Data from: Movement-responsive deep brain stimulation for Parkinson’s Disease using a remotely optimized neural decoder

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 an
aDBS algorithm intended to mitigate movement slowness by delivering
targeted stimulation increases during movement using decoded motor signals
from the brain. Our approach demonstrated improvements in dominant hand
movement speeds and patient-reported therapeutic efficacy compared to an
inverted control, as well as increased typing speed and reduced dyskinesia
compared to cDBS. 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 promising therapeutic approach and
highlights how machine learning assisted programming can simplify complex
optimization to facilitate translational scalability.