Joint estimation of multiple graphical models for an fMRI study of brain connectivity networks

Investigating changes and similarities in brain connectivity networks across task conditions is a central topic in neuroscience. We propose a novel framework for jointly estimating multiple graphical models using a hybrid Bayesian integration technique that can handle high-dimensional or large-scale data sets. This framework accommodates multiple graphical models and performs a series of conditional independence tests to infer the underlying network structures. Theoretical justification for consistency is established, and synthetic experiments demonstrate that our approach outperforms existing methods in both accuracy and robustness. We further apply this framework to an functional magnetic resonance imaging study of dynamic functional connectivity among regions of interest during an emotion-processing task. Results reveal that inter- and intra-module interactions involving the subcortical-cerebellum module are reduced during emotion processing compared to shape processing, highlighting this module's key role in emotional processing.