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Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning

Abstract : Preclinical studies of psychiatric disorders use animal models to investigate the impact of environmental factors or genetic mutations on complex traits such as decision-making and social interactions. Here, we introduce a method for the real-time analysis of the behaviour of mice housed in groups of up to four over several days and in enriched environments. The method combines computer vision through a depth-sensing infrared camera, machine learning for animal and posture identification, and radio-frequency identification to monitor the quality of mouse tracking. It tracks multiple mice accurately, extracts a list of behavioural traits of both individuals and the groups of mice, and provides a phenotypic profile for each animal. We used the method to study the impact of Shank2 and Shank3 gene mutations—mutations that are associated with autism—on mouse behaviour. Characterization and integration of data from the behavioural profiles of Shank2 and Shank3 mutant female mice revealed their distinctive activity levels and involvement in complex social interactions.
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https://hal-pasteur.archives-ouvertes.fr/pasteur-02344329
Contributor : Elodie Ey <>
Submitted on : Monday, November 4, 2019 - 9:41:53 AM
Last modification on : Monday, August 31, 2020 - 12:26:21 PM

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Fabrice de Chaumont, Elodie Ey, Nicolas Torquet, Thibault Lagache, Stéphane Dallongeville, et al.. Real-time analysis of the behaviour of groups of mice via a depth-sensing camera and machine learning. Nature Biomedical Engineering, Nature Publishing Group, 2019, 3, pp.930-942. ⟨10.1038/s41551-019-0396-1⟩. ⟨pasteur-02344329⟩

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