The lack of sensitive and robust behavioral assessments of pain in preclinical models has been a major limitation for both pain research and the development of novel analgesics. Here, we demonstrate a novel data acquisition and analysis platform that provides automated, quantitative, and objective measures of naturalistic rodent behavior in an observer-independent and unbiased fashion. The technology records freely behaving mice, in the dark, over extended periods for continuous acquisition of 2 parallel video data streams: (1) near-infrared frustrated total internal reflection for detecting the degree, force, and timing of surface contact and (2) simultaneous ongoing video graphing of whole-body pose. Using machine vision and machine learning, we automatically extract and quantify behavioral features from these data to reveal moment-by-moment changes that capture the internal pain state of rodents in multiple pain models. We show that these voluntary pain-related behaviors are reversible by analgesics and that analgesia can be automatically and objectively differentiated from sedation. Finally, we used this approach to generate a paw luminance ratio measure that is sensitive in capturing dynamic mechanical hypersensitivity over a period and scalable for highthroughput preclinical analgesic efficacy assessment.
United States Department of Defense
Defense Advanced Research Projects Agency (DARPA) HR0011-19-2-0022
United States Department of Health & Human Services
National Institutes of Health (NIH) - USA
NIH National Institute of Neurological Disorders & Stroke (NINDS) F31 NS084716-02 R35 NS105076 R01 NS089521 F31 NS108450 R01 NA114202
Bertarelli Foundation
Simons Collaboration on the Global Brain
NIH BRAIN Initiative U19 NS113201 U24 NS109520 R01AT011447
Boston Children's Hospital Technology Development Fund
Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) 229356/2013-3