Abstract #4060

Graph theoretical measures predict volumetric changes in multiple sclerosis

Thalis Charalambous¹, Carmen Tur¹, Ferran Prados^1,2, Steven H.P. van de Pavert¹, Declan T. Chard¹, David H. Miller¹, Sebastien Ourselin², Jonathan D. Clayden³, Claudia A.M. Gandini Wheeler-Kingshott^1,4,5, 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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