Robust brain causality network construction based on Bayesian multivariate autoregression

Abstract Background Cognitive processes involve information integration among multiple encephalic regions, which can be measured by causal networks. However, the estimation of causal networks by means of some traditional methods with the least square will lead to distorted networks because of the unexpected outlier noise and the small number of signal samples in real applications. New method In this work, we adopted Bayesian inference to estimate parameters in a multivariate autoregression model (MVAR), to restrain the influence of outliers. Results Through the simulation study, we observed that our proposed method can efficiently suppress outlier influence and shows stable performance when sample sizes become small. Application to real motor imagery functional magnetic resonance imaging (fMRI) also revealed that the proposed approach can capture the inherent hemispheric lateralization of motor imagery even with a small number of fMRI samples. Comparison with existing methods We compared our proposed Bayesian-based Granger analysis with traditional Granger causality analysis. Conclusions The analyses conducted in the current work demonstrate the robustness of Bayesian-based Granger analysis to outlier conditions or physiological signals with small sample sizes.