Inflammatory modulation of the associations between prenatal maternal depression and neonatal brain
Bibliographic record
Abstract
Inflammatory signaling has a role in sensing intrauterine environment, which may be moderators in altering fetal brain development upon maternal environment. This study integrated cytokine transcriptome of post-mortem fetal brains, neonatal brain imaging and genetic variants (n = 161) to examine whether cytokines are candidates for modulating the relationship between prenatal maternal depression and fetal brain development. This study obtained the transcriptome data of 208 cytokine genes in 12 fetal brain regions from the BrainSpan database. We also included 161 mother-child dyads with prenatal maternal depressive symptoms assessed at 26 weeks of gestation, cytokine genotype data extracted from umbilical cord specimens, and neonatal brain images from a longitudinal prospective birth cohort. We revealed that 22 cytokine genes are expressed in specific brain regions in utero, whose variants have roles in modulating the effects of the prenatal environment on the accelerated fetal development of the hippocampus, auditory, parietal, orbitofrontal, and dorsal prefrontal cortex. Neonates high in the genetic expression score (GES) of TNFRSF19 and IL17RB showed a larger right hippocampal volume, high in the GES of BMPR1B showed the thicker thickness of the sensorimotor cortex, and high in the GES of IL1RAP and CXCR4 demonstrated the thicker thickness of the dorsal and orbital prefrontal cortex in relation with greater prenatal maternal depressive symptoms. Our findings suggest that in humans, the cytokine genes are expressed in a brain region-specific manner in utero and may have potential roles in modulating the fetal development of the corresponding brain regions in response to the maternal environment.
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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".