Beta burst characteristics predict instantaneous hand movements in Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by bradykinesia, rigidity, and tremor. Amplification of beta-band oscillations in both cortical and subcortical areas have often been implicated in the pathoetiology of PD symptomatology. Although previous work has linked prolonged beta bursts to motor impairment, few studies have examined the moment-by-moment causal role of beta bursting in driving movement kinematics. We hypothesize that beta burst characteristics predict instantaneous hand movement dynamics and may, therefore, play a fundamental role in the pathophysiology of disease and guiding. We analyzed the characteristics of beta burst dynamics within and between cortical and subcortical regions and their causal relationship with hand kinematics. We recorded neural and hand-movement data from the motor (M1), premotor (PM), and internal Globus Pallidus (GPi) of 20 PD patients during deep brain stimulation (DBS) implantation surgery. We used Generalized Linear Models and Linear Mixed-Effects Models to identify how beta burst characteristics, including power, duration, and bursting rate, predict instantaneous hand motion. High beta burst duration, power, and bursting rate in M1, PM, and GPi correlated with movement kinematics, with significant interactions noted between high beta burst dynamics in GPi and low beta power in M1 (p < 0.05). Collectively, these parameters predicted hand motion 35.6 % of the time. The findings underscore that both low- and high-frequency beta oscillations across interconnected cortical-subcortical networks contribute to bradykinesia and dynamically influence movement kinematics. By delineating the temporal and spectral parameters that predict hand motion, our work advances the understanding of bradykinesia's neural underpinnings and suggests new opportunities for targeted closed-loop therapeutic interventions.