Decreased Cerebral Blood Flow in Young Children With Prenatal Alcohol Exposure
Bibliographic record
Abstract
Background: Alcohol exposure during pregnancy can hinder neurodevelopment, causing a range of behavioral and neurological deficits, including structural and functional brain alterations. Moreover, prenatal alcohol exposure (PAE) is associated with cerebral blood flow (CBF) abnormalities in preclinical models. However, it remains unclear to what extent CBF is affected by PAE in humans. In this study, we investigated CBF in young children with PAE. Methods: A total of 171 scans collected from 99 children (35 children [51 scans] with PAE) between the ages of 3 to 8 years were examined. Children underwent a magnetic resonance imaging scan to acquire arterial spin labeling images to quantify CBF. CBF maps were segmented into 110 gray matter regions, and linear mixed models were used to test CBF differences between children with PAE and unexposed children in each region. Results: Children with PAE had decreased CBF compared with unexposed control children, with the largest effects seen in subcortical and medial frontal regions. Conclusions: CBF is negatively altered in children with PAE. CBF reductions may alter nutrient and oxygen delivery to the brain, resulting in impaired neurodevelopment and helping to explain functional deficits seen in PAE. The largest effects were seen in regions associated with cognitive and behavioral functions that are commonly impaired in individuals with PAE. Our findings contribute additional insight into the adverse effects of PAE on neurodevelopment and lay the groundwork for future studies to investigate CBF effects and how they relate to behavior.
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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 | 0.001 |
| 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".