Associations between white matter asymmetry and communication skills in children with prenatal alcohol exposure
Bibliographic record
Abstract
BACKGROUND: Prenatal alcohol exposure (PAE) occurs in ~10 % of pregnancies and can cause behavioral and neurological deficits, including alterations to white matter pathways involved in language processing. Language and communication skills are generally left-lateralized in the brain, and this asymmetry is associated with better performance in typically developing individuals, while alterations to this association are found in children with language challenges. However, the degree of asymmetry and its relationship with language skills remain poorly understood in children with PAE. METHODS: 200 datasets collected from 98 children (46 with PAE) aged 4-8 years were included here. Language skills were assessed using the Children's Communication Checklist, 2nd edition (CCC-2) parent report. Diffusion MRI was used to examine white matter microstructure and asymmetry in five major language white matter pathways. Measures of white matter microstructure were extracted (fractional anisotropy and mean diffusivity), and a laterality index was calculated. Linear mixed models were used to test associations between language scores and white matter laterality, and whether PAE moderates this relationship. RESULTS: Children with PAE had lower language scores than controls across all CCC-2 indices. Both groups had similar patterns of white matter asymmetry; however, leftward white matter lateralization was associated with worse language scores in children with PAE, but better language scores in unexposed children. CONCLUSION: Our findings show alterations to the white matter asymmetry-language relationship in children with PAE. This may indicate an altered language processing mechanism that could underlie language deficits observed in many individuals with PAE.
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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 | 0.003 |
| 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 | 0.001 |
| 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".