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Clinically deployed deep brain stimulation (DBS) for the treatment of Parkinson’s disease operates in an open loop with fixed stimulation parameters, and this may result in high energy consumption and suboptimal therapy. The objective of this manuscript is to establish, through simulation in a computational model, a closed-loop control system that can automatically adjust the stimulation parameters to recover normal activity in model neurons. Exaggerated beta band activity is recognized as a hallmark of Parkinson’s disease and beta band activity in model neurons of the globus pallidus internus (GPi) was used as the feedback signal to control DBS of the GPi. Traditional proportional controller and proportional-integral controller were not effective in eliminating the error between the target level of beta power and the beta power under Parkinsonian conditions. To overcome the difficulties in tuning the controller parameters and improve tracking performance in the case of changes in the plant, a supervisory control algorithm was implemented by introducing a Radial Basis Function (RBF) network to build the inverse model of the plant. Simulation results show the successful tracking of target beta power in the presence of changes in Parkinsonian state as well as during dynamic changes in the target level of beta power. Our computational study suggests the feasibility of the RBF network-driven supervisory control algorithm for real-time modulation of DBS parameters for the treatment of Parkinson’s disease.

Parkinson's disease (PD) is a degenerative disorder of central nervous system that endangers the olds' health seriously. The motor symptoms of PD can be attributed to the distorted relay reliability of thalamus to cortical sensorimotor input that results from the increase of inhibitory input from internal segment of the globus pallidum (GPi). Based on this, we construct the GPi-thalamocortical computational model to generate the normal and pathological firing patterns by varying GPi spike train input. A kind of closed-loop deep brain stimulation (DBS) strategy is proposed here. Our control objective is to make the controlled membrane potential of the thalamic neuron return to the normal firing pattern. The control input that directly acts on the thalamus is the DBS waveform, which is adjusted in real time according to the feedback signal. Aimed at a certain system without the change of object parameters or stochastic disturbance, the input-output feedback linearization method is able to eliminate the error between the system output and the desired output. When uncertain elements taken into consideration in the system, the simulation results indicate that sliding mode control scheme provides better effectiveness and higher robustness.