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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 Lu3,4, Muhamed Barakovic2,3, Matthias Weigel2,3,5, Ludwig Kappos3,4, Jens Kuhle3,4, Alessandro Daducci1, and Cristina Granziera2,3,4
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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