Computational model for control of hand movement in Parkinson's disease using deep brain stimulation

Parkinson's disease (PD) is a progressive neurological disorder characterized by the loss of dopamine in the substantia nigra resulting in movement disorder. Although several computational models have been proposed to explore different aspects of PD, a comprehensive computational model of PD and its suppression remains elusive. This study presents a computational model of the Cortico-Basal Ganglia Thalamus (CBGT) network, and demonstrates the effects of close-loop deep brain stimulation (DBS) as a potential therapeutic intervention. The model focuses on addressing abnormal brain wave patterns associated with PD-related hand movement through DBS. To assess the model performance, a three-link manipulator is incorporated into the CBGT model, with the joints corresponding to shoulder, elbow and wrist of human arm. PD-like symptoms are simulated by modulating the dopaminergic input. The striatal (STR) neurons were selected as target neurons for application of DBS. A proportional-integral (PI) controller regulates DBS at different frequencies in striatal neurons based on errors in manipulator movement. The effectiveness of DBS at STR was compared with the DBS at globus pallidus externus and subthalamic nucleus. DBS suppressed neuronal signal oscillations at 13-30 Hz and reduced abnormal hand movements. The results demonstrate that application of DBS at STR could correct manipulator movement. Additionally, the trajectory of movement by the end-effector were compared with DBS at different target neurons in CBGT. These findings suggest the therapeutic potential of the proposed computational model in development of neuroprosthesis for PD patients.