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Record W4408006876 · doi:10.1101/2025.02.26.25322913

Early White Matter Microstructure Alterations in Infants with Down Syndrome

2025· preprint· en· W4408006876 on OpenAlexaff
Omar Azrak, Dea Garic, Meghan R. Swanson, Rebecca Grzadzinski, Khalid Al‐Ali, Mark D. Shen, Jessica B. Girault, Tanya St. John, Juhi Pandey, Lonnie Zwaigenbaum, Annette Estes, Jason J. Wolff, Stephen R. Dager, Robert T. Schultz, Alan C. Evans, Jed T. Elison, Essa Yacoub, Sun Hyung Kim, Robert C. McKinstry, Guido Gerig, John R. Pruett, Joseph Piven, Kelly N Botteron, Heather C. Hazlett, Natasha Marrus, Martin Styner

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalUniversity of Alberta
FundersNational Institutes of Health
KeywordsWhite matterMicrostructureWhite (mutation)MedicineMaterials scienceMagnetic resonance imagingMetallurgyBiologyGenetics

Abstract

fetched live from OpenAlex

Importance: Down syndrome, resulting from trisomy 21, is the most prevalent chromosomal disorder and a leading cause of intellectual disability. Despite its significant impact on brain development, research on the white matter microstructure in infants with Down syndrome remains limited. Objective: To investigate early white matter microstructure in infants with Down syndrome using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). Design: Infants were recruited and scanned between March 2019 and May 2024 as participants in prospective studies conducted by the Infant Brain Imaging Study (IBIS) Network. Data were analyzed in October 2024. Setting: Data collection occurred at five research centers in Minnesota, Missouri, North Carolina, Pennsylvania, and Washington. Participants: Down syndrome and control infants were scanned at 6 months of age. Control infants had no Down syndrome diagnosis and either had a typically developing older sibling or, if they had an older sibling with autism, were confirmed not to meet clinical best estimate criteria for an autism diagnosis. Exposure: Diagnosis of Down syndrome. Main Outcomes and Measures: The outcome of interest was white matter microstructure quantified using DTI and NODDI measures. Results: A total of 49 Down syndrome (28 [57.14%] female) and 37 control (18 [48.65%] female) infants were included. Infants with Down syndrome 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. These tracts also demonstrated increased radial diffusivity, suggesting delayed myelination. The inferior fronto-occipital fasciculus and uncinate fasciculus exhibited increased neurite dispersion and fanning in Down syndrome infants, reflected by elevated orientation dispersion index. Notably, the optic tracts in Down syndrome infants exhibited a distinct pattern of elevated fractional anisotropy and axial diffusivity, and lower radial diffusivity and orientation dispersion index, suggesting an early maturation of these pathways. Conclusions and Relevance: This first characterization of white matter microstructure in Down syndrome infants reveals widespread white matter developmental delays. These findings provide new insights into the early neurodevelopment of Down syndrome and may inform early therapeutic interventions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.020
GPT teacher head0.303
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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