did not decrease compared to exploration periods (Figure 7F).

DISCUSSION

The diaphragm, one of the main muscle targets of the neuronal respiratory network, and the olfactory bulb, the first brain structure to receive input from olfactory sensory neurons, were recorded during free behavior of rats exploring their home cages. We found that rats spontaneously displayed a full range of respiratory frequencies from 1 to 12 Hz and could switch rapidly between frequencies, in 1–2 respiratory cycles (Figures 1–2). The higher end of this frequency band is also associated with sniffing behavior seen in learning and odor discrimination contexts (Rojas-Líbano and Kay, 2012). We found a close relationship between motor respiratory activity and sensory LFPs in the OB, expressed as spectral coherence between the signals (Figure 3) and as a correlation of the cycle durations measured from both signals (Figure 4). Our results indicate a delay of about 80 ms from inhalation onset at the diaphragm and signal arrival in the OB (Figure 4), for sniffing (4–12 Hz) respiratory frequencies, and a delay of 200 ms for lower (<1.5 Hz) respiratory frequencies. We found as well a consistent LFP phase-amplitude coupling in the OB between respiratory-related frequencies (1–15 Hz) and gamma frequencies (40–100 Hz), as shown in Figure 6. In addition, we found a regular association between the behavioral modes exhibited in our setup and specific motor and sensory patterns of the recorded structures.

A sequence of several events is known to translate the diaphragm motor output to the respiratory rhythm in the OB. As the diaphragm contracts, air is drawn into the nose and generates a pressure wave that mechanically stimulates the OSNs and brings odorant molecules into contact with the mucosa. The OSNs appear to respond to both odorant binding at the receptors and mechanical stimulation (Grosmaitre et al., 2007), which in turn sends a volley of activity through the olfactory nerve that reaches the OB. This volley is translated by intraglomerular circuits and read by electrodes as a respiratory-linked oscillation of the LFP. If sensory drive by the olfactory nerve is interrupted by tracheotomy, naris closure or nerve transection, OB theta oscillations are almost completely abolished (Domino and Ueki, 1960; Gault and Leaton, 1963; Gray and Skinner, 1988; Courtiol et al., 2011a). Changes in respiratory cycles through the manipulation of pressure changes in the nasopharynx, change the corresponding theta frequency in the OB (Fontanini et al., 2003; Courtiol et al., 2011b; Esclassan et al., 2012).

Respiratory modulation of OB neural activity also occurs at the single neuron level (Walsh, 1956; Macrides and Chorover, 1972; Onoda and Mori, 1980; Ravel et al., 1987), with most reports recording from mitral/tufted cells. In this context, modulation means that the spiking probability of a fraction of the recorded cells is a function of the respiratory phase. Whereas some studies report that a little modulation survives after bypassing nasal breathing (Ravel et al., 1987), some others report complete abolition of modulation in conditions of no nasal breathing (Phillips et al., 2012). At any rate, it seems that the majority of the modulation comes from nasal neural input and a relatively small fraction from centrifugal inputs to the OB.

Despite reference to the respiratory rhythm in the literature over many decades, it was not known if the respiratory-related theta oscillation in the OB would follow the changing respiratory rhythms in the normal range of frequencies displayed spontaneously by behaving rats. Previous studies that had addressed this question were done in anesthetized animals that breathe at very low rates. If the rhythms are tightly linked at all frequencies, then this indicates that the OB circuit has access to and incorporates the motor program at all times. In all the rats we recorded we were able to show not only a spectral coherence between the OB LFP and the diaphragm EMG, but also a cycle-by-cycle correlation between the duration of respiratory and theta LFP cycles, with the LFP cycle lagging respiration, for all the respiratory frequencies exhibited by the rats, which in our case had the range 1–15 Hz. We found a consistent delay estimate of approximately 80 ms from the diaphragm (motor output) signal to the OB theta band LFP (cortical) signal at sniffing frequencies. It takes on the order of 20–40 ms for the diaphragm contraction to begin changing the airflow in the nose (estimated from Katz et al., 1962); we therefore conclude that the remainder of the 80 ms delay between the diaphragm signal and the OB theta oscillation is due to sorption and diffusion of odorants, transduction of odorant binding and mechanical stimulation by ORNs, transduction delays of the sensory signal and the time it takes for the target neural circuits in the OB to respond to the sensory volley.

Another prominent feature of the OB theta oscillation previously described is the phase-amplitude coupling (PAC) between the theta (1–15 Hz in the rat) and the faster gamma (40–100 Hz in the rat) oscillations. The PAC is characterized by bursts of gamma that occur phase-locked to the second half (from phase = 0 to phase = π) of the theta cycle. Extensive work done in anesthetized rats has confirmed this PAC, at least under urethane anesthesia (Buonviso et al., 2003; Cenier et al., 2009). In the awake rat, it is customary to assume that the PAC is always present, although only example traces are available in the literature (Freeman, 1976; Kay et al., 1996; Martin et al., 2004; Courtiol et al., 2011b). Again, in the entire range of frequencies exhibited by our rats we were able to detect this PAC, quantified by the Modulation Index (MI) created by Tort et al. (2010). The MI analysis of the OB LFPs showed that the bursts of gamma activity (40–100 Hz in our rats) occurred during the second half of the theta cycle (Figure 6) irrespective of the theta frequency. A recent report shows that this PAC is abolished during sleep (Manabe and Mori, 2013), where the “nested” gamma oscillations, as the authors refer to, could not be detected either during short-wave sleep or during rapid-eye movement sleep. These authors also report high and low frequencies for gamma oscillations, peaking around 80 and 60 Hz respectively and appearing sequentially and link these to a recent report about firing probabilities of tufted vs. mitral cells at the inhalation to exhalation transition and during late exhalation, respectively (Fukunaga et al., 2012). In our sessions, we did not see much low-frequency gamma, with most activity located between 80 and 100 Hz. However, we have reported previously the occurrence of these two types of gamma (Kay, 2003; Rojas-Líbano and Kay, 2008), especially during grooming, waiting or immobility behaviors. The fact that we did not report them here is mainly related to the types of spontaneous behaviors displayed by our rats within the span of the 10–15 min of our recordings, which was for the most part active exploration. In addition, in our rats we saw little or no beta LFP activity (20–35 Hz), which has been reported to occur mainly in response to high volatility chemicals delivered close to the animal's nostrils or as a result of operant associative olfactory learning (Ravel et al., 2003; Lowry and Kay, 2007; Martin et al., 2007; Aylwin et al., 2009).

We also report a spontaneous switch between two behavioral modes, which we found in all recorded rats. They either explored the cage, moving around it actively sniffing, or they nose-poked at a hole in the cage and stayed there, locking their respiration frequency at about 6 Hz. The switch between these respiration/behavioral modes was reflected clearly in the OB LFP, with marked changes in power at all frequencies. In turn, spectral coherence between respiration and LFP was not affected by these modes, reflecting again the capabilities of the system to maintain coupling at different frequencies (Figure 7).

Sniffing strategies show many connections to behaviors associated with learning and remembering odors. The sniffing rhythm itself, although it overlaps in frequency with the hippocampal theta rhythm implicated in learning and memory processes, is not normally coherent with the hippocampal rhythm. This means that there is no consistent phase relationship between the signals. However, in some learning conditions the rhythms do become coherent. One study showed that when rats learned a reward contingency reversal, sniffing showed coherence with the hippocampal theta rhythm which decayed with learning (Macrides et al., 1982). The opposite effect was shown during learning and performance of a difficult go/no-go task in which rats had to keep track of timing (Kay, 2005). In this condition OB and hippocampal theta rhythm coherence was positively correlated with performance.

Rats also adjust their sniffs as they learn; recent work from our laboratory has shown that rats use variable sniff flow strategies to detect odors dependent on their sorptive properties (Rojas-Líbano and Kay, 2012). As rats learn to discriminate odors, they increase sampling duration and use longer inhalations for low than high sorption odorants, but upon reaching criterion performance they produce lower flow sniffs for low sorption than for high sorption odorants. The current study now shows that the theta rhythm in the OB can be used to study respiratory frequency on a cycle-by-cycle basis and to track the sensorimotor representation within the neural circuit.

ACKNOWLEDGMENTS

Funding was provided by the NIDCD, R01-DC007995 (Leslie M. Kay). We thank James Schadt for advice on EMG electrode fabrication, and Meagen Scott for help in animal care, behavior and surgery. We also thank Cristóbal Córdova for help with Figures 1, 7.

REFERENCES

Adrian, E. D. (1950). The electrical activity of the mammalian olfactory bulb. EEG Clin. Neurophysiol. 2, 377–388.

Aylwin, M. L., Aguilar, G. A., Flores, F. J., and Maldonado, P. E. (2009). Odorant modulation of neuronal activity and local field potential in sensory-deprived olfactory bulb. Neuroscience. 162, 1265–1278. doi: 10.1016/j.neuroscience.2009.05.051

Buonviso, N., Amat, C., Litaudon, P., Roux, S., Royet, J. P., Farget, V., et al. (2003). Rhythm sequence through the olfactory bulb layers during the time window of a respiratory cycle. Eur. J. Neurosci. 17, 1811–1819. doi: 10.1046/j.1460-9568.2003.02619.x

Carey, R. M., Verhagen, J. V., Wesson, D. W., Pírez, N., and Wachowiak, M. (2009). Temporal structure of receptor neuron input to the olfactory bulb imaged in behaving rats. J. Neurophysiol. 101, 1073–1088. doi: 10.1152/jn.90902.2008

Carey, R. M., and Wachowiak, M. (2011). Effect of sniffing on the temporal structure of mitral/tufted cell output from the olfactory bulb. J. Neurosci. 31, 10615–10626. doi: 10.1523/JNEUROSCI.1805-11.2011

Cenier, T., David, F., Litaudon, P., Garcia, S., Amat, C., and Buonviso, N. (2009). Respiration-gated formation of gamma and beta neural assemblies in the mammalian olfactory bulb. Eur. J. Neurosci. 29, 921–930. doi: 10.1111/j.1460-9568.2009.06651.x

Courtiol, E., Amat, C., Thévenet, M., Messaoudi, B., Garcia, S., and Buonviso, N. (2011a). Reshaping of bulbar odor response by nasal flow rate in the rat. PLoS ONE 6:e16445. doi: 10.1371/journal.pone.0016445

Courtiol, E., Hegoburu, C., Litaudon, P., Garcia, S., Fourcaud-Trocmé, N., and Buonviso, N. (2011b). Individual and synergistic effects of sniffing frequency and flow rate on olfactory bulb activity. J. Neurophysiol. 106, 2813–2824. doi: 10.1152/jn.00672.2011

Cunliffe-Beamer, T. L. (1993). Applying principles of aseptic surgery to rodents. AWIC Newsl. 4, 3–6.

Diamond, M. E., von Heimendahl, M., Knutsen, P. M., Kleinfeld, D., and Ahissar, E. (2008). ‘Where’ and ‘what’ in the whisker sensorimotor system. Nat. Rev. Neurosci. 9, 601–612. doi: 10.1038/nrn2411

Domino, E. F., and Ueki, S. (1960). An analysis of the electrical burst phenomenon in some rhinencephalic structures of the dog and monkey. Electroencephalogr. Clin. Neurophysiol. 12, 635–648. doi: 10.1016/0013-4694(60)90107-3

Esclassan, F., Courtiol, E., Thévenet, M., Garcia, S., Buonviso, N., and Litaudon, P. (2012). Faster, deeper, better: the impact of sniffing modulation on bulbar olfactory processing. PLoS ONE 7:e40927. doi: 10.1371/journal.pone.0040927

Feldman, J. L., Del Negro, C. A., and Gray, P. A. (2013). Understanding the rhythm of breathing: so near, yet so far. Annu. Rev. Physiol. 75, 423–452. doi: 10.1146/annurev-physiol-040510-130049

Fontanini, A., Spano, P., and Bower, J. M. (2003). Ketamine-xylazine-induced slow (<1.5 Hz) oscillations in the rat piriform (olfactory) cortex are functionally correlated with respiration. J. Neurosci. 23, 7993–8001. doi: 10.1152/jn.01320.2004

Freeman, W. J. (1972). Depth recording of averaged evoked potential of olfactory bulb. J. Neurophysiol. 35, 780–796.

Freeman, W. J. (1975). Mass Action in the Nervous System. Academic Press.

Freeman, W. J. (1976). “Quantitative patterns of integrated neural activity,” in Simpler Networks and Behavior, ed. J. C. Fentress (Sunderland, MA: Sinauer Assoc.), 280–296.

Fukunaga, I., Berning, M., Kollo, M., Schmaltz, A., and Schaefer, A. T. (2012). Two distinct channels of olfactory bulb output. Neuron 75, 320–329. doi: 10.1016/j.neuron.2012.05.017

Gault, F. P., and Leaton, R. N. (1963). Electrical activity of the olfactory system. EEG Clin. Neurophysiol. 15:299–304.

Gray, C. M., and Skinner, J. E. (1988). Centrifugal regulation of neuronal activity in the olfactory bulb of the waking rabbit as revealed by reversible cryogenic blockade. Exp. Brain Res. 69, 378–386.

Grosmaitre, X., Santarelli, L. C., Tan, J., Luo, M., and Ma, M. (2007). Dual functions of mammalian olfactory sensory neurons as odor detectors and mechanical sensors. Nat. Neurosci. 10, 348–354. doi: 10.1038/nn1856

Katz, R., Fink, B., and Ngai, S. (1962). Relationship between electrical activity of the diaphragm and ventilation. Proc. Soc. Exp. Biol. Med. 110, 792–794.

Kay, L. M. (2003). Two species of gamma oscillations in the olfactory bulb: dependence on behavioral state and synaptic interactions. J. Integr. Neurosci. 2, 31–44. doi: 10.1142/S0219635203000196

Kay, L. M. (2005). Theta oscillations and sensorimotor performance. Proc. Natl. Acad. Sci. USA. 102, 3863–3868. doi: 10.1073/pnas.0407920102

Kay, L. M., Lancaster, L. R., and Freeman, W. J. (1996). Reafference and attractors in the olfactory system during odor recognition. Int. J. Neural Syst. 7, 489–495.

Liversedge, S. P., and Findlay, J. M. (2000). Saccadic eye movements and cognition. Trends Cogn. Sci. 4, 6–14. doi: 10.1016/S1364-6613(99)01418-7

Lowry, C. A., and Kay, L. M. (2007). Chemical factors determine olfactory system beta oscillations in waking rats. J. Neurophysiol. 98, 394–404. doi: 10.1152/jn.00124.2007

Macrides, F., and Chorover, S. L. (1972). Olfactory bulb units: activity correlated with inhalation cycles and odor quality. Science 175, 84–87.

Macrides, F., Eichenbaum, H. B., and Forbes, W. B. (1982). Temporal relationship between sniffing and the limbic theta rhythm during odor discrimination reversal learning. J. Neurosci. 2, 1705–1717.

Manabe, H., and Mori, K. (2013). Sniff rhythm-paced fast and slow gamma-oscillations in the olfactory bulb: relation to tufted and mitral cells and behavioral states. J. Neurophysiol. 110, 1593–1599. doi: 10.1152/jn.00379.2013

Martin, C., Beshel, J., and Kay, L. M. (2007). An olfacto-hippocampal network is dynamically involved in odor-discrimination learning. J. Neurophysiol. 98, 2196–2205. doi: 10.1152/jn.00524.2007

Martin, C., Gervais, R., Hugues, E., Messaoudi, B., and Ravel, N. (2004). Learning modulation of odor-induced oscillatory responses in the rat olfactory bulb: a correlate of odor recognition? J. Neurosci. 24, 389–397. doi: 10.1523/JNEUROSCI.3433-03.2004

Mitchinson, B., Martin, C. J., Grant, R. A., and Prescott, T. J. (2007). Feedback control in active sensing: rat exploratory whisking is modulated by environmental contact. Proc. Biol. Sci. 274, 1035–1041. doi: 10.1098/rspb.2006.0347

Onoda, N., and Mori, K. (1980). Depth distribution of temporal firing patterns in olfactory bulb related to air-intake cycles. J. Neurophysiol. 44, 29–39.

Percival, D. B., and Walden, A. T. (1993). Spectral Analysis for Physical Applications: Multitaper and Conventional Univariate Techniques. Cambridge: Cambridge University Press.

Phillips, M. E., Sachdev, R. N., Willhite, D. C., and Shepherd, G. M. (2012). Respiration drives network activity and modulates synaptic and circuit processing of lateral inhibition in the olfactory bulb. J. Neurosci. 32, 85–98. doi: 10.1523/JNEUROSCI.4278-11.2012

Ravel, N., Caille, D., and Pager, J. (1987). A centrifugal respiratory modulation of olfactory bulb unit activity: a study on acute rat preparation. Exp. Brain Res. 65, 623–628.

Ravel, N., Chabaud, P., Martin, C., Gaveau, V., Hugues, E., Tallon-Baudry, C., et al. (2003). Olfactory learning modifies the expression of odour-induced oscillatory responses in the gamma (60-90 Hz) and beta (15-40 Hz) bands in the rat olfactory bulb. Eur. J. Neurosci. 17, 350–358.

Rojas-Líbano, D., and Kay, L. M. (2008). Olfactory system gamma oscillations: the physiological dissection of a cognitive neural system. Cogn. Neurodyn. 2, 179–194. doi: 10.1007/s11571-008-9053-1

Rojas-Líbano, D., and Kay, L. M. (2012). Interplay between sniffing and odorant sorptive properties in the rat. J. Neurosci. 32, 15577–15589. doi: 10.1523/JNEUROSCI.1464-12.2012

Rosero, M. A., and Aylwin, M. L. (2011). Sniffing shapes the dynamics of olfactory bulb gamma oscillations in awake behaving rats. Eur. J. Neurosci. 34, 787–799. doi: 10.1111/j.1460-9568.2011.07800.x

Shafford, H. L., Strittmatter, R. R., and Schadt, J. C. (2006). A novel electrode design for chronic recording of electromyographic activity. J. Neurosci. Methods 156, 228–230. doi: 10.1016/j.jneumeth.2006.03.009

Slepian, D. (1978). Prolate spheroidal wave functions, Fourier analysis, and uncertainty V: the discrete case. Bell Syst. Tech. J. 57, 1371–1429.

Thomson, D. J. (1982). Spectrum estimation and harmonic analysis. Proc. IEEE 70, 1055–1096.

Thomson, D. J. (2001). “Multitaper analysis of nonstationary and nonlinear time series data,” in Nonlinear and Nonstationary Signal Processing, eds W. Fitzgerald, R. Smith, A. Walden, and P. Young (Cambridge: Cambridge University Press).

Tort, A. B., Komorowski, R., Eichenbaum, H., and Kopell, N. (2010). Measuring phase-amplitude coupling between neuronal oscillations of different frequencies. J. Neurophysiol. 104, 1195–1210. doi: 10.1152/jn.00106.2010

Tort, A. B., Komorowski, R. W., Manns, J. R., Kopell, N. J., and Eichenbaum, H. (2009). Theta-gamma coupling increases during the learning of item-context associations. Proc. Natl. Acad. Sci. U.S.A. 106, 20942–20947. doi: 10.1073/pnas.0911331106

Ueki, S., and Domino, E. F. (1961). Some evidence for a mechanical receptor in olfactory function. J. Neurophysiol. 24, 12–25.

Verhagen, J. V., Wesson, D. W., Netoff, T. I., White, J. A., and Wachowiak, M. (2007). Sniffing controls an adaptive filter of sensory input to the olfactory bulb. Nat. Neurosci. 10, 631–639. doi: 10.1038/nn1892

Wachowiak, M. (2011). All in a sniff: olfaction as a model for active sensing. Neuron 71, 962–973. doi: 10.1016/j.neuron.2011.08.030

Walsh, R. R. (1956). Single cell spike activity in the olfactory bulb. Am. J. Physiol. 186, 255–257.

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 25 April 2014; Accepted: 27 May 2014; Published online: 11 June 2014.

Citation: Rojas-Líbano D, Frederick DE, Egaña JI and Kay LM (2014) The olfactory bulb theta rhythm follows all frequencies of diaphragmatic respiration in the freely behaving rat. Front. Behav. Neurosci. 8:214. doi: 10.3389/fnbeh.2014.00214

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Rojas-Líbano, Frederick, Egaña and Kay. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 15 May 2014
doi: 10.3389/fnbeh.2014.00176

Infant rats can learn time intervals before the maturation of the striatum: evidence from odor fear conditioning

Julie Boulanger Bertolus[1][*], Chloe Hegoburu[1], Jessica L. Ahers[2], Elizabeth Londen[2], Juliette Rousselot[1], Karina Szyba[2], Marc Thévenet[1], Tristan A. Sullivan-Wilson[2], Valérie Doyère[3], Regina M. Sullivan[2] and Anne-Marie Mouly[1][*]

1Lyon Neuroscience Research Center, INSERM U1028, CNRS UMR5292, University Lyon1, Lyon, France

2Child and Adolescent Psychiatry, Emotional Brain Institute, Nathan Kline Institute, New York University School of Medicine, New York, NY, USA

3Centre de Neurosciences Paris-Sud, CNRS UMR 8195, University Paris-Sud, Orsay, France

* Correspondence: Julie Boulanger Bertolus and Anne-Marie Mouly, CRNL, UMR 5292, INSERM 1028, Université Lyon1, 50 Avenue Tony Garnier, 69633 Lyon Cedex 7, France e-mail: julie.boulanger-bertolus@olfac.univ-lyon1.fr; ammouly@olfac.univ-lyon1.fr

Edited by:
Carmen Sandi, Ecole Polytechnique Federale De Lausanne, Switzerland

Reviewed by:
Daniel W. Wesson, Case Western Reserve University, USA
Antonios Stamatakis, National and Kapodistrian University of Athens, Greece

Interval timing refers to the ability to perceive, estimate and discriminate durations in the range of seconds to minutes. Very little is currently known about the ontogeny of interval timing throughout development. On the other hand, even though the neural circuit sustaining interval timing is a matter of debate, the striatum has been suggested to be an important component of the system and its maturation occurs around the third post-natal (PN) week in rats. The global aim of the present study was to investigate interval timing abilities at an age for which striatum is not yet mature. We used odor fear conditioning, as it can be applied to very young animals. In odor fear conditioning, an odor is presented to the animal and a mild footshock is delivered after a fixed interval. Adult rats have been shown to learn the temporal relationships between the odor and the shock after a few associations. The first aim of the present study was to assess the activity of the striatum during odor fear conditioning using 2-Deoxyglucose autoradiography during development in rats. The data showed that although fear learning was displayed at all tested ages, activation of the striatum was observed in adults but not in juvenile animals. Next, we assessed the presence of evidence of interval timing in ages before and after the inclusion of the striatum into the fear conditioning circuit. We used an experimental setup allowing the simultaneous recording of freezing and respiration that have been demonstrated to be sensitive to interval timing in adult rats. This enabled the detection of duration-related temporal patterns for freezing and/or respiration curves in infants as young as 12 days PN during odor fear conditioning. This suggests that infants are able to encode time durations as well as and as quickly as adults while their striatum is not yet functional. Alternative networks possibly sustaining interval timing in infant rats are discussed.

Keywords: olfactory fear conditioning, ontogeny, memory, interval timing, striatum, respiration, freezing, infant rats

INTRODUCTION

The ability to time events is continuously used in humans and other animals. It leads to the prediction of events, to the production of appropriate responses, and to the detection of errors in usual temporal patterns. It allows us, for example, to estimate if we have time to cross the street or if we have to stop when the traffic light turns yellow by estimating when it will turn red. Pavlov (1927) was the first to describe the encoding of the temporal relationships between events. Pavlovian conditioning is now defined as the pairing of an initially neutral stimulus (the conditioned stimulus, CS) with an unconditioned one (US). After repeated presentations of this association, the CS elicits conditioned responses which appear to be anticipatory to the arrival of the US (Pavlov, 1927). This suggests an encoding of the temporal relationships between the CS and the US.

The ability to perceive, estimate and discriminate durations in the range of seconds to minutes is referred to as interval timing. Very little is currently known about the ontogeny of interval timing throughout development. Fitzgerald et al. (1967) demonstrated that human infants as young as 1-month-old can be conditioned to temporal regularities. Indeed, when exposed to repeated light/dark switches, they exhibit pupillary dilatation, which becomes regular regardless if the light stimulus is presented or not. Since then, several studies have compared time judgments during development and showed that time estimation improves throughout childhood (see a meta-analysis by Block et al., 1999; a review by Droit-Volet, 2011). In animals, most of the studies carried out to investigate the ontogeny of time encoding in rats have used eyeblink conditioning, which involves interval durations in the milliseconds range. This procedure usually consists of pairing an auditory stimulus with an eyeblink-eliciting US (e.g., a mild air-puff to the eye or a mild shock to the eyelid). After many CS-US pairings (usually 100–300), a conditioned eyeblink response occurs such that the peak amplitude of the conditioned response occurs at or just before the onset time of the US (Smith, 1968; Smith et al., 1969). Eyeblink conditioning has been shown to emerge gradually between post-natal (PN) day 17 and PN24 (Stanton et al., 1992; Freeman et al., 1993). However, while interstimulus interval discrimination of the conditioned eyeblink response occurs in juvenile rats, performance shows protracted development through adulthood (Freeman et al., 2003; Brown et al., 2006). Concerning the timing of longer duration intervals (seconds to minutes range), only two studies (Lejeune et al., 1986; Lejeune, 1992) investigated interval timing in juvenile (20–30 days of age) rats using a Fixed Interval procedure, where a food reinforcer was delivered in response to lever presses on a fixed 60 s schedule. This procedure is used to investigate the ability of animals to adjust to the temporal regularities of their environment. The studies by Lejeune et al. (1986) and Lejeune (1992) reported excellent temporal regulation of behavior in juvenile rats. However, since Fixed Interval procedures require multiple training days, juvenile rats reached the age of 30 days at the end of training. To our knowledge, no experiment has been conducted to test interval timing at younger ages in rats.

The neural circuits sustaining the encoding and memorization of temporal information are still a matter of debate. Numerous structures have been suggested to be involved, with considerable differences depending on the paradigms, and no structure has been exclusively related to time encoding. Yet, there is a consensus in the literature that the areas critically involved in timing depend on the timescale considered. Indeed, the cerebellum might be crucial for sub-second durations, while the striatum and prefrontal cortex might be more involved in processing seconds to minutes interval durations (for reviews, see Buhusi and Meck, 2005; Meck et al., 2008). More specifically, a number of recent studies have demonstrated that the striatum is an important component of the interval timing system both in humans and animals (reviewed in Buhusi and Meck, 2005; Coull et al., 2011). However, anatomical studies showed that the striatum morphological maturation occurs around PN15 (Chronister et al., 1976; Tepper and Trent, 1993). Moreover, Tepper et al. (1998) reported that the electrophysiological characteristics of the striatal neurons continue to mature throughout the preweaning period. This delayed ontogenesis of the striatum raises the question of whether rats are capable of timing intervals prior to striatum development. The global aim of the present study was to investigate interval timing abilities in rats at an age for which the striatum was not yet mature.

We addressed this question using odor fear conditioning. Indeed, in comparison with the Fixed Interval procedure, fear conditioning makes very little demand on the rat motoric capabilities. Furthermore, odor fear conditioning can be applied to very young animals (Sullivan et al., 2000) as their sense of smell is fully functional at birth, contrary to vision and audition.

We first investigated whether the striatum, known for its role in time processing, was activated by odor fear conditioning in developing rats using 2-DG autoradiography. Although fear learning was shown at all tested ages, activation of the striatum was observed in adults but not in juvenile animals. Next, we assessed the presence of evidence of interval timing in ages before and after the inclusion of the striatum into the fear conditioning circuit. For this, we used an experimental setup allowing the simultaneous recording of freezing and respiration which have been demonstrated to be sensitive to interval timing in adult rats (Hegoburu et al., 2011; Shionoya et al., 2013). This enabled us to assess the emergence of temporal patterns during the acquisition of the odor-shock association in both adult and infant rats. Such analysis revealed evidence of interval timing in ages prior to and after the functional maturation of the striatum.

METHODS

ANIMALS

The subjects were male and female Long Evans rats born and bred either in the Nathan Kline Institute colony (originally from Harlan, USA) or in the Lyon Neuroscience Research Center (originally from Janvier, France). Only one female and one male pup per litter per treatment/test condition were used for all experiments and animals from the same litters were used in the different treatment/test conditions and ages. A total of 20 litters were used. Three groups of ages were used: PN day 12–15 (PN12–15, infants), PN22–24 (juveniles) and older than PN75 (adults). Day of birth was considered PN0. Pups were maintained with their litters up to the end of the experiments, including juvenile pups. Adults were housed by pairs at 23°C and maintained under a 12 h light-dark cycle (lights on from 6:00 am to 6:00 pm). Food and water were available ad libitum and abundance of wood shavings was supplied for nest building. All experiments were conducted in strict accordance with the Institutional Animal Care and Use Committee of the Nathan Kline Institute, which follows the guidelines from the American National Institutes of Health, and with the European Community Council Directive of November 24, 1984 (84/609/EEC) and the French National Committee (87/848) for care and use of laboratory animals. Care was taken at all stages to minimize stress and discomfort to the animals. An overlap in personnel conditioning/testing both infant and adult rats in France and the USA ensured consistency of conditioning and testing of infant and adult animals between labs.

TRAINING APPARATUS

Equivalent conditioning apparatus and procedures were used between labs as previously described (USA: Coulbourn equipment, described in Sevelinges et al., 2008; France: Emka and Coulbourn equipment, described in Hegoburu et al., 2011). In the USA, a standard Coulbourn shock chamber was used. In France, a custom built Plexiglas conditioning chamber for fear conditioning was used and equipped with features allowing fine-grain freezing analysis (4 camera views, B/W CMOS PINHOLE camera, Velleman, Belgium, and an homemade acquisition software using Matrox Imaging Library and acquisition card, Matrox video, UK) and a plethysmograph (diameter 30 cm, Emka Technologies, France) for measuring respiratory frequency (see Hegoburu et al., 2011 for further description of the plethysmograph). The height of the plethysmograph was adapted to the age of the animal in order to optimize the signal-noise ratio, leading to a height of 30 cm for the adults and 16.5 cm for the infants.

ODOR FEAR CONDITIONING PROCEDURE

CS-US parameters were standardized between labs. The American lab used the Coulbourn FreezeFrame software for stimuli delivery control and video recording. The French lab used a custom build program for stimuli delivery and recording. Conditioning took place in a sound attenuation chamber with deodorized air constantly flowing through the cage (2 L/min). The odor CS was a 30-s peppermint odor (McCormick Pure Peppermint; 2 L/min; 1:10 peppermint vapor to air) and was controlled with a solenoid valve that diverted the airflow to the peppermint air stream, thus minimizing pressure change. The 1-s mild electric shock was delivered through a grid floor. In the US, for the youngest pups, the shock was delivered through an electrode to one hind limb. At both sites, adult rats were handled for about 4 days and placed into the conditioning chamber for context habituation. Juveniles received only 1 day of handling and habituation while infants, for which conditioning to context is not yet developed (Raineki et al., 2010), were not handled to minimize distress from separation from the mother.

Three training conditions were used throughout the experiments: Odor-shock pairings (Paired condition), Odor-shock unpaired presentation (Unpaired condition) and Odor-alone presentation (Odor group). In the paired groups, during the first 10 min of the conditioning session, the animals were allowed an adaptation period of free exploration. Then the CS odor was introduced into the cage for 30 s, the last second of which overlapped with the shock. The animals received ten odor-shock trials, with an inter-trial interval of 4 min. In the Unpaired groups, the same procedure was carried out except that the shock and the odor were explicitly unpaired using a fixed long duration (180 s) between the odor onset and the shock arrival. In the Odor groups, the odor was presented alone for 30 s.

RETENTION TEST

A subset of animals at the three ages (Infants: n = 17; Juveniles: n = 22; Adults: n = 26) were tested the day after conditioning for learned fear responses. Testing was done by an experimenter blind to the training conditions.

Infants were tested in a y-maze (start box: 8.5 × 10 × 8 cm; choice arms: 8.5 × 24 × 8 cm), one arm containing the peppermint odor CS (20 μL peppermint on Kim Wipe), and the other containing familiar pine shavings (20 mL clean bedding). Pups were given 5 trials. For each trial, the pup was placed in the start box (5 s), the door to each alley opened and the animals were given 60 s to choose an arm. The number of choices of the arm containing the CS odor was compared between groups using a One-Way ANOVA with the Group as an independent factor.

For adults and juveniles, the rat was placed in a novel experimental cage with new contextual (tactile and visual) cues and allowed a 10-min odor-free period. The CS odor was then presented five times (30 s with a 4-min inter-trial interval) and the animal's freezing response was hand scored by an experimenter blind to the training conditions. The average freezing behavior of a 25 s period preceding odor delivery (Pre-CS period) was compared with the full odor presentation period (CS period) using a Two-Way ANOVA with Group (Paired, Unpaired and Odor) as an independent factor and Period (Pre-CS vs. CS) as a repeated measures factor. Significant ANOVAs were followed by Post-hoc Fisher comparisons.

STRIATUM 2-DG AUTORADIOGRAPHY

In the 14C 2-DG autoradiography, the animals (adults: n = 17, juveniles: n = 18) were injected with 2-DG (20 μCi/100 g) 5 min prior to conditioning (Paired, Unpaired and Odor). Brains were removed immediately after the 45 min of conditioning, frozen in 2-methylbutane (−45°C) and stored at −70°C. Brains were then cut into 20 μm coronal sections (following equilibration to −20°C) and placed in exposure cassettes for 4 days with standards (14C methylmethacrylate standard 10 × 0.02 mCi; American Radiolabeled Chemicals, Inc.), scanned (Epson) and analyzed using NIH Image J Software for quantitative optical densitometry. The dorsal striatum was divided into anterior (Bregma +1.9 mm, Paxinos and Watson, 1986) and posterior dorsal striatum (Bregma +0.1). Within each part, the lateral and medial dorsal striatum were differentiated, as they receive inputs from different parts of the brain (McGeorge and Faull, 1989). Coordinates were adjusted in pups to obtain similar measure locations in both adults and pups. To control for potential differences in section thickness and autoradiograph exposure, 2-DG uptake was measured relative to 2-DG uptake in the corpus callosum that did not vary between conditioning groups (Sullivan and Leon, 1986; Sullivan et al., 2000). For each developmental age, the data from the anterior and posterior parts of the dorsal striatum were analyzed using a Three-Way ANOVA with Group as an independent factor and Anteriority (anterior vs. posterior part) and Laterality (lateral vs. medial part) as repeated measures factors. A One-Way ANOVA was then carried out on the data of each of the four subparts, followed by post-hoc Fisher comparisons allowed by the ANOVA results.

ASSESSMENT OF TEMPORAL PATTERNS OF FREEZING AND RESPIRATION DURING ACQUISITION

To assess interval timing during development we used olfactory fear conditioning in PN12–15 infant (n = 30) and adult rats (n = 25), ages representative of before and after functional striatal development. Three experimental groups of animals per age were trained using either 30 s (Paired 30 s group), or 20 s (Paired 20 s group) Odor-Shock interval duration, or Odor alone presentation (Odor group).

In each experimental group, the time course of respiration and behavior was monitored throughout the acquisition session. Offline, the respiratory signal was analyzed and momentary respiratory frequency was determined. The animal's freezing behavior was automatically detected using a LabView homemade software that had been validated by comparison to hand scoring by an experimenter blind to the rats' condition. Definition of the freezing at the different ages followed the methods defined by Takahashi (1992) and takes into account the immaturity of infants' musculoskeletal system. Instant respiratory frequency and freezing were averaged on a second by second basis, leading to 1-s time bins curves. The resulting individual curves were then averaged among animals of the same experimental group.

The temporal dynamics of the recorded parameters in presence of the CS odor was compared using a Two-Way ANOVA with Group as an independent factor and Time as a repeated measures factor. Post-hoc pairwise comparisons were then carried out when allowed by the ANOVA results. In addition in each group, a One-Way ANOVA for repeated measures was carried out to compare each CS-bin (i.e., seconds 1–19 in the Paired 20 s group and seconds 1–29 in the Paired 30 s group) to the averaged parameter value during 25 s prior the odor arrival as a baseline. Post-hoc within group comparisons were then carried out when allowed by the ANOVA results. For all the statistical comparisons performed, the significance level was set at 0.05.

In order to assess whether the temporal patterns of freezing and respiratory rate observed for the Paired 20 s and Paired 30 s groups are related to the CS-US interval, we assessed whether they respected the scalar property. Indeed, a remarkable property of interval timing is that the error of time estimation varies linearly with the estimated interval. This is known as the scalar property of time estimation (Gibbon, 1977). As a consequence, when the behavioral temporal response functions are normalized by the criterion time (multiplicative rescaling), they superimpose (reviewed in Matell and Meck, 2000). To test if this property was respected for the freezing and respiratory rate temporal responses, the time axis for the individual curves of the Paired 30 s rats was multiplicatively rescaled so that the 19 bins of time of CS for both groups represented the same proportions of elapsed time from CS onset to shock presentation. The scalar timing rule predicts superior superposition of the functions in relative time (multiplicative transform, comparison of the Paired 20 s average curve to the rescaled Paired 30 s average curve), compared to no rescaling (comparison of the Paired 20 s average curve to bins 1–19 of the Paired 30 s average curve) or rescaling by an additive shift of x-axes (additive transform, comparison of the Paired 20 s average curve to bins 11 to 29 of the Paired 30 s average curve). Superposition was indexed by eta-square (η^2), a measure of the proportion of variance accounted for by the mean of the two functions (Brown et al., 1992). When superposition is perfect, η^2 is at its maximum value of 1. Superposition was assessed on curves obtained with absolute freezing and respiratory rate values as well as with data normalized in relative response rate (maximum minus minimum values) on the Y axis.

RESULTS

FEAR LEARNING: ODOR-SHOCK CONDITIONING PRODUCED LEARNING AT ALL AGES

To assess learning from odor-shock conditioning at all ages, some animals were conditioned and tested for retention approximately 24 h later. As illustrated on Figure 1, all paired odor-shock conditioning animals showed learning at testing. Infants tested in a Y-Maze (Figure 1A) showed a significant Group effect [F(2, 14) = 13.404, p = 0.001] and post-hoc Fisher analysis indicated that the paired groups were significantly different from each of the controls (Paired vs. Unpaired p = 0.002; Paired vs. Odor p < 0.001). Juvenile and adult rats received a cue test (CS presentations) in a novel context with learning assessed through freezing rate and analyzed with Two-Way ANOVA with repeated measures (Pre-CS vs. CS). Juvenile animals (Figure 1B) showed a significant Group × Period interaction [F(2, 20) = 4.234, p = 0.029]. Post-hoc analysis evidenced a significant freezing increase during the CS odor in the Paired group (p = 0.02) while this increase was not present in the Unpaired and Odor groups (p > 0.05). Adult rats (Figure 1C) showed a significant Group × Period interaction [F(2, 23) = 6.462, p = 0.006]. In the Paired group, the level of freezing was higher during the CS presentation than during the Pre-CS period (p < 0.05), while it remained unchanged in the Unpaired and Odor groups (p > 0.05).

FIGURE 1. Learning retention session. Animals were tested the day after conditioning. (A) Infant rats (Paired, n = 5; Unpaired, n = 6; Odor, n = 6) were given a Y-Maze test and the number of CS choices (mean ± s.e.m.) out of 5 were used as a measure of learning. (B) Juveniles (Paired, n = 7; Unpaired, n = 9; Odor, n = 6) and (C) adults (Paired, n = 8; Unpaired, n = 8; Odor, n = 10) received a cue test in a novel environment and the freezing rate (mean ± s.e.m.) was compared between pre-CS and CS Odor. #Significant intragroup difference (p < 0.05); *significant intergroup difference (p < 0.05).

Therefore, the training paradigm used in the present study resulted in good memory at the three developmental ages considered.

STRIATAL^14C 2-DG AUTORADIOGRAPHY: THE POSTERIOR DORSAL STRIATUM IS ACTIVATED DURING ODOR-SHOCK CONDITIONING IN ADULTS BUT NOT IN JUVENILES

The level of activation of the dorsal striatum (Figure 2A) during odor fear acquisition was assessed in adults and juveniles using 14C 2-DG autoradiography.

FIGURE 2. Striatum 2-DG autoradiography during odor fear conditioning. (A) 2-DG autoradiograph with the corresponding coronal brain sections from Paxinos and Watson (1986; numbers on the right indicate distance relative to bregma in mm). Anterior and posterior parts of the dorsal striatum were analyzed separately and subdivided in lateral (L) and medial (M) portions for optical densitometric measurements. (B,C) Mean 2-DG uptake ratio (±s.e.m.) in the dorsal striatum relative to corpus callosum during odor fear conditioning in adult (B) and juvenile animals (C) in the different experimental conditions: Adult Paired (n = 5); Adult Unpaired (n = 6); Adult Odor (n = 6); Juvenile Paired (n = 6); Juvenile Unpaired (n = 6); Juvenile Odor (n = 6). #p = 0.066, *p = 0.021, Fisher test comparison with the Paired group.

In adults (Figure (2B), the Three-Way ANOVA revealed a significant effect of Anteriority [F(1, 14) = 21.65, p < 0.001], Laterality [F(1, 14) = 19.67, p = 0.001] and Laterality × Group interaction [F(1, 14) = 4.51, p = 0.03]. Further One-Way ANOVA revealed that in the anterior part of the dorsal striatum (upper panel) the level of activation did not vary between groups for both the lateral [F(2,14) < 1] and medial [F(2, 14) = 1.24, p = 0.32] parts. In the posterior part of the striatum (lower panel), the One-Way ANOVA revealed a main effect of the Group in the medial part [F(2, 14) = 3.62, p = 0.05] but no significant effect in the lateral part [F(2, 14) = 2.50, p = 0.12] although the same tendency was observed. Post-hoc Fisher tests revealed that in the medial part, the Paired group exhibited a higher level of 2-DG uptake compared to the Odor group (p = 0.021) and the unpaired group (tendency, p = 0.066).

In juvenile rats (Figure 2C), the activation of the striatum was at a similar level between all groups regardless if the measure was taken on the anterior or the posterior dorsal striatum, medial or lateral parts (for all comparisons: F < 1).

In summary, in adult rats, odor fear conditioning was associated with an increased activation in the posterior part of the dorsal striatum, while in juveniles no increase was observed.

TEMPORAL PATTERNS FOR FREEZING AND RESPIRATION DURING ACQUISITION: INFANTS SHOW A TEMPORAL PATTERN OF US EXPECTANCY

In order to assess the ability of animals of the different ages to encode interval duration in our odor fear conditioning paradigm, the temporal patterns of responses of animals conditioned to a 30 s and to a 20 s CS-US interval were compared in adult and infant rats. For both ages, we examined the temporal patterns of freezing and respiration during the CS presentation in 1-s time bins. The average curves of the last four odor-shock pairings were pooled together for each parameter (i.e., freezing and respiratory rate). Both parameters were compared between and within groups using a Two-Way ANOVA with factor Group (Paired 20 s, Paired 30 s, and Odor) and repeated factor Time. The comparison was made on the common CS duration between the Paired 20 s and Paired 30 s groups (1–19 s).

Adults

For freezing (Figure 3, left panel), the ANOVA revealed a significant effect of Group [F(2, 22) = 51.709, p < 0.001], Time [F(18, 396) = 1.906, p = 0.014] and Group × Time interaction [F(36, 396) = 1.831, p = 0.003]. Further analyses revealed that both Paired groups showed higher levels of freezing than the Odor group [Paired 20 s vs. Odor, Group: F(1, 15) = 52.737, p < 0.001; Paired 30 s vs. Odor, Group: F(1, 15) = 125.292, p < 0.001]. In addition, there was a significant difference between Paired 20 s and Paired 30 s groups temporal patterns during CS presentation [Paired 20 s vs. Paired 30 s, Group × Time: F(18, 252) = 2.456, p = 0.001]. Post-hoc within group comparisons showed that, in Paired 30 s animals, introduction of the CS induced a significant decrease in freezing (at seconds 10, 14 to 19, 21, 22, and 28 of the CS, p < 0.05) compared to baseline. In the Paired 20 s or in the Odor group no change in freezing rate was observed in presence of the CS odor.

FIGURE 3. Effect of odor-shock conditioning on fine-grain temporal pattern of freezing and respiration during odor presentation in adult rats. The temporal pattern is represented with a 1-s bin precision, from the odor onset (green vertical line on each graph) to shock arrival (red and blue vertical line on each graph) for the paired groups. Point B on the x-axis represents the averaged baseline. Paired 20 s group: red (n = 8), Paired 30 s group: blue (n = 8) and Odor group: green (n = 9). Filled circles on each curve indicate the values that are significantly different from the baseline (p < 0.05).

Concerning respiration (Figure 3, right panel), the ANOVA revealed a significant effect of Group [F(2, 22) = 14.498, p < 0.001], Time [F(18, 396) = 13.033, p < 0.001] and Group × Time interaction [F(36, 396) = 3.341, p < 0.001]. Further analyses revealed a significantly higher respiratory rate in the Paired groups compared to the Odor group [Paired 20 s vs. Odor, Group: F(1, 15) = 20.85, p < 0.001; Paired 30 s vs. Odor, Group: F(1, 15) = 22.927, p < 0.001], as well as a significant difference between Paired 20 s and Paired 30 s groups temporal patterns [Paired 20 s vs. Paired 30 s, Group × Time: F(18, 252) = 2.551, p = 0.001]. In addition, within-group comparisons showed that while Odor animals showed almost no reaction upon odor arrival compared to baseline due to habituation, Paired animals' respiration rate increased significantly following odor onset. In particular, in Paired 20 s animals the increase was significant from seconds 5 to 14 of the CS (p < 0.05), after which the rate returned to baseline value (p > 0.05). In Paired 30 s animals, the respiratory rate increased significantly from second 4 of the CS until the shock arrival (p < 0.05).

In order to assess whether the temporal patterns of freezing and respiratory rate described above for the Paired groups are related to the CS-US interval, we tested whether they respected the scalar property as explained in the Methods.

When considering the freezing curves of Paired 20 s and Paired 30 s groups (Figure 4, upper part), superposition was better under the additive transform (η^2 = 0.49, right side) than under the multiplicative transform (η^2 = 0.26, middle) or no transform (η^2 = 0.24, left side). In contrast, for respiration (Figure 4, lower part), superposition was better under the multiplicative transform (η^2 = 0.85, middle) than under the additive transform (η^2 = 0.58, right side) or no transform (η^2 = 0.82, left side). The foregoing analyses were based on absolute response values for each dependent measure. When data were re-plotted in relative response rate on the Y axis, the results were similar, showing superior superposition with additive transform for freezing (additive: η^2 = 0.67, multiplicative: η^2 = 0.36, no transform: η^2 = 0.20), and with multiplicative transform for respiration (multiplicative: η^2 = 0.82, additive: η^2 = 0.53, no transform: η^2 = 0.80). Thus, the scalar property was best respected for respiration while it was not observed for freezing.

FIGURE 4. Scalar property test on the freezing (upper part) and respiration (lower part) temporal patterns in adult rats. The scalar timing rule predicts better superposition of the curves in relative time (multiplicative transform, middle panel), compared to no rescaling (left panel) or rescaling by an additive shift of the x-axis (right panel). Superposition between the two curves was indexed by eta-square (η^2) indicated in the bottom right of each graph, the highest values being highlighted in bold characters.

In summary, adult rats exhibited different temporal patterns of freezing and respiration rates for the two CS-US intervals. Scalar rules were respected for the respiratory rate but not for freezing, which confirms our previous data (Shionoya et al., 2013).

Infants

The temporal patterns of freezing and respiration rate were assessed in infants using the same analysis parameters as for adults.

Concerning freezing (Figure 5, left side), the ANOVA evidenced a tendency for a Group effect [F(2, 27) = 3.01, p = 0.066], and a significant effect of Time [F(18, 486) = 12.988, p < 0.001] and Group × Time interaction [F(36, 486) = 6.656, p < 0.001]. Further analysis revealed that both Paired groups showed significantly different levels of freezing than Odor group [Paired 20 s vs. Odor, Group: F(1, 18) = 4.256; p = 0.054; Paired 30 s vs. Odor, Group: F(1, 18) = 4.489, p = 0.048]. In addition, the temporal patterns of Paired 20 s and Paired 30 s animals were significantly different [Paired 20 s vs. Paired 30 s, Group × Time: F(18, 324) = 4.811, p < 0.001], although similar levels of baseline and pre-shock (3 last seconds before shock) freezing were observed in both groups (p > 0.05). In infants, introduction of the CS odor induced a strong decrease in freezing in the Paired groups. Indeed, within group comparisons showed that, in Paired 20 s animals, this decrease was significantly different from baseline from second 6 until shock arrival (p < 0.05), whereas in the Paired 30 s group significance was reached from second 17 until shock arrival (p < 0.05).

FIGURE 5. Effect of odor-shock conditioning on fine-grain temporal pattern of freezing and respiration during odor presentation in infant pups. The temporal pattern is represented with a 1-s bin precision, from the odor onset (green vertical line on each graph) to shock arrival (red and blue vertical line on each graph) for the paired groups. Point B on the x-axis represents the averaged baseline. Paired 20 s group: red (n = 10), Paired 30 s group: blue (n = 10) and Odor group: green (n = 10). Filled circles on each curve indicate the points that are significantly different from the baseline (p < 0.05).

Concerning the respiration (Figure 5, right panel), the ANOVA revealed an effect of Group [F(2, 27) = 17.588, p < 0.001], Time [F(18, 486) = 22.209, p < 0.001] and of the Group × Time interaction [F(36, 486) = 5.123, p < 0.001]. Further analysis evidenced that in both Paired groups the respiratory rate was significantly higher than in the Odor group [Paired 20 s vs. Odor, Group: F(1, 18) = 98.076, p < 0.001; Paired 30 s vs. Odor, Group: F(1, 18) = 10.581, p = 0.004]. In addition, the temporal patterns of Paired 20 s and Paired 30 s animals were significantly different [Paired 20 s vs. Paired 30 s, Group × Time: F(18, 324) = 3.753, p < 0.001], although similar respiratory rates were observed at baseline and prior the shock arrival in the two groups (p > 0.05). Within group comparisons showed that, in the Paired 20 s group, introduction of the CS Odor induced a significant increase in respiratory rates compared to baseline from second 3 of the CS until shock arrival (p < 0.05), whereas in Paired 30 s animals, the increase reached significance from second 6 until the end of the CS (p < 0.05).

When the scalar property was tested on the freezing curves (Figure 6, upper part), the highest superposition index was obtained for the multiplicative transform (η^2 = 0.82, middle) compared to additive transform (η^2 = 0.71, right side) or no transform (η^2 = 0.70, left side). Similar results were observed for the respiratory rate curves (Figure 6, lower panel; multiplicative: η^2 = 0.61, additive: η^2 = 0.53, no transform: η^2 = 0.48). When response rates were re-plotted in relative values on the Y axis, the results were similar, showing superior superposition with multiplicative transform for freezing (multiplicative: η^2 = 0.85, additive: η^2 = 0.78, no transform: η^2 = 0.67), and for respiration (multiplicative: η^2 = 0.91, additive: η^2 = 0.63, no transform: η^2 = 0.82). Thus, in infant rats, the scalar property was respected for both respiration and freezing.

FIGURE 6. Scalar property test on the freezing (upper part) and respiration (lower part) temporal patterns in infant rats. The scalar timing rule predicts better superposition of the curves in relative time (multiplicative transform, middle panel), compared to no rescaling (left panel) or rescaling by an additive shift of the x-axis (right panel). Superposition between the two curves was indexed by eta-square (η^2) indicated in the bottom right of each graph, the highest values being highlighted in bold characters.

In summary, infant rats showed different temporal patterns of freezing and respiration rates for the two duration intervals. Both freezing and respiration curves followed the scalar rules, thus supporting the hypothesis that infant pups expressed temporal learning.

DISCUSSION

The present study investigated the ontogeny of time durations encoding in odor fear conditioning. We first assessed whether the striatum, which is a brain area implicated in timing, was differentially activated in odor fear conditioning throughout development. This is the first study to assess striatum activity across development within this paradigm. The 2-DG metabolic mapping study revealed that, while dorsal striatum was activated during odor fear acquisition in adults, its activity remained unchanged in juveniles. These data are in agreement with data on striatum development, which indicates the juvenile striatum is still maturing (Chronister et al., 1976; Tepper and Trent, 1993; Tepper et al., 1998). To investigate interval timing abilities before and after the striatum functional maturation, we performed fine-grain analysis of behavioral and physiological responses shown to be valuable for assessment of timing in adults during the early stages of odor fear learning. Specifically, we monitored the time course of the animal's respiratory rate and freezing behavior during conditioning at two developmental ages: one with striatum activity correlated with learning (adult) and the other for which the striatum has been shown to be immature in the literature (infant). The data showed that for both adult and infant rats, duration-related temporal patterns can be detected for freezing and/or respiration curves suggesting that infants are able to encode time durations as well and as quickly as adults while their striatum is not yet functional.

IN ADULT ANIMALS RESPIRATION RATE IS A RELIABLE INDEX OF TIME ENCODING

The present data show that in adult animals, the temporal patterns of respiration rate in the Paired 20 and 30 s groups were significantly different and followed the scalar property. In contrast, freezing temporal patterns, although different, did not respect scalar rules. These data confirm the results we obtained in a previous study using a slightly different paradigm (Shionoya et al., 2013). Indeed in that study, the same animals were first conditioned to a 20 s odor-shock interval and then shifted to a 30 s interval. This resulted in a shift of the temporal pattern of respiration toward the new duration. However, while for analytical reasons studies of timing usually manipulate the interval durations within rather than between subjects (Brown et al., 1992), this procedure may lead to biases in the temporal pattern of conditioned responses due to the previously learned duration. Here we show that differential patterns are observed when different groups of animals are used for the 20 and 30 s CS-US intervals, thus when the two groups underwent an equivalent amount of training.

The present study also confirms that, in adults, respiration is a more sensitive index than freezing to investigate the emergence of duration-related temporal patterns within a few trials (Shionoya et al., 2013). Indeed, the respiratory rate pattern respected the scalar property (Gibbon, 1977). This property refers to the observation that, in interval timing, the variability in the temporal behavior of an animal grows proportionally with the duration of the timed stimulus. This was not observed for the freezing temporal patterns suggesting that respiration is a more reliable index than freezing to assess interval timing in odor fear conditioning. This property might be due to the fact that respiratory rate is a highly fluctuant signal which can be modulated both by the sampling of odorants (Macrides et al., 1982; Youngentob et al., 1987; Kepecs et al., 2007; Wesson et al., 2008) and by the acquired emotional valence of the stimulus (Freeman et al., 1983; Monod et al., 1989; Nsegbe et al., 1997) thus allowing the observation of subtle transient variations in the animal's fear levels.

INFANT ANIMALS SHOW INTERVAL TIMING ABILITIES

In infants, both the respiration and the freezing rates showed clear temporal patterns respecting the scalar property. To our knowledge, our study is the first to show interval timing in 12–15 days old infant rats. Indeed previous studies devoted to investigate the ontogenesis of temporal learning of seconds-to-minutes intervals in rats were performed on animals aged of 21 days at the beginning of training (Lejeune et al., 1986; Lejeune, 1992). Only slightly younger ages had been investigated on the sub-second range (17-day-old: Stanton et al., 1992). The lack of data in the literature concerning younger ages can be explained by the fact that classical paradigms used to investigate interval timing in animals use peak interval procedures (Catania, 1971) or temporal discrimination tasks (Stubbs, 1968), both of which require numerous conditioning sessions and behavioral responses beyond the infants' motor abilities. In the present study, the use of fine-grain analysis of the temporal patterns of both respiration and freezing permitted us to highlight interval timing abilities occurring after only a few odor-shock pairings. This observation in rats is in line with data collected in human babies showing that they can be conditioned to temporal regularities within a few reinforced trials as early as at 1 month of age (Fitzgerald et al., 1967). In this study, Fitzgerald and colleagues used light/dark regularly spaced switches to evidence that, after about 10 switches, human infants show regular pupillary constriction or dilatation regardless if the switch occurs or not. A recent study using operant discriminative conditioning in 4-months-old babies confirmed a relatively high sensitivity to time at early ages (Provasi et al., 2011).

Interestingly in our study the use of complementary indices such as freezing and respiration permitted us to highlight parameters that revealed timing indices changes throughout development. Indeed, in infants, contrary to adults, freezing appears to be a good index of interval timing. Infant rats respond to the CS odor by a strong decrease in freezing. While it is unclear why freezing was a better measure of timing in pups compared to adults, it may be related to pups' immature freezing response (Hunt and Campbell, 1997).

INVOLVEMENT OF THE DORSAL STRIATUM IN ODOR FEAR CONDITIONING

The 2-DG autoradiograph revealed that, in adult Paired rats, the medial part of the posterior striatum showed an increased activity compared to Unpaired and Odor animals. A growing literature suggests the involvement of the striatum in interval timing (Allen et al., 1972; Hikosaka et al., 1989; Matell et al., 2003; Höhn et al., 2011). According to McGeorge and Faull (1989), the lateral dorsal striatum receives projections from the sensori-motor cortex, while the medial posterior dorsal striatum receives inputs from the piriform cortex (McGeorge and Faull, 1989) and the amygdala (McDonald, 1991). Therefore, the difference we observe between localizations of the striatum activation might be due to the paradigm and modalities used.

The dorsal striatum has also been suggested to be involved in aversive learning in general such as in auditory fear conditioning (Ferreira et al., 2003; Kishioka et al., 2009; Wendler et al., 2014), or in a two-way active avoidance task (Darvas et al., 2011; Wendler et al., 2014) but not in contextual fear conditioning (Ferreira et al., 2003). Interestingly, both auditory fear conditioning and two-way avoidance present some temporal regularities for the animal to learn, while contextual fear conditioning usually does not. This suggests that the striatum might be preferentially involved in time learning rather than in the learning of the aversion. This is also supported by the results in Unpaired animals of the present study, which showed a similar level of activation in the striatum as the control animals.

