@article{e7c7370c19074d6cba7a8cf550923a10,
title = "Deconstructing the mouse olfactory percept through an ethological atlas",
abstract = "Odor perception in non-humans is poorly understood. Here, we generated the most comprehensive mouse olfactory ethological atlas to date, consisting of behavioral responses to a diverse panel of 73 odorants, including 12 at multiple concentrations. These data revealed that mouse behavior is incredibly diverse and changes in response to odorant identity and concentration. Using only behavioral responses observed in other mice, we could predict which of two odorants was presented to a held-out mouse 82% of the time. Considering all 73 possible odorants, we could uniquely identify the target odorant from behavior on the first try 20% of the time and 46% within five attempts. Although mouse behavior is difficult to predict from human perception, they share three fundamental properties: first, odor valence parameters explained the highest variance of olfactory perception. Second, physicochemical properties of odorants can be used to predict the olfactory percept. Third, odorant concentration quantitatively and qualitatively impacts olfactory perception. These results increase our understanding of mouse olfactory behavior and how it compares to human odor perception and provide a template for future comparative studies of olfactory percepts among species.",
keywords = "behavior, chemoinformatics, machine learning, odor, olfactory, perception",
author = "Diogo Manoel and Melanie Makhlouf and Arayata, {Charles J.} and Abbirami Sathappan and Sahar Da'as and Doua Abdelrahman and Senthil Selvaraj and Reem Hasnah and Mainland, {Joel D.} and Gerkin, {Richard C.} and Saraiva, {Luis R.}",
note = "Funding Information: We would like to thank Dr. Darren W. Logan and the members of the Saraiva Lab for the constructive feedback and Alex Williams for the ridge-regularized CCA code. This work was supported by Sidra Medicine (SDR400077), a member of Qatar Foundation, and the National Institute of Health (U19NS112953 and R01DC018455). D.M. participated in the design of the project, analyzed data, and wrote the initial version of the manuscript. M.M. C.J.A. R.H. A.S. S.D. D.A. and S.S. analyzed data. J.D.M. analyzed data and wrote the final version of the manuscript. R.C.G. designed the data analysis methodology, analyzed data, and wrote the final version of the manuscript. L.R.S. analyzed data, conceived and supervised the project, and wrote the final version of the manuscript. J.D.M. receives research funding from Google Research and Procter & Gamble and was on the scientific advisory board of Aromyx and received compensation for these activities. R.C.G. receives research funding from Google Research and The Taylor Corporation and is on the advisory board of Climax Foods. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. All other authors declare that they have no competing interests. Funding Information: We would like to thank Dr. Darren W. Logan and the members of the Saraiva Lab for the constructive feedback and Alex Williams for the ridge-regularized CCA code. This work was supported by Sidra Medicine ( SDR400077 ), a member of Qatar Foundation , and the National Institute of Health ( U19NS112953 and R01DC018455 ). Funding Information: J.D.M. receives research funding from Google Research and Procter & Gamble and was on the scientific advisory board of Aromyx and received compensation for these activities. R.C.G. receives research funding from Google Research and The Taylor Corporation and is on the advisory board of Climax Foods. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. All other authors declare that they have no competing interests. Publisher Copyright: {\textcopyright} 2021 The Author(s)",
year = "2021",
month = jul,
day = "12",
doi = "10.1016/j.cub.2021.04.020",
language = "English (US)",
volume = "31",
pages = "2809--2818.e3",
journal = "Current Biology",
issn = "0960-9822",
publisher = "Cell Press",
number = "13",
}