Brain morphology in children with 47,<scp>XYY</scp> syndrome: a voxel‐ and surface‐based morphometric study
Bibliographic record
Abstract
The neurocognitive and behavioral profile of individuals with 47,XYY is increasingly documented; however, very little is known about the effect of a supernumerary Y-chromosome on brain development. Establishing the neural phenotype associated with 47,XYY may prove valuable in clarifying the role of Y-chromosome gene dosage effects, a potential factor in several neuropsychiatric disorders that show a prevalence bias toward males, including autism spectrum disorders. Here, we investigated brain structure in 10 young boys with 47,XYY and 10 age-matched healthy controls by combining voxel-based morphometry (VBM) and surface-based morphometry (SBM). The VBM results show the existence of altered gray matter volume (GMV) in the insular and parietal regions of 47,XYY relative to controls, changes that were paralleled by extensive modifications in white matter (WM) bilaterally in the frontal and superior parietal lobes. The SBM analyses corroborated these findings and revealed the presence of abnormal surface area and cortical thinning in regions with abnormal GMV and WMV. Overall, these preliminary results demonstrate a significant impact of a supernumerary Y-chromosome on brain development, provide a neural basis for the motor, speech and behavior regulation difficulties associated with 47,XYY and may relate to sexual dimorphism in these areas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".