Machine-Learning-Guided Video Analysis Identifies Sound-Evoked Pain Behaviors from Facial Grimace and Body Cues in Mice

Abstract

Humans can experience sound-evoked pain, either from extremely loud sounds or in cases of pain hyperacusis from typically tolerable sounds. However, the mechanisms underlying sound-evoked pain remain poorly understood. Developing behavioral methods to measure sound-evoked pain in animal models is critical for elucidating these mechanisms. Here, a machine learning-based approach was developed to measure sound-evoked pain in freely moving mice by analyzing facial grimace and body position from video recordings during sound exposure. Facial grimace, a commonly used method to detect pain in mice, and body position, which can be used to measure postural and movement changes that also indicate pain, were both quantified using a deep neural network model trained to extract established facial and body features from video recorded by a single camera. To validate the models capability to detect pain, a known painful state, migraine induced by the injection of the neuropeptide calcitonin gene-related peptide (CGRP), was used. Using this machine learning-based approach, the ability to quantify a pain response from CGRP-induced migraine, distinct from baseline behavior, was demonstrated, resulting in a defined pain threshold. Sound exposures at high intensities elicited significant changes in facial grimace and body position, in comparison, surpassing the pain threshold calculated from CGRP-induced migraine. These behavioral changes were absent in Tmie-knockout mice, which lack functional sound transduction in the cochlea. This automated, high-throughput framework enables objective and sensitive analysis of pain providing a foundation for future studies investigating the peripheral and central mechanisms of sound-evoked pain. SignificanceThis study introduces a quantitative framework for assessing pain using a single-camera setup and machine learning guided analysis to capture and analyze mouse behavior. By integrating two established pain metrics, facial grimace and attenuated movement, this method enables precise, non-invasive quantification of pain-related behaviors. The approach was validated with a well-characterized pain model, migraine, induced by injection of the neuropeptide CGRP, demonstrating the ability to quantify a pain response distinct from baseline behavior. By applying this framework to sound-evoked pain, the data revealed that exposure to intense sound triggers significant pain behavioral responses. These novel findings provide insights into the behavioral manifestations and neural underpinnings of sound-evoked pain, offering a robust tool for studying the mechanisms of pain perception.