In juveniles, our 2-DG metabolic mapping study showed similar levels of activation in the striatum of Paired animals compared to Unpaired and Odor animals. In infants, both morphological (Chronister et al., 1976; Tepper and Trent, 1993) and electrophysiological studies (Tepper et al., 1998) suggest that the striatum is immature. Therefore, at these two developmental ages, the striatum does not seem to be involved in the learning while infant animals in our study and juvenile animals in the literature (Lejeune et al., 1986; Lejeune, 1992; Stanton et al., 1992; Freeman et al., 1993) show clear evidence of learning of interval duration. This raises the question of the neuronal network supporting timing in young animals. It has been demonstrated that neural circuits underlying learning can evolve throughout the development of the organism. The neural substrate of odor-shock associative learning, for example, changes dramatically around PN10. Prior to this age, the association is supported by the olfactory bulb and the anterior piriform cortex (Moriceau et al., 2006). After PN10, the olfactory bulb disengages (Rangel and Leon, 1995), the piriform activation switches from anterior to posterior (Roth and Sullivan, 2005) and the amygdala gets involved in the encoding of the association (Moriceau et al., 2006). Another example of modification of the neural circuit underlying a cognitive process can be found for extinction of conditioned fear. Indeed, while PN24 rats present an adult-like extinction that requires the ventromedial prefrontal cortex (vmPFC), extinction in PN17 rats does not involve the vmPFC (for a review, see Kim and Richardson, 2010). A similar switch throughout the brain maturation could be suggested to underlie time encoding in the brain. While the current model of interval timing encoding proposed in the literature requires a complex communication between the cortex and the striatum (Matell and Meck, 2004), an alternative pathway could be involved in the encoding of interval durations in pups. The olfactory cortex in particular could play a role as it is functional at birth and is known to be involved in odor fear conditioning (Sevelinges et al., 2004, 2008; Jones et al., 2007). Interestingly in a previous study we showed that glutamate and GABA release in the olfactory cortex during odor fear conditioning was correlated to the time of arrival of the CS-US trial suggesting a role for this structure in time encoding (Hegoburu et al., 2009). This is in line with data from the literature showing that sensory cortices are implicated in the processing of temporal information (Quirk et al., 1997; Shuler and Bear, 2006; Bueti et al., 2008). The amygdala could also be involved in interval timing in infants. Indeed a recent study carried out in adult rats reported that changing the CS–US interval during auditory fear memory reactivation induced a selective increase in Zif-268 activity in the lateral nucleus of the amygdala (Díaz-Mataix et al., 2013), and a growing literature suggest that the amygdala may play a role in timing the CS-US interval (Díaz-Mataix et al., 2014). As mentioned above the amygdala is involved in odor fear conditioning from the age of 10 days PN and could thus take part in time processing in infant animals. Finally, alternatively to the striatum, the olfactory tubercle could be considered. Indeed, based on its embryological, anatomical and neuro-chemical properties, this structure is considered as part of the striatum, and has been shown to be functional at early developmental ages (Alheid and Heimer, 1988; Voorn et al., 2004). A recent review by Wesson and Wilson (2011) highlighted the involvement of this structure in both basic and complex olfactory functions and its potentially critical role in interfacing sensory processing and behavioral response. Indeed the olfactory tubercle receives direct olfactory sensory input from the olfactory bulb and piriform cortex (Luskin and Price, 1983) and additional input from the olfactory amygdala (Krettek and Price, 1978; Ubeda-Bañon et al., 2007), thus enabling the association of a given olfactory stimulus with its learned emotional valence. Therefore, although the involvement of the olfactory tubercle in timing has not been investigated yet, this structure could act as a short pathway to link perception with the production of temporally structured actions, specifically at early developmental ages.

In conclusion, the present study shows that in odor fear conditioning, interval durations are learned after only few trials from a very early age. Although the underlying neural network remains to be elucidated, and may evolve with ontogeny, these findings support the hypothesis of a simultaneous encoding of the associative link between two events together with their temporal relationships (Balsam and Gallistel, 2009; Balsam et al., 2010). While further experiments are needed to assess temporal learning at earlier ages than those used in the present study, our data suggest that associative and temporal learning might be two sides of the same coin.

ACKNOWLEDGMENTS

This work was supported by CNRS-PICS program, Partner University Fund Emotion & Time, LIA CNRS-NYU LearnEmoTime, ANR-Memotime, ANR-TDE, COST-Action TIMELY, NIH-DC009910, NIH-MH091451. This work was performed within the framework of the LABEX CORTEX (ANR-11-LABX-0042) of Université de Lyon, within the program “Investissements d'Avenir” (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR). The authors gratefully acknowledge Ounsa Ben-Hellal for taking care of the animals.

REFERENCES

Alheid, G., and Heimer, L. (1988). New perspectives in basal forebrain organization of special relevance for neuropsychiatric disorders: the striatopallidal, amygdaloid, and corticopetal components of substantia innominata. Neuroscience 27, 1–39.

Allen, J. D., Mitcham, J. C., and Byrd, J. I. (1972). Effects of caudate lesions on the acquisition and retention of Sidman avoidance in the rat. Psychon. Sci. 27, 157–160.

Balsam, P. D., Drew, M. R., and Gallistel, C. (2010). Time and associative learning. Comp. Cogn. Behav. Rev. 5, 1. doi: 10.3819/ccbr.2010.50001

Balsam, P. D., and Gallistel, C. R. (2009). Temporal maps and informativeness in associative learning. Trends Neurosci. 32, 73–78. doi: 10.1016/j.tins.2008.10.004

Block, R. A., Zakay, D., and Hancock, P. A. (1999). Developmental changes in human duration judgments: a meta-analytic review. Dev. Rev. 19, 183–211.

Brown, B. L., Hemmes, N. S., and De Vaca, S. C. (1992). Effects of intratrial stimulus change on fixed-interval performance: the roles of clock and memory processes. Anim. Learn. Behav. 20, 83–93.

Brown, K. L., Pagani, J. H., and Stanton, M. E. (2006). The ontogeny of interstimulus interval (ISI) discrimination of the conditioned eyeblink response in rats. Behav. Neurosci. 120, 1057. doi: 10.1037/0735-7044.120.5.1057

Bueti, D., Bahrami, B., and Walsh, V. (2008). Sensory and association cortex in time perception. J. Cogn. Neurosci. 20, 1054–1062. doi: 10.1162/jocn.2008.20060

Buhusi, C. V., and Meck, W. H. (2005). What makes us tick? Functional and neural mechanisms of interval timing. Nat. Rev. Neurosci. 6, 755–765. doi: 10.1038/nrn1764

Catania, A. C. (1971). Reinforcement schedules: the role of resposes preceding the one that produces the reinforcer. J. Exp. Anal. Behav. 15, 271–287.

Chronister, R., Farnell, K., Marco, L., and White, L. Jr. (1976). The rodent neostriatum: a Golgi analysis. Brain Res. 108, 37–46.

Coull, J. T., Cheng, R.-K., and Meck, W. H. (2011). Neuroanatomical and neurochemical substrates of timing. Neuropsychopharmacology 36, 3–25. doi: 10.1038/npp.2010.113

Darvas, M., Fadok, J. P., and Palmiter, R. D. (2011). Requirement of dopamine signaling in the amygdala and striatum for learning and maintenance of a conditioned avoidance response. Learn. Mem. 18, 136–143. doi: 10.1101/lm.2041211

Díaz-Mataix, L., Ruiz Martinez, R. C., Schafe, G. E., Ledoux, J. E., and Doyère, V. (2013). Detection of a temporal error triggers reconsolidation of amygdala-dependent memories. Curr. Biol. 23, 467–472. doi: 10.1016/j.cub.2013.01.053

Díaz-Mataix, L., Tallot, L., and Doyère, V. (2014). The amygdala: a potential player in timing CS–US intervals. Behav. Processes 101, 112–122. doi: 10.1016/j.beproc.2013.08.007

Droit-Volet, S. (2011). “Child and time,” in Multidisciplinary Aspects of Time and Time Perception, eds A. Vatakis, A. Esposito, M. Giagkou, F. Cummins and G. Papadelis (Berlin, Heidelberg: Springer-Verlag), 151–172.

Ferreira, T. L., Moreira, K. M., Ikeda, D. C., Bueno, O. F., and Oliveira, M. G. M. (2003). Effects of dorsal striatum lesions in tone fear conditioning and contextual fear conditioning. Brain Res. 987, 17–24. doi: 10.1016/S0006-8993(03)03217-7

Fitzgerald, H. E., Lintz, L. M., Brackbill, Y., and Adams, G. (1967). Time perception and conditioning an autonomic response in human infants. Percept. Mot. Skills 24, 479–486.

Freeman, J. H., Nicholson, D. A., Muckler, A. S., Rabinak, C. A., and Dipietro, N. T. (2003). Ontogeny of eyeblink conditioned response timing in rats. Behav. Neurosci. 117, 283. doi: 10.1037/0735-7044.117.2.283

Freeman, J. H., Spencer, C. O., Skelton, R. W., and Stanton, M. E. (1993). Ontogeny of eyeblink conditioning in the rat: effects of US intensity and interstimulus interval on delay conditioning. Psychobiology 21, 233–242.

Freeman, W. J., Viana Di Prisco, G., Davis, G., and Whitney, T. M. (1983). Conditioning of relative frequency of sniffing by rabbits to odors. J. Comp. Psychol. 97, 12.

Gibbon, J. (1977). Scalar expectancy theory and Weber's law in animal timing. Psychol. Rev. 84, 279.

Hegoburu, C., Sevelinges, Y., Thévenet, M., Gervais, R., Parrot, S., and Mouly, A.-M. (2009). Differential dynamics of amino acid release in the amygdala and olfactory cortex during odor fear acquisition as revealed with simultaneous high temporal resolution microdialysis. Learn. Mem. 16, 687–697. doi: 10.1101/lm.1584209

Hegoburu, C., Shionoya, K., Garcia, S., Messaoudi, B., Thévenet, M., and Mouly, A.-M. (2011). The RUB cage: respiration–ultrasonic vocalizations–behavior acquisition setup for assessing emotional memory in rats. Front. Behav. Neurosci. 5:25. doi: 10.3389/fnbeh.2011.00025

Hikosaka, O., Sakamoto, M., and Usui, S. (1989). Functional properties of monkey caudate neurons. III. Activities related to expectation of target and reward. J. Neurophysiol. 61, 814–832.

Höhn, S., Dallérac, G., Faure, A., Urbach, Y. K., Nguyen, H. P., Riess, O., et al. (2011). Behavioral and in vivo electrophysiological evidence for presymptomatic alteration of prefrontostriatal processing in the transgenic rat model for huntington disease. J. Neurosci. 31, 8986–8997. doi: 10.1523/JNEUROSCI.1238-11.2011

Hunt, P. S., and Campbell, B. A. (1997). “Developmental dissociation of the components of conditioned fear,” in Learning, Motivation and Cognition: the Functional Behaviorism of Robert C. Bolles, eds M. E. Bouton and M. S. Fanselow (Washington, DC: American Psychological Association), 53–74.

Jones, S. V., Stanek-Rattiner, L., Davis, M., and Ressler, K. J. (2007). Differential regional expression of brain-derived neurotrophic factor following olfactory fear learning. Learn. Mem. 14, 816–820. doi: 10.1101/lm.781507

Kepecs, A., Uchida, N., and Mainen, Z. F. (2007). Rapid and precise control of sniffing during olfactory discrimination in rats. J. Neurophysiol. 98, 205–213. doi: 10.1152/jn.00071.2007

Kim, J. H., and Richardson, R. (2010). New findings on extinction of conditioned fear early in development: theoretical and clinical implications. Biol. Psychiatry 67, 297–303. doi: 10.1016/j.biopsych.2009.09.003

Kishioka, A., Fukushima, F., Ito, T., Kataoka, H., Mori, H., Ikeda, T., et al. (2009). A novel form of memory for auditory fear conditioning at a low-intensity unconditioned stimulus. PLoS ONE 4:e4157. doi: 10.1371/journal.pone.0004157

Krettek, J., and Price, J. (1978). Amygdaloid projections to subcortical structures within the basal forebrain and brainstem in the rat and cat. J. Comp. Neurol. 178, 225–253.

Lejeune, H. (1992). Response timing and development: Fixed-interval performance in precociously weaned rats. Q. J. Exp. Psychol. B 44, 101–122.

Lejeune, H., Jasselette, P., Nagy, J., and Peree, F. (1986). Fixed interval performance in weanling rats: a comparison with adult and senile subjects. Physiol. Behav. 38, 337–343.

Luskin, M. B., and Price, J. L. (1983). The topographic organization of associational fibers of the olfactory system in the rat, including centrifugal fibers to the olfactory bulb. J. Comp. Neurol. 216, 264–291.

Macrides, F., Eichenbaum, H. B., and Forbes, W. B. (1982). Temporal relationship between sniffing and the limbic rhythm during odor discrimination reversal learning. J. Neurosci. 2, 1705–1717.

Matell, M. S., and Meck, W. H. (2000). Neuropsychological mechanisms of interval timing behavior. Bioessays 22, 94–103. doi: 10.1002/(SICI)1521-1878(200001)22:1<94::AID-BIES14>3.0.CO;2-E

Matell, M. S., and Meck, W. H. (2004). Cortico-striatal circuits and interval timing: coincidence detection of oscillatory processes. Cogn. Brain Res. 21, 139–170. doi: 10.1016/j.cogbrainres.2004.06.012

Matell, M. S., Meck, W. H., and Nicolelis, M. A. (2003). Interval timing and the encoding of signal duration by ensembles of cortical and striatal neurons. Behav. Neurosci. 117, 760. doi: 10.1037/0735-7044.117.4.760

McDonald, A. (1991). Organization of amygdaloid projections to the prefrontal cortex and associated striatum in the rat. Neuroscience 44, 1–14.

McGeorge, A., and Faull, R. (1989). The organization of the projection from the cerebral cortex to the striatum in the rat. Neuroscience 29, 503–537.

Meck, W. H., Penney, T. B., and Pouthas, V. (2008). Cortico-striatal representation of time in animals and humans. Curr. Opin. Neurobiol. 18, 145–152. doi: 10.1016/j.conb.2008.08.002

Monod, B., Mouly, A.-M., Vigouroux, M., and Holley, A. (1989). An investigation of some temporal aspects of olfactory coding with the model of multi-site electrical stimulation of the olfactory bulb in the rat. Behav. Brain Res. 33, 51–63.

Moriceau, S., Wilson, D. A., Levine, S., and Sullivan, R. M. (2006). Dual circuitry for odor–shock conditioning during infancy: corticosterone switches between fear and attraction via amygdala. J. Neurosci. 26, 6737–6748. doi: 10.1523/JNEUROSCI.0499-06.2006

Nsegbe, E., Vardon, G., Perruchet, P., and Gallego, J. (1997). Classic conditioning of the ventilatory responses in rats. J. Appl. Physiol. 83, 1174–1183.

Pavlov, I. P. (1927). Conditioned Reflexes. An Investigation of the Physiological Activity of the Cerebral Cortex. London: Oxford University Press.

Paxinos, G., and Watson, C. (1986). The Rat Brain in Stereotaxic Coordinates, Compact, 6th Edn. New-York, NY: Academic.

Provasi, J., Rattat, A.-C., and Droit-Volet, S. (2011). Temporal bisection in 4-month-old infants. J. Exp. Psychol. 37, 108. doi: 10.1037/a0019976

Quirk, G. J., Armony, J. L., and Ledoux, J. E. (1997). Fear conditioning enhances different temporal components of tone-evoked spike trains in auditory cortex and lateral amygdala. Neuron 19, 613–624. doi: 10.1016/S0896-6273(00)80375-X

Raineki, C., Holman, P. J., Debiec, J., Bugg, M., Beasley, A., and Sullivan, R. M. (2010). Functional emergence of the hippocampus in context fear learning in infant rats. Hippocampus 20, 1037–1046. doi: 10.1002/hipo.20702

Rangel, S., and Leon, M. (1995). Early odor preference training increases olfactory bulb norepinephrine. Dev. Brain Res. 85, 187–191. doi: 10.1016/0165-3806(94)00211-H

Roth, T. L., and Sullivan, R. M. (2005). Memory of early maltreatment: neonatal behavioral and neural correlates of maternal maltreatment within the context of classical conditioning. Biol. Psychiatry 57, 823–831. doi: 10.1016/j.biopsych.2005.01.032

Sevelinges, Y., Gervais, R., Messaoudi, B., Granjon, L., and Mouly, A.-M. (2004). Olfactory fear conditioning induces field potential potentiation in rat olfactory cortex and amygdala. Learn. Mem. 11, 761–769. doi: 10.1101/lm.83604

Sevelinges, Y., Sullivan, R. M., Messaoudi, B., and Mouly, A.-M. (2008). Neonatal odor-shock conditioning alters the neural network involved in odor fear learning at adulthood. Learn. Mem. 15, 649–656. doi: 10.1101/lm.998508

Shionoya, K., Hegoburu, C., Brown, B. L., Sullivan, R. M., Doyère, V., and Mouly, A.-M. (2013). It's time to fear! Interval timing in odor fear conditioning in rats. Front. Behav. Neurosci. 7:128. doi: 10.3389/fnbeh.2013.00128

Shuler, M. G., and Bear, M. F. (2006). Reward timing in the primary visual cortex. Science 311, 1606–1609. doi: 10.1126/science.1123513

Smith, M. C. (1968). CS-US interval and US intensity in classical conditioning of the rabbit's nictitating membrane response. J. Comp. Physiol. Psychol. 66, 679. doi: 10.1037/h0026550

Smith, M. C., Coleman, S. R., and Gormezano, I. (1969). Classical conditioning of the rabbit's nictitating membrane response at backward, simultaneous, and forward CS-US intervals. J. Comp. Physiol. Psychol. 69, 226. doi: 10.1037/h0028212

Stanton, M. E., Freeman, J. H., and Skelton, R. W. (1992). Eyeblink conditioning in the developing rat. Behav. Neurosci. 106, 657. doi: 10.1037/0735-7044.106.4.657

Stubbs, A. (1968). The discrimination of stimulus duration by pigeons. J. Exp. Anal. Behav. 11, 223–238. doi: 10.1901/jeab.1968.11-223

Sullivan, R. M., Landers, M., Yeaman, B., and Wilson, D. A. (2000). Neurophysiology: Good memories of bad events in infancy. Nature 407, 38–39. doi: 10.1038/35024156

Sullivan, R. M., and Leon, M. (1986). Early olfactory learning induces an enhanced olfactory bulb response in young rats. Dev. Brain Res. 27, 278–282. doi: 10.1016/0165-3806(86)90256-7

Takahashi, L. K. (1992). Ontogeny of behavioral inhibition induced by unfamiliar adult male conspecifics in preweanling rats. Physiol. Behav. 52, 493–498. doi: 10.1016/0031-9384(92)90336-Z

Tepper, J. M., Sharpe, N. A., Koos, T., and Trent, F. (1998). Postnatal development of the rat neostriatum: electrophysiological, light-and electron-microscopic studies. Dev. Neurosci. 20, 125–145. doi: 10.1159/000017308

Tepper, J. M., and Trent, F. (1993). In vivo studies of the postnatal development of rat neostriatal neurons. Prog. Brain Res. 99, 35–35. doi: 10.1016/S0079-6123(08)61337-0

Ubeda-Bañon, I., Novejarque, A., Mohedano-Moriano, A., Pro-Sistiaga, P., De La Rosa-Prieto, C., Insausti, R., et al. (2007). Projections from the posterolateral olfactory amygdala to the ventral striatum: neural basis for reinforcing properties of chemical stimuli. BMC Neurosci. 8:103. doi: 10.1186/1471-2202-8-103

Voorn, P., Vanderschuren, L. J., Groenewegen, H. J., Robbins, T. W., and Pennartz, C. (2004). Putting a spin on the dorsal–ventral divide of the striatum. Trends Neurosci. 27, 468–474. doi: 10.1016/j.tins.2004.06.006

Wendler, E., Gaspar, J. C., Ferreira, T. L., Barbiero, J. K., Andreatini, R., Vital, M. A., et al. (2014). The roles of the nucleus accumbens core, dorsomedial striatum, and dorsolateral striatum in learning: performance and extinction of Pavlovian fear-conditioned responses and instrumental avoidance responses. Neurobiol. Learn. Mem. 109, 27–36. doi: 10.1016/j.nlm.2013.11.009

Wesson, D. W., Donahou, T. N., Johnson, M. O., and Wachowiak, M. (2008). Sniffing behavior of mice during performance in odor-guided tasks. Chem. Senses 33, 581–596. doi: 10.1093/chemse/bjn029

Wesson, D. W., and Wilson, D. A. (2011). Sniffing out the contributions of the olfactory tubercle to the sense of smell: hedonics, sensory integration, and more? Neurosci. Biobehav. Rev. 35, 655–668. doi: 10.1016/j.neubiorev.2010.08.004

Youngentob, S. L., Mozell, M. M., Sheehe, P. R., and Hornung, D. E. (1987). A quantitative analysis of sniffing strategies in rats performing odor detection tasks. Physiol. Behav. 41, 59–69. doi: 10.1016/0031-9384(87)90131-4

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 28 March 2014; Paper pending published: 07 April 2014; Accepted: 25 April 2014; Published online: 15 May 2014.

Citation: Boulanger Bertolus J, Hegoburu C, Ahers JL, Londen E, Rousselot J, Szyba K, Thévenet M, Sullivan-Wilson TA, Doyère V, Sullivan RM and Mouly A-M (2014) Infant rats can learn time intervals before the maturation of the striatum: evidence from odor fear conditioning. Front. Behav. Neurosci. 8:176. doi: 10.3389/fnbeh.2014.00176

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Boulanger Bertolus, Hegoburu, Ahers, Londen, Rousselot, Szyba, Thévenet, Sullivan-Wilson, Doyère, Sullivan and Mouly. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 18 November 2014
doi: 10.3389/fnbeh.2014.00399

Rodent ultrasonic vocalizations are bound to active sniffing behavior

Yevgeniy B. Sirotin[1][†], Martín Elias Costa[2] and Diego A. Laplagne1,3[*][†]

1Shelby White and Leon Levy Center for Brain, Mind and Behavior, The Rockefeller University, New York, NY, USA

2Integrative Neuroscience Lab, Department of Physics, University of Buenos Aires, Buenos Aires, Argentina

3Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil

* Correspondence: Diego A. Laplagne, Brain Institute, Federal University of Rio Grande do Norte, Av. Nascimento de Castro, 2155, Natal, RN 59056-450, Brazil e-mail: dlaplagne@neuro.ufrn.edu.

[†]These authors have contributed equally to this work.

Edited by:
Anne-Marie Mouly, Centre de Recherche en Neurosciences de Lyon, France

Reviewed by:
Markus Wöhr, Philipps-University of Marburg, Germany
Stefan Brudzynski, Brock University, Canada

During rodent active behavior, multiple orofacial sensorimotor behaviors, including sniffing and whisking, display rhythmicity in the theta range (~5–10 Hz). During specific behaviors, these rhythmic patterns interlock, such that execution of individual motor programs becomes dependent on the state of the others. Here we performed simultaneous recordings of the respiratory cycle and ultrasonic vocalization emission by adult rats and mice in social settings. We used automated analysis to examine the relationship between breathing patterns and vocalization over long time periods. Rat ultrasonic vocalizations (USVs, “50 kHz”) were emitted within stretches of active sniffing (5–10 Hz) and were largely absent during periods of passive breathing (1–4 Hz). Because ultrasound was tightly linked to the exhalation phase, the sniffing cycle segmented vocal production into discrete calls and imposed its theta rhythmicity on their timing. In turn, calls briefly prolonged exhalations, causing an immediate drop in sniffing rate. Similar results were obtained in mice. Our results show that ultrasonic vocalizations are an integral part of the rhythmic orofacial behavioral ensemble. This complex behavioral program is thus involved not only in active sensing but also in the temporal structuring of social communication signals. Many other social signals of mammals, including monkey calls and human speech, show structure in the theta range. Our work points to a mechanism for such structuring in rodent ultrasonic vocalizations.

Keywords: ultrasonic vocalizations, rat, mouse, respiration, speech breathing, theta, rhythm, orofacial

INTRODUCTION

Many behaviors are organized into repetitive cycles. In active rodents, orofacial sensorimotor behaviors like sniffing, whisking, and head movements are organized into cycles with a characteristic frequency in the theta range ~5–10 Hz (Welker, 1964; Macrides, 1975; Deschênes et al., 2012). The cyclical nature of these behaviors serves to structure both sensory input and motor output (Ganguly and Kleinfeld, 2004; Kepecs et al., 2006). However, while each behavior can independently display characteristic patterns, they often phase lock to each other (Welker, 1964; Moore et al., 2013; Ranade et al., 2013). This not only yields coordinated patterns of behavior, but also coordinated activity in associated neural circuits (Kay, 2005; Grosmaitre et al., 2007; Cury and Uchida, 2010; Shusterman et al., 2011; Deschênes et al., 2012; Miura et al., 2012; Moore et al., 2013). Indeed, both hippocampal and cortical theta rhythms can transiently phase lock to motor theta rhythms during specific behaviors (Komisaruk, 1970; Macrides et al., 1982; Ganguly and Kleinfeld, 2004; Kay, 2005; Shusterman et al., 2011). Such structuring suggests that our understanding of each individual behavior can benefit from consideration of the broader behavioral context.

The vocal behavior of rats and mice is proposed to feature two mechanisms of sound production. Audible vocal output of fundamental frequency below 20 kHz is produced, as in human speech, when air flowing out through tensed vocal folds causes them to vibrate resulting in sound pressure waves of rich harmonic content (Roberts, 1975a). Vocalization of fundamental frequency in the ultrasonic range (>20 kHz) is believed to be produced when air flowing through a small orifice formed by tight vocal folds produces ultrasound of nearly pure single frequencies via an aerodynamic whistle mechanism (Roberts, 1975b; Riede, 2011). Rat ultrasonic vocalization falls in two families with distinct ethological and neurophysiological parallels (Brudzynski, 2009). Aversive settings such as the anticipation of pain or danger can result in prolonged emission of ultrasound in the 20–25 kHz range with little or no frequency modulation, named “22 kHz” ultrasonic vocalizations (USVs). Ultrasound in the ~30–90 kHz range (“50 kHz USV”) is generally emitted by males and females in mating and other social interactions. Emission of 50 kHz USVs has been further linked to expectation of reward and activation of mesolimbic dopaminergic pathways (reviewed in Brudzynski, 2013). In turn, listening to 50 kHz USVs effectively induces approach behavior in both male and female rats, suggesting they may promote social contact (Wöhr and Schwarting, 2007; Seffer et al., 2014; Willadsen et al., 2014). Mice lack a 22 kHz-like alarm vocalization, and emit brief USVs in the ~50–100 kHz range, mostly studied in the context of mating (Holy and Guo, 2005). Vocalizations are usually segmented by experimenters into individual packets (“calls” or “syllables”) based on silences and/or spectral discontinuities (Liu et al., 2003; Wright et al., 2010). Interestingly, when segmenting by silences of 40 ms and over, adult rat and mouse calls are found to come in bouts with instantaneous rates in the theta range (Liu et al., 2003; Kim and Bao, 2009).

Vocal output depends critically on air flowing through the larynx, which is temporally structured by the breathing cycle (Roberts, 1975a). As in birds and humans, ultrasonic vocalizations in rats have been shown to be associated with increased subglottal pressure, indicating a phasic relationship with the breathing cycle (Roberts, 1972; Hegoburu et al., 2011; Riede, 2011, 2013). Highly vocal animals like humans and birds developed exquisite control mechanisms that coordinate breathing with activity in muscles used for vocalization in order to produce complex vocal output (MacLarnon and Hewitt, 1999; Andalman et al., 2011). As previously shown by us and others, rats show this control to some degree as they are able to maintain exhalations of over 2 s during the emission of prolonged 22 kHz alarm calls (Hegoburu et al., 2011; Assini et al., 2013). Rat breathing patterns are additionally constrained by sniffing, which is an active breathing behavior used to sample the olfactory environment (Welker, 1964; Wachowiak, 2011). Breathing patterns associated with normal respiration can be distinguished from active sniffing based on their frequency. Normal respiration in adult rats is typically below 3 Hz whereas active sniffing is typically in the theta range (Welker, 1964; Hegoburu et al., 2011; Wachowiak, 2011). However, despite clear dependence of vocalizations on breathing, the interplay between 50 kHz USVs and respiratory dynamics has not been previously investigated.

Here we examined, in detail, the relationship between respiration and ultrasonic vocal output of rats in a social environment. We find that ultrasonic vocalization of the 50 kHz family is largely restricted to periods of active sniffing (5–10 Hz). Within each sniff, both the initiation and cessation of vocal output was precisely linked to specific phases of the sniff, initiating just after the end of the inhalation and finishing just prior to the peak of the exhalation. As a result, the sniff cycle segments ultrasound production into individual calls, which inherit its theta rhythmicity. In turn, vocal output deforms ongoing sniff rhythms, briefly stretching the exhalation period as necessary to accommodate the full duration of the produced vocalization.

Our results show that orofacial behaviors with theta rhythmicity are not only involved in active sampling but also temporally structure outgoing communication signals at this rate. Moreover, we show that the sniffing and ultrasound production systems in rodents are linked on a millisecond scale, suggesting a tight coupling between the neural centers controlling sniffing and vocalizations.

MATERIALS AND METHODS

ANIMAL SUBJECTS

All procedures were approved by The Rockefeller University Institutional Animal Care and Use Committee. Simultaneous recording of ultrasonic vocalizations and intranasal pressure were carried out on 5 Long Evans adult male rats (Charles River, ages 3–8 months, single housed from 2 months of age), and 2 CBA/CaJ adult male mice (Jackson Labs, ages 10–11 weeks, pair housed). Male mice were recorded in the presence of an adult female C57 mouse. Rats were held on an inverted light cycle and all recordings were carried out during the dark phase under infrared illumination.

RECORDING SESSIONS

Rats were placed in a custom built social arena in a single-walled soundproof room. The purpose of this setup was to promote vocal production from social interaction while still being able to unequivocally assign each call to the rat it originated from. The arena (see Figure 1A) was split in two halves, 46 × 33 × 74 cm (W × L × H) each, 25 cm apart on the wide side. Walls were made of thin vertical bars and surrounded by 5 cm thick wedged foam to minimize echoes. The separation between halves was packed with foam from 20 cm above the floor to the top to minimize cross-talk between microphones (see below). The acrylic floor was covered with Aspen Chips bedding (NEPCO, Warrensburg, NY, USA), chosen to minimize locomotion related noise (the same bedding was used in the home cages). One rat was placed on each side of the arena where they could hear and smell each other for sessions lasting up to 2 h. Male-female mice pairs were recorded together in a 20 × 40 × 30 cm (W × L × H) acrylic box with Aspen Chips bedding. The respiration of the female mouse was not monitored. Intranasal pressure and ultrasound signals were simultaneously digitized by a data acquisition board at 250 kHz sampling frequency (PCIe-6259 DAQ with BNC-2110 connector, National Instruments). Animals were monitored from outside the room through video under infrared illumination.

FIGURE 1. Simultaneous recording of respiration and ultrasonic vocalization. (A) Left: Schematic of the recording arena as viewed from the top (top) and side (bottom). The position of the ultrasonic microphones (red) and video cameras (green) is shown. Tubing (gray) connects the nasal cannulae with pressure sensors. Right: Snapshot of rats simultaneously behaving in the arena. (B) Segment of intranasal pressure (black) recorded from a rat in a social setting. Red bars: periods of ultrasonic vocal output detected for this rat. Scale bar: 2 s. From here on, inhalations are plotted as positive deflections of the pressure trace. (C) Detailed view of respiration (bottom) and ultrasonic vocalizations (top; sonogram). From here on, black arrowheads denote zero relative intranasal pressure. Scale bar: 250 ms. (D) Autocorrelations of respiration (black) and ultrasonic vocalizations (red) from a 10 min recording segment. Note signals show similar periodicity, with first peaks at 130 and 150 ms respectively (eq. 7.7 and 6.7 Hz). (A–C) same data set.

ULTRASONIC VOCALIZATIONS

Recording and detection

One condenser microphone with nearly flat (±5 dB) response from 10 to 150 kHz (CM16/CMPA-5V, Avisoft Bioacustics) was positioned above each rat at a height of 72 cm to selectively pick up calls from the rat beneath (Figure 1A). All USV analysis was performed on the raw sound recordings with custom built MATLAB routines (The Mathworks). To efficiently handle the large recorded datasets, we developed automated techniques for detecting ultrasound emissions and assigning them to the rat of origin (Figure S1). The performance of our detection and assignment methodology was assessed in an independent set of recordings (see below). We first obtain the sonogram for each microphone (Figure S1A, 2 ms time window, 0.25 ms time step, 1 kHz bandwidth, 3 tapers; http://chronux.org/; Mitra and Bokil, 2007). Each time step of the spectrogram constitutes a vector P where each point is the power at a given frequency (18–100 kHz). We next normalize this vector by its sum (to ensure all values span between 0 and 1) and calculate the entropy of this normalized vector Pn as H = −Pn · log_2Pn. For rodent vocalizations, sound power is concentrated at a single frequency, reducing the entropy, while unwanted noise is typically broadband and thus of high entropy (Figure S1A). Segments lasting at least 3 ms with entropy below a fixed threshold of 6.5 bits and bounded by silences of >20 ms are selected as putative USVs. These are then curated by automatically discarding as noise those with high power in the sonic range (5–18 kHz) and visually inspecting those with intermediate levels of ultrasonic entropy and sonic power. In a dataset of 31 recording sessions we estimated 94% of emitted USVs (47866 of an estimated total of 51095) were effectively detected in this way (Figure S1B).

Detected USVs are assigned to the emitting rat by comparing the signals from both microphones. When ultrasound is detected (crosses the entropy threshold) at only one microphone, the USV is assigned to the rat on the same side of the arena. If the same USV is detected at both microphones, it is assigned to the rat under the microphone with lowest entropy (examples in Figure S1A). To assess the accuracy of the USV assignment we analyzed 11 recording sessions with just one rat in the arena. 77% of calls (20653 of 26815) were detected only by the microphone on the rat's side (Figure S1C). Of those detected in both, the entropy difference was large enough to unambiguously assign them to the correct side of the arena (Figure S1D). Overall, 99.8 ± 0.1% of USVs were properly assigned at each session. In the special case of two rats vocalizing at the same time, they will typically produce USVs with different fundamental frequency profiles at each microphone. When these profiles are found to differ by >1 kHz during >3 ms we deduce both rats vocalized simultaneously and assign to each the USV detected by the microphone on its side (Figure S1E).

Mice USVs were recorded from a single condenser microphone positioned 30 cm above the floor and detected in a similar fashion. As justified in section Structuring of Mouse Ultrasonic Vocalizations by Sniffing, all calls were assigned to the male mouse.

Analysis

“Vocal ratio” was defined as the fraction of time (0–1) spent producing ultrasound in a window of 3 s. This measurement is independent of any segmentation of vocal production. A “call” was defined as the ultrasound emitted within an individual sniff. “Call rate” as the number of detected calls per second in a 3 s window. “Instant call rate” was calculated for calls occurring on consecutive sniffs as the reciprocal of the time between the onsets of the two calls (Figure 6D).

SNIFFING

Cannula implantation

To monitor respiration, the end of a thin 2-cm-long stainless cannula (gage 22) was implanted in the nasal cavity. The cannula was bent to an S-shape so as to end above the temporal bone. Animals were anesthetized using isoflurane gas anesthesia. A skin incision was made exposing the frontal bone and most of the nasal bone. A small hole was drilled in either the left or the right nasal bone, into which the tip of the cannula was inserted from above so as to protrude into the nasal cavity. The cannula was affixed to the hole with a small drop of cyanoacrylate glue (All-purpose Krazy Glue), and stabilized on the skull with methyl methacrylate dental cement around skull screws. Animals were given at least 2 days after a surgery for recovery.

Data acquisition and pre-processing

During experiments, the cannula was connected to a pressure sensor located above the arena (24PCAFA6G, Honeywell; modified to reduce internal air volume) with ~100 cm of Teflon tubing (AWG# 22 STD, Pennsylvania Fluorocarbon) via a plastic fluid swivel (375/22PS, Instech). The output of the pressure sensor bridge was coupled to an instrumentation amplifier (AD620, Analog Devices) for recording. For analysis, signals were downsampled to 1 kHz Inhalations caused an inward flow of air through the nose that resulted in a decrease in measured pressure whereas exhalations caused an outward flow of air through the nose resulting in an increase in the measured pressure signal. Throughout the figures, inhalations are shown as upward deflections and zero denotes atmospheric pressure.

The tubing connecting the cannula to the pressure sensor filters down fast fluctuations and imposes a time delay to the pressure signal. To measure this distortion we generated broadband pressure signals with an electrodynamic transducer (ET-132-203; Labworks Inc.) driven by a linear power amplifier (PA119; Labworks Inc.). We then recorded the same signal with our pressure sensor directly at the output of the transducer and after distortion by the tubing (Figure S2A). We used these two signals to calculate the transfer function of the tubing through Fourier deconvolution (http://terpconnect.umd.edu/~toh/spectrum/Deconvolution.html) and used this transfer function to reconstruct the undistorted intranasal pressure signal in all recordings (see Figure S2 for validation).

Analysis

To identify individual respiratory cycles (“sniffs”), we developed MATLAB routines to segment the recorded pressure traces as follows. Slow drifts in sensor output were removed (400 Hz low pass Butterworth filter). Signals were then mean subtracted and divided by their standard deviation. Sniff cycles were defined to start at the inhalation onset and end at the exhalation offset (onset of the next inhalation). Inhalation onsets were detected as positive slope crossings of a fixed threshold. The end of each inhalation was defined as the negative slope crossing of the same threshold. Sniffs with aberrant inhalation durations (<20 ms) were rejected from subsequent analyses.

The phase within the sniffing cycle was computed using a previously described algorithm (Shusterman et al., 2011). Briefly, we determined three points in time for each cycle: inhalation onset, inhalation offset (exhalation onset), and exhalation offset, as described above. We then morphed each sniff cycle so that the duration of its inhalation and exhalation matched the average durations across all recorded sniffs. Phase within the sniff was then defined as the normalized time (0–1) within the morphed sniff (see Figures 1A,B in Shusterman et al., 2011).

The instant rate of a sniff cycle was defined as the reciprocal of the time between the start of its inhalation and that of the next cycle. “Ongoing sniff rate” is calculated as the mean instant rate in 3 s windows. Only silent sniffs were included to specifically quantify the respiratory rhythm without direct effects from USVs (see Figures 6A,D).

BOUT ANALYSIS

For the analysis of call bouts, a binary vector was constructed for each recording session. Each vector element corresponded to a single sniff and was assigned 1 if the sniff was vocal and 0 if the sniff was silent. A call bout was defined as a stretch of calls occurring over consecutive sniff cycles (a stretch of ones in the vector). Distributions of bout lengths were obtained by pooling across sessions for each rat. Two random models were used to generate surrogate binary vectors. First, we constructed a constant probability model, where a single call probability was used for each vector element (i.e., sniff). Each sniff was randomly assigned a call with a fixed probability obtained by dividing the total number of calls over the total number of sniffs. For the variable probability model, we simulated the effect of a varying call production rate within a session. The probability of assigning a call to each surrogate element was obtained from the measured data as follows. We convolved the observed binary vector with a Gaussian kernel to estimate an underlying local call production probability. In this analysis, “rate estimation window” corresponds to the full width at half maximum of this kernel (measured in number of sniffs). To capture potential call probability fluctuations at different time scales, we generated surrogate datasets with models of different rate estimation window from 4 to 256 sniffs. For each session and model, we generated 1000 pseudorandom surrogate vectors, calculating the distribution of bout lengths for each. For each session, we calculated the log likelihood of observing a given bout length in the real vs. surrogate data as log_10 of the ratio between the probability of observing a bout of a given length in the real data and that of the surrogates. For example, a value of 1 is obtained if a given bout length is 10 times more likely in the real data.

STATISTICAL ANALYSIS

Relationships showing apparent linearity were analyzed with linear regression (Figures 3B, 6E,F, 7B). Others with repeated measures ANOVA (Figures 2C, 6B,C).

FIGURE 2. Ultrasonic vocalization occurs during periods of fast sniffing. (A) Top: Spectrogram of a section of the recorded respiration. Warmer tones denote higher power (AU). Note the alternation between periods of fast (~7 Hz) and slow (~2 Hz) respiration. Bottom: simultaneous vocal production from this rat quantified as fraction of time spent vocalizing within a 3 s sliding window (vocal ratio). Blue shading: periods of silence (vocal ratio = 0). Red shading: high vocal production (vocal ratio > 0.025). Top right: mean frequency spectrum of respiration for periods of high vocal production (red; peak = 6.8 Hz) and silence (blue; peak = 2.2 Hz) in the example. (B) Distribution of sniff rates during periods of high vocal production (red) or silence (blue). Mean ± s.e.m., N = 5 rats. (C) Vocal ratio as a function of sniff rate. To account for varying average vocal output of individual rats, curves were normalized by their maximum prior to averaging. Effect of sniff rate on mean vocal ratio: p < 0.0001 (ANOVA, N = 5 rats). (D) Autocorrelations of vocal ratio (red) and sniff rate (black), averaged in 3 s intervals.

RESULTS

To examine the relationship between respiration dynamics and ultrasonic vocal output of rats, we developed a split social arena. In the arena, adult male rats separated by a wire divider could hear and smell each other in the dark (Figure 1A). Analysis of audio from a pair of overhead microphones allowed us to unequivocally assign vocalizations to each rat. To monitor respiration, we implanted the rats with intranasal cannulae coupled to pressure sensors (see Materials and Methods). We recorded respiration and vocalizations for extended periods of time (30–120 min) at high sampling frequency (250 kHz), which allowed us to examine relationships between these behaviors across multiple timescales (Figure 1). Rats showed large variations in the rate of respiration and ultrasonic vocalization (Figure 1B). Under these conditions, all vocal output was restricted to USVs of the 50 kHz family (Figure 1C). As expected, intranasal pressure traces showed strong periodicity in the theta range imposed by the inhalation-exhalation cycle. Interestingly, vocal output was also periodic at theta (Figure 1D).

RATS PRODUCE ULTRASOUND DURING FAST SNIFFING

Respiration rate in awake rats varies with behavioral state over a wide range (1–10 Hz) (Wachowiak, 2011). In our recordings, rats also alternated between periods of silence and high vocal production (Figure 2A). Visual inspection of respiration and vocalization records suggested that rats vocalized mostly during periods of active sniffing (e.g., Figure 1B). To quantify this relationship, we computed “vocal ratio” as the fraction of time spent producing ultrasound in a sliding window of 3 s (Figure 2A bottom; Methods). We calculated average ongoing sniff rate in this same window by segmenting the continuous intranasal pressure traces into individual sniff cycles (sniffs) and computing their average instantaneous rate (Methods). To avoid possible interactions between ultrasound production and sniffing, we excluded sniff cycles associated with vocal production from the calculation of sniff rate. During silent periods (vocal ratio = 0), rats were either breathing passively (rate < 4 Hz) or actively sniffing (rate > 5 Hz), spending similar periods of time in each mode. In contrast, periods of high vocal output (vocal ratio > 0.025) were exclusively associated with active sniffing (Figure 2B). Overall, this results in a strong positive correlation between vocal production and ongoing sniff rate with maximal vocal output during periods of 8 Hz sniffing (Figure 2C). Changes in vocal ratio were, however, faster than those of respiratory rate (Figure 2D), reflecting that brief periods of high vocal production occurred within longer periods of fast sniffing (e.g., Figure 2A).

ULTRASOUND PRODUCTION PROLONGS THE SNIFF CYCLE

Mammalian vocalization usually prolongs the respiratory cycle (Smotherman et al., 2010). We analyzed whether this is also the case for the brief rat vocalizations of the 50 kHz family. During silent respiration, recorded intranasal pressure typically followed a sinusoidal pattern, indicating roughly equal time spent inhaling and exhaling (e.g., Figure 1, blue trace in Figure 3A). Of our full population of recorded sniffs (N = 256991 sniffs in 5 rats), vocal sniffs accounted for 15 percent (N = 37593). Despite our observation that ultrasound is produced during periods of high ongoing sniff rate, vocal sniffs were on average longer than silent sniffs (163 ± 64 vs. 131 ± 55 ms; median ± inter-quartile-range; p ≅ 0, Wilcoxon rank sum test for equal medians). Within each vocal sniff, we quantified the total duration of ultrasound production as the difference between the first and last time-point having ultrasound. We found that overall sniff length increased with ultrasound duration (Figure 3A). Specifically, it was exhalation durations that increased, while inhalations remained largely unaltered (Figure 3B). Exhalations grew with ultrasound duration with a mean linear slope of 0.85 (Figure 3C). As a consequence, the emission of ultrasound during a given sniff cycle was accompanied by an instantaneous drop in the sniffing rate (Figure 3D).

FIGURE 3. Ultrasound emission instantaneously lowers the sniffing rate. (A) Average waveforms for silent sniff cycles (blue) or cycles simultaneous to the emission of ultrasonic vocalizations of increasing duration (reds; vocal sniffs) for one example recording. Data was binned by ultrasound duration with bin centers labeled on the right. Traces are aligned to the onset of the inhalation (Time = 0). (B) Inhalation (gray) and exhalation (black) durations for individual vocal sniff cycles vs. vocalization duration. Same data as in A. Lines: linear regressions; R^2 = 0.34 (exh) and 0.01 (inh). (C) Slopes for inhalation (“I”) and exhalation (“E”) regression lines for individual rats. Red lines: medians across animals. Values from B highlighted in blue. (D) Mean instantaneous sniff rate (1/sniff duration) aligned on a vocal sniff (sniff number = 0). Calculation of sniff rate for non-zero sniff numbers excludes vocal sniffs. Instantaneous sniff rate of vocal sniffs is lower than that of the preceding silent sniff (p < 0.001, paired t-test). Open circle: sniff rate computed after subtracting vocalization duration from the period of the vocal sniff. (Means ± s.e.m., N = 5 rats).

ULTRASONIC VOCALIZATION OCCURS AT SPECIFIC PHASES OF THE SNIFF CYCLE

We next examined the detailed temporal alignment between ultrasound production and the inhalation-exhalation cycle. Prior work established that ultrasound is produced during exhalations, corresponding to periods of high subglottal pressure (Riede, 2011). Interestingly, during production of ultrasound, relative intranasal pressure remained close to zero, indicating reduced airflow through the nose (Figure 4A). This relationship held up to the millisecond timescale as brief drops in the power of the emitted ultrasound co-occurred with sharp peaks in nasal flow (Figure S3). We examined the coupling of ultrasound production to inhalations and exhalations by warping each sniff to a common phase axis aligning inhalation onsets, inhalation-exhalation transitions, and exhalation offsets (Methods). The average vocal sniff had a distinctly different shape than a silent sniff, with a pronounced deviation from a sinusoid after inhalation corresponding to the period of low airflow through the nose (Figure 4B, top). Indeed these shape differences were so pronounced that sniff shape alone was often an excellent predictor of the presence of vocalization (Figure S4). For all vocal sniffs, ultrasound production onsets and offsets were tightly coupled to sniff phase. Ultrasound production began shortly after the end of inhalation and ended prior to the peak of exhalation (Figure 4B, bottom). This tight coupling was observed in each of our tested animals (Figure 4C).

FIGURE 4. Ultrasound emission is restricted to specific phases of the sniff cycle. (A) Spectrograms from 3 ultrasound emissions (top) aligned to their corresponding intranasal pressure traces (bottom). Blue arrowheads mark exhalation onset. Note ultrasound is produced during low-pressure region following exhalation onset. (B) Top: mean sniff waveforms from silent (blue) or vocal (red) sniffs for one example rat. All waveforms were warped to align at three points: onsets of inhalation and exhalation and the end of exhalation. Bottom: distribution of ultrasound onset (black) and offset (gray) phases in the vocal sniffs. Inhalation onset: phase = 0, exhalation onset: phase = 0.33, end of exhalation: phase = 1. Gray line: exhalation onset. Time between most frequent vocalization onset and offset marked in pink. (C) Distribution of ultrasound onset (black) and offset (gray) phases for all rats. Boxes: median and 25–75th percentiles. Whiskers: 10–90th percentiles.

THE SNIFF CYCLE NATURALLY SEGMENTS EMITTED ULTRASOUND INTO CALLS

Ultrasound appears to be emitted in brief units separated by silences, usually named “calls” or “syllables.” A clear rationale for this segmentation is, however, missing. It is clear from our data that rats are silent during inhalations. To understand how this structures the emission of ultrasound in time, we quantified the distribution of silence durations and its relation to the sniff cycle. We defined silences as intervals longer than 2 ms with no detectable vocal output. The analysis revealed identical multimodal distributions for all rats (Figure 5A). Silences were either shorter than 20 ms (58 ± 3%) or longer than 60 ms (41 ± 3%). Short silences occurred between ultrasound emissions within a single sniff cycle whereas long silences included at least one inhalation and thus separated emissions across sniffs (Figure 5B). In consequence, segmenting calls by a minimum silence of 20–60 ms is equivalent to segmenting by sniff cycle as all calls are moored to a single sniff and each sniff harbors at most one call (Figure 5C). The sniff cycle thus provides a natural segmentation of ultrasound production into individual calls.

FIGURE 5. Sniff cycles segment ultrasonic vocalization into calls. (A) Distribution of silence durations (Mean ± s.e.m.; N = 5 rats). Inset: detail of short silences. (B) Example of ultrasonic vocalizations (top) and simultaneous sniffing (bottom). Gray and black bars mark the occurrence of long (>40 ms) and short (<40 ms) silences. Note short silences are contained within single exhalations while long ones span more than one sniff cycle. Scale bar: 100 ms. (C) Segmentation of calls as a function of silence duration threshold. Orange: Percentage of segmented calls that do not share a sniff cycle with other calls. Green: Percentage of calls that do not span more than one sniff cycle. The gray area shows the range of silence duration thresholds that effectively segment over 95% of calls by sniff cycles (20–80 ms).

ONGOING SNIFF RATE MODULATES CALL DYNAMICS

Studies on USVs typically correlate measurements like call rate and duration with experimental conditions. Having now defined a “call,” we analyzed to what extent their properties depend on the ongoing respiratory rate, assessed in neighboring silent sniffs (Figure 6A). As expected from our previous results, ongoing sniff rate strongly influenced measured call rates, which were maximal when sniffing at theta frequency (Figure 6B). The probability of emitting a call on each sniff also peaked during theta sniffing demonstrating that increased call rates were not trivially due to having more sniffs per unit time (Figure 6C).

FIGURE 6. Effects of sniff rate on call dynamics. (A) Call rate is defined as the number of calls (red lines) in a time window of 3 s. Ongoing sniff rate is defined as the mean instant rate (1/sniff duration) of all sniffs with no USV (black sniffs) in the same window. (B) Call rate vs. ongoing sniff rate. Effect of sniff rate p < 0.0001, repeated measures ANOVA, N = 5 rats. (C) Percentage of sniffs accompanied by calls vs. ongoing sniff rate. Effect of sniff rate p < 0.0001, repeated measures ANOVA, N = 5 rats. (D) Instant call rate is defined as 1 over the time between the onsets of calls in two consecutive sniffs (“t” in figure). Instant sniff rate is that of the immediately preceding silent sniff. (E) Call duration vs. instant sniff rate. Red: linear regression; R^2 = 0.12, p < 0.001. (F) Instant call rate vs. instant sniff rate. R^2 = 0.67, p < 0.0001. Inset: distribution of instant call rates.

So far we showed that sniff frequency strongly alters the quantity of calls produced. Does sniffing also alter the detailed dynamics of call production (Figure 6D)? We found that calls had a characteristic duration that was largely independent of sniff rate up to 8 Hz sniffing. However, for faster rates mean duration dropped by 25%, highlighting an interaction between the ongoing sniffing behavior and the vocal motor plan (Figure 6E).

We studied call rates in finer temporal detail by measuring the instant rate between calls occurring in consecutive sniffs (Figure 6D). As previously observed (Kim and Bao, 2009), rat calls have a characteristic instant rate of ~6 Hz (Figure 6F, inset). If this was a fixed property of USV emission mechanisms, instant call rate should be largely independent of ongoing respiratory rates. On the contrary, it was positively correlated to the rate of the immediately preceding silent sniff (Figure 6F). Thus, instant call rates carry information about ongoing sniffing frequency. This interaction is bidirectional, as calling immediately affects respiratory rate, bringing it to a narrower range centered at 6 Hz (Figure 6F).

STRUCTURING OF MOUSE ULTRASONIC VOCALIZATIONS BY SNIFFING

We next extended our analysis to the ultrasonic vocalizations of the laboratory mouse (Mus musculus). We simultaneously recorded vocal output with intranasal pressure in male CBA/CaJ adults (N = 2) during encounters with a female. Previous studies have concluded females rarely, if ever, emit USVs during mating so detected ultrasonic calls can be assigned to the male partner (White et al., 1998). Indeed, all calls detected from our recordings matched the breathing pattern of the male (Figure 7A). The sniff cycles of mice differed from that of rats in that even for silent sniffs, inhalations were followed by a brief period of constant low relative intranasal pressure before going into full exhalation (Figure 7A), whereas in the rat this pattern was strongly indicative of USVs (see Figures 1C, 3A, 4A,B and Figure S2). As in the rats, the emission of USVs significantly prolonged the sniff cycle, with a positive correlation between exhalation duration and the duration of USV (Figure 7B). The slope of this relationship was less pronounced (compare Figures 7B, 3A,B). Nonetheless, the locking of the ultrasound production to the phase of the sniff cycle was comparable to that found for rats, with USVs starting after the end of the inhalation and ending prior to the peak of the exhalation (Figure 7C).

FIGURE 7. Structuring of mouse ultrasonic vocalizations by sniffing. (A) Detailed view of ultrasonic vocalizations (top; sonogram) and respiration (bottom) for a mouse. Scale bar: 250 ms. Compare to Figure 1C. (B) Top: average waveforms for silent sniff cycles (blue) or cycles simultaneous to the emission of ultrasonic vocalizations of increasing duration (reds; vocal sniffs) for a mouse. Data was binned by ultrasound duration (mean durations: 14, 42, and 81 ms). Traces are aligned to inhalation onset (dotted line). Compare with Figure 3A. Bottom: Inhalation (gray) and exhalation (black) durations for individual vocal sniff cycles vs. vocalization duration across mice. Lines: linear regressions; Slope = 0.32 (exh) and 0.02 (inh), R^2 = 0.08 (exh) and 0.01 (inh). Compare with Figure 3B. (C) Top: mean sniff waveforms from silent (blue) or vocal (red) sniffs across mice. All waveforms were warped to align at three points: onsets of inhalation and exhalation and the end of exhalation. Bottom: Distribution of ultrasound onset (black) and offset (gray) phases in the vocal sniffs. Inhalation onset: phase = 0, exhalation onset: phase = 0.2, end of exhalation: phase = 1. Gray line: exhalation onset. Time between most frequent vocalization onset and offset marked in pink. Compare with Figure 4B. (D) Segmentation of calls as a function of silence duration threshold in mice. Orange: percentage of segmented calls that do not share a sniff cycle with other calls. Green: percentage of calls that do not span more than one sniff cycle. The gray area shows the range of silence duration thresholds that effectively segment over 95% of calls by sniff cycles (40–60 ms). Compare with Figure 5C. (E) Blue: distribution of silent sniff rates. Red: distribution of instant call rates for calls made on consecutive sniffs.

The temporal properties of ultrasonic calls in the mouse were qualitatively similar to the rat. Silence durations of at least 40–60 ms segmented ultrasonic output into calls (mean duration = 46 ms) occurring within a single sniff (Figure 7D). The distribution of instantaneous rates of calls produced on consecutive sniffs peaked at 6.5 Hz whereas instantaneous rates of silent sniffs peaked at 8 Hz (Figure 7E). This shift is a direct result of prolongation of exhalations by calls, as also observed for the rats.

CALL BOUTS ARE DIFFERENT IN RATS AND MICE

While rodent USVs appear to cluster in time (Nyby and Whitney, 1978; Brudzynski and Pniak, 2002), it is not clear whether the call “bout” is a fundamental unit of their vocal production. Alternatively, calls could appear to be grouped in time simply because of continuous fluctuations in call rate (Nawrot, 2010). We took advantage of the natural segmentation provided by the sniff cycle to explore this in rats and mice. We defined a bout as a series of calls emitted on consecutive sniffs and asked whether their occurrence was a statistically significant event. At first glance, no strong tendency for emitting bouts was observed for rats, as the distribution of bout lengths decayed monotonically with 72 ± 4% (N = 5 rats) composed of a single call and only 2.5 ± 0.7% containing 5 or more calls (Figure 8A). To test for structure in the vocal production we compared this distribution with a random model where rats have a constant probability of emitting a call on each sniff given by their mean call rate (see Materials and Methods). Bouts of 3 or more calls occurred more frequently than chance, while isolated calls were in fact less probable (Figure 8A). However, when comparing with a family of random models that account for call rate variations, the grouping of calls into bouts matched models where calls are randomly emitted with a probability fluctuating with a temporal resolution of 1–2 s (Figure 8A, inset). This analysis suggests that call bouts defined in this way are not a fundamental feature of rat vocal production but rather reflect fast modulations in their behavioral state. Mouse calls were emitted in strikingly longer bouts than for those of rats, with only ~45% of them composed of a single call and ~20% containing 5 calls or more (Figure 8B). This high structuring could not be accounted for by random models with slow call rate fluctuations (Figure 8B, inset), suggesting mice USVs are indeed preferentially grouped into bouts.

FIGURE 8. Call bouts are different in rats and mice. (A) Probability of observing rat call bouts of a given length (i.e., the number of consecutive sniffs with calls). Blue: real measured data. Red: surrogate data constructed assuming constant vocalization rate (see Materials and Methods). Inset: Comparison of measured bout length probabilities to a family of surrogate models with varying rate estimation windows (4–256 sniffs; x-axis; see Materials and Methods). Y-axis: log likelihood ratio between measured and surrogate bout length probabilities (for bout length 1–5). Positive values indicate that bouts of a given length are more likely in real vs. surrogate data. Red arrowhead: surrogate model with a rate estimation window of width 12 sniffs matches real data for all bout lengths (log-likelihood ≅ 0). Panels show mean ± s.e.m.; N = 5 rats. (B) Same analysis as in A for mice. Note lower probability of bout length = 1 for mice (46%) than for rats (72%). Surrogate data with a 4-sniff rate estimation window approximates observed bout distribution in mice, compared with 12-sniff window for rats.

DISCUSSION

By examining long periods of simultaneously recorded respiration and ultrasonic vocalization patterns we found a profound relationship between these two behaviors across timescales. Overall, vocal production is largely restricted to periods of active sniffing. During these periods, both sniffs and calls are periodic at theta frequencies (6–8 Hz). USVs are not, however, a byproduct of olfactory behavior as rats can sniff fast without vocalizing. Calls are produced exclusively during exhalations and prolong sniffs causing an instantaneous reduction in sniff rate. Most calls are, however, brief, producing only a modest drop in sniff rate from 8 to 6 Hz. In this way, the rate of ongoing sniffing effectively imparts its theta rhythmicity onto calls.

