Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease

• DOI: 10.7554/elife.75126
• Publication Date: 2022-05-27T15:00:50Z
• Authors: Timon Merk; Victoria Peterson; Witold J Lipski; Benjamin Blankertz; Robert S Turner; Ningfei Li; Andreas Horn; Robert Mark Richa...; Wolf-Julian Neumann
• Keywords: QH301-705.5; Science; Deep Brain Stimulation; Movement; 610; 03 medical and health sciences; BASAL GANGLIA; 0302 clinical medicine; Humans; https://purl.org/becyt/ford/1.2; Biology (General); https://purl.org/becyt/ford/1; NEUROMODULATION; Q; DEEP BRAIN STIMULATION; R; HUMAN; Brain; Parkinson Disease; deep brain stimulation; 3. Good health; machine learning; NEUROSCIENCE; SYSTEMS BIOLOGY; neuromodulation; basal ganglia; Medicine; Electrocorticography; MACHINE LEARNING; COMPUTATIONAL BIOLOGY; 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit; Computational and Systems Biology; Human

Brain signal decoding promises significant advances in the development of clinical brain computer interfaces (BCI). In Parkinson’s disease (PD), first bidirectional BCI implants for adaptive deep brain stimulation (DBS) are now available. Brain signal decoding can extend the clinical utility of adaptive DBS but the impact of neural source, computational methods and PD pathophysiology on decoding performance are unknown. This represents an unmet need for the development of future neurotechnology. To address this, we developed an invasive brain-signal decoding approach based on intraoperative sensorimotor electrocorticography (ECoG) and subthalamic LFP to predict grip-force, a representative movement decoding application, in 11 PD patients undergoing DBS. We demonstrate that ECoG is superior to subthalamic LFP for accurate grip-force decoding. Gradient boosted decision trees (XGBOOST) outperformed other model architectures. ECoG based decoding performance negatively correlated with motor impairment, which could be attributed to subthalamic beta bursts in the motor preparation and movement period. This highlights the impact of PD pathophysiology on the neural capacity to encode movement vigor. Finally, we developed a connectomic analysis that could predict grip-force decoding performance of individual ECoG channels across patients by using their connectomic fingerprints. Our study provides a neurophysiological and computational framework for invasive brain signal decoding to aid the development of an individualized precision-medicine approach to intelligent adaptive DBS.