Brain Wiring at Birth Predicts Toddlers’ Social and Language Skills

Every parent wonders what their newborn will become, and a new study suggests that part of the answer may already be written in the infant brain. Researchers at Yale University and Columbia University report that the way white matter connections are organized at birth can forecast how toddlers will fare socially, emotionally, and linguistically eighteen months later. The work, published in Translational Psychiatry, analyzed brain scans from 530 infants and used a sophisticated mathematical framework borrowed from engineering—network control theory—to measure how effectively each neural connection can steer the developing brain between different states of activity. The findings offer some of the strongest evidence yet that the architecture present in the first days of life carries measurable consequences for the milestones of toddlerhood.

The central concept in the study is edge controllability, a quantity that describes the ability of an individual white matter connection to drive transitions between diverse brain states. In network control theory, the brain is treated as a dynamic system: regions of gray matter are nodes, the white matter fibers linking them are edges, and the system evolves over time under the influence of external inputs. A connection with high controllability is one that, when stimulated, can push the brain into many different configurations, supporting cognitive flexibility. A connection with low controllability constrains the system to a narrower repertoire of states. By computing this property for every edge in each infant’s connectome, the team produced a detailed map of how much influence each fiber bundle exerts over the newborn brain’s dynamics.

The data came from the developing Human Connectome Project, a landmark effort that acquires high-quality magnetic resonance imaging of infants in the first weeks of life. Diffusion imaging allowed the researchers to reconstruct the white matter pathways of each baby, and from those reconstructions they calculated edge controllability across the whole brain. The infants were then followed longitudinally. At eighteen months of age, caregivers completed the Quantitative Checklist for Autism in Toddlers, a screening instrument that captures social-emotional risk, and the children were assessed with the Bayley Scales of Infant and Toddler Development, Third Edition, which provide standardized measures of language ability. The question was whether patterns of controllability measured at birth could predict these outcomes at the level of the individual child.

To answer it, the team turned to connectome-based predictive modeling, a machine learning approach that has become a workhorse of modern predictive neuroscience. The method works by identifying which features of the connectome—in this case, which edges’ controllability values—are most strongly correlated with a behavioral measure across the training sample, building a predictive model from those features, and then testing how well the model forecasts outcomes in data it has never seen. Crucially, the researchers ran this procedure separately for the two outcome domains. One model predicted Q-CHAT scores, indexing social-emotional development, and another predicted Bayley language scores. The approach guards against overfitting and yields an honest estimate of whether birth-time brain measures genuinely carry predictive signal rather than statistical noise.

The results were striking. From the social-emotional model emerged what the investigators call the social-emotional network, a distributed set of edges whose controllability at birth predicted later Q-CHAT performance. From the language model emerged a parallel language network predicting Bayley scores. Both networks were complex and spanned the entire brain rather than clustering in a single lobe or tract, consistent with the understanding that higher-order developmental skills depend on widespread, interacting circuitry rather than isolated modules. Perhaps most intriguingly, the two networks overlapped significantly in their anatomical composition. The same white matter connections that supported social-emotional development also supported language, suggesting that these two domains, often studied separately, rest on a shared neurobiological foundation established before birth.

The overlap was not merely anatomical. The networks also generalized across measures, meaning that controllability values in the social-emotional network carried predictive information about language outcomes, and vice versa. The researchers then examined the relationship between the two domains statistically and found that controllability in the social-emotional network at birth partially mediated the association between Q-CHAT scores and Bayley language scores at eighteen months. In practical terms, part of the reason social-emotional functioning and language ability track together in toddlers is that both are shaped by the same early control architecture of the brain. This mediating role positions white matter controllability as a candidate mechanism linking the two most consequential domains of early development.

The study also addressed one of the most clinically urgent questions in developmental neuroscience: whether such measures can identify risk in vulnerable populations. When the researchers compared term and preterm infants, controllability in the social-emotional network differed significantly between the groups, reflecting the well-documented impact of preterm birth on white matter maturation. More importantly, the network derived from the main sample successfully predicted Q-CHAT scores in an external sample of preterm infants, demonstrating that the predictive relationship holds beyond the cohort in which it was discovered. External validation of this kind is rare in infant neuroimaging and substantially strengthens the case that the findings reflect a robust biological phenomenon rather than idiosyncrasies of a single dataset.

The implications reach into several domains at once. For basic neuroscience, the study shows that network control theory, originally developed to understand power grids and other engineered systems, can extract meaningful developmental signal from the newborn connectome. The idea that a single scalar property of each connection—its capacity to drive state transitions—captured at birth contains information about functioning a year and a half later is a powerful demonstration that early white matter organization constrains the trajectory of cognitive growth. For developmental science, the identification of overlapping social-emotional and language networks that emerge in the first weeks of life reframes how researchers think about the origins of skills that typically become observable much later, when children begin to speak and to navigate social relationships.

The clinical stakes are equally significant. Deficits in language and social-emotional skills are hallmark features of several neurodevelopmental disorders, including autism spectrum disorder, and delays in these domains have prolonged effects on children’s lives. Screening instruments such as the Q-CHAT can only be administered once children are old enough for their behavior to be informative, which means families often wait many months before risk becomes apparent. A biomarker measurable in the first days of life could compress that timeline dramatically, allowing families and clinicians to identify infants who may benefit from heightened monitoring and early intervention during a period when the brain’s plasticity is at its peak. The authors emphasize that the networks may assist with identifying early risk for developmental delays and disorders, and the successful prediction in preterm infants—a population at elevated risk for exactly these outcomes—points toward concrete clinical applications.

Important caveats remain. Predictive models of this kind capture statistical associations at the group level and cannot determine outcomes for any individual child; a given infant’s trajectory will be shaped by genetics, environment, caregiving, and experience interacting with the biology measured at birth. The study also reports prediction rather than causal manipulation, so future work will need to test whether modulating early white matter development changes the trajectories the model forecasts. Still, the convergence of findings—whole-brain networks, cross-domain overlap, mediation, and external validation in preterm infants—marks this as a milestone in the effort to read the developmental future from the newborn brain. As imaging technology spreads and datasets grow, the vision of pediatric medicine in which a scan in the first weeks of life informs a personalized plan for supporting each child’s social, emotional, and linguistic growth moves steadily closer to reality.

Subject of Research: Neonatal white matter network controllability as a predictor of toddler social-emotional and language development

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

Article References: Sun, H., Vernetti, A., Spann, M., Chawarska, K., Ment, L., & Scheinost, D. (2026). White matter controllability at birth predicts social-emotional and language outcomes in toddlerhood. Translational Psychiatry. https://doi.org/10.1038/s41398-026-04504-6

Image Credits: AI Generated

DOI: 10.1038/s41398-026-04504-6

Keywords: white matter, network control theory, infant neuroimaging, connectome, language development, social-emotional development, preterm infants, predictive modeling, developing Human Connectome Project, autism screening, Bayley Scales, translational psychiatry