The incidence of neurological disorders is constantly growing; hence, the scientific community is intensifying the efforts spent in order to design approaches capable of determining the onset of such disorders. In this paper we focus on a specific neurological disorder, namely Multiple Sclerosis, a chronic disease of the central nervous system. We propose a method for identifying specific brain substructures that might underpin a worsening disease, thus allowing to delineate a number of potentially vulnerable brain regions. The task is addressed by means of a simulation procedure which iteratively disrupt brain regions. Experimental results show that the proposed simulation produces reliable graphs with respect to the used dataset.
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|Titolo:||Inducing Clinical Course Variations in Multiple Sclerosis White Matter Networks|
|Data di pubblicazione:||2019|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|