Classification of Multiple Sclerosis Clinical Forms Using Structural Connectome
Résumé
Multiple sclerosis (MS) is the most frequent disabling neurological disease in young adults with a national prevalence of 95/100 000 in France. Today’s neurologist challenge is to predict the individual patient evolution and response to therapy based on the clinical, biological and imaging markers available from disease onset. Since brain neural network constitutes one of the most complex network, graph theory constitutes a promising approach to characterize its connectivity properties. In this work, we applied this technique to diffusion tensor imaging data acquired in multiple sclerosis (MS) patients in order to classify their clinical forms. Support Vector Machine (SVM) algorithm in combination with graph kernel were used to classify 65 MS patients in the three different clinical forms. Results showed high classification performances using both weighted and unweighted connectivity graphs, the later being more stable, and less dependent to the pathological conditions.