Abstract
People living with schizophrenia (SCZ) experience severe brain network deterioration. The brain is constantly fizzling with non-linear causal activities measured by electroencephalogram (EEG) and despite the variety of effective connectivity methods, only few approaches can quantify the direct non-linear 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 non-linear 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.
Highlights
The brain usually fizzles with the non-linear causal activity of electroencephalogram (EEG) at a microscopic level (Gourévitch et al, 2006; Sabesan et al, 2010; Mehta and Kliewer, 2018)
We evaluated the strength of the networks produced by Granger causal analysis (GCA), Bivariate transfer entropy (BVTE), and multivariate transfer entropy (MTE) by considering the total number of edges in the network
Effective Network Since, we aimed to investigate the brain network deterioration of the SCZ in the oddball task, in this study, only the EEG datasets of the four runs of P300 tasks were included in the following analyses
Summary
The brain usually fizzles with the non-linear causal activity of electroencephalogram (EEG) at a microscopic level (Gourévitch et al, 2006; Sabesan et al, 2010; Mehta and Kliewer, 2018). The behavioral and psychological attitudes of people with psychiatric disorders call for the need to effectively investigate the transient information exchange in the brain (Zhang et al, 2011; Mehta and Kliewer, 2016). Multiple techniques or measures for linear and non-linear brain connectivity such as structural, Non-linear Directed Information Flow in Schizophrenia functional, and effective connectivity are in use for this purpose (Selskii et al, 2017; Hristopulos et al, 2019). Exploring the linear and non-linear interactions, more importantly when the system structure is unknown, holds promise for deepening the knowledge of the causal mechanism in the brain for the SCZ (Pereda et al, 2005; Zhao et al, 2013)
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