Electrocorticography During Deep Brain Stimulation Surgery for Movement Disorders: Single-Center Experience
Bibliographic record
Abstract
Abstract Background Electrocorticography (ECoG) can be used as an intraoperative research tool during deep brain stimulation (DBS) implantation procedures. Its application has contributed to understanding the neurophysiology of movement disorders and the therapeutic effects of DBS. The aim of this report is to demonstrate the feasibility, safety, and utility of high-density ECoG for acquiring high-resolution neurophysiological data during DBS surgery. Methods Data were obtained from patients undergoing awake DBS surgery for the treatment of Parkinson’s disease (PD) or essential tremor (ET) at Johns Hopkins Hospital between February 2021 and September 2024. Burr holes created for the DBS lead implantation were used for ECoG strip placement. Electrophysiological and anatomical data were analyzed using MATLAB FieldTrip and Freesurfer, with localization in the anterior commissure and posterior commissure (ACPC) and Montreal Neurological Institute (MNI) coordinate systems. Surgical complications were monitored for at least six months postoperatively. Results Thirty-six patients (26 PD, 10 ET) were enrolled in the study. In one case, anatomical placement was inadequate for neurophysiological analysis. Postoperative complications included three infections (8.3%) and one chronic subdural hematoma (2.8%), with no permanent neurological deficits. The total complication rate was 11.1%, and all complications were unlikely to be related to ECoG strip placement. Anatomical and neurophysiological analysis demonstrated high-resolution cortical mapping. Multiple-subject level analysis using high-density ECoG yielded over 1,300 electrode positions. Conclusion ECoG during DBS is a valuable research method for movement disorders without additional risk to the standard procedure. The use of high-density intraoperative ECoG grids and the analysis of multiple-subject data in a standardized anatomical mapping space allows for high-resolution neurophysiological data acquisition and analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".