A Covariate-Assisted Community Detection Algorithm with Applied to Functional Brain Network Data

Music City Center 

The brain is a high-dimensional object. Graphical models and network analysis methods are promising tools to characterize complex brain structures at different disease stages. Community detection is particularly valuable to enhance our understanding of the intrinsic connections between different brain modules. In this study, we introduce a new method that integrates brain topology and crucial node-level information for community detection. The estimation is done in a Bayesian framework, where a scalable algorithm based on variational Bayes is proposed. Extensive simulations are conducted to evaluate the performance of the proposed method and related algorithms. The method is applied to brain networks generated from functional magnetic resonance imaging (fMRI) data of Alzheimer's disease for a case study.