Overlapping brain Community detection using Bayesian tensor decomposition

Abstract

It has been found that specific regions in the brain are dedicated to specific functions. Detection and analysis of the constituent functional networks of the brain is of great importance for understanding the brain functionality and diagnosing some neuropsychiatric illnesses. In this paper, we introduce Non-negative Tensor Factorization (NTF) methods to identify the overlapping communities in brain networks using resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Instead of taking average over a group of subjects, we use individual subject connectivity matrices to build the tensor data. Decomposed factors indicate the community membership probabilities and inter-subject variability indices modeling the community strengths over subjects. In contrast to the methods based on Non-negative Matrix Factorization (NMF) which are generally applied to the average connectivity matrices, using tensor factorization modeling preserves the information conveyed by the individual subjects. The experiments are carried out on simulated data as well as real Human Connectome Project (HCP) rs-fMRI datasets. To evaluate the effectiveness of the proposed framework, we have computed reproducibility over time and groups of subjects. Test-retest reliability is also examined through computing the intra-class correlation coefficient (ICC) index. The results show that the proposed NTF-based frameworks lead to stable and accurate results.