A Pipeline for the Analysis of Multilayer Brain Networks

The formalism of multilayer networks (MLN) makes possible to model and understand the multiple relationships between entities in a system. Indeed, this representation has found its way into a wide range of disciplines, particularly in the fields of neuroscience and neuroimaging. Human brain modelling made possible the identification of the basis for the construction of morphological, structural and functional brain connectivity networks. In this work, we propose the design and implementation of a software pipeline for the construction and analysis of multilayer brain networks. This approach aims to identify groups of strongly connected nodes within the network and to evaluate the resulting communities. We examined 10 healthy subjects and 10 patients with multiple sclerosis. We analyzed the brain MLN by applying community detection algorithm that identified recurrent communities in patients with multiple sclerosis. To assess the structure of communities within the network, we calculate modularity indices for each subject. Finally, we confirm what has already been found in the literature, i.e. a high modularity in the brain networks of diseased subjects compared to those of healthy subjects. Future developments could involve aligning these networks to identify common patterns among multiple sclerosis patients and potentially identify subgroups of patients with similar neural characteristics.