A machine-vision approach for automated pain measurement at millisecond timescales

  1. Jessica M Jones
  2. William Foster
  3. Colin Twomey
  4. Justin Burdge
  5. Osama Ahmed
  6. Talmo D Pereira
  7. Jessica A Wojick
  8. Gregory Corder
  9. Joshua B Plotkin
  10. Ishmail Abdus-Saboor  Is a corresponding author
  1. University of Pennsylvania, United States
  2. Princeton University, United States

Abstract

Objective and automatic measurement of pain in mice remains a barrier for discovery in neuroscience. Here we capture paw kinematics during pain behavior in mice with high-speed videography and automated paw tracking with machine and deep learning approaches. Our statistical software platform, PAWS (Pain Assessment at Withdrawal Speeds), uses a univariate projection of paw position over time to automatically quantify seven behavioral features that are combined into a single, univariate pain score. Automated paw tracking combined with PAWS reveals a behaviorally-divergent mouse strain that displays hyper-sensitivity to mechanical stimuli. To demonstrate the efficacy of PAWS for detecting spinally- versus centrally-mediated behavioral responses, we chemogenetically activated nociceptive neurons in the amygdala, which further separated the pain-related behavioral features and the resulting pain score. Taken together, this automated pain quantification approach will increase objectivity in collecting rigorous behavioral data, and it is compatible with other neural circuit dissection tools for determining the mouse pain state.

Data availability

Raw data are now associated with figures as source data.

Article and author information

Author details

  1. Jessica M Jones

    Biology, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3638-255X
  2. William Foster

    Biology, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Colin Twomey

    Biology, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Justin Burdge

    Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Osama Ahmed

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Talmo D Pereira

    Princeton Neuroscience Institute, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9075-8365
  7. Jessica A Wojick

    Psychiatry and Neuroscience, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Gregory Corder

    Psychiatry and Neuroscience, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Joshua B Plotkin

    Department of Biology, University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2349-6304
  10. Ishmail Abdus-Saboor

    Biology, University of Pennsylvania, Philadelphia, United States
    For correspondence
    ishmail@sas.upenn.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2120-0063

Funding

National Institutes of Health (R00-DE026807)

  • Ishmail Abdus-Saboor

National Institutes of Health (R00-DE026807)

  • Jessica M Jones

National Institutes of Health (R01 NS104899)

  • Osama Ahmed

National Institutes of Health (R01 NS104899)

  • Talmo D Pereira

National Institutes of Health (R00-DA043609)

  • Gregory Corder

National Institutes of Health (R00-DA043609)

  • Jessica A Wojick

Army Research Office (W911NF-17-1-0083)

  • Joshua B Plotkin

Defense Advanced Research Projects Agency (D17AC00005)

  • Joshua B Plotkin

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

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#806519) of the University of Pennsylvania.

Reviewing Editor

  1. Rebecca Seal, University of Pittsburgh School of Medicine, United States

Publication history

  1. Received: March 26, 2020
  2. Accepted: August 5, 2020
  3. Accepted Manuscript published: August 6, 2020 (version 1)
  4. Version of Record published: August 18, 2020 (version 2)

Copyright

© 2020, Jones 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. Jessica M Jones
  2. William Foster
  3. Colin Twomey
  4. Justin Burdge
  5. Osama Ahmed
  6. Talmo D Pereira
  7. Jessica A Wojick
  8. Gregory Corder
  9. Joshua B Plotkin
  10. Ishmail Abdus-Saboor
(2020)
A machine-vision approach for automated pain measurement at millisecond timescales
eLife 9:e57258.
https://doi.org/10.7554/eLife.57258

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