Accelerated Repetitive Transcranial Magnetic Stimulation to Treat Major Depression: The Past, Present, and Future
Bibliographic record
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an effective and evidence-based therapy for treatment-resistant major depressive disorder. A conventional course of rTMS applies 20-30 daily sessions over 4-6 weeks. The schedule of rTMS delivery can be accelerated by applying multiple stimulation sessions per day, which reduces the duration of a treatment course with a predefined number of sessions. Accelerated rTMS reduces time demands, improves clinical efficiency, and potentially induces faster onset of antidepressant effects. However, considerable heterogeneity exists across study designs. Stimulation protocols vary in parameters such as the stimulation target, frequency, intensity, number of pulses applied per session or over a course of treatment, and duration of intersession intervals. In this article, clinician-researchers and neuroscientists who have extensive research experience in accelerated rTMS synthesize a consensus based on two decades of investigation and development, from early studies ("Past") to contemporaneous theta burst stimulation, a time-efficient form of rTMS gaining acceptance in clinical settings ("Present"). We propose descriptive nomenclature for accelerated rTMS, recommend avenues to optimize therapeutic and efficiency potential, and suggest using neuroimaging and electrophysiological biomarkers to individualize treatment protocols ("Future"). Overall, empirical studies show that accelerated rTMS protocols are well tolerated and not associated with serious adverse effects. Importantly, the antidepressant efficacy of accelerated rTMS appears comparable to conventional, once daily rTMS protocols. Whether accelerated rTMS induces antidepressant effects more quickly remains uncertain. On present evidence, treatment protocols incorporating high pulse dose and multiple treatments per day show promise and improved efficacy.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".