1. Neuroscience

DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels

  1. James P Bohnslav
  2. Nivanthika K Wimalasena
  3. Kelsey J Clausing
  4. Yu Y Dai
  5. David A Yarmolinsky
  6. Tomás Cruz
  7. Adam D Kashlan
  8. M Eugenia Chiappe
  9. Lauren L Orefice
  10. Clifford J Woolf
  11. Christopher D Harvey  Is a corresponding author
  1. Harvard Medical School, United States
  2. Boston Children's Hospital, United States
  3. Massachusetts General Hospital, United States
  4. Champalimaud Center for the Unknown, Portugal
https://doi.org/10.7554/eLife.63377
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Cite this article
  1. James P Bohnslav
  2. Nivanthika K Wimalasena
  3. Kelsey J Clausing
  4. Yu Y Dai
  5. David A Yarmolinsky
  6. Tomás Cruz
  7. Adam D Kashlan
  8. M Eugenia Chiappe
  9. Lauren L Orefice
  10. Clifford J Woolf
  11. Christopher D Harvey
(2021)
DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels
eLife 10:e63377.
https://doi.org/10.7554/eLife.63377
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  3. Download .RIS

Abstract

Videos of animal behavior are used to quantify researcher-defined behaviors-of-interest to study neural function, gene mutations, and pharmacological therapies. Behaviors-of-interest are often scored manually, which is time-consuming, limited to few behaviors, and variable across researchers. We created DeepEthogram: software that uses supervised machine learning to convert raw video pixels into an ethogram, the behaviors-of-interest present in each video frame. DeepEthogram is designed to be general-purpose and applicable across species, behaviors, and video-recording hardware. It uses convolutional neural networks to compute motion, extract features from motion and images, and classify features into behaviors. Behaviors are classified with above 90% accuracy on single frames in videos of mice and flies, matching expert-level human performance. DeepEthogram accurately predicts rare behaviors, requires little training data, and generalizes across subjects. A graphical interface allows beginning-to-end analysis without end-user programming. DeepEthogram's rapid, automatic, and reproducible labeling of researcher-defined behaviors-of-interest may accelerate and enhance supervised behavior analysis.

Data availability

Code is posted publicly on Github and linked in the paper. Video datasets and human annotations are publicly available and linked in the paper.

The following previously published data sets were used

Article and author information

Author details

  1. James P Bohnslav

    Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  2. Nivanthika K Wimalasena

    F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  3. Kelsey J Clausing

    Molecular Biology, Massachusetts General Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  4. Yu Y Dai

    Molecular Biology, Massachusetts General Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  5. David A Yarmolinsky

    F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  6. Tomás Cruz

    Champalimaud Neuroscience Programme, Champalimaud Center for the Unknown, Lisbon, Portugal
    Competing interests
    The authors declare that no competing interests exist.

  7. Adam D Kashlan

    F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  8. M Eugenia Chiappe

    Champalimaud Neuroscience Porgramme, Champalimaud Center for the Unknown, Lisbon, Portugal
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1761-0457

  9. Lauren L Orefice

    Molecular Biology, Massachusetts General Hospital, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  10. Clifford J Woolf

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.

  11. Christopher D Harvey

    Neurobiology, Harvard Medical School, Boston, United States
    For correspondence
    harvey@hms.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9850-2268

Funding

National Institutes of Health (R01MH107620)

  • Christopher D Harvey

National Science Foundation (GRFP)

  • Nivanthika K Wimalasena

Fundacao para a Ciencia ea Tecnologia (PD/BD/105947/2014)

  • Tomás Cruz

Harvard Medical School Dean's Innovation Award

  • Christopher D Harvey

Harvard Medical School Goldenson Research Award

  • Christopher D Harvey

National Institutes of Health (DP1 MH125776)

  • Christopher D Harvey

National Institutes of Health (R01NS089521)

  • Christopher D Harvey

National Institutes of Health (R01NS108410)

  • Christopher D Harvey

National Institutes of Health (F31NS108450)

  • James P Bohnslav

National Institutes of Health (R35NS105076)

  • Clifford J Woolf

National Institutes of Health (R01AT011447)

  • Clifford J Woolf

National Institutes of Health (R00NS101057)

  • Lauren L Orefice

National Institutes of Health (K99DE028360)

  • David A Yarmolinsky

European Research Council (ERC-Stg-759782)

  • M Eugenia Chiappe

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

Reviewing Editor

  1. Mackenzie W Mathis, EPFL, Switzerland

Ethics

Animal experimentation: All experimental procedures were approved by the Institutional Animal Care and Use Committees at Boston Children's Hospital (protocol numbers 17-06-3494R and 19-01-3809R) or Massachusetts General Hospital (protocol number 2018N000219) and were performed in compliance with the Guide for the Care and Use of Laboratory Animals.

Version history

  1. Received: September 23, 2020
  2. Preprint posted: September 25, 2020 (view preprint)
  3. Accepted: September 1, 2021
  4. Accepted Manuscript published: September 2, 2021 (version 1)
  5. Version of Record published: September 21, 2021 (version 2)

Copyright

© 2021, Bohnslav 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. James P Bohnslav
  2. Nivanthika K Wimalasena
  3. Kelsey J Clausing
  4. Yu Y Dai
  5. David A Yarmolinsky
  6. Tomás Cruz
  7. Adam D Kashlan
  8. M Eugenia Chiappe
  9. Lauren L Orefice
  10. Clifford J Woolf
  11. Christopher D Harvey
(2021)
DeepEthogram, a machine learning pipeline for supervised behavior classification from raw pixels
eLife 10:e63377.
https://doi.org/10.7554/eLife.63377

Share this article

https://doi.org/10.7554/eLife.63377

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