White matter controllability at birth predicts social-emotional and language outcomes in toddlerhood

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.

Data were provided by the developing Human Connectome Project, KCL-Imperial-Oxford Consortium, funded by the European Research Council under the European Union Seventh Framework Programme (FP/2007–2013) / ERC Grant Agreement no. [319456]. We are grateful to the families who generously supported this trial. This study was supported by NIMH (P50MH115716 to K.C., 1R01MH137609-01 and 5R01MH126133-02 to D.S.).

Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA Child Study Center, Yale School of Medicine, New Haven, CT, USA Angelina Vernetti, Katarzyna Chawarska & Dustin Scheinost Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, 10032, USA Department of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA Department of Statistics and Data Science, Yale University, New Haven, CT, USA Department of Pediatrics, Yale School of Medicine, New Haven, CT, USA Department of Neurology, Yale School of Medicine, New Haven, CT, USA Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA Wu Tsai Institute, Yale University, New Haven, CT, 06510, USA Yale Biomedical Imaging Institute, Yale School of Medicine, New Haven, CT, 06510, USA The authors declare no competing interests. The study was approved by the National Research Ethics Service West London committee. Written informed consent was obtained from participating families before imaging.