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Abstract #4060

Graph theoretical measures predict volumetric changes in multiple sclerosis

Thalis Charalambous1, Carmen Tur1, Ferran Prados1,2, Steven H.P. van de Pavert1, Declan T. Chard1, David H. Miller1, Sebastien Ourselin2, Jonathan D. Clayden3, Claudia A.M. Gandini Wheeler-Kingshott1,4,5, Alan J. Thompson 1, and Ahmed T. Toosy1

1UCL Institute of Neurology, Queen Square MS Centre, University College London, London, United Kingdom, 2Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 3UCL GOS Institute of Child Health, University College London, London, United Kingdom, 4Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, 5Brain MRI 3T Mondino Research Center, C. Mondino National Neurological Institute, Pavia, Italy

Numerous studies demonstrated structural network changes in patients with multiple sclerosis (MS). However, the predictive nature of the graph-derived metrics is not yet examined. In this longitudinal study, we constructed baseline diffusion-based structural networks and we used multiple linear regression analysis to assess the ability of the network measures to predict follow-up increased lesion load and brain atrophy in MS (n=49). Our results suggest that edge density, global and local efficiency can predict follow-up brain atrophy after adjusting for the nuisance variables, signifying that network analysis can provide new insights into disease trajectories and offer potential biomarkers for MS progression.

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