A Closed-Loop Deep Brain Stimulation Biomedical Model of Parkinson's Disease

Abstract

In this paper, we introduce a new approach to the study of Parkinson's Disease (PD) through the implementation of a neurorobotics model via the integration of computational neuroscience with the latest robotic technology. Using the iCub as our robot platform, we adapted a computational model of the Basal Ganglia-Thalamo-Cortical (BG-T-C) circuit to investigate the efficiency of Deep Brain Stimulation (DBS) in mitigating PD's motor symptoms.Central to our methodology is the use of closed-loop DBS, a technique that adjusts stimulation parameters in real time based on specific kinematic and neuronal biomarkers of PD severity, such as fluctuations in beta band activity and tremor movements. This dynamic approach allows for a more personalized and efficient treatment regimen compared to traditional, static open-loop systems, which cannot adapt to the patient's changing conditions.The findings of our study corroborate the feasibility of using a neurorobotics model to simulate the motor symptoms of PD and provide evidence that closed-loop DBS can effectively modulate these symptoms. This was achieved by reducing the power spectral density at the beta-band frequency range (8-30 Hz) of the neural activity to below threshold levels and revealing a complex relationship between the severity of the disease and the effectiveness of the treatment.