A deep-learning driven investigation of circuit basis for reflexive hypersensitivity to pain

Abstract

Objectively measuring animal behavior is key to understanding the neural circuits underlying pain. Recent progress in machine vision has presented us with unprecedented scope in behavioral analysis. Here, we apply DeeplabCut (DLC) to dissect mouse behavior on the thermal-plate test -- a commonly used paradigm to ascertain supraspinal contributions to noxious thermal sensation and pain hypersensitivity. We determine the signature characteristics of the pattern of mouse movement and posture in 3D in response to a range of temperatures from innocuous to noxious on the thermal-plate test. Next, we test how acute chemical and chronic inflammatory injuries sensitize mouse behaviors. Repeated exposure to noxious temperatures on the thermal-plate can induce learning, and in this study, we design a novel assay and formulate an analytical pipeline that will facilitate the dissection of plasticity mechanisms in pain circuits in the brain. Last, we record and test how activating Tacr1 expressing PBN neurons -- a population responsive to sustained noxious stimuli-affects mouse behavior on the thermal plate test. Taken together, we demonstrate that by tracking a single body part of a mouse, we can reveal the behavioral signatures of mice exposed to noxious surface temperatures, report the alterations of the same when injured, and determine if a molecularly and anatomically defined pain responsive circuit plays a role in the reflexive hypersensitivity to thermal pain.