Socioeconomic factors, brain-derived neurotrophic factor Val66Met polymorphism, and cortical structure in children and adolescents
Bibliographic record
Abstract
Variability in associations between socioeconomic status and cortical gray matter may be due in part to the common, functional brain-derived neurotrophic factor (BDNF) Val66Met polymorphism, which alters BDNF signaling. In this study, we examined whether BDNF Val66Met genotype moderated the associations between socioeconomic factors (family income, parental education) and cortical surface area (SA) and thickness (CT) in two large independent samples of typically-developing children and adolescents. Participants were 3- to 21-year-olds (N = 383; 47% female) from the Pediatric Imaging, Neurocognition, and Genetics (PING) study and 11- to 14-year-olds (N = 2566; 46% female) in the Adolescent Brain Cognitive Development (ABCD) study. High-resolution, T1-weighted magnetic resonance imaging data were acquired in both studies. Analyses were conducted on global and regional SA and CT. In the PING sample, BDNF Val66Met genotype significantly moderated the association between family income and total SA and SA in the left fusiform gyrus. In the ABCD sample, there were no significant interactions for global or regional SA or CT. Collectively, these results suggest that BDNF Val66Met genotype may not explain variability in associations between socioeconomic factors and SA or CT in children and adolescents.
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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 | 0.002 |
| 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) | 0.001 | 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".