Automated preclinical detection of mechanical pain hypersensitivity and analgesia. Issue 12 (11th December 2022) - Zhang, Zihe; Roberson, David P.; Kotoda, Masakazu; Boivin, Bruno; Bohnslav, James P.; González-Cano, Rafael; Yarmolinsky, David A.; Turnes, Bruna Lenfers; Wimalasena, Nivanthika K.; Neufeld, Shay Q.; Barrett, Lee B.; Quintão, Nara L. M.; et al.
Abstract : Supplemental Digital Content is Available in the Text. Frustrated total internal reflection–enhanced machine vision and machine learning approaches permit objective, sensitive, high-throughput measurement of the pain state of freely moving rodents and its reversal by analgesics. Abstract: 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 high-throughput preclinical analgesic efficacy assessment.