Keywords: Neuro, Neonatal, Congenital Heart Disease, Structural Covariance Network
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords