Journal
ELIFE
Volume 9, Issue -, Pages -Publisher
ELIFE SCIENCES PUBLICATIONS LTD
DOI: 10.7554/eLife.57258
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Funding
- National Institutes of Health [R00-DE026807, R01 NS104899, R00-DA043609]
- Army Research Office [W911NF-17-1-0083]
- Defense Advanced Research Projects Agency [D17AC00005]
- Burroughs Wellcome Fund
- mindCORE
- David and Lucile Packard Foundation
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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 hypersensitivity 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.
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