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Record W4405573987 · doi:10.1186/s11689-024-09581-6

Brain volumes, cognitive, and adaptive skills in school-age children with Down syndrome

2024· article· en· W4405573987 on OpenAlexafffund
Rebecca Grzadzinski, Kattia Mata, Alapika Jatkar, Dea Garic, Mark D. Shen, Jessica B. Girault, Tanya St. John, Juhi Pandey, Lonnie Zwaigenbaum, Annette Estes, Audrey M. Shen, Stephen R. Dager, Robert T. Schultz, Kelly N. Botteron, Natasha Marrus, Martin Styner, Alan Evans, Sun Hyung Kim, Robert C. McKinstry, Guido Gerig, Joseph Piven, Heather C. Hazlett, C. Chappell, Daniel J. Shaw, J. Constantino, Jed T. Elison, Jason J. Wolff, J. R. Pruett, D. L. Collins, Vladimir Fonov, Leigh MacIntyre, Samir Das, K. Truong, Heather E. Volk, Dani Fallin

Bibliographic record

VenueJournal of Neurodevelopmental Disorders · 2024
Typearticle
Languageen
FieldMedicine
TopicDown syndrome and intellectual disability research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and HospitalUniversity of Alberta
FundersNational Center for Advancing Translational SciencesNational Institute of Mental HealthYork UniversityWashington University in St. LouisUniversity of AlbertaUniversity of WashingtonEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentUniversity of North Carolina at Chapel HillNational Institutes of HealthJohns Hopkins UniversityUniversity of MinnesotaChildren's Hospital of Philadelphia
KeywordsBrain sizeCognitionNeuropsychologyPsychologyNeurologyDown syndromeEffects of sleep deprivation on cognitive performanceTypically developingAutismAudiologyClinical psychologyMedicineDevelopmental psychologyPediatricsPsychiatryMagnetic resonance imaging

Abstract

fetched live from OpenAlex

BACKGROUND: Down syndrome (DS) is the most common congenital neurodevelopmental disorder, present in about 1 in every 700 live births. Despite its prevalence, literature exploring the neurobiology underlying DS and how this neurobiology is related to behavior is limited. This study fills this gap by examining cortical volumes and behavioral correlates in school-age children with DS. METHODS: School-age children (mean = 9.7 years ± 1.1) underwent comprehensive assessments, including cognitive and adaptive assessments, as well as an MRI scan without the use of sedation. Children with DS (n = 35) were compared to available samples of typically developing (TD; n = 80) and ASD children (n = 29). ANOVAs were conducted to compare groups on cognitive and adaptive assessments. ANCOVAs (covarying for age, sex, and total cerebral volume; TCV) compared cortical brain volumes between groups. Correlations between behavioral metrics and cortical and cerebellar volumes (separately for gray (GM) and white matter (WM)) were conducted separately by group. RESULTS: As expected, children with DS had significantly lower cognitive skills compared to ASD and TD children. Daily Living adaptive skills were comparable between ASD children and children with DS, and both groups scored lower than TD children. Children with DS exhibited a smaller TCV compared to ASD and TD children. Additionally, when controlling for TCV, age, and sex, children with DS had significantly smaller total GM and tissue volumes. Cerebellum volumes were significantly correlated with Daily Living adaptive behaviors in the DS group only. CONCLUSIONS: Despite children with DS exhibiting lower cognitive skills and smaller brain volume overall than children with ASD, their deficits in Socialization and Daily Living adaptive skills are comparable. Differences in lobar volumes (e.g., Right Frontal GM/WM, Left Frontal WM, and Left and Right Temporal WM) were observed above and beyond overall differences in total volume. The correlation between cerebellum volumes and Daily Living adaptive behaviors in the DS group provides a novel area to explore in future research.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.057
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.276
Teacher spread0.264 · 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 teacher head, 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

Citations3
Published2024
Admission routes2
Has abstractyes

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