Maternal antenatal anxiety and electrophysiological functioning amongst a sub-set of preschoolers participating in the GUSTO cohort
Bibliographic record
Abstract
BACKGROUND: Antenatal maternal anxiety is a risk for offspring psychological and cognitive difficulties. The preschool years represent an important time for brain development, and so may be a window for intervention. However, electrophysiological investigations of maternal anxiety and preschoolers' brain functioning are lacking. We ask whether anxiety symptoms predict neurophysiology, and consider timing specificity (26-weeks antenatal or 24-months postnatal), form of insult (anxiety symptoms, per se, or also depression symptoms), and offspring gender. METHODS: The sample consisted of a subset of 71 mothers and their 3 year old children taking part in the prospective birth cohort, GUSTO. Mothers provided antenatal (26 weeks) and postnatal (2 years) anxiety and depressive symptomatology data, respectively via the "State Trait Anxiety Questionnaire" and the "Edinburgh Postpartum Depression Scale." Offspring provided electrophysiological data, obtained while they indicated the emotional expression of actors whose facial expressions remained consistent throughout a pre-switch block, but were reversed at "post-switch." RESULTS: Three electrophysiological components linked to different information processing stages were identified. The two earliest occurring components (i.e., the N1 and P2) differed across blocks. During post-switch, both were significantly predicted by maternal anxiety, after controlling for pre-switch neurophysiology. Similar results were observed with depression. Antenatal mental health remained a significant predictor after controlling for postnatal mental health. CONCLUSION: In combination with past work, these findings suggest the importance of reducing symptoms in women prior to and during pregnancy, and offering support to offspring early in development.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".