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

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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.

Original languageEnglish (US)
Article numbere1006606
JournalPLoS computational biology
Issue number12
StatePublished - Dec 2018

Bibliographical note

Funding Information:
This work was supported by the following sources: The National Institute of Neurological Disorders and Stroke ( grant F31-NS103487-01A1 (LLG), R01-NS094206 (MDJ, TIN), and P50-NS098573 (MDJ, TIN), and the National Science Foundation ( grant CBET-1264432 (TIN). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank the Minnesota Supercomputing Institute (MSI) for providing the computational resources. We also thank the non-author members of the Johnson and Netoff labs for their help and support. Finally, we thank Andy Lamperski for sharing his expertise in optimization and control theory.

Publisher Copyright:
© 2018 Grado et al.


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