Though it is commonplace in the field to talk about rodent “calls,” a proper delineation of the term is missing. Segmenting a stream of vocal output into meaningful units is an important first step in any semantic or syntactic study. The working hypothesis behind defining animal “calls” is that there are a finite number of distinct motor plans for the production of vocalizations which could differentially correlate with the emitter's physiological or behavioral state and the receiver's responses. Segmentation of the produced sound by this underlying structure results in a more compact description of the vocal repertoire and aids in the understanding of vocal communication systems. Animal vocalization is usually broken up in calls at spectrotemporal discontinuities, but the choice of parameters is not trivial. We propose a physiologically grounded segmentation strategy such that a call is defined as the ultrasound emitted during a single exhalation. We further show this rule can be accurately implemented without recording respiration by choosing a silence duration threshold between 20 and 60 ms for rats and 40–60 ms for mice. Of those studies where the segmentation method is reported, some used silence durations within or close to these ranges (Liu et al., 2003; Holy and Guo, 2005; Wright et al., 2010) while others used thresholds too short to match the sniffing structure (Sewell, 1970; Takahashi et al., 2010).

Welker's detailed examination of rat behavior demonstrated the phasic relationship between sniffing, whisking, and head movement. During active periods, these behaviors are produced in cycles coherent at theta frequency (Welker, 1964; Deschênes et al., 2012; Moore et al., 2013; Ranade et al., 2013). This shared oscillatory patterning has been proposed to be relevant for information exchange between brain areas (Kay, 2005; Kepecs et al., 2006). Our results add the emission of ultrasonic vocalizations to the family of orofacial behaviors with theta rhythmicity observed in rodents (Figure 9). As such, the detailed properties of USVs are not independent but bounded by this rhythmic frame. Any research into the neural or broader behavioral correlates of any such motor behaviors would thus benefit from considering the broad context of the others to identify any individual contributions and interactions (Assini et al., 2013; Moore et al., 2014). During ultrasound production, motoneurons in the nucleus ambiguus control larynx geometry via activation of specific muscles (Yajima and Hayashi, 1983; Riede, 2011). The observed phase locking of vocalizations with the sniff cycle suggests a precise coordination between activity in this motoneuron pool and the brainstem nuclei responsible for orchestrating the respiratory rhythm (Moore et al., 2014). The mechanistic links posited by our observations should be confirmed by experimental manipulation of activity in these nuclei, as is being done for dissecting the interactions between the sniffing and whisking rhythms (Moore et al., 2013). Our results show that constriction of the larynx associated with ultrasound production is associated with a delay in the onset of the following respiratory cycle, similar to that observed for swallowing (McFarland and Lund, 1993). USVs are natural and frequent perturbations of the sniffing cycle. Understanding how they affect (and are affected by) the instantaneous phase of other orofacial rhythms like whisking and head movements could aid in understanding the hierarchical organization of their associated motor nuclei. Of particular interest is the coordination of ultrasonic vocalization with active whisking, as it is likely that both are simultaneously acting as rhythmic communication signals during close distance social interactions (Wolfe et al., 2011).

FIGURE 9. Theta-linked orofacial behaviors in rodents. Periodic motor actions during active behavioral states are coordinated in phase along a theta frequency rhythm. When vocalizations occur (red), they are inserted immediately after the end of inhalation. Adapted after Welker (1964) and Kepecs et al. (2006).

The rate of respiration is strongly correlated with the behavioral state of the animal (Welker, 1964; Hegoburu et al., 2011). We show that calls carry detailed information about sniff dynamics at both slow and fast timescales. At slow scales, the co-occurrence of high rates of 50 kHz USVs and fast sniffing could reflect their common drive by the ascending dopaminergic system (Costall and Naylor, 1975; Brudzynski, 2007). Given this link, 50 kHz USVs could preferentially promote social contact in individuals in positively aroused, exploratory states. At faster time scales, calls group together in time resulting in bouts where calls are emitted in consecutive sniffs. We found that the statistics of rat call bouts do not support their status as a fundamental unit of vocal production, but rather appear secondary to changes in the drive to produce calls on the timescale of 1–2 s. In contrast, mouse calls are organized into longer bouts that cannot be accounted for by slow rate fluctuations, in agreement with a proposed song-like production (Holy and Guo, 2005). Call instant rates within bouts are centered on theta, with their precise value closely reflecting the underlying sniffing rate. Thus, the instantaneous call rate could transmit detailed information about the ongoing sniffing rate of the emitter, which is intimately linked with behavioral state. Interestingly, sounds presented at these rates are privileged in their processing by the auditory system of rats. During development, the auditory cortex selectively enhances the representation of sounds presented within theta band ~7 Hz (Kim and Bao, 2009), suggesting that theta patterning is important for the learning of species specific vocalizations. In adults, auditory responses to sounds are heavily attenuated at presentation rates above 10 Hz (Kilgard and Merzenich, 1998), which corresponds to the upper limit of our observed distribution of instantaneous call rates. Thus, the auditory system of rodents may be preferentially tuned to the sniff-driven dynamics of conspecific vocalizations.

Other mammalian orofacial communication signals are temporally structured at theta frequencies, such as marmoset twitter calls (Wang et al., 1995), macaque lip-smacking (Ghazanfar et al., 2010) and human speech (Chandrasekaran et al., 2009). Specific disruption of this rhythmicity results in impaired intelligibility (Saberi and Perrott, 1999; Ghitza and Greenberg, 2009; Ghazanfar et al., 2013) and cortical oscillations at matching frequencies are proposed to play a role in their selective perception (Giraud and Poeppel, 2012). Whether theta rhythms in primate and rodent social signals are evolutionarily linked and whether emission and perception of all of them are linked to cortical theta oscillations remains unknown.

ACKNOWLEDGMENTS

Experimental work was conducted at the Levy Center for Mind, Brain and Behavior of The Rockefeller University, New York, NY, USA. Final analysis and writing was carried out at the Brain Institute, Federal University of Rio Grande do Norte, Natal, RN, Brazil. Funding was provided by the Leon Levy Foundation. The authors would like to thank Andrew Widmer and Robert Assini for assistance with the recordings and Pawel Wojcik for contribution in the respiration sensor design.

SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: http://www.frontiersin.org/journal/10.3389/fnbeh.2014.00399/abstract

REFERENCES

Andalman, A. S., Foerster, J. N., and Fee, M. S. (2011). Control of vocal and respiratory patterns in birdsong: dissection of forebrain and brainstem mechanisms using temperature. PLoS ONE 6:e25461. doi: 10.1371/journal.pone.0025461

Assini, R., Sirotin, Y. B., and Laplagne, D. A. (2013). Rapid triggering of vocalizations following social interactions. Curr. Biol. 23, R996–R997. doi: 10.1016/j.cub.2013.10.007

Brudzynski, S. M. (2007). Ultrasonic calls of rats as indicator variables of negative or positive states: acetylcholine-dopamine interaction and acoustic coding. Behav. Brain Res. 182, 261–273. doi: 10.1016/j.bbr.2007.03.004

Brudzynski, S. M. (2009). Communication of adult rats by ultrasonic vocalization: biological, sociobiological, and neuroscience approaches. ILAR J. 50, 43–50. doi: 10.1093/ilar.50.1.43

Brudzynski, S. M. (2013). Ethotransmission: communication of emotional states through ultrasonic vocalization in rats. Curr. Opin. Neurobiol. 23, 310–317. doi: 10.1016/j.conb.2013.01.014

Brudzynski, S. M., and Pniak, A. (2002). Social contacts and production of 50-kHz short ultrasonic calls in adult rats. J. Comp. Psychol. 116, 73–82. doi: 10.1037/0735-7036.116.1.73

Chandrasekaran, C., Trubanova, A., Stillittano, S., Caplier, A., and Ghazanfar, A. A. (2009). The natural statistics of audiovisual speech. PLoS Comput. Biol. 5:e1000436. doi: 10.1371/journal.pcbi.1000436

Costall, B., and Naylor, R. J. (1975). The behavioural effects of dopamine applied intracerebrally to areas of the mesolimbic system. Eur. J. Pharmacol. 32, 87–92.

Cury, K. M., and Uchida, N. (2010). Robust odor coding via inhalation-coupled transient activity in the mammalian olfactory bulb. Neuron 68, 570–585. doi: 10.1016/j.neuron.2010.09.040

Deschênes, M., Moore, J., and Kleinfeld, D. (2012). Sniffing and whisking in rodents. Curr. Opin. Neurobiol. 22, 243–250. doi: 10.1016/j.conb.2011.11.013

Ganguly, K., and Kleinfeld, D. (2004). Goal-directed whisking increases phase-locking between vibrissa movement and electrical activity in primary sensory cortex in rat. Proc. Natl. Acad. Sci. U.S.A. 101, 12348–12353. doi: 10.1073/pnas.0308470101

Ghazanfar, A., a, Chandrasekaran, C., and Morrill, R. J. (2010). Dynamic, rhythmic facial expressions and the superior temporal sulcus of macaque monkeys: implications for the evolution of audiovisual speech. Eur. J. Neurosci. 31, 1807–1817. doi: 10.1111/j.1460-9568.2010.07209.x

Ghazanfar, A., a, Morrill, R. J., and Kayser, C. (2013). Monkeys are perceptually tuned to facial expressions that exhibit a theta-like speech rhythm. Proc. Natl. Acad. Sci. U.S.A. 110, 1959–1963. doi: 10.1073/pnas.1214956110

Ghitza, O., and Greenberg, S. (2009). On the possible role of brain rhythms in speech perception: intelligibility of time-compressed speech with periodic and aperiodic insertions of silence. Phonetica 66, 113–126. doi: 10.1159/000208934

Giraud, A.-L., and Poeppel, D. (2012). Cortical oscillations and speech processing: emerging computational principles and operations. Nat. Neurosci. 15, 511–517. doi: 10.1038/nn.3063

Grosmaitre, X., Santarelli, L. C., Tan, J., Luo, M., and Ma, M. (2007). Dual functions of mammalian olfactory sensory neurons as odor detectors and mechanical sensors. Nat. Neurosci. 10, 348–354. doi: 10.1038/nn1856

Hegoburu, C., Shionoya, K., Garcia, S., Messaoudi, B., Thévenet, M., and Mouly, A.-M. (2011). The RUB cage: respiration-ultrasonic vocalizations-behavior acquisition setup for assessing emotional memory in rats. Front. Behav. Neurosci. 5:25. doi: 10.3389/fnbeh.2011.00025

Holy, T. E., and Guo, Z. (2005). Ultrasonic songs of male mice. PLoS Biol. 3:e386. doi: 10.1371/journal.pbio.0030386

Kay, L. M. (2005). Theta oscillations and sensorimotor performance. Proc. Natl. Acad. Sci. U.S.A. 102, 3863. doi: 10.1073/pnas.0407920102

Kepecs, A., Uchida, N., and Mainen, Z. F. (2006). The sniff as a unit of olfactory processing. Chem. Senses 31, 167–179. doi: 10.1093/chemse/bjj016

Kilgard, M. P., and Merzenich, M. M. (1998). Plasticity of temporal information processing in the primary auditory cortex. Nat. Neurosci. 1, 727–731. doi: 10.1038/3729

Kim, H., and Bao, S. (2009). Selective increase in representations of sounds repeated at an ethological rate. J. Neurosci. 29, 5163–5169. doi: 10.1523/JNEUROSCI.0365-09.2009

Komisaruk, B. R. (1970). Synchrony between limbic system theta activity and rhythmical behavior in rats. J. Comp. Physiol. Psychol. 70, 482–492. doi: 10.1037/h0028709

Liu, R. C., Miller, K. D., Merzenich, M. M., and Schreiner, C. E. (2003). Acoustic variability and distinguishability among mouse ultrasound vocalizations. J. Acoust. Soc. Am. 114, 3412. doi: 10.1121/1.1623787

Macrides, F. (1975). Temporal relationships between hippocampal slow waves and exploratory sniffing in hamsters. Behav. Biol. 14, 295–308.

Macrides, F., Eichenbaum, H., and Forbes, W. B. (1982). Temporal relationship between sniffing and the limbic rhythm during odor discrimination reversal learning. J. Neurosci. 2, 1705–1717.

McFarland, D. H., and Lund, J. P. (1993). An investigation of the coupling between respiration, mastication, and swallowing in the awake rabbit. J. Neurophysiol. 69, 95–108.

MacLarnon, A. M., and Hewitt, G. P. (1999). The evolution of human speech: the role of enhanced breathing control. Am. J. Phys. Anthropol. 109, 341–363. doi: 10.1002/(SICI)1096-8644(199907)109:3<341::AID-AJPA5>3.0.CO;2-2

Mitra, P., and Bokil, H. (2007). Observed Brain Dynamics. Oxford: Oxford University Press.

Miura, K., Mainen, Z. F., and Uchida, N. (2012). Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity. Neuron 74, 1087–1098. doi: 10.1016/j.neuron.2012.04.021

Moore, J. D., Desch~enes, M., Furuta, T., Huber, D., Smear, M. C., Demers, M., et al. (2013). Hierarchy of orofacial rhythms revealed through whisking and breathing. Nature 497, 205–210. doi: 10.1038/nature12076

Moore, J. D., Kleinfeld, D., and Wang, F. (2014). How the brainstem controls orofacial behaviors comprised of rhythmic actions. Trends Neurosci. 37, 370–380. doi: 10.1016/j.tins.2014.05.001

Nawrot, M. P. (2010). “Analysis and interpretation of interval and count variability in neural spike trains,” in Analysis of Parallel Spike Trains Springer Series in Computational Neuroscience, eds S. Grün and S. Rotter (New York, NY: Springer), 37–58.

Nyby, J., and Whitney, G. (1978). Ultrasonic communication of adult myomorph rodents. Neurosci. Biobehav. Rev. 2, 1–14. doi: 10.1016/0149-7634(78)90003-9

Ranade, S., Hangya, B., and Kepecs, A. (2013). Multiple modes of phase locking between sniffing and whisking during active exploration. J. Neurosci. 33, 8250–8256. doi: 10.1523/JNEUROSCI.3874-12.2013

Riede, T. (2011). Subglottal pressure, tracheal airflow and intrinsic laryngeal muscle activity during rat ultrasound vocalization. J. Neurophysiol., 2580–2592. doi: 10.1152/jn.00478.2011

Riede, T. (2013). Stereotypic laryngeal and respiratory motor patterns generate different call types in rat ultrasound vocalization. J. Exp. Zool. A. Ecol. Genet. Physiol. 319, 213–224. doi: 10.1002/jez.1785

Roberts, L. H. (1972). Correlation of respiration and ultrasound production in rodents and bats. J. Zool. 168, 439–449. doi: 10.1111/j.1469-7998.1972.tb01360.x

Roberts, L. H. (1975a). Evidence for the laryngeal source of ultrasonic and audible cries of rodents. J. Zool. 175, 243–257.

Roberts, L. H. (1975b). The rodent ultrasound production mechanism. Ultrasonics 13, 83–88.

Saberi, K., and Perrott, D. R. (1999). Cognitive restoration of reversed speech. Nature 398, 760. doi: 10.1038/19652

Seffer, D., Schwarting, R. K. W., and Wöhr, M. (2014). Pro-social ultrasonic communication in rats: insights from playback studies. J. Neurosci. Methods 234, 73–81. doi: 10.1016/j.jneumeth.2014.01.023

Sewell, G. D. (1970). Ultrasonic signals from rodents. Ultrasonics 8, 26–30.

Shusterman, R., Smear, M. C., Koulakov, A. A., and Rinberg, D. (2011). Precise olfactory responses tile the sniff cycle. Nat. Neurosci. 14, 1039–1044. doi: 10.1038/nn.2877

Smotherman, M., Schwartz, C., and Metzner, W. (2010). “Vocal–respiratory interactions in the parabrachial nucleus,” Handbook of Mammalian Vocalization—An Integrative Neuroscience Approach. Handbook of Behavioral Neuroscience, Vol. 19 (Amsterdam; Boston: Elsevier), 383–392.

Takahashi, N., Kashino, M., and Hironaka, N. (2010). Structure of rat ultrasonic vocalizations and its relevance to behavior. PLoS ONE 5:e14115. doi: 10.1371/journal.pone.0014115

Wachowiak, M. (2011). All in a sniff: olfaction as a model for active sensing. Neuron 71, 962–973. doi: 10.1016/j.neuron.2011.08.030

Wang, X., Merzenich, M. M., Beitel, R., and Schreiner, C. E. (1995). Representation of a species-specific vocalization in the primary auditory cortex of the common marmoset: temporal and spectral characteristics. J. Neurophysiol. 74, 2685–2706.

Welker, W. I. (1964). Analysis of sniffing of the albino rat. Behaviour 22, 223–244.

White, N. R., Prasad, M., Barfield, R. J., and Nyby, J. G. (1998). 40- and 70-kHz vocalizations of mice (Mus musculus) during copulation. Physiol. Behav. 63, 467–73.

Willadsen, M., Seffer, D., Schwarting, R. K. W., and Wöhr, M. (2014). Rodent ultrasonic communication: male prosocial 50-kHz ultrasonic vocalizations elicit social approach behavior in female rats (Rattus norvegicus). J. Comp. Psychol. 128, 56–64. doi: 10.1037/a0034778

Wöhr, M., and Schwarting, R. K. W. (2007). Ultrasonic communication in rats: can playback of 50-kHz calls induce approach behavior? PLoS ONE 2:e1365. doi: 10.1371/journal.pone.0001365

Wolfe, J., Mende, C., and Brecht, M. (2011). Social facial touch in rats. Behav. Neurosci. 125, 900–910. doi: 10.1037/a0026165

Wright, J. M., Gourdon, J. C., and Clarke, P. B. S. (2010). Identification of multiple call categories within the rich repertoire of adult rat 50-kHz ultrasonic vocalizations: effects of amphetamine and social context. Psychopharmacology (Berl). 211, 1–13. doi: 10.1007/s00213-010-1859-y

Yajima, Y., and Hayashi, Y. (1983). Ambiguous motoneurons discharging synchronously with ultrasonic vocalization in rats. Exp. Brain Res. 50, 359–366. doi: 10.1007/BF00239201

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 16 July 2014; Accepted: 30 October 2014; Published online: 18 November 2014.

Citation: Sirotin YB, Elias Costa M and Laplagne DA (2014) Rodent ultrasonic vocalizations are bound to active sniffing behavior. Front. Behav. Neurosci. 8:399. doi: 10.3389/fnbeh.2014.00399

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Sirotin, Elias Costa and Laplagne. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 10 April 2014
doi: 10.3389/fnbeh.2014.00119

Repeated exposure to odors induces affective habituation of perception and sniffing

Camille Ferdenzi[*], Johan Poncelet, Catherine Rouby and Moustafa Bensafi

Centre National de la Recherche Scientifique UMR5292, INSERM U1028, Centre de Recherche en Neurosciences de Lyon, Université Claude Bernard Lyon 1, Lyon, France

* Correspondence: Camille Ferdenzi, Centre National de la Recherche Scientifique UMR5292, INSERM U1028, Centre de Recherche en Neurosciences de Lyon, Université Claude Bernard Lyon 1, 50 Avenue Tony Garnier, F-69366 Lyon Cedex 07, France e-mail: cferdenzi@crnl.cnrs.fr

Edited by:
Donald A. Wilson, New York University School of Medicine, USA

Reviewed by:
Ilona Croy, University of Gothenburg, Sweden
Monique A. Smeets, Utrecht University, Netherlands

Olfactory perception, and especially hedonic evaluation of odors, is highly flexible, but some mechanisms involved in this flexibility remain to be elucidated. In the present study we aimed at better understanding how repeated exposure to odors can affect their pleasantness. We tested the hypothesis of an affective habituation to the stimuli, namely a decrease of emotional intensity over repetitions. More specifically, we tested whether this effect is subject to inter-individual variability and whether it can also be observed at the olfactomotor level. Twenty-six participants took part in the experiment during which they had to smell two odorants, anise and chocolate, presented 20 times each. On each trial, sniff duration and volume were recorded and paired with ratings of odor pleasantness and intensity. For each smell, we distinguished between “likers” and “dislikers,” namely individuals giving positive and negative initial hedonic evaluations. Results showed a significant decrease in pleasantness with time when the odor was initially pleasant (“likers”), while unpleasantness remained stable or slightly decreased when the odor was initially unpleasant (“dislikers”). This deviation toward neutrality was interpreted as affective habituation. This effect was all the more robust as it was observed for both odors and corroborated by sniffing, an objective measurement of odor pleasantness. Affective habituation to odors can be interpreted as an adaptive response to stimuli that prove over time to be devoid of positive or negative outcome on the organism. This study contributes to a better understanding of how olfactory preferences are shaped through exposure, depending on the individual's own initial perception of the odor.

Keywords: pleasantness, smell, repeated exposure, sniff, habituation

INTRODUCTION

Olfactory perception is known to be highly flexible as a function of perceiver's age, sex, or motivation state, of the context where the odor is perceived or of the characteristic of the odorant itself like its structure or its concentration. Another prominent factor of variations in odor perception is repeated exposure, which is able to improve olfactory detection thresholds (Stevens and O'Connell, 1995; Dalton et al., 2002) and can even boost olfactory sensitivity in seemingly anosmic participants (Wysocki et al., 1989; Mainland et al., 2002). Some studies also investigated the effect of exposure on discrimination abilities. There is now clear evidence that unreinforced exposure to odors can improve discrimination between odorants in humans (Rabin, 1988; Jehl et al., 1995). In line with this, it has been shown that exposure to odor mixtures can alter the perceived quality of the individual components (Stevenson, 2001). For instance, exposure to wine or beer through personal experience or through controlled training improves the ability to discriminate between different wines or beers (Owen and Machamer, 1979; Peron and Allen, 1988; Melcher and Schooler, 1996).

Although these studies show that exposure improves odor perception through differentiation of stimulus features, dimensions, or categories, how repeated exposure to odors affects one of the most prominent dimension of olfaction, namely pleasantness, remains understudied. What happens when we are exposed to the same odorant repeatedly? Do we like it more, or on the contrary do we like it less, or does liking remain stable overtime? A pioneer work in the field conducted by Cain and Johnson (1978) showed that mere presentation of a given odor significantly changed its hedonic value. More specifically, repeated presentation of a pleasant odor (citral) led to a decreased pleasantness whereas repeated presentation of an unpleasant odor (isobutyric acid) led to a decreased unpleasantness. In others words, repeated exposure shifted odor pleasantness ratings toward neutrality, a phenomenon called by Cain and Johnson “affective habituation.” However, to the best of our knowledge, non-verbal correlates of self-reported decrease in pleasantness (for pleasant odors) and unpleasantness (for unpleasant ones), such as psychophysiological responses, remain very scarcely investigated in the olfactory domain (but see evoked potentials for the unpleasant pole in Croy et al., 2013). Such a physiological indicator would be of particular interest, because it would strengthen the notion that affective habituation phenomenon is not due to experimental demand or even to a change in the use of the subjective scale over time.

Non-verbal measures of odor hedonics include autonomous (Alaoui-Ismaili et al., 1997; Bensafi et al., 2002a), or motor responses as reflected by reaction time studies (Bensafi et al., 2002b; Jacob and Wang, 2006; Boesveldt et al., 2010) and by sniffing responses to odors (Bensafi et al., 2003, 2007). Indeed, research in animals and humans has shown that sniffing behavior, i.e., the motor component of olfaction, is of considerable importance in odor perception. Sniffing is driven by stimulus attributes such as odor concentration (Laing, 1983; Frank et al., 2003; Johnson et al., 2003), and induces by itself activation in human primary olfactory cortex (Sobel et al., 1998). Furthermore, there is psychophysiological evidence that sniffing is modulated by subjective pleasantness of an odor: sniff duration and sniff volume increase when pleasant odors are sampled compared to unpleasant ones (Frank et al., 2003; Mainland and Sobel, 2006; Bensafi et al., 2007, 2003). Moreover, even when participants are asked to maintain their sniff for a specific duration irrespective of odor content, they sniff pleasant odors stronger and for a longer time (Bensafi et al., 2007). Thus, measuring sniffing patterns has two main advantages in studies on odor hedonics. First, it allows testing whether modulations in pleasantness are consistent with modulations in physiological/motor response. Second, this measure appears less vulnerable than verbal ratings to modulation by explicit or voluntary strategies, which makes it a more objective measure of hedonic responses. The main aim of the present study was therefore to examine whether affective habituation is not only observed at the self-reported level but also reflected at the psychophysiological level, by modulating sniffing responses to pleasant and unpleasant odors.

One striking particularity of odor hedonic responses is their variation between individuals: whereas affective evaluation of a given odor is positive for some individuals, the same smell may be considered unpleasant by others. For example, Doty (1975) emphasized the “large differences between observers in regard to the assessment of odorant hedonicity” (p. 495) based on 10 odorants, and noted for example that benzaldehyde had a bimodal distribution with half of the participants describing it as unpleasant and the other half as pleasant. In the same line, Bensafi et al. (2012) showed that two CO_2-odor mixtures received varied hedonic ratings from one participant to another, and revealed differential activations in the brain according to whether the stimulation was perceived as pleasant or unpleasant. Moreover, Lundström et al. (2006) evidenced variability between individuals as regards pleasantness of the smell of androstenone, going from unpleasant to neutral. These hedonic rating differences were accompanied by distinctive verbal descriptions and neural responses in olfactory evoked potentials: Individuals who gave the lowest pleasantness ratings described the smell as “sweaty” and “urinous” and showed larger P3 amplitudes than individuals who gave higher pleasantness ratings and who described the smell with non-body descriptors (“smoky,” “fresh,” “sweet,” and “chemical”). In accordance with this finding, Keller et al. (2007) showed that variation of olfactory receptor expression accounted for a significant part of olfactory perceptual differences, especially between likers and dislikers of androstenone. Neuroimaging studies also shed light on these inter-individual differences in hedonic ratings of smells. In an fMRI study, Rolls and McCabe (2007) showed that chocolate cravers rated this flavor as more pleasant than non-cravers, and that an increasing level of pleasantness was associated with an enhanced activity in the pregenual cingulate cortex, the medial orbitofrontal cortex, and the dorsolateral prefrontal cortex. Altogether, these findings suggest that in olfaction studies, and especially in these dealing with pleasantness, it is of the utmost importance to take into account inter-individual differences because they have significant implications at the peripheral and central levels of olfactory processing.

Therefore, the secondary aim of the present study was to investigate inter-individual variability of the effect of repeated exposure on perceptual ratings and sniffing activity. To this end, participants were exposed to odors for which a previous study revealed large hedonic variability between raters (anise and chocolate; Barkat et al., 2008). In our study, participants were classified as “likers” or “dislikers” for each particular smell based on their initial hedonic ratings. They were then exposed 20 times to each odorant while hedonic ratings and sniffing behavior were recorded. We hypothesized that: (1) olfactory repeated exposure should decrease odor pleasantness in “likers” and odor unpleasantness in “dislikers,” (2) such affective habituation should be accompanied by changes in sniff parameters, namely decreased sniff volume and duration in “likers” and increased sniff volume and duration in “dislikers.”

MATERIALS AND METHODS

PARTICIPANTS

Twenty-six young adults (mean age ± s.e.m: 21.5 ± 0.46, range 19–29; 18 women and 8 men) attending the Claude Bernard University of Lyon (France) participated in the experiment. The experimental procedure was explained in great detail to the participants, who provided written consent prior to participation. The study was conducted according to the Declaration of Helsinki and was approved by the local ethical committee. Based on participant's reports, exclusion criteria were: abnormal olfaction, history of neurological disease or injury, or history of nasal insult (broken nose or surgery).

ODORANTS AND OLFACTOMETRY

Based on the results of a hedonic ranking task involving 8 odorants in a previous study (Barkat et al., 2008), two odorants, anise and chocolate (Euracli, France), were chosen because (1) they received a medium mean rank, and (2) they exhibited a large inter-individual variability. Odorants were diluted in mineral oil (10%) and presented to both nostrils via a nasal mask (Figure 1A). They were presented 20 times each in a random order, with an inter-stimulus interval of 30 s and duration of 3 s. Stimulations were delivered via a computer-controlled air-dilution olfactometer whereby odorants were diffused synchronously with the beginning of participant's inspiration (respiration was recorded continuously during the study).

FIGURE 1. (A) Experimental device ensuring odor delivery and sniff recording. Nasal respiration was monitored with a flow sensor connected both to the subject's nose via a nasal mask and to the olfactometer. The nasal inspiration, detected by the flow sensor, triggers the sending of the odorant by the olfactometer during the requested duration to the subject's nose. To clean the mask chamber between stimulations, and to decrease the risk of odor contamination, the mask was connected to a Ruben valve (Intersurgical®, 7562700, 22F-22M/15F, UK) so that the odorized air contained in the chamber was sent out on each expiration. (B) Grand average of the sniffs for the odors of anise (AN) and chocolate (CH) across all trials and all participants, showing the maximum flow rate, duration, and volume.

The general principle of the olfactometer is to mix two airflows (odorized and pure air) to deliver a constant odorized or non-odorized airflow to the participant's nose. Pure air is sent by a compressor and cleaned by an activated carbon filter before being carried to the olfactometer input line (6 mm diameter, 5 m length tube). A manometer allows selecting the air input pressure. Then, air enters two channels: (1) a channel where it works as air carrier, and (2) an “odor” channel (one per odorant). For each odorant, a glass tube is set with polypropylene marbles where the odor is adsorbed. At the exit of each channel, an electric valve is programmed to be closed or open so that the odorant is pushed into the airflow for a given duration and pressure. The output odorous air is led by a 4 mm tube (20 cm length) into the nasal mask.

The experimental room was well-ventilated and included two areas, one for the experimenter and one for the participant. The experimenter area contained the computer controlling the olfactometer and two control screens showing the processing of the olfactometer and the answers the participant was giving on his/her own screen. The participant's area included the olfactometer output, as well as a screen and a mouse allowing them to read the instructions and give their ratings after each olfactory stimulation.

PROCEDURE

After providing written informed consent and reading instructions, participants were taken into the testing room. At this point, the experimenter fitted the sniffing equipment to the participants. Sniffing was recorded using an airflow sensor (TSL®, 4000 series, Model 40211, USA) connected to the nasal mask delivering odors to both nostrils. Sniffing signal was amplified and digitally recorded at 100 Hz using Python software®.

Upon installation of the nasal mask, the experiment started. Each trial was timed, and cued by the computer-generated visual instructions “please prepare to smell,” displayed for 3 s and announcing odor delivery. Once the instruction disappeared, participants were to sniff, which enabled the airflow sensor to detect the beginning of subject's inspiration and trigger odor delivery via the olfactometer. Following each odor presentation, participants rated stimulus pleasantness and intensity on an on-screen visual analog scale: the left end of the scale was labeled “extremely unpleasant” or “no stimulus perceived” (0), and the right end “extremely pleasant” or “extremely strong” (100). Instructions, odor presentation and sniffing recordings were all time-locked through one central computer.

DATA ANALYSIS

For each participant, we recorded intensity and pleasantness ratings (0–100) and sniff parameters on 20 occasions per odor (T1 to T20, for anise and chocolate). Sniffs (see Figure 1B) were pre-processed by removing baseline offsets and aligned in time by setting the point where the sniff entered the inspiratory phase as time zero. Sniff maximum flow rate, duration, and volume (see Figure 1B) were calculated for the first sniff of every trial, for every participant. Before analyzing how the ratings and sniff parameters changed with repeated exposure, outliers defined as values exceeding three standard deviations from the participant's mean were removed (0.65% of the trials). Then, analyses of the time-related changes in ratings and sniff parameters were performed (1) at the group level, by comparing time-related changes of “likers” (participants giving the highest pleasantness scores) and “dislikers” (participants giving the lowest pleasantness scores), and (2) at the individual level, by correlating each participant's initial pleasantness rating at T1 with the time-related changes across the trials T1 to T20. Time-related changes in hedonic and intensity ratings, sniff maximum flow rate, duration, and volume were represented by the slope of each variable as a function of trial number (1 to 20). A positive slope and a negative slope, respectively correspond to an increase and a decrease of the measured variable over time. For the group analysis, slopes were computed on average scores for “likers” and “dislikers” on each trial T1 to T20. The significance of the increases/decreases was assessed by using linear regressions with trial number as predictive variable. For the individual level analysis, slopes were computed for each participant individually. Correlation between individual pleasantness rating at T1 and the slopes of pleasantness, intensity and sniff parameters were investigated using Spearman rank correlation coefficient. Here, we expect “likers” to exhibit negative slopes and “dislikers” to display positive slopes in both perceptual and sniffing variables. This should be confirmed at the individual level by negative correlations between individual hedonic scores at T1 and individual slopes.

RESULTS

INTER-RATER VARIABILITY IN ODOR PLEASANTNESS

Anise and chocolate were selected for their average neutral valence and the variability of pleasantness ratings they receive in the population. To verify that this was true in our sample, we examined both the average and individual ratings on the very first trial of each odor (i.e., at T1). As expected, anise and chocolate had moderate average pleasantness on the 0–100 hedonic scale, with large inter-individual variations (anise: M ± SD = 40.5 ± 25.2, range 1–100; chocolate M ± SD = 40.5 ± 25.2, range 1–85). The large variations in pleasantness ratings across participants allowed categorizing them as either “dislikers” or “likers” for each odorant. There were 14 “dislikers” (pleasantness ratings between 1 and 31 at T1) and 12 “likers” (ratings 47–100) for anise, and 13 “dislikers” (ratings 1–47) and 13 “likers” (ratings 50–85) for chocolate.

GROUP ANALYSES: “LIKERS” AND “DISLIKERS”

Average pleasantness, intensity, sniff maximum flow rate, sniff duration, and sniff volume of “likers” and “dislikers” across the 20 trials are shown in Figure 2. Results of the linear regressions between the five variables and time (Table 1) suggest that repeated exposure induced a significant decrease in pleasantness and intensity ratings, sniff duration, and sniff volume in “likers,” while these variables increased without reaching statistical significance in “dislikers.” In both groups, repeated-exposure resulted in a convergence of pleasantness ratings toward neutrality. Indeed, while hedonic ratings of “likers” and “dislikers” significantly differed at T1 (t-tests for independent samples, Table 1), they did not differ any more at T20. “Likers” and “dislikers” did not significantly differ on the other variables at T1 or T20, except for sniff maximum flow rate, higher in “dislikers” at T20 for chocolate. Finally, pleasantness ratings did not significantly correlate with intensity nor with sniffing parameters at T1 (Spearman rank correlations).

FIGURE 2. Average hedonic ratings (A), intensity ratings (B), sniff maximum flow rate (C), sniff duration (D), sniff volume (E) (Mean ± s.e.m) of the “likers” and “dislikers” for the odors of anise and chocolate together, on each of the 20 repeated odor presentations. The linear trend curve of each participant group is presented. Slopes and results of the linear regressions are shown in Table 1.

Table 1. (A) Linear regressions between pleasantness, intensity, sniff maximum flow rate, sniff duration, sniff volume, and trial number (1 to 20) for “likers” and “dislikers” separately, and for the odors of anise and chocolate separately and together. (B) t-tests for independent samples between “likers” (L) and “dislikers” (D) at trial 1 and trial 20.

CORRELATION BETWEEN INDIVIDUAL INITIAL PLEASANTNESS AND TIME-RELATED PERCEPTUAL CHANGES

To go further, we then focused on each participant's pleasantness ratings at T1 and we correlated it with the time-related changes in pleasantness, intensity, and sniff parameters represented by the slopes of these variables as a function of trial number. The slopes were positive or negative depending on the participants (e.g., pleasantness ratings: range = −2.21 to +3.57, mean = 0.00 for anise, and range = −3.35 to +2.06, mean = −0.50 for chocolate). As expected, Spearman coefficients showed significant negative correlations between initial pleasantness and the slopes of the variables—except sniff maximum flow rate—for one or both odors. These results, illustrated in Figures 3, 4, mean that: (i) higher initial odor pleasantness ratings were associated with larger decreases of pleasantness, intensity, sniff volume and duration during repeated exposure (more negative slopes), and (ii) lower initial odor pleasantness ratings were associated with smaller decreases (slopes closer to zero) and even to increases of these variables (positive slopes), especially for the pleasantness ratings (Figures 3A,B) and the sniff volume (Figures 4E,F).

FIGURE 3. Correlations between pleasantness ratings at T1 (first odor presentation) and time-related changes of pleasantness (A,B) and intensity (C,D) ratings represented by their slopes across the 20 odor presentations, for anise (AN) and chocolate (CH).

FIGURE 4. Correlations between pleasantness ratings at T1 (first odor presentation) and time-related changes of the following sniff parameters: maximum flow rate (A,B), duration (C,D), and volume (E,F) represented by their slopes across the 20 odor presentations, for anise (AN) and chocolate (CH).

DISCUSSION

In the present study, we aimed at testing how hedonic perception of odors varies with repeated exposure, and whether inter-individual differences in hedonic perception of a given odor can modulate this variation. Namely, we used two odors people did not agree to find pleasant or unpleasant and presented them twenty times each (T1 to T20) in a random sequence. We explored time-related perceptual and motor (sniffing) changes for each odor, according to the participant's initial hedonic judgment. First, when considering the groups of “likers” (who rated the odor as pleasant at T1) and of “dislikers” (who rated the odor as unpleasant at T1), we found that pleasantness significantly decreased with time in “likers.” In “dislikers,” unpleasantness tended to decrease with time but the effect did not reach significance. These effects were paralleled by similar changes in intensity ratings, sniff duration, and sniff volume. We noticed that these effects led to a decrease in affective responsiveness since pleasantness ratings of both groups did not differ any more after 20 odor presentations. Second, when investigating more precisely the level of initial pleasantness rating at T1, we found negative correlations with the slopes (or time-related changes) of pleasantness, intensity, and sniff volume and duration for at least one odor. Correlation graphs (Figures 3, 4) show that higher initial pleasantness was mostly associated with more negative slopes (decrease in ratings and sniffing) and lower initial pleasantness was mostly associated with more positive slopes (increase in ratings and sniffing). In sum, we showed that affective habituation occurs with repeated exposure, which can be observed both at the self-reported level and at the olfactomotor level. We also provided evidence that repeated exposure influences individuals differently according to whether they initially liked or disliked the odor, affective habituation being more significant for odor “likers.”

One can wonder whether peripheral mechanisms such as olfactory adaptation may explain the present findings. Peripheral olfactory adaptation (or olfactory fatigue) is a phenomenon characterized by a decrease of the olfactory receptors' sensitivity due to prolonged or repeated exposure. Our experimental procedure was designed to limit such phenomenon by using appropriate inter-stimulus intervals (minimum 30 s) and by presenting two different odors randomly. Moreover, olfactory adaptation is characterized by a decrease in perceived intensity (Cain, 1969). Thus, if adaptation had occurred in our study, all participants should have displayed a decrease in perceived intensity, paralleled with an increase in sniff magnitude (see Laing, 1983; Frank et al., 2003, for the link between odor intensity and sniff volume/duration). However, this was not the case since a substantial number of participants displayed positive slopes over time for intensity, sniff volume and sniff duration (see Figures 3C,D, 4C–F). Rather, the time-related variation of pleasantness toward neutrality observed in our study is likely due to more central processes, and may therefore be preferentially qualified of affective habituation. It must be kept in mind that both processes are not independent (central processing can reflect changes in peripheral response) and the origin of response reduction due to repeated exposure remains unclear (Dalton, 2000).

Affective habituation is a form of learning that has been observed in previous studies, through decreasing strength of responses to repeated emotional stimuli of various nature, at the psychophysiological level (reduction of the electrodermal and electromyographic response: Bradley et al., 1993) and at the neurophysiological level (decrement in amydgala activation: Wright et al., 2001; Mutschler et al., 2010). At the behavioral level, few studies have described affective habituation using odors with contrasted pleasantness. Cain and Johnson (1978), who measured pleasantness of odors before and after repeated exposure, found a shift in the direction of hedonic neutrality: the positively valenced odor of citral became less pleasant and the negatively valenced odor of isobutyric acid became less unpleasant after exposure. Similarly, Prescott et al. (2008) showed an increase of pleasantness of two (neutral and unpleasant) odors after an exposure phase, as did Croy et al. (2013) after three presentations of the unpleasant odor of H_2S. The latter result was corroborated by a reduced neuronal activation at the cerebral level and was interpreted as a decrease in emotional salience. With a more time-related approach, our study provided further evidence that this effect exists and is gradual: using a linear model of the pleasantness change across 20 odor presentations, we showed that pleasantness follows different trajectories, depending on the initial hedonic rating of the participants.

In this study, sniffing behaviors followed the same pattern as pleasantness ratings. This result reinforces the hypothesis that affective habituation occurs when an odor is repeated in a short period of time. Odor pleasantness is known to co-vary with sniffing behavior parameters, whether the odor is really smelled or whether it is imagined: compared to unpleasant odors (like rotten egg or fish), pleasant ones (like strawberry or rose) have been repeatedly found to be associated with larger and longer sniffs (Bensafi et al., 2003, 2007; Joussain et al., 2013). This motor correlate of odor pleasantness seems to be a robust mechanism since it is observed even when participants are asked to maintain constant sniffs across conditions (Bensafi et al., 2007). In line with this, we found that, as for pleasantness, sniff volume and duration mostly decreased over time in “likers” and tended to increase or stagnated in “dislikers.” In sum, not only did repeated exposure cause pleasantness to become more neutral, it also caused more involuntary parameters of olfactory perception (sniff duration and volume) to reflect this tendency toward neutrality. One may be surprised by the fact that “likers” and “dislikers” did not differ in their sniffing patterns for any of the two odors, and that pleasantness ratings did not correlate with sniffing volume or duration at T1. Relationship between sniff and pleasantness reported in the literature was usually found in response to odors with different qualities and more importantly, with highly contrasted valence (e.g., rose vs. rotten egg in Bensafi et al., 2003). In our study we compared individual responses to the same odor: not only are differences thus likely to be less marked but also inter-individual variability may have prevented the difference between “likers” and “dislikers” to reach statistical significance. Sniffing may nonetheless be considered as a reliable measure because, for a given individual, fine time-related changes paralleling changes in pleasantness were found in our study.

Why would affective habituation occur when odors are presented repeatedly? And how can this be interpreted in relation to another apparently contradictory theory, the mere exposure effect, according to which exposure leads to familiarization and higher liking (Zajonc, 1968)? In the conditions of our experimental design, namely 20 repeated presentations of two odors within about an hour, responsiveness to the repeated stimuli decreased. As nicely explained by Dijksterhuis and Smith (2002), habituation is a very useful mechanism that prevents us to be overwhelmed by the numerous stimulations of our environment. When encountering an emotional stimulus, such as an appetitive or a repulsive odor, we may first react intensely, but if subsequent repeated or prolonged exposure proves not to have any positive or negative consequence on the organism, such an intense response becomes unnecessary. On the course of time, the stimulus becomes less relevant, leading to reduced responsiveness. The effect of repeated exposure can be more pronounced or even reversed if the stimulus has effective or supposed consequences on the organism. For example, repeated chocolate ingestion, which has physiological outcomes, leads an initially very positive stimulus (chocolate) to lose its pleasantness (like in our study) and even to become aversive, and activates accordingly two different cerebral substrates related to reward and punishment, respectively (Small et al., 2001). Another example refers to unpleasant odors. If the odor were associated with the belief that it is harmful, by itself or via its source, responsiveness to the odor would then be more likely to increase rather than to decrease or remain stable like in our study. Indeed, in a study by Dalton (1996), perceived intensity of an odor increased over time for an odorous substance presented as being hazardous (sensitization), whereas it decreased in participants who believed that this substance was healthy. If pleasantness of the repeated harmful substance were measured, it probably would decrease over time (instead of increasing or remaining stable like in our study). These results highlight the importance of cognitive influences on odor perception, both at a given time (Herz and von Clef, 2001; De Araujo et al., 2005) and over time. The mere exposure effect, where novel (never encountered) stimuli that become more familiar with exposure also become more appreciated (Zajonc, 1968), may have the same origins as the habituation pattern of initially negative stimuli found in Cain and Johnson (1978) and more moderately in “dislikers” in our study. It is also the phenomenon that might occur in the case of cultural influences on odor perception: learning to associate initially negative smells with positive consequences (taste enjoyment of smelly cheese in France or of the foul-smelling durian fruit in Asia; Ayabe-Kanamura et al., 1998; Ferdenzi et al., 2013) may decrease its unpleasantness possibly to the point where it even reaches the positive side of the pleasantness scale.

In sum, repeated presentation of emotional stimuli such as odors may produce gradual decrease in responsiveness (tendency to neutral hedonic valence), but cognitive influences related to the consequences on the organism can modulate this pattern, by increasing responsiveness to repeated stimuli that have harmful or beneficial outcomes. In future studies, the asymmetry between affective habituation to pleasant and unpleasant odors (or of “likers” and “dislikers”) should be investigated further. Indeed, our study suggested that habituation was much less pronounced in “dislikers” than in “likers.” In “dislikers,” pleasantness and sniff magnitude seemed to have a tendency to increase with repeated exposure but the effect did not reach significance (Table 1), while reverse time-related changes were highly significant in “likers.” It might be that unpleasant odors are more resistant to the effect of familiarization, because maintaining an aversion for potentially harmful stimuli is an adaptive behavior (Delplanque et al., 2008; Ferdenzi et al., 2013). Affective habituation to unpleasant odors may thus be more limited in amplitude and/or might require longer exposure to reach the same magnitude as with pleasant stimuli, but this remains to be tested.

Finally, our study shows that it is highly relevant for olfaction studies to take into account inter-individual differences in hedonic perception. Agreement between raters and between cultures seems to be lower for neutral and pleasant odors than for unpleasant ones (Schaal et al., 1998). Hedonically neutral odors, in particular, may not be truly “neutral” and may rather receive highly contrasted odor ratings with some participants finding them pleasant and others finding them unpleasant (as in Doty, 1975), which leads to a moderate average score. Our study highlights significant differences from one person to another in the changes of perception and sniffing over time, for the same odor. When investigating odor hedonics, it is hazardous to consider the object per se independently of the perceiver (Robin et al., 1999; Rouby and Bensafi, 2002; Forestell and Mennella, 2005) because pleasantness is subjective and depends on personal past experience, current needs and goals.

AUTHOR CONTRIBUTIONS

Moustafa Bensafi and Catherine Rouby designed the research, Camille Ferdenzi and Moustafa Bensafi analyzed the data and wrote the paper, Johan Poncelet conducted the data collection.

ACKNOWLEDGMENTS

This study was supported by a grant from the ANR to Camille Ferdenzi (PDOC Program, ATTRASENS Project).

REFERENCES

Alaoui-Ismaili, O., Vernet-Maury, E., Dittmar, A., Delhomme, G., and Chanel, J. (1997). Odor hedonics: connection with emotional response estimated by autonomic parameters. Chem. Senses 22, 237–248. doi: 10.1093/chemse/22.3.237

Ayabe-Kanamura, S., Schicker, I., Laska, M., Hudson, R., Distel, H., Kobayakawa, T., et al. (1998). Differences in perception of everyday odors: a Japanese-German cross-cultural study. Chem. Senses 23, 31–38. doi: 10.1093/chemse/23.1.31

Barkat, S., Poncelet, J., Landis, B. N., Rouby, C., and Bensafi, M. (2008). Improved smell pleasantness after odor-taste associative learning in humans. Neurosci. Lett. 434, 108–112. doi: 10.1016/j.neulet.2008.01.037

Bensafi, M., Iannilli, E., Poncelet, J., Seo, H.-S., Gerber, J., Rouby, C., et al. (2012). Dissociated representations of pleasant and unpleasant olfacto-trigeminal mixtures: an FMRI study. PLoS ONE 7:e38358. doi: 10.1371/journal.pone.0038358

Bensafi, M., Porter, J., Pouliot, S., Mainland, J., Johnson, B., Zelano, C., et al. (2003). Olfactomotor activity during imagery mimics that during perception. Nat. Neurosci. 6, 1142–1144. doi: 10.1038/nn1145

Bensafi, M., Rouby, C., Farget, V., Bertrand, B., Vigouroux, M., and Holley, A. (2002a). Psychophysiological correlates of affects in human olfaction. Neurophysiol. Clin. 32, 326–332. doi: 10.1016/S0987-7053(02)00339-8

Bensafi, M., Rouby, C., Farget, V., Vigouroux, M., and Holley, A. (2002b). Asymmetry of pleasant vs. unpleasant odor processing during affective judgment in humans. Neurosci. Lett. 328, 309–313. doi: 10.1016/S0304-3940(02)00548-7

Bensafi, M., Sobel, N., and Khan, R. M. (2007). Hedonic-specific activity in piriform cortex during odor imagery mimics that during odor perception. J. Neurophysiol. 98, 3254–3262. doi: 10.1152/jn.00349.2007

Boesveldt, S., Frasnelli, J., Gordon, A. R., and Lundström, J. N. (2010). The fish is bad: negative food odors elicit faster and more accurate reactions than other odors. Biol. Psychol. 84, 313–317. doi: 10.1016/j.biopsycho.2010.03.006

Bradley, M. M., Lang, P. J., and Cuthbert, B. N. (1993). Emotion, novelty, and the startle reflex: habituation in humans. Behav. Neurosci. 107, 970–980. doi: 10.1037/0735-7044.107.6.970

Cain, W. S. (1969). Olfactory adaptation and direct scaling of odor intensity. Diss. Abstr. Int. 30:696.

Cain, W. S., and Johnson, F. (1978). Lability of odor pleasantness: influence of mere exposure. Perception 7, 459–465. doi: 10.1068/p070459

Croy, I., Maboshe, W., and Hummel, T. (2013). Habituation effects of pleasant and unpleasant odors. Int. J. Psychophysiol. 88, 104–108. doi: 10.1016/j.ijpsycho.2013.02.005

Dalton, P. (1996). Odor perception and beliefs about risk. Chem. Senses 21, 447–458. doi: 10.1093/chemse/21.4.447

Dalton, P. (2000). Psychophysical and behavioral characteristics of olfactory adaptation. Chem. Senses 25, 487–492. doi: 10.1093/chemse/25.4.487

Dalton, P., Doolittle, N., and Breslin, P. A. (2002). Gender-specific induction of enhanced sensitivity to odors. Nat. Neurosci. 5, 199–200. doi: 10.1038/nn803

De Araujo, I. E., Rolls, E. T., Velazco, M. I., Margot, C., and Cayeux, I. (2005). Cognitive modulation of olfactory processing. Neuron 46, 671–679. doi: 10.1016/j.neuron.2005.04.021

Delplanque, S., Grandjean, D., Chrea, C., Aymard, L., Cayeux, I., Le Calvé, B., et al. (2008). Emotional processing of odors: evidence for a nonlinear relation between pleasantness and familiarity evaluations. Chem. Senses 33, 469–479. doi: 10.1093/chemse/bjn014

Dijksterhuis, A., and Smith, P. K. (2002). Affective habituation: subliminal exposure to extreme stimuli decreases their extremity. Emotion 2, 203–214. doi: 10.1037/1528-3542.2.3.203

Doty, R. L. (1975). An examination of relationships between the pleasantness, intensity, and concentration of 10 odorous stimuli. Percept. Psychophys. 17, 492–496. doi: 10.3758/BF03203300

Ferdenzi, C., Roberts, S. C., Schirmer, A., Delplanque, S., Cekic, S., Porcherot, C., et al. (2013). Variability of affective responses to odors: culture, gender, and olfactory knowledge. Chem. Senses 38, 175–186. doi: 10.1093/chemse/bjs083

Forestell, C. A., and Mennella, J. A. (2005). Children's hedonic judgments of cigarette smoke odor: effects of parental smoking and maternal mood. Psychol. Addict. Behav. 19, 423–432. doi: 10.1037/0893-164X.19.4.423

Frank, R. A., Dulay, M. F., and Gesteland, R. C. (2003). Assessment of the Sniff Magnitude Test as a clinical test of olfactory function. Physiol. Behav. 78, 195–204. doi: 10.1016/S0031-9384(02)00965-4

Herz, R. S., and von Clef, J. (2001). The influence of verbal labeling on the perception of odors: evidence for olfactory illusions? Perception 30, 381–391. doi: 10.1068/p3179

Jacob, T. J. C., and Wang, L. (2006). A new method for measuring reaction times for odour detection at iso-intensity: comparison between an unpleasant and pleasant odour. Physiol. Behav. 87, 500–505. doi: 10.1016/j.physbeh.2005.11.018

Jehl, C., Royet, J. P., and Holley, A. (1995). Odor discrimination and recognition memory as a function of familiarization. Percept. Psychophys. 57, 1002–1011. doi: 10.3758/BF03205459

Johnson, B. N., Mainland, J. D., and Sobel, N. (2003). Rapid olfactory processing implicates subcortical control of an olfactomotor system. J. Neurophysiol. 90, 1084–1094. doi: 10.1152/jn.00115.2003

Joussain, P., Thevenet, M., Rouby, C., and Bensafi, M. (2013). Effect of aging on hedonic appreciation of pleasant and unpleasant odors. PloS ONE 8:e61376. doi: 10.1371/journal.pone.0061376

Keller, A., Zhuang, H., Chi, Q., Vosshall, L. B., and Matsunami, H. (2007). Genetic variation in a human odorant receptor alters odour perception. Nature 449, 468–472. doi: 10.1038/nature06162

Laing, D. G. (1983). Natural sniffing gives optimum odour perception for humans. Perception 12, 99–117. doi: 10.1068/p120099

Lundström, J. N., Seven, S., Olsson, M. J., Schaal, B., and Hummel, T. (2006). Olfactory event-related potentials reflect individual differences in odor valence perception. Chem. Senses 31, 705–711. doi: 10.1093/chemse/bjl012

Mainland, J. D., Bremner, E. A., Young, N., Johnson, B. N., Khan, R. M., Bensafi, M., et al. (2002). Olfactory plasticity: one nostril knows what the other learns. Nature 419, 802. doi: 10.1038/419802a

Mainland, J., and Sobel, N. (2006). The sniff is part of the olfactory percept. Chem. Senses 31, 181–196. doi: 10.1093/chemse/bjj012

Melcher, J. M., and Schooler, J. W. (1996). The misremembrance of wines past: verbal and perceptual expertise differentially mediate verbal overshadowing of taste memory. J. Mem. Lang. 35, 231–245. doi: 10.1006/jmla.1996.0013

Mutschler, I., Wieckhorst, B., Speck, O., Schulze-Bonhage, A., Hennig, J., Seifritz, E., et al. (2010). Time scales of auditory habituation in the amygdala and cerebral cortex. Cereb. Cortex 20, 2531–2539. doi: 10.1093/cercor/bhq001

Owen, D. H., and Machamer, P. K. (1979). Bias-free improvement in wine discrimination. Perception 8, 199–209. doi: 10.1068/p080199

Peron, R. M., and Allen, G. L. (1988). Attempts to train novices for beer flavor discrimination: a matter of taste. J. Gen. Psychol. 115, 403–418. doi: 10.1080/00221309.1988.9710577

Prescott, J., Kim, H., and Kim, K.-O. (2008). Cognitive mediation of hedonic changes to odors following exposure. Chemosens. Percept. 1, 2–8. doi: 10.1007/s12078-007-9004-y

Rabin, M. D. (1988). Experience facilitates olfactory quality discrimination. Percept. Psychophys. 44, 532–540. doi: 10.3758/BF03207487

Robin, O., Alaoui-Ismaili, O., Dittmar, A., and Vernet-Maury, E. (1999). Basic emotions evoked by eugenol odor differ according to the dental experience. A neurovegetative analysis. Chem. Senses 24, 327–335. doi: 10.1093/chemse/24.3.327

Rolls, E. T., and McCabe, C. (2007). Enhanced affective brain representations of chocolate in cravers vs. non-cravers. Eur. J. Neurosci. 26, 1067–1076. doi: 10.1111/j.1460-9568.2007.05724.x

Rouby, C., and Bensafi, M. (2002). “Is there a hedonic dimension to odors?” in Olfaction, Taste, and Cognition, eds C. Rouby, B. Schaal, D. Dubois, R. Gervais, and A. Holley (Cambridge: Cambridge University Press), 140–159. doi: 10.1017/CBO9780511546389.015

Schaal, B., Rouby, C., Marlier, L., Soussignan, R., Kontar, F., and Tremblay, R. E. (1998). “Variabilité et universaux au sein de l'espace perçu des odeurs: approches inter-culturelles de l'hédonisme olfactif” in Géographie des Odeurs, eds R. Dulau and J.-R. Pitte (Montréal, QC: L'Harmattan), 25–47.

Small, D. M., Zatorre, R. J., Dagher, A., Evans, A. C., and Jones-Gotman, M. (2001). Changes in brain activity related to eating chocolate: from pleasure to aversion. Brain J. Neurol. 124, 1720–1733. doi: 10.1093/brain/124.9.1720

Sobel, N., Prabhakaran, V., Desmond, J. E., Glover, G. H., Goode, R. L., Sullivan, E. V., et al. (1998). Sniffing and smelling: separate subsystems in the human olfactory cortex. Nature 392, 282–286. doi: 10.1038/32654

Stevens, D. A., and O'Connell, R. J. (1995). Enhanced sensitivity to androstenone following regular exposure to pemenone. Chem. Senses 20, 413–419. doi: 10.1093/chemse/20.4.413

Stevenson, R. J. (2001). The acquisition of odour qualities. Q. J. Exp. Psychol. A 54, 561–577. doi: 10.1080/713755972

Wright, C. I., Fischer, H., Whalen, P. J., McInerney, S. C., Shin, L. M., and Rauch, S. L. (2001). Differential prefrontal cortex and amygdala habituation to repeatedly presented emotional stimuli. Neuroreport 12, 379–383. doi: 10.1097/00001756-200102120-00039

Wysocki, C. J., Dorries, K. M., and Beauchamp, G. K. (1989). Ability to perceive androstenone can be acquired by ostensibly anosmic people. Proc. Natl. Acad. Sci. U.S.A. 86, 7976–7978. doi: 10.1073/pnas.86.20.7976

Zajonc, R. B. (1968). Attitudinal effects of mere exposure. J. Pers. Soc. Psychol. 9, 1–27. doi: 10.1037/h0025848

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 11 February 2014; Accepted: 21 March 2014; Published online: 10 April 2014.

