Hierarchical Bayesian inference for community detection and connectivity of functional brain networks

TL;DR

This paper introduces a hierarchical Bayesian multilayer community detection method for functional brain networks, accounting for individual variability and validated with synthetic and real fMRI data.

Contribution

It develops a novel Bayesian latent block model that robustly detects community structures at individual and group levels, outperforming existing methods.

Findings

The method accurately recovers known community structures in synthetic data.

It demonstrates higher reproducibility and accuracy on real fMRI data.

Outperforms traditional modularity models in reliability and precision.

Abstract

Most functional magnetic resonance imaging studies rely on estimates of hierarchically organized functional brain networks whose segregation and integration reflect the cognitive and behavioral changes in humans. However, most existing methods for estimating the community structure of networks from both individual and group-level analysis methods do not account for the variability between subjects. In this paper, we develop a new multilayer community detection method based on Bayesian latent block model (LBM). The method can robustly detect the community structure of weighted functional networks with an unknown number of communities at both individual and group levels and retain the variability of the individual networks. For validation, we propose a new community structure-based multivariate Gaussian generative model to simulate synthetic signal. Our simulation study shows that the community memberships estimated by hierarchical Bayesian inference are consistent with the predefined node labels in the generative model. The method is also tested via split-half reproducibility using working memory task fMRI data of 100 unrelated healthy subjects from the Human Connectome Project. Analyses using both synthetic and real data show that our proposed method is more accurate and reliable compared with the commonly used (multilayer) modularity models.