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.

中文翻译：

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通过多元传递熵测量精神分裂症的非线性定向信息流


精神分裂症 (SCZ) 患者的大脑网络会出现严重恶化。大脑不断地充满通过脑电图（EEG）测量的非线性因果活动，尽管有多种有效的连接方法，但只有少数方法可以量化直接的非线性因果相互作用。为了解决这个问题，我们通过多元转移熵（MTE）来定量测量有效连通性，该方法已被证明能够有效地捕获线性和非线性因果关系。在这项工作中，我们建议通过 MTE 构建 EEG 有效网络，并进一步将其性能与格兰杰因果分析（GCA）和双变量传递熵（BVTE）进行比较。模拟结果定量表明，在不同的信噪比条件、边缘恢复、灵敏度和特异性下，MTE 优于 GCA 和 BVTE。此外，其对健康对照（HC）和 SCZ 患者的 P300 任务脑电图的应用进一步清楚地表明，与 HC 相比，SCZ 的网络相互作用恶化。 MTE 提供了一种新颖的工具，有可能加深我们对 SCZ 大脑网络恶化的了解。