Early White Matter Microstructure Alterations in Infants with Down Syndrome
Bibliographic record
Abstract
Down syndrome (DS), resulting from Trisomy 21, is the most prevalent chromosomal disorder and a leading cause of intellectual disability. Despite the significant impact of Trisomy 21 on brain development, research on white matter (WM) microstructure in infants with DS remains limited. While widespread reductions in WM integrity have been identified in children and young adults with DS, no study has examined WM microstructure in infancy. This study investigates early WM microstructure in infants with DS using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). Forty-nine infants with DS (28 [57.14%] female) and 36 control (18 [48.65%] female) infants were scanned at 6 months of age. Infants with DS showed significant reductions in fractional anisotropy and neurite density index across multiple association tracts, particularly in the inferior fronto-occipital fasciculus and superior longitudinal fasciculus II, consistent with reduced structural integrity and neurite density. Increased radial diffusivity was observed in these tracts, a feature associated with disrupted myelination. In the inferior fronto-occipital fasciculus, superior longitudinal fasciculus II, and uncinate fasciculus, an elevated orientation dispersion index suggested increased neurite dispersion and fanning in infants with DS. These findings reveal widespread WM developmental alterations in DS, providing new insights into the early neurodevelopment of DS, which may inform timing of early therapeutic interventions.
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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".