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Ye Y, Wang Y, Zhuang Y, Tan H, Zuo Z, Yun H, Yuan K, Zhou W. Decomposition of an odorant in olfactory perception and neural representation. Nat Hum Behav 2024; 8:1150-1162. [PMID: 38499771 DOI: 10.1038/s41562-024-01849-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 02/19/2024] [Indexed: 03/20/2024]
Abstract
Molecules-the elementary units of substances-are commonly considered the units of processing in olfactory perception, giving rise to undifferentiated odour objects invariant to environmental variations. By selectively perturbing the processing of chemical substructures with adaptation ('the psychologist's microelectrode') in a series of psychophysical and neuroimaging experiments (458 participants), we show that two perceptually distinct odorants sharing part of their structural features become significantly less discernible following adaptation to a third odorant containing their non-shared structural features, in manners independent of olfactory intensity, valence, quality or general olfactory adaptation. The effect is accompanied by reorganizations of ensemble activity patterns in the posterior piriform cortex that parallel subjective odour quality changes, in addition to substructure-based neural adaptations in the anterior piriform cortex and amygdala. Central representations of odour quality and the perceptual outcome thus embed submolecular structural information and are malleable by recent olfactory encounters.
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Affiliation(s)
- Yuting Ye
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- Institute of Psychology, School of Public Affairs, Xiamen University, Xiamen, China
| | - Yanqing Wang
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- School of Psychology, Northwest Normal University, Lanzhou, China
| | - Yuan Zhuang
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Huibang Tan
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Zhentao Zuo
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Sino-Dannish College, University of Chinese Academy of Sciences, Beijing, China
| | - Hanqi Yun
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Kaiqi Yuan
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Wen Zhou
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
- Chinese Institute for Brain Research, Beijing, China.
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2
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Bratman GN, Bembibre C, Daily GC, Doty RL, Hummel T, Jacobs LF, Kahn PH, Lashus C, Majid A, Miller JD, Oleszkiewicz A, Olvera-Alvarez H, Parma V, Riederer AM, Sieber NL, Williams J, Xiao J, Yu CP, Spengler JD. Nature and human well-being: The olfactory pathway. SCIENCE ADVANCES 2024; 10:eadn3028. [PMID: 38748806 DOI: 10.1126/sciadv.adn3028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 04/12/2024] [Indexed: 07/04/2024]
Abstract
The world is undergoing massive atmospheric and ecological change, driving unprecedented challenges to human well-being. Olfaction is a key sensory system through which these impacts occur. The sense of smell influences quality of and satisfaction with life, emotion, emotion regulation, cognitive function, social interactions, dietary choices, stress, and depressive symptoms. Exposures via the olfactory pathway can also lead to (anti-)inflammatory outcomes. Increased understanding is needed regarding the ways in which odorants generated by nature (i.e., natural olfactory environments) affect human well-being. With perspectives from a range of health, social, and natural sciences, we provide an overview of this unique sensory system, four consensus statements regarding olfaction and the environment, and a conceptual framework that integrates the olfactory pathway into an understanding of the effects of natural environments on human well-being. We then discuss how this framework can contribute to better accounting of the impacts of policy and land-use decision-making on natural olfactory environments and, in turn, on planetary health.
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Affiliation(s)
- Gregory N Bratman
- School of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USA
- Department of Psychology, University of Washington, Seattle, WA 98195, USA
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA
| | - Cecilia Bembibre
- Institute for Sustainable Heritage, University College London, London, UK
| | - Gretchen C Daily
- Natural Capital Project, Stanford University, Stanford, CA 94305, USA
- Department of Biology, Stanford University, Stanford, CA 94305, USA
- Woods Institute, Stanford University, Stanford, CA 94305, USA
| | - Richard L Doty
- Smell and Taste Center, Department of Otorhinolaryngology: Head and Neck Surgery, University of Pennsylvania Perelman School of Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Thomas Hummel
- Interdisciplinary Center Smell and Taste, Department of Otorhinolaryngology, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Lucia F Jacobs
- Department of Psychology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Peter H Kahn
- School of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USA
- Department of Psychology, University of Washington, Seattle, WA 98195, USA
| | - Connor Lashus
- School of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USA
| | - Asifa Majid
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | | | - Anna Oleszkiewicz
- Interdisciplinary Center Smell and Taste, Department of Otorhinolaryngology, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Institute of Psychology, University of Wroclaw, Wrocław, Poland
| | | | | | - Anne M Riederer
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA
| | - Nancy Long Sieber
- T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA
| | - Jonathan Williams
- Air Chemistry Department, Max Planck Institute for Chemistry, 55128 Mainz, Germany
- Climate and Atmosphere Research Center, The Cyprus Institute, Nicosia, Cyprus
| | - Jieling Xiao
- College of Architecture, Birmingham City University, Birmingham, UK
| | - Chia-Pin Yu
- School of Forestry and Resource Conservation, National Taiwan University, Taiwan
- The Experimental Forest, College of Bio-Resources and Agriculture, National Taiwan University, Taiwan
| | - John D Spengler
- T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA
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3
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Abstract
Historically, the human sense of smell has been regarded as the odd stepchild of the senses, especially compared to the sensory bravado of seeing, touching, and hearing. The idea that the human olfaction has little to contribute to our experience of the world is commonplace, though with the emergence of COVID-19 there has rather been a sea change in this understanding. An ever increasing body of work has convincingly highlighted the keen capabilities of the human nose and the sophistication of the human olfactory system. Here, we provide a concise overview of the neuroscience of human olfaction spanning the last 10-15 years, with focus on the peripheral and central mechanisms that underlie how odor information is processed, packaged, parceled, predicted, and perturbed to serve odor-guided behaviors. We conclude by offering some guideposts for harnessing the next decade of olfactory research in all its shapes and forms.
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Affiliation(s)
| | - Jay A Gottfried
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA; ,
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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4
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Perrot NM, Roche A, Tonda A, Lutton E, Thomas-Danguin T. Predicting odor profile of food from its chemical composition: Towards an approach based on artificial intelligence and flavorists expertise. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:20528-20552. [PMID: 38124564 DOI: 10.3934/mbe.2023908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Odor is central to food quality. Still, a major challenge is to understand how the odorants present in a given food contribute to its specific odor profile, and how to predict this olfactory outcome from the chemical composition. In this proof-of-concept study, we seek to develop an integrative model that combines expert knowledge, fuzzy logic, and machine learning to predict the quantitative odor description of complex mixtures of odorants. The model output is the intensity of relevant odor sensory attributes calculated on the basis of the content in odor-active comounds. The core of the model is the mathematically formalized knowledge of four senior flavorists, which provided a set of optimized rules describing the sensory-relevant combinations of odor qualities the experts have in mind to elaborate the target odor sensory attributes. The model first queries analytical and sensory databases in order to standardize, homogenize, and quantitatively code the odor descriptors of the odorants. Then the standardized odor descriptors are translated into a limited number of odor qualities used by the experts thanks to an ontology. A third step consists of aggregating all the information in terms of odor qualities across all the odorants found in a given product. The final step is a set of knowledge-based fuzzy membership functions representing the flavorist expertise and ensuring the prediction of the intensity of the target odor sensory descriptors on the basis of the products' aggregated odor qualities; several methods of optimization of the fuzzy membership functions have been tested. Finally, the model was applied to predict the odor profile of 16 red wines from two grape varieties for which the content in odorants was available. The results showed that the model can predict the perceptual outcome of food odor with a certain level of accuracy, and may also provide insights into combinations of odorants not mentioned by the experts.
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Affiliation(s)
- N Mejean Perrot
- UMR 518 MIA-PS, INRAE, AgroParisTech, Université Paris-Saclay, 22 place de l'Agronomie, 91120, Palaiseau, France
- Institut des Systèmes Complexes de Paris Île-de-France (ISC-PIF), UAR 3611 CNRS, 75013 Paris, France
| | - Alice Roche
- Centre des Sciences du Goût et de l'Alimentation, INRAE, CNRS, Institut Agro Dijon, Université de Bourgogne Franche-Comté, F-21000 Dijon, France
| | - Alberto Tonda
- UMR 518 MIA-PS, INRAE, AgroParisTech, Université Paris-Saclay, 22 place de l'Agronomie, 91120, Palaiseau, France
- Institut des Systèmes Complexes de Paris Île-de-France (ISC-PIF), UAR 3611 CNRS, 75013 Paris, France
| | - Evelyne Lutton
- UMR 518 MIA-PS, INRAE, AgroParisTech, Université Paris-Saclay, 22 place de l'Agronomie, 91120, Palaiseau, France
- Institut des Systèmes Complexes de Paris Île-de-France (ISC-PIF), UAR 3611 CNRS, 75013 Paris, France
| | - Thierry Thomas-Danguin
- Centre des Sciences du Goût et de l'Alimentation, INRAE, CNRS, Institut Agro Dijon, Université de Bourgogne Franche-Comté, F-21000 Dijon, France
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5
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Kumari P, Besold T, Spranger M. Perceptual metrics for odorants: Learning from non-expert similarity feedback using machine learning. PLoS One 2023; 18:e0291767. [PMID: 37939067 PMCID: PMC10631653 DOI: 10.1371/journal.pone.0291767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 09/05/2023] [Indexed: 11/10/2023] Open
Abstract
Defining perceptual similarity metrics for odorant comparisons is crucial to understanding the mechanism of olfactory perception. Current methods in olfaction rely on molecular physicochemical features or discrete verbal descriptors (floral, burnt, etc.) to approximate perceptual (dis)similarity between odorants. However, structural or verbal descriptors alone are limited in modeling complex nuances of odor perception. While structural features inadequately characterize odor perception, language-based discrete descriptors lack the granularity needed to model a continuous perception space. We introduce data-driven approaches to perceptual metrics learning (PMeL) based on two key insights: a) by combining physicochemical features with the user's perceptual feedback, we can leverage both structural and perceptual attributes of odors to define dissimilarity, and b) instead of discrete labels, user's perceptual feedback can be gathered as relative similarity comparisons, such as "Does molecule-A smell more like molecule-B, or molecule-C?" These triplet comparisons are easier even for non-experts users and offer a more effective representation of the continuous perception space. Experimental results on several defined tasks show the effectiveness of our approach in evaluating perceptual dissimilarity between odorants. Finally, we investigate how closely our model, trained on non-expert feedback, aligns with the expert's similarity judgments. Our effort aims to reduce reliance on expert annotations.
