Graph network measures of brain connectivity and its relation with cognitive performance in high-risk children

Understanding rate and variability of connectivity in normal brain development can offer insight into the developmental origin of childhood and adult brain disorders. Indeed, there is increasing interest towards assessing the development of white matter (WM) fibers underlying the complex brain connectivity [1], since the development of functional connections is clearly dependent on the establishment of cortical fiber pathways [2,3], their appropriate maturation and myelination. However, correlation of structural connectivity with specific brain cognitive and behavioral brain functioning can open the way to define quantitative and qualitative MRI biomarkers with the final scope of understanding brain organization and function. To goal of this work is to study the brain connectivity and network model based segregation of structural connectivity associated with cognitive and behavioral scores in prematurely born children. We used graph theory-based connectivity analysis and stepwise linear regression models to assess the contribution of brain connectivity as well as subjects co-variates as gestational age (GA) and birth weight (BW) in cognitive and behavioral performance of young children.