Neurophysiologically Realistic Environment for Comparing Adaptive Deep Brain Stimulation Algorithms in Parkinson Disease

TL;DR

This paper presents a neurophysiologically realistic benchmark environment for evaluating adaptive deep brain stimulation algorithms in Parkinson's disease, incorporating detailed brain dynamics and physiological variability.

Contribution

It introduces the first comprehensive, realistic simulation framework for aDBS, including physiological attributes and a platform for training reinforcement learning algorithms.

Findings

Includes 15 physiological attributes like noise and neural drift

Models spatially distributed and temporally registered features

Provides a structured environment for RL algorithm development

Abstract

Adaptive deep brain stimulation (aDBS) has emerged as a promising treatment for Parkinson disease (PD). In aDBS, a surgically placed electrode sends dynamically altered stimuli to the brain based on neurophysiological feedback: an invasive gadget that limits the amount of data one could collect for optimizing the control offline. As a consequence, a plethora of synthetic models of PD and those of the control algorithms have been proposed. Herein, we introduce the first neurophysiologically realistic benchmark for comparing said models. Specifically, our methodology covers not only conventional basal ganglia circuit dynamics and pathological oscillations, but also captures 15 previously dismissed physiological attributes, such as signal instabilities and noise, neural drift, electrode conductance changes and individual variability - all modeled as spatially distributed and temporally registered features via beta-band activity in the brain and a feedback. Furthermore, we purposely built our framework as a structured environment for training and evaluating deep reinforcement learning (RL) algorithms, opening new possibilities for optimizing aDBS control strategies and inviting the machine learning community to contribute to the emerging field of intelligent neurostimulation interfaces.