Abstract #0491

An investigation of the sensitivity of diffusion-based microstructure combined with network analysis in multiple sclerosis

Sara Bosticardo¹, Simona Schiavi¹, Sabine Schaedelin², Po-Jui Lu^3,4, Muhamed Barakovic^2,3, Matthias Weigel^2,3,5, Ludwig Kappos^3,4, Jens Kuhle^3,4, Alessandro Daducci¹, and Cristina Granziera^2,3,4

¹Department of Computer Science, University of Verona, Verona, Italy, ²Departments of Medicine, Clinical Research and Biomedical Engineering, Neurology, University Hospital Basel and University of Basel, Basel, Switzerland, ³Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Neurologic Clinic and Policlinic, Translational Imaging in Neurology (ThINk), Basel, Switzerland, ⁴Research Center for Clinical Neuroimmunology and Neuroscience, Basel, Switzerland, ⁵Department of Radiology, Division of Radiological Physics, University Hospital Basel, Basel, Switzerland

Graph measures derived from structural connectomes are widely used to study neurodegenerative diseases such as multiple sclerosis (MS). Usually, the connection strength is assessed by counting the number of streamlines connecting pairs of grey-matter regions. Here we used different ways to weight the edges to compare the sensitivity to MS structural disruptions of three diffusion-based microstructural models and their derived maps combined with network analysis. We found that the most sensitive are those whose derived maps are associated to intra-axonal signal fraction. Moreover, the segregation of the network appeared to be the most important in explaining clinical motor disability.

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