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6
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Li Q, Zhang YF, Zhang TM, Wan JH, Zhang YD, Yang H, Huang Y, Xu C, Li G, Lu HM. iORbase: A database for the prediction of the structures and functions of insect olfactory receptors. INSECT SCIENCE 2023; 30:1245-1254. [PMID: 36519267 DOI: 10.1111/1744-7917.13162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 11/01/2022] [Accepted: 11/27/2022] [Indexed: 06/17/2023]
Abstract
Insect olfactory receptors (iORs) with atypical 7-transmembrane domains, unlike Chordata olfactory receptors, are not in the GPCR protein family. iORs selectively bind to volatile ligands in the environment and affect essential insect behaviors. In this study, we constructed a new platform (iORbase, https://www.iorbase.com) for the structural and functional analysis of iORs based on a combined algorithm for gene annotation and protein structure prediction. Moreover, it provides the option to calculate the binding affinities and binding residues between iORs and pheromone molecules by virtual screening of docking. Furthermore, iORbase supports the automatic structural and functional prediction of user-submitted iORs or pheromones. iORbase contains the well-analyzed results of approximately 6 000 iORs and their 3D protein structures identified from 59 insect species and 2 077 insect pheromones from the literature, as well as approximately 12 million pairs of simulated interactions between functional iORs and pheromones. We also built 4 online modules, iORPDB, iInteraction, iModelTM, and iOdorTool to easily retrieve and visualize the 3D structures and interactions. iORbase can help greatly improve the experimental efficiency and success rate, identify new insecticide targets, or develop electronic nose technology. This study will shed light on the olfactory recognition mechanism and evolutionary characteristics from the perspectives of omics and macroevolution.
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Affiliation(s)
- Qian Li
- School of Life Sciences, Key Laboratory for Space Bioscience and Biotechnology, Northwestern Polytechnical University, Xi'an, China
| | - Yi-Feng Zhang
- School of Life Sciences, Key Laboratory for Space Bioscience and Biotechnology, Northwestern Polytechnical University, Xi'an, China
| | - Tian-Min Zhang
- School of Life Sciences, Key Laboratory for Space Bioscience and Biotechnology, Northwestern Polytechnical University, Xi'an, China
- College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Jia-Hui Wan
- School of Life Sciences, Key Laboratory for Space Bioscience and Biotechnology, Northwestern Polytechnical University, Xi'an, China
| | - Yu-Dan Zhang
- School of Life Sciences, Key Laboratory for Space Bioscience and Biotechnology, Northwestern Polytechnical University, Xi'an, China
| | - Hui Yang
- School of Life Sciences, Key Laboratory for Space Bioscience and Biotechnology, Northwestern Polytechnical University, Xi'an, China
| | - Yuan Huang
- College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Chang Xu
- College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Gang Li
- College of Life Sciences, Shaanxi Normal University, Xi'an, China
| | - Hui-Meng Lu
- School of Life Sciences, Key Laboratory for Space Bioscience and Biotechnology, Northwestern Polytechnical University, Xi'an, China
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7
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Lee BK, Mayhew EJ, Sanchez-Lengeling B, Wei JN, Qian WW, Little KA, Andres M, Nguyen BB, Moloy T, Yasonik J, Parker JK, Gerkin RC, Mainland JD, Wiltschko AB. A principal odor map unifies diverse tasks in olfactory perception. Science 2023; 381:999-1006. [PMID: 37651511 DOI: 10.1126/science.ade4401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 08/03/2023] [Indexed: 09/02/2023]
Abstract
Mapping molecular structure to odor perception is a key challenge in olfaction. We used graph neural networks to generate a principal odor map (POM) that preserves perceptual relationships and enables odor quality prediction for previously uncharacterized odorants. The model was as reliable as a human in describing odor quality: On a prospective validation set of 400 out-of-sample odorants, the model-generated odor profile more closely matched the trained panel mean than did the median panelist. By applying simple, interpretable, theoretically rooted transformations, the POM outperformed chemoinformatic models on several other odor prediction tasks, indicating that the POM successfully encoded a generalized map of structure-odor relationships. This approach broadly enables odor prediction and paves the way toward digitizing odors.
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Affiliation(s)
- Brian K Lee
- Google Research, Brain Team, Cambridge, MA, USA
| | - Emily J Mayhew
- Monell Chemical Senses Center, Philadelphia, PA, USA
- Department of Food Science and Human Nutrition, Michigan State University, East Lansing, MI, USA
| | | | | | - Wesley W Qian
- Google Research, Brain Team, Cambridge, MA, USA
- Osmo Labs, PBC, Cambridge, MA, USA
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | | | | | | | - Theresa Moloy
- Monell Chemical Senses Center, Philadelphia, PA, USA
| | - Jacob Yasonik
- Google Research, Brain Team, Cambridge, MA, USA
- Osmo Labs, PBC, Cambridge, MA, USA
| | - Jane K Parker
- Department of Food and Nutritional Sciences, University of Reading, Reading, Berkshire, UK
| | - Richard C Gerkin
- Google Research, Brain Team, Cambridge, MA, USA
- Osmo Labs, PBC, Cambridge, MA, USA
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Joel D Mainland
- Monell Chemical Senses Center, Philadelphia, PA, USA
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA
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8
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Sagar V, Shanahan LK, Zelano CM, Gottfried JA, Kahnt T. High-precision mapping reveals the structure of odor coding in the human brain. Nat Neurosci 2023; 26:1595-1602. [PMID: 37620443 PMCID: PMC10726579 DOI: 10.1038/s41593-023-01414-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 07/18/2023] [Indexed: 08/26/2023]
Abstract
Odor perception is inherently subjective. Previous work has shown that odorous molecules evoke distributed activity patterns in olfactory cortices, but how these patterns map on to subjective odor percepts remains unclear. In the present study, we collected neuroimaging responses to 160 odors from 3 individual subjects (18 h per subject) to probe the neural coding scheme underlying idiosyncratic odor perception. We found that activity in the orbitofrontal cortex (OFC) represents the fine-grained perceptual identity of odors over and above coarsely defined percepts, whereas this difference is less pronounced in the piriform cortex (PirC) and amygdala. Furthermore, the implementation of perceptual encoding models enabled us to predict olfactory functional magnetic resonance imaging responses to new odors, revealing that the dimensionality of the encoded perceptual spaces increases from the PirC to the OFC. Whereas encoding of lower-order dimensions generalizes across subjects, encoding of higher-order dimensions is idiosyncratic. These results provide new insights into cortical mechanisms of odor coding and suggest that subjective olfactory percepts reside in the OFC.
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Affiliation(s)
- Vivek Sagar
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Christina M Zelano
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Jay A Gottfried
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Thorsten Kahnt
- National Institute on Drug Abuse Intramural Research Program, Baltimore, MD, USA.
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9
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Ward RJ, Wuerger SM, Ashraf M, Marshall A. Physicochemical features partially explain olfactory crossmodal correspondences. Sci Rep 2023; 13:10590. [PMID: 37391587 PMCID: PMC10313698 DOI: 10.1038/s41598-023-37770-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 06/27/2023] [Indexed: 07/02/2023] Open
Abstract
During the olfactory perception process, our olfactory receptors are thought to recognize specific chemical features. These features may contribute towards explaining our crossmodal perception. The physicochemical features of odors can be extracted using an array of gas sensors, also known as an electronic nose. The present study investigates the role that the physicochemical features of olfactory stimuli play in explaining the nature and origin of olfactory crossmodal correspondences, which is a consistently overlooked aspect of prior work. Here, we answer the question of whether the physicochemical features of odors contribute towards explaining olfactory crossmodal correspondences and by how much. We found a similarity of 49% between the perceptual and the physicochemical spaces of our odors. All of our explored crossmodal correspondences namely, the angularity of shapes, smoothness of textures, perceived pleasantness, pitch, and colors have significant predictors for various physicochemical features, including aspects of intensity and odor quality. While it is generally recognized that olfactory perception is strongly shaped by context, experience, and learning, our findings show that a link, albeit small (6-23%), exists between olfactory crossmodal correspondences and their underlying physicochemical features.
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Affiliation(s)
- Ryan J Ward
- School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool, L3 3AF, UK.
- Digital Innovation Facility, University of Liverpool, Liverpool, L69 3RF, UK.
| | - Sophie M Wuerger
- Department of Psychology, University of Liverpool, Liverpool, L69 7ZA, UK
| | - Maliha Ashraf
- Department of Psychology, University of Liverpool, Liverpool, L69 7ZA, UK
| | - Alan Marshall
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, L69 3GJ, UK
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10
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Schicker D, Singh S, Freiherr J, Grasskamp AT. OWSum: algorithmic odor prediction and insight into structure-odor relationships. J Cheminform 2023; 15:51. [PMID: 37150811 PMCID: PMC10164323 DOI: 10.1186/s13321-023-00722-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 04/16/2023] [Indexed: 05/09/2023] Open
Abstract
We derived and implemented a linear classification algorithm for the prediction of a molecule's odor, called Olfactory Weighted Sum (OWSum). Our approach relies solely on structural patterns of the molecules as features for algorithmic treatment and uses conditional probabilities combined with tf-idf values. In addition to the prediction of molecular odor, OWSum provides insights into properties of the dataset and allows to understand how algorithmic classifications are reached by quantitatively assigning structural patterns to odors. This provides chemists with an intuitive understanding of underlying interactions. To deal with ambiguities of the natural language used to describe odor, we introduced descriptor overlap as a metric for the quantification of semantic overlap between descriptors. Thus, grouping of descriptors and derivation of higher-level descriptors becomes possible. Our approach poses a large leap forward in our capabilities to understand and predict molecular features.
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Affiliation(s)
- Doris Schicker
- Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Straße 35, 85354, Freising, Germany.
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054, Erlangen, Germany.
| | - Satnam Singh
- Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Straße 35, 85354, Freising, Germany
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054, Erlangen, Germany
| | - Jessica Freiherr
- Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Straße 35, 85354, Freising, Germany
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054, Erlangen, Germany
| | - Andreas T Grasskamp
- Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Straße 35, 85354, Freising, Germany.
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11
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Wang Y, Zhao Q, Ma M, Xu J. Olfactory perception prediction model inspired by olfactory lateral inhibition and deep feature combination. APPL INTELL 2023. [DOI: 10.1007/s10489-023-04517-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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12
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Deroy O. Olfactory abstraction: a communicative and metacognitive account. Philos Trans R Soc Lond B Biol Sci 2023; 378:20210369. [PMID: 36571118 PMCID: PMC9791486 DOI: 10.1098/rstb.2021.0369] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 08/05/2022] [Indexed: 12/27/2022] Open
Abstract
The usual puzzle raised about olfaction is that of a deficit of abstraction: smells, by contrast notably with colours, do not easily lend themselves to abstract categories and labels. Some studies have argued that the puzzle is culturally restricted and that abstraction is more common outside urban Western societies. Here, I argue that the puzzle is misconstrued and should be reversed: given that odours are constantly changing and that their commonalities are difficult for humans to identify, what is surprising is not that abstract terms are rare, but that they should be used at all for olfaction. Given the nature of the olfactory environment and our cognitive equipment, concrete labels referring to sources seem most adaptive. To explain the use and presence of abstract terms, we need to examine their social and communicative benefits. Here these benefits are spelt out as securing a higher agreement among individuals varying in their olfactory experiences as well as the labels they use, as well as feeling a heightened sense of confidence in one's naming capacities. This article is part of the theme issue 'Concepts in interaction: social engagement and inner experiences'.
