The association between prenatal maternal anxiety, infant brain volumes, and temperament during the COVID-19 pandemic
Bibliographic record
Abstract
Prenatal maternal stress (PNMS), anxiety, and depression are associated with altered trajectories of infant socio-emotional and brain development, including the amygdala and prefrontal cortex (PFC). During the COVID-19 pandemic, prenatal anxiety and depression was significantly elevated, yet the impact on infant neurodevelopment remains uncertain. The objective of this study was to determine whether PNMS and mental health during the pandemic was associated with infant amygdala and PFC volumes as well as temperament. Participants were enrolled in the Canadian 'Pregnancy during the COVID-19 Pandemic' cohort study. Pregnant individuals had their perceived stress, pandemic-related objective hardship, and mental health measured via questionnaires. Infant magnetic resonance imaging (MRI) scans (n = 100) were conducted at 3 months of age, and parents reported on infant temperament at 6 months of age. General linear models were used to examine the associations among PNMS, mental health, brain volumes, and developmental outcomes. Prenatal maternal anxiety negatively predicted 3-month left infant amygdala volumes (B = -5.919; p = 0.016; 95% CI, -10.748 to -1.089). Smaller left amygdala volumes were associated with greater infant 6-month negative affectivity (B = -0.003; p = 0.002; 95% CI, -0.006--0.001). This study provides evidence for infant brain alterations related to prenatal maternal anxiety, indicating that the impact of anxiety on infant development during the COVID-19 pandemic may have long-lasting implications for children's health. Our findings suggest that prenatal anxiety may be a key area for screening and intervention during pregnancy to best support healthy infant development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".