ArticleeLife2022
Electrocorticography is superior to subthalamic local field potentials for movement decoding in Parkinson's disease.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed, 77 citations in OpenAlex.
- Network state transitions under deep brain stimulation: A Wilson-Cowan model of Parkinson's Disease.Journal of computational neuroscience · 2026Article
- Cortico-basal oscillations index naturalistic movements during deep brain stimulation.Brain : a journal of neurology · 2026Article
- Cerebellar Electrical Activity as a Marker for Predicting Brain Health and Disease: A Review.Brain sciences · 2026Review
- Brain-Computer Interfaces: The Dawn of a New Era in Disease Treatment.Exploration (Beijing, China) · 2026Review
- Adaptive Deep Brain Stimulation for Parkinson's Disease: Navigating the Roadblocks to Clinical Implementation.Movement disorders : official journal of the Movement Disorder Society · 2026Review
- Electrocorticography During Deep Brain Stimulation Surgery for Movement Disorders: Single-Center Experience.Brain sciences · 2026Article
- Multitarget brain implants enable generalized decoding of Parkinson's disease symptoms from chronic home recordings.Research square · 2026Article
- Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants.Nature biomedical engineering · 2026Article
- Decoding pre-movement neural activity from thalamic LFPs for adaptive neurostimulation in tremor patients.Neurocomputing · 2026Article
- Inferring neural sources from electroencephalography: foundations and frontiers.Journal of neural engineering · 2026Review
- Decoding the impact of visual states on adaptive deep brain stimulation feedback signals in movement disorders.NPJ Parkinson's disease · 2026Article
- Hydrogels in Neurological Disorders: Emerging Diagnostic and Therapeutic Applications.International journal of nanomedicine · 2026Review
- Movement-responsive deep brain stimulation for Parkinson's disease using a remotely optimized neural decoder.Nature biomedical engineering · 2026Article
- Local field potential biomarkers for adaptive deep brain stimulation in Parkinson's disease: A scoping review.Surgical neurology international · 2026Review
- Translational prospectives for deep brain stimulation and low-intensity focused ultrasound neuromodulation: IFCN Handbook chapter.Clinical neurophysiology practice · 2026Review
- BrainTRACE (Brain Tumor Registration and Cortical Electrocorticography): A novel tool for localizing electrocorticography electrodes in patients with brain tumors.Journal of neuroscience methods · 2025Article
- Electrophysiological signatures predict the therapeutic window of deep brain stimulation electrode contacts.NPJ digital medicine · 2025Article
- From adaptive deep brain stimulation to adaptive circuit targeting.Nature reviews. Neurology · 2025Review
- Differential modulation of movement speed with state-dependent deep brain stimulation in Parkinson's disease.Science advances · 2025Article
- Modeling and optimizing deep brain stimulation to enhance gait in Parkinson's disease: personalized treatment with neurophysiological insights.NPJ Parkinson's disease · 2025Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.