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Affiliation(s)
- Ophelia Deroy
- Faculty of Philosophy, Ludwig Maximilian University, D-80539 Munich, Germany
- Munich Center for Neuroscience, Ludwig Maximilian University, D-80539 Munich, Germany
- Institute of Philosophy, School of Advanced Study, University of London, London EC1E 7HU, UK
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13
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Dhurandhar A, Li H, Cecchi GA, Meyer P. Expansive linguistic representations to predict interpretable odor mixture discriminability. Chem Senses 2023; 48:bjad018. [PMID: 37262433 DOI: 10.1093/chemse/bjad018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Indexed: 06/03/2023] Open
Abstract
Language is often thought as being poorly adapted to precisely describe or quantify smell and olfactory attributes. In this work, we show that semantic descriptors of odors can be implemented in a model to successfully predict odor mixture discriminability, an olfactory attribute. We achieved this by taking advantage of the structure-to-percept model we previously developed for monomolecular odorants, using chemical descriptors to predict pleasantness, intensity and 19 semantic descriptors such as "fish," "cold," "burnt," "garlic," "grass," and "sweet" for odor mixtures, followed by a metric learning to obtain odor mixture discriminability. Through this expansion of the representation of olfactory mixtures, our Semantic model outperforms state of the art methods by taking advantage of the intermediary semantic representations learned from human perception data to enhance and generalize the odor discriminability/similarity predictions. As 10 of the semantic descriptors were selected to predict discriminability/similarity, our approach meets the need of rapidly obtaining interpretable attributes of odor mixtures as illustrated by the difficulty of finding olfactory metamers. More fundamentally, it also shows that language can be used to establish a metric of discriminability in the everyday olfactory space.
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Affiliation(s)
- Amit Dhurandhar
- Foundations of Trusted Artificial Intelligence, T.J. Watson IBM Research Laboratory, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, United States
| | - Hongyang Li
- Healthcare and Life Sciences, T.J. Watson IBM Research Laboratory, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, United States
| | - Guillermo A Cecchi
- Healthcare and Life Sciences, T.J. Watson IBM Research Laboratory, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, United States
| | - Pablo Meyer
- Healthcare and Life Sciences, T.J. Watson IBM Research Laboratory, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, United States
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14
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Ghaninia M, Zhou Y, Knauer AC, Schiestl FP, Sharpee TO, Smith BH. Hyperbolic odorant mixtures as a basis for more efficient signaling between flowering plants and bees. PLoS One 2022; 17:e0270358. [PMID: 35830455 PMCID: PMC9278781 DOI: 10.1371/journal.pone.0270358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 06/08/2022] [Indexed: 11/19/2022] Open
Abstract
Animals use odors in many natural contexts, for example, for finding mates or food, or signaling danger. Most analyses of natural odors search for either the most meaningful components of a natural odor mixture, or they use linear metrics to analyze the mixture compositions. However, we have recently shown that the physical space for complex mixtures is ‘hyperbolic’, meaning that there are certain combinations of variables that have a disproportionately large impact on perception and that these variables have specific interpretations in terms of metabolic processes taking place inside the flower and fruit that produce the odors. Here we show that the statistics of odorants and odorant mixtures produced by inflorescences (Brassica rapa) are also better described with a hyperbolic rather than a linear metric, and that combinations of odorants in the hyperbolic space are better predictors of the nectar and pollen resources sought by bee pollinators than the standard Euclidian combinations. We also show that honey bee and bumble bee antennae can detect most components of the B. rapa odor space that we tested, and the strength of responses correlates with positions of odorants in the hyperbolic space. In sum, a hyperbolic representation can be used to guide investigation of how information is represented at different levels of processing in the CNS.
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Affiliation(s)
- Majid Ghaninia
- School of Life Sciences, Arizona State University, Tempe, AZ, United States of America
| | - Yuansheng Zhou
- The Salk Institute for Biological Studies, Computational Neurobiology Laboratory, La Jolla, CA, United States of America
- University of California, San Diego, La Jolla, CA, United States of America
| | - Anina C. Knauer
- Institute of Systematic and Evolutionary Botany University of Zurich, Zollikerstrasse, Zurich, Switzerland
| | - Florian P. Schiestl
- Institute of Systematic and Evolutionary Botany University of Zurich, Zollikerstrasse, Zurich, Switzerland
| | - Tatyana O. Sharpee
- The Salk Institute for Biological Studies, Computational Neurobiology Laboratory, La Jolla, CA, United States of America
- University of California, San Diego, La Jolla, CA, United States of America
- * E-mail: (TOS); , (BHS)
| | - Brian H. Smith
- School of Life Sciences, Arizona State University, Tempe, AZ, United States of America
- * E-mail: (TOS); , (BHS)
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15
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Nakayama H, Gerkin RC, Rinberg D. A behavioral paradigm for measuring perceptual distances in mice. CELL REPORTS METHODS 2022; 2:100233. [PMID: 35784646 PMCID: PMC9243525 DOI: 10.1016/j.crmeth.2022.100233] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 02/20/2022] [Accepted: 05/17/2022] [Indexed: 01/22/2023]
Abstract
Perceptual similarities between a specific stimulus and other stimuli of the same modality provide valuable information about the structure and geometry of sensory spaces. While typically assessed in human behavioral experiments, perceptual similarities-or distances-are rarely measured in other species. However, understanding the neural computations responsible for sensory representations requires the monitoring and often manipulation of neural activity, which is more readily achieved in non-human experimental models. Here, we develop a behavioral paradigm that enables the quantification of perceptual similarity between sensory stimuli using mouse olfaction as a model system.
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Affiliation(s)
| | - Richard C. Gerkin
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Dmitry Rinberg
- Neuroscience Institute, NYU Langone Health, New York, NY 10016, USA
- Center for Neural Science, New York University, New York, NY 10003, USA
- Department of Physics, New York University, New York, NY 10003, USA
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16
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Malfeito-Ferreira M. Fine wine flavour perception and appreciation: Blending neuronal processes, tasting methods and expertise. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.06.053] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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17
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Abstract
Olfaction is fundamentally distinct from other sensory modalities. Natural odor stimuli are complex mixtures of volatile chemicals that interact in the nose with a receptor array that, in rodents, is built from more than 1,000 unique receptors. These interactions dictate a peripheral olfactory code, which in the brain is transformed and reformatted as it is broadcast across a set of highly interconnected olfactory regions. Here we discuss the problems of characterizing peripheral population codes for olfactory stimuli, of inferring the specific functions of different higher olfactory areas given their extensive recurrence, and of ultimately understanding how odor representations are linked to perception and action. We argue that, despite the differences between olfaction and other sensory modalities, addressing these specific questions will reveal general principles underlying brain function.
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Affiliation(s)
- David H Brann
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115, USA;
| | - Sandeep Robert Datta
- Department of Neurobiology, Harvard Medical School, Boston, Massachusetts 02115, USA;
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18
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Gronowitz ME, Liu A, Qiu Q, Yu CR, Cleland TA. A physicochemical model of odor sampling. PLoS Comput Biol 2021; 17:e1009054. [PMID: 34115747 PMCID: PMC8221795 DOI: 10.1371/journal.pcbi.1009054] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 06/23/2021] [Accepted: 05/09/2021] [Indexed: 11/19/2022] Open
Abstract
We present a general physicochemical sampling model for olfaction, based on established pharmacological laws, in which arbitrary combinations of odorant ligands and receptors can be generated and their individual and collective effects on odor representations and olfactory performance measured. Individual odor ligands exhibit receptor-specific affinities and efficacies; that is, they may bind strongly or weakly to a given receptor, and can act as strong agonists, weak agonists, partial agonists, or antagonists. Ligands interacting with common receptors compete with one another for dwell time; these competitive interactions appropriately simulate the degeneracy that fundamentally defines the capacities and limitations of odorant sampling. The outcome of these competing ligand-receptor interactions yields a pattern of receptor activation levels, thereafter mapped to glomerular presynaptic activation levels based on the convergence of sensory neuron axons. The metric of greatest interest is the mean discrimination sensitivity, a measure of how effectively the olfactory system at this level is able to recognize a small change in the physicochemical quality of a stimulus. This model presents several significant outcomes, both expected and surprising. First, adding additional receptors reliably improves the system’s discrimination sensitivity. Second, in contrast, adding additional ligands to an odorscene initially can improve discrimination sensitivity, but eventually will reduce it as the number of ligands increases. Third, the presence of antagonistic ligand-receptor interactions produced clear benefits for sensory system performance, generating higher absolute discrimination sensitivities and increasing the numbers of competing ligands that could be present before discrimination sensitivity began to be impaired. Finally, the model correctly reflects and explains the modest reduction in odor discrimination sensitivity exhibited by transgenic mice in which the specificity of glomerular targeting by primary olfactory neurons is partially disrupted. We understand most sensory systems by comparing the responses of the system against objective external physical measurements. For example, we know that our ability to distinguish small changes in color is greater for some colors than for others, and that we can distinguish sounds more acutely when they are within the range of pitches used for speech. Similar principles presumably apply to the sense of smell, but odorous chemicals are harder to physically quantify than light or sound because they cannot be organized in terms of a straightforward physical variable like wavelength or frequency. That said, the physical properties of interactions between chemicals and cellular receptors (such as those in the olfactory system) are well understood. What we lack is a systematic framework within which these pharmacological principles can be organized to study odor sampling in the way that we have long studied visual and auditory sampling. We here propose and describe such a framework for odor sampling, and show that it successfully replicates some established but unexplained experimental results.
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Affiliation(s)
- Mitchell E. Gronowitz
- Department of Psychology, Cornell University, Ithaca, New York, United States of America
| | - Adam Liu
- Department of Psychology, Cornell University, Ithaca, New York, United States of America
| | - Qiang Qiu
- Stowers Institute for Medical Research, Kansas City, Missouri, United States of America
| | - C. Ron Yu
- Stowers Institute for Medical Research, Kansas City, Missouri, United States of America
- Department of Anatomy and Cell Biology, University of Kansas Medical Center, Kansas City, Kansas, United States of America
| | - Thomas A. Cleland
- Department of Psychology, Cornell University, Ithaca, New York, United States of America
- * E-mail:
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19
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Ma Y, Tang K, Xu Y, Thomas-Danguin T. A dataset on odor intensity and odor pleasantness of 222 binary mixtures of 72 key food odorants rated by a sensory panel of 30 trained assessors. Data Brief 2021; 36:107143. [PMID: 34041322 PMCID: PMC8144660 DOI: 10.1016/j.dib.2021.107143] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 05/06/2021] [Accepted: 05/07/2021] [Indexed: 11/20/2022] Open
Abstract
This paper describes data collected on a set of 222 binary mixtures, based on a set of 72 odorants chiefly found in food, rated by 30 selected and trained assessors for odor intensity and pleasantness. The data included odor intensity (IAB) and pleasantness (PAB) of the mixtures, the intensity (IA, IB) and the pleasantness (PA, PB) of the two components. Moreover, the intensity (IAmix, IBmix) of the two components’ odor perceived within the mixture are included. The quality of the dataset was evaluated by checking subjects’ performance and by testing repeatability using the 24 duplicated trials for each attribute. This set of experimental data would be especially valuable to investigate theories of odor mixture perception in human and to test new models to predict odor perception of odor mixtures.
