Suppression of information spreading in MEG source estimation using a functionally-structured Bayesian approach

Magnetoencephalography (MEG) is a noninvasive method that can measure human brain activity with high temporal resolution. However, the spatial resolution of MEG is limited because MEG signals are recorded by sensors located outside the head. Although MEG source estimation overcomes this problem to some extent, the combination of MEG source estimation and multivariate analysis results in "information spreading", where significant predictions are observed in brain areas outside the true signal source location. In this study, we examine a Bayesian estimation approach that incorporates functional structure of the human brain in order to explore whether both source estimation accuracy and suppression of information spreading can be improved simultaneously. We compare the performance of the proposed model with simulated data generated under various signal-to-noise ratio conditions. The results show that the functionally-structured Bayesian model achieves source estimation accuracy that is better than that of conventional source estimation models. Additionally, the comparison of information spreading among the models reveals that our model outperforms the conventional ones. These results suggest that information spreading in the MEG source estimation can be suppressed while maintaining high source estimation accuracy.