Neural correlates of disaster-related prenatal maternal stress in young adults from Project Ice Storm: Focus on amygdala, hippocampus, and prefrontal cortex
Bibliographic record
Abstract
Background: Studies have shown that prenatal maternal stress alters volumes of the amygdala and hippocampus, and alters functional connectivity between the amygdala and prefrontal cortex. However, it remains unclear whether prenatal maternal stress (PNMS) affects volumes and functional connectivity of these structures at their subdivision levels. Methods: = 65, 30 male) exposure to PNMS deriving from the 1998 ice storm. Volumes of amygdala nuclei, hippocampal subfields and prefrontal subregions were computed, and seed-to-seed functional connectivity analyses were conducted. Results: Compared to controls, young adult offspring exposed to disaster-related PNMS had larger volumes of bilateral whole amygdala, driven by the lateral, basal, central, medial, cortical, accessory basal nuclei, and corticoamygdaloid transition; larger volumes of bilateral whole hippocampus, driven by the CA1, HATA, molecular layer, fissure, tail, CA3, CA4, and DG; and larger volume of the prefrontal cortex, driven by the left superior frontal. Inversely, young adult offspring exposed to disaster-related PNMS had lower functional connectivity between the whole amygdala and the prefrontal cortex (driven by bilateral frontal poles, the left superior frontal and left caudal middle frontal); and lower functional connectivity between the hippocampal tail and the prefrontal cortex (driven by the left lateral orbitofrontal). Conclusion: These results suggest the possibility that effects of disaster-related PNMS on structure and function of subdivisions of offspring amygdala, hippocampus and prefrontal cortex could persist into 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 | 0.000 |
| 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".