Brain grey-matter volume alteration in adult patients with bipolar disorder under different conditions: a voxel-based meta-analysis
Bibliographic record
Abstract
Background: The literature on grey-matter volume alterations in bipolar disorder is heterogeneous in its findings. Methods: Using effect-size differential mapping, we conducted a meta-analysis of grey-matter volume alterations in patients with bipolar disorder compared with healthy controls. Results: We analyzed data from 50 studies that included 1843 patients with bipolar disorder and 2289 controls. Findings revealed lower grey-matter volumes in the bilateral superior frontal gyri, left anterior cingulate cortex and right insula in patients with bipolar disorder and in patients with bipolar disorder type I. Patients with bipolar disorder in the euthymic and depressive phases had spatially distinct regions of altered grey-matter volume. Meta-regression revealed that the proportion of female patients with bipolar disorder or bipolar disorder type I was negatively correlated with regional grey-matter alteration in the right insula; the proportion of patients with bipolar disorder or bipolar disorder type I taking lithium was positively correlated with regional grey-matter alterations in the left anterior cingulate/paracingulate gyri; and the proportion of patients taking antipsychotic medications was negatively correlated with alterations in the anterior cingulate/paracingulate gyri. Limitations: This study was cross-sectional; analysis techniques, patient characteristics and clinical variables in the included studies were heterogeneous. Conclusion: Structural grey-matter abnormalities in patients with bipolar disorder and bipolar disorder type I were mainly in the prefrontal cortex and insula. Patients' mood state might affect grey-matter alterations. Abnormalities in regional grey-matter volume could be correlated with patients' specific demographic and clinical features.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.024 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".