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Abstract #1864

Brain graph representation of structural disconnectivity estimated with an atlas-based approach in multiple sclerosis

Veronica Ravano1,2,3, Michaela Andelova4, Mazen Fouad A-Wali Mahdi1, Reto Meuli2, Tomas Uher4, Jan Krasensky5, Manuela Vaneckova5, Dana Horakova4, Tobias Kober1,2,6, and Jonas Richiardi1,2
1Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland, 2Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 3Medical Imaging Processing, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 4Department of Neurology and Center of Clinical Neuroscience First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic, 5MR unit, Department of Radiology First Facutly of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic, 6LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

In multiple sclerosis, the correlation between clinical scores and classical radiological metrics is poor (“clinico-radiological paradox”). To improve the prediction of future disease course, we suggest to study structural brain disconnectivity resulting from white matter lesions. We proposed an atlas-based approach to quantify structural disconnectomes without diffusion imaging, as it is typically not part of clinical routine MR protocols for multiple sclerosis. The disconnectome was modelled as a graph where brain regions are vertices and affected connections edges. Our method provides a new representation of brain disconnectivity that enables to stratify multiple sclerosis patients in two groups with different prognosis.

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