Investigating EEG biomarkers of clinical response to low frequency rTMS in depression
Bibliographic record
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an effective intervention for major depressive disorder (MDD). Completing a full treatment course, however, is costly and time-consuming. Biomarkers of clinical outcome such as baseline resting-state brain activity measured with electroencephalography (EEG) may spare people futile treatment and conserve limited clinical resources. Additionally, investigating changes in EEG power post-treatment could provide insights into the working mechanism of rTMS. 39 MDD patients received 6 daily sessions of accelerated low-frequency (LF) rTMS over the right dorsolateral prefrontal cortex (DLPFC) for 5 days followed by a tapering course of 25 once-daily sessions. Resting-state EEG and heart rate (HR) measures were acquired immediately before and after a single rTMS session at 3 different timepoints: baseline, one week after the final accelerated session, and upon completion of the tapering course. The primary clinical outcome measure was the Beck Depression Inventory II (BDI-II). High relative baseline theta power in prefrontal areas and high baseline HR were associated with poorer clinical outcome. HR decreased acutely at the beginning of the patients’ first rTMS session but this effect was not associated with treatment outcome. The main limitations were small sample size and a lack of sham and healthy control group. Our results suggest that high relative theta power at baseline may be a marker of poorer response to right-sided LF rTMS. If validated, this easily applicable measure could inform rTMS protocol choice for the individual, thereby potentially speeding up patient recovery and saving clinical resources.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".