Citation: Ferdenzi C, Poncelet J, Rouby C and Bensafi M (2014) Repeated exposure to odors induces affective habituation of perception and sniffing. Front. Behav. Neurosci. 8:119. doi: 10.3389/fnbeh.2014.00119

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Ferdenzi, Poncelet, Rouby and Bensafi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

REVIEW ARTICLE
published: 07 July 2014
doi: 10.3389/fnbeh.2014.00238

Properties and mechanisms of olfactory learning and memory

Michelle T. Tong[1][*], Shane T. Peace[2] and Thomas A. Cleland[1]

1Computational Physiology Lab, Department of Psychology, Cornell University, Ithaca, NY, USA

2Computational Physiology Lab, Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, USA

* Correspondence: Michelle T. Tong, Department of Psychology, Uris Hall, Cornell University, Ithaca, NY 14853, USA e-mail: tt389@cornell.edu

Edited by:
Anne-Marie Mouly, Centre de Recherche en Neurosciences de Lyon, France

Reviewed by:
Justus V. Verhagen, The John B. Pierce Laboratory, USA
Carolyn Harley, Memorial University of Newfoundland, Canada

Memories are dynamic physical phenomena with psychometric forms as well as characteristic timescales. Most of our understanding of the cellular mechanisms underlying the neurophysiology of memory, however, derives from one-trial learning paradigms that, while powerful, do not fully embody the gradual, representational, and statistical aspects of cumulative learning. The early olfactory system—particularly olfactory bulb—comprises a reasonably well-understood and experimentally accessible neuronal network with intrinsic plasticity that underlies both one-trial (adult aversive, neonatal) and cumulative (adult appetitive) odor learning. These olfactory circuits employ many of the same molecular and structural mechanisms of memory as, for example, hippocampal circuits following inhibitory avoidance conditioning, but the temporal sequences of post-conditioning molecular events are likely to differ owing to the need to incorporate new information from ongoing learning events into the evolving memory trace. Moreover, the shapes of acquired odor representations, and their gradual transformation over the course of cumulative learning, also can be directly measured, adding an additional representational dimension to the traditional metrics of memory strength and persistence. In this review, we describe some established molecular and structural mechanisms of memory with a focus on the timecourses of post-conditioning molecular processes. We describe the properties of odor learning intrinsic to the olfactory bulb and review the utility of the olfactory system of adult rodents as a memory system in which to study the cellular mechanisms of cumulative learning.

Keywords: olfactory bulb, synaptic plasticity, generalization, representational learning, behavior

1. INTRODUCTION

Odor learning, like all learning, is distributed across multiple regions of the brain. Studies of learning within associative brain regions such as the hippocampus and prefrontal cortex—particularly in rodents—can utilize and manipulate olfactory stimuli just as they do other forms of sensory input (Eichenbaum et al., 1996; Eichenbaum, 1998; Law and Smith, 2012; Peters et al., 2013). Importantly, however, a substantial component of odor learning is intrinsic to the olfactory bulb (OB), and to its interactions with the piriform (olfactory) cortex to which OB mitral cells (second-order sensory neurons of the OB) project (Figure 1). Within OB proper, several lines of evidence, including N-methyl-D-aspartate (NMDA)-based synaptic plasticity (Wilson, 1995; McNamara et al., 2008), the long-term potentiation of ascending piriform pyramidal projections onto OB granule cells (Gao and Strowbridge, 2009) and odor memory persistence linked to the selective retention of adult-born interneurons in the OB (Moreno et al., 2009; Kermen et al., 2010; Sultan et al., 2010) indicate that the OB itself supports sophisticated intrinsic plasticity mechanisms that regulate the transformation of olfactory signals across the first principal sensory synapse.

FIGURE 1. Circuit diagram of the mammalian olfactory bulb. The axons of olfactory sensory neurons (OSNs) expressing the same odorant receptor type converge and arborize together to form glomeruli (shaded ovals; two depicted) on the surface layer of the olfactory bulb. Intrinsic OB interneurons innervate each glomerulus, including olfactory nerve driven periglomerular cells (PGo), external tufted cell-driven periglomerular cells (PGe), and external tufted cells (ET). Superficial short-axon cells (sSA), closely related to PG cells and possibly part of the same heterogeneous population, are not associated with specific glomeruli but project broadly and laterally within the deep glomerular layer. Principal neurons include mitral cells and tufted cells (collectively depicted as M/T), which interact via reciprocal connections in the external plexiform layer (EPL) with the dendrites of inhibitory granule cells (Gr), thereby receiving recurrent and lateral inhibition, and project out of the OB to several regions of the brain. The heterogeneous deep short-axon cell population (dSAC) includes cells that deliver GABAergic inhibition onto granule cells and one another, and, along with granule cells, receive centrifugal cortical input from piriform pyramidal cells. OE, olfactory epithelium (in the nasal cavity); GL, glomerular layer; EPL, external plexiform layer; MCL, mitral cell layer; IPL, internal plexiform layer; GCL, granule cell layer. Filled triangles denote excitatory synapses; open circles denote inhibitory synapses. Speckles surrounding OSN terminals denote volume-released GABA and dopamine approaching presynaptic GABAB and dopamine D2 receptors. Note that sSA-PG and sSA-sSA synapses are depicted as excitatory despite being GABAergic (discussed in Cleland, 2014). Figure adapted from Cleland et al. (2012).

The elucidation of these intrinsic learning mechanisms within OB presents both theoretical and practical opportunities. While the OB is highly interconnected with multiple cortical and subcortical regions, it is morphologically isolated. This facilitates, for example, the specific delivery of neurochemicals or virally-packaged transgenes to the OB via cannulation. The neural circuitry of the OB and the physiology of its diverse neurons are reasonably well-described (Figure 1), enabling the development of biophysically realistic models of OB function that can associate specific cellular properties and mechanisms with systems-level function and performance (Migliore and Shepherd, 2002; Li and Cleland, 2013). Specific odor-dependent behavioral paradigms have been developed that are strongly sensitive to OB manipulations and are likely to depend on OB intrinsic learning, enabling some segregation of OB-specific learning from odor learning dependent on other brain regions (Wilson and Linster, 2008). As the direct target of primary sensory neurons, the OB responds to, and differentiates among, the physical stimulus representations of odorants, but also closely apposes these bottom-up inputs with powerful top-down state-dependent and neuromodulatory influences. The olfactory system thus provides a powerful model to study representational learning (Bieszczad and Weinberger, 2010)—that is, the integrated effects of learning on the forms (or shapes) of neural representations as well as their persistence (see section 2.1). However, the OB remains underdeveloped as a model system for the neuroscientific study of learning and memory circuits. Critical elements such as the factors influencing memory persistence, the mechanistic differences between associative and nonassociative conditioning, and the signature molecular mechanisms of cellular and synaptic learning have been observed in OB but require further exploration. We here outline the features of OB-dependent intrinsic learning and review work on the structural and molecular mechanisms of memory formation, with a focus on the timecourses of learning-initiated signaling cascades and the roles of extracellular signals such as classical neuromodulators and the peptide brain-derived neurotrophic factor (BDNF). In particular, we discuss how research into representational, appetitive and cumulative learning mediated by plasticity in the olfactory system can most productively contribute to a broad understanding of general learning and memory mechanisms.

2. LEARNING IN THE OLFACTORY BULB

2.1. ODOR LEARNING IS REPRESENTATIONAL

Learning alters the transformation of information by a neural circuit, and memory refers to the persistence of that altered transformation function over time. In olfactory representational learning, the forms of odor representations are sensitive to learning and can be measured using behavioral generalization gradients (Cleland et al., 2002, 2009; Fernandez et al., 2009). Olfactory generalization gradients define the range of variance in odor quality that an animal will respond to as representative of a given odor, and reflect the statistical reliability of odor features (Wright and Smith, 2004). The area under the gradient, or consequential region (Shepard, 1987), describes the degree of certainty expressed by the animal that a stimulus of a given quality is likely to represent that learned odor or its implications. Increased pairings of odor with reward progressively sharpen the generalization gradient (Figure 2A), and manipulations of other training parameters indicate that factors that increase classical learning also increase the rate of sharpening of olfactory generalization gradients (Cleland et al., 2009). If the odor being paired with reward is itself variable in quality, however, it becomes clear that the generalization gradient does not sharpen per se, but progressively conforms to the actual environmental distribution of reward-predicting odor qualities as experienced by the animal (Cleland et al., 2012 Figure 2B). That is, the learning-dependent regulation of generalization gradients describes a statistical learning process by which an animal's internal odor representations become gradually and probabilistically categorical (Tenenbaum and Griffiths, 2001), evolving to correctly reflect the meaningful categories of the external olfactory environment (Cleland et al., 2012). This aspect of odor learning has been hypothesized to rely on OB circuitry both for theoretical reasons and based on results from the experimental manipulation of OB circuit function (Mandairon et al., 2006; Guérin et al., 2008; McNamara et al., 2008; Linster and Cleland, 2010; Devore and Linster, 2012; de Almeida et al., 2013; Dillon et al., 2013). Hence, in contrast to odorants—which are chemical stimuli, whether simple or complex—odors here are psychometrically defined as probability density functions of odorant quality that the animal has learned imply the same consequences, embedded within a high-dimensional similarity space that is best defined by odorant receptor activation levels (Cleland, 2008, 2014). Behaviorally-measured generalization gradients constitute one-dimensional trajectories within this high-dimensional space, essentially estimating the changing form of the odor representation via sampling. The key point is that memory content is not a constant, but changes with learning and over time just as memory strength and persistence do. By providing quantifiable, interpretable measures of memory content as it evolves, odor generalization gradients illustrate the advantages of representational learning systems for the study of learning and memory mechanisms.

FIGURE 2. Olfactory generalization gradients in mice. (A) Mice received either 3, 6, or 12 pairings of an odor CS with buried reward, after which their perseverance in digging following randomized presentation of the odor CS, a similar odorant (S1) and a dissimilar odorant (D) was measured. Increasing the number of training trials prior to testing progressively increased perseverance and sharpened associative generalization gradients. 3x: three training trials; 6x: six training trials; 12x: 12 training trials. Asterisks denote significant pairwise differences. Figure adapted from Cleland et al. (2009). (B) Generalization gradients adapt to the variance of the conditioning odor. Mice received 12 pairings of an odor CS with buried reward; this CS was either a 50:50 mixture of odorants C4 and C5 (Trained on 50:50 mix group) or was a variable mixture of the same two odorants, varying from 95:5 to 5:95 on different trials (Trained on high variance group). The high-variance training group generalized significantly more broadly than the low-variance training group. Abscissa comprises a sequentially-similar homologous series of different odorants (C3, C4, C5, C6) and a dissimilar odorant (D). Figure adapted from Cleland et al. (2012).

2.2. APPETITIVE ODOR LEARNING IS CUMULATIVE AND INCREMENTAL

The cellular and synaptic neurophysiology of mammalian learning and memory is substantially based on fear conditioning. The advantage of the conditioned fear model is that strong, discrete, and easily measurable memories can be generated by single learning trials, avoiding the complexity and additional questions imposed by the need to integrate the cumulative effects of multiple learning events. The persistence of these memories is a function of the unconditioned stimulus amplitude—e.g., footshock current—but commonly extends to several days (Bekinschtein et al., 2007), enabling study of the sequential transitions in their structural, biochemical, and molecular substrates that occur over time. The clearest example of these gradually transforming dependencies is the protein synthesis requirement for long-term (many hours to days or more) but not short-term (up to a few hours) memory (Davis and Squire, 1984; DeZazzo and Tully, 1995), though additional phases of memory have been defined in some systems. Moreover, many specific cellular signaling cascades, induced by fear conditioning events and underlying the relevant learning, have been described; whereas most of these processes are initiated immediately after the causal event, several have been described that are initiated minutes or even hours later (Figure 3).

FIGURE 3. Timecourses of activity for learning-associated molecular mechanisms in hippocampal tissue following one-trial inhibitory avoidance (IA) training. Leftmost dashed vertical line (marked with yellow triangle) denotes the time of IA conditioning. Subsequent vertical lines denote timepoints following the trial. Rows correspond to particular mechanisms (signaling molecule, receptor, or neuromodulator). Downward-pointing arrows indicate timepoints when antagonists to the corresponding receptor or signaling pathway were infused into the hippocampus; solid arrows denote that the antagonists had an amnesic effect on LTM (when tested 24–48 h after conditioning); open arrows indicate no amnesic effect on LTM. Filled shapes denote the experimentally-measured expression levels of receptors or signaling molecules. Increasing or decreasing levels are depicted when known; rectangles depict measurable levels when relative abundances were not measured. Light green shapes represent intracellular signaling pathways, including Ca2+/calmodulin-dependent protein kinase II (CaMKII), cAMP response element-binding protein (CREB), protein kinase A (PKA), cyclic adenosine monophosphate (cAMP), and protein kinase C (PKC) (Data from: Jerusalinsky et al., 1994; Wolfman et al., 1994; Bernabeu et al., 1996, 1997a,b; Bevilaqua et al., 1997; Cammarota et al., 1997; Izquierdo et al., 1997). White shapes represent ionotropic receptors for neurotransmitters, including γ-Aminobutyric acid A (GABAA) receptors, N-methyl-D-aspartate (NMDA) receptors, and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors (Data from: Izquierdo et al., 1992; Jerusalinsky et al., 1992; Bernabeu et al., 1997c). Gray shapes represent the activity of receptors for neuromodulators, including β-noradrenergic receptors, dopamine type 1 receptors (D_1R), and serotonin 1A receptors (5-HT1A) (Data from: Izquierdo et al., 1992; Bernabeu et al., 1996; Bevilaqua et al., 1997). Blue shapes represent brain-derived neurotrophic factor (BDNF; Data from Bekinschtein et al., 2007).

Olfactory appetitive learning, in contrast, is gradual, cumulative, and statistical (Cleland et al., 2009, 2012 Figure 2). The richness of the OB learning model that is gained by its statistical and representational character also imposes a cost in terms of unavoidable complexity. For example, the distribution of repeated training events in time will always be a factor; massed versus spaced learning schedules are well known to affect memory persistence (Tsao, 1948; Menzel et al., 2001; Kermen et al., 2010), and intertrial interval timing can even determine which areas of the brain are most immediately responsible for nonassociative odor learning (McNamara et al., 2008). This complexity, however, is manageable, and is substantially mitigated by the theoretical tractability and experimental accessibility of the OB as well as the elucidation of plasticity-related molecular cascades in other cortical memory systems. Studies of representational plasticity and memory processes in primary and secondary sensory cortices offer a singular opportunity to understand a new dimension of memory—plasticity in form as well as persistence—in preparations within which its form can be measured physiologically as well as psychophysically (Bieszczad and Weinberger, 2010). This is critical for a mechanistic understanding of statistical learning, in which repeated stimulus experiences are not identical and part of the challenge of learning is to estimate the intrinsic variability of meaningful stimuli and the relationship between stimulus quality and outcome.

It is worth noting that odor learning in OB is not always gradual and cumulative. Fear conditioning to odorants induces strong one-trial learning, which appears to involve plasticity-related molecular changes in the OB (Jones et al., 2007). However, this aversive learning appears to substantially broaden olfactory generalization, rather than sharpen it as is observed in appetitive odor learning (Chen et al., 2011). This can be interpreted as adaptive, in that broad generalization is the safe, conservative response to a dangerous odor of uncertain variance, but it raises important mechanistic questions: what would be the cumulative effect of multiple aversive conditioning trials, for example, or how would this broad generalization gradient interact with strong, preexisting, non-aversive odor representations? It also suggests that the mechanisms underlying the effects of aversive conditioning in OB may differ qualitatively from those underlying appetitive conditioning at some level of organization.

Odor learning in neonates also is qualitatively different from that described in adults. First, neonatal odor learning is more heavily OB-dependent than it is in adults, in part because downstream learning areas such as the amygdala are not yet functional (Berdel et al., 1997; Sullivan, 2003). Second, neonatal odor learning is substantially stronger and less conditional than it is in adults, exhibiting a nearly all-or-none quality that contributes to the rapid learning of maternal and nest odors (Sullivan et al., 1991, 2000). Accordingly, studies of the molecular cascades and mechanisms underlying intrinsic OB learning are most advanced in neonates (McLean and Harley, 2004; McLean et al., 2005; Grimes et al., 2012; Lethbridge et al., 2012). Many of these mechanisms, however, are likely to be conserved in some form to underlie the incremental appetitive learning exhibited by adults. For example, norepinephrine (NE) and the intracellular cascades that it initiates play a central role in neonatal olfactory learning (Sullivan et al., 1989; Yuan et al., 2003). Indeed, the innate hyperresponsivity of the neonatal locus coeruleus (LC) is the underlying mechanism of one-trial odor learning in neonates (Sullivan, 2001, 2003; Moriceau and Sullivan, 2005), and NE delivery to the OB is sufficient to induce odor preference learning in neonates (see section 4.1). In adults, in contrast, LC responsivity is much more measured and conditional. Nevertheless, NE in the adult OB is essential for even the basic nonassociative learning processes underlying odor habituation (Guérin et al., 2008; Shea et al., 2008; Moreno et al., 2012), and selective NE receptor antagonists infused into OB impair conditioned odor preference learning, recognition memory, and near-threshold odor identification (Guérin et al., 2008; Escanilla et al., 2010, 2012; Linster et al., 2011; Manella et al., 2013). In this way, the cellular mechanisms of memory elaborated by one-trial learning paradigms can serve as the basis for study of the more complex problem of ongoing appetitive learning.

2.3. ADVANTAGES OF OLFACTORY BULB LEARNING MODELS

The OB provides both practical and theoretical advantages for study of the molecular and structural mechanisms involved in memory. Practically, pharmacological agents can be infused selectively and locally into the OB. Intrinsic OB circuits display functional plasticity similar to other regions of the brain, including long-term synaptic potentiation (Gao and Strowbridge, 2009) and adult neurogenesis (Lledo et al., 2006), and are reconfigured substantially by neuromodulatory inputs (Devore and Linster, 2012). Established behavioral paradigms enable insight into the changing form as well as the persistence of odor representations over time, and physiological studies enable measurements of direct correspondence between environmental changes, behavioral performance, and the synaptic and molecular changes that occur in neural circuitry (Abraham et al., 2012, 2014; Qiu et al., 2014). In particular, odor learning exhibits varying memory durations that are related to behavioral task parameters and depend on evolving physiological substrates for short-term memory (Figure 4; McNamara et al., 2008), intermediate-term memory (Grimes et al., 2011), and long-term memory (Figure 5; Lazarini and Lledo, 2011).

FIGURE 4. Olfactory generalization gradients measured at various latencies after conditioning. (A) Progressive decay of a newly learned olfactory generalization gradient over an STM timescale. Mice received 12 massed training trials in which they dug in a dish of sand scented with a 1.0 Pa conditioned odor to retrieve a food reward. Separate cohorts of conditioned mice then were tested at different latencies for their perseverative responses (digging times) to the odor CS, a highly similar odorant S1, a moderately similar odorant S2, and a structurally and perceptually different odorant D. Responses declined and generalization gradients flattened with greater training-testing latencies. Methodology follows that of Cleland et al. (2009). Four of six latencies tested are depicted for clarity. (B) Lin-log plot of digging time in the CS during testing at all six latencies tested (2, 10, 30, 60, 180, 1440 min). Data are fit with the regression line y = −0.687ln(x) + 9.278, R^2 = 0.683.

FIGURE 5. Learning curves for an odor discrimination task. (A) Mice received 20 trials of discrimination training (Cleland et al., 2002) in which they learned to choose a rewarded conditioned odor (1.0 Pa) over a distractor odor (Day 1). Twenty-four hours later, the discrimination training was repeated (Day 2). The correct trials were scored and averaged across animals. Trials are grouped into 4 blocks of 5 consecutive trials for display and analysis. A steady improvement across trials on Day 1 is remembered 1 day later. (B) Data from trial blocks 1 and 4 replotted for comparison. Comparing trial block 1 (trials 1–5) between days, mice performed significantly better on the second training session, indicating a robust retention of odor memory [asterisk: t(8) = 7.5593, p < 0.001]; no other comparisons approach statistical significance. Comparable findings have been observed by Schellinck et al. (2001).

2.3.1. Habituation and cross-habituation

In this non-associative olfactory learning paradigm, animals first are habituated to an odorant, responding to repeated presentations with progressively lower investigation times. Some time after habituation, they are presented again with that odorant, or with a series of structurally and perceptually similar odorants (cross-habituation, also referred to as spontaneous discrimination). Perceptually distinct odors elicit normal, non-habituated investigation times, but odorants similar to the habituated odorant elicit reduced, partially-habituated responses depending on the degree of similarity between the habituated and test odorants. A generalization gradient therefore can be constructed by presenting a battery of similar odorants to habituated animals and measuring the pattern of cross-habituation among odorants (Cleland et al., 2002). Interestingly, memory for odorant habituation acquired on short timescales (tens of seconds) is predominantly mediated within piriform cortex (Wilson, 2009), whereas habituation on the minutes timescale is localized within OB (McNamara et al., 2008; Chaudhury et al., 2010). Habituation and cross-habituation memory persistence is sensitive to the degree of habituation, declining over a 10–20 min period in standard protocols (Freedman et al., 2013). Both the extent and persistence of cross-habituation memory are regulated by neuromodulatory and hormonal effects in the OB as well as task parameters and state variables (Mandairon et al., 2006, 2008; Dillon et al., 2013; Manella et al., 2013).

2.3.2. Associative generalization

Generalization gradients also can be measured in response to odorants that are conditioned via associative pairing with reward (Cleland et al., 2002, 2009). After conditioning, animals are tested with batteries of structurally and perceptually similar odorants, often in a digging task where the odorant cue signals a buried reward. The animals' perseverance, measured as time spent digging, in pursuit of an expected reward (that is not present in test trials) declines with increasing perceptual dissimilarity between the conditioned and test odorants. The breadths and forms of these gradients are sensitive to determinants of learning and to the statistical variance in odorant CS quality across conditioning trials (Cleland et al., 2009, 2012) and also are sensitive to the pharmacological and neuromodulatory manipulation of OB circuitry (Zimering and Cleland, 2011). Associative odor learning based on a standard short-term conditioning paradigm (a single series of up to twelve massed conditioning trials) progressively decays over a timescale of several hours (Figure 4), though this timescale is likely to be sensitive to training parameters.

2.3.3. Odor discrimination

Odor discrimination is the most commonly used olfactory learning model, and subsumes many radically different conditioning paradigms and performance metrics. The distinguishing feature of this task is that animals are motivated to distinguish between two or more odors with different learned contingencies (e.g., one is rewarded and the other not), such that it tends to measure an animal's capacity to learn a given discrimination rather than to measure an odor representation per se. Automated tasks with relatively nonintuitive metrics (e.g., odor-specified left-right selection or go/no-go tasks) may utilize hundreds of training trials, whereas tasks with more intuitive (to the animal) metrics such as odor-cued digging often require less than 20 trials to reach criterion. The dependence of odor discrimination performance on OB circuitry corresponds closely with the difficulty of the discrimination (Rinberg et al., 2006), which corroborates theoretical proposals that OB circuitry serves in large part to identify which statistical differences among inputs correspond to meaningfully different odorants, and which are simply variations of a single odor that should be generalized (Cleland et al., 2012).

2.3.4. Olfactory performance depends on memory

In olfaction, memory does not serve only to remember odors past, but is also a critical factor in realtime perceptual processing, even within OB and piriform cortex (Wilson and Stevenson, 2003a,b; Zucco et al., 2014). Hence, short-term and long-term memory processes are likely to be highly interactive and conditional; e.g., the form of a long-term memory should acquire the evolving characteristics of accumulating short-term memory processes during multitrial odor learning tasks or natural learning scenarios. That is, though it is established in general that STM and LTM processes are initiated separately—i.e., LTM is not simply a continuation of STM (Figure 6; Izquierdo et al., 1999)—it also is true that LTM must be able to be repeatedly updated based on new information even before it is first behaviorally expressed. One likely scenario is that short-term learning and memory processes contribute to this updating—a hypothesis that the olfactory appetitive learning and memory model is well-poised to test.

FIGURE 6. Diagram of the separate molecular pathways underlying short-term and long-term memory as derived from studies using single-trial aversive training paradigms. Leftmost dashed vertical line (marked with yellow triangle) denotes the the time of conditioning; subsequent vertical lines denote timepoints following the trial. The lower diagram illustrates the theoretical bifurcation of memory mechanisms into a protein synthesis-independent STM stream that governs behavior for the first few hours after training and a separate, protein synthesis-dependent LTM stream that begins to govern behavior only thereafter. The relationship, if any, between these two streams during multiple repeated conditioning trials is not clear.

3. MOLECULAR AND STRUCTURAL MECHANISMS OF LEARNING AND MEMORY

Memory mechanisms are heterogeneous in form, structure, and timecourse, yet exhibit many commonalities across regions of the brain. We here separate these mechanisms into two broad categories: molecular, which includes intracellular cascades, molecular signaling, neuromodulatory influences, activity-dependent protein synthesis, and epigenetic modifications, and structural, which includes physiological changes such as long-term potentiation or other synaptic weight modifications, alterations to neuronal morphology such as dendritic branching, changes to terminal shapes or numbers, and ancillary modifications such as effects on glia or cell adhesion to the extracellular matrix, as well as changes to neuron number via adult neurogenesis or selective apoptosis. Mechanisms from these categories often are interdependent, and exhibit characteristic response timecourses that underlie memory-related changes. In this section, we review selected learning models and mechanisms drawn primarily from the hippocampal literature, focusing on models with well-developed response timecourses and signaling mechanisms for which there is evidence of relevance to OB learning as well.

3.1. MOLECULAR MECHANISMS

Inhibitory avoidance (IA) is a well-established behavioral paradigm for one-trial fear conditioning that offers a simple analog measure of memory “strength.” IA memories can persist strongly for days, enabling study of both short-term and long-term memory mechanisms. If entering a darkened chamber or stepping down from a platform results in footshock on the conditioning trial, a normal animal will hesitate, in subsequent test trials, before again entering that chamber or stepping down. The delay in seconds before again entering the chamber or stepping down is a robust measure of the strength of the action-consequence association. Much of what is known about the molecular mechanisms of memory and their timecourses in mammalian systems has been developed using this task.

3.1.1. Long-term memory

IA conditioning leads to a rapid elevation in calcium/calmodulin-dependent protein kinase II (CaMKII) levels in the hippocampus. This in turn enhances the phosphorylation of cyclic AMP (cAMP) response element binding protein (CREB) (Miyamoto, 2006) and promotes the formation of complexes with ionotropic glutamate NMDA receptors (Sanhueza and Lisman, 2013), which have been shown to play a functional role in learning and memory (reviewed in Danysz et al., 1995). Blocking CaMKII activity immediately after IA training substantially reduced animals' fear responses when measured 24 h later (i.e., LTM). However, blocking CaMKII activity 30 min after IA resulted in a weaker LTM deficit, and blockade 2–4 h after IA had no effect on LTM at all (Figure 3; Wolfman et al., 1994). These findings indicate that CaMKII plays a crucial role early in the memory induction process, and that its functional role in LTM formation is confined to a specific period following learning. The neurotrophin BDNF also plays a critical signaling role in LTM induction (Figure 3). For example, blockade of BDNF signaling through its TrkB receptor, or through function-blocking anti-BDNF, disrupted LTM but not STM for a conditioned IA event, whereas infusion of recombinant BDNF into hippocampus rescued IA memory from amnesia induced by glucocorticoid receptor blockade (Chen et al., 2012).

Other studies have demonstrated the early involvement of the cAMP–protein kinase A (PKA)–CREB pathway in LTM formation. Cyclic AMP levels in the hippocampus begin to rise about 30 min following IA training, peak at 3 h after training, and decrease to baseline levels circa 6 h after training (Bernabeu et al., 1996, 1997a). PKA activity and CREB phosphorylation (pCREB levels), in contrast, both exhibit two distinct peaks: one immediately following training and another beginning roughly 3 h thereafter and persisting until 6 h, but not 9 h, post-conditioning. The second of these peaks coincides with peak hippocampal cAMP levels (Bernabeu et al., 1997a). Injection of the PKA inhibitor KT5720 into the hippocampus 0–6 h after conditioning impairs IA memory when tested 24 h after training (Bernabeu et al., 1997a). Similarly, injections of CREB antisense oligonucleotides into the amygdala impaired LTM in the IA task (Canal et al., 2008), and infusions of antisense CREB oligonucleotides into the hippocampus prior to water maze training blocked 48-h LTM while sparing 4-h STM (Guzowski and McGaugh, 1997). Mutant mice that lack the α and β isoforms of CREB also exhibit impaired LTM consolidation, but normal STM, on a contextual fear conditioning task (Bourtchuladze et al., 1994).

In a multi-trial, appetitive learning paradigm based on the radial arm maze task, increased PKA activity and CREB phosphorylation levels were observed in the hippocampus after the fourth consecutive day of training, but not after the first day, in contrast to the immediate same-day effects observed in IA studies (Mizuno et al., 2002). A similar contrast between IA conditioning and appetitive learning effects has been described with learning-associated BDNF activation. Infusions of function-blocking anti-BDNF antibody into the hippocampus prior to, but not 6 h after, IA training block LTM, indicating that BDNF activity around the time of learning sets the stage for eventual LTM consolidation (Alonso et al., 2002). In contrast, on an appetitive radial arm maze task, BDNF mRNA levels in hippocampus increased only after 8, but not 4, consecutive days of conditioning, and even then mRNA levels were significantly elevated only after 15 min, but not immediately, following training (Mizuno et al., 2002). A similar increase in BDNF mRNA levels, with a comparable 15-min delay, also was observed after 28 days of training with this task (Mizuno et al., 2000). (It remains unclear whether the levels observed at 8 and 28 days represent a continuous elevation in task-induced BDNF mRNA transcription across those days or reflect multiple peaks in BDNF mRNA activity). These findings suggest that similar molecular mechanisms can mediate multi-trial appetitive learning as underlie fear-based single-trial learning, but that the timecourses can differ. It is in these latter contingencies that the richness of appetitive learning studies is likely to contribute most significantly to general studies of learning and memory. Should LTM be modeled as a statistical evidence accumulation system, in which LTM is formed only after enough evidence has accumulated that the cue-reward association is reliable and likely to remain true over time? How is this compatible with the evidence that, in IA training, LTM induction is initiated immediately after learning (and is not dependent upon intact STM), even though it cannot govern behavioral responses until hours later? Once LTM is induced, how is the persistence of that memory governed? How does existing LTM contribute to STM formation over multi-day training sequences? What factors contribute to the timescales, selectivity, and stringency of new memory formation?

3.1.2. Short-term memory

The formation and maintenance of short-term memory (STM), which are independent of protein synthesis, rely on different molecular mechanisms than those underlying LTM (Izquierdo et al., 1999). To elucidate these different mechanisms, animals were conditioned using the IA protocol, immediately infused with one of a battery of antagonists into the hippocampus, and behaviorally tested for memory retention at 1.5 h (STM) and 24 h (LTM) after training (Izquierdo, 2000). The study showed that STM formation required cyclic GMP (cGMP), mitogen-activated protein kinase kinase (MAPKK), and PKA, but did not depend on protein kinase G (PKG), protein kinase C (PKC), or CaMKII. (Note that this dependence of STM on PKA differs from the PKA-independent STM of neonatal OB as described above). Additionally, infusions of either an AMPA receptor antagonist or a γ-aminobutyric acid (GABA) subtype A receptor agonist into the entorhinal cortex prior to training impaired STM when tested at 1.5 h following training, but did not impair LTM when tested 24 h after conditioning (Izquierdo et al., 1998). Critically, these results demonstrated that LTM does not derive from STM representations, in that LTM formation does not depend on intact STM. Instead, at least two distinct cascades of events are set in motion after training (Figure 6), one of which enables rapid behavioral adjustment but decays in several hours (STM), and the other of which is longer-lasting but cannot be behaviorally expressed for the first few hours after conditioning (LTM). Interestingly, the distinct STM and LTM pathways - and many of their mechanistic elements - are common across widely divergent clades, including mollusks and insects as well as vertebrates (Davis and Squire, 1984; DeZazzo and Tully, 1995; Blum et al., 2009), suggesting that these properties have been strongly conserved.

A distinct, intermediate phase of memory, termed intermediate-term memory (ITM), also has been defined, originally in Aplysia californica. It is characterized primarily by its dependence on protein translation but not on transcription (Sutton et al., 2001, 2002), although a separate, mechanistically distinct form of ITM in Aplysia also has been described (Sutton et al., 2004). Though the timescales of these memory phases in Aplysia differ from their mammalian analogs, a translation-dependent, transcription-independent ITM for conditioned odor preference also has been identified in neonatal rat OB (Grimes et al., 2011). Infusion of anisomycin, a translation blocker, immediately after conditioning had no effect on odor memory when the rat pups were tested one or 3 h later, but eliminated the memory when tested 5 or 20 h after conditioning. When actinomycin, a transcription blocker, was similarly infused, memory at 1, 3, and 5 h was comparable to control animals, but an impairment of odor memory was observed at 24 h. It is likely that the mechanism underlying memory during this intermediate period (~5 h after conditioning) is simply the LTM mechanism; that is, it is in this time window that new proteins begin to be required for memory maintenance, but the translation of existing mRNA transcripts provides a sufficient supply for a limited time. This interpretation is supported by subsequent results demonstrating that PKA blockade blocks 5-h ITM and 24-h LTM, but not 3-h STM (Grimes et al., 2012).

3.2. STRUCTURAL MECHANISMS

Long-term memory has long been associated with persistent structural changes in specific brain regions involved in the formation of the memory. Many of the molecular mechanisms characterized in LTM induction and maintenance also have been shown to influence these structural changes, which may in some cases be the primary effectors of the memory. We briefly review some of these structural mechanisms here.

3.2.1. Long-term potentiation

Over decades of research, considerable debate has arisen about whether, and to what extent, long-term synaptic potentiation (LTP, Bliss and Lømo, 1973) underlies or otherwise corresponds to behaviorally-measured LTM (Izquierdo, 1993). The arguments in favor of their relationship were strengthened by the elucidation of two distinct forms of hippocampal long-term plasticity (LTP), a short-duration early form (E-LTP) and a longer-lasting late form (L-LTP) distinguished primarily by the latter's dependence on protein synthesis. Specifically, the persistence of LTP in the CA1 region beyond roughly 4 h depends on mRNA and protein synthesis (Frey et al., 1988; Bliss and Collingridge, 1993); translation blockers injected into rat dentate gyrus during in vivo LTP induction caused synaptic potentiation to decay within 3–4 h (Krug et al., 1984). This timescale closely resembles the protein-synthesis dependency of LTM observed in behavioral studies. Similarly, after LTP induction by a tetanic stimulation of afferent fibers in hippocampal slices, any further tetanus to the afferent within 3 h generates only short-term plasticity across the synapse, whereas after 4 h the same tetanus could generate a longer-lasting potentiation over and above the initially induced LTP level (Frey et al., 1995). The timescale of this effect also corresponds with the STM/LTM distinctions described above, and additionally suggests that LTM expression may free up resources needed for further learning. Finally, several molecular mechanisms associated with memory induction and persistence also regulate LTP. For example, CaMKII activity is necessary for LTP induction (Malinow et al., 1989), PKC inhibition immediately following induction leads to early decay of potentiation (Wang and Feng, 1992), and PKA inhibition prior to LTP induction limits the persistence of LTP to roughly 3 h (Frey et al., 1993). BDNF also facilitates the induction of LTP in hippocampal slices (Korte et al., 1995), and the application of BDNF in the presence of protein synthesis inhibitors is sufficient to transform a short-lasting LTP to a longer-lasting form (Lu et al., 2008), suggesting a role for BDNF in the determination of long-term functional plasticity that is comparable to its necessary and sufficient role in determining LTM persistence (Bekinschtein et al., 2008). Moreover, blockade of BDNF signaling immediately following LTP induction reduced LTP persistence. Specifically, LTP induction in slices generated a transient peak in the phosphorylated form of the TrkB receptor for BDNF; pTrkB levels rose 15 min following induction, peaked at 30 min, and slowly declined to baseline over 2 h (Lu et al., 2011). Preventing TrkB activation with TrkB-IgG at the 30-min peak, but not at 60 min post-induction, inhibited persistent LTP (Kang et al., 1997). The timecourses of these interactions also correspond to those of the early biochemical cascades involved in LTM formation as discussed above.

3.2.2. Neuronal and synaptic morphology

Changes in neuronal morphology, such as the growth of new dendritic spines, have been shown to accompany novel experiences (Leggio et al., 2005; Jung and Herms, 2014). Importantly, the stabilization of new dendritic spines underlies at least some LTMs (Yang et al., 2009), indicating that durable modifications of the synaptic weights within neuronal networks mediated by physical spines is a structural mechanism underlying memory persistence (reviewed in Ramiro-Cortés et al., 2014; Sotelo and Dusart, 2014). The specific roles of these morphological elements are further emphasized by the dependence of LTM on intact cytoskeletal dynamics (Lamprecht, 2014). Notably, BDNF and other neurotrophins associated with memory regulation have been strongly implicated in the modification and maintenance of both synaptic efficacy and dendritic morphology (reviewed in McAllister et al., 1995; Castello et al., 2014; Zagrebelsky and Korte, 2014).

3.2.3. Adult neurogenesis

Learning and memory in the hippocampus and olfactory bulb also are associated with the incorporation of new adult-born neurons. The proliferation of new neurons ceases prior to adulthood in most brain regions, with the exception of the hippocampus and OB, and possibly the hypothalamus (Cheng, 2013). Hippocampal progenitor cells are produced in the subgranular zone (SGZ) of the hippocampus and migrate a short distance to the granule cell layer of the dentate gyrus (DG); in contrast, OB progenitor cells are produced in the subventricular zone (SVZ) and migrate to the OB along the rostral migratory stream for 10–14 days before arriving in the OB and differentiating within the granule cell and glomerular layers (Petreanu and Alvarez-Buylla, 2002). The observation that olfactory learning increases the odor-specific survival of adult-born neurons in OB (Alonso et al., 2006; Kermen et al., 2010; Sultan et al., 2010) and, conversely, that the selective activation of these adult-born neurons facilitates olfactory performance and memory (Alonso et al., 2012), has led to a broad and well-supported hypothesis that adult neurogenesis underlies LTM in OB as it does in the hippocampus (reviewed in Sahay et al., 2011; Gheusi et al., 2013; Lepousez et al., 2013). However, the observation that this constant integration of new neurons does not result in a progressively increasing total neuron number in the OB (Mouret et al., 2008) suggests that these new neurons may be relatively short-lived, or may replace older neurons, or both, rendering unclear some essential aspects of the role of adult neurogenesis in long-term odor memory within OB.

In the hippocampus, environmental enrichment and experience increase the survival rates of adult-generated neurons within the dentate gyrus (Kee et al., 2007; Tashiro et al., 2007). Moreover, critically, the selective destruction of adult-born neurons that recently had been incorporated into the hippocampal network impaired spatial memory in the Morris water maze task when animals were tested seven days after training (Arruda-Carvalho et al., 2011). This latter result indicates that these newly-incorporated neurons were substantially mediating the new spatial memory; indeed, it has been suggested that adult-born neurons in HPC are employed specifically for new learning (i.e., initial acquisition), as opposed to the expression or reacquisition of memory (Anderson et al., 2011). A similar principle is emerging in the OB, within which the selective ablation of newly-incorporated adult-born neurons following appetitive odor conditioning eliminated animals' memory for that odor (Akers et al., 2011).

Interestingly, some of the signaling mechanisms most strongly associated with LTM formation also appear to be involved in the learning-dependent survival of adult-born neurons. Besides a basic activity-dependence arising from glutamate and GABA receptor activation (Platel et al., 2010; Platel and Bordey, 2011), the survival of adult-born neurons is also enhanced by stimulation with NE (Veyrac et al., 2009; Moreno et al., 2012) or BDNF (Scharfman et al., 2005). For example, infusions of BDNF into the hippocampus, when delivered to adult rats over 2 weeks, increased the number of adult-born granule cells when compared against control animals infused with saline vehicle or bovine serum albumin (Scharfman et al., 2005). In heterozygous BDNF knockout mice, the number of surviving new neurons in the hippocampus did not change (despite increased proliferation in the SGZ); however, adult-born neurons continued to express markers of immature neurons as well as reduced dendritic growth, suggesting that reduced BDNF levels impaired their processes of maturation and differentiation. Other studies have emphasized a role for BDNF in the survival, rather than the proliferation or differentiation, of adult-born neurons (e.g., Sairanen et al., 2005).

4. MECHANISMS OF ODOR LEARNING IN THE OB

Odor learning in the OB offers rare opportunities to study the molecular and structural mechanisms of learning and memory in concert with well-controlled perceptual and behavioral tasks. During appetitive learning, OB circuitry integrates information about the statistical properties of the conditioned stimulus, perhaps also incorporating other features of the odor environment, and supports persistent representations of this learning. Insofar as has been studied, the molecular and structural determinants of OB memory appear similar to those described for hippocampal fear conditioning and other memory systems. The particular value of OB-dependent behavioral learning paradigms is that they enable study of these molecular and structural mechanisms in the more complex milieu of cumulative, multi-trial, representational learning, in which the instantiation of LTM is delayed and conditional in nature, and based on information acquired over time. The representational aspect of OB learning further enables study of how learning alters the form, as well as the strength and persistence, of acquired memories.

4.1. MOLECULAR MECHANISMS IN THE OB

Intrinsic memory mechanisms within the OB appear to share common pathways and adhere to similar pharmacologically-elaborated phases as have been elucidated in IA-based neural plasticity and memory studies. For example, PKA activity in the neonatal rat OB increases 10 min after one-trial olfactory appetitive conditioning, and blocking PKA activation in the OB with the competitive inhibitor Rp-cAMPS disrupted odor preference memory when tested 5 or 24 h, but not 3 h, after training. Moreover, exogenous administration of the PKA activator Sp-cAMPs into the OB prior to odor exposure sufficed to induce intermediate (5 h) and long-term (24 h) odor preference memory. Higher dosages of Sp-cAMPs into the OB further extended the persistence of this odor preference memory up to 72 h (Grimes et al., 2012). Odor-reward conditioning, but not odor or reward alone, also induced increased CREB phosphorylation in neonatal OB mitral cell nuclei 10 min after training, suggesting that pCREB-related plasticity in mitral cells may be important for the formation of odor LTM (McLean et al., 1999). The MAPK/extracellular signal-related kinase (ERK) pathway also is activated by odor learning in neonates; odor stimulation induced ERK phosphorylation in selective populations of OB neurons related to the identity of the learned odor (Mirich et al., 2004). Neonatal odor learning, like hippocampal LTM, appears to rely on NMDA receptor activation (Lethbridge et al., 2012) and the increased expression of synaptic AMPA receptors (Cui et al., 2011); notably, the PKA-dependent phosphorylation of AMPA receptor subunit GluA1 rises with a similar timecourse as does the level of CREB phosphorylation in mitral cells, peaking at about 10 min post-conditioning (Cui et al., 2011). BDNF mRNA levels increase in the OB and piriform cortex within 2 h of olfactory fear learning (Jones et al., 2007). To the extent that a substantially common set of essential molecular mechanisms is employed, the important distinctions between one-trial learning and appetitive statistical learning become within which neurons, under what conditions, and to what extent these mechanisms are invoked.

Most studies of olfactory learning and memory that measure the form of the odor memory (typically via generalization gradients) have been performed in adult animals and at STM timescales. There is little research to date on the molecular mechanisms underlying bulbar STM, though there is a substantial literature on the effects of neuromodulators, hormones (Dillon et al., 2013), and other extracellular signaling molecules. Noradrenergic effects within OB, in particular, have been studied in both nonassociative and associative olfactory representational learning studies (reviewed in Linster et al., 2011), which suggest that NE in the OB may be necessary for even the simplest forms of odor learning. Notably, a nonspecific infusion of NE into OB suffices to restore the nonassociative learning deficits arising from depletion of cortically-projecting NE fibers (Guérin et al., 2008), though dosage is critical, and bulbar NE levels induced by moderate stress can suppress OB-dependent STM in some contexts (Manella et al., 2013). In neonatal rats, as noted in section 2.2, bulbar NE is heavily released into OB during odor learning, and exogenous application of NE into the OB suffices to induce odor preference learning when paired with an odorant, essentially serving as an unconditioned stimulus as no external source of reward is required (Sullivan et al., 1989; Harley, 2004; Grimes et al., 2012). Activation of the PKA pathway with Sp-cAMPs also acts as an unconditioned stimulus in neonatal OB in this context (Grimes et al., 2012). There is no evidence, however, that bulbar NE can serve as an unconditioned stimulus for adult odor learning, and even in neonates this property may be epiphenomenal. If NE serves to gate activity-dependent plasticity in OB circuits, then known properties of neonatal physiology ensure that in neonates this learning will always be strong, always depend on odor-induced activation of OB circuits, and always be appetitive (neonates respond appetitively and can be positively conditioned to even normally-aversive unconditioned stimuli such as electric shocks; Sullivan, 2001). Consequently, simply gating circuit plasticity in the OB could be expected to directly generate a positive association in neonates. In any event, analogous pairings of odor presentation with bulbar NE infusions in adult mice demonstrate that NE facilitates habituation to presented odors, but does not innately generate odor preferences as it does in neonatal animals (Shea et al., 2008). Of course, other classical neuromodulators, notably acetylcholine acting at muscarinic receptors within OB, also exert effects within OB circuitry on odor learning and STM maintenance (Devore and Linster, 2012; Devore et al., 2012).

BDNF also is clearly implicated in LTM formation for IA learning. While it has been much less thoroughly studied in the olfactory system, BDNF transcription is activated in OB and piriform cortex after odor conditioning (Jones et al., 2007), and olfactory sensory deprivation reduces BDNF expression in neonatal OB (McLean et al., 2001). BDNF and its precursor proBDNF exert distinct physiological effects on OB neuronal excitability and plasticity (Mast and Fadool, 2012). BDNF heterozygous knockout mice and BDNF(Val66Met) point mutants exhibit reduced activity-dependent secretion of BDNF and behavioral deficits in an OB-dependent task. Both mutants habituate normally to odors but exhibit greatly reduced spontaneous discrimination in the cross-habituation task (Bath et al., 2008), suggesting an impairment in their ability to form specific odor representations. The clearest effects of BDNF on the OB, however, are structural in nature, substantially affecting dendritic arborization and adult neurogenesis.

4.2. STRUCTURAL MECHANISMS IN THE OB

4.2.1. Long-term potentiation

Long-term potentiation has been clearly if sparsely observed in the early olfactory system, notably within piriform cortex and its ascending synapses into OB. NMDA receptor-dependent LTP has been demonstrated at afferent and associative fiber synapses within piriform cortical slices, and coactivation of the two can facilitate a form of associative LTP if local inhibition is suppressed (Kanter and Haberly, 1990, 1993). Piriform pyramidal neuron feedback projections onto OB granule cells also exhibit spike timing-dependent LTP (Gao and Strowbridge, 2009), which may be a particularly powerful computational element given the importance of dynamical, timing-dependent interactions within OB circuitry. Contemporary models of OB-piriform computations have regarded these circuits as a pattern separation/completion network not unlike the dentate gyrus/CA1 circuit of hippocampus, in which piriform association fibers underlie pattern completion (Hasselmo et al., 1992; Barnes et al., 2008) and their feedback projections onto inhibitory granule cells within OB underlie pattern separation (Strowbridge, 2009), within a common recurrent circuit. This rich and structured plasticity requires further experimental and theoretical development, but exemplifies the capacities of the olfactory system as a model for understanding complex memory systems.

4.2.2. Neuronal and synaptic morphology

Spine densities in OB and piriform cortex are affected by odor learning and by learning-associated trophic factors, notably BDNF. In piriform cortex, spine density on pyramidal neurons increased in odor-conditioned rats compared with pseudoconditioned or naïve controls, an effect potentially corresponding to increased synaptic weights in the association fiber network (Knafo et al., 2001). In the neonatal OB, dendritic branching and spine morphology is substantially regulated by BDNF signaling mediated by the TrkB receptor (Matsutani and Yamamoto, 2004; Imamura and Greer, 2009). In adults, BDNF expression in the OB persists (Malkovska et al., 2006), and continues to regulate OB dendritogenesis, at least among parvalbumin-expressing neurons of the external plexiform layer (Berghuis et al., 2006). In combination with the integration of adult-born neurons into the OB network (below), it is clear that the regulation of dendritic connectivity among OB neurons is a significant determinant of OB functional plasticity, and that BDNF is a crucial regulator of the underlying mechanisms.

4.2.3. Adult neurogenesis

Adult neurogenesis in the OB has been studied extensively with regard to its effects on, and mediation of, odor learning. The differentiation of adult-born neurons within OB and its relevance for olfactory perception and odor learning have been extensively studied and reviewed elsewhere (Lazarini and Lledo, 2011; Lepousez et al., 2013; Gheusi et al., 2013). Of particular interest for present purposes, though, is the regulation of these neuronal differentiation processes by signaling molecules and other established mediators of olfactory learning, as well as timing and task dependencies that may suggest points of particular mechanistic importance.

The incorporation of new neurons is most widely associated with olfactory LTM; as described above, the selective ablation of newly differentiated OB neurons specifically disrupted a long-term odor memory (Akers et al., 2011). However, there also are indications that adult-born neurons may participate in STM processes. Infusions of the antimitotic drug AraC into the lateral ventricle of rats abolished the arrival of new neurons into the OB, while largely sparing hippocampal neurogenesis, and impaired short-term nonassociative memory for odors learned thereafter (Breton-Provencher et al., 2009). Specifically, the absence of new neurons in OB did not affect memory for a habituated odor after 30 min, but 60-, 90-, and 120-min odor memories were disrupted compared with control animals. In contrast, AraC treatment did not affect 24-h or 7-day preference memory for an odorant paired with reward over 4 days. It remains unclear whether this difference depends more on the multi-day spacing of the trials or on the associative nature of the task.

Interestingly, it has been proposed that nonassociative and associative odor learning preferentially activate neurons of different ages within OB (Belnoue et al., 2011). Specifically, nonassociative perceptual learning preferentially activated newly-arrived neurons (~2 weeks old), as measured by c-Fos immunoreactivity, whereas water-rewarded odor discrimination training in a go/no-go task preferentially activated more mature, though still recently generated, interneurons (5–9 weeks of age). This result is consistent with the results described above, in that the OBs of AraC-infused mice in that study were devoid of neurons younger than 3–4 weeks, as required for nonassociative odor learning, but possessed a full complement of neurons in the 5–9 week age range, as were most heavily utilized in the rewarded task. (Also of potential interest is that activation does not necessarily correspond to increased survival; olfactory go/no-go training has been associated with enhancing the survival of 2–4 week old neurons in OB, while increasing apoptosis in 5-week old neurons, and not affecting fully mature interneurons 9 weeks of age or older; Mouret et al., 2008). These results still beg the question, of course, of what factors in these different training paradigms underlie the selective recruitment of different cohorts of new neurons. These results illustrate another advantage of the olfactory system for studies of complex and naturalistic learning, in which task parameters may determine the differential utilization of OB (and non-OB; Luu et al., 2012) circuit elements for odor-dependent learning.

BDNF signaling is a significant contributor to the survival of new neurons in the OB. BDNF levels are similar in both the site of neurogenesis in the SVZ and in the OB, the target of migration, and regulate both neuronal migration and differentiation (the latter via the MAPK pathway) (Petridis and El Maarouf, 2011). Infusions of BDNF into the lateral ventricle of adult rats significantly increased the generation and/or survival of adult-born neurons in the OB (Zigova et al., 1998; Benraiss et al., 2001); in an analogous in vitro study, BDNF administered to neurons arising from the subependymal zone of rats promoted their survival (Kirschenbaum and Goldman, 1995). Mice heterozygous for either the BDNF gene or its TrkB receptor exhibit reduced neuron survival in the OB, as do mice with the Val66Met point mutation in the BDNF gene, which impairs activity-dependent BDNF secretion (Bath et al., 2008); these mutants also exhibited impaired nonassociative odor learning as described above. Neuronal proliferation was not affected by these mutations, suggesting that the effects of BDNF primarily relate to survival and differentiation. The powerful effects of this neurotrophin on olfactory learning and neuronal differentiation, and its association with established learning-associated molecular cascades, render it a strong candidate for study in order to elucidate the complex relationships underlying these representational, statistical learning processes in naturalistic contexts.

5. IN SUMMARY

Understanding the neurophysiological basis of natural learning and memory is one of the great challenges of neuroscience. Much of what is known about the cellular mechanisms underlying learning derives from one-trial learning paradigms of inhibitory avoidance (fear conditioning), though research in other plastic neural systems has indicated that they share many, though not all, of the same underlying molecular and structural mechanisms of plasticity. One-trial odor learning studies, which induce plasticity in olfactory bulb, suggest that these cortical circuits also rely on these common mechanisms for plasticity—although bulbar memory also depends on adult neurogenesis, a structural mechanism which it shares only with the hippocampus.

Most natural learning, however, is less categorical than these one-trial paradigms, requiring multiple encounters in order to elucidate relevant stimuli and learn appropriate associations. Appetitive learning in adults, for example, tends to be gradual, conditional, and statistical in nature. This raises new mechanistic questions: how does learning accumulate over multiple trials? How do STM and LTM mechanisms interact over the extended timescales of natural experience? How are the relevant features of the sensory scene identified, selected, and represented? How does learning change the form, or quality, of a sensory representation in response to accumulating information? Developing the olfactory system as a neurophysiological learning and memory model enables engagement with these rich questions.

AUTHOR CONTRIBUTIONS

Michelle T. Tong and Thomas A. Cleland conceived of and wrote the paper, and designed the figures. Shane T. Peace designed and performed the research featured in Figure 4. Michelle T. Tong designed and performed the research featured in Figure 5.

FUNDING

This work was supported by NIH/NIDCD grant DC012249 to Thomas A. Cleland.

ACKNOWLEDGMENTS

The authors would like to thank undergraduate research assistants Alan Leung, Chin Ho Fung, Rahul Krishnan, Dmitriy Migdalovich, Simon Wong, and Zhongming Chen for help with the research conducted in Figures 4, 5. We would also like to thank SiWei Luo (Department of Psychology, Cornell University) for providing valuable discussion and references.

REFERENCES

Abraham, N. M., Guerin, D., Bhaukaurally, K., and Carleton, A. (2012). Similar odor discrimination behavior in head-restrained and freely moving mice. PLoS ONE 7:e51789. doi: 10.1371/journal.pone.0051789

Abraham, N. M., Vincis, R., Lagier, S., Rodriguez, I., and Carleton, A. (2014). Long term functional plasticity of sensory inputs mediated by olfactory learning. eLife 3:e02109 doi: 10.7554/eLife.02109

Akers, K. G., Arruda-Carvalho, M., and Frankland, P. W. (2011). Ablation of adult-generated olfactory bulb neurons produces retrograde impairment in an associative odor memory task. Soc. Neurosci. Abstr. 834.08.

Alonso, M., Lepousez, G., Wagner, S., Bardy, C., Gabellec, M.-M., Torquet, N., et al. (2012). Activation of adult-born neurons facilitates learning and memory. Nat. Neurosci. 15, 897–904. doi: 10.1038/nn.3108

Alonso, M., Vianna, M. R., Depino, A. M., Mello e Souza, T., Pereira, P., Szapiro, G., et al. (2002). BDNF–triggered events in the rat hippocampus are required for both short- and long-term memory formation. Hippocampus 12, 551–560. doi: 10.1002/hipo.10035

Alonso, M., Viollet, C., Gabellec, M.-M., Meas-Yedid, V., Olivo-Marin, J.-C., and Lledo, P.-M. (2006). Olfactory discrimination learning increases the survival of adult-born neurons in the olfactory bulb. J. Neurosci. 26, 10508–10513. doi: 10.1523/JNEUROSCI.2633-06.2006

Anderson, M. L., Sisti, H. M., Curlik, D. M., and Shors, T. J. (2011). Associative learning increases adult neurogenesis during a critical period. Eur. J. Neurosci. 33, 175–181. doi: 10.1111/j.1460-9568.2010.07486.x

Arruda-Carvalho, M., Sakaguchi, M., Akers, K. G., Josselyn, S. A., and Frankland, P. W. (2011). Posttraining ablation of adult-generated neurons degrades previously acquired memories. J. Neurosci. 31, 15113–15127. doi: 10.1523/JNEUROSCI.3432-11.2011

Barnes, D. C., Hofacer, R. D., Zaman, A. R., Rennaker, R. L., and Wilson, D. A. (2008). Olfactory perceptual stability and discrimination. Nat. Neurosci. 11, 1378–1380. doi: 10.1038/nn.2217

Bath, K. G., Mandairon, N., Jing, D., Rajagopal, R., Kapoor, R., Chen, Z.-Y., et al. (2008). Variant brain-derived neurotrophic factor (Val66Met) alters adult olfactory bulb neurogenesis and spontaneous olfactory discrimination. J. Neurosci. 28, 2383–2393. doi: 10.1523/JNEUROSCI.4387-07.2008

Bekinschtein, P., Cammarota, M., Igaz, L. M., Bevilaqua, L. R., Izquierdo, I., and Medina, J. H. (2007). Persistence of long-term memory storage requires a late protein synthesis- and BDNF-dependent phase in the hippocampus. Neuron 53, 261–277. doi: 10.1016/j.neuron.2006.11.025

Bekinschtein, P., Cammarota, M., Katche, C., Slipczuk, L., Rossato, J. I., Goldin, A., et al. (2008). BDNF is essential to promote persistence of long-term memory storage. Proc. Natl. Acad. Sci. U.S.A. 105, 2711–2716. doi: 10.1073/pnas.0711863105

Belnoue, L., Grosjean, N., Abrous, D. N., and Koehl, M. (2011). A critical time window for the recruitment of bulbar newborn neurons by olfactory discrimination learning. J. Neurosci. 31, 1010–1016. doi: 10.1523/JNEUROSCI.3941-10.2011

Benraiss, A., Chmielnicki, E., Lerner, K., Roh, D., and Goldman, S. A. (2001). Adenoviral brain–derived neurotrophic factor induces both neostriatal and olfactory neuronal recruitment from endogenous progenitor cells in the adult forebrain. J. Neurosci. 21, 6718–6731.

Berdel, B., Moryś, J., and Maciejewska, B. (1997). Neuronal changes in the basolateral complex during development of the amygdala of the rat. Int. J. Dev. Neurosci. 15, 755–765. doi: 10.1016/S0736-5748(97)00022-1

Berghuis, P., Agerman, K., Dobszay, M. B., Minichiello, L., Harkany, T., and Ernfors, P. (2006). Brain-derived neurotrophic factor selectively regulates dendritogenesis of parvalbumin-containing interneurons in the main olfactory bulb through the PLCγ pathway. J. Neurobiol. 66, 1437–1451. doi: 10.1002/neu.20319

Bernabeu, R., Bevilaqua, L., Ardenghi, P., Bromberg, E., Schmitz, P., Bianchin, M., et al. (1997a). Involvement of hippocampal cAMP/cAMP-dependent protein kinase signaling pathways in a late memory consolidation phase of aversively motivated learning in rats. Proc. Natl. Acad. Sci. U.S.A. 94, 7041–7046.

Bernabeu, R., Cammarota, M., Izquierdo, I., and Medina, J. H. (1997b). Involvement of hippocampal AMPA glutamate receptor changes and the cAMP/protein kinase A/CREB-P signalling pathway in memory consolidation of an avoidance task in rats. Brazil. J. Med. Biol. Res. 30, 961–965.

Bernabeu, R., Schmitz, P., Faillace, M. P., Izquierdo, I., and Medina, J. H. (1996). Hippocampal cGMP and cAMP are differentially involved in memory processing of inhibitory avoidance learning. Neuroreport 7, 585–588.

Bernabeu, R., Schroder, N., Quevedo, J., Izquierdo, I., and Medina, J. H. (1997c). Further evidence for the involvement of a hippocampal cGMP/cGMP-dependent protein kinase cascade in memory consolidation. Neuroreport 8, 2221–2224.

