Wan Hazlin Zaini1, Zhang Chen1, Min Liu1, Fabrizio Giuliani2, Chris Hanstock1, Christian Beaulieu1
1Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada; 2Division of Neurology, University of Alberta, Edmonton, Alberta, Canada
Conventional magnetic resonance imaging has low ability to predict disease progression in multiple sclerosis patients. We used diffusion tensor brain network analysis to assess differences among early relapsing-remitting MS patients with low disability and various levels of lesion load. Relative to controls, the high lesion load group, but not the low lesion load group, had reduced global and local network efficiency and increased shortest path length that all correlated with lesion load within that group. Diffusion tensor brain network analysis identifies altered white matter network properties that may provide a potential biomarker of disease progression.