@misc{10481/78398,
year = {2022},
month = {12},
url = {https://hdl.handle.net/10481/78398},
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 highthroughput
preclinical analgesic efficacy assessment.},
organization = {United States Department of Defense},
organization = {Defense Advanced Research Projects Agency (DARPA)	HR0011-19-2-0022},
organization = {United States Department of Health & Human Services},
organization = {National Institutes of Health (NIH) - USA},
organization = {NIH National Institute of Neurological Disorders & Stroke (NINDS)	F31 NS084716-02	
R35 NS105076	
R01 NS089521	
F31 NS108450	
R01 NA114202},
organization = {Bertarelli Foundation},
organization = {Simons Collaboration on the Global Brain},
organization = {NIH BRAIN Initiative	U19 NS113201	
U24 NS109520	
R01AT011447},
organization = {Boston Children's Hospital Technology Development Fund},
organization = {Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)	229356/2013-3},
publisher = {Lippincot Williams & Wilkins},
keywords = {Preclinical pain models},
keywords = {Machine learning},
keywords = {Machine vision},
keywords = {Automated pain detection},
title = {Automated preclinical detection of mechanical pain hypersensitivity and analgesia},
doi = {10.1097/j.pain.0000000000002680},
author = {Zhang, Zihe and González Cano, Rafael},
}