PROJECT SUMMARY Rodent models are highly valuable for elucidating the molecular and cellular mechanisms of chronic pain. Because rodents cannot articulate their sensation, “pain-like” behaviors have been used as the proxy. However, sensitivity and specificity of many existing methods for measuring rodent “pain” sensation, especially “chronic pain”, are uncertain. Here we propose to explore the feasibility of a largely automated and data-driven behavioral assay for identifying spontaneous pain in freely behaving mice. Specifically, we will take advantage of recent advances in 3D motion analysis, which enable precise and robust measurements of movements without human intervention, to extract movement features from freely moving mice in various pain states (baseline, induced acute pain, chronic pain, and with painkiller treatment). We will generate a database of movement features of control mice and mice with induced acute cheek/leg pain or chronic neuropathic cheek/leg pain, using both sexes of two mouse strains. We will then use machine-learning algorithms to identify the best combination of movement features for predicting the pain state (a “mouse chronic pain scale”). These efforts are expected to produce a novel and objective method to assess spontaneous pain, a characteristic feature of chronic pain, in mice. This method can supplement our recent method in measurements of evoked responses (a “mouse acute pain scale”) to provide efficient, robust, and comprehensive assessments of pain-related rodent behaviors and facilitate mechanistic investigations of brain circuits in mediating and modulating pain. Our interdisciplinary team is well suited to complete these Aims, utilizing combined expertise in mouse somatosensory/pain system (PI Luo), behavioral, systems and computational neuroscience (PI Ding), and 3D imaging and computer vision (PI Park).