Defining physiological and behavioral… (F31DE036418) – Project Grant to Regents Of The University Of…

Abstract: Chronic pain affects 1 in 5 americans, yet effective, non-addictive treatments remain elusive. The development of effective analgesics depends on the use of rodent preclinical models to probe the circuits and molecules that drive or relieve pain. However, many treatments developed in the lab have failed to translate successfully to the clinic. This is due, in part, to the difficulty of measuring pain in rodents. Traditional pain assays rely on using human observation to measure reflex and facial expressions which introduce subjectivity, bias, and error when determining if an experimental manipulation drives or relieves pain. To address the drawbacks of using human observation to quantify pain, researchers have started applying high-resolution videography and machine learning to track changes in animal behavior (e.g., facial expressions or withdrawal/reflex kinematics) during the induction of pain and/or analgesia. These studies have uncovered key signatures of animal behavior during pain, but this work has predominantly focused on easy-to-access structures of the body, such as the skin of the PAW. However, these techniques have not yet been applied to measuring pain in many craniofacial structures of the head. There remains a need for the development of sophisticated pain metrics that can sensitively detect and quantify pain in the structures of the head. To address this need, this project will apply videography and machine learning to 1) evaluate pupil dilation as an indicator of pain across tissues of the head and body, and 2) quantify spontaneous pain and evoked pain during chewing by tracking facial features/body position after the induction of inflammation in the tooth. These approaches will advance the rigor and reproducibility of preclinical pain research and facilitate the discovery of novel molecules and circuits that drive or relieve pain. This project will serve as an excellent training opportunity for the applicant who will work under the primary mentorship of DR. Joshua emrick, who is a dentist-scientist within the university of michigan's school of dentistry. DR. Emrick provides expertise in oral biology, molecular neuroscience, and somatosensation. Additionally, the applicant will receive mentorship from DR. Bing YE, who provides expertise in the application of machine learning to quantify behavior in rodents. The mentorship team will jointly support the applicant's training in experimental design, analysis, and interpretation to prepare her for a competitive postdoctoral fellowship.

Defining physiological and behavioral signatures of orofacial pain in mice

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