A spatiotemporal hypergraph self-attention neural networks framework for the identification and pharmacological efficacy assessment of Parkinson's disease motor symptoms.

L-DOPA-induced dyskinesia (LID) is a common complication in the treatment of Parkinson's disease (PD), characterized by involuntary excessive movements. The traditional Abnormal Involuntary Movement Scale (AIMs), used for quantifying abnormal involuntary movements, relies heavily on manual observation and is highly subjective. Unsupervised behavior classification typically requires joint modeling on the entire dataset, making it inflexible when dealing with new samples. Here, we propose an automated behavioral recognition framework integrating multi-view 3D motion reconstruction with a hypergraph self-attention neural network to precisely delineate LID behavioral phenotypes and evaluate pharmacological interventions. Using a synchronized four-camera setup, we collected large-scale motion data from WT, PD, and LID mice, tracking 16 key body points to reconstruct accurate 3D trajectories. By combining unsupervised clustering with manual annotation, we established a standardized behavioral database. We introduced a spatiotemporal hypergraph neural network model incorporating a self-attention mechanism, which demonstrated excellent recognition accuracy across all behaviors and effectively distinguished the behavioral profiles of WT, PD, and LID mice. Based on this, we compared the behavioral differences in treatment effects between amantadine (AMAN) and clozapine (CLZ). Overall, our automated 3D behavioral analysis framework offers a high-throughput, objective, and precise approach to behavioral quantification, presenting a powerful tool for unraveling the mechanisms underlying LID and other movement disorders, as well as for advancing pharmacological research.