Abstract / Summary
The development of language and social-emotional skills is an important milestone supported by rapid brain maturation during early infancy. Deficits in these skills are hallmarks of several developmental disorders and have a prolonged impact on children’s lives. However, how white matter connections at birth support these complex skills is less well known. We investigated whether white matter at birth predicts social-emotional and language outcomes at toddlerhood in 530 infants. We used edge-centric network control theory to quantify edge controllability, or the ability of white-matter connections to drive transitions between diverse brain states, after birth. Connectome-based predictive modeling was used to predict the Quantitative Checklist for Autism in Toddlers (Q-CHAT) for social-emotional risks and the Bayley Scales of Infant and Toddler Development (BSID-III) for language skills at 18 months from the edge controllability data. We identified the social-emotional 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, but also significantly overlapped in anatomy and generalized across measures. Controllability in the SEN after birth partially mediated the association 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. White matter controllability at birth predicts individual differences in social-emotional and language development at 18 months, revealing overlapping brain networks that emerge early in life. These networks may assist with identifying early risk for developmental delays and disorders.