In-Vivo Training for Deep Brain Stimulation
This work addresses the challenge of personalizing DBS treatment for Parkinson's patients by using measurable in-vivo brain activity, though it is incremental as it builds on existing RL approaches.
The paper tackles the problem of adapting deep brain stimulation (DBS) parameters for Parkinson's Disease using reinforcement learning, achieving greater suppression of biomarkers correlated with disease severity compared to standard clinical methods.
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.