A machine-vision approach for automated pain measurement at millisecond timescales
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 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 06, 2020
- Source ID
- 10.7554/elife.57258
Entities
People
- Colin R Twomey
- Gregory Corder
- Ishmail Abdus-Saboor
- Jessica A Wojick
- Jessica M Jones
- Joshua B. Plotkin
- Justin Burdge
- Osama M Ahmed
- Talmo D Pereira
- William Foster
Organizations
- Army Research Office
- Burroughs Wellcome Fund
- David and Lucile Packard Foundation
- Defense Advanced Research Projects Agency
- National Institutes of Health
- Princeton University
- University of Pennsylvania