Evaluation of Brain Alterations and Behavior in Children With Low Levels of Prenatal Alcohol Exposure
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».