Abstract
Diabetic neuropathy (DN) is a prevalent and debilitating complication of diabetes, significantly impairing quality of life through chronic pain, sensory deficits, and motor dysfunction. Despite its widespread impact, current rodent behavioral assessments using 2D tracking methods primarily quantify basic locomotion, such as distance and speed, but lack resolution to detect subtle, pattern-based motor impairments characteristic of DN. This study employed MoSeq-based 3D behavioral profiling combined with unsupervised machine learning to identify subtle yet significant alterations in nicotinamide (NA)- and streptozotocin (STZ)-induced DN mouse models. Our analysis identified 22 distinct behavioral syllables, with DN mice exhibiting increased stress-associated behaviors such as head weaving, wall jumping, and nasal hesitancy, while displaying decreased locomotor activities including walking and rearing. These alterations were accompanied by heightened mechanical sensitivity indicative of neuropathic pain and a more predictable, less exploratory behavioral transition pattern, suggesting a restricted behavioral repertoire rather than improved motor coordination. Additionally, MoSeq-based profiling enabled detailed analysis of movement organization and temporal transitions, highlighting stereotyped behavioral sequences and notably decreased exploratory behaviors in DN mice. These behavioral patterns indicate that DN-associated pain is more strongly related to impairments in behavioral adaptability and higher-order motor planning than to simple reductions in movement, suggesting underlying dysfunctions in sensorimotor or cognitive control circuits. These findings indicate that MoSeq can be used as a valuable tool for high-resolution behavioral quantification in diabetic neuropathic animal pain model, enabling refined evaluation of neuropathic phenotypes and therapeutic efficacy in preclinical studies.