Automated preclinical detection of mechanical pain hypersensitivity and analgesia
Metadatos
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Lippincot Williams & Wilkins
Materia
Preclinical pain models Machine learning Machine vision Automated pain detection
Date
2022-12Referencia bibliográfica
Zhang, Zihe... [et al.]. Automated preclinical detection of mechanical pain hypersensitivity and analgesia. PAIN: December 2022 - Volume 163 - Issue 12 - p 2326-2336 doi: [10.1097/j.pain.0000000000002680]
Patrocinador
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) 229356/2013-3Résumé
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.