Electrophysiological Biomarkers Reflect Target Engagement and Response Using Deep Brain Stimulation for Obsessive-Compulsive Disorder
Bibliographic record
Abstract
Background: Deep brain stimulation (DBS) of the anterior limb of the internal capsule (ALIC) is an effective treatment for severe, treatment-resistant obsessive-compulsive disorder (OCD). However, optimizing lead placement and stimulation parameters remains a challenge. DBS evoked potentials (EPs) recorded with electroencephalography (EEG) during surgical lead placement could serve as intraoperative biomarkers for target engagement and clinical efficacy. Methods: We obtained intraoperative EEG recordings on the forehead from 10 patients (2 nonresponders) undergoing ALIC DBS surgery for OCD. Monopolar stimulation at 2 Hz was delivered through all electrode contacts, and EEG EPs were analyzed in relation to stimulation contact, white matter connectivity to the prefrontal cortical regions of interest (assessed via probabilistic tractography), and reduction in symptom severity (assessed with the Yale-Brown Obsessive Compulsive Scale). Results: We observed consistent DBS EPs with 3 oscillatory peaks (∼35, ∼75, and ∼120 ms) across all patients. EP amplitude varied across contacts, with the largest responses occurring when the location of stimulation overlapped with the preoperatively defined tractographic target. Higher EP amplitudes recorded on the forehead correlated with greater white matter connectivity to the ventromedial prefrontal cortex/orbitofrontal cortex and ventrolateral prefrontal cortex. Treatment nonresponders exhibited less consistent EP waveforms across lead contacts. Conclusions: These findings suggest that intraoperative EPs provide valuable insights into ALIC DBS target engagement. EP characteristics may serve as biomarkers to refine DBS targeting and predict clinical response, offering a potential tool for optimizing DBS therapy for OCD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".