Modelling and prediction of neurodevelopment in preterm infants using structural connectome data

Each year worldwide, millions of babies are born very preterm (before 32 weeks postmenstral age). Very preterm birth puts infants at higher risk for delayed or altered neurodevelopment. While the mechanisms causing these alterations are not fully understood, it has been shown that image-based biomarkers of the fragile connective white matter brain tissue are correlated with neurodevelopmental outcomes. Diffusion MRI (dMRI) is a non-invasive imaging modality that allows in-vivo analysis of an infant's white matter brain network (known as a structural connectome) and can be used to better understand neurodevelopment. The purpose of this thesis is to study how the structural connectome can be used for analysis of development and early prediction of outcomes for better informed care. The thesis begins with a thorough examination of the literature on studies that have applied machine learning to brain network data from MRI. It proceeds with a connectome based analysis of the early neurodevelopment of normative preterm infants. Finally, this thesis tackles the problem of early prediction of cognitive and motor neurodevelopmental outcomes using machine learning on connectome data. Three novel prediction methods are proposed for this task, which are found to be able to accurately predict the 18-month neurodevelopmental outcomes of a cohort of preterm infants from the BC Childrens' Hospital. The thesis concludes with a discussion of how the proposed models may be applicable to a broader set prediction problems and of important future directions for research.