AbstractBetween 50-80% of very preterm infants (≤32 weeks gestational age) exhibit increased white matter signal intensity on T2 MRI at term-equivalent age, known as diffuse white matter abnormality (DWMA). A few studies have linked DWMA with microstructural abnormalities, but the exact relationship remains poorly understood. We used graph theory methods to relate DWMA extent to measures of efficient information processing at term in a representative cohort of 343 very preterm infants. We performed anatomic and diffusion MRI at term and quantified DWMA volume using our novel, semi-automated algorithm. From structural connectomes, we calculated graph theory metrics: local efficiency and clustering coefficient, which measure the ability of groups of nodes to perform specialized processing, and global efficiency, which assesses the ability of brain regions to efficiently combine information. We computed partial correlations between these measures and DWMA volume, adjusted for confounders. Increasing DWMA volume was associated with decreased global efficiency of the entire brain network (r= - 0.27, p= 8.36E-07) and decreased local efficiency and clustering coefficient within individual networks supporting cognitive, linguistic, and motor functions. We show that DWMA is associated with widespread decreased brain network connectivity in very preterm infants, suggesting it is pathologic and likely has adverse developmental consequences.
Objective and Automated Detection of Diffuse White Matter Abnormality in Preterm Infants Using Deep Convolutional Neural Networks