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

  1. Brian Q Geuther
  2. Asaf Peer
  3. Hao He
  4. Gautam Sabnis
  5. Vivek M Philip
  6. Vivek Kumar  Is a corresponding author
  1. The Jackson Laboratory, United States

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 and 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 2,457 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.

Data availability

The all machine learning datasets are available here: https://www.kumarlab.org/2021/03/11/grooming-behavioral-data/ The code is available here: https://github.com/KumarLabJax/MouseGrooming Behavioral data has been deposited into Mouse Phenome Database. The access for this data will be https://mpdpreview.jax.org/projects/Project1051

Article and author information

Author details

  1. Brian Q Geuther

    Mammalian Genetics, The Jackson Laboratory, Bar Harbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Asaf Peer

    Mammalian Genetics, The Jackson Laboratory, Bar Harbor, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7577-353X
  3. Hao He

    Mammalian Genetics, The Jackson Laboratory, Bar Harbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Gautam Sabnis

    Mammalian Genetics, The Jackson Laboratory, Bar Harbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Vivek M Philip

    Mammalian Genetics, The Jackson Laboratory, Bar Harbor, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Vivek Kumar

    Mammalian Genetics, The Jackson Laboratory, Bar Harbor, United States
    For correspondence
    Vivek.Kumar@jax.org
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6643-7465

Funding

Jackson Laboratory Director's Innovation Fund

  • Vivek Kumar

National Institutes of Health (DA041668)

  • Vivek Kumar

National Institutes of Health (DA048634)

  • Vivek Kumar

National Science Foundation (TG-DBS170004)

  • Vivek Kumar

Brain and Behavioral Foundation

  • Vivek Kumar

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: All studies were performed in accordance with approved protocols from The Jackson Laboratory Institutional Animal Care and Use Committee guidelines (Animal Protocol Number 14010).

Reviewing Editor

  1. Bianca Jones Marlin, Columbia University, United States

Publication history

  1. Received: September 17, 2020
  2. Accepted: March 5, 2021
  3. Accepted Manuscript published: March 17, 2021 (version 1)
  4. Version of Record published: April 13, 2021 (version 2)

Copyright

© 2021, Geuther et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Brian Q Geuther
  2. Asaf Peer
  3. Hao He
  4. Gautam Sabnis
  5. Vivek M Philip
  6. Vivek Kumar
(2021)
Action detection using a neural network elucidates the genetics of mouse grooming behavior
eLife 10:e63207.
https://doi.org/10.7554/eLife.63207

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