• Zihe Zhang, David P Roberson, Masakazu Kotoda, Bruno Boivin, James P Bohnslav, Rafael González-Cano, David A Yarmolinsky, Bruna Lenfers Turnes, Nivanthika K Wimalasena, Shay Q Neufeld, Lee B Barrett, QuintãoNara L MNLMBoston Children's Hospital, F.M. Kirby Neurobiology Center, Boston, MA, United States. D.P. Roberson is now with Blackbox Bio, LLC, Dallas, TX, United States. R. González-Cano is now with the Department of Pharmacology, University of Gra, Victor Fattori, Daniel G Taub, Alexander B Wiltschko, Nick A Andrews, Christopher D Harvey, Sandeep Robert Datta, and Clifford J Woolf.
• Boston Children's Hospital, F.M. Kirby Neurobiology Center, Boston, MA, United States. D.P. Roberson is now with Blackbox Bio, LLC, Dallas, TX, United States. R. González-Cano is now with the Department of Pharmacology, University of Granada, Granada, Spain . N.K. Wimalasena is now with Decibel Therapeutics, Boston, MA, United States. N.L.M. Quintão is now with the Postgraduate Programe in Pharmaceutical Science, Universidade do Vale do Itajaí (UNIVALI), Itajaí, Santa Catarina, Brazil . V. Fattori is now with the Laboratory of Pain, Inflammation, Neuropathy, and Cancer, Department of Pathology, Londrina State University, Londrina, Paraná, Brazil . A.B. Wiltschko is now with the Google Research, Brain Team, Cambridge, MA, United States. N.A. Andrews is now with the Salk Institute for Biological Studies, La Jolla, CA, United States.
• Pain. 2022 Dec 1; 163 (12): 232623362326-2336.

AbstractThe 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.Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the International Association for the Study of Pain.

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