In-Vivo Training for Deep Brain Stimulation

Nicholas Carter, Arkaprava Gupta, Prateek Ganguli, Vibhor Krishna, Benedikt Dietrich, Samarjit Chakraborty

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Keywords: deep brain stimulation, parkinson's disease, reinforcement learning, personalized medicine

TL;DR: Our work proposes a reinforcement learning based approach to closed loop deep brain stimulation (DBS) for treatment of Parkinson's disorder, and find that our algorithm is highly adaptable to patient needs while outperforming clinical DBS standards.

Abstract:

Deep Brain Stimulation (DBS) is a highly effective
treatment for Parkinson’s Disease (PD). Recent research uses
reinforcement learning (RL) for DBS, with RL agents modulating
the stimulation frequency and amplitude. But, these models rely
on biomarkers that are not measurable in patients and are only
present in brain-on-chip (BoC) simulations. In this work, we
present an RL-based DBS approach that adapts these stimulation
parameters according to brain activity measurable in vivo. Using
a TD3 based RL agent trained on a model of the basal ganglia
region of the brain, we see a greater suppression of biomarkers
correlated with PD severity, compared to modern clinical DBS
implementations. Our agent outperforms the standard clinical
approaches in suppressing PD biomarkers while relying on
information that can be measured in a real world environment,
thereby opening up the possibility of training personalized RL
agents specific to individual patient needs.

Track: 11. Optimization and personalization for computational health with digital health technologies

NominateReviewer: Nicholas Carter, Arkaprava Gupta

Submission Number: 46