Aims: Patients with Parkinson's disease (PD) have various motor difficulties, including standing up, gait initiation and freezing of gait. These abnormalities are associated with cortico-subthalamic dysfunction. We aimed to reveal the characteristics of cortico-subthalamic activity in PD patients during different motor statuses.
Methods: Potentials were recorded in the superior parietal lobule (SPL), the primary motor cortex (M1), premotor cortex (PMC), and the bilateral subthalamic nucleus (STN) in 18 freely walking patients while sitting, standing, walking, dual-task walking, and freezing in medication "off" (Moff) and "on" (Mon) states. Different motor status activities were compared in band power, and a machine learning classifier was used to differentiate the motor statuses.
Results: SPL beta power was specifically inhibited from standing to walking, and negatively correlated with walking speed; M1 beta power reflected the degree of rigidity and was reversed by medication; XGBoost algorithm classified the five motor statuses with acceptable accuracy (68.77% in Moff, 60.58% in Mon). SPL beta power ranked highest in feature importance in both Moff and Mon states.
Conclusion: SPL beta power plays an essential role in walking status classification and could be a physiological biomarker for walking speed, which would aid the development of adaptive DBS.
Keywords: Parkinson's disease; local field potentials; machine learning; motor decoding; parietal lobule.
© 2023 The Authors. CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.