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.
CITATION STYLE
Melissari, G., Marzullo, A., Stamile, C., Calimeri, F., Durand-Dubief, F., & Sappey-Marinier, D. (2019). Inducing Clinical Course Variations in Multiple Sclerosis White Matter Networks. In Advances in Intelligent Systems and Computing (Vol. 997, pp. 900–917). Springer Verlag. https://doi.org/10.1007/978-3-030-22871-2_64
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