Adaptive (closed-loop) DBS
Sensing-enabled DBS systems that adjust stimulation in real time based on neural biomarkers.
State of the art
No update yet for Adaptive (closed-loop) DBS. An update is a standalone state-of-the-art for the topic — what someone with Parkinson's needs to know about where this approach stands today.
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The Place of Adaptive Deep Brain Stimulation in Parkinson's Disease: Spatial before Temporal Optimization
This expert viewpoint argues that adaptive DBS — which modulates stimulation amplitude in real time based on subthalamic beta activity — delivers its best results only after conventional spatial optimisation (contact selection, directional field steering) has been completed. It positions aDBS as the final layer of a programming sequence rather than a standalone upgrade, offering clinicians a practical framework now that sensing-enabled devices such as the FDA-approved BrainSense system are in routine use. -
Patient-Calibrated Dynamical Modeling and Embedded Trend-Zone Predictive Control for Closed-Loop Deep Brain Stimulation in Parkinson's Disease
This preprint introduces a patient-calibrated mathematical model of individual beta-oscillation dynamics paired with a "trend-zone" model predictive controller — an algorithm that anticipates the direction brain activity is heading and adjusts stimulation within defined acceptable bands, rather than simply reacting to a fixed threshold. The approach is designed to personalise closed-loop DBS to each patient's recorded brain data, potentially improving both symptom control and energy efficiency compared to simpler adaptive algorithms.