Maternal Perinatal Depressive Symptoms, Prenatal Maternal Selective Serotonin Reuptake Inhibitor Antidepressants, and Executive Functions in Children: A 12-Year Longitudinal Study
Bibliographic record
Abstract
OBJECTIVE: To determine whether mothers' depressive symptoms with or without exposure to selective serotonin reuptake inhibitor (SSRI) antidepressant treatments during pregnancy were associated with executive functions (EFs) in offspring at 6 and 12 years of age. METHODS: A prospective cohort of 191 mothers and their children participated in the study. Clinician-rated reports of mothers' depressive symptoms were obtained spanning the third trimester during pregnancy to 12 years later. Children's EFs were measured using 2 computer-based tasks (Flanker/Reverse Flanker, Hearts and Flowers [HF]) and mothers' reports of EFs using the Behavior Rating Inventory of Executive Function (BRIEF) when the child was 6 and 12 years old. RESULTS: Longitudinal analyses showed that all children were both faster and more accurate on both Flanker/Reverse Flanker and HF with age. Fewer maternal prenatal depressive symptoms were associated with better accuracy on HF in children at 6 years of age and better EF skills as measured by the BRIEF at 6 and 12 years. Mothers' ratings of their children at 12 years indicated more executive dysfunction in children with prenatal SSRI exposure than for children without prenatal SSRI exposure, but this was no longer significant once prenatal depressive symptoms were taken into account. CONCLUSION: Prenatal and later depressive symptoms, not prenatal SSRI exposure, seems to affect offspring that continues into preadolescence, highlighting the importance of long-term mental health follow-up in mothers to ensure optimal development of children's EFs and hence their optimal development in school, in social relations, and in life generally.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".