Communities and Cliques in Functional Brain Network Using Multiscale Consensus Approach

The modular organization of the functional brain connectome implies its functional segregation. Correlation matrices extracted from fMRI data are used as adjacency matrices of the connectome, i.e., the functional connectivity network (FCN). The modular organization of FCN is widely solved using node-community detection methods, albeit with a requirement of edge filtering, mostly. However, network sparsification potentially leads to the loss of correlation information. With no ideal threshold values for edge filtering in literature, there is growing interest in finding communities in the complete weighted network. To address this requirement, we propose the use of exploratory factor analysis (EFA), thus, exploiting the semantics of the correlation matrix. In our recent work on using EFA for FCN analysis, we have proposed a novel consensus-based algorithm using a multiscale approach, where the number of factors n_F is treated as the scale. The consensus procedure is employed for transforming the network before performing community detection. Here, we propose a novel extension to our multiscale EFA for finding relevant cliques. We use an ensemble of experiments and extensive quantitative analysis of its outcomes to identify the optimal set of scales for efficient node-partitioning. We perform case studies of datasets of FCN of the human brain at resting state, with different sizes and parcellation atlases (AAL, Schaefer). Our results of consensus communities and cliques correspond to relevant brain activity in its resting state, thus showing the effectiveness of consensus-based multiscale EFA.