Model-based rational feedback controller design for closed-loop deep brain stimulation of Parkinson's disease.

OBJECTIVE: To explore the use of classical feedback control methods to achieve an improved deep brain stimulation (DBS) algorithm for application to Parkinson's disease (PD). APPROACH: A computational model of PD dynamics was employed to develop model-based rational feedback controller design. The restoration of thalamocortical relay capabilities to patients suffering from PD is formulated as a feedback control problem with the DBS waveform serving as the control input. Two high-level control strategies are tested: one that is driven by an online estimate of thalamic reliability, and another that acts to eliminate substantial decreases in the inhibition from the globus pallidus interna (GPi) to the thalamus. Control laws inspired by traditional proportional-integral-derivative (PID) methodology are prescribed for each strategy and simulated on this computational model of the basal ganglia network. MAIN
RESULTS: For control based upon thalamic reliability, a strategy of frequency proportional control with proportional bias delivered the optimal control achieved for a given energy expenditure. In comparison, control based upon synaptic inhibitory output from the GPi performed very well in comparison with those of reliability-based control, with considerable further reduction in energy expenditure relative to that of open-loop DBS. The best controller performance was amplitude proportional with derivative control and integral bias, which is full PID control. We demonstrated how optimizing the three components of PID control is feasible in this setting, although the complexity of these optimization functions argues for adaptive methods in implementation. SIGNIFICANCE: Our findings point to the potential value of model-based rational design of feedback controllers for Parkinson's disease.