Longitudinal associations between mother-child attachment security in toddlerhood and white matter microstructure in late childhood: a preliminary investigation
Bibliographic record
Abstract
Early childhood experiences are considered to influence the strength and effectiveness of neural connections and thus the development of brain connectivity. As one of the most pervasive and potent early relational experiences, parent-child attachment is a prime candidate to account for experience-driven differences in brain development. Yet, knowledge of the effects of parent-child attachment on brain structure in typically developing children is scarce and largely limited to grey matter, whereas caregiving influences on white matter (i.e. neural connections) have seldom been explored. This study examined whether normative variation in mother-child attachment security predicts white matter microstructure in late childhood and explored associations with cognitive-inhibition. Mother-child attachment security was assessed using home observations when children (N = 32, 20 girls) were 15 and 26 months old. White matter microstructure was assessed using diffusion magnetic resonance imaging when children were 10 years old. Child cognitive-inhibition was tested when children were 11 years old. Results revealed a negative association between mother-toddler attachment security and child white matter microstructure organization, which in turn related to better child cognitive-inhibition. While preliminary given the sample size, these findings add to the growing literature that suggests that rich and positive experiences are likely to decelerate brain 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".