Simple Electroencephalographic Treatment-Emergent Marker Can Predict Repetitive Transcranial Magnetic Stimulation Antidepressant Response—A Feasibility Study
Bibliographic record
Abstract
OBJECTIVES: Prefrontal repetitive transcranial magnetic stimulation (rTMS) repeated daily for 4 to 6 weeks is used to treat major depressive disorder, but more than 50% of patients do not achieve significant response. Here we test the validity of a simple electroencephalographic (EEG) marker that predicts nonresponse to rTMS. Such a marker could potentially increase rTMS effectiveness by directing nonresponders to alternative treatments or by guiding early modification of stimulation parameters. METHODS: We retrospectively analyzed 2-channel EEG data captured in the OPT-TMS National Institute of Mental Health-sponsored, multicenter study. Cumulative Brain Engagement Index (cBEI), a measure derived from template matching that allows scoring EEG dynamics along treatment, was computed. RESULTS: Six hundred sixty-five EEG recordings were analyzed. In the rTMS group, the median cBEI was found to increase in the responder group but remained unchanged in the nonresponder group. The difference between the cBEI of the groups became statistically significant by the third valid EEG sample. Within 5 samples, 91% of the responders presented with a cBEI above a preset threshold. Within 9 samples, 17% of the nonresponders had a cBEI above the threshold. CONCLUSIONS: This study demonstrates the feasibility of a simple-to-capture EEG marker as a treatment-emergent marker of response to rTMS treatment of depression. In the OPT-TMS study, discontinuing treatment when the cBEI dropped below the threshold between the fifth to ninth treatment potentially could have avoided administration of 485 (63%) of 765 treatments. Because the marker can be generated online, it would be of interest to evaluate, in future studies, whether it could be used to tune treatment parameters and improve remission rates.
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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 | 0.002 | 0.006 |
| 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".