Quality of maternal behaviour during infancy predicts functional connectivity between default mode network and salience network 9 years later
Bibliographic record
Abstract
Infants' experiences are considered to determine to a large degree the strength and effectiveness of neural connections and fine tune the development of brain networks. As one of the most pervasive and potent relational experiences of infancy, parent-child relationships appear to be prime candidates to account for experience-driven differences in children's brain development. Yet, studies linking parenting and functional connectivity are surprisingly scarce, and restricted to the connectivity of limbic structures. Accordingly, this longitudinal study explored whether normative variation in the quality of early maternal behaviour predicts the functional connectivity of large-scale brain networks in late childhood. Maternal mind-mindedness and autonomy support were assessed with 28 children when they were 13 and 15 months old respectively. When children were 10 years of age, children underwent a resting-state functional MRI exam. Functional connectivity was assessed between key regions of the default mode network (DMN), salience network (SN), and frontal-parietal central executive network (CEN). Results revealed that higher mind-mindedness and autonomy support predicted stronger negative connectivity between DMN and SN regions. These findings are the first to provide preliminary evidence suggestive of a long-lasting impact of variation within the normative range of early maternal behaviour on functional connectivity between large-scale brain networks.
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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 | 0.002 |
| 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".