Block-structured Bayesian source estimation model for magnetoencephalography signals

The human brain consists of functionally specialized areas that process specific types of information and interact with one another. Magnetoencephalography (MEG) is a neuroimaging technique that captures brain activity at high temporal resolution. However, its spatial resolution is insufficient to accurately localize neural activation, and thus cannot capture the interactive nature of human brain activity. To resolve this issue, various MEG source estimation models have been proposed. In particular, models incorporating prior information from functional magnetic resonance imaging (fMRI), which offers superior spatial resolution, have improved source estimation accuracy. However, these models typically ignore the similarity of activity across different brain areas, and still fall short in tracking dynamic brain activity pattern at a sufficient spatial scale. In this study, we developed a block-structured model that integrates information about functional areas and the inter-areal relationships of the human brain into MEG source estimation based on a hierarchical Bayesian framework. We evaluated our model performance using simulation data with sequential activation across multiple brain areas. Results showed that our model outperformed conventional approaches in source estimation accuracy, suggesting that incorporating the functional areal information and inter-areal relationships may enhance MEG source estimation, enabling human neuroimaging at high spatiotemporal resolution.