Differential impact of manic versus depressive episode recurrence on longitudinal gray matter volume changes in bipolar disorder
Bibliographic record
Abstract
Bipolar disorder (BD) is a severe mental disorder, characterized by episodes of mania and depression. The longitudinal neurobiological impact of BD episodes on brain structure remains largely unknown. In 124 age-sex-matched participants (62 BD patients; 62 healthy controls; HCs), aged 20-62 years, we investigated the longitudinal relationship between BD episodes and whole-brain gray matter volume (GMV) changes (3 Tesla MRI) during a two-year interval, using voxel-based morphometry in SPM12/CAT12. We compared GMV trajectories between BD patients with at least one depressive or manic episode during the two-year interval, BD patients without an episode, and HCs. We explored associations between GMV changes and clinical variables, like the number and duration of depressive or manic episodes both during the two-year interval and before baseline assessment. BD patients showed GMV increases in the right exterior cerebellum with an increasing number of depressive episodes during the two-year interval. BD patients without recurrence showed GMV reductions in this area, relative to BD patients with recurrence and HCs. Notably, BD patients without recurrence exhibited greater GMV reductions during the two-year interval, the longer they had spent in a manic episode before baseline. Our findings underscore the dynamic nature of brain changes in BD. GMV increases in BD patients with recurrence may be due to acute neuroinflammatory mechanisms including glial cell proliferation, whereas GMV reductions in BD patients without recurrence may result from abnormal synaptic refinement or pruning, as a consequence of past neuroinflammation during BD episodes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".