Preservation of Gray Matter Volume in Early Stage of Bipolar Disorder: A Case for Early Intervention: Préservation du volume de matière grise au stade précoce du trouble bipolaire: un cas pour intervention précoce
Bibliographic record
Abstract
Objective: It has been proposed that different stages of the bipolar disorder might have distinct neurobiological changes. However, the evidence for this has not been consistent, as the studies in early stages of the illness are limited by small sample sizes. The purpose of this study was to investigate the gray matter volume changes in bipolar patients who recently recovered from their first episode of mania (FEM). Methods: Using a whole-brain voxel-based analysis, we compared the regional gray matter volumes of 61 bipolar patients who have recovered from their FEM in the past 3 months with 43 age- and gender-matched healthy participants. We also performed a series of subgroup analyses to determine the effects of hospitalization during the FEM, history of depressive episodes, and exposure to lithium. Results: No statistically significant difference was found between gray matter volumes of FEM patients and healthy participants, even at a more liberal threshold ( P < 0.001, uncorrected for multiple comparisons). Voxel-based subgroup analyses did not reveal significant gray matter differences except for a trend toward decreased gray matter volume in left lateral occipital cortex ( P < 0.001, uncorrected) in patients with a previous history of depression. Conclusion: This study represents the largest structural neuroimaging investigation of FEM published to date. Early stage of bipolar disorder was not found to be associated with significant gray matter volume changes. Our findings suggest that there might be a window of opportunity for early intervention strategies to prevent or delay neuroprogression in bipolar disorder.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".