Neurobiological mechanisms of repetitive transcranial magnetic stimulation of the dorsolateral prefrontal cortex in depression: a systematic review
Bibliographic record
Abstract
Depression is one of the most prevalent mental illnesses worldwide and a leading cause of disability, especially in the setting of treatment resistance. In recent years, repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising alternative strategy for treatment-resistant depression and its clinical efficacy has been investigated intensively across the world. However, the underlying neurobiological mechanisms of the antidepressant effect of rTMS are still not fully understood. This review aims to systematically synthesize the literature on the neurobiological mechanisms of treatment response to rTMS in patients with depression. Medline (1996-2014), Embase (1980-2014) and PsycINFO (1806-2014) were searched under set terms. Three authors reviewed each article and came to consensus on the inclusion and exclusion criteria. All eligible studies were reviewed, duplicates were removed, and data were extracted individually. Of 1647 articles identified, 66 studies met both inclusion and exclusion criteria. rTMS affects various biological factors that can be measured by current biological techniques. Although a number of studies have explored the neurobiological mechanisms of rTMS, a large variety of rTMS protocols and parameters limits the ability to synthesize these findings into a coherent understanding. However, a convergence of findings suggest that rTMS exerts its therapeutic effects by altering levels of various neurochemicals, electrophysiology as well as blood flow and activity in the brain in a frequency-dependent manner. More research is needed to delineate the neurobiological mechanisms of the antidepressant effect of rTMS. The incorporation of biological assessments into future rTMS clinical trials will help in this regard.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".