Tracking functional brain networks in preterm and term infants using precision mapping

Preterm birth is a known risk factor for neurodevelopmental disabilities, but early neurobehavioral assessments and structural imaging often fail to predict long-term outcomes. This limitation underscores the need for alternative biomarkers that reflect early brain organization. Resting-state functional connectivity offers a powerful tool to track functional brain organization by characterizing resting-state networks (RSNs), potentially offering more sensitive biomarkers. However, most fMRI studies in infant populations use group-level analyses that average subject-specific data across several weeks of development, reducing sensitivity to subtle, time-sensitive deviations from typical brain trajectories, particularly in higher-order association networks. Using a recently introduced precision mapping approach, we estimated individual resting-state networks (RSNs) in a large cohort of term and preterm neonates from the developing Human Connectome Project. RSN connectivity strength increased linearly with age at scan, with primary sensory networks maturing earlier and higher-order association networks, including the default mode network (DMN), showing more gradual but pronounced changes, evolving from an immature organization in preterm infants to a more adult-like pattern in term-born infants. Longitudinal data from a subset of preterm infants confirmed ongoing network development shortly after birth. Despite this maturation, preterm infants did not reach the connectivity levels of term-born infants by term-equivalent age. These findings demonstrate that individualized RSN mapping captures heterogeneous developmental trajectories in the neonatal brain and highlights higher-order association networks, particularly the DMN, as promising early markers for monitoring neurodevelopmental outcomes in neonates.