Accelerated Small-World Property of Structural Brain Networks in Preterm Infants at Term-Equivalent Age

Abstract

Background: The prediction of neurodevelopmental outcomes in preterm infants is one of the clinical challenges of pediatrics. Despite the recent interest in brain development and white matter connectivity using a network-based analysis, very little is known about the brain network of at term-equivalent age in preterm infants. Objective: We aimed to investigate the structural brain network using diffusion MRI following preterm delivery at term-equivalent age compared with term infants and explored the influence of gestational age (GA) and clinical factors. Method: Diffusion tensor imaging data were acquired prospectively from 55 preterm neonates without apparent brain abnormalities (mean gestational age: 29.43 weeks) and 21 full-term infants at term-equivalent age. The global structural brain networks were produced by probabilistic white matter tractography in combination with the Johns Hopkins University neonate atlas to quantify connectivity between different cortical regions. Results: Compared with full-term infants, preterm infants had significantly lower global efficiency (p = 0.048) and increased small worldness (p = 0.012) after correcting for sex and age at MRI scan. The increased small worldness in the brain network at term-equivalent age was significantly linearly correlated with lower GA after adjusting for sex and the effects of postmenstrual age at MRI scan on the data in preterm infants (beta = -0.020, p = 0.037). In multivariate analysis, infants with chronic lung disease had significantly decreased changes in clustering (p = 0.014) and local efficiency (p = 0.027). Conclusion: The accelerated small worldness in preterm infants suggests that the structural brain network after preterm birth is reorganized in maximizing integrated and segregated processing, implying resilience against prematurity-associated pathology.