Multi-level static and dynamic graph-theoretical analyses of resting-state functional networks in a Chinese cohort of preterm neonates

Abstract

Preterm birth influences early functional brain maturation at term-equivalent age. Using resting-state functional magnetic resonance imaging from a large Chinese neonatal cohort (62 term-born and 107 preterm neonates), we examined static and dynamic network organization using multi-level graph-theoretical analyses. Preterm neonates exhibited reduced global integration and segregation, reflected by lower global efficiency, clustering coefficient, and local efficiency, together with increased characteristic path length. Widespread nodal and modular alterations were observed across multiple networks. Dynamic analyses revealed selective edge-level disturbances involving the right parahippocampal gyrus and its connections with default mode, visual, and limbic networks, despite limited group differences in global or nodal dynamic metrics. Developmental analyses further showed associations between specific global metrics and postmenstrual age at scan. Network measures were also associated with prenatal factors, including multiple pregnancy and cesarean delivery. These findings characterize early alterations in static and dynamic functional network organization after preterm birth and highlight prenatal factors potentially related to neonatal brain development.