Repetitive Transcranial Magnetic Stimulation for Comorbid Major Depressive Disorder and Alcohol Use Disorder
Bibliographic record
Abstract
Major depressive disorder (MDD) and alcohol use disorder (AUD) are leading causes of disability, and patients are frequently affected by both conditions. This comorbidity is known to confer worse outcomes and greater illness severity. Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive neuromodulation method that has demonstrated antidepressant effects. However, the study of rTMS for patients with MDD and commonly associated comorbidities, such as AUD, has been largely overlooked, despite significant overlap in clinical presentation and neurobiological mechanisms. This narrative review aims to highlight the interrelated aspects of the literature on rTMS for MDD and rTMS for AUD. First, we summarize the available evidence on the effectiveness of rTMS for each condition, both most studied through stimulation of the dorsolateral prefrontal cortex (DLPFC). Second, we describe common symptom constructs that can be modulated by rTMS, such as executive dysfunction, that are transdiagnostic across these disorders. Lastly, we describe promising approaches in the personalization and optimization of rTMS that may be applicable to both AUD and MDD. By bridging the gap between research efforts in MDD and AUD, rTMS is well positioned to be developed as a treatment for the many patients who have both conditions concurrently.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".