Abstract #1864

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

Veronica Ravano^1,2,3, Michaela Andelova⁴, Mazen Fouad A-Wali Mahdi¹, Reto Meuli², Tomas Uher⁴, Jan Krasensky⁵, Manuela Vaneckova⁵, Dana Horakova⁴, Tobias Kober^1,2,6, and Jonas Richiardi^1,2

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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