Decoding basal ganglia motor circuit dysfunction from handwriting: a physics-informed neural signal interpretation framework for Parkinson's disease screening.

Abstract

The decoding of latent neural states from observable signals is a key focus of modern brain-AI research. Although most neural decoding models are based on electrophysiological recordings, peripheral motor outputs also convey information about circuit-level dynamics. Handwriting is a channeled behavioral signal that indexes the health of the cortico-basal ganglia-thalamo-cortical loop. In Parkinson's disease (PD), dopaminergic loss disrupts this circuit, leading to tremor oscillations, micrographia, and movement irregularities. The challenge of decoding behavior-encoded neural signals from handwriting images constitutes a principled neural signal interpretation problem. We propose a physics-informed and interpretable AI framework for decoding basal ganglia motor dysfunction through harmonic oscillator perturbation analysis. Six energy-inspired measures are extracted to quantify different aspects of motor system dynamics: intensity variation, spatial gradients, multi-scale stability, deviation variability, directional anisotropy, and cross-scale interactions. The transparent mapping between computational models and neurophysiological processes is enabled by these steps, which have their basis in mechanistic theories of amplitude modulation and oscillatory instability. High levels of discrimination were achieved when 594 handwriting trials (279 with PD and 315 controls) were assessed using repeated 10-fold cross-validation for spiral, circle, and meander tasks. Support Vector Machines achieved 84.06% accuracy and 93.56% sensitivity, with highly significant group differences across all features (p < 10^-33; Cohen's |d| = 0.87-1.51). Through the integration of physics-based modeling and interpretable machine learning, the proposed framework extends neural signal interpretation beyond direct neural recordings, establishing handwriting as a low-cost, behaviorally encoded biomarker of circuit state and advancing AI-driven decoding of brain dysfunction.