3D-AI mouse behavior analysis system has the capability to detect abnormalities in R6/1 model mice with Huntington’s disease during the pre-symptomatic phase

Introduction Huntington's disease (HD), a dominantly inherited neurodegenerative disorder caused by CAG repeat expansions in the HTT gene, manifests with progressive motor dysfunction, cognitive decline, and psychiatric disturbances. While current transgenic mouse models recapitulate key pathological features, they exhibit rapid disease progression, and early behavioural phenotypes are not analyzed comprehensively to understand their progression. Methods We employed a high-resolution 3-dimensional motion capture and unsupervised machine learning to dissect behavioral dynamics in the R6/1 HD mouse model at 8 weeks of age, a stage analogous to human pre-diagnostic HD. Results Through unsupervised learning-based clustering analysis, we identified 40 major movement categories in mice. Using a subsequent supervised learning approach, we recognized 13 fundamental spontaneous behavioral movements and identified disrupted behavioral modules in R6/1 mice, including reduced locomotor fraction, increased pausing frequency, and altered exploratory patterns. Our key findings revealed that HD mice exhibited reduced velocity and increased stride length during running and trotting behaviors, mirroring bradykinesia and gait abnormalities observed in HD patients. These mice also showed preferential exploration of the peripheral zone and decreased sniffing frequency, which might suggest that they have displayed behaviors analogous to anxiety or depression.Furthermore, an escalating frequency of pausing was observed over 30-minute sessions, suggesting early-onset motor fatigue. Additionally, lower behavioral entropy and fewer transitions from exploratory or maintenance states to locomotion were detected, pointing to executive dysfunction. A LDA classifier integrating these core behavioral metrics achieved an AUC of 0.917, surpassing the performance of traditional coarse motor assessments. Conclusion These results establish precision behavioral analytics as a sensitive platform for detecting premanifest HD pathology, providing a framework for evaluating presymptomatic therapeutics and scientific base for developing early diagnostic and treatment strategies for HD.