Effects of maternal childhood trauma on child emotional health: maternal mental health and frontoamygdala pathways
Bibliographic record
Abstract
BACKGROUND: Experiences of early life adversity pose significant psychological and physical health risks to exposed individuals. Emerging evidence suggests that these health risks can be transmitted across generations; however, the mechanisms underlying the intergenerational impacts of maternal early-life trauma on child health remain unknown. METHODS: The current study used a prospective longitudinal design to determine the unique and joint contributions of maternal childhood trauma (neglect and abuse) and maternal prenatal and postnatal mental health (anxiety and depressive symptoms) (N = 541) to children's resting frontoamygdala functional connectivity at 6 years (N = 89) and emotional health at 7-8 years, as indexed by parent-reported internalizing problems and child self-reported anxiety and depressive symptoms (N = 268-418). RESULTS: Greater maternal childhood neglect was indirectly associated with greater internalizing problems serially through a pathway of worse maternal prenatal and postnatal mental health (greater maternal anxiety and depressive symptoms). Worse maternal postnatal mental health was also uniquely associated with more negative child frontoamygdala resting-state functional connectivity, over and above maternal childhood trauma (both neglect and abuse) and prenatal mental health. More negative frontoamygdala functional connectivity was, in turn, associated with poorer child emotional health outcomes. CONCLUSIONS: Findings from the current study provide support for the existence of intergenerational influences of parental exposure to childhood trauma on childhood risk for psychopathology in the next generation and point to the importance of maternal factors proximal to the second generation (maternal prenatal and postnatal mental health) in determining the intergenerational impact of maternal early experiences.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".