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Affiliation(s)
- Yue Ma
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu 214122, China.,Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu 214122, P. R. China.,Centre des Sciences du Goût et de l'Alimentation, INRAE, CNRS, AgroSup Dijon, Université Bourgogne Franche-Comté, Dijon, France
| | - Ke Tang
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu 214122, China.,Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu 214122, P. R. China
| | - Yan Xu
- State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu 214122, China.,Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu 214122, P. R. China
| | - Thierry Thomas-Danguin
- Centre des Sciences du Goût et de l'Alimentation, INRAE, CNRS, AgroSup Dijon, Université Bourgogne Franche-Comté, Dijon, France
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20
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Olfactory Perception in Relation to the Physicochemical Odor Space. Brain Sci 2021; 11:brainsci11050563. [PMID: 33925220 PMCID: PMC8146962 DOI: 10.3390/brainsci11050563] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/23/2021] [Accepted: 04/26/2021] [Indexed: 11/29/2022] Open
Abstract
A growing body of research aims at solving what is often referred to as the stimulus-percept problem in olfactory perception. Although computational efforts have made it possible to predict perceptual impressions from the physicochemical space of odors, studies with large psychophysical datasets from non-experts remain scarce. Following previous approaches, we developed a physicochemical odor space using 4094 molecular descriptors of 1389 odor molecules. For 20 of these odors, we examined associations with perceived pleasantness, intensity, odor quality and detection threshold, obtained from a dataset of 2000 naïve participants. Our results show significant differences in perceptual ratings, and we were able to replicate previous findings on the association between perceptual ratings and the first dimensions of the physicochemical odor space. However, the present analyses also revealed striking interindividual variations in perceived pleasantness and intensity. Additionally, interactions between pleasantness, intensity, and olfactory and trigeminal qualitative dimensions were found. To conclude, our results support previous findings on the relation between structure and perception on the group level in our sample of non-expert raters. In the challenging task to relate olfactory stimulus and percept, the physicochemical odor space can serve as a reliable and helpful tool to structure the high-dimensional space of olfactory stimuli. Nevertheless, human olfactory perception in the individual is not an analytic process of molecule detection alone, but is part of a holistic integration of multisensory inputs, context and experience.
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21
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Gerkin RC. Parsing Sage and Rosemary in Time: The Machine Learning Race to Crack Olfactory Perception. Chem Senses 2021; 46:6226923. [PMID: 33860304 DOI: 10.1093/chemse/bjab020] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Color and pitch perception are largely understandable from characteristics of physical stimuli: the wavelengths of light and sound waves, respectively. By contrast, understanding olfactory percepts from odorous stimuli (volatile molecules) is much more challenging. No intuitive set of molecular features is up to the task. Here in Chemical Senses, the Ray lab reports using a predictive modeling framework-first breaking molecular structure into thousands of features and then using this to train a predictive statistical model on a wide range of perceptual descriptors-to create a tool for predicting the odor character of hundreds of thousands of available but previously uncharacterized molecules (Kowalewski et al. 2021). This will allow future investigators to representatively sample the space of odorous molecules as well as identify previously unknown odorants with a target odor character. Here, I put this work into the context of other modeling efforts and highlight the urgent need for large new datasets and transparent benchmarks for the field to make and evaluate modeling breakthroughs, respectively.
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Affiliation(s)
- Richard C Gerkin
- School of Life Sciences, Arizona State University, 427 East Tyler Mall, Tempe, AZ 85287, USA
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22
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Kowalewski J, Huynh B, Ray A. A System-Wide Understanding of the Human Olfactory Percept Chemical Space. Chem Senses 2021; 46:6153471. [PMID: 33640959 DOI: 10.1093/chemse/bjab007] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The fundamental units of olfactory perception are discrete 3D structures of volatile chemicals that each interact with specific subsets of a very large family of hundreds of odorant receptor proteins, in turn activating complex neural circuitry and posing a challenge to understand. We have applied computational approaches to analyze olfactory perceptual space from the perspective of odorant chemical features. We identify physicochemical features associated with ~150 different perceptual descriptors and develop machine-learning models. Validation of predictions shows a high success rate for test set chemicals within a study, as well as across studies more than 30 years apart in time. Due to the high success rates, we are able to map ~150 percepts onto a chemical space of nearly 0.5 million compounds, predicting numerous percept-structure combinations. The chemical structure-to-percept prediction provides a system-level view of human olfaction and opens the door for comprehensive computational discovery of fragrances and flavors.
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Affiliation(s)
- Joel Kowalewski
- Interdepartmental Neuroscience Program, University of California, 3401 Watkins Drive, Riverside, CA 92521, USA
| | - Brandon Huynh
- Department of Molecular, Cell and Systems Biology, University of California, 3401 Watkins Drive, Riverside, CA 92521, USA
| | - Anandasankar Ray
- Interdepartmental Neuroscience Program, University of California, 3401 Watkins Drive, Riverside, CA 92521, USA.,Department of Molecular, Cell and Systems Biology, University of California, 3401 Watkins Drive, Riverside, CA 92521, USA
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23
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Jraissati Y, Deroy O. Categorizing Smells: A Localist Approach. Cogn Sci 2021; 45:e12930. [PMID: 33389758 DOI: 10.1111/cogs.12930] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 11/16/2020] [Accepted: 11/19/2020] [Indexed: 11/30/2022]
Abstract
Humans are poorer at identifying smells and communicating about them, compared to other sensory domains. They also cannot easily organize odor sensations in a general conceptual space, where geometric distance could represent how similar or different all odors are. These two generalities are more or less accepted by psychologists, and they are often seen as connected: If there is no conceptual space for odors, then olfactory identification should indeed be poor. We propose here an important revision to this conclusion: We believe that the claim that there is no odor space is true only if by odor space, one means a conceptual space representing all possible odor sensations, in the paradigmatic sense used for instance for color. However, in a less paradigmatic sense, local conceptual spaces representing a given subset of odors do exist. Thus the absence of a global odor space does not warrant the conclusion that there is no olfactory conceptual map at all. Here we show how a localist account provides a new interpretation of experts and cross-cultural categorization studies: Rather than being exceptions to the poor olfactory identification and communication usually seen elsewhere, experts and cross-cultural categorization are here taken to corroborate the existence of local conceptual spaces.
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Affiliation(s)
- Yasmina Jraissati
- Ronin Institute.,Department of Philosophy, American University of Beirut
| | - Ophelia Deroy
- Faculty of Philosophy, Ludwig Maximilian University.,Munich Centre for Neuroscience, Ludwig Maximilian University.,Institute of Philosophy, School of Advanced Study, University of London
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24
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A measure of smell enables the creation of olfactory metamers. Nature 2020; 588:118-123. [DOI: 10.1038/s41586-020-2891-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 08/19/2020] [Indexed: 11/09/2022]
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25
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de Groot JHB, Croijmans I, Smeets MAM. More Data, Please: Machine Learning to Advance the Multidisciplinary Science of Human Sociochemistry. Front Psychol 2020; 11:581701. [PMID: 33192899 PMCID: PMC7642605 DOI: 10.3389/fpsyg.2020.581701] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 10/05/2020] [Indexed: 12/12/2022] Open
Abstract
Communication constitutes the core of human life. A large portion of our everyday social interactions is non-verbal. Of the sensory modalities we use for non-verbal communication, olfaction (i.e., the sense of smell) is often considered the most enigmatic medium. Outside of our awareness, smells provide information about our identity, emotions, gender, mate compatibility, illness, and potentially more. Yet, body odors are astonishingly complex, with their composition being influenced by various factors. Is there a chemical basis of olfactory communication? Can we identify molecules predictive of psychological states and traits? We propose that answering these questions requires integrating two disciplines: psychology and chemistry. This new field, coined sociochemistry, faces new challenges emerging from the sheer amount of factors causing variability in chemical composition of body odorants on the one hand (e.g., diet, hygiene, skin bacteria, hormones, genes), and variability in psychological states and traits on the other (e.g., genes, culture, hormones, internal state, context). In past research, the reality of these high-dimensional data has been reduced in an attempt to isolate unidimensional factors in small, homogenous samples under tightly controlled settings. Here, we propose big data approaches to establish novel links between chemical and psychological data on a large scale from heterogeneous samples in ecologically valid settings. This approach would increase our grip on the way chemical signals non-verbally and subconsciously affect our social lives across contexts.
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Affiliation(s)
- Jasper H. B. de Groot
- Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht, Netherlands
- Behavioural Science Institute, Radboud University, Nijmegen, Netherlands
| | - Ilja Croijmans
- Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht, Netherlands
| | - Monique A. M. Smeets
- Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht, Netherlands
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26
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Shepherd GM, Hines ML, Migliore M, Chen WR, Greer CA. Predicting brain organization with a computational model: 50-year perspective on lateral inhibition and oscillatory gating by dendrodendritic synapses. J Neurophysiol 2020; 124:375-387. [PMID: 32639901 DOI: 10.1152/jn.00175.2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The first compartmental computer models of brain neurons using the Rall method predicted novel and unexpected dendrodendritic interactions between mitral and granule cells in the olfactory bulb. We review the models from a 50-year perspective on the work that has challenged, supported, and extended the original proposal that these interactions mediate both lateral inhibition and oscillatory activity, essential steps in the neural basis of olfactory processing and perception. We highlight strategies behind the neurophysiological experiments and the Rall methods that enhance the ability of detailed compartmental modeling to give counterintuitive predictions that lead to deeper insights into neural organization at the synaptic and circuit level. The application of these methods to mechanisms of neurogenesis and plasticity are exciting challenges for the future.
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Affiliation(s)
- Gordon M Shepherd
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Michael L Hines
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
| | | | - Charles A Greer
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut
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27
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Chong E, Moroni M, Wilson C, Shoham S, Panzeri S, Rinberg D. Manipulating synthetic optogenetic odors reveals the coding logic of olfactory perception. Science 2020; 368:368/6497/eaba2357. [PMID: 32554567 DOI: 10.1126/science.aba2357] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 05/01/2020] [Indexed: 12/26/2022]
Abstract
How does neural activity generate perception? Finding the combinations of spatial or temporal activity features (such as neuron identity or latency) that are consequential for perception remains challenging. We trained mice to recognize synthetic odors constructed from parametrically defined patterns of optogenetic activation, then measured perceptual changes during extensive and controlled perturbations across spatiotemporal dimensions. We modeled recognition as the matching of patterns to learned templates. The templates that best predicted recognition were sequences of spatially identified units, ordered by latencies relative to each other (with minimal effects of sniff). Within templates, individual units contributed additively, with larger contributions from earlier-activated units. Our synthetic approach reveals the fundamental logic of the olfactory code and provides a general framework for testing links between sensory activity and perception.
