Action detection using a neural network elucidates the genetics of mouse grooming behavior

eLife

Vivek M Philip¹  Asaf Peer¹  Hao He¹  Gautam Sabnis¹  Vivek Kumar¹  Brian Q Geuther¹ 

[1] The Jackson Laboratory, Bar Harbor, United States;

关键词: action detection;    machine learning;    grooming;    neural network;    GWAS;    Mouse;   

DOI  :  10.7554/eLife.63207

来源: eLife Sciences Publications, Ltd

【 摘 要 】

Automated detection of complex animal behaviors remains a challenging problem in neuroscience, particularly for behaviors that consist of disparate sequential motions. Grooming is a prototypical stereotyped behavior that is often used as an endophenotype in psychiatric genetics. Here, we used mouse grooming behavior as an example and developed a general purpose neural network architecture capable of dynamic action detection at human observer-level performance and operating across dozens of mouse strains with high visual diversity. We provide insights into the amount of human annotated training data that are needed to achieve such performance. We surveyed grooming behavior in the open field in 2457 mice across 62 strains, determined its heritable components, conducted GWAS to outline its genetic architecture, and performed PheWAS to link human psychiatric traits through shared underlying genetics. Our general machine learning solution that automatically classifies complex behaviors in large datasets will facilitate systematic studies of behavioral mechanisms.

【 授权许可】

CC BY