Amygdala and Prefrontal Cortex Maturational Differences in Children and Adolescents With Prenatal Alcohol Exposure
Bibliographic record
Abstract
BACKGROUND: Prenatal alcohol exposure (PAE) has widespread effects on brain development. Alterations to the maturational timing of the amygdala, prefrontal cortex (PFC), and the white matter tracts connecting them may underlie behavioral differences, such as elevated risk taking and impulsivity in youth with PAE. METHODS: Here, we used T1 and diffusion-weighted magnetic resonance imaging to evaluate amygdala and PFC macrostructure (volume) and uncinate fasciculus and amygdala-PFC white matter tract microstructure (fractional anisotropy, mean diffusivity) development longitudinally in children and adolescents with PAE (n = 92 individuals [165 scans], ages 2-18 years) and unexposed participants (n = 148 individuals [606 scans], ages 2-17 years). We used generalized additive mixed-effects models to examine age-related changes in volume, fractional anisotropy, and mean diffusivity. RESULTS: Children and adolescents with PAE showed no significant amygdala volume development across the age range, and, compared with their unexposed counterparts, had shorter and delayed PFC development, earlier uncinate fasciculus development, and more protracted amygdala-PFC tract development in our age range. Participants with PAE also had smaller amygdala and PFC volumes, higher fractional anisotropy, and lower mean diffusivity in both tracts than unexposed individuals. CONCLUSIONS: Our findings show altered maturational patterns in amygdala-PFC structures and circuitry among children and adolescents with PAE that suggest reduced brain plasticity. Differences in the developmental timing of these regions may underlie behavioral challenges, such as elevated risk taking and impulsivity, in those 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 | 0.000 | 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) | 0.001 | 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".