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Affiliation(s)
- Edmund Chong
- Neuroscience Institute, NYU Langone Health, New York, NY 10016, USA.
| | - Monica Moroni
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy. .,CIMeC, University of Trento, Rovereto, Italy
| | | | - Shy Shoham
- Neuroscience Institute, NYU Langone Health, New York, NY 10016, USA.,Center for Neural Science, New York University, New York, NY 10003, USA.,Tech4Health Institute, NYU Langone Health, New York, NY 10010, USA.,Department of Ophthalmology, NYU Langone Health, New York, NY 10017, USA
| | - Stefano Panzeri
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy
| | - Dmitry Rinberg
- Neuroscience Institute, NYU Langone Health, New York, NY 10016, USA. .,Center for Neural Science, New York University, New York, NY 10003, USA
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28
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Sprayberry JDH. Compounds without borders: A mechanism for quantifying complex odors and responses to scent-pollution in bumblebees. PLoS Comput Biol 2020; 16:e1007765. [PMID: 32320390 PMCID: PMC7197864 DOI: 10.1371/journal.pcbi.1007765] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 05/04/2020] [Accepted: 03/02/2020] [Indexed: 11/21/2022] Open
Abstract
Bumblebees are critical pollinators whose populations have been experiencing troubling declines over the past several decades. Successful foraging improves colony fitness, thus understanding how anthropogenic influences modulate foraging behavior may aid conservation efforts. Odor pollution can have negative impacts on bumble- and honey-bees foraging behavior. However, given the vast array of potential scent contaminants, individually testing pollutants is an ineffective approach. The ability to quantitatively measure how much scent-pollution of a floral-odor bumblebees can tolerate would represent a paradigm shift in odor-pollution studies. Current statistical methods for analyzing complex odors have poor predictive power because statistically-derived odor-spaces are rewritten when new odors are added. This study presents an alternative method of analyzing complex odor blends based on the encoding properties of insect olfactory systems. This “Compounds Without Borders” (CWB) method vectorizes odors in a multidimensional space representing relevant functional group and carbon characteristics of their component odorants. A single vector can be built for any scent, which allows the angular distance between any two odors to be calculated–including a learned odor and its polluted counterpart. Data presented here indicate that CWB-angles are capable of both describing and predicting bumblebee odor-discrimination behavior: odor pairs with angular distances in the 20–29° range appear to be generalized, while odor pairs over 30 degrees are differentiated. The neurophysiological properties underlying CWB-vectorization of odors are not unique to bumblebees; CWB-angle analysis of a small sample of published odor-data supports the idea that this method may have broader applications.
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Affiliation(s)
- Jordanna D H Sprayberry
- Departments of Biology & Neuroscience, Muhlenberg College, Allentown, Pennsylvania, United States of America
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29
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Freeman AR, Ophir AG, Sheehan MJ. The giant pouched rat (Cricetomys ansorgei) olfactory receptor repertoire. PLoS One 2020; 15:e0221981. [PMID: 32240170 PMCID: PMC7117715 DOI: 10.1371/journal.pone.0221981] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 03/06/2020] [Indexed: 12/12/2022] Open
Abstract
For rodents, olfaction is essential for locating food, recognizing mates and competitors, avoiding predators, and navigating their environment. It is thought that rodents may have expanded olfactory receptor repertoires in order to specialize in olfactory behavior. Despite being the largest clade of mammals and depending on olfaction relatively little work has documented olfactory repertoires outside of conventional laboratory species. Here we report the olfactory receptor repertoire of the African giant pouched rat (Cricetomys ansorgei), a Muroid rodent distantly related to mice and rats. The African giant pouched rat is notable for its large cortex and olfactory bulbs relative to its body size compared to other sympatric rodents, which suggests anatomical elaboration of olfactory capabilities. We hypothesized that in addition to anatomical elaboration for olfaction, these pouched rats might also have an expanded olfactory receptor repertoire to enable their olfactory behavior. We examined the composition of the olfactory receptor repertoire to better understand how their sensory capabilities have evolved. We identified 1145 intact olfactory genes, and 260 additional pseudogenes within 301 subfamilies from the African giant pouched rat genome. This repertoire is similar to mice and rats in terms of size, pseudogene percentage and number of subfamilies. Analyses of olfactory receptor gene trees revealed that the pouched rat has 6 expansions in different subfamilies compared to mice, rats and squirrels. We identified 81 orthologous genes conserved among 4 rodent species and an additional 147 conserved genes within the Muroid rodents. The orthologous genes shared within Muroidea suggests that there may be a conserved Muroid-specific olfactory receptor repertoire. We also note that the description of this repertoire can serve as a complement to other studies of rodent olfaction, as the pouched rat is an outgroup within Muroidea. Thus, our data suggest that African giant pouched rats are capable of both natural and trained olfactory behaviors with a typical Muriod olfactory receptor repertoire.
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Affiliation(s)
- Angela R. Freeman
- Department of Psychology, Cornell University, Ithaca, NY, United States of America
- * E-mail:
| | - Alexander G. Ophir
- Department of Psychology, Cornell University, Ithaca, NY, United States of America
| | - Michael J. Sheehan
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY, United States of America
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30
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Snitz K, Perl O, Honigstein D, Secundo L, Ravia A, Yablonka A, Endevelt-Shapira Y, Sobel N. SmellSpace: An Odor-Based Social Network as a Platform for Collecting Olfactory Perceptual Data. Chem Senses 2020; 44:267-278. [PMID: 30873534 PMCID: PMC6462760 DOI: 10.1093/chemse/bjz014] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
A common goal in olfaction research is modeling the link between odorant structure and odor perception. Such modeling efforts require large data sets on olfactory perception, yet only a few of these are publicly and freely available. Given that individual odor perception may be informative on personal makeup and interpersonal relationships, we hypothesized that people would gladly provide olfactory perceptual estimates in the context of an odor-based social network. We developed a web-based infrastructure for such a network we called SmellSpace and distributed 10 000 scratch-and-sniff registration booklets each containing a subset of 12 out of 35 microencapsulated odorants. Within ~100 days, we obtained data from ~1000 participants who rated the odorants along 13 verbal descriptors. To verify that these estimates are comparable to lab-collected estimates we tested 26 participants in a controlled lab setting using the same odorants and descriptors. We observed remarkably high overall group correlations between lab and SmellSpace data, implying that this method provides for credible group-representations of odorants. We further estimated the usability of the data by applying to it two previously published models that used odorant structure alone to predict either odorant pleasantness or pairwise odorant perceptual similarity. We observed statistically significant predictions in both cases, thus further implying that the current data may be helpful toward future efforts of modeling olfactory perception from structure. We conclude that an odor-based social network is a potentially useful instrument for collecting extensive data on olfactory perception and here post the complete raw data set from the first ~1000 participants.
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Affiliation(s)
- Kobi Snitz
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | - Ofer Perl
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | | | - Lavi Secundo
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | - Aharon Ravia
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | - Adi Yablonka
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
| | | | - Noam Sobel
- Department of Neurobiology, Weizmann Institute of Science, Rehovot, Israel
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31
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Perl O, Nahum N, Belelovsky K, Haddad R. The contribution of temporal coding to odor coding and odor perception in humans. eLife 2020; 9:49734. [PMID: 32031520 PMCID: PMC7007219 DOI: 10.7554/elife.49734] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 01/15/2020] [Indexed: 11/17/2022] Open
Abstract
Whether neurons encode information through their spike rates, their activity times or both is an ongoing debate in systems neuroscience. Here, we tested whether humans can discriminate between a pair of temporal odor mixtures (TOMs) composed of the same two components delivered in rapid succession in either one temporal order or its reverse. These TOMs presumably activate the same olfactory neurons but at different times and thus differ mainly in the time of neuron activation. We found that most participants could hardly discriminate between TOMs, although they easily discriminated between a TOM and one of its components. By contrast, participants succeeded in discriminating between the TOMs when they were notified of their successive nature in advance. We thus suggest that the time of glomerulus activation can be exploited to extract odor-related information, although it does not change the odor perception substantially, as should be expected from an odor code per se.
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Affiliation(s)
- Ofer Perl
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
| | - Nahum Nahum
- Department of Psychology, Bar-Ilan University, Ramat-Gan, Israel
| | - Katya Belelovsky
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
| | - Rafi Haddad
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
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32
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Young BD, Escalon JA, Mathew D. Odors: from chemical structures to gaseous plumes. Neurosci Biobehav Rev 2020; 111:19-29. [PMID: 31931034 DOI: 10.1016/j.neubiorev.2020.01.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 01/07/2020] [Accepted: 01/07/2020] [Indexed: 10/25/2022]
Abstract
We are immersed within an odorous sea of chemical currents that we parse into individual odors with complex structures. Odors have been posited as determined by the structural relation between the molecules that compose the chemical compounds and their interactions with the receptor site. But, naturally occurring smells are parsed from gaseous odor plumes. To give a comprehensive account of the nature of odors the chemosciences must account for these large distributed entities as well. We offer a focused review of what is known about the perception of odor plumes for olfactory navigation and tracking, which we then connect to what is known about the role odorants play as properties of the plume in determining odor identity with respect to odor quality. We end by motivating our central claim that more research needs to be conducted on the role that odorants play within the odor plume in determining odor identity.
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Affiliation(s)
- Benjamin D Young
- Philosophy and Neuroscience, University of Nevada, 1664 N Virginia St, Reno, NV 89557, United States.
| | | | - Dennis Mathew
- Biology and Neuroscience, University of Nevada, Reno, United States.
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33
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Bae J, Yi JY, Moon C. Odor quality profile is partially influenced by verbal cues. PLoS One 2019; 14:e0226385. [PMID: 31830119 PMCID: PMC6907808 DOI: 10.1371/journal.pone.0226385] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 11/25/2019] [Indexed: 11/19/2022] Open
Abstract
Characterizing an odor quality is difficult for humans. Ever-increasing physiological and behavioral studies have characterized odor quality and demonstrated high performance of human odor categorization. However, there are no precise methods for measuring the multidimensional axis of an odor quality. Furthermore, it can be altered by individual experience, even when using existing measurement methods for the multidimensional axis of odor such as odor profiling. It is, therefore, necessary to characterize patterns of odor quality with odor profiling and observe alterations in odor profiles under the influence of subjective rating conditions such as verbal cues. Considering the high performance of human odor categorization, we hypothesized that odor may have specific odor quality that is scarcely altered by verbal cues. We assessed odor responses to isovaleric acid with and without verbal cues and compared the results in each stimulation condition. We found that verbal cues influenced the rating of odor quality descriptors. Verbal cues weakly influenced the odor quality descriptors of high-rated value (upper 25%) compared to odor quality descriptors of low-rated value (lower 75%) by the survey test. Even under different verbal cue conditions, the same odor was classified in the same class when using high-rated odor quality descriptors. Our study suggests that people extract essential odor quality descriptors that represent the odor itself in order to efficiently quantify odor quality.
