Sex-specific cortical brain differences in children at familial high risk for schizophrenia or bipolar disorder
Bibliographic record
Abstract
Abstract Background Schizophrenia (SZ) and bipolar disorder (BP) are severe psychiatric disorders with neurodevelopmental underpinnings. Familial high risk (FHR) is the strongest predictor of developing SZ and BP. Children at FHR offer a unique opportunity to identify early brain markers of vulnerability. However, previous studies often span wide age ranges and neglect sex differences, despite evidence of distinct sex-specific brain developmental trajectories. We investigated sex-specific group differences in brain morphometry among 11-12-year-old children at FHR for SZ (FHR-SZ) or BP (FHR-BP). Methods This study included 278 children from the Danish High Risk and Resilience Study (VIA11): 101 FHR-SZ, 64 FHR-BP, and 113 population-based controls (PBCs). Groups matched on age and sex. Structural MRI scans were acquired on 3T scanners at two sites. Brain volume, cortical volume, surface area, and cortical thickness were extracted using FreeSurfer. Results Significant group-by-sex interactions were observed for brain volume, cortical volume, and surface area (eta 2 =0.030-0.038; p=0.006-0.016). Males at FHR-SZ exhibited smaller brain volume, cortical volume, and surface area than PBC males (Cohen’s d =-0.624--0.489; p=0.002-0.015), while FHR-BP females had larger brain and cortical volumes than PBC females (Cohen’s d =0.525-0.537; p=0.017-0.020). No significant differences were observed for cortical thickness (p>0.210). Conclusions Children at FHR-SZ and FHR-BP exhibited sex-specific morphometric differences, potentially reflecting sex-specific endophenotypic markers of risk. Our findings highlight the importance of incorporating sex as a factor in neurodevelopmental psychiatric research. Longitudinal studies are needed to track how these neuroanatomical differences evolve over time and to evaluate their predictive value for transition to SZ or BP.
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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) | 0.002 | 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".