Structural Magnetic Resonance Imaging-Based Surface Morphometry Analysis of Pediatric Down Syndrome
Bibliographic record
Abstract
Down syndrome (DS) is a genetic disorder characterized by intellectual disability whose etiology includes an additional partial or full copy of chromosome 21. Brain surface morphometry analyses can potentially assist in providing a better understanding of structural brain differences, and may help characterize DS-specific neurodevelopment. We performed a retrospective surface morphometry study of 73 magnetic resonance imaging (MRI) examinations of DS patients (aged 1 day to 22 years) and compared them to a large cohort of 993 brain MRI examinations of neurotypical participants, aged 1 day to 32 years. Surface curvature measurements, absolute surface area measurements, and surface areas as a percentage of total brain surface area (%TBSA) were extracted from each brain region in each examination. Results demonstrate broad reductions in surface area and abnormalities of surface curvature measurements across the brain in DS. After adjusting our regional surface area measurements as %TBSA, abnormally increased presentation in DS relative to neurotypical controls was observed in the left precentral, bilateral entorhinal, left parahippocampal, and bilateral perirhinal cortices, as well as Brodmann's area 44 (left), and the right temporal pole. Findings suggest the presence of developmental abnormalities of regional %TBSA in DS that can be characterized from clinical MRI examinations.
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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 | 0.001 |
| 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".