Bevilaqua, L., Ardenghi, P., Schroder, N., Bromberg, E., Schmitz, P., Schaeffer, E., et al. (1997). Drugs acting upon the cyclic adenosine monophosphate/protein kinase A signalling pathway modulate memory consolidation when given late after training into rat hippocampus but not amygdala. Behav. Pharmacol. 8, 331–338.

Bieszczad, K. M., and Weinberger, N. M. (2010). Representational gain in cortical area underlies increase of memory strength. Proc. Natl. Acad. Sci. U.S.A. 107, 3793–3798. doi: 10.1073/pnas.1000159107

Bliss, T. V., and Collingridge, G. L. (1993). A synaptic model of memory: long-term potentiation in the hippocampus. Nature 361, 31–39. doi: 10.1038/361031a0

Bliss, T. V., and Lømo, T. (1973). Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J. Physiol. 232, 331–356.

Blum, A. L., Li, W., Cressy, M., and Dubnau, J. (2009). Short- and long-term memory in Drosophila require cAMP signaling in distinct neuron types. Curr. Biol. 19, 1341–1350. doi: 10.1016/j.cub.2009.07.016

Bourtchuladze, R., Frenguelli, B., Blendy, J., Cioffi, D., Schütz, G., and Silva, A. J. (1994). Deficient long-term memory in mice with a targeted mutation of the cAMP-responsive element-binding protein. Cell 79, 59–68. doi: 10.1016/0092-8674(94)90400-6

Breton-Provencher, V., Lemasson, M., Peralta, M. R., and Saghatelyan, A. (2009). Interneurons produced in adulthood are required for the normal functioning of the olfactory bulb network and for the execution of selected olfactory behaviors. J. Neurosci. 29, 15245–15257. doi: 10.1523/JNEUROSCI.3606-09.2009

Cammarota, M., Paratcha, G., Levi de Stein, M., Bernabeu, R., Izquierdo, I., and Medina, J. H. (1997). B-50/GAP-43 phosphorylation and PKC activity are increased in rat hippocampal synaptosomal membranes after an inhibitory avoidance training. Neurochem. Res. 22, 499–505.

Canal, C. E., Chang, Q., and Gold, P. E. (2008). Intra-amygdala injections of CREB antisense impair inhibitory avoidance memory: role of norepinephrine and acetylcholine. Learn. Mem. 15, 677–686. doi: 10.1101/lm.904308

Castello, N. A., Nguyen, M. H., Tran, J. D., Cheng, D., Green, K. N., and LaFerla, F. M. (2014). 7,8-dihydroxyflavone, a small molecule TrkB agonist, improves spatial memory and increases thin spine density in a mouse model of Alzheimer disease-like neuronal loss. PLoS ONE 9:e91453. doi: 10.1371/journal.pone.0091453

Chaudhury, D., Manella, L., Arellanos, A., Escanilla, O., Cleland, T. A., and Linster, C. (2010). Olfactory bulb habituation to odor stimuli. Behav. Neurosci. 124, 490–499. doi: 10.1037/a0020293

Chen, C. F., Barnes, D. C., and Wilson, D. A. (2011). Generalized vs. stimulus-specific learned fear differentially modifies stimulus encoding in primary sensory cortex of awake rats. J. Neurophysiol. 106, 3136–3144. doi: 10.1152/jn.00721.2011

Chen, D. Y., Bambah-Mukku, D., Pollonini, G., and Alberini, C. M. (2012). Glucocorticoid receptors recruit the CaMKIIα-BDNF-CREB pathways to mediate memory consolidation. Nat. Neurosci. 15, 1707–1714. doi: 10.1038/nn.3266

Cheng, M. F. (2013). Hypothalamic neurogenesis in the adult brain. Front. Neuroendocrinol. 34, 167–178. doi: 10.1016/j.yfrne.2013.05.001

Cleland, T. A. (2008). “The construction of olfactory representations,” in Mechanisms of Information Processing in the Brain: Encoding of Information in Neural Populations, eds C. Holscher and M. Munk (Cambridge, UK: Cambridge University Press), 247–280.

Cleland, T. A. (2014). “Construction of odor representations by olfactory bulb microcircuits,” in Progress in Brain Research: Odor Memory and Perception, eds E. Barkai and D. Wilson (New York, NY: Elsevier), 177–203.

Cleland, T. A., Chen, S. Y., Hozer, K. W., Ukatu, H. N., Wong, K. J., and Zheng, F. (2012). Sequential mechanisms underlying concentration invariance in biological olfaction. Front. Neuroeng. 4:21. doi: 10.3389/fneng.2011.00021.

Cleland, T. A., Morse, A., Yue, E. L., and Linster, C. (2002). Behavioral models of odor similarity. Behav. Neurosci. 116, 222–231. doi: 10.1037/0735-7044.116.2.222

Cleland, T. A., Narla, V. A., and Boudadi, K. (2009). Multiple learning parameters differentially regulate olfactory generalization. Behav. Neurosci. 123, 26–35. doi: 10.1037/a0013991

Cui, W., Darby-King, A., Grimes, M. T., Howland, J. G., Wang, Y. T., McLean, J. H., et al. (2011). Odor preference learning and memory modify GluA1 phosphorylation and GluA1 distribution in the neonate rat olfactory bulb: testing the AMPA receptor hypothesis in an appetitive learning model. Learn. Mem. 18, 283–291. doi: 10.1101/lm.1987711

Danysz, W., Zajaczkowski, W., and Parsons, C. G. (1995). Modulation of learning processes by ionotropic glutamate receptor ligands. Behav. Pharmacol. 6, 455–474.

Davis, H. P., and Squire, L. R. (1984). Protein synthesis and memory: a review. Psychol. Bull. 96, 518–559.

de Almeida, L., Idiart, M., and Linster, C. (2013). A model of cholinergic modulation in olfactory bulb and piriform cortex. J. Neurophysiol. 109, 1360–1377.

Devore, S., and Linster, C. (2012). Noradrenergic and cholinergic modulation of olfactory bulb sensory processing. Front. Behav. Neurosci. 6:52. doi: 10.3389/fnbeh.2012.00052

Devore, S., Manella, L. C., and Linster, C. (2012). Blocking muscarinic receptors in the olfactory bulb impairs performance on an olfactory short-term memory task. Front. Behav. Neurosci. 6:59. doi: 10.3389/fnbeh.2012.00059

DeZazzo, J., and Tully, T. (1995). Dissection of memory formation: from behavioral pharmacology to molecular genetics. Trends Neurosci. 18, 212–218. doi: 10.1016/0166-2236(95)93905-D

Dillon, T. S., Fox, L. C., Han, C., and Linster, C. (2013). 17β-estradiol enhances memory duration in the main olfactory bulb in CD-1 mice. Behav. Neurosci. 127, 923–931. doi: 10.1037/a0034839

Eichenbaum, H. (1998). Using olfaction to study memory. Ann. N.Y. Acad. Sci. 855, 657–669. doi: 10.1111/j.1749-6632.1998.tb10642.x

Eichenbaum, H., Schoenbaum, G., Young, B., and Bunsey, M. (1996). Functional organization of the hippocampal memory system. Proc. Natl. Acad. Sci. U.S.A. 93, 13500–13507. doi: 10.1073/pnas.93.24.13500

Escanilla, O., Alperin, S., Youssef, M., Ennis, M., and Linster, C. (2012). Noradrenergic but not cholinergic modulation of olfactory bulb during processing of near threshold concentration stimuli. Behav. Neurosci. 126, 720–728. doi: 10.1037/a0030006

Escanilla, O., Arrellanos, A., Karnow, A., Ennis, M., and Linster, C. (2010). Noradrenergic modulation of behavioral odor detection and discrimination thresholds in the olfactory bulb. Eur. J. Neurosci. 32, 458–468. doi: 10.1111/j.1460-9568.2010.07297.x

Fernandez, P. C., Locatelli, F. F., Person-Rennell, N., Deleo, G., and Smith, B. H. (2009). Associative conditioning tunes transient dynamics of early olfactory processing. J. Neurosci. 29, 10191–10202. doi: 10.1523/JNEUROSCI.1874-09.2009

Freedman, K. G., Radhakrishna, S., Escanilla, O., and Linster, C. (2013). Duration and specificity of olfactory nonassociative memory. Chem. Sens. 38, 369–375. doi: 10.1093/chemse/bjt010

Frey, U., Huang, Y. Y., and Kandel, E. R. (1993). Effects of cAMP simulate a late stage of LTP in hippocampal CA1 neurons. Science 260, 1661–1664. doi: 10.1126/science.8389057

Frey, U., Krug, M., Reymann, K. G., and Matthies, H. (1988). Anisomycin, an inhibitor of protein synthesis, blocks late phases of LTP phenomena in the hippocampal CA1 region in vitro. Brain Res. 452, 57–65. doi: 10.1016/0006-8993(88)90008-X

Frey, U., Schollmeier, K., Reymann, K., and Seidenbecher, T. (1995). Asymptotic hippocampal long-term potentiation in rats does not preclude additional potentiation at later phases. Neuroscience 67, 799–807. doi: 10.1016/0306-4522(95)00117-2

Gao, Y., and Strowbridge, B. W. (2009). Long-term plasticity of excitatory inputs to granule cells in the rat olfactory bulb. Nat. Neurosci. 12, 731–733. doi: 10.1038/nn.2319

Gheusi, G., Lepousez, G., and Lledo, P.-M. (2013). “Adult-born neurons in the olfactory bulb: integration and functional consequences,” in Neurogenesis and Neural Plasticity, eds C. Belzung and P. Wigmore (Berlin: Springer), 49–72.

Grimes, M. T., Harley, C. W., Darby-King, A., and McLean, J. H. (2012). PKA increases in the olfactory bulb act as unconditioned stimuli and provide evidence for parallel memory systems: pairing odor with increased PKA creates intermediate- and long-term, but not short-term, memories. Learn. Mem. 19, 107–115. doi: 10.1101/lm.024489.111

Grimes, M. T., Smith, M., Li, X., Darby-King, A., Harley, C. W., and McLean, J. H. (2011). Mammalian intermediate-term memory: New findings in neonate rat. Neurobiol. Learn. Mem. 95, 385–391. doi: 10.1016/j.nlm.2011.01.012

Guérin, D., Peace, S. T., Didier, A., Linster, C., and Cleland, T. A. (2008). Noradrenergic neuromodulation in the olfactory bulb modulates odor habituation and spontaneous discrimination. Behav. Neurosci. 122, 816–826. doi: 10.1037/a0012522

Guzowski, J. F., and McGaugh, J. L. (1997). Antisense oligodeoxynucleotide-mediated disruption of hippocampal cAMP response element binding protein levels impairs consolidation of memory for water maze training. Proc. Natl. Acad. Sci. U.S.A. 94, 2693–2698. doi: 10.1073/pnas.94.6.2693

Harley, C. W. (2004). Norepinephrine and dopamine as learning signals. Neural Plast. 11, 191–204.

Hasselmo, M. E., Anderson, B. P., and Bower, J. M. (1992). Cholinergic modulation of cortical associative memory function. J. Neurophysiol. 67, 1230–1246. doi: 10.1155/NP.2004.191

Imamura, F., and Greer, C. A. (2009). Dendritic branching of olfactory bulb mitral and tufted cells: regulation by TrkB. PLoS ONE 4:e6729. doi: 10.1371/journal.pone.0006729

Izquierdo, I. (1993). Long-term potentiation and the mechanisms of memory. Drug Dev. Res. 30, 1–17. doi: 10.1002/ddr.430300102

Izquierdo, I., Barros, D. M., Mello e Souza, T., de Souza, M. M., Izquierdo, L. A., and Medina, J. H. (1998). Mechanisms for memory types differ. Nature 393, 635–636. doi: 10.1038/31371

Izquierdo, I., Da Cunha, C., Rosat, R., Jerusalinsky, D., Ferreira, M., and Medina, J. H. (1992). Neurotransmitter receptors involved in post-training memory processing by the amygdala, medial septum and hippocampus of the rat. Behav. Neural Biol. 58, 16–26. doi: 10.1016/0163-1047(92)90847-W

Izquierdo, I., Medina, J. H., Vianna, M. R., Izquierdo, L. A., and Barros, D. M. (1999). Separate mechanisms for short-and long-term memory. Behav. Brain Res. 103, 1–11. doi: 10.1016/S0166-4328(99)00036-4

Izquierdo, L. (2000). Short- and long-term memory are differentially affected by metabolic inhibitors given into hippocampus and entorhinal cortex. Neurobiol. Learn. Mem. 73, 141–149. doi: 10.1006/nlme.1999.3925

Izquierdo, L., Schroder, N., Ardenghi, P., Quevedo, J., Bevilaqua, L., Netto, C., et al. (1997). Systemic administration of ACTH or vasopressin reverses the amnestic effect of posttraining beta-endorphin or electroconvulsive shock but not that of intrahippocampal infusion of protein kinase inhibitors. Learn. Mem. 68, 197–202. doi: 10.1006/nlme.1997.3794

Jerusalinsky, D., Ferreira, M. B., Walz, R., Da Silva, R. C., Bianchin, M., Ruschel, A. C., et al. (1992). Amnesia by post-training infusion of glutamate receptor antagonists into the amygdala, hippocampus and entorhinal cortex. Behav. Neural Biol. 58, 76–80. doi: 10.1016/0163-1047(92)90982-A

Jerusalinsky, D., Quillfeldt, J. H., Walz, R., Da Silva, R., Medina, J. H., and Izquierdo, I. (1994). Post-training intrahippocampal infusion of protein kinase C inhibitors causes amnesia in rats. Behav. Neural Biol. 61, 107–109. doi: 10.1016/S0163-1047(05)80063-9

Jones, S. V., Stanek-Rattiner, L., Davis, M., and Ressler, K. J. (2007). Differential regional expression of brain-derived neurotrophic factor following olfactory fear learning. Learn. Mem. 14, 816–820. doi: 10.1101/lm.781507

Jung, C. K., and Herms, J. (2014). Structural dynamics of dendritic spines are influenced by an environmental enrichment: an in vivo imaging study. Cereb. Cortex 24, 377–384. doi: 10.1093/cercor/bhs317

Kang, H., Welcher, A. A., Shelton, D., and Schuman, E. M. (1997). Neurotrophins and time: different roles for TrkB signaling in hippocampal long-term potentiation. Neuron 19, 653–664. doi: 10.1016/S0896-6273(00)80378-5

Kanter, E. D., and Haberly, L. B. (1993). Associative long-term potentiation in piriform cortex slices requires GABAA blockade. J. Neurosci. 13, 2477–2482.

Kanter, E. D., and Haberly, L. B. (1990). NMDA-dependent induction of long-term potentiation in afferent and association fiber systems of piriform cortex in vitro. Brain Res. 525, 175–179. doi: 10.1016/0006-8993(90)91337-G

Kee, N., Teixeira, C. M., Wang, A. H., and Frankland, P. W. (2007). Preferential incorporation of adult-generated granule cells into spatial memory networks in the dentate gyrus. Nat. Neurosci. 10, 355–362. doi: 10.1038/nn1847

Kermen, F., Sultan, S., Sacquet, J., Mandairon, N., and Didier, A. (2010). Consolidation of an olfactory memory trace in the olfactory bulb is required for learning-induced survival of adult-born neurons and long-term memory. PLoS ONE 5:e12118. doi: 10.1371/journal.pone.0012118

Kirschenbaum, B., and Goldman, S. A. (1995). Brain–derived neurotrophic factor promotes the survival of neurons arising from the adult rat forebrain subependymal zone. Proc. Natl. Acad. Sci. U.S.A. 92, 210–214. doi: 10.1073/pnas.92.1.210

Knafo, S., Grossman, Y., Barkai, E., and Benshalom, G. (2001). Olfactory learning is associated with increased spine density along apical dendrites of pyramidal neurons in the rat piriform cortex. Eur. J. Neurosci. 13, 633–638. doi: 10.1046/j.1460-9568.2001.01422.x

Korte, M., Carroll, P., Wolf, E., Brem, G., Thoenen, H., and Bonhoeffer, T. (1995). Hippocampal long-term potentiation is impaired in mice lacking brain-derived neurotrophic factor. Proc. Natl. Acad. Sci. U.S.A. 92, 8856–8860. doi: 10.1073/pnas.92.19.8856

Krug, M., Lössner, B., and Ott, T. (1984). Anisomycin blocks the late phase of long-term potentiation in the dentate gyrus of freely moving rats. Brain Res. Bull. 13, 39–42. doi: 10.1016/0361-9230(84)90005-4

Lamprecht, R. (2014). The actin cytoskeleton in memory formation. Prog. Neurobiol. 117C, 1–19. doi: 10.1016/j.pneurobio.2014.02.001

Law, L. M., and Smith, D. M. (2012). The anterior thalamus is critical for overcoming interference in a context-dependent odor discrimination task. Behav. Neurosci. 126, 710–719. doi: 10.1037/a0029698

Lazarini, F., and Lledo, P.-M. (2011). Is adult neurogenesis essential for olfaction? Trends Neurosci. 34, 20–30. doi: 10.1016/j.tins.2010.09.006

Leggio, M. G., Mandolesi, L., Federico, F., Spirito, F., Ricci, B., Gelfo, F., et al. (2005). Environmental enrichment promotes improved spatial abilities and enhanced dendritic growth in the rat. Behav. Brain Res. 163, 78–90. doi: 10.1016/j.bbr.2005.04.009

Lepousez, G., Valley, M. T., and Lledo, P.-M. (2013). The impact of adult neurogenesis on olfactory bulb circuits and computations. Annu. Rev. Physiol. 75, 339–363. doi: 10.1146/annurev-physiol-030212-183731

Lethbridge, R., Hou, Q., Harley, C. W., and Yuan, Q. (2012). Olfactory bulb glomerular NMDA receptors mediate olfactory nerve potentiation and odor preference learning in the neonate rat. PLoS ONE 7:e35024. doi: 10.1371/journal.pone.0035024

Li, G., and Cleland, T. A. (2013). A two-layer biophysical model of cholinergic neuromodulation in olfactory bulb. J. Neurosci. 33, 3037–3058. doi: 10.1523/JNEUROSCI.2831-12.2013

Linster, C., and Cleland, T. A. (2010). Decorrelation of odor representations via spike timing-dependent plasticity. Front. Comput. Neurosci. 4:157. doi: 10.3389/fncom.2010.00157

Linster, C., Nai, Q., and Ennis, M. (2011). Nonlinear effects of noradrenergic modulation of olfactory bulb function in adult rodents. J. Neurosci. 105, 1432–1443. doi: 10.1152/jn.00960.2010

Lledo, P.-M., Alonso, M., and Grubb, M. S. (2006). Adult neurogenesis and functional plasticity in neuronal circuits. Nat. Rev. Neurosci. 7, 179–193. doi: 10.1038/nrn1867

Lu, Y., Christian, K., and Lu, B. (2008). BDNF: a key regulator for protein synthesis-dependent LTP and long-term memory? Neurobiol. Learn. Mem. 89, 312–323. doi: 10.1016/j.nlm.2007.08.018

Lu, Y., Ji, Y., Ganesan, S., Schloesser, R., Martinowich, K., Sun, M., et al. (2011). TrkB as a potential synaptic and behavioral tag. J. Neurosci. 31, 11762–11771. doi: 10.1523/JNEUROSCI.2707-11.2011

Luu, P., Sill, O. C., Gao, L., Becker, S., Wojtowicz, J. M., and Smith, D. M. (2012). The role of adult hippocampal neurogenesis in reducing interference. Behav. Neurosci. 126, 381–391. doi: 10.1037/a0028252

Malinow, R., Schulman, H., and Tsien, R. W. (1989). Inhibition of postsynaptic PKC or CaMKII blocks induction but not expression of LTP. Science 245, 862–866. doi: 10.1126/science.2549638

Malkovska, I., Kernie, S. G., and Parada, L. F. (2006). Differential expression of the four untranslated BDNF exons in the adult mouse brain. J. Neurosci. Res. 83, 211–221. doi: 10.1002/jnr.20728

Mandairon, N., Ferretti, C. J., Stack, C. M., Rubin, D. B., Cleland, T. A., and Linster, C. (2006). Cholinergic modulation in the olfactory bulb influences spontaneous olfactory discrimination in adult rats. Eur. J. Neurosci. 24, 3234–3244. doi: 10.1111/j.1460-9568.2006.05212.x

Mandairon, N., Peace, S., Karnow, A., Kim, J., Ennis, M., and Linster, C. (2008). Noradrenergic modulation in the olfactory bulb influences spontaneous and reward-motivated discrimination, but not the formation of habituation memory. Eur. J. Neurosci. 27, 1210–1219. doi: 10.1111/j.1460-9568.2008.06101.x

Manella, L. C., Alperin, S., and Linster, C. (2013). Stressors impair odor recognition memory via an olfactory bulb-dependent noradrenergic mechanism. Front. Integr. Neurosci. 7:97. doi: 10.3389/fnint.2013.00097

Mast, T. G., and Fadool, D. A. (2012). Mature and precursor brain-derived neurotrophic factor have individual roles in the mouse olfactory bulb. PLoS ONE 7:e31978. doi: 10.1371/journal.pone.0031978

Matsutani, S., and Yamamoto, N. (2004). Brain-derived neurotrophic factor induces rapid morphological changes in dendritic spines of olfactory bulb granule cells in cultured slices through the modulation of glutamatergic signaling. Neuroscience 123, 695–702. doi: 10.1016/j.neuroscience.2003.10.030

McAllister, K. A., Lo, D. C., and Katz, L. C. (1995). Neurotrophins regulate dendritic growth in developing visual cortex. Neuron 15, 791–803.

McLean, J. H., Darby-King, A., and Bonnell, W. S. (2001). Neonatal olfactory sensory deprivation decreases BDNF in the olfactory bulb of the rat. Dev. Brain Res. 128, 17–24. doi: 10.1016/S0165-3806(01)00144-4

McLean, J. H., Darby-King, A., and Harley, C. W. (2005). Potentiation and prolongation of long-term odor memory in neonate rats using a phosphodiesterase inhibitor. Neuroscience 135, 329–334. doi: 10.1016/j.neuroscience.2005.06.029

McLean, J. H., and Harley, C. W. (2004). Olfactory learning in the rat pup: a model that may permit visualization of a mammalian memory trace. Neuroreport 15, 1691–1697. doi: 10.1097/01.wnr.0000134988.51310.c3

McLean, J. H., Harley, C. W., Darby-King, A., and Yuan, Q. (1999). pCREB in the neonate rat olfactory bulb is selectively and transiently increased by odor preference-conditioned training. Learn. Mem. 6, 608–618. doi: 10.1101/lm.6.6.608

McNamara, A. M., Magidson, P. D., Linster, C., Wilson, D. A., and Cleland, T. A. (2008). Distinct neural mechanisms mediate olfactory memory formation at different timescales. Learn. Mem. 15, 117–125. doi: 10.1101/lm.785608

Menzel, R., Manz, G., Menzel, R., and Greggers, U. (2001). Massed and spaced learning in honeybees: the role of CS, US, the intertrial interval, and the test interval. Learn. Mem. 8, 198–208. doi: 10.1101/lm.40001

Migliore, M., and Shepherd, G. M. (2002). Emerging rules for the distributions of active dendritic conductances. Nat. Rev. Neurosci. 3, 362–370. doi: 10.1038/nrn810

Mirich, J. M., Illig, K. R., and Brunjes, P. C. (2004). Experience-dependent activation of extracellular signal-related kinase (ERK) in the olfactory bulb. J. Comp. Neurol. 479, 234–241. doi: 10.1002/cne.20325

Miyamoto, E. (2006). Molecular mechanism of neuronal plasticity: induction and maintenance of long-term potentiation in the hippocampus. J. Pharmacol. Sci. 100, 433–442. doi: 10.1254/jphs.CPJ06007X

Mizuno, M., Yamada, K., Maekawa, N., Saito, K., Seishima, M., and Nabeshima, T. (2002). CREB phosphorylation as a molecular marker of memory processing in the hippocampus for spatial learning. Behav. Brain Res. 133, 135–141. doi: 10.1016/S0166-4328(01)00470-3

Mizuno, M., Yamada, K., Olariu, A., Nawa, H., and Nabeshima, T. (2000). Involvement of brain-derived neurotrophic factor in spatial memory formation and maintenance in a radial arm maze test in rats. J. Neurosci. 20, 7116–7121.

Moreno, M., Bath, K., Kuczewski, N., Sacquet, J., Didier, A., and Mandairon, N. (2012). Action of the noradrenergic system on adult-born cells is required for olfactory learning in mice. J. Neurosci. 32, 3748–3758. doi: 10.1523/JNEUROSCI.6335-11.2012

Moreno, M. M., Linster, C., Escanilla, O., Sacquet, J., Didier, A., and Mandairon, N. (2009). Olfactory perceptual learning requires adult neurogenesis. Proc. Natl. Acad. Sci. U.S.A. 106, 17980–17985. doi: 10.1073/pnas.0907063106

Moriceau, S., and Sullivan, R. M. (2005). Neurobiology of infant attachment. Dev. Psychobiol. 47, 230–242. doi: 10.1002/dev.20093

Mouret, A., Gheusi, G., Gabellec, M. M., de Chaumont, F., Olivo-Marin, J. C., and Lledo, P. M. (2008). Learning and survival of newly generated neurons: when time matters. J. Neurosci. 28, 11511–11516. doi: 10.1523/JNEUROSCI.2954-08.2008

Peters, G. J., David, C. N., Marcus, M. D., and Smith, D. M. (2013). The medial prefrontal cortex is critical for memory retrieval and resolving interference. Learn. Mem. 20, 201–209. doi: 10.1101/lm.029249.112

Petreanu, L., and Alvarez-Buylla, A. (2002). Maturation and death of adult-born olfactory bulb granule neurons: role of olfaction. J. Neurosci. 22, 6106–6113.

Petridis, A. K., and El Maarouf, A. (2011). Brain-derived neurotrophic factor levels influence the balance of migration and differentiation of subventricular zone cells, but not guidance to the olfactory bulb. J. Clin. Neurosci. 18, 265–270. doi: 10.1016/j.jocn.2010.06.021

Platel, J.-C., and Bordey, A. (2011). “Control of adult-born neuron production by converging GABA and glutamate signals,” in Neurogenesis in the Adult Brain I. Neurobiology, eds T. Seki, K. Sawamoto, J. M. Parent, and A. Alvarez-Buylla (Berlin: Springer), 395–406. doi: 10.1007/978-4-431-53933-9_17

Platel, J.-C., Dave, K. A., Gordon, V., Lacar, B., Rubio, M. E., and Bordey, A. (2010). NMDA receptors activated by subventricular zone astrocytic glutamate are critical for neuroblast survival prior to entering a synaptic network. Neuron 65, 859–872. doi: 10.1016/j.neuron.2010.03.009

Qiu, Q., Scott, A., Scheerer, H., Sapkota, N., Lee, D. K., Ma, L., et al. (2014). Automated analyses of innate olfactory behaviors in rodents. PLoS ONE 9:e93468. doi: 10.1371/journal.pone.0093468

Ramiro-Cortés, Y., Hobbiss, A. F., and Israely, I. (2014). Synaptic competition in structural plasticity and cognitive function. Phil. Trans. R. Soc. B Biol. Sci. 369:20130157. doi: 10.1098/rstb.2013.0157

Rinberg, D., Koulakov, A., and Gelperin, A. (2006). Speed-accuracy tradeoff in olfaction. Neuron 51, 351–358. doi: 10.1016/j.neuron.2006.07.013

Sahay, A., Wilson, D. A., and Hen, R. (2011). Pattern separation: a common function for new neurons in hippocampus and olfactory bulb. Neuron 70, 582–588. doi: 10.1016/j.neuron.2011.05.012

Sairanen, M., Lucas, G., Ernfors, P., Castrén, M., and Castrén, E. (2005). Brain-derived neurotrophic factor and antidepressant drugs have different but coordinated effects on neuronal turnover, proliferation, and survival in the adult dentate gyrus. J. Neurosci. 25, 1089–1094. doi: 10.1523/JNEUROSCI.3741-04.2005

Sanhueza, J., and Lisman, J. (2013). The CaMKII/NMDAR complex as a molecular memory. Mol. Brain 6:10. doi: 10.1186/1756-6606-6-10

Scharfman, H., Goodman, J., Macleod, A., Phani, S., Antonelli, C., and Croll, S. (2005). Increased neurogenesis and the ectopic granule cells after intrahippocampal BDNF infusion in adult rats. Exp. Neurol. 192, 348–356. doi: 10.1016/j.expneurol.2004.11.016

Schellinck, H. M., Forestell, C. A., and LoLordo, V. M. (2001). A simple and reliable test of olfactory learning and memory in mice. Chem. Sens. 26, 663–672. doi: 10.1093/chemse/26.6.663

Shea, S. D., Katz, L. C., and Mooney, R. (2008). Noradrenergic induction of odor-specific neural habituation and olfactory memories. J. Neurosci. 28, 10711–10719. doi: 10.1523/JNEUROSCI.3853-08.2008

Shepard, R. N. (1987). Toward a universal law of generalization for psychological science. Science 237, 1317–1323. doi: 10.1126/science.3629243

Sotelo, C., and Dusart, I. (2014). “Structural plasticity in adult nervous system: an historic perspective,” in Endogenous Stem Cell-Based Brain Remodeling in Mammals, eds M.-P. Junier and S. G. Kernie (New York, NY: Springer), 5–41.

Strowbridge, B. W. (2009). Role of cortical feedback in regulating inhibitory microcircuits. Ann. N.Y. Acad. Sci. 1170, 270–274. doi: 10.1111/j.1749-6632.2009.04018.x

Sullivan, R. M. (2001). Unique characteristics of neonatal classical conditioning: the role of the amygdala and locus coeruleus. Integr. Physiol. Behav. Sci. 36, 293–307. doi: 10.1007/BF02688797

Sullivan, R. M. (2003). Developing a sense of safety: the neurobiology of neonatal attachment. Ann. N.Y. Acad. Sci. 1008, 122–131. doi: 10.1196/annals.130.013

Sullivan, R. M., Landers, M., Yeaman, B., and Wilson, D. A. (2000). Neurophysiology: good memories of bad events in infancy. Nature 407, 38–39. doi: 10.1038/35024156

Sullivan, R. M., McGaugh, J. L., and Leon, M. (1991). Norepinephrine-induced plasticity and one-trial olfactory learning in neonatal rats. Brain Res. Dev. Brain Res. 60, 219–228. doi: 10.1016/0165-3806(91)90050-S

Sullivan, R. M., Wilson, D. A., and Leon, M. (1989). Norepinephrine and learning-induced plasticity in infant rat olfactory system. J. Neurosci. 9, 3998–4006.

Sultan, S., Mandairon, N., Kermen, F., Garcia, S., Sacquet, J., and Didier, A. (2010). Learning-dependent neurogenesis in the olfactory bulb determines long-term olfactory memory. FASEB J. 24, 2355–2363. doi: 10.1096/fj.09-151456

Sutton, M. A., Bagnall, M. W., Sharma, S. K., Shobe, J., and Carew, T. J. (2004). Intermediate-term memory for site-specific sensitization in Aplysia is maintained by persistent activation of protein kinase C. J. Neurosci. 24, 3600–3609. doi: 10.1523/JNEUROSCI.1134-03.2004

Sutton, M. A., Ide, J., Masters, S. E., and Carew, T. J. (2002). Interaction between amount and pattern of training in the induction of intermediate- and long-term memory for sensitization in Aplysia. Learn. Mem. 9, 29–40. doi: 10.1101/lm.44802

Sutton, M. A., Masters, S. E., Bagnall, M. W., and Carew, T. J. (2001). Molecular mechanisms underlying a unique intermediate phase of memory in Aplysia. Neuron 31, 143–154. doi: 10.1016/S0896-6273(01)00342-7

Tashiro, A., Makino, H., and Gage, F. H. (2007). Experience-specific functional modification of the dentate gyrus through adult neurogenesis: a critical period during an immature stage. J. Neurosci. 27, 3252–3259. doi: 10.1523/JNEUROSCI.4941-06.2007

Tenenbaum, J. B., and Griffiths, T. L. (2001). Generalization, similarity, and Bayesian inference. Behav. Brain Sci. 24, 629–640. doi: 10.1017/S0140525X01000061

Tsao, J. C. (1948). Studies in spaced and massed learning: II. Meaningfulness of material and distribution of practice. Q. J. Exp. Physiol. Cogn. Med. Sci. 1, 79–84. doi: 10.1080/17470214808416748

Veyrac, A., Sacquet, J., Nguyen, V., Marien, M., Jourdan, F., and Didier, A. (2009). Novelty determines the effects of olfactory enrichment on memory and neurogenesis through noradrenergic mechanisms. Neuropsychopharmacology 34, 786–795. doi: 10.1038/npp.2008.191

Wang, J.-H., and Feng, D.-P. (1992). Postsynaptic protein kinase C essential to induction and maintenance of long-term potentiation in the hippocampal CA1 region. Proc. Natl. Acad. Sci. U.S.A. 89, 2576–2580.

Wilson, D. A. (1995). NMDA receptors mediate expression of one form of functional plasticity induced by olfactory deprivation. Brain Res. 677, 238–242. doi: 10.1016/0006-8993(95)00151-F

Wilson, D. A. (2009). Olfaction as a model system for the neurobiology of mammalian short-term habituation. Neurobiol. Learn. Mem. 92, 199–205. doi: 10.1016/j.nlm.2008.07.010

Wilson, D. A., and Linster, C. (2008). Neurobiology of a simple memory. J. Neurophysiol. 100, 2–7. doi: 10.1152/jn.90479.2008

Wilson, D. A., and Stevenson, R. J. (2003a). The fundamental role of memory in olfactory perception. Trends Neurosci. 26, 243–247. doi: 10.1016/S0166-2236(03)00076-6

Wilson, D. A., and Stevenson, R. J. (2003b). Olfactory perceptual learning: the critical role of memory in odor discrimination. Neurosci. Biobehav. Rev. 27, 307–328. doi: 10.1016/S0149-7634(03)00050-2

Wolfman, C., Fin, C., Dias, M., Bianchin, M., Da Silva, R. C., Schmitz, P. K., et al. (1994). Intrahippocampal or intraamygdala infusion of KN62, a specific inhibitor of calcium/calmodulin-dependent protein kinase II, causes retrograde amnesia in the rat. Behav. Neural Biol. 61, 203–205. doi: 10.1016/S0163-1047(05)80001-9

Wright, G. A., and Smith, B. H. (2004). Variation in complex olfactory stimuli and its influence on odor recognition. Proc. R. Soc. Lond. B 271, 147–152. doi: 10.1098/rspb.2003.2590

Yang, G., Pan, F., and Gan, W.-B. (2009). Stably maintained dendritic spines are associated with lifelong memories. Nature 462, 920–924. doi: 10.1038/nature08577

Yuan, Q., Harley, C. W., and McLean, J. H. (2003). Mitral cell beta1 and 5-HT2A receptor colocalization and cAMP coregulation: a new model of norepinephrine-induced learning in the olfactory bulb. Learn. Mem. 10, 5–15. doi: 10.1101/lm.54803

Zagrebelsky, M., and Korte, M. (2014). Form follows function: BDNF and its involvement in sculpting the function and structure of synapses. Neuropharmacology 76C, 628–638. doi: 10.1016/j.neuropharm.2013.05.029

Zigova, T., Pencea, V., Wiegand, S. J., and Luskin, M. B. (1998). Intraventricular administration of BDNF increases the number of newly generated neurons in the adult olfactory bulb. Mol. Cell. Neurosci. 11, 234–245. doi: 10.1006/mcne.1998.0684

Zimering, J. H., and Cleland, T. A. (2011). Dihydrokainate restores recall of olfactory reward-based associative learning following impairment by isoflurane. FASEB J. 25:674.3

Zucco, G. M., Hummel, T., Tomaiuolo, F., and Stevenson, R. J. (2014). The influence of short-term memory on standard discrimination and cued identification olfactory tasks. J. Neurosci. Methods 222, 138–141. doi: 10.1016/j.jneumeth.2013.11.006

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 23 April 2014; Accepted: 16 June 2014; Published online: 07 July 2014.

Citation: Tong MT, Peace ST and Cleland TA (2014) Properties and mechanisms of olfactory learning and memory. Front. Behav. Neurosci. 8:238. doi: 10.3389/fnbeh.2014.00238

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Tong, Peace and Cleland. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

REVIEW ARTICLE
published: 22 April 2014
doi: 10.3389/fnbeh.2014.00134

Sleep and olfactory cortical plasticity

Dylan C. Barnes1,2,3 and Donald A. Wilson1,2,3,4[*]

1Emotional Brain Institute, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA

2Behavioral and Cognitive Neuroscience Program, City University of New York, New York, NY, USA

3Department of Biology, University of Oklahoma, Norman, OK, USA

4Department of Child and Adolescent Psychiatry, New York University Langone School of Medicine, New York, NY, USA

*Correspondence:

Donald A. Wilson, Department of Child and Adolescent Psychiatry, New York University Langone School of Medicine, 1 Park Avenue, 7th floor, New York, NY 10016, USA
e-mail: donald.wilson@nyumc.org

Edited by:
Anne-Marie Mouly, Centre de Recherche en Neurosciences de Lyon, France

Reviewed by:
Kensaku Mori, University of Tokyo, Japan
Nadine Ravel, Center for Research in Neuroscience of LYON (CRNL), France

In many systems, sleep plays a vital role in memory consolidation and synaptic homeostasis. These processes together help store information of biological significance and reset synaptic circuits to facilitate acquisition of information in the future. In this review, we describe recent evidence of sleep-dependent changes in olfactory system structure and function which contribute to odor memory and perception. During slow-wave sleep, the piriform cortex becomes hypo-responsive to odor stimulation and instead displays sharp-wave activity similar to that observed within the hippocampal formation. Furthermore, the functional connectivity between the piriform cortex and other cortical and limbic regions is enhanced during slow-wave sleep compared to waking. This combination of conditions may allow odor memory consolidation to occur during a state of reduced external interference and facilitate association of odor memories with stored hedonic and contextual cues. Evidence consistent with sleep-dependent odor replay within olfactory cortical circuits is presented. These data suggest that both the strength and precision of odor memories is sleep-dependent. The work further emphasizes the critical role of synaptic plasticity and memory in not only odor memory but also basic odor perception. The work also suggests a possible link between sleep disturbances that are frequently co-morbid with a wide range of pathologies including Alzheimer’s disease, schizophrenia and depression and the known olfactory impairments associated with those disorders.

Keywords: olfaction, piriform cortex, slow-wave sleep, odor memory, odor perception, memory consolidation

INTRODUCTION

The olfactory system (Figure 1) is remarkably plastic. This experience-dependent plasticity is not reserved for higher-order olfactory cortical areas or zones of multisensory integration, but rather is expressed throughout the olfactory pathway from the nose to cortex. For example, in the olfactory epithelium, odor experience and associative training can modify olfactory sensory neuron receptor gene expression, survival and axonal targeting (Tyler et al., 2007; Jones et al., 2008; Tian and Ma, 2008; Kass et al., 2013). In the olfactory bulb (OB), such experience can modify OB glomerular size, number of juxtaglomerular neurons and glomerular responses (Fletcher, 2012; Miura et al., 2012). In addition, second-order neuron (mitral/tufted cell) structure (Gheusi et al., 2000; Davison and Ehlers, 2011), sensory physiology and excitability (Moreno et al., 2009; Boland and Alloy, 2013; Tononi and Cirelli, 2014) have also been shown to be sensitive to odor experience. Not only are OB principal neurons changed, but also the large population of OB inhibitory interneurons, granule cells undergo extreme odor experience-dependent changes in survival and physiology (Killgore and McBride, 2006; Moreno et al., 2009; Yokoyama et al., 2011). These learned changes in OB structure lead to changes in OB function, often measured as changes in local circuit oscillations (Grajski and Freeman, 1989; Martin et al., 2006; Chapuis et al., 2009; Kay and Beshel, 2010). Beyond the OB, odor experience has been shown to modify piriform cortical pyramidal cell synaptic and sensory physiology (Mouly et al., 2001; Wilson, 2003, 2010; Roesch et al., 2007; Cohen et al., 2008; Chapuis and Wilson, 2011; Saar et al., 2012), membrane excitability (Saar and Barkai, 2003) and dendritic structure (Knafo et al., 2004), as well as piriform cortical local circuit oscillations (Martin et al., 2006; Kay and Beshel, 2010; Chapuis and Wilson, 2011). Finally, functional connectivity between the OB and olfactory cortex (Martin et al., 2006), and between these primary olfactory structures and higher-order cortical areas such as the hippocampus (Martin et al., 2007) and orbitofrontal cortex (Cohen et al., 2008) are also highly experience-dependent. This list is not meant to be exhaustive of all possible identified changes, but rather meant to exemplify the extent and diversity of changes induced by either passive odor exposure (or lack thereof) or associative conditioning.

FIGURE 1. Simplified schematic diagram of the main olfactory system. Olfactory sensory neurons in the olfactory epithelium express one of a very large set of olfactory receptor genes. Here, cells expressing different genes are colored differently. Sensory neurons expressing the same gene converge onto glomeruli in the olfactory bulb (OB), where they synapse onto second order neurons, mitral and tufted cells. Individual mitral cells receive excitatory input from a single glomerulus, and thus received input from a homogeneous population of sensory neurons. Excitability of, and lateral interactions between, mitral cells are mediated in part by OB granule cells. Granule cells undergo continual neurogenesis and replacement throughout life. Furthermore, they are a primary target of descending inputs from olfactory cortical areas. The primary olfactory cortex (piriform cortex) functions as an combinatorial, auto-associative array, allowing convergence of input conveyed from different olfactory sensory neurons. This allows merging of odorant features into odor objects. In addition to merging odorant features, the piriform cortex also has extensive, reciprocal connections with a variety of limbic and cortical areas.

Together, these experience-induced changes can modify olfactory system sensitivity and acuity, as well as link odor quality to associative meaning or hedonic valence. The changes can last as short as a few seconds, in the case of short-term odor adaptation (Best and Wilson, 2004) to as long as days, weeks or years in the case of associative memory (Fillion and Blass, 1986) or following long-term odor-exposure (Dalton and Wysocki, 1996). Apparently the most stable feature of the olfactory system is its constant change. Thus, as described elsewhere (Wilson and Stevenson, 2003; Wilson and Sullivan, 2011), olfactory perception lies at the interface between memory and sensation. That is, experience-dependent plasticity is required not only to allow the associative memory that strawberry aroma signals a pleasant, energy dense fruit, but also to allow the implicit memory underlying perception of the complex mixture of odorants emanating from the fruit as a unique odor object which English-speaking humans label “strawberry”.

Most forms of long-term memory require a process of consolidation, wherein temporary traces of encoded information become more permanently stored through post-experience modulation (McGaugh, 2013). Post-experience consolidation is in essence a “save now” process. Not all experiences need be permanently stored. Those events or stimuli that signaled something biologically significant (e.g., a source of food, safety or danger, or mating opportunity) may be more likely to be consolidated than other events. Thus for example, events that elevate peripheral epinephrine or central norepinephrine are more strongly consolidated than those that do not (McGaugh, 2013).

However, it is increasingly apparent that memory consolidation can also involve a sleep-dependent stage. Sleeping within a few hours of learning a new skill or new information enhances memory for that skill or information, compared to staying awake after the initial learning. This consolidation enhancement (enhanced long-term memory) can occur for motor skill memories, declarative/episodic memories, emotional memories and sensory memories/perceptual learning (Stickgold, 2005; Diekelmann and Born, 2010; Rasch and Born, 2013; Tononi and Cirelli, 2014).

Recent work has begun exploring the role of sleep in olfactory memory. Olfaction is interesting in this regard given the unusual anatomy of the olfactory pathway compared to all other sensory systems, most notably the relatively limited involvement of a thalamic nucleus prior to the primary sensory cortex. Two major approaches have been used in this research. First, odors have been used as contextual cues while some other task was ongoing, such as declarative or emotional memory task (Rasch et al., 2007; Eschenko et al., 2008; Hauner et al., 2013). This research has primarily utilized the odor context to manipulate sleep-dependent memory consolidation of these other forms of information (Rasch et al., 2007; Diekelmann et al., 2012; Hauner et al., 2013). Several recent reviews that include this work have been published (Rasch and Born, 2013; Shanahan and Gottfried, 2014) and this work will not be the focus of this paper. The second approach has been to focus on odor memory and the olfactory system itself. This work has examined state-dependent changes in olfactory system activity and connectivity, and explored how sleep directly modifies the olfactory system after odor experience. Based on this work, we argue here that olfactory system structure and function, and thus olfactory perception itself, is shaped in an odor-specific manner during post-odor experience sleep. These modifications are critical to both the associative meaning of learned odors, as well as the acuity of odor perception and memory. The work suggests that odor perception not only depends on the odors we smell, but also on the odors of which we dream.

SLEEP

Sleep or sleep-like states appear to occur in all animals, including invertebrates and vertebrates. Sleep is generally defined as a period of behavioral dormancy, though is not necessarily associated with a quiescent nervous system. In fact, the mammalian brain is highly active during sleep and undergoes shifts between several different sleep-related physiological states that together are referred to as sleep structure. Although sleep-related activity can occur unilaterally in marine mammals and some birds, generally the entire brain enters specific stages of sleep nearly simultaneously.

SLEEP STRUCTURE

In non-human mammals, there are two widely accepted behavioral phases of sleep: rapid eye movement (REM) or paradoxical sleep and slow wave sleep (SWS), also known as non-paradoxical or NREM sleep (Figure 2). In the circadian rhythm of most mammals, early nocturnal and diurnal sleep contains mostly SWS and late sleep is largely devoted to REM sleep (Tobler, 1995). Both of these sleep stages are characterized by specific field potential oscillations of brain activity. REM sleep is characterized by fast, low-amplitude oscillatory activity in the θ-band (4–8 Hz) and higher frequency bands characteristic of waking (van der Helm et al., 2011). REM sleep also includes ponto-geniculo-occipital (PGO) waves that are intense bursts of synchronized activity propagating from the pontine region of the brain stem to the lateral geniculate nucleus and visual cortex (Datta and O’Malley, 2013). Behavioral hallmarks of REM sleep are phasic bouts of REM (hence the phase’s name) and muscle atonia (Jones, 1979).

FIGURE 2. (A) During a typical human sleep bout early stages of the sleep bout are dominated by slow-wave sleep, while REM sleep becomes more prominent later in the bout. The same is true in rodents. (B) local field potential (LFP) recordings in piriform cortex showing spontaneous shifts between slow-wave and fast-wave activity. (C) During slow-wave sleep, piriform cortical single-units fire in phase with the sharp-waves as shown by the raster plot and peri-stimulus time histogram. (D) Piriform cortical single-unit and LFP activity are decoupled from respiration during slow-wave sleep compared to waking. Histograms show respiratory events as a function of single-unit spiking. LFP’s are mean waveforms triggered on single-unit spikes. Overdrawn waveform of single-unit used for analysis in D in shown at bottom.

The predominant oscillatory activity in SWS is in the δ-band (0.5–4.0 Hz), which includes the less than 1 Hz slow oscillation (Buzsáki, 1986; Mölle et al., 2002; Montgomery et al., 2008). These oscillations are comprised of alternating periods of membrane depolarization, the “up” states, and hyperpolarization, “down” states. Most cortical neurons engage in slow oscillations and the firing patterns produce high synchrony across cellular populations in different brain regions (Amzica and Steriade, 1998; Steriade et al., 2001; Volgushev et al., 2006). The widespread synchrony of neuronal activity during SWS is the backbone of the slow oscillation and allows for a relatively global time scale in which activity is limited to the depolarized up states and terminated with the hyperpolarized down states (Luczak et al., 2007; Mölle and Born, 2011). Early sleep SWS is classically thought of as important for processing declarative and hippocampal based memories (Wagner and Born, 2008), although recent studies may point to complementary, rather than separate processes for memory consolidation (see below) (Rasch and Born, 2013).

A special type of oscillation seen in the hippocampus (Eschenko et al., 2008) and the piriform cortex (Wilson, 2010; Manabe et al., 2011; Narikiyo et al., 2014) during periods of quiet wakefulness and, more predominantly during SWS, is the sharp-wave ripple (SPW-R). These are fast depolarizing waves that were first seen in the CA3 region of the hippocampus and are superimposed by high frequency (100–300 Hz) ripple oscillations (Diekelmann and Born, 2010). During sharp-wave ripple events, small subpopulations of pyramidal cells that were active during prior wakefulness spontaneously fire in the same pattern as during learning (Pavlides and Winson, 1989) in a much shorter amount of time as compared to their original timing during encoding. The number of sharp-wave ripple events during post-learning sleep is also significantly correlated with the formation and strength of memories (Axmacher et al., 2008; Ramadan et al., 2009). The olfactory system is special in relation to other sensory systems as sharp-waves have also been described within the main olfactory cortex, the piriform cortex (Wilson and Yan, 2010; Manabe et al., 2011; Narikiyo et al., 2014) that are similar to those described in the hippocampus (Buzsáki, 1986), and that these sharp-waves appear to be generated independent of hippocampal origination, perhaps originating in the endopiriform nucleus (Behan and Haberly, 1999).

SLEEP IS IMPLICATED IN SYNAPTIC HOMEOSTASIS

SWS is traditionally thought of as the marker for homeostatically regulated sleep pressure. After prolonged periods of wakefulness, sleep pressure is greatest as evidenced by a shorter latency to enter SWS following sleep onset and an increased power of slow wave activity (SWA), which gradually decreases from early to late sleep (Borbély, 1982; Borbély and Achermann, 1999; Riedner et al., 2007). However, in addition to sleep homeostasis, SWS may also play an important role in the homeostasis of synaptic weight. That is, sleep may be a time when the brain can re-adjust synaptic weights according to their recent history of use, returning them to a state where they can be maximally efficient at encoding new information—avoiding saturation and incapable of further strengthening (Tononi and Cirelli, 2003). This hypothesis of synaptic homeostasis allows for a widespread decrease in the strength of synaptic connections that take place during SWS. Plasticity-related gene expression follows a circadian rhythm and sleep deprivation causes a decrease in the expression of these genes (Guzman-Marin et al., 2006). During wakefulness, and especially during learning, there is a buildup of molecular correlates associated with long-term potentiation (LTP) such as BDNF and cAMP response element-binding protein (Silva, 2003). During SWS, however, the expression of these genes is significantly reduced or downscaled (Tononi and Cirelli, 2003). LTP also involves an increase in dendritic spine growth. Sleep, however, seems to act as a pruning mechanism during which time there is a decrease in the number of spines (Bushey et al., 2011). Synaptic downscaling, therefore, prevents saturation of the synaptic weight and reduces place and energy demands of a neural network, thereby allowing the system to reset to prepare for the encoding of new information during succeeding wakefulness (Horn et al., 1998a,b). The hypothesis promotes the consolidation of stronger memories, however, that were encoded during the pre-sleep period to remain and replay spontaneously while more trivial synapses can be reset. This active system consolidation implicates a certain selectivity of which memories to consolidate. It is likely that the widespread consolidation of everything recently learned would produce a system overflow. In fact, sleep does not benefit all memories, although the mechanisms that determine which memory will be tagged for consolidation and which will be forgotten during sleep is still unclear.

SLEEP AND PERCEPTUAL LEARNING IN THALAMOCORTICAL SYSTEMS

As noted above, sleep-dependent consolidation has been demonstrated for a variety of forms of information including declarative/episodic, motor, emotional and sensory memory. The prevailing view is that during sleep, recently acquired information is replayed, both within local neural networks (e.g., hippocampus or sensory cortex) and across networks (e.g., linking hippocampal and neocortical networks). This replay allows recently strengthened synapses to solidify those changes either biochemically or structurally, while other less critical synapses are reset to basal strengths, ready for plasticity another day (Stickgold, 2005; Cirelli and Tononi, 2008; Rasch and Born, 2013; Tononi and Cirelli, 2014). Performing replay during sleep, especially SWS, may be ideal as it is a period of hypo-responsiveness to external sensory inputs, and thus replay may be less prone to external interference.

Although sleep-dependent consolidation influences most forms of memory, here we focus on the perceptual learning to highlight evidence of sleep-dependent consolidation within thalamocortical sensory systems. Perceptual learning is an enhancement in perceptual acuity or sensitivity induced by training. Perceptual learning is associated with sensory system plasticity and is remarkably specific. For example, learning to make very precise judgments of the alignment of two vertical lines (e.g., a vernier scale) does not transfer to judgments of diagonal or horizontal lines. Similar selective improvements can be made in the other thalamocortical senses with appropriate training. The learned enhancement in sensory acuity is associated with narrowed receptive fields in sensory neocortex and/or enlargement of the sensory cortical region devoted to that segment of the sensory world (e.g., Recanzone et al., 1992; Godde et al., 1996; Kilgard and Merzenich, 1998; Xerri et al., 1999). The improvement in sensory acuity following perceptual learning is facilitated by sleep (Karni et al., 1994; Allen, 2003; Fenn et al., 2003; Atienza et al., 2004; Gottselig et al., 2004; Censor et al., 2006; Yotsumoto et al., 2009), although see Hussain et al. (2008) and Aberg et al. (2009) for counter–examples. Both REM (Karni et al., 1994) and SWS (Aeschbach et al., 2008) have been implicated in consolidation of perceptual learning. As noted above, SWS may be a very effective period for sleep-dependent replay of learned stimuli given that the sensory evoked activity in the thalamus becomes highly variable and reduced during SWS (McCormick and Bal, 1994; Steriade et al., 2001). This shift away from thalamic monitoring of external events may reduce interference during memory replay.

In a variety of paradigms, sleep structure during the post-training period is modified, with prolonged bouts of either REM or SWS, depending on the specific task (Tononi and Cirelli, 2014). This prolongation of sleep bouts is hypothesized to reflect the additional pressure required for memory storage. In addition, perceptual learning can influence sleep related activity in neocortical sensory systems in other ways. First, SWS related activity (EEG oscillation power or fMRI activation) is enhanced in sensory cortex during sleep after perceptual learning (Cantero et al., 2002; Yotsumoto et al., 2009; Bang et al., 2013), and specifically in those regions of sensory neocortex encoding the learned stimulus (Yotsumoto et al., 2009). Second, while slow waves are cortical-wide events generally driven by thalamocortical oscillations, recent evidence suggests that following visual perceptual learning, slow-waves may be preferentially initiated in primary visual cortex (Mascetti et al., 2013).

Together, these changes in sleep structure, and sleep-related oscillatory activity within the primary sensory system may promote consolidation and refinement of newly learned sensory representations, allowing enhanced perceptual acuity after sleep compared to a similar period of waking (Karni et al., 1994; Mednick et al., 2002; Allen, 2003; Fenn et al., 2003; Aeschbach et al., 2008). However, neocortical sensory systems evolved well after the primary olfactory system. Can the mammalian olfactory system, with a very different structure and relationship to thalamus, also support sleep-dependent memory consolidation?

SLEEP AND OLFACTORY SYSTEM PHYSIOLOGY

Sleep-dependent changes in neocortical function are largely shaped by changes in thalamic activity (Steriade et al., 2001; Buzsaki, 2006). For example, slow-waves derive from a synchronization of slow, δ-frequency oscillations generated in thalamic and neocortical networks. During this thalamocortical slow-wave activity, sensory evoked activity in the thalamus is reduced and more variable, resulting in reduced sensory cortical activation. In contrast, although there is an olfactory thalamic nucleus that contributes to odor processing and memory (Lu and Slotnick, 1990; Courtiol and Wilson, 2014), it is downstream of the primary olfactory cortex, not between the cortex and the periphery. Thus, the state-dependent gating performed by the thalamus in thalamocortical systems is missing in olfaction. Furthermore, rather than displaying slow-wave activity during SWS as in thalamocortical systems, the olfactory cortex (piriform cortex) displays sharp-wave ripples, similar to that observed in the hippocampal formation. Nonetheless, important parallels exist between the olfactory cortex and thalamocortical systems in sleep-wake state dependent physiology.

Although there is no thalamic state-dependent gate between the nose and the piriform cortex, the piriform cortex shows robust state-dependent fluctuations in odor responsiveness. During SWS, both piriform cortical single-units (Murakami et al., 2005; Wilson, 2010) and local field potentials (Barnes et al., 2011) show greatly attenuated odor responses. This SWS-dependent modulation is expressed both in unanesthetized animals spontaneously cycling between waking and SWS (Barnes et al., 2011; Manabe et al., 2011), and in urethane-anesthetized animals (Murakami et al., 2005; Wilson, 2010) that also show spontaneous fast-wave/slow-wave cycling. It should be emphasized that cortical odor-evoked activity is not completely eliminated during SWS, but is greatly reduced. In unanesthetized animals, odor responses appear unaffected by REM sleep states (Barnes et al., 2011). The human olfactory system appears similarly depressed during SWS (Carskadon and Herz, 2004).

During SWS, while the piriform cortex responsivity to the outside world is reduced, its activity shifts to sharp-wave/ripple like activity, reminiscent of that observed in the hippocampal formation. However, the piriform cortical sharp-wave activity is relatively independent of that observed simultaneously in the hippocampus (Manabe et al., 2011). Although the generator of these large cortical sharpwaves in unknown, one hypothesis is that they are driven by the highly auto-excitatory endopiriform nucleus, which has broad excitatory connections throughout piriform cortex (Behan and Haberly, 1999). In fact, the current sink for piriform cortical sharp-waves is located in layers II/III which is consistent with a intracortical association fiber/endopiriform driven potential (Wilson, 2010; Manabe et al., 2011). Piriform cortical layer II/III single-unit activity during these sharpwaves coincides with the deep recorded negative peak (Wilson, 2010; Manabe et al., 2011; Narikiyo et al., 2014). That is, units shift from primarily firing in phase with the respiratory cycle during waking or fast-wave states to firing en masse in phase with the sharpwaves during SWS.

This coherent firing of large piriform cortical pyramidal cell ensembles in phase with sharpwave evokes strong responses in monosynaptic targets of the piriform, including feedback to the OB (Manabe et al., 2011; Narikiyo et al., 2014). Functional connectivity/coherence of piriform cortex with limbic structures such as the basolateral amygdala and dorsal hippocampus, as well as neocortical areas is significantly enhanced during SWS compared to fast-wave states (Wilson and Yan, 2010; Wilson et al., 2011). Combined with the reduced response to sensory afferents, these changes in functional connectivity suggest a turning inward, perhaps consistent with the needs of replay and strengthening odor associations with meaning and hedonics.

