White-matter controllability at birth predicts social engagement and language outcomes in toddlerhood
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 significantly. 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 associates 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.