Impact of repetitive transcranial magnetic stimulation on clinical and cognitive outcomes, and brain-derived neurotrophic factor levels in treatment-resistant depression
Bibliographic record
Abstract
Introduction: Treatment-resistant depression (TRD) affects approximately 30% of patients with major depressive disorder (MDD), for whom effective treatment options are limited. Repetitive transcranial magnetic stimulation (rTMS) has shown efficacy in alleviating depressive symptoms in TRD. However, it remains unclear if these improvements are driven or mediated by changes in cognitive function or biological markers, such as brain-derived neurotrophic factor (BDNF). Methods: This study examines the effects of rTMS on depressive symptoms, cognition, and BDNF levels, as well as the potential moderating role of lifetime suicidal attempts (LSA) on cognition and the predictive value of baseline BDNF for clinical outcomes. Twenty-five TRD patients were included, with 13 in the rTMS treatment group (receiving 20 sessions of rTMS over four weeks) and 12 as control group. Depression severity, cognitive function (Mini-Mental State Examination, Verbal Fluency, Digit Span), and serum BDNF levels were measured pre- and post-treatment. Mixed-effects linear regression models assessed clinical and biological associations. Results: rTMS significantly reduced HAM-D (p < 0.001) and CGI (p < 0.001) scores compared to controls. Cognitive performance improved significantly in MMSE (p = 0.049) and Digit Span (p = 0.04), with no significant changes in BDNF levels (p = 0.39). LSA did not moderate cognitive outcomes, and baseline BDNF did not predict clinical improvement (p = 0.68). Discussion: rTMS reduced depressive symptoms in TRD patients, with modest cognitive benefits. Baseline BDNF did not predict outcomes, though the lack of significant change suggests complex neuroplastic responses. Future studies should include larger samples and refined biomarker assessments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".