Maternal Depressive Symptoms During Pregnancy and Brain Age in Young Adult Offspring: Findings from a Prenatal Birth Cohort
Bibliographic record
Abstract
Maternal depression during pregnancy is associated with elevated risk of anxiety and depression in offspring, but the mechanisms are incompletely understood. Here we conducted a neuroimaging follow-up of a prenatal birth cohort from the European Longitudinal Study of Pregnancy and Childhood (n = 131; 53% women, age 23-24) to test whether deviations from age-normative structural brain development in young adulthood may partially underlie this link. Structural brain age was calculated based on previously published neuroanatomical age prediction models using cortical thickness maps from healthy controls aged 6-89. Brain age gap was computed as the difference between chronological and structural brain age. Participants also completed self-report measures of anxiety and mood dysregulation. Further, mothers of a subset of participants (n = 103, 54% women) answered a self-report questionnaire in 1990-1992 about depressive symptoms during pregnancy. Higher exposure to maternal depressive symptoms in utero showed a linear relationship with elevated brain age gap, which showed a quadratic relationship with anxiety and mood dysregulation in the young adult offspring. Our findings suggest that exposure to maternal depressive symptoms in utero may be associated with accelerated brain maturation and that deviations from age-normative structural brain development in either direction predict more anxiety and dysregulated mood in young adulthood.
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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 | 0.000 | 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".