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Affiliation(s)
- Jisub Bae
- Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea
| | - Ju-Yeon Yi
- Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea
| | - Cheil Moon
- Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea
- Convergence Research Advanced Centre for Olfaction, Daegu Gyeongbuk Institute of Science and Technology, Daegu, Republic of Korea
- * E-mail:
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34
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An argument for hyperbolic geometry in neural circuits. Curr Opin Neurobiol 2019; 58:101-104. [PMID: 31476550 DOI: 10.1016/j.conb.2019.07.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 07/25/2019] [Indexed: 11/22/2022]
Abstract
This review connects several lines of research to argue that hyperbolic geometry should be broadly applicable to neural circuits as well as other biological circuits. The reason for this is that networks that conform to hyperbolic geometry are maximally responsive to external and internal perturbations. These networks also allow for efficient communication under conditions where nodes are added or removed. We will argue that one of the signatures of hyperbolic geometry is the celebrated Zipf's law (also sometimes known as the Pareto distribution) that states that the probability to observe a given pattern is inversely related to its rank. Zipf's law is observed in a variety of biological systems - from protein sequences, neural networks to economics. These observations provide further evidence for the ubiquity of networks with an underlying hyperbolic metric structure. Recent studies in neuroscience specifically point to the relevance of a three-dimensional hyperbolic space for neural signaling. The three-dimensional hyperbolic space may confer additional robustness compared to other dimensions. We illustrate how the use of hyperbolic coordinates revealed a novel topographic organization within the olfactory system. The use of such coordinates may facilitate representation of relevant signals elsewhere in the brain.
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35
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Wakayama H, Sakasai M, Yoshikawa K, Inoue M. Method for Predicting Odor Intensity of Perfumery Raw Materials Using Dose–Response Curve Database. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b01225] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Hideki Wakayama
- Sensory Science Research, Global R&D, Kao Corporation, 2-1-3 Bunka Sumida-ku, Tokyo 131-8501, Japan
| | - Mitsuyoshi Sakasai
- Sensory Science Research, Global R&D, Kao Corporation, 2-1-3 Bunka Sumida-ku, Tokyo 131-8501, Japan
| | - Keiichi Yoshikawa
- Sensory Science Research, Global R&D, Kao Corporation, 2606 Haga-Gun, Tochigi 321-3497, Japan
| | - Michiaki Inoue
- Sensory Science Research, Global R&D, Kao Corporation, 2-1-3 Bunka Sumida-ku, Tokyo 131-8501, Japan
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36
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Licon CC, Bosc G, Sabri M, Mantel M, Fournel A, Bushdid C, Golebiowski J, Robardet C, Plantevit M, Kaytoue M, Bensafi M. Chemical features mining provides new descriptive structure-odor relationships. PLoS Comput Biol 2019; 15:e1006945. [PMID: 31022180 PMCID: PMC6504111 DOI: 10.1371/journal.pcbi.1006945] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 05/07/2019] [Accepted: 03/11/2019] [Indexed: 12/30/2022] Open
Abstract
An important goal in researching the biology of olfaction is to link the perception of smells to the chemistry of odorants. In other words, why do some odorants smell like fruits and others like flowers? While the so-called stimulus-percept issue was resolved in the field of color vision some time ago, the relationship between the chemistry and psycho-biology of odors remains unclear up to the present day. Although a series of investigations have demonstrated that this relationship exists, the descriptive and explicative aspects of the proposed models that are currently in use require greater sophistication. One reason for this is that the algorithms of current models do not consistently consider the possibility that multiple chemical rules can describe a single quality despite the fact that this is the case in reality, whereby two very different molecules can evoke a similar odor. Moreover, the available datasets are often large and heterogeneous, thus rendering the generation of multiple rules without any use of a computational approach overly complex. We considered these two issues in the present paper. First, we built a new database containing 1689 odorants characterized by physicochemical properties and olfactory qualities. Second, we developed a computational method based on a subgroup discovery algorithm that discriminated perceptual qualities of smells on the basis of physicochemical properties. Third, we ran a series of experiments on 74 distinct olfactory qualities and showed that the generation and validation of rules linking chemistry to odor perception was possible. Taken together, our findings provide significant new insights into the relationship between stimulus and percept in olfaction. In addition, by automatically extracting new knowledge linking chemistry of odorants and psychology of smells, our results provide a new computational framework of analysis enabling scientists in the field to test original hypotheses using descriptive or predictive modeling. An important issue in olfaction sciences deals with the question of how a chemical information can be translated into percepts. This is known as the stimulus-percept problem. Here, we set out to better understand this issue by combining knowledge about the chemistry and cognition of smells with computational olfaction. We also assumed that not only one, but several physicochemical models may describe a given olfactory quality. To achieve this aim, a first challenge was to set up a database with ~1700 molecules characterized by chemical features and described by olfactory qualities (e.g. fruity, woody). A second challenge consisted in developing a computational model enabling the discrimination of olfactory qualities based on these chemical features. By meeting these 2 challenges, we provided for several olfactory qualities new chemical models describing why an odorant molecule smells fruity or woody (among others). For most qualities, multiple (rather than a single) chemical models were generated. These findings provide new elements of knowledge about the relationship between odorant chemistry and perception. They also make it possible to envisage concrete applications in the aroma and fragrance field where chemical characterization of smells is an important step in the design of new products.
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Affiliation(s)
- Carmen C. Licon
- Lyon Neuroscience Research Center, University Lyon, CNRS UMR5292, France
- Food Science and Nutrition Department, California State University, Fresno, California, United States of America
| | - Guillaume Bosc
- INSA Lyon, CNRS, LIRIS UMR5205, France
- Infologic, Bourg-lès-Valence, France
| | - Mohammed Sabri
- Lyon Neuroscience Research Center, University Lyon, CNRS UMR5292, France
- Ecole Nationale Polytechnique d’Oran—Maurice Audin, Département de Mathématiques et Informatique, Oran, Algérie
| | - Marylou Mantel
- Lyon Neuroscience Research Center, University Lyon, CNRS UMR5292, France
| | - Arnaud Fournel
- Lyon Neuroscience Research Center, University Lyon, CNRS UMR5292, France
| | - Caroline Bushdid
- Institute of Chemistry of Nice, UMR CNRS 7272, Université Côte d’Azur, Nice, France
| | - Jerome Golebiowski
- Institute of Chemistry of Nice, UMR CNRS 7272, Université Côte d’Azur, Nice, France
- Department of Brain & Cognitive Sciences, DGIST, Daegu, Republic of Korea
| | | | | | - Mehdi Kaytoue
- INSA Lyon, CNRS, LIRIS UMR5205, France
- Infologic, Bourg-lès-Valence, France
| | - Moustafa Bensafi
- Lyon Neuroscience Research Center, University Lyon, CNRS UMR5292, France
- * E-mail:
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37
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Teşileanu T, Cocco S, Monasson R, Balasubramanian V. Adaptation of olfactory receptor abundances for efficient coding. eLife 2019; 8:39279. [PMID: 30806351 PMCID: PMC6398974 DOI: 10.7554/elife.39279] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Accepted: 02/13/2019] [Indexed: 01/27/2023] Open
Abstract
Olfactory receptor usage is highly heterogeneous, with some receptor types being orders of magnitude more abundant than others. We propose an explanation for this striking fact: the receptor distribution is tuned to maximally represent information about the olfactory environment in a regime of efficient coding that is sensitive to the global context of correlated sensor responses. This model predicts that in mammals, where olfactory sensory neurons are replaced regularly, receptor abundances should continuously adapt to odor statistics. Experimentally, increased exposure to odorants leads variously, but reproducibly, to increased, decreased, or unchanged abundances of different activated receptors. We demonstrate that this diversity of effects is required for efficient coding when sensors are broadly correlated, and provide an algorithm for predicting which olfactory receptors should increase or decrease in abundance following specific environmental changes. Finally, we give simple dynamical rules for neural birth and death processes that might underlie this adaptation.
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Affiliation(s)
- Tiberiu Teşileanu
- Center for Computational BiologyFlatiron InstituteNew YorkUnited States,Initiative for the Theoretical Sciences, The Graduate CenterCity University of New YorkNew YorkUnited States,David Rittenhouse LaboratoriesUniversity of PennsylvaniaPhiladelphiaUnited States
| | - Simona Cocco
- Laboratoire de Physique StatistiqueÉcole Normale Supérieure and CNRS UMR 8550, PSL Research, UPMC Sorbonne UniversitéParisFrance
| | - Rémi Monasson
- Laboratoire de Physique ThéoriqueÉcole Normale Supérieure and CNRS UMR 8550, PSL Research, UPMC Sorbonne UniversitéParisFrance
| | - Vijay Balasubramanian
- Initiative for the Theoretical Sciences, The Graduate CenterCity University of New YorkNew YorkUnited States,David Rittenhouse LaboratoriesUniversity of PennsylvaniaPhiladelphiaUnited States
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38
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Li H, Panwar B, Omenn GS, Guan Y. Accurate prediction of personalized olfactory perception from large-scale chemoinformatic features. Gigascience 2018; 7:4750780. [PMID: 29267859 PMCID: PMC5824779 DOI: 10.1093/gigascience/gix127] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2017] [Accepted: 12/07/2017] [Indexed: 12/19/2022] Open
Abstract
Background The olfactory stimulus-percept problem has been studied for more than a century, yet it is still hard to precisely predict the odor given the large-scale chemoinformatic features of an odorant molecule. A major challenge is that the perceived qualities vary greatly among individuals due to different genetic and cultural backgrounds. Moreover, the combinatorial interactions between multiple odorant receptors and diverse molecules significantly complicate the olfaction prediction. Many attempts have been made to establish structure-odor relationships for intensity and pleasantness, but no models are available to predict the personalized multi-odor attributes of molecules. In this study, we describe our winning algorithm for predicting individual and population perceptual responses to various odorants in the DREAM Olfaction Prediction Challenge. Results We find that random forest model consisting of multiple decision trees is well suited to this prediction problem, given the large feature spaces and high variability of perceptual ratings among individuals. Integrating both population and individual perceptions into our model effectively reduces the influence of noise and outliers. By analyzing the importance of each chemical feature, we find that a small set of low- and nondegenerative features is sufficient for accurate prediction. Conclusions Our random forest model successfully predicts personalized odor attributes of structurally diverse molecules. This model together with the top discriminative features has the potential to extend our understanding of olfactory perception mechanisms and provide an alternative for rational odorant design.
