Deep Reinforcement Learning for Closed-Loop STN Brain Stimulation in Parkinson's and Circuit Mechanisms

Abstract

Parkinson's disease represents a debilitating neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta, leading to pathological oscillatory synchronization within the basal ganglia-thalamocortical network. Deep Brain Stimulation of the Subthalamic Nucleus is a well-established symptomatic treatment; however, conventional continuous stimulation approaches often result in suboptimal clinical outcomes and adverse side effects due to their open-loop nature. This paper presents a comprehensive investigation into the application of Deep Reinforcement Learning for developing closed-loop, adaptive stimulation protocols. By formulating the neural modulation problem as a Markov Decision Process, we employ a deep neural network agent to learn optimal stimulation strategies that minimize pathological beta-band oscillations while optimizing energy consumption. Our computational approach utilizes a biophysically plausible mean-field model of the basal ganglia to simulate the complex circuit mechanisms underlying Parkinsonian states. The results demonstrate that the reinforcement learning agent successfully identifies non-linear control policies that outperform traditional proportional-integral-derivative controllers and continuous stimulation paradigms. Furthermore, the agent exhibits the capacity to adapt to biological variability and signal noise, suggesting a robust pathway toward patient-specific neuroprosthetics. This study elucidates the intersection of computational intelligence and neural circuit dynamics, offering a promising trajectory for the next generation of precision neuromodulation therapies.