Evaluation of Brain Alterations and Behavior in Children With Low Levels of Prenatal Alcohol Exposure
Bibliographic record
Abstract
Importance: High levels of prenatal alcohol exposure (PAE) are associated with widespread behavioral and cognitive problems as well as structural alterations of the brain. However, it remains unclear whether low levels of PAE affect brain structure and function, and prior studies generally have not had well-matched control populations (eg, for sociodemographic variables). Objective: To compare structural brain alterations and behavioral changes in children with lower levels of PAE with those of well-matched controls with no PAE. Design, Setting, and Participants: In this cross-sectional study, participants were selected from the Adolescent Brain Cognitive Development study. Children with PAE were compared with controls matched for age, sex, family income, maternal educational level, and caregiver status. Neither group had prenatal exposure to other adverse substances (eg, tobacco, cannabis, illicit drugs). Data were collected from September 1, 2016, to November 15, 2018, and analyzed from October 14, 2020, to February 14, 2022. Exposures: Diffusion tensor imaging, resting-state functional magnetic resonance imaging (MRI), and Child Behavior Checklist (CBCL) administration. Main Outcomes and Measures: Fractional anisotropy (FA); mean, axial, and radial diffusivity from diffusion tensor imaging; brain functional signal variations from functional MRI; and several scores, including internalizing and externalizing behavior problems, from the CBCL. Spearman correlation coefficients between diffusion tensor imaging and functional MRI measures and the CBCL scores were calculated. Results: A total of 270 children were included in the analysis (mean [SD] age, 9.86 [0.46] years; 141 female [52.2%] and 129 male [47.8%]), consisting of 135 children with PAE (mean [SD] age, 9.85 [0.65] years; 73 female [54.1%] and 62 male [45.9%]) (mean exposure, 1 drink/wk) and 135 unexposed controls (mean [SD] age, 9.87 [0.04] years; 68 female [50.4%] and 67 male [49.6%]). Children with PAE had lower mean (SD) FA in white matter of the left postcentral (0.35 [0.05] vs 0.36 [0.04]; mean difference, -0.02 [95% CI, -0.03 to -0.01]), left inferior parietal (0.31 [0.07] vs 0.33 [0.06]; mean difference, -0.03 [95% CI, -0.04 to -0.01]), left planum temporale (0.26 [0.04] vs 0.28 [0.03]; mean difference, -0.02 [95% CI, -0.03 to -0.01]), left inferior occipital (0.30 [0.07] vs 0.32 [0.05]; mean difference, -0.03 [95% CI, -0.04 to -0.01]), and right middle occipital (0.30 [0.04] vs 0.31 [0.04]; mean difference, -0.01 [95% CI, -0.02 to -0.01]) areas compared with controls, and higher FA in the gray matter of the putamen (0.22 [0.03] vs 0.21 [0.02]; mean difference, 0.01 [95% CI, 0.005-0.02]). Externalizing behavior scores were higher (worse) in children with PAE than in controls (mean [SD], 45.2 [9.0] vs 42.8 [9.0]; mean difference, 2.39 [95% CI, 0.30-4.47]). Several of these regions had significant group-behavior interactions, such that the higher FA was associated with less problematic behaviors in controls (ρ range, -0.24 to -0.08) but no associations were present in the PAE group (ρ range, 0.02-0.16). Conclusions and Relevance: In this cross-sectional study, children with low levels of PAE had lower FA and more behavioral problems compared with a well-matched control group. These results suggest that PAE, even in small amounts, has a measurable effect on brain structure in children.
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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 | 0.002 | 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".