ABSTRACT

Deep brain stimulation (DBS) has garnered widespread use as an effective treatment for advanced Parkinson’s Disease (PD). Conventional DBS (cDBS) provides electrical stimulation to the basal ganglia at fixed amplitude and frequency, yet patients’ therapeutic needs are often dynamic with residual symptom fluctuations or side-effects. Adaptive DBS (aDBS) is an emerging technology that modulates stimulation with respect to real-time clinical, physiological, or behavioral states, enabling therapy to dynamically align with patient-specific symptoms. Here, we report a novel aDBS algorithm intended to mitigate movement slowness by delivering targeted stimulation increases during movement using decoded motor signals from the brain. Compared to cDBS and a control algorithm that decreases stimulation during movement, our approach demonstrates enhanced clinical efficacy, with improvements in movement speed, reduced dyskinesia, and better patient-reported therapeutic quality. Furthermore, we demonstrate proof-of-principle of a machine learning pipeline capable of remotely optimizing aDBS parameters in a home setting. This work illustrates the potential of movement-responsive aDBS as a new therapeutic approach and highlights how machine learning assisted programming can simplify optimization to facilitate translational scalability.