RT Journal Article
SR Electronic
T1 Comparing Community Detection Methods in Brain Functional Connectivity Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.02.06.935783
DO 10.1101/2020.02.06.935783
A1 Reddy Rani Vangimalla
A1 Jaya Sreevalsan-Nair
YR 2020
UL http://biorxiv.org/content/early/2020/02/07/2020.02.06.935783.abstract
AB Brain functional networks are essential for understanding functional connectome. Computing the temporal dependencies between the regions of brain activities of functional magnetic resonance imaging (fMRI) gives us the functional connectivity between the regions. The pairwise connectivities in matrix form correspond to the functional network (fNet), also referred to as a functional connectivity network (FCN). We start with analyzing a correlation matrix, which is an adjacency matrix of the FCN. In this work, we perform a case study of comparison of different analytical approaches in finding node-communities of the brain network. We use five different methods of community detection, out of which two methods are implemented on the network after filtering out the edges with weight below a predetermined threshold. We additionally compute and observe the following characteristics of the outcomes: (i) modularity of the communities, (ii) symmetrical node-partition between the left and right hemispheres of the brain, i.e., hemispheric symmetry, and (iii) hierarchical modular organization. Our contribution is in identifying an appropriate test-bed for comparison of outcomes of approaches using different semantics, such as network science, information theory, multivariate analysis, and data mining.