Grey matter abnormalities in first‐episode mania: A systematic review and meta‐analysis of voxel‐based morphometry studies
Bibliographic record
Abstract
OBJECTIVES: It has been proposed that different stages of bipolar disorder may be underpinned by distinct neurobiological substrates. However, structural neuroimaging studies in early stages of the illness are limited by small sample sizes yielding inconsistent findings. The purpose of this systematic review and meta-analysis, therefore, was to identify regional grey matter volume (GMV) changes that are consistently associated with first episode of mania (FEM). METHODS: Following PRISMA guidelines, we conducted a systematic search of the literature to identify Voxel-Based Morphometry (VBM) studies in FEM patients compared with healthy individuals. We then conducted a voxel-wise meta-analysis using Seed-based d-Mapping technique. Finally, we performed univariate meta-regression analyses to explore the potential effects of moderator variables including age, gender, and percentage of lithium users on GMV alterations. RESULTS: We identified 15 VBM studies and included 12 studies in the meta-analysis. Four studies found no regional differences in GM volumes while other 11 studies reported volume changes in frontal and temporal regions as well as anterior cingulate cortex (ACC), cerebellum and basal ganglia. The meta-analysis revealed a single cluster of GMV reduction in bilateral pregenual ACC in patients with FEM compared to healthy individuals (P < .001). The Egger's test showed no evidence of publication bias at peak voxel level (P = .447). Meta-regression analyses revealed no significant effects of moderators evaluated. CONCLUSIONS: Structural brain changes are evident in the early stages of bipolar disorder. GMV reduction in bilateral pregenual ACC is the most consistent finding in VBM studies of FEM.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| 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".