Abstract
Although developmental language delays affect approximately 10% of children in the general population, the neurodevelopmental mechanisms that support normative language acquisition, and atypicalities that may predict later language delay, across the first year of life are poorly understood. Here, resting-state fMRI data from the Baby Connectome Project was used to evaluate age-related changes in language network functional connectivity and alterations associated with suboptimal language development. Additionally, a data-driven machine learning algorithm was used to partition our sample into three groups who showed Delayed, Typical, and Advanced trajectories of language development. These groups reliably differed on several assessments of language ability during infancy and toddlerhood. Using a priori brain regions involved in adult language processing, a seed-based functional connectivity analysis showed broad age-related increases in functional synchrony and specialization throughout the infant language network. Additionally, the Delayed group showed several atypical patterns of functional connectivity with language regions. Importantly, the magnitude of connectivity differences consistently predicted later language scores at two-year outcome across several different language assessments. These findings add to our understanding of normative neurodevelopmental patterns underlying language acquisition, and identify several potential biomarkers associated with language delay that could serve as future targets to inform diagnoses and clinical interventions.