A machine-vision approach for automated pain measurement at millisecond timescales
- University of Pennsylvania, United States
- 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
Ishmail Abdus-Saboor
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.
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.
Metrics
-
- 7,483
- views
-
- 803
- downloads
-
- 63
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Citations by DOI
-
- 63
- citations for umbrella DOI https://doi.org/10.7554/eLife.57258
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
A machine-vision approach for automated pain measurement at millisecond timescales
eLife 9:e57258.
https://doi.org/10.7554/eLife.57258