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Affiliation(s)
- Hongyang Li
- Department of Computational Medicine and Bioinformatics and Departments of Internal Medicine and Human Genetics and School of Public Health, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Bharat Panwar
- Department of Computational Medicine and Bioinformatics and Departments of Internal Medicine and Human Genetics and School of Public Health, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics and Departments of Internal Medicine and Human Genetics and School of Public Health, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA.,Departments of Internal Medicine and Human Genetics and School of Public Health, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics and Departments of Internal Medicine and Human Genetics and School of Public Health, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI 48109, USA
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39
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Iatropoulos G, Herman P, Lansner A, Karlgren J, Larsson M, Olofsson JK. The language of smell: Connecting linguistic and psychophysical properties of odor descriptors. Cognition 2018; 178:37-49. [DOI: 10.1016/j.cognition.2018.05.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2017] [Revised: 04/12/2018] [Accepted: 05/07/2018] [Indexed: 10/16/2022]
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40
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Zhou Y, Smith BH, Sharpee TO. Hyperbolic geometry of the olfactory space. SCIENCE ADVANCES 2018; 4:eaaq1458. [PMID: 30167457 PMCID: PMC6114987 DOI: 10.1126/sciadv.aaq1458] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Accepted: 07/19/2018] [Indexed: 05/20/2023]
Abstract
In the natural environment, the sense of smell, or olfaction, serves to detect toxins and judge nutritional content by taking advantage of the associations between compounds as they are created in biochemical reactions. This suggests that the nervous system can classify odors based on statistics of their co-occurrence within natural mixtures rather than from the chemical structures of the ligands themselves. We show that this statistical perspective makes it possible to map odors to points in a hyperbolic space. Hyperbolic coordinates have a long but often underappreciated history of relevance to biology. For example, these coordinates approximate the distance between species computed along dendrograms and, more generally, between points within hierarchical tree-like networks. We find that both natural odors and human perceptual descriptions of smells can be described using a three-dimensional hyperbolic space. This match in geometries can avoid distortions that would otherwise arise when mapping odors to perception.
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Affiliation(s)
- Yuansheng Zhou
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Brian H. Smith
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Tatyana O. Sharpee
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA
- Corresponding author.
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41
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Tromelin A, Chabanet C, Audouze K, Koensgen F, Guichard E. Multivariate statistical analysis of a large odorants database aimed at revealing similarities and links between odorants and odors. FLAVOUR FRAG J 2017. [DOI: 10.1002/ffj.3430] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Anne Tromelin
- UMR CSGA: CNRS, INRA; Université de Bourgogne Franche-Comté; 21000 Dijon France
| | - Claire Chabanet
- UMR CSGA: CNRS, INRA; Université de Bourgogne Franche-Comté; 21000 Dijon France
| | - Karine Audouze
- MTi, Sorbonne Paris Cité; Université Paris Diderot; INSERM UMR-S 973 75013 Paris France
| | - Florian Koensgen
- UMR CSGA: CNRS, INRA; Université de Bourgogne Franche-Comté; 21000 Dijon France
| | - Elisabeth Guichard
- UMR CSGA: CNRS, INRA; Université de Bourgogne Franche-Comté; 21000 Dijon France
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42
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Keller A, Gerkin RC, Guan Y, Dhurandhar A, Turu G, Szalai B, Mainland JD, Ihara Y, Yu CW, Wolfinger R, Vens C, Schietgat L, De Grave K, Norel R, Stolovitzky G, Cecchi GA, Vosshall LB, Meyer P. Predicting human olfactory perception from chemical features of odor molecules. Science 2017; 355:820-826. [PMID: 28219971 DOI: 10.1126/science.aal2014] [Citation(s) in RCA: 126] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Accepted: 01/27/2017] [Indexed: 01/02/2023]
Abstract
It is still not possible to predict whether a given molecule will have a perceived odor or what olfactory percept it will produce. We therefore organized the crowd-sourced DREAM Olfaction Prediction Challenge. Using a large olfactory psychophysical data set, teams developed machine-learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models accurately predicted odor intensity and pleasantness and also successfully predicted 8 among 19 rated semantic descriptors ("garlic," "fish," "sweet," "fruit," "burnt," "spices," "flower," and "sour"). Regularized linear models performed nearly as well as random forest-based ones, with a predictive accuracy that closely approaches a key theoretical limit. These models help to predict the perceptual qualities of virtually any molecule with high accuracy and also reverse-engineer the smell of a molecule.
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Affiliation(s)
- Andreas Keller
- Laboratory of Neurogenetics and Behavior, The Rockefeller University, New York, NY 10065, USA
| | - Richard C Gerkin
- School of Life Sciences, Arizona State University, Tempe, AZ 85281, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Amit Dhurandhar
- Thomas J. Watson Computational Biology Center, IBM, Yorktown Heights, NY 10598, USA
| | - Gabor Turu
- Department of Physiology, Faculty of Medicine, Semmelweis University, 1085 Budapest, Hungary.,Laboratory of Molecular Physiology, Hungarian Academy of Science, Semmelweis University (MTA-SE), 1085 Budapest, Hungary
| | - Bence Szalai
- Department of Physiology, Faculty of Medicine, Semmelweis University, 1085 Budapest, Hungary.,Laboratory of Molecular Physiology, Hungarian Academy of Science, Semmelweis University (MTA-SE), 1085 Budapest, Hungary
| | - Joel D Mainland
- Monell Chemical Senses Center, Philadelphia, PA 19104, USA.,Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yusuke Ihara
- Monell Chemical Senses Center, Philadelphia, PA 19104, USA.,Institution for Innovation, Ajinomoto Co., Inc., Kawasaki, Kanagawa 210-8681, Japan
| | - Chung Wen Yu
- Monell Chemical Senses Center, Philadelphia, PA 19104, USA
| | | | - Celine Vens
- Department of Public Health and Primary Care, KU Leuven, Kulak, 8500 Kortrijk, Belgium
| | | | - Kurt De Grave
- Department of Computer Science, KU Leuven, 3001 Leuven, Belgium.,Flanders Make, 3920 Lommel, Belgium
| | - Raquel Norel
- Thomas J. Watson Computational Biology Center, IBM, Yorktown Heights, NY 10598, USA
| | | | - Gustavo Stolovitzky
- Thomas J. Watson Computational Biology Center, IBM, Yorktown Heights, NY 10598, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Guillermo A Cecchi
- Thomas J. Watson Computational Biology Center, IBM, Yorktown Heights, NY 10598, USA
| | - Leslie B Vosshall
- Laboratory of Neurogenetics and Behavior, The Rockefeller University, New York, NY 10065, USA.,Howard Hughes Medical Institute, New York, NY 10065, USA
| | - Pablo Meyer
- Thomas J. Watson Computational Biology Center, IBM, Yorktown Heights, NY 10598, USA. .,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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43
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Soh Z, Nishikawa S, Kurita Y, Takiguchi N, Tsuji T. A Mathematical Model of the Olfactory Bulb for the Selective Adaptation Mechanism in the Rodent Olfactory System. PLoS One 2016; 11:e0165230. [PMID: 27992433 PMCID: PMC5167254 DOI: 10.1371/journal.pone.0165230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Accepted: 10/07/2016] [Indexed: 11/19/2022] Open
Abstract
To predict the odor quality of an odorant mixture, the interaction between odorants must be taken into account. Previously, an experiment in which mice discriminated between odorant mixtures identified a selective adaptation mechanism in the olfactory system. This paper proposes an olfactory model for odorant mixtures that can account for selective adaptation in terms of neural activity. The proposed model uses the spatial activity pattern of the mitral layer obtained from model simulations to predict the perceptual similarity between odors. Measured glomerular activity patterns are used as input to the model. The neural interaction between mitral cells and granular cells is then simulated, and a dissimilarity index between odors is defined using the activity patterns of the mitral layer. An odor set composed of three odorants is used to test the ability of the model. Simulations are performed based on the odor discrimination experiment on mice. As a result, we observe that part of the neural activity in the glomerular layer is enhanced in the mitral layer, whereas another part is suppressed. We find that the dissimilarity index strongly correlates with the odor discrimination rate of mice: r = 0.88 (p = 0.019). We conclude that our model has the ability to predict the perceptual similarity of odorant mixtures. In addition, the model also accounts for selective adaptation via the odor discrimination rate, and the enhancement and inhibition in the mitral layer may be related to this selective adaptation.
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Affiliation(s)
- Zu Soh
- Department of System Cybernetics, Institute of Engineering, Hiroshima University, Higashi-Hiroshima, Japan
- * E-mail: (ZS); (TT)
| | - Shinya Nishikawa
- Department of System Cybernetics, Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima, Japan
| | - Yuichi Kurita
- Department of System Cybernetics, Institute of Engineering, Hiroshima University, Higashi-Hiroshima, Japan
| | - Noboru Takiguchi
- Division of Material Sciences, Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan
| | - Toshio Tsuji
- Department of System Cybernetics, Institute of Engineering, Hiroshima University, Higashi-Hiroshima, Japan
- * E-mail: (ZS); (TT)
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44
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Keller A, Vosshall LB. Olfactory perception of chemically diverse molecules. BMC Neurosci 2016; 17:55. [PMID: 27502425 PMCID: PMC4977894 DOI: 10.1186/s12868-016-0287-2] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2016] [Accepted: 07/08/2016] [Indexed: 11/10/2022] Open
Abstract
Background Understanding the relationship between a stimulus and how it is perceived reveals fundamental principles about the mechanisms of sensory perception. While this stimulus-percept problem is mostly understood for color vision and tone perception, it is not currently possible to predict how a given molecule smells. While there has been some progress in predicting the pleasantness and intensity of an odorant, perceptual data for a larger number of diverse molecules are needed to improve current predictions. Towards this goal, we tested the olfactory perception of 480 structurally and perceptually diverse molecules at two concentrations using a panel of 55 healthy human subjects. Results For each stimulus, we collected data on perceived intensity, pleasantness, and familiarity. In addition, subjects were asked to apply 20 semantic odor quality descriptors to these stimuli, and were offered the option to describe the smell in their own words. Using this dataset, we replicated several previous correlations between molecular features of the stimulus and olfactory perception. The number of sulfur atoms in a molecule was correlated with the odor quality descriptors “garlic,” “fish,” and “decayed,” and large and structurally complex molecules were perceived to be more pleasant. We discovered a number of correlations in intensity perception between molecules. We show that familiarity had a strong effect on the ability of subjects to describe a smell. Many subjects used commercial products to describe familiar odorants, highlighting the role of prior experience in verbal reports of olfactory perception. Nonspecific descriptors like “chemical” were applied frequently to unfamiliar odorants, and unfamiliar odorants were generally rated as neither pleasant nor unpleasant. Conclusions We present a very large psychophysical dataset and use this to correlate molecular features of a stimulus to olfactory percept. Our work reveals robust correlations between molecular features and perceptual qualities, and highlights the dominant role of familiarity and experience in assigning verbal descriptors to odorants. Electronic supplementary material The online version of this article (doi:10.1186/s12868-016-0287-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Andreas Keller
- Laboratory of Neurogenetics and Behavior, The Rockefeller University, 1230 York Avenue, Box 63, New York, NY, 10065, USA
| | - Leslie B Vosshall
- Laboratory of Neurogenetics and Behavior, The Rockefeller University, 1230 York Avenue, Box 63, New York, NY, 10065, USA. .,Howard Hughes Medical Institute, New York, USA. .,Kavli Neural Systems Institute, The Rockefeller University, New York, USA.
