Altered neural activities during emotion regulation in depression: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: Deficient neural activities during emotion regulation have been reported in depression. We sought to conduct a meta-analysis to provide a comprehensive description of these neural alterations during use of emotion regulation strategies among patients with depression, including major depressive disorder (MDD) and bipolar disorder (BD). METHODS: mapping, we conducted voxel-wise meta-analyses of the neural activation pattern differences between the 2 groups across conditions involving emotion regulation and those where emotion regulation was not needed. RESULTS: We included 33 studies reporting 34 data sets, including 23 involving MDD (571 people with MDD and 578 matched controls) and 11 involving BD (358 people with BD and 369 matched controls). Overall, compared with controls, patients with depression showed hyperactivity in the insula and postcentral gyrus, and hypoactivity in the prefrontal part of the inferior, middle, and superior frontal gyrus, the middle temporal gyrus, and the supplementary motor area. In subgroup analyses, data from patients with MDD and studies focused on decreasing negative emotions or using the emotional strategy of reappraisal reported specific hypoactivity in the middle cerebellar peduncles. LIMITATIONS: Given limited studies involving patients with BD, we were unable to detect the common and distinct abnormalities in neural activation between MDD and BD. We did not conduct any meta-regression analyses because of limited information. CONCLUSION: In this meta-analysis, we identified hyperactivity in brain regions associated with emotional experience and hypoactivity in brain regions associated with cognitive control during emotion regulation among patients with depression, relative to healthy controls. These findings could help indicate a target for future interventions aimed at increasing emotion regulation capacity for patients with depression.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.031 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".