One contributor to the state-dependent shift in piriform cortical activity is a change in neuromodulatory tone over the sleep-wake cycle, particularly acetylcholine (ACh). ACh plays a major role in odor processing and plasticity throughout the olfactory pathway from the OB (Ravel et al., 1994; Tsuno et al., 2008; Chaudhury et al., 2009), to olfactory cortex (Barkai et al., 1994; Wilson, 2001; Chapuis and Wilson, 2013). In the piriform cortex, ACh muscarinic receptor (AChmR) activation suppresses association fiber synaptic efficacy through a reduction in pre-synaptic glutamate release, with minimal effects on afferent fiber synapses from mitral cells (Hasselmo and Bower, 1992). This means that during waking or vigilance, when ACh levels are high, the piriform cortex would be strongly driven by afferent input from the bulb, while intracortical association fibers would be suppressed. During SWS however, when ACh levels drop, the intracortical association fiber system would be released from cholinergic suppression and could more effectively influence cortical activity. Such intracortical excitation between recently activated piriform cortical pyramidal cells during SWS may be important in replay of recently experienced odors.

SLEEP AND ODOR MEMORY

As noted above, a number of groups have used odors and odor contexts as cues in declarative, procedural or emotional memory tasks (Rasch et al., 2007; Eschenko et al., 2008; Diekelmann et al., 2011; Arzi et al., 2012; Hauner et al., 2013). This work has emphasized how odors delivered during sleep can modulate consolidation of various forms of memory in limbic or other brain regions. Here, we focus specifically on work examining how sleep related activity shapes the olfactory system itself, in turn shaping odor coding, perception and memory.

ODOR EXPERIENCE AFFECTS SLEEP STRUCTURE

Laboratory housed rats and mice spend approximately 40% of a 24 h day in SWS (Barnes et al., 2011). Left alone for a 4 h period during the light cycle, rodents enter SWS within 5–15 min and then cycle between SWS, REM sleep and waking, with REM sleep bouts emerging after the initial SWS bouts. As is true in a variety of conditioning tasks, during a 4 h period following odor-fear conditioning rats spend significantly more time in SWS than control rats, as recorded within the piriform cortex (Barnes et al., 2011; Barnes and Wilson, 2014). In our paradigm, there was a slight, non-significant decrease in REM sleep, thus the total duration of sleep did not change (Barnes et al., 2011). Importantly, the percentage increase in post-training SWS duration strongly correlated with the strength of fear memory 24 h later (Barnes et al., 2011).

Thus, during SWS, the primary olfactory cortex is hypo-responsive to environmental stimulation, instead displaying sharp-wave activity, and following fear conditioning, animals spend more time in this state. It is hypothesized that the piriform cortical sharpwave activity during SWS may contribute to olfactory system reorganization and odor memory consolidation during a time of reduced interference from new odor stimulation.

ODOR REPLAY MODULATES MEMORY STRENGTH AND PRECISION

As an initial examination of whether odors are replayed in the piriform cortex during sharp-wave activity, we asked whether the temporal structure of single-unit activity during sharp-waves is affected by recent (15–30 min) odor experience that occurred during fast-wave state (Wilson, 2010). Piriform cortical single-units were recorded in anesthetized rats, which, though surgically anesthetized, naturally cycle between fast-wave and slow-wave states (Fontanini and Bower, 2005; Murakami et al., 2005; Wilson, 2010). Single-unit activity during piriform cortical sharpwaves was characterized and then the animal was allowed to spontaneously shift to fastwave state. During fastwave state, the animal was repeatedly exposed to an odor to which the recorded unit responded, or in control animals to no odor or to an odor to which the cell did not respond. After the stimulation, the animal was allowed to spontaneously return to slow-wave state, and again unit activity was characterized relative to sharpwave events. In cells that had been stimulated with odor during the fast-wave state, the temporal structure of sharp-wave related unit activity shifted and became more variable, compared to either control group (Wilson, 2010). Thus, the temporal structure of piriform cortical single-unit activity during slow-wave state was shaped by past odor experience. These data do not confirm that the cells were replaying the odor during a sharpwave, but are consistent with that interpretation. Unfortunately spontaneous single-unit activity in the piriform cortex is very slow (Roesch et al., 2007; Chen et al., 2011), thus some of the analytical techniques to discern actual “replay” that are used in other brain regions are currently less effective here. To address this problem, we decided to impose replay directly following odor fear conditioning.

Odor fear conditioning can induce either highly odor-specific fear responses (e.g., freezing) or more generalized odor-evoked freezing, depending on the nature of the training protocol (Chen et al., 2011). Differential conditioning, involving both a CS+ (predicts footshock) and a CS− (does not predict footshock) induces fear responses selectively to the CS+. Conditioning with a CS+ but no CS−, in contrast, induces generalized fear responses to a wide range of odors. These different behavioral outcomes are associated with distinct changes in piriform cortical single-unit odor coding (Chen et al., 2011). Differential conditioning, which induces odor specific fear, results in a narrowing of piriform cortical single-unit odor receptive fields compared to pseudo-conditioned controls, i.e., an enhancement in odor acuity. In contrast, conditioning without a CS−, which results in generalized odor fear, is associated with a broadening of piriform cortical single-unit odor receptive fields, i.e., a reduction in acuity. This suggests that the precision of the odor memory is at least in part due to changes in piriform cortical odor coding. Similar results can be observed after appetitive conditioning (Chapuis and Wilson, 2011). Does cortical activity during post-training sleep contribute to the strength and/or precision of this odor memory?

To test this, we utilized olfactomimetic electrical stimulation of the OB (Mouly et al., 2001) as the CS+ and CS− in a differential fear conditioning paradigm, with different spatial patterns of electrical stimulation serving as the different stimuli. We chose this type of stimulus because it allowed us to deliver identical stimuli, with precise temporal control during training and different post-training states, regardless of the animal’s posture or respiration in the different states. We further hypothesized that the strength of the direct OB stimulation would allow activation of the piriform cortex regardless of behavioral state.

Rats were trained using these olfactomimetic stimuli in a differential fear conditioning task, and then placed in a quiet chamber for the 4 h post-training. Piriform cortical local field potentials and neck muscle EMG were recorded to identify sleep/wake states. Animals that were trained and then left alone during the 4 h post-training period displayed CS+ specific freezing the following day. However, the strength and accuracy of this memory could be modulated by post-training CS+ imposed replay (Barnes and Wilson, 2014). For example, animals that received imposed post-training replay (i.e., OB stimulation identical to the CS+ delivered during training) while they were awake showed reduced CS+ evoked freezing the following day, consistent with extinction. In contrast, animals that received the identical imposed replay selectively during slow-wave sleep showed a significant enhancement in the strength of the CS+ evoked response. This enhanced memory was selective for the CS+. Importantly, imposing replay during slow-wave sleep of a novel olfactomimetic stimulus not previously encountered, while the animal was hypothetically spontaneously replaying the learned stimulus resulted in normal memory strength for the CS+, but now the memory was generalized to all olfactomimetic stimuli tested (Barnes and Wilson, 2014).

We hypothesize that during post-training slow-wave sleep, ensembles of piriform cortical neurons that had been recently co-activated during training, preferentially fire together during piriform cortical sharp-waves in a form of spontaneous replay. This co-activation is facilitated by a release of intracortical association fiber synapse suppression due to low levels of cholinergic activity during slow-wave sleep (Hasselmo and Bower, 1992; Hasselmo and McGaughy, 2004). The co-activation during sharp-waves should strengthen excitatory synapses between neurons within the ensemble, enhancing representation of the learned odor (Linster et al., 2003, 2009). Adding noise to the replay by imposing the novel stimulation during slow-wave sleep should expand the membership of the ensemble to include irrelevant neurons, thus degrading the precision of the odor representation and inducing generalized fear responses.

If this model is correct, then disrupting intracortical association fiber synapses during the post-training period should impair odor memory strength and/or memory precision. To test this, we infused the GABAB receptor agonist baclofen bilaterally into the piriform cortex during the post-training period. Baclofen selectively suppresses association fiber synapses in the piriform cortex (Tang and Hasselmo, 1994; Poo and Isaacson, 2011; Barnes and Wilson, 2014), and suppresses sharp-wave amplitude (Barnes and Wilson, 2014). Animals with suppressed association fiber synapses during the 4 h post-training period showed normal freezing to the CS+, however the freezing strongly generalized across odors. Thus, the post-training association fiber activity, presumably during spontaneous sleep-dependent replay, is necessary for odor memory precision the following day (Barnes and Wilson, 2014).

Together, this work suggests that piriform cortical sharp-wave activity during post-training slow-wave sleep allows for a strengthening of ensemble representation of the learned odor. This post-training replay can modify both the strength and the precision of the odor memory, at least in part via plasticity within the piriform cortex itself. However, it must be noted that co-activation of large piriform cortical ensembles during sharp-waves also result in propagation of spike trains out of the cortex to its monosynaptic neighbors. Thus, this provides an opportunity for piriform cortical manipulation not only of intra-cortical synapses, but also of its efferent targets during slow-wave sleep. These “downstream” effects have been the focus of recent work in Kensaku Mori’s group (Manabe et al., 2011; Yokoyama et al., 2011; Yamaguchi et al., 2013; Narikiyo et al., 2014).

ODOR REPLAY MODULATES ADULT-BORN OLFACTORY BULB NEURON SURVIVAL

One important role for odor learning is for memory of novel flavors. The majority of the perception of flavor is derived from volatile food odorants delivered to the olfactory epithelium via retronasal smell. While humans are believed to have the most advanced retronasal olfactory abilities (Shepherd, 2012), rodents also experience retronasal smell (Chapuis et al., 2009). In omnivore’s such as mus musculus and rattus norvegicus, learning about food odors, and their nutritional and/or illness producing characteristics is critical for survival. Interestingly, a common behavioral response in many mammals following a satiating meal is drowsiness and sleep; referred to a post-prandial sleep. For example, 50% or more of food deprived mice begin resting or fall asleep within 1 h of being given access to food (Yokoyama et al., 2011). Could post-prandial sleep contribute to memory for the odors and flavors of the consumed food?

As noted above, odor memory is associated with changes throughout the olfactory pathway. Perhaps the most extreme neural correlate of odor memory is differential survival of OB granule cells. OB granule cells are inhibitory interneurons which modulate the excitability of OB mitral and tufted cells and are the primary target of descending inputs from olfactory cortex. Importantly, granule cells display adult neurogenesis (Bayer, 1983), with survival of adult born neurons dependent on olfactory stimulation and activity (Killgore and McBride, 2006; Moreno et al., 2009). Differential experience-dependent granule cell survival may contribute to olfactory acuity and information storage (Gheusi et al., 2013). Yokoyama et al. (2011) have demonstrated that there is enhanced granule cell death in the few hours post-feeding. The extent of cell death is correlated with the amount of time spent in slow-wave sleep (though not REM sleep) (Yokoyama et al., 2011). Granule cell death is enhanced even more if the bulb is odor deprived during the food exposure. This suggests that those granule cells not activated by the food odors are selectively targeted for apoptosis during post-prandial sleep.

Mori’s group suggests that the strong descending sharpwave-associated pyramidal cell spiking from the olfactory cortex to the OB during slow-wave sleep (Manabe et al., 2011) may be the critical signal differentiating granule cell death and survival. Thus, it is hypothesized that during waking odor exposure, for example during the meal, granule cells activated by the odors are tagged by descending piriform cortical axonal input. Those cells not activated by the odor do not receive the same tagging. During subsequent post-prandial slow-wave sleep, cortical sharp-wave evoked descending activity, perhaps in concert with sleep-associated neuromodulatory inputs initiate cascades leading to non-tagged granule cell apoptosis (Yokoyama et al., 2011). Similar events may occur following other odor learning experiences, for example the fear conditioning protocols described above.

SUMMARY OF MAJOR EFFECTS OF SLEEP ON OLFACTORY CORTEX

Combining the findings from these two paradigms leads to the suggestion that post-odor exposure slow-wave sleep contributes to changes across the olfactory system that contribute to both the strength and precision of odor memory (Figure 3). Slow-wave sleep associated piriform cortical sharp-waves allow strengthening of associations within cortical ensembles encoding specific odors in a replay-like manner. Reducing this association or imposing noise during replay impairs the precision of the odor memory. Sharp-wave evoked piriform cortical activity also induces strong activation of cortical efferent targets, such as the OB and perhaps other mono-synaptic targets (Courtiol and Wilson, 2014; Narikiyo et al., 2014), contributing to memory-associated changes in those structures.

FIGURE 3. A schematic representation of changes in olfactory system activity between waking and slow-wave sleep. Odor stimulation during waking (symbolized by the rose) evokes odor-specific patterns of activity in the OB and mitral/tufted cell output to the piriform cortex. This afferent activity is respiration entrained and evokes intra-cortical association fiber activity linking co-active piriform cortical neurons. It also results in piriform cortical output, including feedback to OB granule cells (red dots) as well as to other regions of olfactory cortex and non-olfactory regions. During slow-wave sleep, the balance of afferent and intracortical activity shifts, with decreases in sensory-evoked input to piriform cortex and enhanced intra-cortical mediated activity, primarily during sharp-wave events. The sharp-wave associated activity replays cortical ensemble activity evoked by odors during waking. These strong, synchronous sharp-wave events help strengthen synaptic connections within odor-coding ensembles, as well as help shape OB granule cell survival in an odor-specific manner. Abbreviations: GL = glomerular layer, M = mitral cell layer, G = granule cell layer.

SLEEP, PATHOLOGY AND ODOR PERCEPTION

Olfactory deficits are associated with a variety of disorders including, but not limited to Alzheimer’s Disease (Murphy, 1999), Parkinson’s disease (Doty, 2012), schizophrenia (Malaspina et al., 2012) and major depression (van Mill et al., 2010). All of these disorders are also associated with sleep disturbances such as insomnia and sleep fragmentation (Spiegelhalder et al., 2013). While originally thought of as side effects of the primary disorder, sleep disturbance is increasingly seen as integral component of many disorders. For example, specifically treating sleep disorders in individuals with major depression helps alleviate depressive symptoms (Sánchez-Ortuño and Edinger, 2012; Spiegelhalder et al., 2013). Furthermore, given the importance of sleep related memory consolidation and synaptic homeostasis as described here, it is easy to see how sleep disturbance could contribute to cognitive deficits.

Thus, we speculate that a contributing factor to the widespread occurrence of olfactory disorders, particularly odor identification, across diverse pathologies may be related to underlying sleep disorders. Memory and neural plasticity are integral not only to odor memory, but also to basic odor perception (Wilson and Stevenson, 2003; Wilson and Sullivan, 2011). As described here and elsewhere, sleep, especially slow-wave sleep, is now known to play an important role in that plasticity, including modulation of synaptic connectivity (Tsuno et al., 2008) and survival (Yokoyama et al., 2011) of OB neurons known to be critical for precise odor discrimination (Gheusi et al., 2000; Moreno et al., 2009). Furthermore, disruption of normal sleep-related activity within the olfactory cortex can impair the strength and accuracy of odor memory, leading to impaired odor-guided behavior (Barnes and Wilson, 2014). In addition, 24 h of sleep deprivation has been shown to impair odor identification in humans (Killgore and McBride, 2006). Thus, olfactory perception and memory may benefit from a good night’s sleep.

ACKNOWLEDGMENTS

Dylan C. Barnes was supported by a pre-doctoral fellowship from NIDCD (F31-DC012284). Donald A. Wilson was funded by grants from NIDCD (R01- DC03906) and NIA (R01-AG037693).

REFERENCES

Aberg, K. C., Tartaglia, E. M., and Herzog, M. H. (2009). Perceptual learning with Chevrons requires a minimal number of trials, transfers to untrained directions, but does not require sleep. Vision Res. 49, 2087–2094. doi: 10.1016/j.visres.2009.05.020

Aeschbach, D., Cutler, A. J., and Ronda, J. M. (2008). A role for non-rapid-eye-movement sleep homeostasis in perceptual learning. J. Neurosci. 28, 2766–2772. doi: 10.1523/JNEUROSCI.5548-07.2008

Allen, R. P. (2003). Take afternoon naps to improve perceptual learning. Sleep Med. 4, 589–590. doi: 10.1016/j.sleep.2003.09.002

Amzica, F., and Steriade, M. (1998). Electrophysiological correlates of sleep delta waves. Electroencephalogr. Clin. Neurophysiol. 107, 69–83. doi: 10.1016/s0013-4694(98)00051-0

Arzi, A., Shedlesky, L., Ben-Shaul, M., Nasser, K., Oksenberg, A., Hairston, I. S., et al. (2012). Humans can learn new information during sleep. Nat. Neurosci. 15, 1460–1465. doi: 10.1038/nn.3193

Atienza, M., Cantero, J. L., and Stickgold, R. (2004). Posttraining sleep enhances automaticity in perceptual discrimination. J. Cogn. Neurosci. 16, 53–64. doi: 10.1162/089892904322755557

Axmacher, N., Elger, C. E., and Fell, J. (2008). Ripples in the medial temporal lobe are relevant for human memory consolidation. Brain 131, 1806–1817. doi: 10.1093/brain/awn103

Bang, J. W., Khalilzadeh, O., Hamalainen, M., Watanabe, T., and Sasaki, Y. (2013). Location specific sleep spindle activity in the early visual areas and perceptual learning. Vision Res. doi: 10.1016/j.visres.2013.12.014. [Epub ahead of print].

Barkai, E., Bergman, R. E., Horwitz, G., and Hasselmo, M. E. (1994). Modulation of associative memory function in a biophysical simulation of rat piriform cortex. J. Neurophysiol. 72, 659–677.

Barnes, D. C., Chapuis, J., Chaudhury, D., and Wilson, D. A. (2011). Odor fear conditioning modifies piriform cortex local field potentials both during conditioning and during post-conditioning sleep. PLoS One 6:e18130. doi: 10.1371/journal.pone.0018130

Barnes, D. C., and Wilson, D. A. (2014). Slow-wave sleep imposed-replay modulates both strength and precision of memory. J. Neurosci. 34, 5134–5142.

Bayer, S. A. (1983). 3H-thymidine-radiographic studies of neurogenesis in the rat olfactory bulb. Exp. Brain Res. 50, 329–340. doi: 10.1007/bf00239197

Behan, M., and Haberly, L. B. (1999). Intrinsic and efferent connections of the endopiriform nucleus in rat. J. Comp. Neurol. 408, 532–548. doi: 10.1002/(sici)1096-9861(19990614)408:4<532::aid-cne74>3.0.co;2-s

Best, A. R., and Wilson, D. A. (2004). Coordinate synaptic mechanisms contributing to olfactory cortical adaptation. J. Neurosci. 24, 652–660. doi: 10.1523/jneurosci.4220-03.2004

Boland, E. M., and Alloy, L. B. (2013). Sleep disturbance and cognitive deficits in bipolar disorder: toward an integrated examination of disorder maintenance and functional impairment. Clin. Psychol. Rev. 33, 33–44. doi: 10.1016/j.cpr.2012.10.001

Borbély, A. A. (1982). A two process model of sleep regulation. Hum. Neurobiol. 1, 195–204.

Borbély, A. A., and Achermann, P. (1999). Sleep homeostasis and models of sleep regulation. J. Biol. Rhythms 14, 557–568. doi: 10.1177/074873099129000894

Bushey, D., Tononi, G., and Cirelli, C. (2011). Sleep and synaptic homeostasis: structural evidence in Drosophila. Science 332, 1576–1581. doi: 10.1126/science.1202839

Buzsáki, G. (1986). Hippocampal sharp waves: their origin and significance. Brain Res. 398, 242–252. doi: 10.1016/0006-8993(86)91483-6

Buzsaki, G. (2006). Rhythms of the Brain. New York: Oxford University Press.

Cantero, J. L., Atienza, M., Salas, R. M., and Dominguez-Marin, E. (2002). Effects of prolonged waking-auditory stimulation on electroencephalogram synchronization and cortical coherence during subsequent slow-wave sleep. J. Neurosci. 22, 4702–4708.

Carskadon, M. A., and Herz, R. S. (2004). Minimal olfactory perception during sleep: why odor alarms will not work for humans. Sleep 27, 402–405.

Censor, N., Karni, A., and Sagi, D. (2006). A link between perceptual learning, adaptation and sleep. Vision Res. 46, 4071–4074. doi: 10.1016/j.visres.2006.07.022

Chapuis, J., Garcia, S., Messaoudi, B., Thevenet, M., Ferreira, G., Gervais, R., et al. (2009). The way an odor is experienced during aversive conditioning determines the extent of the network recruited during retrieval: a multisite electrophysiological study in rats. J. Neurosci. 29, 10287–10298. doi: 10.1523/JNEUROSCI.0505-09.2009

Chapuis, J., and Wilson, D. A. (2011). Bidirectional plasticity of cortical pattern recognition and behavioral sensory acuity. Nat. Neurosci. 15, 155–161. doi: 10.1038/nn.2966

Chapuis, J., and Wilson, D. A. (2013). Cholinergic modulation of olfactory pattern separation. Neurosci. Lett. 545, 50–53. doi: 10.1016/j.neulet.2013.04.015

Chaudhury, D., Escanilla, O., and Linster, C. (2009). Bulbar acetylcholine enhances neural and perceptual odor discrimination. J. Neurosci. 29, 52–60. doi: 10.1523/JNEUROSCI.4036-08.2009

Chen, C. F., Barnes, D. C., and Wilson, D. A. (2011). Generalized versus stimulus-specific learned fear differentially modifies stimulus encoding in primary sensory cortex of awake rats. J. Neurophysiol. 106, 3136–3144. doi: 10.1152/jn.00721.2011

Cirelli, C., and Tononi, G. (2008). Is sleep essential? PLoS Biol. 6:e216. doi: 10.1371/journal.pbio.0060216

Cohen, Y., Reuveni, I., Barkai, E., and Maroun, M. (2008). Olfactory learning-induced long-lasting enhancement of descending and ascending synaptic transmission to the piriform cortex. J. Neurosci. 28, 6664–6669. doi: 10.1523/JNEUROSCI.0178-08.2008

Courtiol, E., and Wilson, D. A. (2014). Thalamic olfaction: characterizing odor processing in the mediodorsal thalamus of the rat. J. Neurophysiol. 111, 1274–1285. doi: 10.1152/jn.00741.2013

Dalton, P., and Wysocki, C. J. (1996). The nature and duration of adaptation following long-term odor exposure. Percept. Psychophys. 58, 781–792. doi: 10.3758/bf03213109

Datta, S., and O’Malley, M. W. (2013). Fear extinction memory consolidation requires potentiation of pontine-wave activity during REM sleep. J. Neurosci. 33, 4561–4569. doi: 10.1523/JNEUROSCI.5525-12.2013

Davison, I. G., and Ehlers, M. D. (2011). Neural circuit mechanisms for pattern detection and feature combination in olfactory cortex. Neuron 70, 82–94. doi: 10.1016/j.neuron.2011.02.047

Diekelmann, S., Büchel, C., Born, J., and Rasch, B. (2011). Labile or stable: opposing consequences for memory when reactivated during waking and sleep. Nat. Neurosci. 14, 381–386. doi: 10.1038/nn.2744

Diekelmann, S., Biggel, S., Rasch, B., and Born, J. (2012). Offline consolidation of memory varies with time in slow wave sleep and can be accelerated by cuing memory reactivations. Neurobiol. Learn. Mem. 98, 103–111. doi: 10.1016/j.nlm.2012.07.002

Diekelmann, S., and Born, J. (2010). The memory function of sleep. Nat. Rev. Neurosci. 11, 114–126. doi: 10.1038/nrn2762

Doty, R. L. (2012). Olfactory dysfunction in Parkinson disease. Nat. Rev. Neurol. 8, 329–339. doi: 10.1038/nrneurol.2012.80

Eschenko, O., Ramadan, W., Molle, M., Born, J., and Sara, S. J. (2008). Sustained increase in hippocampal sharp-wave ripple activity during slow-wave sleep after learning. Learn. Mem. 15, 222–228. doi: 10.1101/lm.726008

Fenn, K. M., Nusbaum, H. C., and Margoliash, D. (2003). Consolidation during sleep of perceptual learning of spoken language. Nature 425, 614–616. doi: 10.1038/nature01951

Fillion, T. J., and Blass, E. M. (1986). Infantile experience with suckling odors determines adult sexual behavior in male rats. Science 231, 729–731. doi: 10.1126/science.3945807

Fletcher, M. L. (2012). Olfactory aversive conditioning alters olfactory bulb mitral/tufted cell glomerular odor responses. Front. Syst. Neurosci. 6:16. doi: 10.3389/fnsys.2012.00016

Fontanini, A., and Bower, J. M. (2005). Variable coupling between olfactory system activity and respiration in ketamine/xylazine anesthetized rats. J. Neurophysiol. 93, 3573–3581. doi: 10.1152/jn.01320.2004

Gheusi, G., Cremer, H., McLean, H., Chazal, G., Vincent, J. D., and Lledo, P. M. (2000). Importance of newly generated neurons in the adult olfactory bulb for odor discrimination. Proc. Natl. Acad. Sci. U S A 97, 1823–1828. doi: 10.1073/pnas.97.4.1823

Gheusi, G., Lepousez, G., and Lledo, P. M. (2013). Adult-born neurons in the olfactory bulb: integration and functional consequences. Curr. Top. Behav. Neurosci. 15, 49–72. doi: 10.1007/7854_2012_228

Godde, B., Spengler, F., and Dinse, H. R. (1996). Associative pairing of tactile stimulation induces somatosensory cortical reorganization in rats and humans. Neuroreport 8, 281–285. doi: 10.1097/00001756-199612200-00056

Gottselig, J. M., Hofer-Tinguely, G., Borbely, A. A., Regel, S. J., Landolt, H. P., Retey, J. V., et al. (2004). Sleep and rest facilitate auditory learning. Neuroscience 127, 557–561. doi: 10.1016/j.neuroscience.2004.05.053

Grajski, K. A., and Freeman, W. J. (1989). Spatial EEG correlates of nonassociative and associative olfactory learning in rabbits. Behav. Neurosci. 103, 790–804. doi: 10.1037/0735-7044.103.4.790

Guzman-Marin, R., Ying, Z., Suntsova, N., Methippara, M., Bashir, T., Szymusiak, R., et al. (2006). Suppression of hippocampal plasticity-related gene expression by sleep deprivation in rats. J. Physiol. 575, 807–819. doi: 10.1113/jphysiol.2006.115287

Hasselmo, M. E., and Bower, J. M. (1992). Cholinergic suppression specific to intrinsic not afferent fiber synapses in rat piriform (olfactory) cortex. J. Neurophysiol. 67, 1222–1229.

Hasselmo, M. E., and McGaughy, J. (2004). High acetylcholine levels set circuit dynamics for attention and encoding and low acetylcholine levels set dynamics for consolidation. Prog. Brain Res. 145, 207–231. doi: 10.1016/S0079-6123(03)45015-2

Hauner, K. K., Howard, J. D., Zelano, C., and Gottfried, J. A. (2013). Stimulus-specific enhancement of fear extinction during slow-wave sleep. Nat. Neurosci. 16, 1553–1555. doi: 10.1038/nn.3527

Horn, D., Levy, N., and Ruppin, E. (1998a). Memory maintenance via neuronal regulation. Neural Comput. 10, 1–18. doi: 10.1162/089976698300017863

Horn, D., Levy, N., and Ruppin, E. (1998b). Neuronal regulation versus synaptic unlearning in memory maintenance mechanisms. Network 9, 577–586. doi: 10.1088/0954-898x/9/4/011

Hussain, Z., Sekuler, A. B., and Bennett, P. J. (2008). Robust perceptual learning of faces in the absence of sleep. Vision Res. 48, 2785–2792. doi: 10.1016/j.visres.2008.09.003

Jones, B. E. (1979). Elimination of paradoxical sleep by lesions of the pontine gigantocellular tegmental field in the cat. Neurosci. Lett. 13, 285–293. doi: 10.1016/0304-3940(79)91508-8

Jones, S. V., Choi, D. C., Davis, M., and Ressler, K. J. (2008). Learning-dependent structural plasticity in the adult olfactory pathway. J. Neurosci. 28, 13106–13111. doi: 10.1523/JNEUROSCI.4465-08.2008

Karni, A., Tanne, D., Rubenstein, B. S., Askenasy, J. J., and Sagi, D. (1994). Dependence on REM sleep of overnight improvement of a perceptual skill. Science 265, 679–682. doi: 10.1126/science.8036518

Kass, M. D., Rosenthal, M. C., Pottackal, J., and McGann, J. P. (2013). Fear learning enhances neural responses to threat-predictive sensory stimuli. Science 342, 1389–1392. doi: 10.1126/science.1244916

Kay, L. M., and Beshel, J. (2010). A beta oscillation network in the rat olfactory system during a 2-alternative choice odor discrimination task. J. Neurophysiol. 104, 829–839. doi: 10.1152/jn.00166.2010

Kilgard, M. P., and Merzenich, M. M. (1998). Cortical map reorganization enabled by nucleus basalis activity. Science 279, 1714–1718. doi: 10.1126/science.279.5357.1714

Killgore, W. D., and McBride, S. A. (2006). Odor identification accuracy declines following 24 h of sleep deprivation. J. Sleep Res. 15, 111–116. doi: 10.1111/j.1365-2869.2006.00502.x

Knafo, S., Ariav, G., Barkai, E., and Libersat, F. (2004). Olfactory learning-induced increase in spine density along the apical dendrites of CA1 hippocampal neurons. Hippocampus 14, 819–825. doi: 10.1002/hipo.10219

Linster, C., Maloney, M., Patil, M., and Hasselmo, M. E. (2003). Enhanced cholinergic suppression of previously strengthened synapses enables the formation of self-organized representations in olfactory cortex. Neurobiol. Learn. Mem. 80, 302–314. doi: 10.1016/s1074-7427(03)00078-9

Linster, C., Menon, A. V., Singh, C. Y., and Wilson, D. A. (2009). Odor-specific habituation arises from interaction of afferent synaptic adaptation and intrinsic synaptic potentiation in olfactory cortex. Learn. Mem. 16, 452–459. doi: 10.1101/lm.1403509

Lu, X.-C. M., and Slotnick, B. M. (1990). Acquisition of an olfactory learning-set in rats with lesions of the mediodorsal thalamic nucleus. Chem. Senses 15, 713–724. doi: 10.1093/chemse/15.6.713

Luczak, A., Barthó, P., Marguet, S. L., Buzsaki, G., and Harris, K. D. (2007). Sequential structure of neocortical spontaneous activity in vivo. Proc. Natl. Acad. Sci. U S A 104, 347–352. doi: 10.1073/pnas.0605643104

Mölle, M., and Born, J. (2011). Slow oscillations orchestrating fast oscillations and memory consolidation. Prog. Brain Res. 193, 93–110. doi: 10.1016/B978-0-444-53839-0.00007-7

Mölle, M., Marshall, L., Gais, S., and Born, J. (2002). Grouping of spindle activity during slow oscillations in human non-rapid eye movement sleep. J. Neurosci. 22, 10941–10947.

Malaspina, D., Goetz, R., Keller, A., Messinger, J. W., Bruder, G., Goetz, D., et al. (2012). Olfactory processing, sex effects and heterogeneity in schizophrenia. Schizophr. Res. 135, 144–151. doi: 10.1016/j.schres.2011.11.025

Manabe, H., Kusumoto-Yoshida, I., Ota, M., and Mori, K. (2011). Olfactory cortex generates synchronized top-down inputs to the olfactory bulb during slow-wave sleep. J. Neurosci. 31, 8123–8133. doi: 10.1523/JNEUROSCI.6578-10.2011

Martin, C., Beshel, J., and Kay, L. M. (2007). An olfacto-hippocampal network is dynamically involved in odor-discrimination learning. J. Neurophysiol. 98, 2196–2205. doi: 10.1152/jn.00524.2007

Martin, C., Gervais, R., Messaoudi, B., and Ravel, N. (2006). Learning-induced oscillatory activities correlated to odour recognition: a network activity. Eur. J. Neurosci. 23, 1801–1810. doi: 10.1111/j.1460-9568.2006.04711.x

Mascetti, L., Muto, V., Matarazzo, L., Foret, A., Ziegler, E., Albouy, G., et al. (2013). The impact of visual perceptual learning on sleep and local slow-wave initiation. J. Neurosci. 33, 3323–3331. doi: 10.1523/JNEUROSCI.0763-12.2013

McCormick, D. A., and Bal, T. (1994). Sensory gating mechanisms of the thalamus. Curr. Opin. Neurobiol. 4, 550–556. doi: 10.1016/0959-4388(94)90056-6

McGaugh, J. L. (2013). Making lasting memories: remembering the significant. Proc. Natl. Acad. Sci. U S A 110(Suppl. 2), 10402–10407. doi: 10.1073/pnas.1301209110

Mednick, S. C., Nakayama, K., Cantero, J. L., Atienza, M., Levin, A. A., Pathak, N., et al. (2002). The restorative effect of naps on perceptual deterioration. Nat. Neurosci. 5, 677–681. doi: 10.1038/nn864

Miura, K., Mainen, Z. F., and Uchida, N. (2012). Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity. Neuron 74, 1087–1098. doi: 10.1016/j.neuron.2012.04.021

Montgomery, S. M., Sirota, A., and Buzsaki, G. (2008). Theta and gamma coordination of hippocampal networks during waking and rapid eye movement sleep. J. Neurosci. 28, 6731–6741. doi: 10.1523/JNEUROSCI.1227-08.2008

Moreno, M. M., Linster, C., Escanilla, O., Sacquet, J., Didier, A., and Mandairon, N. (2009). Olfactory perceptual learning requires adult neurogenesis. Proc. Natl. Acad. Sci. U S A 106, 17980–17985. doi: 10.1073/pnas.0907063106

Mouly, A. M., Fort, A., Ben-Boutayab, N., and Gervais, R. (2001). Olfactory learning induces differential long-lasting changes in rat central olfactory pathways. Neuroscience 102, 11–21. doi: 10.1016/s0306-4522(00)00476-0

Murakami, M., Kashiwadani, H., Kirino, Y., and Mori, K. (2005). State-dependent sensory gating in olfactory cortex. Neuron 46, 285–296. doi: 10.1016/j.neuron.2005.02.025

Murphy, C. (1999). Loss of olfactory function in dementing disease. Physiol. Behav. 66, 177–182. doi: 10.1016/s0031-9384(98)00262-5

Narikiyo, K., Manabe, H., and Mori, K. (2014). Sharp wave-associated synchronized inputs from the piriform cortex activate olfactory tubercle neurons during slow-wave sleep. J. Neurophysiol. 111, 72–81. doi: 10.1152/jn.00535.2013

Pavlides, C., and Winson, J. (1989). Influences of hippocampal place cell firing in the awake state on the activity of these cells during subsequent sleep episodes. J. Neurosci. 9, 2907–2918.

Poo, C., and Isaacson, J. S. (2011). A major role for intracortical circuits in the strength and tuning of odor-evoked excitation in olfactory cortex. Neuron 72, 41–48. doi: 10.1016/j.neuron.2011.08.015

Ramadan, W., Eschenko, O., and Sara, S. J. (2009). Hippocampal sharp wave/ripples during sleep for consolidation of associative memory. PLoS One 4:e6697. doi: 10.1371/journal.pone.0006697

Rasch, B., Büchel, C., Gais, S., and Born, J. (2007). Odor cues during slow-wave sleep prompt declarative memory consolidation. Science 315, 1426–1429. doi: 10.1126/science.1138581

Rasch, B., and Born, J. (2013). About sleep’s role in memory. Physiol. Rev. 93, 681–766. doi: 10.1016/j.smrv.2004.11.002

Ravel, N., Elaagouby, A., and Gervais, R. (1994). Scopolamine injection into the olfactory bulb impairs short-term olfactory memory in rats. Behav. Neurosci. 108, 317–324. doi: 10.1037/0735-7044.108.2.317

Recanzone, G. H., Merzenich, M. M., and Dinse, H. R. (1992). Expansion of the cortical representation of a specific skin field in primary somatosensory cortex by intracortical microstimulation. Cereb. Cortex 2, 181–196. doi: 10.1093/cercor/2.3.181

Riedner, B. A., Vyazovskiy, V. V., Huber, R., Massimini, M., Esser, S., Murphy, M., et al. (2007). Sleep homeostasis and cortical synchronization: III. A high-density EEG study of sleep slow waves in humans. Sleep 30, 1643–1657.

Roesch, M. R., Stalnaker, T. A., and Schoenbaum, G. (2007). Associative encoding in anterior piriform cortex versus orbitofrontal cortex during odor discrimination and reversal learning. Cereb. Cortex 17, 643–652. doi: 10.1093/cercor/bhk009

Sánchez-Ortuño, M. M., and Edinger, J. D. (2012). Cognitive-behavioral therapy for the management of insomnia comorbid with mental disorders. Curr. Psychiatry Rep. 14, 519–528. doi: 10.1007/s11920-012-0312-9

Saar, D., and Barkai, E. (2003). Long-term modifications in intrinsic neuronal properties and rule learning in rats. Eur. J. Neurosci. 17, 2727–2734. doi: 10.1046/j.1460-9568.2003.02699.x

Saar, D., Reuveni, I., and Barkai, E. (2012). Mechanisms underlying rule learning-induced enhancement of excitatory and inhibitory synaptic transmission. J. Neurophysiol. 107, 1222–1229. doi: 10.1152/jn.00356.2011

Shanahan, L. K., and Gottfried, J. A. (2014). “Olfactory insights into sleep-dependent learning and memory,” in Odor Memory and Perception, eds. E. Barkai and D. A. Wilson (Oxford, UK: Elsevier), 309–344.

Shepherd, G. M. (2012). Neurogastronomy. New York: Columbia University Press.

Silva, A. J. (2003). Molecular and cellular cognitive studies of the role of synaptic plasticity in memory. J. Neurobiol. 54, 224–237. doi: 10.1002/neu.10169

Spiegelhalder, K., Regen, W., Nanovska, S., Baglioni, C., and Riemann, D. (2013). Comorbid sleep disorders in neuropsychiatric disorders across the life cycle. Curr. Psychiatry Rep. 15:364. doi: 10.1007/s11920-013-0364-5

Steriade, M., Timofeev, I., and Grenier, F. (2001). Natural waking and sleep states: a view from inside neocortical neurons. J. Neurophysiol. 85, 1969–1985.

Stickgold, R. (2005). Sleep-dependent memory consolidation. Nature 437, 1272–1278. doi: 10.1038/nature04286

Tang, A. C., and Hasselmo, M. E. (1994). Selective suppression of intrinsic but not afferent fiber synaptic transmission by baclofen in the piriform (olfactory) cortex. Brain Res. 659, 75–81. doi: 10.1016/0006-8993(94)90865-6

Tian, H., and Ma, M. (2008). Activity plays a role in eliminating olfactory sensory neurons expressing multiple odorant receptors in the mouse septal organ. Mol. Cell. Neurosci. 38, 484–488. doi: 10.1016/j.mcn.2008.04.006

Tobler, I. (1995). Is sleep fundamentally different between mammalian species? Behav. Brain Res. 69, 35–41. doi: 10.1016/0166-4328(95)00025-o

Tononi, G., and Cirelli, C. (2003). Sleep and synaptic homeostasis: a hypothesis. Brain Res. Bull. 62, 143–150. doi: 10.1016/j.brainresbull.2003.09.004

Tononi, G., and Cirelli, C. (2014). Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration. Neuron 81, 12–34. doi: 10.1016/j.neuron.2013.12.025

Tsuno, Y., Kashiwadani, H., and Mori, K. (2008). Behavioral state regulation of dendrodendritic synaptic inhibition in the olfactory bulb. J. Neurosci. 28, 9227–9238. doi: 10.1523/JNEUROSCI.1576-08.2008

Tyler, W. J., Petzold, G. C., Pal, S. K., and Murthy, V. N. (2007). Experience-dependent modification of primary sensory synapses in the mammalian olfactory bulb. J. Neurosci. 27, 9427–9438. doi: 10.1523/JNEUROSCI.0664-07.2007

van der Helm, E., Yao, J., Dutt, S., Rao, V., Saletin, J. M., and Walker, M. P. (2011). REM sleep depotentiates amygdala activity to previous emotional experiences. Curr. Biol. 21, 2029–2032. doi: 10.1016/j.cub.2011.10.052

van Mill, J. G., Hoogendijk, W. J., Vogelzangs, N., van Dyck, R., and Penninx, B. W. (2010). Insomnia and sleep duration in a large cohort of patients with major depressive disorder and anxiety disorders. J. Clin. Psychiatry 71, 239–246. doi: 10.4088/JCP.09m05218gry

Volgushev, M., Chauvette, S., Mukovski, M., and Timofeev, I. (2006). Precise long-range synchronization of activity and silence in neocortical neurons during slow-wave oscillations [corrected]. J. Neurosci. 26, 5665–5672. doi: 10.1523/JNEUROSCI.0279-06.2006

Wagner, U., and Born, J. (2008). Memory consolidation during sleep: interactive effects of sleep stages and HPA regulation. Stress 11, 28–41. doi: 10.1080/10253890701408822

Wilson, D. A. (2001). Scopolamine enhances generalization between odor representations in rat olfactory cortex. Learn. Mem. 8, 279–285. doi: 10.1101/lm.42601

Wilson, D. A. (2003). Rapid, experience-induced enhancement in odorant discrimination by anterior piriform cortex neurons. J. Neurophysiol. 90, 65–72. doi: 10.1152/jn.00133.2003

Wilson, D. A. (2010). Single-unit activity in piriform cortex during slow-wave state is shaped by recent odor experience. J. Neurosci. 30, 1760–1765. doi: 10.1523/JNEUROSCI.5636-09.2010

Wilson, D. A., Hoptman, M. J., Gerum, S. V., and Guilfoyle, D. N. (2011). State-dependent functional connectivity of rat olfactory system assessed by fMRI. Neurosci. Lett. 497, 69–73. doi: 10.1016/j.neulet.2011.04.031

Wilson, D. A., and Stevenson, R. J. (2003). The fundamental role of memory in olfactory perception. Trends Neurosci. 26, 243–247. doi: 10.1016/S0166-2236(03)00076-6

Wilson, D. A., and Sullivan, R. M. (2011). Cortical processing of odor objects. Neuron 72, 506–519. doi: 10.1037/0735-7044.122.3.643

Wilson, D. A., and Yan, X. (2010). Sleep-like states modulate functional connectivity in the rat olfactory system. J. Neurophysiol. 104, 3231–3239. doi: 10.1152/jn.00711.2010

Xerri, C., Merzenich, M. M., Jenkins, W., and Santucci, S. (1999). Representational plasticity in cortical area 3b paralleling tactual-motor skill acquisition in adult monkeys. Cereb. Cortex 9, 264–276. doi: 10.1093/cercor/9.3.264

Yamaguchi, M., Manabe, H., Murata, K., and Mori, K. (2013). Reorganization of neuronal circuits of the central olfactory system during postprandial sleep. Front. Neural Circuits 7:132. doi: 10.3389/fncir.2013.00132

Yokoyama, T. K., Mochimaru, D., Murata, K., Manabe, H., Kobayakawa, K., Kobayakawa, R., et al. (2011). Elimination of adult-born neurons in the olfactory bulb is promoted during the postprandial period. Neuron 71, 883–897. doi: 10.1016/j.neuron.2011.05.046

Yotsumoto, Y., Sasaki, Y., Chan, P., Vasios, C. E., Bonmassar, G., Ito, N., et al. (2009). Location-specific cortical activation changes during sleep after training for perceptual learning. Curr. Biol. 19, 1278–1282. doi: 10.1016/j.cub.2009.06.011

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 07 March 2014; accepted: 03 April 2014; published online: 22 April 2014.

Citation: Barnes DC and Wilson DA (2014) Sleep and olfactory cortical plasticity. Front. Behav. Neurosci. 8:134. doi: 10.3389/fnbeh.2014.00134

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Barnes and Wilson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

REVIEW ARTICLE
published: 23 June 2014
doi: 10.3389/fnbeh.2014.00218

Beta and gamma oscillatory activities associated with olfactory memory tasks: different rhythms for different functional networks?

Claire Martin[1][*] and Nadine Ravel[2][*]

1Laboratory Imagerie et Modélisation en Neurobiologie et Cancérologie, CNRS UMR 8165, Université Paris Sud, Université Paris Diderot, Orsay, France

2Team “Olfaction: Du codage à la mémoire,” Centre de Recherche en Neurosciences de Lyon CNRS UMR 5292, INSERM U1028, Université Lyon 1, Lyon, France

* Correspondence: Claire Martin, Laboratory Imagerie et Modélisation en Neurobiologie et Cancérologie, CNRS UMR 8165, Université Paris Sud, Université Paris Diderot, Batiment 440, 91405 Orsay, France e-mail: martin@imnc.in2p3.fr;

Nadine Ravel, Equipe “Olfaction: Du codage à la mémoire,” Centre de Recherche en Neurosciences de Lyon, 50 Avenue Tony Garnier, 69366 Lyon Cedex 07, France e-mail: nravel@olfac.univ-lyon1.fr

Edited by:
Donald A. Wilson, New York University School of Medicine, USA

Reviewed by:
Daniel W. Wesson, Case Western Reserve University, USA
Maria Luz Aylwin, University of Chile, Chile

Olfactory processing in behaving animals, even at early stages, is inextricable from top down influences associated with odor perception. The anatomy of the olfactory network (olfactory bulb, piriform, and entorhinal cortices) and its unique direct access to the limbic system makes it particularly attractive to study how sensory processing could be modulated by learning and memory. Moreover, olfactory structures have been early reported to exhibit oscillatory population activities easy to capture through local field potential recordings. An attractive hypothesis is that neuronal oscillations would serve to “bind” distant structures to reach a unified and coherent perception. In relation to this hypothesis, we will assess the functional relevance of different types of oscillatory activity observed in the olfactory system of behaving animals. This review will focus primarily on two types of oscillatory activities: beta (15–40 Hz) and gamma (60–100 Hz). While gamma oscillations are dominant in the olfactory system in the absence of odorant, both beta and gamma rhythms have been reported to be modulated depending on the nature of the olfactory task. Studies from the authors of the present review and other groups brought evidence for a link between these oscillations and behavioral changes induced by olfactory learning. However, differences in studies led to divergent interpretations concerning the respective role of these oscillations in olfactory processing. Based on a critical reexamination of those data, we propose hypotheses on the functional involvement of beta and gamma oscillations for odor perception and memory.

Keywords: beta and gamma oscillations, odor learning, behavior, olfactory bulb, piriform cortex

INTRODUCTION

Among the functional particularities of the olfactory system, we wish to stress its privileged access to limbic structures and its predisposition to rhythmicity. In the absence of thalamic relay, the olfactory receptors are only two and three synapses distant from the cortical amygdala and the hippocampus respectively. This singularity could partly explain why olfactory experience has been reported to be so efficient to shape odor representations (Wilson and Stevenson, 2003; Davis, 2004). In adults, anatomic and functional plasticity related to odor learning occur at every step of the olfactory system. As early as in the olfactory mucosa, olfactory learning increases the number of sensory neurons specific to a trained odorant (Jones et al., 2008; Dias and Ressler, 2014). Studies carried out in the main olfactory bulb (MOB) and the piriform cortex (PCx) reported long-lasting modifications of neuronal activity and synaptic efficiency in various learning contexts (Barkai and Saar, 2001; Mouly et al., 2001; Mouly and Gervais, 2002; Martin et al., 2004b; Sevelinges et al., 2004; Mandairon and Linster, 2009; Restrepo et al., 2009; Wilson and Sullivan, 2011; Royet et al., 2013).

The olfactory system is also highly dynamic. On the one hand, odorant detection and coding are constrained by respiratory modulation through breathing. The sniff cycle controls the firing pattern of olfactory neurons in time and is suggested to be the functional time unit for odor processing (Buonviso et al., 2006; Kepecs et al., 2006; Wachowiak, 2011). On the other hand, odor processing has been associated with oscillations of the local field potential (LFP) both in insects (Perez-Orive et al., 2002) and mammals (Kay et al., 2009). Those signals reflect a weighted average of synchronized dendro-somatic components of neuronal processing within a neural population (Buzsáki et al., 2012). Because they underlie coincident activity, oscillations would favor temporal coordination of sensory information within brain areas and facilitation of its transfer across regions (Varela et al., 2001; Siegel et al., 2012). Accordingly, they are ideally suited to subserve memory processes such as encoding, consolidation and retrieval (Engel et al., 2001; Varela et al., 2001; Tallon-Baudry et al., 2004; Fell and Axmacher, 2011).

The present review will leave apart the respiratory modulation which has already been the object of several recent reviews (i.e., Buonviso et al., 2006; Kepecs et al., 2006; Scott, 2006; Wachowiak, 2011). The aim here is to synthesize our current knowledge about the conditions in which the other two main oscillatory rhythms linked to odor processing, namely beta (15–40 Hz) and gamma (60–90 Hz), are observed at the first stages of olfactory processing, the MOB and the PCx. A majority of studies designed to decipher odor coding have been performed in anesthetized animals. These studies have been essential for understanding the activity of single neurons in response to odorants both in the MOB and the PCx (Buonviso et al., 2003; Fletcher and Wilson, 2003; Litaudon et al., 2003; Chapuis and Wilson, 2012; Fukunaga et al., 2012). In the present review, we will focus exclusively on LFP recordings performed in awake, behaving animals. One benefit of chronic LFP recordings as compared to single unit recordings is the ability to follow the evolution of rhythmic activities across cerebral areas throughout training as a means of tracking learning-related changes. Because oscillatory activities are transient, their detection and precise description requires operant devices, in which the timing of odorant onset and offset can be precisely controlled. Comparing the relation between behavior and LFP oscillations in various conditions, we came to propose hypotheses on the functional involvement of beta and gamma oscillations in the context of odor processing. Far to be exhaustive, the scope of this review is to consider the respective putative role of these two oscillatory rhythms in odor coding and memory.

THE OLFACTORY SYSTEM AND ITS OSCILLATIONS

More than any other sensory system the olfactory system has early been reported to be oscillatory (Adrian, 1942; Freeman and Schneider, 1982; Gray, 1994). This specificity is most probably due to two parameters, the nature of the stimulus and the organization of olfactory areas. First, odorant molecules are slow to reach the detector, compared to sound or light. They travel with nasal airflow, and do not reach simultaneously the different parts of the nasal cavity. Because odorant onset cannot be sharp, it most often fails to elicit evoked potential. Second, as it will be described below, the central olfactory relays (MOB and PCx) are tightly interconnected and host specific features; the mitral-granule dendrodendritic reciprocal synapses in the MOB, and a dense network of associational fibers in the PCx.

The olfactory sensory neurons present in the nasal cavity are the point where the odorant chemical information is transduced and transmitted to the brain (Zufall and Munger, 2001). All the olfactory sensory neurons that express the same molecular receptor converge onto a few glomeruli, well identified microdomains containing the first synapse of the olfactory information path (Zou et al., 2009). In the absence of thalamic relay, the MOB has been considered already as an associative structure where inhibition plays a major role. Olfactory signal that travels in the principal excitatory neurons, mitral and tufted (MT) cells, is gated at two levels within the structure. Surrounding glomeruli, juxtaglomerular cells include astrocytes and various types of neurons: excitatory external tufted cells, periglomerular cells and short axon cells. While periglomerular are GABAergic cells, short axon cells, which processes extend across several glomeruli, have two opposite actions by releasing both GABA and Dopamine (Liu et al., 2013). Deeper in the structure, the specific interaction between granules and MT cells via dendrodendritic reciprocal synapses is a key element for the large oscillatory activity displayed in the olfactory system. MT cells axons coalesce into the lateral olfactory tract and project to numerous areas termed as the olfactory cortex. Privileged targets of the MOB are the anterior olfactory nucleus and the anterior PCx (Haberly, 2001; Cleland and Linster, 2003; Hintiryan et al., 2012). MT cells also contact in a lesser extent, the posterior PCx, the lateral entorhinal cortex, the olfactory tubercle, the ventral tenia tecta and the anterior cortical complex of the amygdala.

The PCx is anatomically and functionally divided into two parts: a rostral region (anterior PCx) mostly connected to the other olfactory areas and a caudal region (posterior PCx) in connection with higher cognitive regions and characterized by dense associational connectivity (Haberly, 2001; Litaudon et al., 2003; Bekkers and Suzuki, 2013). Indeed, the anterior part of the PCx has strong bidirectional projections to the posterior part of the structure and to the MOB and the anterior olfactory nucleus. On the contrary, the posterior PCx has dense feedforward projections to numerous cortical and subcortical regions including high-order association areas, but lacks functional projections to the anterior PCx. In addition, feedback projections from the posterior PCx to the MOB are sparse. A noticeable point in the functional anatomy of the PCx is the presence of abundant associational connections, sparser in the anterior than in the posterior PCx (Hagiwara et al., 2012). Anatomo-functional connectivity of the PCx already suggests a key role of this structure in the elaboration of complex mechanisms of olfactory perception and memory (Gottfried, 2010; Wilson and Sullivan, 2011). Differences between anterior and posterior PCx would sustain complementary memory processes: as suggested in the literature, the anterior PCx would mediate odors matching such as generalization, discrimination or pattern completion (Wilson and Stevenson, 2003; Chapuis and Wilson, 2012) whereas the posterior PCx would rather link odor to previously learned non-olfactory information (Haberly, 2001).

The MOB and the PCx are densely interconnected. The lateral olfactory tract carries odor information from the MT cells to pyramidal cells. In turn, pyramidal cells send axon collaterals to the MOB. These glutamatergic fibers synapse almost exclusively on the different type of inhibitory interneurons contained in the MOB. They have a major inhibitory effect on the structure at two levels: the glomerulus via periglomerular cells, and the mitral cell via granule cells. Interestingly, the strongest drive is to deep and superficial short axon cells, the main source of inhibition onto granule and periglomerular cells (Boyd et al., 2012). Centrifugal projections to the MOB do not only originate from the PCx (Matsutani, 2010). Moreover, acetylcholine, norepinephrine, and serotonin projections modulate the activity of the MOB and the PCx (Linster and Hasselmo, 2001; Ennis et al., 2007; Rothermel et al., 2014). They can inhibit as well as disinhibit glomerular activity and MT cells.

Dense interconnections between and within olfactory structures are conducive to the emergence of oscillations. Presumably for this reason, the MOB and the PCx have been very early seen as good models to study rhythmic activities in the brain, and have been the target of pioneering electrical recording of brain oscillations (Adrian, 1942, 1950). Few years later, Lavin et al. (1959) performed the first recording of the electrical activity of the MOB in awake, unrestrained cats chronically implanted with electrodes. They reported bursts of activity related to the arousal of the animal. Since that time, numerous studies have recorded intracerebral LFPs in the MOB and the PCx in behaving animals. In these brain areas, even raw signals overtly display different types of oscillations that can be easily defined in sub-classes according to their frequency range and to the moment they occur in relation with external events.

Oscillatory activities in the olfactory system covers a broad frequency band comprised between 1 and 150 Hz. In the MOB and the PCx, three rhythms dominate. The larger and most obvious is linked to the respiration and occurs in a frequency range overlapping with the hippocampal theta rhythm (~1–10 Hz) (Kay et al., 2009). In awake and motivated animals, regular bursts of fast oscillations, i.e., the gamma rhythm (~60–90 Hz) are nested onto the respiratory modulation, occurring at the transition between inspiration and expiration (Buonviso et al., 2006; Manabe and Mori, 2013) (for an example see Figures 1A, 3A). Odorant presentation most often elicits beta oscillations (~15–40 Hz) of variable amplitude, but has also been associated with gamma increase restrained to the MOB (Beshel et al., 2007). Finally, in the MOB, some sporadic long lasting bursts of low frequency gamma (~35–65 Hz) can occur during exploration (Kay, 2003). The boundaries of these rhythms are sometimes variable in frequency, depending on the animal species; as a consequence we will consider their functional condition of emergence rather than their absolute frequency.

FIGURE 1. Odor stimulation modifies beta (15–35 Hz) and gamma (60–90 Hz) oscillations in the olfactory bulb. Example of LFP traces recorded in the olfactory bulb in freely moving mice. (A) Raw LFP signal (0.1–300 Hz) on first row is filtered in the theta (1–10 Hz) and the gamma (60–90 Hz) bands, showing the close relation between gamma bursts and the respiratory modulation. (B) Raw LFP signal (0.1–300 Hz) and corresponding time-frequency power representation in a mouse conditioned in a Go/No-Go task. Time-frequency plot was obtained based on Morlet wavelet analysis. It represents the power of the signal (as indicated by the color scale) as a function of time (x-axis) for each frequency (y-axis). Odorant onset is indicated by the vertical red arrow. Note that odor elicits an overall decrease in the gamma band and an increase in the beta band power.

ODOR-EVOKED MODULATION OF LFPS, INFLUENCES ON BETA AND GAMMA BAND OSCILLATIONS

The rhythm classically studied in the olfactory system has been the gamma band (~60–90 Hz). In the absence of imposed odorant stimulation, in particular when animals freely explore their environment, the presence of gamma bursts, regularly nested at the transition between inspiration and expiration characterizes the LFP in the MOB (Figure 1A) and at least in the anterior PCx. Beside the ubiquitous nature of gamma bursts in the olfactory system, the fact that gamma frequency has been recognized as the gold standard for sensory coding following the work of Wolf Singer in the visual system (Singer, 1993) probably drew the attention of the community on this frequency range. A detailed historical review about gamma oscillations in the olfactory system can be found in Rojas-Líbano and Kay (2008).

Gamma oscillations have been extensively analyzed in several regions of the olfactory system by Walter Freeman (Freeman, 1960; Freeman and Schneider, 1982; Eeckman and Freeman, 1990), who focused analyses on a rather large frequency range (20–90 Hz). As it has been reported in numerous studies, gamma bursts amplitude increases mostly when the animal is in an attentive state (Bressler, 1984; Eeckman and Freeman, 1990). This relationship to attention and motivation is easy to observe in the initial phase of any training. Indeed, our data suggest that when a rat is placed in a novel environment, the amplitude of spontaneous activity related gamma bursts increases very rapidly as the animal becomes more familiar with the arena and aware of what is going to happen (Martin et al., unpublished data). By recording EEG using a 64-electrodes grid at the surface of the MOB of small mammals, Freeman analyzed the spatial distribution of odor-induced gamma bursts amplitude, considering the brain as a chaotic system. In two of the most famous papers (Freeman and Schneider, 1982; Di Prisco and Freeman, 1985), authors examined this pattern during and after either an aversive or appetitive odor conditioning. They reported that spatio-temporal motifs emerging from gamma bursts analysis were relatively independent of odor presentation and more related to the significance of the odor. They proposed that gamma oscillatory activity modulations were mostly related to the context and expectations of animals. These studies were pioneering in considering the MOB as a central element of a broader network underlying odor representations, rather than a passive odor relay. They were also the first evidence that odor processing at this early sensory stage already takes into account the context and the experience of the animal. Interestingly, as reported in several articles from the same group (Di Prisco and Freeman, 1985), the fact that spatial distribution of iso-amplitude gamma bursts is indeed modified by experience does not mean that odorant presentation increases gamma oscillations amplitude. On the contrary, the mean gamma power over the MOB remained stable or decreased by 15–35% during odorant sampling. In a series of experiments we performed at the beginning of the 2000's we observed that odorant sampling in the context of a Go/No-Go task was first associated with a strong and transient decrease of gamma oscillatory activities at the onset of odor. This power decrease was observed in naïve animals and was amplified as rats became experts for the odorants used in the task. Gamma depression was transient and most often followed by a light rebound effect before a return to baseline activity with calibrated regular gamma bursts nested on a slower respiration-related activity (Ravel et al., 2003; Martin et al., 2004b, 2006).

