Structural Covariance Network Analysis Reveals Altered Brain Development in Neonates with Congenital Heart Disease After Surgery

Motivation: Early identification of altered neurodevelopment in neonates with congenital heart disease (CHD) is crucial for improving long-term outcomes. Structural covariance network (SCN) analysis is a data-driven approach to identify regions of synchronously developing structural organization. Goal(s): To identify SCNs that differ between neonates with CHD after cardiac surgery and healthy controls. Approach: Jacobian determinants were calculated using warps from non-linear registration and input into an Independent Component Analysis to identify SCNs. Results: Out of forty identified SCNs, twelve showed significant differences between neonates with CHD and controls, involving extracerebral CSF, white matter, and cortical- and deep grey matter. Impact: Structural covariance network analysis, a data-driven approach to characterise networks of simultaneously developing brain regions, identified 12 networks that were significantly different in neonates with CHD after cardiac surgery compared to healthy neonates, highlighting altered brain development in this population.