Author:
Hong Weizhe,Kennedy Ann,Burgos-Artizzu Xavier P.,Zelikowsky Moriel,Navonne Santiago G.,Perona Pietro,Anderson David J.
Abstract
A lack of automated, quantitative, and accurate assessment of social behaviors in mammalian animal models has limited progress toward understanding mechanisms underlying social interactions and their disorders such as autism. Here we present a new integrated hardware and software system that combines video tracking, depth sensing, and machine learning for automatic detection and quantification of social behaviors involving close and dynamic interactions between two mice of different coat colors in their home cage. We designed a hardware setup that integrates traditional video cameras with a depth camera, developed computer vision tools to extract the body “pose” of individual animals in a social context, and used a supervised learning algorithm to classify several well-described social behaviors. We validated the robustness of the automated classifiers in various experimental settings and used them to examine how genetic background, such as that of Black and Tan Brachyury (BTBR) mice (a previously reported autism model), influences social behavior. Our integrated approach allows for rapid, automated measurement of social behaviors across diverse experimental designs and also affords the ability to develop new, objective behavioral metrics.
Funder
Howard Hughes Medical Institute
Helen Hay Whitney Foundation
National Science Foundation
Sloan-Swartz Foundation
Simons Foundation
Gordon and Betty Moore Foundation
Publisher
Proceedings of the National Academy of Sciences
Cited by
227 articles.
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