Arcuate fasciculus and pre-reading language development in children with prenatal alcohol exposure
Bibliographic record
Abstract
Introduction Prenatal alcohol exposure (PAE) contributes to widespread neurodevelopmental challenges, including reading, and has been associated with altered white matter. Here, we aimed to investigate whether arcuate fasciculus (AF) development is associated with pre-reading language skills in young children with PAE. Methods A total of 51 children with confirmed PAE (25 males; 5.6 ± 1.1 years) and 116 unexposed controls (57 males; 4.6 ± 1.2 years) underwent longitudinal diffusion tensor imaging (DTI), for a total of 111 scans from participants with PAE and 381 scans in the unexposed control group. We delineated the left and right AF and extracted mean fractional anisotropy (FA) and mean diffusivity (MD). Pre-reading language ability was assessed using age-standardized phonological processing (PP) and speeded naming (SN) scores of the NEPSY-II. Linear mixed effects models were run to determine the relationship between diffusion metrics and age, group, sex, and age-by-group interactions, with subject modeled as a random factor. A secondary mixed effect model analysis assessed the influence of white matter microstructure and PAE on pre-reading language ability using diffusion metric-by-age-by-group interactions, with 51 age- and sex-matched unexposed controls. Results Phonological processing (PP) and SN scores were significantly lower in the PAE group (p< 0.001). In the right AF, there were significant age-by-group interactions for FA (p< 0.001) and MD (p= 0.0173). In the left AF, there was a nominally significant age-by-group interaction for MD that failed to survive correction (p= 0.0418). For the pre-reading analysis, a significant diffusion-by-age-by-group interaction was found for left FA (p= 0.0029) in predicting SN scores, and for the right FA (p= 0.00691) in predicting PP scores. Discussion Children with PAE showed altered developmental trajectories for the AF, compared with unexposed controls. Children with PAE, regardless of age, showed altered brain-language relationships that resembled those seen in younger typically developing children. Our findings support the contention that altered developmental trajectories in the AF may be associated with functional outcomes in young children 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 | 0.001 |
| 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) | 0.002 | 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".