Irzan, H;
Hütel, M;
Semedo, C;
O'Reilly, H;
Sahota, M;
Ourselin, S;
Marlow, N;
(2020)
A network-based analysis of the preterm adolescent brain using PCA and graph theory.
In: Bonet-Carne, E and Hutter, J and Palombo, M and Pizzolato, M and Sepehrband, F and Zhang, F, (eds.)
Computational Diffusion MRI.
(pp. pp. 173-181).
Springer: Shenzhen, China.
Preview |
Text
A_network_based_analysis_of_the_preterm_adolescent_brain_using_PCA_and_graph_theory.pdf - Accepted Version Download (1MB) | Preview |
Abstract
The global increase in the rate of premature birth is of great concern since it is associated with an increase in a wide spectrum of neurologic and cognitive disorders. Neuroimaging analyses have been focused on white matter alterations in preterm subjects and findings have linked neurodevelopment impairment to white matter damage linked to premature birth. However, the trajectory of brain development into childhood and adolescence is less well described. Neuroimaging studies of extremely preterm born subjects in their adulthood are now available to investigate the long-term structural alterations of disrupted neurodevelopment. In this paper, we examine white matter pathways in the preterm adolescent brain by combining state-of-the-art diffusion techniques with graph theory and principal component analysis (PCA). Our results suggest that the pattern of connectivity is altered and differences in connectivity patterns result in more vulnerable premature brain network.
Archive Staff Only
 |
View Item |
