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.
- Ishmail Abdus-Saboor
- Jessica M Jones
- Osama Ahmed
- Talmo D Pereira
- Gregory Corder
- Jessica A Wojick
- Joshua B Plotkin
- Joshua B Plotkin
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
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.
- Rebecca Seal, University of Pittsburgh School of Medicine, United States
© 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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