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Abstract #2552

The Role of Node Strength in Investigating Pathology

Elizabeth CA Powell1,2, Thalis Charalambous1, Ferran Prados1,3, Carmen Tur1, Daniel Altmann1,4, Declan Chard1,5, Sebastien Ourselin3, Ahmed Toosy1,5, Jonathan D Clayden6, and Claudia AM Wheeler-Kingshott1,7,8

1Institute of Neurology, University College London, London, United Kingdom, 2Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 3Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Imaging, University College London, London, United Kingdom, 4Medical Statistics Department, London School of Hygiene and Tropical Medicine, London, United Kingdom, 5National Hospital of Neurology and Neurosurgery, London, United Kingdom, 6Institute of Child Health, University College London, London, 7Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, 8Brain MRI 3T Mondino Research Center, C. Mondino National Neurological Institute, Pavia, Italy

Graph theoretical network properties, while successful in exploring topological features of entire brain networks, have limited sensitivity to localized disease effects. This work explores the role of node strength as an objective way to characterize disease. Differences in the default mode network (DMN) between a cohort of relapsing-remitting multiple sclerosis (RRMS) patients and healthy controls (HC) have been explored using standard graph metrics (e.g. efficiency) and node strength. No differences in graph metrics were observed between the groups; however several key regions of the DMN had a significantly reduced strength in RRMS than HC (5% significance level).

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