Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy - Harmah, Dennis Joe; Li, Cunbo; Li, Fali; Liao, Yuanyuan; Wang, Jiuju; Ayedh, Walid M. A.; Bore, Joyce Chelangat; Yao, Dezhong; Dong, Wentian; Xu, Peng (Frontiers in Computational Neuroscience, 2020)
People living with schizophrenia (SCZ) experience severe brain network deterioration. The brain is constantly fizzling with nonlinear causal activities measured by electroencephalogram (EEG) and despite the variety of effective connectivity methods, only few approaches can quantify the direct nonlinear causal interactions. To circumvent this problem, we are motivated to quantitatively measure the effective connectivity by multivariate transfer entropy (MTE) which has been demonstrated to be able to capture both linear and nonlinear causal relationships effectively. In this work, we propose to construct the EEG effective network by MTE and further compare its performance with the Granger causal analysis (GCA) and Bivariate transfer entropy (BVTE). The simulation results quantitatively show that MTE outperformed GCA and BVTE under varied signal-to-noise conditions, edges recovered, sensitivity, and specificity. Moreover, its applications to the P300 task EEG of healthy controls (HC) and SCZ patients further clearly show the deteriorated network interactions of SCZ, compared to that of the HC. The MTE provides a novel tool to potentially deepen our knowledge of the brain network deterioration of the SCZ.