Urban environment in early-life and brain morphology in preadolescents
Bibliographic record
Abstract
Rapid urbanization leads to increased exposure to air pollution, limited greenness, and denser built environments. However, evidence on how these urban factors influence brain development remains limited. We investigated associations between urban characteristics during pregnancy and childhood and brain morphology in preadolescence. The study included 2,895 children from the Dutch Generation R Study, with replication in 92 children from the French PELAGIE cohort. Twelve built environment and four urban natural space indicators were estimated at residential addresses during pregnancy and childhood. Brain outcomes included cortical gray matter, cerebral white matter, cerebellum, corpus callosum, subcortical structures, cortical thickness, and surface area assessed at 9–12 years. We applied multi-exposure regression models with data-driven variable selection and assessed mediation by air pollution and road-traffic noise, adjusting for confounders. In Generation R, higher surrounding greenness during pregnancy was associated with smaller cortical gray matter volume (–5132 mm 3 ; 95% CI: –8611, –1652), and higher facility richness with larger nucleus accumbens volume. During childhood, higher distance to blue space was associated with larger cortical gray matter volume, and higher transport land use with smaller hippocampus. No mediation by air pollution or road-traffic noise was observed. In PELAGIE, associations were consistent but not statistically significant. Cortical thickness was associated with several built environment indicators during childhood, and surrounding greenness was linked to smaller surface area in specific cortical regions. Our findings suggest that early-life exposure to urban environments may influence brain morphology, with distinct contributions from green space, blue space, and built environment factors.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".