The decrease in gamma activity is most often replaced by the emergence of an activity in the beta band (15–40 Hz, centered around 25 Hz) that is never observed in the absence of odorant in normal condition (Figure 1B). This shift in the oscillatory dynamics between gamma and beta frequencies is characteristic of odorant sampling in awake animals and have been reported in numerous studies in the MOB (Gray and Skinner, 1988b; Martin et al., 2004b; Lowry and Kay, 2007; Lepousez and Lledo, 2013; Chery et al., 2014) and the PCx (Martin et al., 2006). Interestingly, beta oscillations elicited by odorant stimulations have been characterized for the first time in the dentate gyrus of the hippocampus where inhalation of toluene by the rat produced fast-wave bursts (Vanderwolf, 1992). This group conducted many studies in awake rats submitted to passive presentations of odorants that were supposed to be innately relevant or naturally aversive to the animals (urine, feces, toluene, predator odors…) (Vanderwolf, 1992; Heale and Vanderwolf, 1994; Zibrowski and Vanderwolf, 1997; Chapman et al., 1998; Zibrowski et al., 1998; Vanderwolf and Zibrowski, 2001; Vanderwolf et al., 2002). They showed that a low frequency wave (around 20 Hz) was elicited by these odorants in a large network covering the MOB, the PCx, and limbic structures (entorhinal cortex, dentate gyrus). They also observed in the PCx that the repeated presentation of odorants (10–15 trials) leads to a gradual enhancement of beta wave amplitude that persists for several days (Vanderwolf and Zibrowski, 2001). In a subsequent study, Lowry and Kay (2007) have also found large beta activity during passive presentation of some specific odorants. However, they reported that all the odorants that showed significantly higher beta power were in a certain range of vapor pressure, between 1 and 120 mmHg. Interestingly, this range includes TMT, a component of fox feces, and toluene. In urethane anesthetized rats, similar observations were made that the molecular feature of odorants influenced the probability of emergence of beta oscillations (Cenier et al., 2008). Consequently, the reason why some odorants elicited higher beta power could be due to their volatility rather than their innate value. However these studies have been conducted using cotton swab presentations, a condition in which the odorant concentration and duration is more difficult to calibrate. Elevated odorant concentration may by itself be fearful and/or aversive for macrosmatic animals such as rodents. Indeed, in both sets of data, beta power enhancement induced by repeated exposure to the same series of odorants suggests that other processes than pure olfactory detection occur like some odor recognition and classification. Oscillatory activities related to naturally meaningful odorant molecules have also been found in the accessory olfactory bulb, which receives its sensory input from the vomeronasal organ. In awake female mice, male urine exposure significantly increases LFP power in frequencies overlapping with beta rhythm (ranging from 8 to 24 Hz) at the vicinity of the MT cells layer. Interestingly, following mating, the power of the LFP oscillations recorded under baseline conditions is dramatically increased across all frequency bands, suggesting that some form of synaptic plasticity has occurred (Binns and Brennan, 2005).

In conclusion, as presented in Table 1, most of the studies using odorant presentation have shown that they elicits a shift in frequency for the oscillatory activities recorded in the MOB and the PCx. Respiration locked gamma band activity (60–90 Hz) decreases and a slower beta oscillation (15–40 Hz) emerges. The reason why the pioneering studies led by Walter Freeman did not described such a systematic shift could be explained by the fact that recording were not performed in deep layers but at the surface of the cortex (Buffalo et al., 2011).

Table 1. Beta oscillations recorded during odor presentation in awake mammals.

NETWORK SUSTAINING BETA AND GAMMA RHYTHMS IN THE OLFACTORY BULB

Is the same network involved in the generation of beta and gamma oscillations? As presented above, odorant presentation often leads to a gamma decrease coupled to a beta increase suggesting that the two rhythms share a common cellular substrate. If this is easily noticeable during odor-reward learning tasks, some studies involving passive and non-reinforced odor presentations find both gamma and beta enhancement during odor sampling (Lowry and Kay, 2007; Carlson et al., 2014). Stimulus delivery, not constraint by a nose poke may not be continuous, which could explain this discrepancy. Indeed, Lowry and Kay (2007) mention that within single investigation period, bursts at each frequency actually alternate, as it is reported in urethane anesthetized rats. In our hands, passive odor presentations induced the same shift from gamma to beta rhythm (Chabaud et al., 2000). The conditions of generation of gamma oscillations in the MOB have been extensively studied by computational modeling and electrophysiology in vivo or in vitro. Much less data have been collected concerning beta oscillations.

Gamma bursts present during spontaneous activity are generated in the MOB under the influence of spontaneous input from the neuroreceptors located in the nasal cavity (Hernandez-Peon et al., 1960; Gray and Skinner, 1988a), and are then transmitted to the PCx (Bressler, 1984; Mori et al., 2013). Indeed, blocking descending centrifugal influences by cooling or local infusion of anesthetic leads to an increase and not a decrease of MOB gamma bursts amplitude (Gray and Skinner, 1988a; Martin et al., 2006). In addition, the section of the lateral olfactory tract, which interrupts the transmission of the olfactory signal from the MOB to the PCx, selectively abolishes gamma bursts in the PCx (Neville and Haberly, 2003). On the contrary, pharmacological removal of centrifugal influences to the MOB abolishes beta oscillations in both the MOB and the PCx (Martin et al., 2006). Therefore, the major difference between the two rhythms is that gamma oscillations are generated locally within the MOB, whereas beta oscillations require intact bidirectional connectivity at least between the MOB and the PCx.

Within the MOB, gamma oscillations have been shown to be supported by the reciprocal synapse between mitral and granule cells (Nusser et al., 2001; Bathellier et al., 2006; Schoppa, 2006; Lagier et al., 2007; David et al., 2009). Computational models agree with the fact that these oscillations require an appropriate balance between excitation and inhibition, consistent with the mechanism proposed for gamma-band generation in other cortical areas (Cannon et al., 2014). Recent data have confirmed these mechanisms in awake mouse. They show that increasing the excitation/inhibition balance of MT cells via a decrease of GABAa receptors inhibition or local injection of glutamatergic agonists boosts gamma oscillatory power (Lepousez and Lledo, 2013). Consistently, selective MT cells drive using optogenetic technique causes a 5–10 fold increase of gamma oscillations without affecting other frequency bands. By scanning different frequency for light pulses (between 25 and 90 Hz), the authors show that the maximal response of the LFP occurs around 66 Hz, which corresponds to the dominant frequency of spontaneous gamma oscillations. Interestingly, the same GABAa receptors antagonist picrotoxin, which enhances gamma oscillations, leads to a reduction of beta oscillations power by more than 65%. On the contrary injection of MK801, an NMDA receptor antagonist, reduce gamma oscillations power without affecting beta oscillations (Lepousez and Lledo, 2013). Finally, modifications of beta and gamma oscillations observed in the presence of glutamate reuptake blockers argue for a role of glutamate spillover in constraining synaptic time constants. They suggest that MT cells glutamate release may locally change NMDA and AMPA mediated excitation (Martin et al., 2012; Lepousez and Lledo, 2013).

Taken together, these data imply that the two rhythms require the MOB network to exist. Even if they are both constrained by inhibition onto MT cells, this inhibition is likely to occur under different forms: either locally within the MOB network (granule and periglomerular cells) or remotely through centrifugal feedback. Because beta oscillations require intact connections between the MOB and at least the PCx, they are likely to emerge when the cerebral network engaged is broader. In the following part, we will examine which behavioral conditions are associated with either of the two oscillations, and how the distinction can have a functional readout in the context of learning.

ODOR LEARNING INDUCED MODIFICATIONS: DIFFERENT RHYTHMS FOR DIFFERENT LEARNING TASKS?

As we discussed earlier, beta oscillatory activity has been observed in the olfactory system in naïve animals in response to toxic or aversive odorants (Zibrowski et al., 1998; Vanderwolf et al., 2002). When exposed to a neutral unfamiliar odorant, only weak beta oscillations are observed but their amplitude increases through training as soon as this odor starts to acquire a behavioral meaning for the animal (Ravel et al., 2003; Martin et al., 2004b). Such a learning-induced increase in beta power has been observed in several structures associated with odor processing (MOB, PCx, entorhinal cortex, and hippocampus) and for a variety of behavioral paradigms (see Table 1): Go/No-Go task (Ravel et al., 2003; Martin et al., 2004b, 2007; Gourévitch et al., 2010; Lepousez and Lledo, 2013), two-alternative choice task (Fuentes et al., 2008) and aversive learning (Chapuis et al., 2009). However, a few studies, with similar operant conditioning, report an odor evoked gamma increase instead of a change in beta activity (Beshel et al., 2007; Rosero and Aylwin, 2011).

It is easily arguable, when comparing the studies where LFPs have been recorded in olfactory structures in different operant tasks that the presence or not of substantial beta oscillations seems to be strongly dependent on the behavioral context of the task, and the cognitive strategy required to solve it (Kay et al., 2006). Go/No-Go and two-alternative choice have been the two main tools used to assess odor discrimination and learning in rodents. In the case of the Go/No-Go task, two odorants are delivered in a random order, one is positively rewarded (CS+; sucrose) and another is not rewarded or associated with a negative reinforcement (CS-; quinine). Initially, both odorants are neutral to the animals, and do not elicit any particular behavior. Over the course of training, animals learn to associate each odorant with the corresponding reward, and exhibit a differential behavior in response to the two odorant stimuli. Reaching the behavioral criterion for good performances takes several sessions, a duration that can vary with the difficulty of the task, which depends itself on the qualitative proximity of the odorants used.

In the Go/No-Go task, we have constantly found beta power increase during learning for both the CS+ and CS− (Martin et al., 2004a,b, 2006), raising the question of the link between this activity and the chemical feature of the odorant in one hand, or the odor meaning on the other hand. Oscillations in the olfactory system are triggered by odorant sampling and are likely to carry some aspects of odorants, as it has been demonstrated in anesthetized animals (Cenier et al., 2008). Indeed, by recording LFP signals from four different locations within the MOB, we showed that the main characteristics of beta oscillations (frequency and amplitude) are not homogeneous across the MOB, contrarily to gamma bursts recorded during spontaneous activity. Moreover, during learning, a stronger beta power is found in the posterior part of the structure (Martin et al., 2004b). Distinct odors evoke different amplitude levels of beta oscillations, irrespectively of the reward they are associated to. For a given animal, two different CS+ odorants can evoke distinct beta amplitude (Martin et al., 2004a), and a reversal procedure for an odorant pair (inverse learning contingencies) does not lead to the mirror image of the beta activity for each odor (Martin et al., 2007). Taken together, these data show that specificity of beta oscillations after learning would convey some feature of the odorant. However, it is likely that beta rhythm also reflects the odor signification acquired through learning.

Contrarily to the Go/No-Go, where only one odor is reinforced, the two-alternative choice task is symmetrically rewarded and a pellet is delivered for each correct response. This task, that seems more demanding for a rodent, is indeed often acquired slowly by the animals, and with a lower final performance (Friedrich, 2006; Slotnick, 2007). However, a recent study has shown that adjustment of parameters could allow to attain the same level of accuracy than for the Go/No-Go task in the same laps of time (Frederick et al., 2011). Besides the difficulty of the task, we can make the hypothesis that these two tasks involve different strategies and thus activate different brain circuits. In the two-alternative choice paradigm, odors can elicit high amplitude beta oscillations (10–30 Hz) and a significant decrease in the gamma band (70–100 Hz) (Fuentes et al., 2008). However, Beshel et al. (2007) using this task to compare successive odor pairs discriminations obtained different results. In this study, as expected, the animals are faster to reach the criterion for molecularly dissimilar odorants than for similar ones. Moreover, once animals are at the criterion for the discrimination, odor evoked gamma (60–85 Hz) power is very high for fine discriminations and almost absent for coarse ones. Within a given session, gamma power increases almost linearly across trial block but resets at the beginning of each session even if performances are improved. Interestingly, gamma increase is restricted to the MOB and does not propagate to the PCx. However, besides this gamma response, beta oscillations are also observed in three interconnected olfactory areas (MOB and anterior and posterior PCx) and only the beta band exhibits consistently elevated coherence levels between these three areas during odor sniffing across all odor pairs, classes (alcohols and ketones), and discrimination types (fine and coarse) (Kay and Beshel, 2010).

As mentioned earlier, the respiratory modulation influences odor processing. In anesthetized-tracheotomized animals, an airflow change is sufficient to change the relative power of beta and gamma frequency bursts (Courtiol et al., 2011). The direct relation between sniffing properties and oscillatory patterns during olfactory conditioning is still an open question in awake animals, who can actively tune their respiratory modulation. Still, we cannot exclude that sniffing properties affect oscillatory activities during odor sampling in the context of learning. A very recent paper showed that sniffing properties can be modulated by the context of the discrimination, i.e., which odor pair is presented during the test (Courtiol et al., 2014). However, the adjustment of sniffing parameters during odorant mixtures discrimination seems to rely largely on differences in sorption quality of the elements (Rojas-Líbano and Kay, 2012). Evolution of sniffing frequency and/or duration during the acquisition of the task is more likely to affect the intensity or the length of oscillations rather than its frequency.

We can rule out the hypothesis that only the concentration of odorant would turn beta into gamma in some conditions as it has been reported in anesthetized preparations (Neville and Haberly, 2003) and suggested in other studies (Rosero and Aylwin, 2011). Indeed, Go/No-Go and two-alternative choice tests were in this case performed in the same laboratory, using the same apparatus and the same odorant concentrations (Beshel et al., 2007; Martin et al., 2007). Beshel et al. (2007) show that the relatedness of the two odors involved in the discrimination increase gamma power in the MOB. However the relationship between the elevation of gamma power and the chemical proximity of the odorants, directly linked with the difficulty of the task, seems to be task specific. Indeed, as illustrated in Figure 2, the use of two chemically related odors heptanol and hexanol leads to different results in the two paradigms: beta band (15–40 Hz) power increase in the Go/No-Go paradigm (Martin et al., 2007) and enhanced gamma power (65–85 Hz) in the two-alternative choice task (Beshel et al., 2007).

FIGURE 2. Odor discrimination in two different behavioral tests led to distinct rhythms in the olfactory bulb. Example of raw LFP traces recorded in the olfactory bulb during a criterion session of discrimination between two alcohols (hexanol and heptanol) in two different paradigms: (A) a two-alternative choice test (Beshel et al., 2007) or (B) a Go/No-Go test (Martin et al., 2007). The two tests are schematized on the top. The main difference is that both odors are rewarded in the 2 alternative-choice whereas one odor is not rewarded in the Go/No-Go. In both cases, the same odor concentration is delivered. Odorants are generated in glass test tubes by bubbling air (100 ml/min) through a column of pure odorant and injecting the odorized air into a carrier air stream (400 ml/min) via a computer-controlled olfactometer achieving ~20% saturated vapor. LFPs recorded during odorant sampling periods are displayed, red arrows pointed out odorant onset. As underlined by shaded regions, in the two-alternative choice test, odors evoke high gamma power (65–85 Hz) whereas in the Go/No-Go context, odorant sampling is associated with a strong beta wave (15–40 Hz). It has to be noted that both rewarded and unrewarded odors trigger beta rhythm in the Go/No-Go test.

How can we explain these discrepancies? One possibility could be that beta and gamma oscillations do correspond to distinct odor-related cognitive processing occurring at different stages of the training. Beta rhythm would be necessary, during the acquisition of the discrimination, to set up a broad network of distant brain structures required for specific rules and odor encoding. Indeed, one consistency across studies is that beta connects different brain areas (olfactory areas and beyond). Gamma could be required only in a subsequent stage of training, when odor discrimination has been learned by the rat, but its resolution is more challenging to reach the criterion. At this stage, the olfactory network has already been modified by learning and odor processing required for a fine sensory discrimination is rather supported by a local network and sustained by gamma band oscillations. This shift could allow a faster and more efficient treatment of odor, requiring less energy expenditure.

In the context of two-alternative choice task, beta rhythm is indeed present during the first learning sessions and disappears in subsequent sessions (Beshel et al., 2007; Kay and Beshel, 2010). In this condition, the number of trials required to reach the criterion for a given odor pair is enhanced compared to Go/No-Go for instance for the pair hexanol/heptanol, 350 trials (Beshel et al., 2007) vs. 72 trials (Martin et al., 2007) respectively (in both case, after rule transfer from previous odor pair discrimination). It is likely that the rat learns the discrimination between the odors during the first session, but that further sessions are necessary for the acquisition of the sensory-motor association. In the case where gamma oscillations are recorded in the context of a Go/No-Go task, the number of trials to criterion is also elevated (close to 1000) (Rosero and Aylwin, 2011).

The hypothesis that beta and gamma are two distinct mechanisms occurring at different time scale of the learning process is consistent with the idea proposed by Engel and Fries (2010) that beta band activity would dominate when top down input are the majority, whereas gamma band would rather reflects bottom-up local processing of sensory input. We will argue this hypothesis in the following part.

GAMMA AND BETA OSCILLATIONS, LOCAL VS. DISTAL NETWORKS?

In agreement with the notion that in brain circuits beta rhythms coordinate long-range communication whereas faster gamma rhythms are more related to local intra structure processing (Kopell et al., 2000; Siegel et al., 2012; Cannon et al., 2014), the emergence in the MOB and PCx of these two rhythms in odor-driven behavioral tasks is thus likely to sustain different network properties and processing.

In line with their implication in memory processes, beta oscillations have been found to sustain long range interactions. They have been recorded in many distant brain structures related to olfactory-driven behavior. Beyond the MOB and the PCx, they have been found in the lateral entorhinal cortex (Martin et al., 2004a; Igarashi et al., 2014), in the tubercle (Carlson et al., 2014), the hippocampus (Martin et al., 2007; Igarashi et al., 2014), in motor cortex M1 (Hermer-Vazquez et al., 2007) in different parts of the prefrontal cortex (infralimbic and orbito-frontal cortex), the basolateral amygdala, and the insular cortex (Chapuis et al., 2009). Interestingly, using an olfactory discrimination Go/No-Go task, van Wingerden et al. (2010) reported an increase in gamma oscillations in the orbitofrontal cortex, where power was correlated with rat training and performance, as shown by Beshel et al. (2007) in the MOB. However, in the same study, the authors also observe some late beta oscillatory activity more associated with odorant sampling and very similar to what was reported in the MOB or the hippocampus (Martin et al., 2004b, 2007). This shift from gamma to beta rhythm observed in several areas associated with odorant sampling just before the animal makes a decision is in agreement with the hypothesis of a general beta synchronization across odor-processing areas that could be the signature of a functional network set up through learning.

Whereas gamma oscillations recorded in these structures are likely to reflect local processing and thus to have a distinct origin from that recorded in primary olfactory structures, we propose that beyond the MOB and PCx, beta oscillations would tag brain structures involved in the behavioral task that the animal is performing and form a unique representation of the odor in this task. Studies where multielectrode recordings have been performed have shown that beta increase occurred specifically in brain regions involved in the task performed by the animal. Indeed, in Martin et al. (2007), beta does not increase in the hippocampus for the first odor discrimination but for the transfer that is more likely to involve the structure. In the same way, after odor aversive conditioning, beta oscillations increase in insular and infralimbic cortices when the odor is ingested but not when it is delivered by airflow (Chapuis et al., 2009). By extension, we postulate that other brain areas, not yet studied, are capable of joining beta oscillatory network if involved in a given olfactory task.

One striking and stable characteristic of the emergence of beta rhythm in olfactory structures is that it is narrowly linked to behavioral output. Interestingly, beta power modulations seem to follow some aspects of the learning curve dynamics at least in the MOB and PCx. Indeed, beta gradually increases across training sessions (Martin et al., 2004b) and a strong beta oscillatory activity is observed just one or two sessions before the learning criterion was reached regardless of the time needed by the rat to acquire the discrimination (Ravel et al., 2003; Martin et al., 2004b, 2007). This is also true when odor learning is achieved in 1 day in the context of aversive odor learning (Chapuis et al., 2009) or following rule transfer to a new odor pair discrimination, which is done within one learning session (Martin et al., 2007). It is important to underline that beta power increases specifically for the learnt odor pair and falls down at the beginning of each new odor pair presentation. If training is continued post-criterion, beta power decreases as a function of overtraining (Martin et al., 2007). On the contrary, we observe that once the discrimination is achieved, if animals are put at rest and not tested for a long period (from a week to a month), beta oscillation emerges again stronger than ever. This latter effect is observed both after appetitive and aversive conditioning (Martin et al., 2004b; Chapuis et al., 2009). Finally, the emergence of beta oscillatory activity in a network seems to be highly specific of the conditioning procedure. Taking advantage of two different experimental situations suitable to induce a conditioned olfactory aversion we were able to demonstrate two different odor-evoked beta networks according to how the odor has been previously experienced by the animal (Chapuis et al., 2009).

All together these observations have therefore spawned the idea that beta rhythm might be necessary to bind together elements of a broad network and contribute to the build-up of memory. Indeed, we make the hypothesis that such a coordinated oscillatory activity could be used to tag preferentially inter or intra area connections that need to be reinforced to be efficiently and rapidly reactivated when the odor is further encountered. This idea has been strongly reinforced by a recent article that identified beta oscillations coupling between the entorhinal cortex and the hippocampus as a mechanism for the emergence of a functional circuit during encoding of odor associative memory (Igarashi et al., 2014).

The shift between gamma and beta when an odor is processed is likely due to a change in bidirectional connections between the MOB and other cerebral structures (Figure 3).

FIGURE 3. Schematic illustration of hypotheses for the generation of beta (15–35 Hz) and gamma (60–90 Hz) rhythms in the olfactory bulb and the piriform cortex in awake behaving animals. Example of raw LFP traces recorded (A) in the MOB in the absence of olfactory stimulation and (B) in MOB and two different regions of the PCx before learning and (C) after learning during a conditioned discrimination paradigm (Go/No-Go). a–c: Corresponding schematic representation of MOB and PCx interconnected networks and centrifugal modulation. The level of neuromodulation is represented by the red arrow on the left, the level of cortical feedback by green arrow on the right. (A) Spontaneous activity: in the absence of olfactory stimulation. Observe the regular theta respiratory modulation (around 2 Hz) and the associated bursts of gamma activity (60–90 Hz). See also in (B) and (C) the portion just preceding the odorant sampling (green square area) how the gamma bursts decrease in the posterior part of the PCx compare to the MOB and anterior part of PCx. a: In the absence of olfactory stimulation, the level of activation in both networks is weak and variable and both structures are dominated by theta and gamma activity. Gamma activity is transmitted from the MOB to the PCx. (B) Before learning: during odorant sampling, occurrence of gamma bursts is reduced but recovered after the animal has left the odor port. b: During this phase, a population of mitral cells of the MOB becomes active, this input is transmitted to a corresponding population of pyramidal cells. Both neuromodulatory and cortical feedback are exerted on the networks. However, no real coordination is set up in the network. (C) During training: In addition to a strong and sustained decrease in gamma activity, a clear beta oscillation is observed in the MOB and two regions of PCx associated to odorant sampling. c: During this phase, we propose that both assemblies of active mitral cells and pyramidal cells reinforce their connections. The result could be a more efficient and rapid transfer of olfactory information. This coordination is under the influence of both cortical feedback and neuromodulatory fibers as suggested by the results we observed with lidocaine inactivation of the peduncle (Martin et al., 2006). Once synaptic contacts are established, if the training is maintained to get over training, the amplitude of beta oscillatory activity decreases. On the contrary, if the animal is left in his home cage for a long interval without training and tested again, both structures exhibit a very strong beta oscillatory activity.

It has been reported for a long time that centrifugal influences are gating synaptic plasticity processes in both MOB and PCx (for a review see Mandairon and Linster, 2009). As expected, manipulations of centrifugal projections alter behavior. The lesion of efferent inputs to the MOB prevents the formation of odor-reward associations, but has no effect on the resolution of spontaneous habituation experiment (Kiselycznyk et al., 2006). Besides, specific manipulation of noradrenergic action to the MOB impairs mice in discrimination learning in a Go/No-Go paradigm (Doucette et al., 2007). In this same task, MT cells undergo a profound change in odor responsiveness throughout a learning session (Doucette and Restrepo, 2008), that is dependent on centrifugal feedback (Restrepo et al., 2009). Both cortical feedback and neuromodulatory influences play a determinant role in the shift between gamma and beta rhythms as indicated by the impact of their local blockade on the odor-evoked activity in both the MOB and the PCx in a Go/No-Go task (Martin et al., 2006), reinforcing their link with expression of plasticity and memory processes. Interestingly, directed coherence analyses have shown that during odorant sampling, the MOB would drive odor related beta activity to the PCx (Boeijinga and Lopes da Silva, 1989; Kay and Beshel, 2010) and to the hippocampus (Chapman et al., 1998; Gourévitch et al., 2010), carrying relevant information in the bottom up direction instead of the reverse. In contrast, it could be the opposite during memory consolidation since in a slow-wave sleep-like state induced under anesthesia, the functional link based on slow waves LFP recordings (<15 Hz) is in the direction of the hippocampus to the PCx (Wilson and Yan, 2010). That beta rhythm could play a role in memory consolidation during sleep remains an open question.

The ultimate argument for a causal link between emergence of oscillatory activities and improvement of behavioral performance would be to degrade beta/gamma oscillatory dynamic in the network and observe behavioral impairment. Few studies have been conducted in MOB mammals that mainly addressed gamma activity. Local injection of low doses of picrotoxin, a GABAa receptor antagonist were reported to enhance gamma oscillations and also led to behavioral modifications: mice displayed an increased odorant sampling time, and their performances were selectively altered in the case of a fine odor discrimination in a Go/No-Go paradigm (Lepousez and Lledo, 2013). Nusser et al. (2001) used a transgenic mouse model, in which GABAa receptors were disrupted specifically on granule cells, i.e., those cells where centrifugal feedback targets and reported gamma oscillations power was enhanced in the MOB. Behavioral testing concluded that whereas mice seemed to perform better on a simple odor identification task, they were impaired on a mixture discrimination test. Interestingly, these two studies show that network modifications that lead to gamma band increase also result in behavioral impairment. Using pharmacological blockade, we have reported that inactivation of feedback projections abolish beta oscillations and conversely increases gamma power (Martin et al., 2006). Consequently, we postulate that those modifications that increase gamma oscillations and also lead to beta weakening impair behavior.

CONCLUSION

In this review, we focused on two different oscillatory rhythms beta (15–40 Hz) and gamma (60–100 Hz) that have been associated with olfactory stimulus processing. We propose that gamma activity is associated with the resting state of a network limited to the first two steps of the olfactory system (MOB and anterior part of the PCx). As reported, this basic activity could be modulated in power during learning according to some experimental conditions but rather reflects the involvement of a limited local network under the control of higher cortical feedback and neuromodulators. In behaving animals, as soon as an odor is processed, this local coordination is disrupted and replaced by a lower frequency oscillation in the beta range (15–40 Hz). Most of the data reported in this review lead to the hypothesis that beta activity is the signature of a larger network including not only olfactory sensory areas but also each structure involved in the processing of the odorant stimulus, which could differ according to the behavioral situation. As stated in the present article, a decent amount of data is in favor of a strong correlation between beta oscillation modulation in power and learning-induced changes, in both rats and mice. Beta rhythm frequency is well suited for long range interactions (Kopell et al., 2000; Von Stein and Sarnthein, 2000) and thus for sustaining memory processes. The presence of beta oscillatory rhythm within and between neuronal networks would optimize information processing, representing a framework for neuronal synchronization. By this mean odor coding would be more efficient and temporal simultaneity would favor hebbian mechanism of plasticity (Cassenaer and Laurent, 2007). However, we still lack evidence to disambiguate whether beta oscillations are instrumental for processes like spike timing plasticity in the network or if on the contrary they are just reflecting these changes. Nevertheless, we propose the idea that mapping such oscillatory activities in a neural network could be a good way to assess learning-induced brain plasticity at least in the context of odor-guided tasks. Recently, beta oscillations have also been used as a tool to reveal impaired network activity preceding behavioral dysfunctions (Wesson et al., 2011) and evaluate the impact of a treatment to enhance clearance of beta-amyloid protein in a mouse model of Alzheimer disease (Cramer et al., 2012). Providing experimental evidence to support a causal link between oscillatory binding and inter area synchronization will be one of the main goal for the future.

ACKNOWLEDGMENT

This review has received the financial support of the excellence network LABEX Cortex.

REFERENCES

Adrian, E. D. (1942). Olfactory reactions in the brain of the hedgehog. J. Physiol. 100, 459–473.

Adrian, E. D. (1950). The electrical activity of the mammalian olfactory bulb. Electroencephalogr. Clin. Neurophysiol. 2, 377–388. doi: 10.1016/0013-4694(50)90075-7

Barkai, E., and Saar, D. (2001). Cellular correlates of olfactory learning in the rat piriform cortex. Rev. Neurosci. 12, 111–120. doi: 10.1515/REVNEURO.2001.12.2.111

Bathellier, B., Lagier, S., Faure, P., and Lledo, P. M. (2006). Circuit properties generating gamma oscillations in a network model of the olfactory bulb. J. Neurophysiol. 95, 2678–2691. doi: 10.1152/jn.01141.2005

Bekkers, J. M., and Suzuki, N. (2013). Neurons and circuits for odor processing in the piriform cortex. Trends Neurosci. 36, 429–438. doi: 10.1016/j.tins.2013.04.005

Beshel, J., Kopell, N., and Kay, L. (2007). Olfactory bulb gamma oscillations are enhanced with task demands. J. Neurosci. 27, 8358–8365. doi: 10.1523/JNEUROSCI.1199-07.2007

Binns, K. E., and Brennan, P. A. (2005). Changes in electrophysiological activity in the accessory olfactory bulb and medial amygdala associated with mate recognition in mice. Eur. J. Neurosci. 21, 2529–2537. doi: 10.1111/j.1460-9568.2005.04090.x

Boeijinga, P. H., and Lopes da Silva, F. H. (1989). Modulations of EEG activity in the entorhinal cortex and forebrain olfactory areas during odour sampling. Brain Res. 478, 257–268. doi: 10.1016/0006-8993(89)91506-0

Boyd, A. M., Sturgill, J. F., Poo, C., and Isaacson, J. S. (2012). Cortical feedback control of olfactory bulb circuits. Neuron 76, 1161–1174. doi: 10.1016/j.neuron.2012.10.020

Bressler, S. (1984). Spatial organization of EEGs from olfactory bulb and cortex. Electroencephalogr. Clin. Neurophysiol. 57, 270–276. doi: 10.1016/0013-4694(84)90128-7

Buffalo, E. A., Fries, P., Landman, R., Buschman, T. J., and Desimone, R. (2011). Laminar differences in gamma and alpha coherence in the ventral stream. Proc. Natl. Acad. Sci. U.S.A. 108, 11262–11267. doi: 10.1073/pnas.1011284108

Buonviso, N., Amat, C., and Litaudon, P. (2006). Respiratory modulation of olfactory neurons in the rodent brain. Chem. Senses 31, 145–154. doi: 10.1093/chemse/bjj010

Buonviso, N., Amat, C., Litaudon, P., Roux, S., Royet, J. P., Farget, V., et al. (2003). Rhythm sequence through the olfactory bulb layers during the time window of a respiratory cycle. Eur. J. Neurosci. 17, 1811–1819. doi: 10.1046/j.1460-9568.2003.02619.x

Buzsáki, G., Anastassiou, C. A., and Koch, C. (2012). The origin of extracellular fields and currents–EEG, ECoG, LFP and spikes. Nat. Rev. Neurosci. 13, 407–420. doi: 10.1038/nrn3241

Cannon, J., McCarthy, M. M., Lee, S., Lee, J., Börgers, C., Whittington, M. A., et al. (2014). Neurosystems: brain rhythms and cognitive processing. Eur. J. Neurosci. 39, 705–719. doi: 10.1111/ejn.12453

Carlson, K. S., Dillione, M. R., and Wesson, D. W. (2014). Odor- and state-dependent olfactory tubercle local field potential dynamics in awake rats. J. Neurophysiol. 111, 2109–2123. doi: 10.1152/jn.00829.2013

Cassenaer, S., and Laurent, G. (2007). Hebbian STDP in mushroom bodies facilitates the synchronous flow of olfactory information in locusts. Nature 448, 709–713. doi: 10.1038/nature05973

Cenier, T., Amat, C., Litaudon, P., Garcia, S., Lafaye de Micheaux, P., Liquet, B., et al. (2008). Odor vapor pressure and quality modulate local field potential oscillatory patterns in the olfactory bulb of the anesthetized rat. Eur. J. Neurosci. 27, 1432–1440. doi: 10.1111/j.1460-9568.2008.06123.x

Chabaud, P., Ravel, N., Wilson, D. A., Mouly, A. M., Vigouroux, M., Farget, V., et al. (2000). Exposure to behaviourally relevant odour reveals differential characteristics in rat central olfactory pathways as studied through oscillatory activities. Chem. Senses 25, 561–573. doi: 10.1093/chemse/25.5.561

Chapman, C. A., Xu, Y., Haykin, S., and Racine, R. J. (1998). Beta-frequency (15-35 Hz) electroencephalogram activities elicited by toluene and electrical stimulation in the behaving rat. Neuroscience 86, 1307–1319. doi: 10.1016/S0306-4522(98)00092-X

Chapuis, J., Garcia, S., Messaoudi, B., Thevenet, M., Ferreira, G., Gervais, R., et al. (2009). The way an odor is experienced during aversive conditioning determines the extent of the network recruited during retrieval: a multisite electrophysiological study in rats. J. Neurosci. 29, 10287–10298. doi: 10.1523/JNEUROSCI.0505-09.2009

Chapuis, J., and Wilson, D. A. (2012). Bidirectional plasticity of cortical pattern recognition and behavioral sensory acuity. Nat. Neurosci. 15, 155–161. doi: 10.1038/nn.2966

Chery, R., Gurden, H., and Martin, C. (2014). Anesthetic regimes modulate the temporal dynamics of local field potential in the mouse olfactory bulb. J. Neurophysiol. 111, 908–917. doi: 10.1152/jn.00261.2013

Cleland, T. A., and Linster, C. (2003). “Central olfactory structures,” in Handbook of Olfaction and Gustation, ed R. L. Doty (New York, NY; Basel: Marcel Dekker, Inc.), 165–180.

Courtiol, E., Amat, C., Thévenet, M., Messaoudi, B., Garcia, S., and Buonviso, N. (2011). Reshaping of bulbar odor response by nasal flow rate in the rat. PLoS ONE 6:e16445. doi: 10.1371/journal.pone.0016445

Courtiol, E., Lefèvre, L., Garcia, S., Thévenet, M., Messaoudi, B., and Buonviso, N. (2014). Sniff adjustment in an odor discrimination task in the rat: analytical or synthetic strategy? Front. Behav. Neurosci. 8:145. doi: 10.3389/fnbeh.2014.00145

Cramer, P. E., Cirrito, J. R., Wesson, D. W., Lee, C. Y., Karlo, J. C., Zinn, A. E., et al. (2012). ApoE-directed therapeutics rapidly clear β-amyloid and reverse deficits in AD mouse models. Science 335, 1503–1506. doi: 10.1126/science.1217697

David, F. O., Hugues, E., Cenier, T., Fourcaud-Trocmé, N., and Buonviso, N. (2009). Specific entrainment of mitral cells during gamma oscillation in the rat olfactory bulb. PLoS Comput. Biol. 5:e1000551. doi: 10.1371/journal.pcbi.1000551

Davis, R. L. (2004). Olfactory learning. Neuron 44, 31–48. doi: 10.1016/j.neuron.2004.09.008

Dias, B. G., and Ressler, K. J. (2014). Parental olfactory experience influences behavior and neural structure in subsequent generations. Nat. Neurosci. 17, 89–96. doi: 10.1038/nn.3594

Di Prisco, G. V., and Freeman, W. J. (1985). Odor-related bulbar EEG spatial pattern analysis during appetitive conditioning in rabbits. Behav. Neurosci. 99, 964–978. doi: 10.1037/0735-7044.99.5.964

Doucette, W., Milder, J., and Restrepo, D. (2007). Adrenergic modulation of olfactory bulb circuitry affects odor discrimination. Learn. Mem. 14, 539–547. doi: 10.1101/lm.606407

Doucette, W., and Restrepo, D. (2008). Profound context-dependent plasticity of mitral cell responses in olfactory bulb. PLoS Biol. 6:e258. doi: 10.1371/journal.pbio.0060258

Dumenko, V. N. (1995). Dynamic shifts in the parameters of the traditional frequency range of the EEG during learning in dogs. Neurosci. Behav. Physiol. 25, 403–412. doi: 10.1007/BF02359597

Eeckman, F. H., and Freeman, W. J. (1990). Correlations between unit firing and EEG in the rat olfactory system. Brain Res. 528, 238–244. doi: 10.1016/0006-8993(90)91663-2

Engel, A. K., and Fries, P. (2010). Beta-band oscillations–signalling the status quo? Curr. Opin. Neurobiol. 20, 156–165. doi: 10.1016/j.conb.2010.02.015

Engel, A. K., Fries, P., and Singer, W. (2001). Dynamic predictions: oscillations and synchrony in top-down processing. Nat. Rev. Neurosci. 2, 704–716. doi: 10.1038/35094565

Ennis, M., Hamilton, K. A., and Hayar, A. (2007). “Neurochemistry of the main olfactory system,” in Handbook of Neurochemistry and Molecular Neurobiology, 3rd Edn, ed A. Lajtha (New York, NY: Springer), 137–204.

Fell, J., and Axmacher, N. (2011). The role of phase synchronization in memory processes. Nat. Rev. Neurosci. 12, 105–118. doi: 10.1038/nrn2979

Fletcher, M. L., and Wilson, D. A. (2003). Olfactory bulb mitral-tufted cell plasticity: odorant-specific tuning reflects previous odorant exposure. J. Neurosci. 23, 6946–6955.

Frederick, D. E., Rojas-Líbano, D., Scott, M., and Kay, L. M. (2011). Rat behavior in go/no-go and two-alternative choice odor discrimination: differences and similarities. Behav. Neurosci. 125, 588–603. doi: 10.1037/a0024371

Freeman, W., and Schneider, W. (1982). Changes in spatial patterns of rabbit olfactory EEG with conditioning to odors. Psychophysiology 19, 44–56. doi: 10.1111/j.1469-8986.1982.tb02598.x

Freeman, W. J. (1960). Correlation of electrical activity of prepyriform cortex and behavior in cat. J. Neurophysiol. 23, 111–131.

Friedrich, R. W. (2006). Mechanisms of odor discrimination: neurophysiological and behavioral approaches. Trends Neurosci. 29, 40–47. doi: 10.1016/j.tins.2005.10.004

Fuentes, R. A., Aguilar, M. I., Aylwin, M. L., and Maldonado, P. E. (2008). Neuronal activity of mitral-tufted cells in awake rats during passive and active odorant stimulation. J. Neurophysiol. 100, 422–430. doi: 10.1152/jn.00095.2008

Fukunaga, I., Berning, M., Kollo, M., Schmaltz, A., and Schaefer, A. T. (2012). Two distinct channels of olfactory bulb output. Neuron 75, 320–329. doi: 10.1016/j.neuron.2012.05.017

Gottfried, J. A. (2010). Central mechanisms of odour object perception. Nat. Rev. Neurosci. 11, 628–641. doi: 10.1038/nrn2883

Gourévitch, B., Kay, L. M., and Martin, C. (2010). Directional coupling from the olfactory bulb to the hippocampus during a go/no-go odor discrimination task. J. Neurophysiol. 103, 2633–2641. doi: 10.1152/jn.01075.2009

Gray, C., and Skinner, J. (1988a). Centrifugal regulation of neuronal activity in the olfactory bulb of the waking rabbit as revealed by reversible cryogenic blockade. Exp. Brain Res. 69, 378–386. doi: 10.1007/BF00247583

Gray, C. M. (1994). Synchronous oscillations in neuronal systems: mechanisms and functions. J. Comput. Neurosci. 1, 11–38. doi: 10.1007/BF00962716

Gray, C. M., and Skinner, J. E. (1988b). Field potential response changes in the rabbit olfactory bulb accompany behavioral habituation during the repeated presentation of unreinforced odors. Exp. Brain Res. 73, 189–197. doi: 10.1007/BF00279672

Haberly, L. (2001). Parallel-distributed processing in olfactory cortex: new insights from morphological and physiological analysis of neuronal circuitry. Chem. Senses 26, 551–576. doi: 10.1093/chemse/26.5.551

Hagiwara, A., Pal, S. K., Sato, T. F., Wienisch, M., and Murthy, V. N. (2012). Optophysiological analysis of associational circuits in the olfactory cortex. Front. Neural Circuits 6:18. doi: 10.3389/fncir.2012.00018

Heale, V. R., and Vanderwolf, C. H. (1994). Dentate gyrus and olfactory bulb responses to olfactory and noxious stimulation in urethane anaesthetized rats. Brain Res. 652, 235–242. doi: 10.1016/0006-8993(94)90232-1

Hermer-Vazquez, R., Hermer-Vazquez, L., Srinivasan, S., and Chapin, J. K. (2007). Beta- and gamma-frequency coupling between olfactory and motor brain regions prior to skilled, olfactory-driven reaching. Exp. Brain Res. 180, 217–235. doi: 10.1007/s00221-007-0850-2

Hernandez-Peon, R., Lavin, A., Alcocer-Cuaron, C., and Marcelin, J. P. (1960). Electrical activity of the olfactory bulb during wakefulness and sleep. Electroencephalogr. Clin. Neurophysiol. 12, 41–58. doi: 10.1016/0013-4694(60)90060-2

Hintiryan, H., Gou, L., Zingg, B., Yamashita, S., Lyden, H. M., Song, M. Y., et al. (2012). Comprehensive connectivity of the mouse main olfactory bulb: analysis and online digital atlas. Front. Neuroanat. 6:30. doi: 10.3389/fnana.2012.00030

Igarashi, K. M., Lu, L., Colgin, L. L., Moser, M. B., and Moser, E. I. (2014). Coordination of entorhinal-hippocampal ensemble activity during associative learning. Nature 510, 143–147. doi: 10.1038/nature13162

Jones, S. V., Choi, D. C., Davis, M., and Ressler, K. J. (2008). Learning-dependent structural plasticity in the adult olfactory pathway. J. Neurosci. 28, 13106–13111. doi: 10.1523/JNEUROSCI.4465-08.2008

Kay, L., Beshel, J., Brea, J., Martin, C., Rojas-Líbano, D., and Kopell, N. (2009). Olfactory oscillations: the what, how and what for. Trends Neurosci. 32, 207–214. doi: 10.1016/j.tins.2008.11.008

Kay, L., Beshel, J., and Martin, C. (2006). When good enough is best. Neuron 51, 277–278. doi: 10.1016/j.neuron.2006.07.015

Kay, L. M. (2003). Two species of gamma oscillations in the olfactory bulb: dependence on behavioral state and synaptic interactions. J. Integr. Neurosci. 2, 31–44. doi: 10.1142/S0219635203000196

Kay, L. M., and Beshel, J. (2010). A beta oscillation network in the rat olfactory system during a 2-alternative choice odor discrimination task. J. Neurophysiol. 104, 829–839. doi: 10.1152/jn.00166.2010

Kay, L. M., and Freeman, W. J. (1998). Bidirectional processing in the olfactory-limbic axis during olfactory behavior. Behav. Neurosci. 112, 541–553. doi: 10.1037/0735-7044.112.3.541

Kepecs, A., Uchida, N., and Mainen, Z. F. (2006). The sniff as a unit of olfactory processing. Chem. Senses 31, 167–179. doi: 10.1093/chemse/bjj016

Kiselycznyk, C. L., Zhang, S., and Linster, C. (2006). Role of centrifugal projections to the olfactory bulb in olfactory processing. Learn. Mem. 13, 575–579. doi: 10.1101/lm.285706

Kopell, N., Ermentrout, G., Whittington, M., and Traub, R. (2000). Gamma rhythms and beta rhythms have different synchronization properties. Proc. Natl. Acad. Sci. U.S.A. 97, 1867–1872. doi: 10.1073/pnas.97.4.1867

Lagier, S., Panzanelli, P., Russo, R. E., Nissant, A., Bathellier, B., Sassoe-Pognetto, M., et al. (2007). GABAergic inhibition at dendrodendritic synapses tunes gamma-oscillations in the olfactory bulb. Proc. Natl. Acad. Sci. U.S.A. 104, 7259–7264. doi: 10.1073/pnas.0701846104

Lavin, A., Alcocer-Cuaron, C., and Hernandez-Peon, R. (1959). Centrifugal arousal in the olfactory bulb. Science 129, 332–333. doi: 10.1126/science.129.3345.332

Lepousez, G., and Lledo, P. M. (2013). Odor discrimination requires proper olfactory fast oscillations in awake mice. Neuron 80, 1010–1024. doi: 10.1016/j.neuron.2013.07.025

Linster, C., and Hasselmo, M. E. (2001). Neuromodulation and the functional dynamics of piriform cortex. Chem. Senses 26, 585–594. doi: 10.1093/chemse/26.5.585

Litaudon, P., Amat, C., Bertrand, B., Vigouroux, M., and Buonviso, N. (2003). Piriform cortex functional heterogeneity revealed by cellular responses to odours. Eur. J. Neurosci. 17, 2457–2461. doi: 10.1046/j.1460-9568.2003.02654.x

Liu, S., Plachez, C., Shao, Z., Puche, A., and Shipley, M. T. (2013). Olfactory bulb short axon cell release of GABA and dopamine produces a temporally biphasic inhibition-excitation response in external tufted cells. J. Neurosci. 33, 2916–2926. doi: 10.1523/JNEUROSCI.3607-12.2013

Lowry, C., and Kay, L. (2007). Chemical factors determine olfactory system beta oscillations in waking rats. J. Neurophysiol. 98, 394–404. doi: 10.1152/jn.00124.2007

Manabe, H., and Mori, K. (2013). Sniff rhythm-paced fast and slow gamma-oscillations in the olfactory bulb: relation to tufted and mitral cells and behavioral states. J. Neurophysiol. 110, 1593–1599. doi: 10.1152/jn.00379.2013

Mandairon, N., and Linster, C. (2009). Odor perception and olfactory bulb plasticity in adult mammals. J. Neurophysiol. 101, 2204–2209. doi: 10.1152/jn.00076.2009

Martin, C., Beshel, J., and Kay, L. M. (2007). An olfacto-hippocampal network is dynamically involved in odor-discrimination learning. J. Neurophysiol. 98, 2196–2205. doi: 10.1152/jn.00524.2007

Martin, C., Gervais, R., Chabaud, P., Messaoudi, B., and Ravel, N. (2004a). Learning-induced modulation of oscillatory activities in the mammalian olfactory system: the role of the centrifugal fibres. J. Physiol. 98, 467–478. doi: 10.1016/j.jphysparis.2005.09.003

Martin, C., Gervais, R., Hugues, E., Messaoudi, B., and Ravel, N. (2004b). Learning modulation of odor-induced oscillatory responses in the rat olfactory bulb: a correlate of odor recognition? J. Neurosci. 24, 389–397. doi: 10.1523/JNEUROSCI.3433-03.2004

Martin, C., Gervais, R., Messaoudi, B., and Ravel, N. (2006). Learning-induced oscillatory activities correlated to odour recognition: a network activity. Eur. J. Neurosci. 23, 1801–1810. doi: 10.1111/j.1460-9568.2006.04711.x

Martin, C., Houitte, D., Guillermier, M., Petit, F., Bonvento, G., and Gurden, H. (2012). Alteration of sensory-evoked metabolic and oscillatory activities in the olfactory bulb of GLAST-deficient mice. Front. Neural Circuits 6:1. doi: 10.3389/fncir.2012.00001

Matsutani, S. (2010). Trajectory and terminal distribution of single centrifugal axons from olfactory cortical areas in the rat olfactory bulb. Neuroscience 169, 436–448. doi: 10.1016/j.neuroscience.2010.05.001

Mori, K., Manabe, H., Narikiyo, K., and Onisawa, N. (2013). Olfactory consciousness and gamma oscillation couplings across the olfactory bulb, olfactory cortex, and orbitofrontal cortex. Front. Psychol. 4:743. doi: 10.3389/fpsyg.2013.00743

Mouly, A. M., Fort, A., Ben-Boutayab, N., and Gervais, R. (2001). Olfactory learning induces differential long-lasting changes in rat central olfactory pathways. Neuroscience 102, 11–21. doi: 10.1016/S0306-4522(00)00476-0

Mouly, A. M., and Gervais, R. (2002). Polysynaptic potentiation at different levels of rat olfactory pathways following learning. Learn. Mem. 9, 66–75. doi: 10.1101/lm.45602

Neville, K. R., and Haberly, L. B. (2003). Beta and gamma oscillations in the olfactory system of the urethane-anesthetized rat. J. Neurophysiol. 90, 3921–3930. doi: 10.1152/jn.00475.2003

Nusser, Z., Kay, L., Laurent, G., Homanics, G., and Mody, I. (2001). Disruption of GABA(A) receptors on GABAergic interneurons leads to increased oscillatory power in the olfactory bulb network. J. Neurophysiol. 86, 2823–2833.

Perez-Orive, J., Mazor, O., Turner, G. C., Cassenaer, S., Wilson, R. I., and Laurent, G. (2002). Oscillations and sparsening of odor representations in the mushroom body. Science 297, 359–365. doi: 10.1126/science.1070502

Ravel, N., Chabaud, P., Martin, C., Gaveau, V., Hugues, E., Tallon-Baudry, C., et al. (2003). Olfactory learning modifies the expression of odour-induced oscillatory responses in the gamma (60-90 Hz) and beta (15-40 Hz) bands in the rat olfactory bulb. Eur. J. Neurosci. 17, 350–358. doi: 10.1046/j.1460-9568.2003.02445.x

Restrepo, D., Doucette, W., Whitesell, J. D., McTavish, T. S., and Salcedo, E. (2009). From the top down: flexible reading of a fragmented odor map. Trends Neurosci. 32, 525–531. doi: 10.1016/j.tins.2009.06.001

Rojas-Líbano, D., and Kay, L. (2008). Olfactory system gamma oscillations: the physiological dissection of a cognitive neural system. Cogn. Neurodyn. 2, 179–194. doi: 10.1007/s11571-008-9053-1

Rojas-Líbano, D., and Kay, L. M. (2012). Interplay between sniffing and odorant sorptive properties in the rat. J. Neurosci. 32, 15577–15589. doi: 10.1523/JNEUROSCI.1464-12.2012

Rosero, M. A., and Aylwin, M. L. (2011). Sniffing shapes the dynamics of olfactory bulb gamma oscillations in awake behaving rats. Eur. J. Neurosci. 34, 787–799. doi: 10.1111/j.1460-9568.2011.07800.x

Rothermel, M., Carey, R. M., Puche, A., Shipley, M. T., and Wachowiak, M. (2014). Cholinergic inputs from Basal forebrain add an excitatory bias to odor coding in the olfactory bulb. J. Neurosci. 34, 4654–4664. doi: 10.1523/JNEUROSCI.5026-13.2014

Royet, J. P., Plailly, J., Saive, A. L., Veyrac, A., and Delon-Martin, C. (2013). The impact of expertise in olfaction. Front. Psychol. 4:928. doi: 10.3389/fpsyg.2013.00928

Schoppa, N. E. (2006). Synchronization of olfactory bulb mitral cells by precisely timed inhibitory inputs. Neuron 49, 271–283. doi: 10.1016/j.neuron.2005.11.038

Scott, J. W. (2006). Sniffing and spatiotemporal coding in olfaction. Chem. Senses 31, 119–130. doi: 10.1093/chemse/bjj013

Sevelinges, Y., Gervais, R., Messaoudi, B., Granjon, L., and Mouly, A. M. (2004). Olfactory fear conditioning induces field potential potentiation in rat olfactory cortex and amygdala. Learn. Mem. 11, 761–769. doi: 10.1101/lm.83604

Siegel, M., Donner, T. H., and Engel, A. K. (2012). Spectral fingerprints of large-scale neuronal interactions. Nat. Rev. Neurosci. 13, 121–134. doi: 10.1038/nrn3137

Singer, W. (1993). Synchronization of cortical activity and its putative role in information processing and learning. Annu. Rev. Physiol. 55, 349–374. doi: 10.1146/annurev.ph.55.030193.002025

Slotnick, B. (2007). Odor-sampling time of mice under different conditions. Chem. Senses 32, 445–454. doi: 10.1093/chemse/bjm013

Tallon-Baudry, C., Mandon, S., Freiwald, W. A., and Kreiter, A. K. (2004). Oscillatory synchrony in the monkey temporal lobe correlates with performance in a visual short-term memory task. Cereb. Cortex 14, 713–720. doi: 10.1093/cercor/bhh031

Vanderwolf, C. (1992). Hippocampal activity, olfaction, and sniffing: an olfactory input to the dentate gyrus. Brain Res. 593, 197–208. doi: 10.1016/0006-8993(92)91308-2

Vanderwolf, C. H., and Zibrowski, E. M. (2001). Pyriform cortex beta-waves: odor-specific sensitization following repeated olfactory stimulation. Brain Res. 892, 301–308. doi: 10.1016/S0006-8993(00)03263-7

Vanderwolf, C. H., Zibrowski, E. M., and Wakarchuk, D. (2002). The ability of various chemicals to elicit olfactory beta-waves in the pyriform cortex of meadow voles (Microtus pennsylvanicus) and laboratory rats (Rattus norvegicus). Brain Res. 924, 151–158. doi: 10.1016/S0006-8993(01)03225-5

van Wingerden, M., Vinck, M., Lankelma, J. V., and Pennartz, C. M. (2010). Learning-associated gamma-band phase-locking of action-outcome selective neurons in orbitofrontal cortex. J. Neurosci. 30, 10025–10038. doi: 10.1523/JNEUROSCI.0222-10.2010

Varela, F., Lachaux, J. P., Rodriguez, E., and Martinerie, J. (2001). The brainweb: phase synchronization and large-scale integration. Nat. Rev. Neurosci. 2, 229–239. doi: 10.1038/35067550

Von Stein, A., and Sarnthein, J. (2000). Different frequencies for different scales of cortical integration: from local gamma to long range alpha/theta synchronization. Int. J. Psychophysiol. 38, 301–313. doi: 10.1016/S0167-8760(00)00172-0

Wachowiak, M. (2011). All in a sniff: olfaction as a model for active sensing. Neuron 71, 962–973. doi: 10.1016/j.neuron.2011.08.030

Wesson, D. W., Borkowski, A. H., Landreth, G. E., Nixon, R. A., Levy, E., and Wilson, D. A. (2011). Sensory network dysfunction, behavioral impairments, and their reversibility in an Alzheimer's β-amyloidosis mouse model. J. Neurosci. 31, 15962–15971. doi: 10.1523/JNEUROSCI.2085-11.2011

Wilson, D. A., and Stevenson, R. J. (2003). The fundamental role of memory in olfactory perception. Trends Neurosci. 26, 243–247. doi: 10.1016/S0166-2236(03)00076-6

Wilson, D. A., and Sullivan, R. M. (2011). Cortical processing of odor objects. Neuron 72, 506–519. doi: 10.1016/j.neuron.2011.10.027

Wilson, D. A., and Yan, X. (2010). Sleep-like states modulate functional connectivity in the rat olfactory system. J. Neurophysiol. 104, 3231–3239. doi: 10.1152/jn.00711.2010

Zibrowski, E. M., Hoh, T. E., and Vanderwolf, C. H. (1998). Fast wave activity in the rat rhinencephalon: elicitation by the odors of phytochemicals, organic solvents, and a rodent predator. Brain Res. 800, 207–215. doi: 10.1016/S0006-8993(98)00494-6

Zibrowski, E. M., and Vanderwolf, C. H. (1997). Oscillatory fast wave activity in the rat pyriform cortex: relations to olfaction and behavior. Brain Res. 766, 39–49. doi: 10.1016/S0006-8993(97)00543-X

Zou, D. J., Chesler, A., and Firestein, S. (2009). How the olfactory bulb got its glomeruli: a just so story? Nat. Rev. Neurosci. 10, 611–618. doi: 10.1038/nrn2666

Zufall, F., and Munger, S. D. (2001). From odor and pheromone transduction to the organization of the sense of smell. Trends Neurosci. 24, 191–193. doi: 10.1016/S0166-2236(00)01765-3

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 14 April 2014; Accepted: 28 May 2014; Published online: 23 June 2014.

Citation: Martin C and Ravel N (2014) Beta and gamma oscillatory activities associated with olfactory memory tasks: different rhythms for different functional networks? Front. Behav. Neurosci. 8:218. doi: 10.3389/fnbeh.2014.00218

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Martin and Ravel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH ARTICLE
published: 29 April 2014
doi: 10.3389/fnbeh.2014.00138

Context-driven activation of odor representations in the absence of olfactory stimuli in the olfactory bulb and piriform cortex

Nathalie Mandairon[1][*], Florence Kermen^1[†], Caroline Charpentier^1[†], Joelle Sacquet[1], Christiane Linster^2[†] and Anne Didier1†

1Centre de Recherche en Neurosciences de Lyon, UMR CNRS 5292 INSERM 1028, Université Lyon1, Lyon, France

2Computational Physiology Lab, Neurobiology and Behavior, Cornell University, Ithaca, NY, USA

* Correspondence: Nathalie Mandairon, Centre de Recherche en Neurosciences de Lyon, UMR CNRS 5292 INSERM 1028, Université Lyon1, 50 Avenue Tony Garnier, F-69007 Lyon, France e-mail: nathalie.mandairon@olfac.univ-lyon1.fr

[†]These authors have contributed equally to this work.

Edited by:
Donald A. Wilson, New York University School of Medicine, USA

Reviewed by:
Frederic Levy, Institut National de la Recherche Agronomique, France
Leslie M. Kay, The University of Chicago, USA

Sensory neural activity is highly context dependent and shaped by experience and expectation. In the olfactory bulb (OB), the first cerebral relay of olfactory processing, responses to odorants are shaped by previous experiences including contextual information thanks to strong feedback connections. In the present experiment, mice were conditioned to associate an odorant with a visual context and were then exposed to the visual context alone. We found that the visual context alone elicited exploration of the odor port similar to that elicited by the stimulus when it was initially presented. In the OB, the visual context alone elicited a neural activation pattern, assessed by mapping the expression of the immediate early gene zif268 (egr-1) that was highly similar to that evoked by the conditioned odorant, but not other odorants. This OB activation was processed by olfactory network as it was transmitted to the piriform cortex. Interestingly, a novel context abolished neural and behavioral responses. In addition, the neural representation in response to the context was dependent on top-down inputs, suggesting that context-dependent representation is initiated in cortex. Modeling of the experimental data suggests that odor representations are stored in cortical networks, reactivated by the context and activate bulbar representations. Activation of the OB and the associated behavioral response in the absence of physical stimulus showed that mice are capable of internal representations of sensory stimuli. The similarity of activation patterns induced by imaged and the corresponding physical stimulus, triggered only by the relevant context provides evidence for an odor-specific internal representation.

