Voxel‐based morphometry meta‐analysis of gray and white matter finds significant areas of differences in bipolar patients from healthy controls
Bibliographic record
Abstract
OBJECTIVE: We present a retrospective meta-analysis of voxel-based morphometry (VBM) of gray (GM) and white matter (WM) differences between patients with bipolar disorder (BD) and behaviorally healthy controls. METHODS: We used the activation likelihood estimation and Sleuth software for our meta-analysis, considering P-value maps at the cluster level inference of .05 with uncorrected P<.001. Results were visualized with the software MANGO. RESULTS: We included twenty-five articles in the analysis, and separated the comparisons where BD patients had lower GM or WM concentrations than controls (573 subjects, 21 experiments, and 117 locations/180 subjects, five experiments, and 15 locations, respectively) and the comparisons where BD patients had greater GM concentrations than controls (217 subjects, nine experiments, and 49 locations). Higher WM concentrations in BD patients were not detected. We observed for BD reduced GM concentrations in the left medial frontal gyrus and right inferior/precentral gyri encompassing the insular cortex, and greater GM concentrations in the left putamen. Further, lower WM concentrations were detected in the left inferior longitudinal fasciculus, left superior corona radiata, and left posterior cingulum. CONCLUSIONS: This meta-analysis confirms deterioration of frontal and insular regions as already found in previous meta-analysis. GM reductions in these regions could be related to emotional processing and decision making, which are typically impaired in BD. Moreover, we found abnormalities in precentral frontal areas and putamen that have been linked to more basic functions, which could point to sensory and specific cognitive deficits. Finally, WM reductions involved circuitry that may contribute to emotional dysregulation in BD.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.021 |
| 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) | 0.003 | 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".