Maternal Adverse Childhood Experience and Depression in Relation with Brain Network Development and Behaviors in Children: A Longitudinal Study
Bibliographic record
Abstract
Maternal childhood maltreatment and depression increase risks for the psychopathology of the offspring. This study employed a longitudinal dataset of mother-child dyads to investigate the developmental trajectories of brain functional networks and behaviors of children in relation with maternal childhood adverse experience and depression. Maternal childhood trauma was retrospectively assessed via childhood trauma questionnaire, whereas maternal depressive symptoms were prospectively evaluated during pregnancy and after delivery (n = 518). Child brain scans were acquired at age of 4.5, 6, and 7.5 years (n = 163) and behavioral problems were measured at 7.5 years using the Child Behavior Checklist. We found the functional connectivity of the language network with the sensorimotor, frontal, and attentional networks as a function of maternal adverse experience that interacted with sex and age. Girls exposed to mothers with depressive symptoms or childhood abuse showed the increased development of the functional connectivity of the language network with the visual networks, which was associated with social problems. Girls exposed to mothers with depressive symptoms showed the slower growth of the functional connectivity of the language network with the sensorimotor networks. Our findings, in a community sample, suggest the language network organization as neuroendophenotypes for maternal childhood trauma and 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 | 0.001 |
| 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 | 0.001 |
| 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".