Repetitive Transcranial Magnetic Stimulation for Major Depressive Disorder: A Review
Bibliographic record
Abstract
Several studies demonstrated that repetitive transcranial magnetic stimulation (rTMS) is an efficacious treatment for treatment-resistant major depressive disorder (TRD). Recent metaanalyses and more recent large multicentre studies provided evidence suggesting that rTMS is indeed a promising treatment; however, its efficacy has often been shown to be modest, compared with sham stimulation. We review these lines of evidence and discuss several reasons that may explain the modest therapeutic efficacy in most of these studies, including: most involved left-sided treatment alone to the dorsolateral prefrontal cortex (DLPFC) only, which may be less optimal than applying bilateral stimulation; suboptimal methods were used to target the DLPFC (that is, the 5-cm anterior method), limiting the treatment potential of inherently a targeted form of treatment; some treatment durations were short (that is, 2 to 4 weeks); and stimulation intensity might have been insufficient by not considering coil-to-cortex distance, which has been linked to rTMS-induced antidepressant response. Future studies attempting to address the above-mentioned limitations are necessary to potentially optimize the efficacy of this already promising treatment option in TRD. Finally, it is also essential that research investigate the mechanisms of therapeutic efficacy, thus increases in understanding can be translated into enhanced treatment. For several reasons that will be reviewed, cortical excitability may represent an important mechanism, linked to the therapeutic efficacy of this disorder.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".