White-matter controllability at birth predicts social engagement and language outcomes in toddlerhood
Bibliographic record
Abstract
Social engagement and language are connected through early development. Alterations in their development can have a prolonged impact on children's lives. However, the role of white matter at birth in this ongoing connection is less well-known. Here, we investigate how white matter at birth jointly supports social engagement and language outcomes in 642 infants. We use edge-centric network control theory to quantify edge controllability, or the ability of white-matter connections to drive transitions between diverse brain states, at 1 month. Next, we used connectome-based predictive modeling (CPM) to predict the Quantitative Checklist for Autism in Toddlers (Q-CHAT) for social engagement risks and the Bayley Scales of Infant and Toddler Development (BSID-III) for language skills at 18 months from edge controllability. We created the social engagement network (SEN) to predict Q-CHAT scores and the language network (LAN) to predict BSID-III scores. The SEN and LAN were complex, spanning the whole brain. They also significantly overlapped in anatomy and generalized across measures. Controllability in the SEN at 1 month partially mediated associations between Q-CHAT and BSID-III language scores at 18 months. Further, controllability in the SEN significantly differed between term and preterm infants and predicted Q-CHAT scores in an external sample of preterm infants. Together, our results suggest that the intertwined nature of social engagement and language development is rooted in an infant's white-matter controllability. Significance Statement: During infancy and toddlerhood, social engagement and language emerge together. Delays are often observed in both simultaneously. These interactions persist into later childhood, potentially affecting life quality. We reveal that the interplay between social engagement and language milestones in toddlerhood is rooted in the infant's structural connectivity, which may assist in early risk identification of developmental delays. Insights into the early brain foundations for emerging social engagement and language skills may open opportunities for individualized interventions to improve developmental outcomes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".