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45
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Odorant receptors of Drosophila are sensitive to the molecular volume of odorants. Sci Rep 2016; 6:25103. [PMID: 27112241 PMCID: PMC4844992 DOI: 10.1038/srep25103] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2015] [Accepted: 04/08/2016] [Indexed: 01/08/2023] Open
Abstract
Which properties of a molecule define its odor? This is a basic yet unanswered question regarding the olfactory system. The olfactory system of Drosophila has a repertoire of approximately 60 odorant receptors. Molecules bind to odorant receptors with different affinities and activate them with different efficacies, thus providing a combinatorial code that identifies odorants. We hypothesized that the binding affinity of an odorant-receptor pair is affected by their relative sizes. The maximum affinity can be attained when the molecular volume of an odorant matches the volume of the binding pocket. The affinity drops to zero when the sizes are too different, thus obscuring the effects of other molecular properties. We developed a mathematical formulation of this hypothesis and verified it using Drosophila data. We also predicted the volume and structural flexibility of the binding site of each odorant receptor; these features significantly differ between odorant receptors. The differences in the volumes and structural flexibilities of different odorant receptor binding sites may explain the difference in the scents of similar molecules with different sizes.
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46
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Poivet E, Peterlin Z, Tahirova N, Xu L, Altomare C, Paria A, Zou DJ, Firestein S. Applying medicinal chemistry strategies to understand odorant discrimination. Nat Commun 2016; 7:11157. [PMID: 27040654 PMCID: PMC4822015 DOI: 10.1038/ncomms11157] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2015] [Accepted: 02/25/2016] [Indexed: 11/09/2022] Open
Abstract
Associating an odorant's chemical structure with its percept is a long-standing challenge. One hindrance may come from the adoption of the organic chemistry scheme of molecular description and classification. Chemists classify molecules according to characteristics that are useful in synthesis or isolation, but which may be of little importance to a biological sensory system. Accordingly, we look to medicinal chemistry, which emphasizes biological function over chemical form, in an attempt to discern which among the many molecular features are most important for odour discrimination. Here we use medicinal chemistry concepts to assemble a panel of molecules to test how heteroaromatic ring substitution of the benzene ring will change the odour percept of acetophenone. This work allows us to describe an extensive rule in odorant detection by mammalian olfactory receptors. Whereas organic chemistry would have predicted the ring size and composition to be key features, our work reveals that the topological polar surface area is the key feature for the discrimination of these odorants. Understanding the basis of odour perception and discrimination is a challenging task, due to the inherent complexity of the olfactory system. Here, the authors use a medicinal chemistry approach to derive biologically relevant rules for odorant classification.
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Affiliation(s)
- Erwan Poivet
- Department of Biological Sciences, Columbia University, New York, New York 10027, USA
| | - Zita Peterlin
- Corporate Research and Development, Firmenich Incorporated, Plainsboro, New Jersey 08536, USA
| | - Narmin Tahirova
- Department of Biological Sciences, Columbia University, New York, New York 10027, USA
| | - Lu Xu
- Department of Biological Sciences, Columbia University, New York, New York 10027, USA
| | - Clara Altomare
- Department of Biological Sciences, Columbia University, New York, New York 10027, USA
| | - Anne Paria
- Department of Biological Sciences, Columbia University, New York, New York 10027, USA
| | - Dong-Jing Zou
- Department of Biological Sciences, Columbia University, New York, New York 10027, USA
| | - Stuart Firestein
- Department of Biological Sciences, Columbia University, New York, New York 10027, USA
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Fournel A, Ferdenzi C, Sezille C, Rouby C, Bensafi M. Multidimensional representation of odors in the human olfactory cortex. Hum Brain Mapp 2016; 37:2161-72. [PMID: 26991044 DOI: 10.1002/hbm.23164] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Revised: 02/09/2016] [Accepted: 02/18/2016] [Indexed: 11/09/2022] Open
Abstract
What is known as an odor object is an integrated representation constructed from physical features, and perceptual attributes mainly mediated by the olfactory and trigeminal systems. The aim of the present study was to comprehend how this multidimensional representation is organized, by deciphering how similarities in the physical, olfactory and trigeminal perceptual spaces of odors are represented in the human brain. To achieve this aim, we combined psychophysics, functional MRI and multivariate representational similarity analysis. Participants were asked to smell odors diffused by an fMRI-compatible olfactometer and to rate each smell along olfactory dimensions (pleasantness, intensity, familiarity and edibility) and trigeminal dimensions (irritation, coolness, warmth and pain). An event-related design was implemented, presenting different odorants. Results revealed that (i) pairwise odorant similarities in anterior piriform cortex (PC) activity correlated with pairwise odorant similarities in chemical properties (P < 0.005), (ii) similarities in posterior PC activity correlated with similarities in olfactory perceptual properties (P <0.01), and (iii) similarities in amygdala activity correlated with similarities in trigeminal perceptual properties (P < 0.01). These findings provide new evidence that extraction of physical, olfactory and trigeminal features is based on specific fine processing of similarities between odorous stimuli in a distributed manner in the olfactory system. Hum Brain Mapp 37:2161-2172, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- A Fournel
- Lyon Neuroscience Research Center, CNRS UMR5292, INSERM U1028, University Lyon, F-69000, France
| | - C Ferdenzi
- Lyon Neuroscience Research Center, CNRS UMR5292, INSERM U1028, University Lyon, F-69000, France
| | - C Sezille
- Lyon Neuroscience Research Center, CNRS UMR5292, INSERM U1028, University Lyon, F-69000, France
| | - C Rouby
- Lyon Neuroscience Research Center, CNRS UMR5292, INSERM U1028, University Lyon, F-69000, France
| | - M Bensafi
- Lyon Neuroscience Research Center, CNRS UMR5292, INSERM U1028, University Lyon, F-69000, France
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48
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Kumar R, Kaur R, Auffarth B, Bhondekar AP. Understanding the Odour Spaces: A Step towards Solving Olfactory Stimulus-Percept Problem. PLoS One 2015; 10:e0141263. [PMID: 26484763 PMCID: PMC4615634 DOI: 10.1371/journal.pone.0141263] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2015] [Accepted: 10/05/2015] [Indexed: 11/23/2022] Open
Abstract
Odours are highly complex, relying on hundreds of receptors, and people are known to disagree in their linguistic descriptions of smells. It is partly due to these facts that, it is very hard to map the domain of odour molecules or their structure to that of perceptual representations, a problem that has been referred to as the Structure-Odour-Relationship. We collected a number of diverse open domain databases of odour molecules having unorganised perceptual descriptors, and developed a graphical method to find the similarity between perceptual descriptors; which is intuitive and can be used to identify perceptual classes. We then separately projected the physico-chemical and perceptual features of these molecules in a non-linear dimension and clustered the similar molecules. We found a significant overlap between the spatial positioning of the clustered molecules in the physico-chemical and perceptual spaces. We also developed a statistical method of predicting the perceptual qualities of a novel molecule using its physico-chemical properties with high receiver operating characteristics(ROC).
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Affiliation(s)
- Ritesh Kumar
- CSIR-Central Scientific Instruments Organisation, Chandigarh, India
- Academy of Scientific and Innovative Research, New Delhi, India
- * E-mail:
| | - Rishemjit Kaur
- CSIR-Central Scientific Instruments Organisation, Chandigarh, India
- Academy of Scientific and Innovative Research, New Delhi, India
- Nagoya University, Nagoya, Japan
| | - Benjamin Auffarth
- Neuroinformatik, Department of Neurobiology, Freie Universität Berlin, Berlin, Germany
| | - Amol P. Bhondekar
- CSIR-Central Scientific Instruments Organisation, Chandigarh, India
- Academy of Scientific and Innovative Research, New Delhi, India
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49
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Use of a modified vector model for odor intensity prediction of odorant mixtures. SENSORS 2015; 15:5697-709. [PMID: 25760055 PMCID: PMC4435142 DOI: 10.3390/s150305697] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2015] [Revised: 02/16/2015] [Accepted: 02/17/2015] [Indexed: 01/25/2023]
Abstract
Odor intensity (OI) indicates the perceived intensity of an odor by the human nose, and it is usually rated by specialized assessors. In order to avoid restrictions on assessor participation in OI evaluations, the Vector Model which calculates the OI of a mixture as the vector sum of its unmixed components’ odor intensities was modified. Based on a detected linear relation between the OI and the logarithm of odor activity value (OAV—a ratio between chemical concentration and odor threshold) of individual odorants, OI of the unmixed component was replaced with its corresponding logarithm of OAV. The interaction coefficient (cosα) which represented the degree of interaction between two constituents was also measured in a simplified way. Through a series of odor intensity matching tests for binary, ternary and quaternary odor mixtures, the modified Vector Model provided an effective way of relating the OI of an odor mixture with the lnOAV values of its constituents. Thus, OI of an odor mixture could be directly predicted by employing the modified Vector Model after usual quantitative analysis. Besides, it was considered that the modified Vector Model was applicable for odor mixtures which consisted of odorants with the same chemical functional groups and similar molecular structures.
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50
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Perez M, Giurfa M, d'Ettorre P. The scent of mixtures: rules of odour processing in ants. Sci Rep 2015; 5:8659. [PMID: 25726692 PMCID: PMC4345350 DOI: 10.1038/srep08659] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2014] [Accepted: 01/29/2015] [Indexed: 11/08/2022] Open
Abstract
Natural odours are complex blends of numerous components. Understanding how animals perceive odour mixtures is central to multiple disciplines. Here we focused on carpenter ants, which rely on odours in various behavioural contexts. We studied overshadowing, a phenomenon that occurs when animals having learnt a binary mixture respond less to one component than to the other, and less than when this component was learnt alone. Ants were trained individually with alcohols and aldehydes varying in carbon-chain length, either as single odours or binary mixtures. They were then tested with the mixture and the components. Overshadowing resulted from the interaction between chain length and functional group: alcohols overshadowed aldehydes, and longer chain lengths overshadowed shorter ones; yet, combinations of these factors could cancel each other and suppress overshadowing. Our results show how ants treat binary olfactory mixtures and set the basis for predictive analyses of odour perception in insects.
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Affiliation(s)
- Margot Perez
- Research Center on Animal Cognition; University of Toulouse; UPS; 118 route de Narbonne, F-31062 Toulouse Cedex 9, France
- Research Center on Animal Cognition; CNRS; 118 route de Narbonne, F-31062 Toulouse Cedex 9, France
- Laboratory of Experimental and Comparative Ethology, University Paris 13, Sorbonne Paris Cité, Villetaneuse, France
| | - Martin Giurfa
- Research Center on Animal Cognition; University of Toulouse; UPS; 118 route de Narbonne, F-31062 Toulouse Cedex 9, France
- Research Center on Animal Cognition; CNRS; 118 route de Narbonne, F-31062 Toulouse Cedex 9, France
| | - Patrizia d'Ettorre
- Laboratory of Experimental and Comparative Ethology, University Paris 13, Sorbonne Paris Cité, Villetaneuse, France
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