Maternal Antenatal Depression and Deviations From Normative Brain Development in Offspring
Bibliographic record
Abstract
BACKGROUND: Maternal mental health during pregnancy is important for optimal brain development in offspring. Exposure to maternal depression in utero has been shown to be associated with accelerated global cortical brain aging in young adulthood. However, it is not clear whether maternal antenatal depression (MAD) also predicts region-specific deviations from normative development of cortical thickness, surface area, and subcortical volume in offspring or whether the region-specific deviations remain stable throughout the third decade of life. METHODS: Two neuroimaging follow-ups of a prenatal birth cohort in young adulthood tested whether MAD was associated with deviations from normative brain development in the offspring in their early and late 20s, as modeled using 37,407 magnetic resonance images from individuals 3 to 90 years of age (CentileBrain). RESULTS: MAD predicted deviations from normative development of thalamus and nucleus accumbens but not other subcortical volumes, surface area, or cortical thickness. Women exposed to greater MAD showed a smaller thalamus and nucleus accumbens in both the early and late 20s than expected based on age- and sex-normative means. In contrast, men exposed to greater MAD showed no deviations from the development of the thalamus but did show a larger nucleus accumbens in their late 20s than expected based on age- and sex-normative means. CONCLUSIONS: Given the importance of the thalamus in the pathogenesis of major depressive disorder and the critical role of the nucleus accumbens in reward and motivation, the altered development of these subcortical structures may contribute to a higher risk of depression.
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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 | 0.000 | 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".