Newborn Brain Function Is Affected by Fetal Exposure to Maternal Serotonin Reuptake Inhibitors
Bibliographic record
Abstract
Recent experimental animal studies have shown that fetal exposure to serotonin reuptake inhibitors (SRIs) affects brain development. Modern recording methods and advanced computational analyses of scalp electroencephalography (EEG) have opened a possibility to study if comparable changes are also observed in the human neonatal brain. We recruited mothers using SRI during pregnancy (n = 22) and controls (n = 62). Mood and anxiety of mothers, newborn neurology, and newborn cortical function (EEG) were assessed. The EEG parameters were compared between newborns exposed to drugs versus controls, followed by comparisons of newborn EEG features with maternal psychiatric assessments. Neurological assessment showed subtle abnormalities in the SRI-exposed newborns. The computational EEG analyses disclosed a reduced interhemispheric connectivity, lower cross-frequency integration, as well as reduced frontal activity at low-frequency oscillations. These effects were not related to maternal depression or anxiety. Our results suggest that antenatal serotonergic treatment might change newborn brain function in a manner compatible with the recent experimental studies. The present EEG findings suggest links at the level of neuronal activity between human studies and animal experiments. These links will also enable bidirectional translation in future studies on the neuronal mechanisms and long-term neurodevelopmental effects of early SRI exposure.
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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".