Brain volumes in psychotic youth with schizophrenia and mood disorders
Bibliographic record
Abstract
Background: We sought to test the hypothesis that deficits in grey matter volume are characteristic of psychotic youth with early-onset schizophrenia-spectrum disorders (EOSS) but not of psychotic youth with early-onset mood disorders (EOMD). Methods: We used magnetic resonance imaging to examine brain volume in 24 psychotic youth (13 male, 11 female) with EOSS ( n = 12) or EOMD ( n = 12) and 17 healthy controls (10 male, 7 female). We measured the volume of grey and white matter using an automated segmentation program. Results: After adjustment for age and intracranial volume, whole brain volume was lower in the EOSS patients than in the healthy controls ( p = 0.001) and EOMD patients ( p = 0.002). The EOSS patients had a deficit in grey matter volume ( p = 0.005), especially in the frontal ( p = 0.003) and parietal ( p = 0.006) lobes, with no significant differences in white matter volume. Limitations: The main limitations of our study were its small sample size and the inclusion of patients with depression and mania in the affective group. Conclusion: Adolescents with EOSS have grey matter deficits compared with healthy controls and psychotic adolescents with EOMD. Our results suggest that grey matter deficits are not generally associated with psychosis but may be specifically associated with schizophrenia. Larger studies with consistent methods are needed to reconcile the contradictory findings among imaging studies involving psychotic youth.
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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 | 0.000 | 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) | 0.001 | 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".