Differential modulation of movement speed with state-dependent deep brain stimulation in Parkinson's disease
Subthalamic deep brain stimulation (STN-DBS) provides unprecedented spatiotemporal precision for the treatment of Parkinsons disease (PD), allowing for direct real-time state-specific adjustments. Inspired by findings from optogenetic stimulation in mice, we hypothesized that STN-DBS effects on movement speed depend on ongoing movement kinematics that patients exhibit during stimulation. To investigate this hypothesis, we implemented a motor state-dependent closed-loop neurostimulation algorithm, adapting DBS burst delivery to ongoing movement speed in 24 PD patients. We found a stronger anti-bradykinetic effect, raising movement speed to the level of healthy controls, when STN-DBS was applied during fast but not slow movements, while only stimulating 5% of overall movement time. To study underlying brain circuits and neurophysiological mechanisms, we investigated the behavioral effects with MRI connectomics and motor cortex electrocorticography. Finally, we demonstrate that machine learning-based brain signal decoding can be used to predict continuous movement speed for fully embedded state-dependent closed-loop algorithms. Our findings provide novel insights into the state-dependency of invasive neuromodulation, which could inspire advanced state-dependent neurostimulation algorithms for brain disorders.