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

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease
 
Identifier https://doi.org/10.7910/DVN/IO2FLM
 
Creator Merk, Timon
Peterson, Victoria
Lipski, Witold
Blankertz, Benjamin
Turner, Robert S.
Li, Ningfei
Horn, Andreas
Richardson, Robert Mark
Neumann, Wolf-Julian
 
Publisher Harvard Dataverse
 
Description This dataset contains the data required for replication of the results published in the paper titled "Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson’s disease". The abstract for the paper is below:

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 kinematics. 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.
 
Subject Medicine, Health and Life Sciences
Parkinson’s disease
Deep brain stimulation (DBS)
Machine learning
Neuromodulation
Basal Ganglia
 
Contributor Saravanan, Varun