Keywords: olfactory bulb, Zif268, cell mapping, conditioning, visual context, modeling

INTRODUCTION

Sensory neural activity is highly context dependent and shaped by experience and expectation. For example, throughout the olfactory system, neural responses to odors are shaped by behavioral relevance of the odor (Kay and Laurent, 1999; Martin et al., 2004; Doucette and Restrepo, 2008; Doucette et al., 2011; Wilson and Sullivan, 2011), by previous experience (Buonviso and Chaput, 2000; Moreno et al., 2009; Wilson, 2009; Chaudhury et al., 2010) and task difficulty (Mandairon et al., 2006; Li et al., 2008). Changes in neural responses to odors can be seen as early as in the olfactory bulb (OB), the target of sensory neurons (Freeman and Schneider, 1982; Mandairon and Linster, 2009). Contextual information is presumably shaped by previous experience and expectation and mediated to first order sensory structures by feedback projections from higher brain areas. The OB is an ideal target structure for the integration of sensory and contextual information because it receives direct inputs from sensory neurons, without thalamic detour, as well as a massive inputs from higher order brain areas such as noradrenergic and cholinergic nuclei, amygdala, piriform cortex, enthorinal cortex (Shipley and Ennis, 1996). Inspired by paradigms used in human imagery experiments which showed that a visual stimulus previously associated with an odorant is able to activate primary olfactory cortical regions (Gottfried et al., 2002, 2004) in the absence of olfactory stimulation, we here tested if association of an odorant with a visual context in mice would allow the visual context alone to elicit a behavioral responses usually associated with an olfactory stimulus as well as neural activation in olfactory pathways. In the OB, odor quality is represented by distributed patterns of activity in both the glomerular and granule cell layers. Each odor is represented by a unique pattern of relative activity across the OB, as visualized by 2DG (Johnson and Leon, 2007) or immediate early gene (IEG) mapping (Inaki et al., 2002) as well as partial visualization using optical methods. In addition to these spatial activity patterns, odors also evoke unique temporal patterns across the OB, as well as stimulus induced oscillations and synchronization (Kay et al., 2009). Because activity in the granule cell layer is susceptible to activity dependent plasticity, is highly odor and experience specific, we here chose relative activation patterns of granule cells as a measure for odor representations in the OB. We find that in mice which had been presented with an odorant repeatedly in the same visual context, the visual context alone elicited a behavioral response similar to that elicited by the stimulus when it was initially presented. In the OB, the visual context alone elicited a neural activation pattern, assessed by mapping the expression of the immediate early gene zif268 (egr-1) that was highly correlated with that elicited by the associated odorant, but not other odorants. Both behavioral and neural activation was not elicited by a novel context and both dependent on intact feedback to the OB from higher brain areas. A computational model of the OB and cortex which incorporated known features of the interactions between these two areas showed that experimentally described plasticity in projections from cortex to bulb, paired with “context neurons” previously used in models of hippocampal processing (Hasselmo and Wyble, 1997) sufficed to reproduce the observed experimental results. We conclude that in rodents, neural representation of an odorant in primary sensory areas can be elicited in its absence by exposure to the context to which the odorant was previously associated. This further suggests that rodents can build internal representation of the olfactory stimulus.

METHODS

SUBJECTS

Sixty adult male C57BL/6J mice (8 weeks old, Charles River, L'Arbresles, France) were used in accordance with the European Community Council Directive of November 24, 1986 (86/609/EEC). Mice were kept in standard mouse cages with full access to food and water. Experimental group contained 5 to 13 animals.

EXPERIMENTAL SET UP

All behavioral experiments were conducted in individual training cages (20 × 27 cm) with visual cues differing in shape, color and pattern added to the outside walls of the cage. The cage lids were pierced in their center and a 3-cm diameter tube was pushed inside the cage through this hole. The tubes were transparent and had 3-mm holes in their bottom through which odor diffused. This system allowed the introduction of the odor without opening the cages during the experiment (Figure 1A).

FIGURE 1. Methodological aspects. (A) Experimental cage. Visual cues are placed on the wall of the cage and a drilled plastic tube protruding from ceiling of the cage receives the odorized swab. (B) Zif268-positive cells in the granule cell layer of the OB.

BEHAVIORAL TRAINING

Thirty minutes per day during 10 days, mice were placed in the training cage (Figure 1A). Five minutes after their introduction, a non-odorized swab or an odorized swab was introduced into the tube.

For odorant presentation, the swab was impregnated with 60 μL of pure +Limonene (Sigma-Aldrich, purity 97%) or pure Decanal (Fluka, purity ≥95%).

BEHAVIORAL TEST

On day 11, mice were placed again into the training cage or novel context cage. After 5 min, the non-odorized or odorized swab introduced, depending on the group. The amount of time that the mice investigated the tube was manually recorded during 10 min after the introduction of the stimulus (Figure 1A). Investigation was defined as active exploration within 1 cm around the odor port.

DATA ANALYSIS

All mice were included in the analyses. Results are expressed as mean ± s.e.m. For behavioral data, Kruskall–Wallis (multiple comparisons) Mann–Whitney (two-group comparisons) tests were applied. For Zif268 expression maps, data showed normal distribution and between-group differences were assessed using ANOVA followed by Fisher post-hoc tests for pair comparisons or unilateral t-tests when appropriate (Systat software). The level of significance was set to 0.05.

ZIF268 EXPRESSION MAPPING

One hour after the test session, animals were killed by intracardiac perfusion (under deep anesthesia, Pentobarbital 3.64 mg/kg) and brains were sectioned using a cryostat. Zif268 immunochemistry (Mandairon et al., 2008) was performed for each animal on serial coronal sections (sampling interval = 70 μm). Zif268-positive cells were counted automatically in the granule cell layer of the OB using mapping software (Mercator, Explora Nova, La Rochelle, France; Figure 1B) coupled to a Zeiss microscope. The cell counts were conducted by experimenters who were blind to the experimental condition of the mice. The number of labeled profiles was divided by the surface of the region of interest to yield the total densities of labeled cells. Maps of Zif268-positive cells were constructed as previously described (Mandairon et al., 2006). Briefly, the granule cell layer was divided into 36 sectors of 10°. The number of labeled cells/μm^2 was calculated for each sector and measurements were then merged into arrays of 10° × 70−μm bins yielding a 2-D map of the granule cell layer. Arrays were averaged within each group, and a colored image plot of the data was constructed. To compare odor and context evoked activation maps, we calculated the pairwise overlap between maps (Python scripts associated to the Scipy library). Following standard procedures, maps were first threshold to keep the 30% highest values (percentile). This threshold was set as best fitted to the clusters delineated by visual inspection of the Zif268 expression maps. We analyzed the similarities between maps by counting the number of overlapping pixels and calculating a percentage of overlap. Between groups comparisons were done using t-tests for comparison of proportions (Mandairon et al., 2006; Sultan et al., 2011).

Zif268 labeling was analyzed in the anterior piriform cortex (layer II) in about eight sections per mice (distributed between 1.18 and 2.46 mm anterior to Bregma, Paxinos Atlas). The boundary between the anterior and posterior cortex was located at the level of the anterior commissure.

CANNULATION

Mice were anesthetized (100 mg/kg ketamine and 6 mg/kg xylazine, i.p.) and implanted as described earlier (Kermen et al., 2011) into both olfactory peduncles at the following coordinates with respect to bregma: AP = +2.4 mm; ML = ± 0.75 mm; DV = −3 mm. Following surgery, mice were allowed to recover for 10 days before beginning the training. Lidocaine (Sigma) (2%, 1 μl/side) was freshly prepared and infused into the each medial peduncle. Behavioral testing began 10 min after lidocaine administration was completed.

COMPUTATIONAL MODELING

We used a computational model of olfactory sensory neurons, OB and piriform cortex (Figure 2A). The individual elements of this model have been described in detail before (Linster and Cleland, 2001, 2002, 2004; Linster et al., 2003) and have been adapted. Synaptic plasticity between pyramidal cells and granule cells, as described experimentally (Gao and Strowbridge, 2009) is new to this model as is the introduction of “context” neurons (Hasselmo and Wyble, 1997). Context neurons here represent the context of the behavioral experiments, or a combination of features of the cage in which odor exposure happened. These context neurons, after training, can drive activity in olfactory cortex, creating context dependent responses as described experimentally (Calu et al., 2007). To enable context learning, synaptic plasticity was also introduced between context neurons and pyramidal cells.

FIGURE 2. Computational model of the olfactory bulb. (A) Sensory neurons (osn) project to glomeruli (glom) and synapses with mitral (mi) and periglomerular cells (pg). Periglomerular cells feed inhibitory synapses to mitral cells. Mitral cell secondary dendrites excite granule cells (gr); granule cells inhibit mitral cells. Mitral outputs project to pyramidal cells; context neurons, responsive to the behavioral context that the mouse is put in, project initially weak synapses exhibiting synaptic plasticity to pyramidal cells. Pyramidal cell (pyr) outputs project back to OB granule cells with initially weak synapses exhibiting plasticity (lot, lateral olfactory tract). (B) Neural activity patterns in response to stimulation with a randomly chosen odorant before training. Activity is color-coded from low (blue) to high (red). (C) Membrane potential and action potentials of 10 neurons in response to a 200 ms stimulation.

In a model simulating 100 OSNs, 100 mitral (Mi), granule (Gr), periglomerular (PG), 100 pyramidal (Pyr) cells and 10 context neurons, synapses between mitral and pyramidal cells were created randomly with each mitral cell projecting to any pyramidal cell with an equal probability of PMit−Pyr = 0.1. Intra-cortical connections and interneurons were omitted from this model. Pyramidal cells projected back to randomly chosen granule cells with an overall connection probability of 0.4 and initially weak synapses. 10 context neurons activated by behavioral context connected to pyramidal cells in an all to all fashion with initially weak synapses. Information flow in the model is both feedforward (OSNs activate mitral cells, mitral cells activate granule cells and cortical pyramidal cells) and feedback (context cells activate pyramidal cells and pyramidal cells activate granule cells). Representative spatial activation patterns and neural firing patterns are depicted in Figures 2B,C.

In the simulations presented here (Figure 2B), simulated exposure to an odorant in a specific context induced activity dependent plasticity of synapses from pyramidal to granule and from context to pyramidal cells. Synaptic strengths were first calculated from the parameters given in Table 1, and responses to simulated odorants were obtained. To simulate perceptual learning in response to repeated exposure to an odorant, synapses between pyramidal and granule and between context and pyramidal cells underwent synaptic potentiation:

where wij is the synaptic strength between the presynaptic pyramidal cell (context cell) j and the postsynaptic granule cell (pyramidal cell) i, η is the rate of potentiation and xj and xi are the total numbers of spikes emitted by the pre and postsynaptic cells during the period of odor stimulation.

Table 1. Model parameters.

RESULTS

CONTEXTUAL PRIMING PRODUCES SIMILAR PATTERNS OF RESPONSIVENESS IN THE OB AS ODOR STIMULATION

To test for contextual activation of OB neurons, mice were trained to associate an odorant (+limonene) with a visual context (visual cues added to a transparent cage, context A) by being introduced into the cage for 30 min per day during 10 consecutive days (Figure 3A). On day 11 (test), one group of mice was placed in the same visual context with the same odorant (Lim-Lim, ctxA), a second group of mice was placed in the same visual context with no odorant (Lim-NO, ctxA) and a third group was placed in novel visual context (context B) with no odorant (Lim-NO-ctxB) (Figure 3A). Upon testing, the group exposed to the visual context only (Lim-NO) investigated the odor delivery device significantly more than both other groups (Lim-Lim and Lim-NO-ctxB) (group effect p = 0.008; Lim-Lim vs. Lim-NO p = 0.011, Lim-NO vs. Lim-NO-ctxB p = 0.004) (Figure 3B), suggesting an expectation of the stimulus. This expectation was specific to the context previously associated with the odor stimulus, because the increased sniffing did not occur in the novel context (Lim-No ctx B) (Figure 3B). At the neural level, the overall Zif268-positive cell density in the granule cell layer of the OB did not vary among the three groups [F(2, 12) = 1.16, p = 0.34] (Figure 3C); however, the similarity between patterns evoked by the training context only (Lim-NO) and odor stimulus (Lim-Lim) was high (72% overlap), whereas overlap between patterns evoked by the training context (Lim-NO) and a new context (Lim-NO-ctxB) was significantly less (47% overlap, p < 0.0001) (Figures 3D,E). These findings show that the training context previously associated with the odorant induced a pattern of activity mimicking odor specific activity as well as a significant behavioral response.

FIGURE 3. Context-evoked behavioral and OB neural responses in the absence of olfactory stimulus. (A) Mice were trained to associate a visual context to an odorant (+limonene: Lim) 30 min per day during 10 days and tested on day 11. The day of the test, the same odorant as during training (“Lim-Lim” group), or an empty swab (“Lim-NO” group, NO: no odor) was introduced in the cage. In a third experimental group, mice were trained with +limonene, but tested in a different context without the odorant (“Lim-NO-ctxB” group). Mice were sacrificed (S) 1 h after the test. (B) Investigation time of the odor delivery device. Mice trained during 10 days with Lim and tested with no odor (Lim-NO) showed an investigation time of the odor port superior to the mice trained and tested with the same odorant (Lim-Lim). Moreover, when the visual context changed the day of the test, the increase of investigation time was no longer observed. (C) The density of Zif268-positive cells was similar between all groups. (D) The overlap between maps of Lim-Lim and Lim-NO was high (72%) indicating the similarity between those two maps. Overlap significantly decreased when Lim-NO was compared to Lim-NO-ctxB. (*p < 0.05; **p < 0.005). (E) Normalized 2-D maps of the density of Zif268-positive cells in the granule cell layer of Lim-Lim and Lim-NO groups showed a similar pattern of Zif268 expression. Cell density in this figure and in following figures is color-coded from low (blue) to high (red). When the context was changed the day of the test, this pattern was altered (Lim-NO-ctxB).

CONTEXT-EVOKED NEURAL ACTIVATION PATTERNS ARE SPECIFIC TO THE ASSOCIATED ODOR

We confirmed this result with an additional set of mice in which context A was associated with a different odorant (decanal, Figure 4A). Results were similar to those found with the previous odorant (Figure 3). Indeed, presentation of the context alone (Dec-NO) induced a significantly increased investigation of the odor delivery device (p = 0.01) (Figure 4B), suggesting an expectation of the odorant. As with limonene, overall levels of Zif268 expression were not significantly different between the two groups (Figure 4C). The patterns of neural activation in the OB evoked by the context alone (Dec-NO) were very similar to those evoked by the odor in the same context (Dec-Dec) (62% overlap) (Figures 4D,E). In contrast, the patterns evoked in mice who associated decanal with context A were significantly different from those evoked by context A in mice who associated context A with limonene [44% overlap between Dec-NO and Lim-NO and 43% between Dec-Dec and Lim-Lim compared to the 62% overlap between Dec-Dec and Dec-NO (p < 0.0004) or compared to the 72% overlap between Lim-NO and Lim-Lim (p < 0.0001)], showing that the activity pattern evoked by context A alone was specific to the odor associated with that context by the mice.

FIGURE 4. Context-evoked neural activation patterns in the OB are specific to the associated odor. (A) Mice were trained to associate a context to an odorant, the decanal (Dec), 30 min per day during 10 days. The day of the test, the same odorant as during training (Dec-Dec), or an empty swab (Dec-NO) was introduced in the cage. Mice were sacrificed (S) 1 h after the test. (B) Mice trained during 10 days with Dec and tested with no odor (Dec-NO) showed an investigation time of the odor port superior to the mice trained and tested with the odorant (Dec-Dec). (C) The density of Zif268-positive cells was similar between both groups. (D) The overlap between maps of Dec-Dec and Dec-NO was high (66%) indicating the similarity between those two maps. Overlaps significantly decreased when other comparisons were performed. (E) Normalized 2-D maps of the density of Zif268-positive cells in the granule cell layer of the OB of Dec-Dec and Dec-NO groups showed similar patterns of Zif268 expression. (*p < 0.05).

CONTEXT-EVOKED NEURAL ACTIVATION IN ANTERIOR PIRIFORM CORTEX IS SPECIFIC TO THE ASSOCIATED CONTEXT

In anterior piriform cortex, to which the OB projects (Shipley and Adamek, 1984; Haberly, 1998), we found that Zif268-positive cell density did not differ between groups smelling the odor and groups exposed to the associated context alone. Because no specific spatial activity pattern is associated with an odorant in cortex (Isaacson, 2010) we did not analyze the overlap between maps but rather compared to a naive control group not exposed to an odorant during training and testing (NO-NO) and found more activation in the limonene-context associated groups compared to naive [F(2, 6) = 13.70, p = 0.006, NO-NO vs. Lim-Lim p = 0.002; NO-NO vs. Lim-NO p = 0.019] (Figure 5A). The same results were obtained in the group of mice presented with decanal during context association [F(2, 5) = 18.07, p = 0.005, NO-NO vs. Dec-Dec p = 0.004; NO-NO vs. Dec-NO p = 0.004] (Figure 5B).

FIGURE 5. Context-evoked activity in the piriform cortex. (A) Zif268-positive cell density in the piriform cortex was increased in response to odorant stimulation (Lim-Lim) and in response to context (Lim-NO) compared to control animals (“NO-NO” group). (B) Same as A, with decanal used instead of limonene (*p < 0.05, **p < 0.005).

CONTEXT-ACTIVATED BEHAVIORAL AND NEURAL RESPONSES DEPEND ON CENTRAL INPUTS TO THE OLFACTORY BULB

Information about a learned context is likely to be transmitted as top-down information to the OB (Gilbert and Sigman, 2007). Mice with surgically implanted cannula in the medial olfactory peduncle were exposed to the visual context and odorant during 1 h daily for 10 days. On day 11, mice were exposed to the context alone. In this experimental group, lidocaine (or saline) was infused into the olfactory peduncle in such a manner as to decrease central inputs to the OB without affecting olfactory input (Martin et al., 2006) (Figure 6A). In response to the context, lidocaine-induced decrease of peduncle activity (Lim-NO-Lido) led to a significant decrease in context-induced investigation time compared to saline-infused animals (Lim-NO-Sal) (p < 0.0005) (Figure 6B). The overall level of Zif268 expression in the OB was not significantly different between animals infused with lidocaine or saline (t-test, p = 0.11) (Figure 6C), suggesting no direct effect of lidocaine in the OB through diffusion from the infusion site. However, the distribution of Zif268-positive cells was deeply altered in Lim-NO-Lido compared to Lim-NO-Sal (47% overlap between Lim-NO-Lido and Lim-NO-Sal vs. 72% overlap between Lim-NO and Lim-Lim, p < 0.0001) (Figures 6D,E). This result shows that context evoked activity in the OB depends on functional feedback inputs from other brain centers.

FIGURE 6. Context-activated behavioral and neural responses depend on central inputs to the olfactory bulb. (A) Mice were trained similarly to previous groups (see Figure 1) and tested with no odor. The day of the test, animals were injected in the olfactory peduncle with lidocaine (“Lim-NO-Lido” group) or saline (“Lim-NO-Sal” group) 10 min before being placed in their training cage. (B) Lidocaine injection induced a decrease of investigation time compared to animals injected with saline. (C) The density of Zif268-positive cells was similar between both groups. (D) The overlap between Lim-Lim and Lim-NO significantly decreased when feedbacks to the OB were altered (***p < 0.0001). (E) The spatial pattern of Zif268-positive cell density was altered when the animals were injected with lidocaine.

COMPUTATIONAL MODELING OF THE NEURAL CIRCUITS UNDERLYING CONTEXT-DRIVEN OB ACTIVATION

We then used a well-described computational model of the olfactory system (Linster et al., 2007; Linster and Cleland, 2009), to which we added an abstract set of context neurons encoding the visual features of the context (Figures 2A–C) (Gao and Strowbridge, 2009) and a connection between context neurons and cortical pyramidal cells. The behavioral association between the context and odor was simulated by stimulating olfactory sensory neurons with an “odor” while simultaneously stimulating the context neurons with a “context;” during the formation of this association the activity-dependent learning rule is turned on (Figures 2B,C). After training, stimulation with odorant and the context drove granule cell activation (Figure 7A, Odor+ctxA, corresponding to Lim-Lim in experimental data). Presence of the trained context only (Figure 7A; No-odor+ctxA, corresponding to Lim-NO in experimental data) stimulated a very similar pattern of activity (r^2 = 0.96) (Figure 7A). When a novel context was presented to the network in the absence of odorant, pyramidal cells were not activated and did not shape granule cell activation: the activation pattern of granule cells was non-specific and the overlap with the representation triggered by the trained context was low (r^2 = 0.14, Figure 7A, No-odor-ctxb corresponding to NO-Lim-ctx B in experimental data). A set of ten simulations with novel networks and randomly chosen odorants confirmed these results to be independent of the choice of odorant. There was a statistically significant difference between the two sets of overlap [F(1, 17) = 816.16; p < 0.001], showing that while the trained context evokes granule cell activity resembling that in response to the odorant, the novel context does not (Figure 7B). We observed a significant effect of group on the discharge rates of granule cells (no-odor naive, odor+ctxA, no-odor+ctxA; [F(2, 27) = 21.285, p < 0.001] (Figure 7C); with individual significant differences between the naive network and both trained networks (p < 0.001) but not between the trained networks (p > 0.2) (Figure 7C), as observed experimentally (Figure 3). Cortical pyramidal cells exhibited significantly higher spike rates in response to the trained odor (Figure 7D, Odor_ctxA) or the context alone (Figure 7D, N-Odor_ctx) than in response to no odor in an untrained network (Figure 7D, No-Odor_naive), as shown experimentally (Figure 5). When cortical feedback inactivation were simulated by decreasing the synaptic weights from pyramidal neurons to granule cells, the overlap between granule cell activation patterns in response to context alone with intact feedback (No−odor+ctxA) and context alone with “lesioned” feedback (No-odor+ctxA-no feedback) was very low (r^2 = 0.24 in the example in Figures 7E,F).

FIGURE 7. Computational modeling of the neural circuits underlying context-driven OB activation. (A) Odor representations in granule cells after training on a randomly chosen odor [mean activity over 200 ms; odor and context (Odor+ctxA), trained context only (No-odor+ctxA), novel, untrained context (No-odor+ctxB)]. (B) Correlation between post-training activation patterns (Odor+ctxA/No-odor+ctxtA and No-odor+ctxA/No-odor+ctxB). (C) Spikes per seconds evoked in granule cells after training in response to the trained odorant and context (Odor+ctxA), the trained context only (No-odor+ctxA), and a novel context (No-odor+ctxB). (D) Spikes per second evoked in pyramidal cells in a naive model exposed to context only (before training; odor naive, No-Odor_naive), to the trained odorant/context (Odor+ctxA), or to the trained context only (No-odor+ctxA). (E) Odor representations in granule cells in response to stimulation with the trained context only with the intact (No-odor+ctxA) or lesioned (No-odor+ctxA-no feedback) feedback. (F) Correlation between Odor+ctxA/No-odor+ctxtA and Odor+ctxA/No-odor+ctxA+no-feedback. (*p < 0.05).

DISCUSSION

In this study, in order to trigger an internal odor representation, we developed a behavioral paradigm which allowed inducing odor expectations by exposing animals to a context previously associated to an odorant. The increase in investigation time directed to the empty odor source the day of testing strongly supports the view that animals actually expected, and even searched for the odorant.

Using this original paradigm, we demonstrated a patterned, odor and context specific activation of olfactory cortices by contextual information in the absence of a physical stimulus. This result is in accordance with a previous fMRI study in humans, in which the same brain regions were activated by imagining visual (Halpern and Zatorre, 1999), auditory (Kosslyn et al., 2001), or olfactory stimuli (Bensafi et al., 2007) and actually viewing, hearing or smelling them. In humans, the previous studies showed that imagining odors activated olfactory structures as the piriform cortex, left insula and amygdala. Here, we showed that not only the piriform cortex but also the OB were activated during context evoked odor expectation. This finding is reminiscent of the “search image” revealed by EEG recordings in rabbits (Freeman, 1983). The “search image” was defined by Freeman as a large-scale pattern of strengthened connections (synaptic template) that could serve to represent an expected stimulus even if it is not present. We mapped Zif268 expression in granule cells because bulbar patterned expressions are odor-specific and replicable across individuals in these cells (Inaki et al., 2002; Mandairon et al., 2006; Busto et al., 2009) and hence allowed us to compare odor and context evoked activation patterns. We found that the context induced activation pattern in the OB was highly similar to the one observed after odor stimulation and was specific to each odorant tested. Taken together, these findings strongly suggested that odor expectation induced an internal neural representation of the odorant in the OB which was context and odor specific and resulted from the specific odor-context association the animal was exposed to.

Both behavioral and OB neural context-driven responses depend on intact centrifugal projections to the OB. When we blocked top-down fibers by infusing mice with an anesthetic (Lidocaine) in the medial part of the olfactory peduncle 10 min before testing we eliminated the context driven behavioral and neural responses. The olfactory peduncle contains fibers projecting from the rest of the brain to the OB including the anterior olfactory nucleus, glutamatergic fibers from the piriform and entorhinal cortices and cortical amygdaloid nuclei (Haberly and Price, 1977, 1978), cholinergic neurons from basal forebrain or noradrenergic (McLean and Shipley, 1991), serotoninergic (McLean and Shipley, 1987) neurons as well as sparse projections from the hypothalamus. We assume that a high percentage of these incoming fibers are blocked by our lidocaine injection; we can therefore not speculate on which centrifugal fibers contribute to our observations. From a computational point of view, any secondary or tertiary olfactory structures receiving odor and context information and projecting back the OB could perform this function. Overall, we observed that top-down inactivation resulted in a decreased investigation time compared to non-injected mice, as if they were not expecting the odor. This result is consistent with data showing that blockade of central inputs to the OB using an infusion of Lidocaine in the olfactory peduncle reduced the amplitude of odor-induced oscillatory beta responses (Martin et al., 2006) which are involved in odor associative learning and in anticipation of odor stimuli (Kay et al., 2009). The simulations presented showed that a combined model of OB, olfactory cortex and context neurons, including experimentally described synaptic plasticity between pyramidal and granule cells (Gao and Strowbridge, 2009) can reproduce the described priming of granule cell activation by visual context. The model suggests that during the learning of the odor-context association, information flows from bulb to cortex and from cortex to bulb, and that activity dependent plasticity in both pathways can suffice to support context-driven bulbar activity in the absence of olfactory stimulation. The association with context needs to be performed outside the OB to prevent changes in odor processing in the absence of the learned context. Piriform cortex is only one of many candidate structures to perform this function; it was chosen for these simulations because (a) cortical pyramidal cells project onto bulbar granule cells with synapses that have been shown to undergo activity dependent plasticity (Gao and Strowbridge, 2009) and (b) because neural activity in piriform cortex has been shown to be modulated by behavioral context (Calu et al., 2007). In theory, any brain area receiving odor inputs from the OB and projecting back to OB granule cells and capable of forming an association with context information would yield equivalent results. Our simulations results are not dependent in a specific structure being implemented.

The data presented, together with the computational results suggest that both odor and contextual information shape rather than create OB neural responses. Odor quality, or the expectation thereof, would therefore be encoded in the spatio-temporal patterns of bulbar activity (Kay et al., 2009; Mori and Sakano, 2011) and can be elicited by sensory or central inputs.

ACKNOWLEDGMENTS

This work was supported by the CNRS and Lyon1 University.

REFERENCES

Bensafi, M., Sobel, N., and Khan, R. M. (2007). Hedonic-specific activity in piriform cortex during odor imagery mimics that during odor perception. J. Neurophysiol. 98, 3254–3262. doi: 10.1152/jn.00349.2007

Buonviso, N., and Chaput, M. (2000). Olfactory experience decreases responsiveness of the olfactory bulb in the adult rat. Neuroscience 95, 325–332. doi: 10.1016/S0306-4522(99)00450-9

Busto, G. U., Elie, J. E., Kermen, F., Garcia, S., Sacquet, J., Jourdan, F., et al. (2009). Expression of Zif268 in the granule cell layer of the adult mouse olfactory bulb is modulated by experience. Eur. J. Neurosci. 29, 1431–1439. doi: 10.1111/j.1460-9568.2009.06689.x

Calu, D. J., Roesch, M. R., Stalnaker, T. A., and Schoenbaum, G. (2007). Associative encoding in posterior piriform cortex during odor discrimination and reversal learning. Cereb. Cortex 17, 1342–1349. doi: 10.1093/cercor/bhl045

Chaudhury, D., Manella, L., Arellanos, A., Escanilla, O., Cleland, T. A., and Linster, C. (2010). Olfactory bulb habituation to odor stimuli. Behav. Neurosci. 124, 490–499. doi: 10.1037/a0020293

Doucette, W., Gire, D. H., Whitesell, J., Carmean, V., Lucero, M. T., and Restrepo, D. (2011). Associative cortex features in the first olfactory brain relay station. Neuron 69, 1176–1187. doi: 10.1016/j.neuron.2011.02.024

Doucette, W., and Restrepo, D. (2008). Profound context-dependent plasticity of mitral cell responses in olfactory bulb. PLoS Biol. 6:e258. doi: 10.1371/journal.pbio.0060258

Freeman, W. J. (1983). The physiological basis of mental images. Biol. Psychiatry 18, 1107–1125.

Freeman, W. J., and Schneider, W. (1982). Changes in spatial patterns of rabbit olfactory EEG with conditioning to odors. Psychophysiology 19, 44–56. doi: 10.1111/j.1469-8986.1982.tb02598.x

Gao, Y., and Strowbridge, B. W. (2009). Long-term plasticity of excitatory inputs to granule cells in the rat olfactory bulb. Nat. Neurosci. 12, 731–733. doi: 10.1038/nn.2319

Gilbert, C. D., and Sigman, M. (2007). Brain states: top-down influences in sensory processing. Neuron 54, 677–696. doi: 10.1016/j.neuron.2007.05.019

Gottfried, J. A., O'Doherty, J., and Dolan, R. J. (2002). Appetitive and aversive olfactory learning in humans studied using event-related functional magnetic resonance imaging. J. Neurosci. 22, 10829–10837.

Gottfried, J. A., Smith, A. P., Rugg, M. D., and Dolan, R. J. (2004). Remembrance of odors past: human olfactory cortex in cross-modal recognition memory. Neuron 42, 687–695. doi: 10.1016/S0896-6273(04)00270-3

Haberly, L. B. (1998). The Synaptic Organization of the Brain. Oxford: Oxford University Press, Inc.

Haberly, L. B., and Price, J. L. (1977). The axonal projection patterns of the mitral and tufted cells of the olfactory bulb in the rat. Brain Res. 129, 152–157. doi: 10.1016/0006-8993(77)90978-7

Haberly, L. B., and Price, J. L. (1978). Association and commissural fiber systems of the olfactory cortex of the rat. II. Systems originating in the olfactory peduncle. J. Comp. Neurol. 181, 781–807. doi: 10.1002/cne.901810407

Halpern, A. R., and Zatorre, R. J. (1999). When that tune runs through your head: a PET investigation of auditory imagery for familiar melodies. Cereb. Cortex 9, 697–704. doi: 10.1093/cercor/9.7.697

Hasselmo, M. E., and Wyble, B. P. (1997). Free recall and recognition in a network model of the hippocampus: simulating effects of scopolamine on human memory function. Behav. Brain Res. 89, 1–34. doi: 10.1016/S0166-4328(97)00048-X

Inaki, K., Takahashi, Y. K., Nagayama, S., and Mori, K. (2002). Molecular-feature domains with posterodorsal-anteroventral polarity in the symmetrical sensory maps of the mouse olfactory bulb: mapping of odourant-induced Zif268 expression. Eur. J. Neurosci. 15, 1563–1574. doi: 10.1046/j.1460-9568.2002.01991.x

Isaacson, J. S. (2010). Odor representations in mammalian cortical circuits. Curr. Opin. Neurobiol. 20, 328–331. doi: 10.1016/j.conb.2010.02.004

Johnson, B. A., and Leon, M. (2007). Chemotopic odorant coding in a mammalian olfactory system. J. Comp. Neurol. 503, 1–34. doi: 10.1002/cne.21396

Kay, L. M., Beshel, J., Brea, J., Martin, C., Rojas-Libano, D., and Kopell, N. (2009). Olfactory oscillations: the what, how and what for. Trends Neurosci. 32, 207–214. doi: 10.1016/j.tins.2008.11.008

Kay, L. M., and Laurent, G. (1999). Odor- and context-dependent modulation of mitral cell activity in behaving rats. Nat. Neurosci. 2, 1003–1009. doi: 10.1038/14801

Kermen, F., Sultan, S., Sacquet, J., Mandairon, N., and Didier, A. (2011). Consolidation of an olfactory memory trace in the olfactory bulb is required for learning-induced survival of adult-born neurons and long-term memory. PLoS ONE 5:e12118. doi: 10.1371/journal.pone.0012118

Kosslyn, S. M., Ganis, G., and Thompson, W. L. (2001). Neural foundations of imagery. Nat. Rev. Neurosci. 2, 635–642. doi: 10.1038/35090055

Li, W., Howard, J. D., Parrish, T. B., and Gottfried, J. A. (2008). Aversive learning enhances perceptual and cortical discrimination of indiscriminable odor cues. Science 319, 1842–1845. doi: 10.1126/science.1152837

Linster, C., and Cleland, T. A. (2001). How spike synchronization among olfactory neurons can contribute to sensory discrimination. J. Comput. Neurosci. 10, 187–193. doi: 10.1023/A:1011221131212

Linster, C., and Cleland, T. A. (2002). Cholinergic modulation of sensory representations in the olfactory bulb. Neural Netw. 15, 709–717. doi: 10.1016/S0893-6080(02)00061-8

Linster, C., and Cleland, T. A. (2004). Configurational and elemental odor mixture perception can arise from local inhibition. J. Comput. Neurosci. 16, 39–47. doi: 10.1023/B:JCNS.0000004840.87570.2e

Linster, C., and Cleland, T. A. (2009). Glomerular microcircuits in the olfactory bulb. Neural Netw. 22, 1169–1173. doi: 10.1016/j.neunet.2009.07.013

Linster, C., Henry, L., Kadohisa, M., and Wilson, D. A. (2007). Synaptic adaptation and odor-background segmentation. Neurobiol. Learn. Mem. 87, 352–360. doi: 10.1016/j.nlm.2006.09.011

Linster, C., Maloney, M., Patil, M., and Hasselmo, M. E. (2003). Enhanced cholinergic suppression of previously strengthened synapses enables the formation of self-organized representations in olfactory cortex. Neurobiol. Learn. Mem. 80, 302–314. doi: 10.1016/S1074-7427(03)00078-9

Mandairon, N., Didier, A., and Linster, C. (2008). Odor enrichment increases interneurons responsiveness in spatially defined regions of the olfactory bulb correlated with perception. Neurobiol. Learn. Mem. 90, 178–184. doi: 10.1016/j.nlm.2008.02.008

Mandairon, N., and Linster, C. (2009). Odor perception and olfactory bulb plasticity in adult mammals. J. Neurophysiol. 101, 2204–2209. doi: 10.1152/jn.00076.2009

Mandairon, N., Sacquet, J., Garcia, S., Ravel, N., Jourdan, F., and Didier, A. (2006). Neurogenic correlates of an olfactory discrimination task in the adult olfactory bulb. Eur. J. Neurosci. 24, 3578–3588. doi: 10.1111/j.1460-9568.2006.05235.x

Martin, C., Gervais, R., Hugues, E., Messaoudi, B., and Ravel, N. (2004). Learning modulation of odor-induced oscillatory responses in the rat olfactory bulb: a correlate of odor recognition? J. Neurosci. 24, 389–397. doi: 10.1523/JNEUROSCI.3433-03.2004

Martin, C., Gervais, R., Messaoudi, B., and Ravel, N. (2006). Learning-induced oscillatory activities correlated to odour recognition: a network activity. Eur. J. Neurosci. 23, 1801–1810. doi: 10.1111/j.1460-9568.2006.04711.x

McLean, J. H., and Shipley, M. T. (1987). Serotonergic afferents to the rat olfactory bulb: I. Origins and laminar specificity of serotonergic inputs in the adult rat. J. Neurosci. 7, 3016–3028.

McLean, J. H., and Shipley, M. T. (1991). Postnatal development of the noradrenergic projection from locus coeruleus to the olfactory bulb in the rat. J. Comp. Neurol. 304, 467–477. doi: 10.1002/cne.903040310

Moreno, M. M., Linster, C., Escanilla, O., Sacquet, J., Didier, A., and Mandairon, N. (2009). Olfactory perceptual learning requires adult neurogenesis. Proc. Natl. Acad. Sci. U.S.A. 106, 17980–17985. doi: 10.1073/pnas.0907063106

Mori, K., and Sakano, H. (2011). How is the olfactory map formed and interpreted in the mammalian brain? Annu. Rev. Neurosci. 34, 467–499. doi: 10.1146/annurev-neuro-112210-112917

Shipley, M. T., and Adamek, G. D. (1984). The connections of the mouse olfactory bulb: a study using orthograde and retrograde transport of wheat germ agglutinin conjugated to horseradish peroxidase. Brain Res. Bull. 12, 669–688. doi: 10.1016/0361-9230(84)90148-5

Shipley, M. T., and Ennis, M. (1996). Functional organization of olfactory system. J. Neurobiol. 30, 123–176. doi: 10.1002/(SICI)1097-4695(199605)30:1<123::AID-NEU11>3.0.CO;2-N

Sultan, S., Lefort, J. M., Sacquet, J., Mandairon, N., and Didier, A. (2011). Acquisition of an olfactory associative task triggers a regionalized down-regulation of adult born neuron cell death. Front. Neurosci. 5:52. doi: 10.3389/fnins.2011.00052

Wilson, D. A. (2009). Olfaction as a model system for the neurobiology of mammalian short-term habituation. Neurobiol. Learn. Mem. 92, 199–205. doi: 10.1016/j.nlm.2008.07.010

Wilson, D. A., and Sullivan, R. M. (2011). Cortical processing of odor objects. Neuron 72, 506–519. doi: 10.1016/j.neuron.2011.10.027

Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 31 January 2014; Accepted: 04 April 2014; Published online: 29 April 2014.

Citation: Mandairon N, Kermen F, Charpentier C, Sacquet J, Linster C and Didier A (2014) Context-driven activation of odor representations in the absence of olfactory stimuli in the olfactory bulb and piriform cortex. Front. Behav. Neurosci. 8:138. doi: 10.3389/fnbeh.2014.00138

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Mandairon, Kermen, Charpentier, Sacquet, Linster and Didier. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

OPINION ARTICLE
published: 23 May 2014
doi: 10.3389/fnbeh.2014.00190

Comparative chemosensory cognition

Alan Gelperin[*]

Department of Molecular Biology, Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA

* Correspondence: gelperin@princeton.edu

Edited by:
Anne-Marie Mouly, Centre de Recherche en Neurosciences de Lyon, France

Reviewed by:
Frederic Levy, Institut National de la Recherche Agronomique, France

Keywords: comparative cognition, olfactory learning, invertebrate consciousness, higher order conditioning, genetic model systems

Don Griffin, one of the foremost students of comparative cognition, offered the following definition of cognition: “The term cognition is ordinarily taken to mean information processing in human and nonhuman central nervous systems that often leads to choices and decisions” (Griffin and Speck, 2004). Clearly we can study cognition by analyzing animal information processing systems and the manner in which behavioral choices and decision making are altered by experience. The use of olfactory information for guiding a wide range of basic biological decisions is ubiquitous in animals, including humans (Gelperin, 2010). Chemosensory processing, and particularly olfactory information processing, is a particularly attractive modality within which to seek comparative insights into cognitive processes underlying learning and memory.

The advent of modern molecular and genetic tools for selectively modifying and perturbing functionally identified groups of neurons, particularly optogenetic methods, has led to a focus on chemosensory processing in a limited number of species for which genetic tools are well developed, including C. elegans (Glater et al., 2014) and D. melanogaster (Wilson, 2013), among others. Looking more broadly for instances of chemosensory learning reveals a remarkable diversity of cognitive plasticity from ants to zebrafish. The renewed interest in comparative cognition (Shettleworth, 2012) plus glimmerings of recognition that a voluminous and highly folded cerebral cortex is not required for implementation of higher order logic operations (Watanabe et al., 2008) is producing renewed interest in exploration of chemosensory processing and learning in an increasingly diverse set of species.

Another impetus for comparative studies is the quest to identify the minimum essential neural circuit that can implement the synaptic operations required for higher-order learning and decision making involved in choosing among alternative neural outputs based on previously learned weighting factors, as in reinforcement learning (Wilson et al., 2014). Modeling studies tightly constrained by demonstrated or plausible neuronal and synaptic properties are essential to this enterprise, having been used to show for example that five neurons with suitable dynamics and plausible synaptic plasticity functions can demonstrate the Kamin blocking effect, previously shown behaviorally in Limax (Goel and Gelperin, 2006). In the face of overwhelming evidence that neuronal properties and synaptic communication are essentially similar from ants to zebrafish (Llinas, 2008), the hope is that gaining new insights into the minimum essential circuitry for higher order information processing functions may be more straightforward in compact nervous systems comprised of fewer computing elements compared to the mammalian central nervous system. The use of transgenic Drosophila mutants as models of human disease (Chen and Crowther, 2012; van Alphen and van Swinderen, 2013) is also fueling this enterprise.

A remarkable example of olfactory learning in a compact brain is the demonstration of food imprinting and prenatal chemosensory conditioning in the predatory mite Neoseiulus (Peralta Quesada and Schausberger, 2012), whose entire brain of 10,325 cells occupies a single synganglion, containing a prominent olfactory lobe, measuring only 100 by 65 microns (van Wijk et al., 2006). A more familiar example of a compact brain with excellent olfactory abilities is that of the no-see-um, e.g., Culicoides sonorensis, best described as a flying nose with gonads and biting mouthparts measuring only a few hundred microns in overall body size. The success of these tiny cognitive engines is shown by the fact that the family containing the no-see-ums, the Ceratopogonidae, contains over 4000 species worldwide. No-see-um olfactory learning is likely as members of a species of larger size, also with obligate blood-sucking by females, are know to modulate their olfactory responses due to associative learning (Sanford et al., 2013).

Consider the noble nematode, C. elegans, possessed of 302 neurons, only a subset of which is needed to implement associative chemosensory conditioning modulated by dopamine (Mersha et al., 2013). Dopamine is a familiar neuromodulator in the mammalian CNS implicated in the processing of signals and synapses mediating reward and expectation (Kobayashi and Schultz, 2014). C. elegans possesses multiple learning types modifying its information processing in non-chemosensory modalities (Ardiel and Rankin, 2010). This suggests that a search for higher order conditioning such as second order conditioning and blocking using chemosensory stimuli might be fruitful.

A detailed prescription for comparing cognitive abilities across wide phyletic boundaries was provided by Bullock (1993), who emphasized the need for quantitative measurement of higher order cognitive operations by invertebrate brains. He also highlighted the need to frame experimental questions assessing complex information processing with special regard to the neuroethological context within which experimental questions are most effectively and insightfully asked of the animal subject. The use of olfactory stimuli to ask questions about cognitive aspects of higher order learning has been particularly fruitful for terrestrial gastropods in general (Gelperin, 2013) and Limax maximus in particular (Watanabe et al., 2008). Limax has been shown to be capable of a variety of higher order learned logic operations on olfactory stimuli, most recently involving the ability to learn the association between olfactory stimuli and the reward of access to water after the animal was subjected to rapid and severe dehydration, a normal stressor for terrestrial slugs. Olfaction is the dominant sensory modality for distance perception in terrestrial mollusks and a brain region with unique neuronal architectures and dynamics, the procerebral lobe, is devoted to learning about odors (Matsuo et al., 2011). Like the mushroom bodies of insects and the vertical lobes of octopi, identification of the sensory inputs to these distinctive central information processing centers in invertebrates can help guide the choice of sensory modalities within which to look for complex cognitive logic operations.

The cognitive abilities of honeybees in the chemosensory domain include not only higher order chemosensory learning, but also the construction and use of cognitive maps incorporating multiple domains of sensory input during their construction, particularly visual and chemosensory inputs (Gould, 1990). In 1973 Karl von Frisch shared the Nobel Prize for his pioneering work on the multimodal communication methods used by bees in communicating and receiving information on potential food sources in the environment surrounding the hive. Several generations of von Frisch's scientific descendants have continued his neuroethological tradition in selecting behavioral questions arising from the use of natural odor, taste, color, and shape information contained in the floral fountains exploited by bees. Processing of these proximate cues is interlaced with the processing of visual and magnetic information for navigation from hive to food source. Thus, chemosensory cues are only one aspect of a multimodal processing system that is capable of concept learning, a form of higher order learning that relies on the relationships between objects (e.g., same/different, left/right, above/below) rather than the specific properties of individual stimuli. These studies used the training protocols of delayed matching to sample and delayed non-matching to sample (Reinhard et al., 2004), protocols that are widely used for probing cognitive aspects of stimulus representation in primates. A recent summary of the cognitive architecture of the honeybee brain, which contains 950,000 neurons packaged in a volume of 1 mm^3, identifies more than 17 discrete categories of computations as demonstrated abilities of the honeybee brain, including a requirement for neuronal circuitry tasked with assigning value to stimulus configurations, a value assignment that changes with experience (Menzel, 2001). Another mammalian cognitive parallel is the demonstration that honeybees consolidate a novel navigation memory during sleep (Menzel and Giurfa, 2001).

Chemosensory cognition in Drosophila has only recently come under experimental examination, although the study of olfactory learning is well developed (Young et al., 2011; Beyaert et al., 2012), augmented by experimentally useful modeling work (Wessnitzer et al., 2012). The seminal initial work on Drosophila learning was done by molecular biologists (Quinn et al., 1974) so work focused initially on development of high throughput screens for memory mutants rather than identification of natural stimulus configurations and contingencies that would provide natural experimental approaches to asking cognitive questions (Tomchik and Davis, 2013). Nonetheless in more recent work behavioral and neurobiological aspects of sleep, dopaminergic arousal, aggression, selective attention and courtship in Drosophila have been identified. Both aggression and courtship in Drosophila have critical chemosensory components involving both olfactory and gustatory receptors that allow male flies to distinguish between potential mates and competing conspecific males (Hollis and Kawecki, 2014). The maintenance of a range of cognitive tasks in male Drosophila, including but not limited to olfactory learning ability, was significantly reduced after 100 generations of enforced monogamy (Anderson and Adolphs, 2014).

Another example of a genetic model system that has engendered work on olfactory cognition is the zebrafish, Danio (Friedrich, 2013). Odors are known to be critical cues for guiding a number of behaviors in fish, including homing, reproduction, ingestion and social and avoidance behaviors. Aqueous odors can also participate in eliciting food-aversion conditioning, accompanied by induction of the immediate early response gene Egr-1 in gustatory areas of the zebrafish brain (Boyer et al., 2013). High throughput methods have been developed for assessment of the effects of genetic and pharmacological manipulations on visual responses of larval zebrafish behavior. Extension of these methods to olfactory conditioning will allow assessment of olfactory learning in a quantitative and unbiased fashion and facilitate the search for higher order learning about odors.

The power of genetic tools has promoted a focus on a limited number of animal species, known as genetic model systems, that enable use of the so-called genetic toolbox. This trend is augmented by multiple demonstrations that human disease genes and their downstream effects can be usefully studied in some of these genetic model systems, particularly Drosophila. Work on comparative cognition among vertebrate species provides more and more examples of cognitive skills among non-hominids, particularly but not exclusively elephants, birds and cetaceans. An interesting generalization from this effort is to look for further examples of higher order learning and other cognitive skills among invertebrates. For example, the debate on whether Octopus is conscious has already begun (Mather, 2008), fueled by descriptions of unique personality types among captive specimens (Mather and Kuba, 2013).

The unique relationship between neural circuits processing olfactory memory and circuits controlling emotions may provide yet another unique vantage point for a comparative approach to chemosensory cognition (Anderson and Adolphs, 2014). The proposal stresses the common features and evolutionary advantages of modes of behavior that are commonly identified as outward manifestations of emotions. An example is provided by negative or positive odor conditioning in male Drosophila, where flies show a conditioned positive place preference to an odor previously paired with mating with a virgin female (Shohat-Ophir et al., 2012). Mating responses to virgin females require neuropeptide Y, also involved in reward learning to ethanol. The concatenation of these findings suggests that these learned responses involve a rewarding internal state, a potential substrate for positive emotions. The idea that certain brain states can be rewarding, as indexed by their ability to promote the increased probability of discrete behaviors with which they are associated, is also supported by the finding that direct electrical stimulation of discrete brain areas in the cerebral ganglion of the terrestrial snail Helix can lead to significant increases in the probability of occurrence of the behaviors yoked to the application of brain stimulation (Balaban and Chase, 1989; Balaban and Maksimova, 1993). The use of implanted electrodes in minimally restrained animals (Cooke and Gelperin, 2001) increases the range of possible analyses of the rewarding effects of direct brain stimulation.

If the concept of consciousness loses its uniquely mammalian brand, the study of olfactory information processing may be the most general and fruitful approach to the study of comparative cognition, including consciousness, in the 96% of animal species in the Invertebrata. Some invertebrates could have a brain state representing a precursor of consciousness, as recently suggested for Drosophila (van Swinderen, 2005). Thus, understanding chemosensory cognition could help unravel some of the mechanisms underlying an evolutionary precursor to hominid consciousness.

This effort was presaged by Vince Dethier in his paper on Microscopic Brains, which ends with the following: “Perhaps these insects are little machines in a deep sleep, but looking at their rigidly armored bodies, their staring eyes, and their mute performances, one cannot help at times wondering if there is anyone inside” (Dethier, 1964).

REFERENCESE

Anderson, D. J., and Adolphs, R. (2014). A framework for studying emotions across species. Cell 157, 187–200. doi: 10.1016/j.cell.2014.03.003

Ardiel, E. L., and Rankin, C. H. (2010). An elegant mind: learning and memory in Caenorhabditis elegans. Learn. Mem. 17, 191–201. doi: 10.1101/lm.960510

Balaban, P. M., and Chase, R. (1989). Self-stimulation in snails. Neurosci. Res. Commun. 4, 139–146.

Balaban, P. M., and Maksimova, O. A. (1993). Positive and negative brain zones in the snail. Eur. J. Neurosci. 5, 768–774. doi: 10.1111/j.1460-9568.1993.tb00541.x

Beyaert, L., Greggers, U., and Menzel, R. (2012). Honeybees consolidate navigation memory during sleep. J. Exp. Biol. 215, 3981–3988. doi: 10.1242/jeb.075499

Boyer, B., Ernest, S., and Rosa, F. (2013). Egr-1 induction provides a genetic response to food aversion in zebrafish. Front. Behav. Neurosci. 7:51. doi: 10.3389/fnbeh.2013.00051

Bullock, T. H. (1993). How are more complex brains different? One view and an agenda for comparative neurobiology. Brain Behav. Evol. 41, 88–96. doi: 10.1159/000113826

Chen, K. F., and Crowther, D. C. (2012). Functional genomics in Drosophila models of human disease. Brief. Funct. Genomics 11, 405–415. doi: 10.1093/bfgp/els038

Cooke, I. R. C., and Gelperin, A. (2001). In vivo recordings of spontaneous and odor-modulated dynamics in the Limax olfactory lobe. J. Neurobiol. 46, 126–141. doi: 10.1002/1097-4695

Dethier, V. G. (1964). Microscopic brains. Science 143, 1138–1145. doi: 10.1126/science.143.3611.1138

Friedrich, R. W. (2013). Neuronal computations in the olfactory system of zebrafish. Annu. Rev. Neurosci. 36, 383–402. doi: 10.1146/annurev-neuro-062111-150504

Gelperin, A. (2010). “Human olfactory perception,” in Chemistry and Biology of Volatiles, ed A. Herrmann (West Sussex: John Wiley and Sons), 253–290.

Gelperin, A. (2013). “Associative memory mechanisms in terrestrial slugs and snails,” in Invertebrate Learning and Memory, eds R. Menzel and P. Benjamin (Waltham, MA: Academic Press—Elsevier), 280–290.

Glater, E. E., Rockman, M. V., and Bargmann, C. I. (2014). Multigenic natural variation underlies Caenorhabditis elegans olfactory preference for the bacterial pathogen Serratia marcescens. G3 (Bethesda) 4, 265–276. doi: 10.1534/g3.113.008649

Goel, P., and Gelperin, A. (2006). A neuronal network for the logic of Limax learning. J. Comput. Neurosci. 21, 259–270. doi: 10.1007/s10827-006-8097-7

Gould, J. L. (1990). Honey bee cognition. Cognition 37, 83–103. doi: 10.1016/0010-0277(90)90019-G

Griffin, D. R., and Speck, G. B. (2004). New evidence of animal consciousness. Anim. Cogn. 7, 5–18. doi: 10.1007/s10071-003-0203-x

Hollis, B., and Kawecki, T. J. (2014). Male cognitive performance declines in the absence of sexual selection. Proc. Biol. Sci. 281, 20132873. doi: 10.1098/rspb.2013.2873

Kobayashi, S., and Schultz, W. (2014). Reward contexts extend dopamine signals to unrewarded stimuli. Curr. Biol. 24, 56–62. doi: 10.1016/j.cub.2013.10.061

Llinas, R. (2008). Neuron. Scholarpedia 3:1490. doi: 10.4249/scholarpedia.1490

Mather, J. A. (2008). Cephalopod consciousness: behavioural evidence. Conscious. Cogn. 17, 37–48. doi: 10.1016/J.Concog.2006.11.006

Mather, J. A., and Kuba, M. J. (2013). The cephalopod specialties: complex nervous system, learning, and cognition. Can. J. Zool. 91, 431–449. doi: 10.1139/cjz-2013-0009

Matsuo, R., Kobayashi, S., Yamagishi, M., and Ito, E. (2011). Two pairs of tentacles and a pair of procerebra: optimized functions and redundant structures in the sensory and central organs involved in olfactory learning of terrestrial pulmonates. J. Exp. Biol. 214, 879–886. doi: 10.1242/jeb.024562

Menzel, R. (2001). Search for the memory trace in a mini-brain, the honeybee. Learn. Mem. 8, 53–62. doi: 10.1101/lm.38801

Menzel, R., and Giurfa, M. (2001). Cognitive architecture of a mini-brain: the honeybee. Trends Cogn. Sci. 5, 62–71. doi: 10.1016/S1364-6613(00)01601-6

Mersha, M., Formisano, R., Mcdonald, R., Pandey, P., Tavernarakis, N., and Harbinder, S. (2013). GPA-14, a Galpha(i) subunit mediates dopaminergic behavioral plasticity in C. elegans. Behav. Brain Funct. 9, 16. doi: 10.1186/1744-9081-9-16

Peralta Quesada, P. C., and Schausberger, P. (2012). Prenatal chemosensory learning by the predatory mite Neoseiulus californicus. PLoS ONE 7:e53229. doi: 10.1371/journal.pone.0053229

Quinn, W. G., Harris, W. A., and Benzer, S. (1974). Conditioned behavior in Drosophila melanogaster. Proc. Natl. Acad. Sci. U.S.A. 71, 708–712. doi: 10.1073/pnas.71.3.708

Reinhard, J., Srinivasan, M. V., Guez, D., and Zhang, S. W. (2004). Floral scents induce recall of navigational and visual memories in honeybees. J. Exp. Biol. 207, 4371–4381. doi: 10.1242/jeb.01306

Sanford, M. R., Olson, J. K., Lewis, W. J., and Tomberlin, J. K. (2013). The effect of sucrose concentration on olfactory-based associative learning in Culex quinquefasciatus Say (Diptera: Culicidae). J. Insect Behav. 26, 494–513. doi: 10.1007/s10905-012-9368-y

Shettleworth, S. J. (2012). Modularity, comparative cognition and human uniqueness. Philos. Trans. R. Soc. Lond. B Biol. Sci. 367, 2794–2802. doi: 10.1098/rstb.2012.0211

Shohat-Ophir, G., Kaun, K. R., Azanchi, R., Mohammed, H., and Heberlein, U. (2012). Sexual deprivation increases ethanol intake in Drosophila. Science 335, 1351–1355. doi: 10.1126/science.1215932

Tomchik, S. M., and Davis, R. L. (2013). “Drosphila memory research through four eras,” in Invertebrate Learning and Memory, eds R. Menzel and P. R. Benjamin (London: John Wiley; Elsevier), 359–377.

van Alphen, B., and van Swinderen, B. (2013). Drosophila strategies to study psychiatric disorders. Brain Res. Bull. 92, 1–11. doi: 10.1016/j.brainresbull.2011.09.007

van Swinderen, B. (2005). The remote roots of consciousness in fruit-fly selective attention? Bioessays 27, 321–330. doi: 10.1002/bies.20195

van Wijk, M., Wadman, W. J., and Sabelis, M. W. (2006). Gross morphology of the central nervous system of a phytoseiid mite. Exp. Appl. Acarol. 40, 205–216. doi: 10.1007/s10493-006-9039-9

Watanabe, S., Kirino, Y., and Gelperin, A. (2008). Neural and molecular mechanisms of microcognition in Limax. Learn. Mem. 15, 633–642. doi: 10.1101/Lm920908

Wessnitzer, J., Young, J. M., Armstrong, J. D., and Webb, B. (2012). A model of non-elemental olfactory learning in Drosophila. J. Comput. Neurosci. 32, 197–212. doi: 10.1007/s10827-011-0348-6

Wilson, R. C., Takahashi, Y. K., Schoenbaum, G., and Niv, Y. (2014). Orbitofrontal cortex as a cognitive map of task space. Neuron 81, 267–279. doi: 10.1016/j.neuron.2013.11.005

Wilson, R. I. (2013). Early olfactory processing in Drosophila: mechanisms and principles. Annu. Rev. Neurosci. 36, 217–241. doi: 10.1146/annurev-neuro-062111-150533

Young, J. M., Wessnitzer, J., Armstrong, J. D., and Webb, B. (2011). Elemental and non-elemental olfactory learning in Drosophila. Neurobiol. Learn. Mem. 96, 339–352. doi: 10.1016/j.nlm.2011.06.009

Conflict of Interest Statement: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Received: 13 April 2014; Accepted: 07 May 2014; Published online: 23 May 2014.

Citation: Gelperin A (2014) Comparative chemosensory cognition. Front. Behav. Neurosci. 8:190. doi:10.3389/fnbeh.2014.00190

This article was submitted to the journal Frontiers in Behavioral Neuroscience.

Copyright © 2014 Gelperin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.