Mother–Infant Interaction and Child Brain Morphology: A Multidimensional Approach to Maternal Sensitivity
Bibliographic record
Abstract
Emerging research suggests that normative variation in parenting quality relates to children's brain development. However, although the young brain is presumed to be especially sensitive to environmental influence, to our knowledge only two studies have examined parenting quality with infants as it relates to indicators of brain development, and both were cross-sectional. This longitudinal study investigated whether different components of maternal sensitivity in infancy predicted the volume of two brain structures presumed to be particularly sensitive to early experience, namely the amygdala and the hippocampus. Three dimensions of sensitivity (Cooperation/Attunement, Positivity, Accessibility/Availability) were observed in 33 mother-infant dyads at 1 year of age and children underwent structural magnetic resonance imaging at age 10. Higher maternal Accessibility/Availability during mother-infant interactions was found to be predictive of smaller right amygdala volume, while greater maternal positivity was predictive of smaller bilateral hippocampal volumes. These longitudinal findings extend those of previous cross-sectional studies and suggest that a multidimensional approach to maternal behavior could be a fruitful way to further advance research in this area, given that different facets of parenting might be differentially predictive of distinct aspects of neurodevelopment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".