Bayesian adaptive dual control of deep brain stimulation in a computational model of Parkinson’s disease

In this paper, we present a novel Bayesian adaptive dual controller (ADC) for autonomously programming deep brain stimulation devices. We evaluated the Bayesian ADC’s performance in the context of reducing beta power in a computational model of Parkinson’s disease, in which it was tasked with finding the set of stimulation parameters which optimally reduced beta power as fast as possible. Here, the Bayesian ADC has dual goals: (a) to minimize beta power by exploiting the best parameters found so far, and (b) to explore the space to find better parameters, thus allowing for better control in the future. The Bayesian ADC is composed of two parts: an inner parameterized feedback stimulator and an outer parameter adjustment loop. The inner loop operates on a short time scale, delivering stimulus based upon the phase and power of the beta oscillation. The outer loop operates on a long time scale, observing the effects of the stimulation parameters and using Bayesian optimization to intelligently select new parameters to minimize the beta power. We show that the Bayesian ADC can efficiently optimize stimulation parameters, and is superior to other optimization algorithms. The Bayesian ADC provides a robust and general framework for tuning stimulation parameters, can be adapted to use any feedback signal, and is applicable across diseases and stimulator designs.Author summary: Deep brain stimulation (DBS) is an effective therapy for treating motor symptoms of Parkinson’s disease. However, the clinical success of DBS relies on selecting stimulation parameters that both relieve symptoms while avoiding side effects. Currently, DBS devices are programmed using a laborious trial-and-error process, requiring multiple clinic visits over the course of months. As DBS leads and algorithms become more complex, it will become impossible to select optimal DBS parameters manually. There is a clear need for an intelligent, automated approach to parameter tuning. We present a novel Bayesian adaptive dual controller (ADC), which can autonomously tune stimulation parameters. It uses a feedback signal measured from the patient to quantify the efficacy of a set of stimulation parameters, and uses this information to intelligently find the parameters which work best for each individual patient. The Bayesian ADC has the potential to improve DBS efficacy and reduce clinic visits by efficiently finding the best stimulation parameters.