Canonical TGF-β signaling regulates the relationship between prenatal maternal depression and amygdala development in early life
Bibliographic record
Abstract
Canonical transforming growth factor-beta (TGF-β) signaling exerts neuroprotection and influences memory formation and synaptic plasticity. It has been considered as a new target for the prevention and treatment of depression. This study aimed to examine its modulatory role in linking prenatal maternal depressive symptoms and the amygdala volumes from birth to 6 years of age. We included mother-child dyads (birth: n = 161; 4.5 years: n = 131; 6 years: n = 162) and acquired structural brain images of children at these three time points. Perinatal maternal depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale (EPDS) questionnaire to mothers at 26 weeks of pregnancy and 3 months postpartum. Our findings showed that the genetic variants of TGF-β type I transmembrane receptor (TGF-βRI) modulated the association between prenatal maternal depressive symptoms and the amygdala volume consistently from birth to 6 years of age despite a trend of significance at 4.5 years of age. Children with a lower gene expression score (GES) of TGF-βRI exhibited larger amygdala volumes in relation to greater prenatal maternal depressive symptoms. Moreover, children with a lower GES of the TGF-β type II transmembrane receptor (TGF-βRII), Smad4, and Smad7 showed larger amygdala volumes at 6 years of age in relation to greater prenatal maternal depressive symptoms. These findings support the involvement of the canonical TGF-β signaling pathway in the brain development of children in the context of in utero maternal environment. Such involvement is age-dependent.
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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 | 0.000 |
| 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".