Robust odor identification in novel olfactory environments in mice

Yan Li, Mitchell Swerdloff, Tianyu She, Asiyah Rahman, Naveen Sharma, Reema Shah, Michael Castellano, Daniel Mogel, Jason Wu, Asim Ahmed, James San Miguel, Jared Cohn, Nikesh Shah, Raddy L. Ramos and Gonzalo H. Otazu ()
Additional contact information
Yan Li: College of Osteopathic Medicine, Northern Boulevard
Mitchell Swerdloff: College of Osteopathic Medicine, Northern Boulevard
Tianyu She: College of Osteopathic Medicine, Northern Boulevard
Asiyah Rahman: College of Osteopathic Medicine, Northern Boulevard
Naveen Sharma: College of Osteopathic Medicine, Northern Boulevard
Reema Shah: College of Osteopathic Medicine, Northern Boulevard
Michael Castellano: College of Osteopathic Medicine, Northern Boulevard
Daniel Mogel: College of Osteopathic Medicine, Northern Boulevard
Jason Wu: College of Osteopathic Medicine, Northern Boulevard
Asim Ahmed: College of Osteopathic Medicine, Northern Boulevard
James San Miguel: College of Osteopathic Medicine, Northern Boulevard
Jared Cohn: College of Osteopathic Medicine, Northern Boulevard
Nikesh Shah: College of Osteopathic Medicine, Northern Boulevard
Raddy L. Ramos: College of Osteopathic Medicine, Northern Boulevard
Gonzalo H. Otazu: College of Osteopathic Medicine, Northern Boulevard

Nature Communications, 2023, vol. 14, issue 1, 1-29

Abstract: Abstract Relevant odors signaling food, mates, or predators can be masked by unpredictable mixtures of less relevant background odors. Here, we developed a mouse behavioral paradigm to test the role played by the novelty of the background odors. During the task, mice identified target odors in previously learned background odors and were challenged by catch trials with novel background odors, a task similar to visual CAPTCHA. Female wild-type (WT) mice could accurately identify known targets in novel background odors. WT mice performance was higher than linear classifiers and the nearest neighbor classifier trained using olfactory bulb glomerular activation patterns. Performance was more consistent with an odor deconvolution method. We also used our task to investigate the performance of female Cntnap2-/- mice, which show some autism-like behaviors. Cntnap2-/- mice had glomerular activation patterns similar to WT mice and matched WT mice target detection for known background odors. However, Cntnap2-/- mice performance fell almost to chance levels in the presence of novel backgrounds. Our findings suggest that mice use a robust algorithm for detecting odors in novel environments and this computation is impaired in Cntnap2-/- mice.

Date: 2023
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DOI: 10.1038/s41467-023-36346-x

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