Non-invasive brain stimulation combined with psychosocial intervention for depression: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Objectives This review investigates the efficacy and safety of non-invasive brain stimulation (NIBS) combined with psychosocial intervention on depressive symptoms. Materials and methods We systematically searched five electronic databases from their inception to June 2021: PubMed, Embase, PsycINFO, Web of Science, and Medline. Randomized or non-randomized clinical trials in which NIBS plus psychosocial intervention was compared to control conditions in people with depressive symptoms were included. Results A total of 17 eligible studies with 660 participants were included. The meta-analysis results showed that NIBS combined with psychosocial therapy had a positive effect on moderate to severe depression ([SMD = − 0.46, 95%CI (− 0.90, − 0.02), I2 = 73%, p < .01]), but did not significantly improve minimal to mild depression ([SMD = − 0.12, 95%CI (− 0.42, 0.18), I2 = 0%, p = .63]). Compared with NIBS alone, the combination treatment had a significantly greater effect in alleviating depressive symptoms ([SMD = − 0.84, 95%CI (− 1.25, − 0.42), I2 = 0%, p = .93]). However, our results suggested that the pooled effect size of ameliorating depression of NIBS plus psychosocial intervention had no significant difference compared with the combination of sham NIBS [SMD = − 0.12, 95%CI (− 0.31, 0.07), I2 = 0%, p = .60] and psychosocial intervention alone [SMD = − 0.97, 95%CI (− 2.32, 0.38), I2 = 72%, p = .01]. Conclusion NIBS when combined with psychosocial intervention has a significant positive effect in alleviating moderately to severely depressive symptoms. Further well-designed studies of NIBS combined with psychosocial intervention on depression should be carried out to consolidate the conclusions and explore the in-depth underlying mechanism.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".