Structural network analysis of brain development in young preterm neonates

Abstract

Preterm infants develop differently than those born at term and are at higher risk of brain pathology. Thus, an understanding of their development is of particular importance. Diffusion tensor imaging (DTI) of preterm infants offers a window into brain development at a very early age, an age at which that development is not yet fully understood. Recent works have used DTI to analyze structural connectome of the brain scans using network analysis. These studies have shown that, even from infancy, the brain exhibits small-world properties. Here we examine a cohort of 47 normal preterm neonates (i.e., without brain injury and with normal neurodevelopment at 18 months of age) scanned between 27 and 45 weeks post-menstrual age to further the understanding of how the structural connectome develops. We use fullbrain tractography to find white matter tracts between the 90 cortical and sub-cortical regions defined in the University of North Carolina Chapel Hill neonatal atlas. We then analyze the resulting connectomes and explore the differences between weighting edges by tract count versus fractional anisotropy. We observe that the brain networks in preterm infants, much like infants born at term, show high efficiency and clustering measures across a range of network scales. Further, the development of many individual region-pair connections, particularly in the frontal and occipital lobes, is significantly correlated with age. Finally, we observe that the preterm infant connectome remains highly efficient yet becomes more clustered across this age range, leading to a significant increase in its small-world structure.

The emergence of functional architecture during early brain development

“…With all major white matter tracts being in place by the end of normal gestation, it is not surprising that structural connectome studies -examining whole brain connectivity on a macroscopic level (Box 1) -have revealed structural network organization in the neonatal brain to show great similarity to the adult human brain Brown et al, 2014;Ratnarajah et al, 2013;Shi et al, 2012;Tymofiyeva et al, 2012;van den Heuvel et al, 2014;Yap et al, 2011). Key features of adult connectome architecture have been demonstrated in the neonatal brain, including a small world organization -combining local specialization with long-range efficiency -, a modular topology -meaning that the network constitutes smaller subnetworks for specialized information processing -, and a heavy tailed degree distribution with brain regions which show the highest number of connections forming a central core or 'rich club' (Box 1).…”

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

“…Previous reports have demonstrated the degree of prematurity at birth was associated with a reduction in density and lower global efficiency in children born very preterm, suggesting that brain networks are less connected and less complex [7, 8]. However, it remains unclear whether the altered structural brain connectivity is affected by prematurity itself or by adverse neonatal conditions encountered during the hospital stay.…”

Prediction of Motor Function in Very Preterm Infants Using Connectome Features and Local Synthetic Instances

“…SMOTE with different costs (SDC) is a variant of SMOTE, improved to better handle imbalanced classes [1]. For SDC, we set K = 1 as it was found to give highest accuracy over range [1,5]. For PCA, once the variation modes were learned, new instances were generated by sampling the Gaussian distribution defined by the variation modes.…”

“…Each connectome was constructed by grouping tracts according to their endpoint regions. Following [5], we constructed three connectome types for each scan, including a mean-FA weighted connectome, a tract-count connectome and a normalized tract-count connectome. Network measures summarizing the connectome's topological properties were computed for each connectome type.…”

“…Prediction of motor function from brain structure at birth is thus very challenging due to the large number of confounding factors affecting brain development, including potentially unknown genetic and environmental factors. It is especially difficult in young infants due to the combination of limited image resolution and small brain sizes, artifacts due to motion and rapid structural change across a small temporal window [5]. Furthermore, datasets are often class-imbalanced, containing fewer abnormal cases.…”