Online prediction of optimal deep brain stimulation contacts from local field potentials in Parkinson’s disease
Bibliographic record
Abstract
Selecting optimal contacts for chronic deep-brain stimulation (DBS) requires a monopolar review, involving time-consuming manual testing by trained personnel, often causing patient discomfort. Neural biomarkers, such as local field potentials (LFP), could streamline this process. This study aimed to validate LFP recordings from chronically implanted neurostimulators for guiding clinical contact-level selection. We retrospectively analysed bipolar LFP recordings from Parkinson's disease patients across three centres (Netherlands: 68, Switzerland: 21, Germany: 32). Using beta-band power measures (13-35 Hz), we ranked channels based on clinical contact-level choices and developed two prediction algorithms: (i) a "decision tree" method for in-clinic use and (ii) a "pattern based" method for offline validation. The "decision tree" method achieved accuracies of 86.5% (NL), 86.7% (CH), and 75.0% (DE) for predicting the top two contact-levels. Both methods outperformed an existing algorithm. These findings suggest LFP-based approaches can enhance DBS programming efficiency, potentially reducing patient burden and clinical workload.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".