The Effects of Prenatal Alcohol Exposure on Structural Brain Connectivity and Early Language Skills in a South African Birth Cohort
Bibliographic record
Abstract
Prenatal alcohol exposure (PAE) is associated with various neurological, behavioral and cognitive deficits, including reading and language. Previous studies have demonstrated altered white matter in children and adolescents with PAE and associations with reading and language performance in children aged 3 years and older. However, little research has focused on the toddler years, despite this being a critical period for behavioral and neural development. We aimed to determine associations between structural brain connectivity and early language skills in toddlers, in the context of PAE. Eighty-eight toddlers (2-3 yr, 56 males), 23 of whom had PAE, underwent a diffusion MRI scan in Cape Town, South Africa, with language skills assessed using the Expressive and Receptive Communication subtests from the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III). Diffusion scans were preprocessed to create a structural network of regions associated with language skills using graph theory analysis. Linear regression models were used to examine moderation effects of PAE on structural network properties and language skills. Toddlers with PAE had higher structural connectivity in language networks than unexposed children. PAE moderated the relationship between structural network properties and Expressive Communication scores. None of the effects survived correction for multiple comparisons. Our findings show weak moderation effects of PAE on structural language network properties and language skills. Our study sheds light on the structural connectivity correlates of early language skills in an understudied population during a critical neurodevelopmental period, laying the foundation for future research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".