Document Type

Article

Publication Date

3-17-2021

Publication Title

Elife

JAX Source

Elife 2021 Mar 17; 10:e63207

Volume

10

ISSN

2050-084X

PMID

33729153

DOI

https://doi.org/10.7554/elife.63207

Abstract

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.

Comments

We thank Drs. Greg Carter and Daniel Skelly for critical feedback on the manuscript. We thank members of the Kumar Lab for helpful advice and Taneli Helenius for editing. We thank JAX Information Technology team members Edwardo Zaborowski, Shane Sanders, Rich Brey, David McKenzie, and Jason Macklin for infrastructure support.

This article is distributed under the terms of the Creative Commons Attribution License.

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