Robust brain causality network construction based on Bayesian multivariate autoregression. (April 2020)

• Record Type: Journal Article
• Authors: Li, Peiyang; Huang, Xiaoye; Zhu, Xuyang; Li, Cunbo; Liu, Huan; Zhou, Weiwei; Bore, Joyce Chelangat; Zhang, Tao; Zhang, Yangsong; Yao, Dezhong; Xu, Peng
• Is Part Of: Biomedical signal processing and control. Volume 58(2020)
• Publication Date: 2020-04
• Subjects: Multivariate regressive model -- Bayesian analysis -- fMRI -- Brain-network construction -- Motor imagery; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.101864
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A Bayesian analysis (BA) based on Granger causality estimation was developed. The proposed model estimated parameters more robustly than the traditional Granger causality analysis for time series with different kinds of noise. The network patterns estimated by our proposed method were more similar to the predefined networks than traditional Granger causality analysis under noise conditions. Both the simulation study and real fMRI data applications revealed that our proposed method was less influenced by the data length compared with traditional Granger causality analysis. 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.