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首页> 外文期刊>Proceedings of the IEEE >Wiener–Granger Causality in Network Physiology With Applications to Cardiovascular Control and Neuroscience
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Wiener–Granger Causality in Network Physiology With Applications to Cardiovascular Control and Neuroscience

机译：网络生理中的维纳-格兰杰因果关系及其在心血管控制和神经科学中的应用

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摘要

Since the operative definition given by C. W. J. Granger of an idea expressed by N. Wiener, the Wiener–Granger causality (WGC) has been one of the most relevant concepts exploited by modern time series analysis. Indeed, in networks formed by multiple components, working according to the notion of segregation and interacting with each other according to the principle of integration, inferring causality has opened a window on the effective connectivity of the network and has linked experimental evidences to functions and mechanisms. This tutorial reviews predictability improvement, information-based and frequency domain methods for inferring WGC among physiological processes from multivariate realizations and quantifying the strength of the cause–effect relations in network physiology. Studies relevant to cardiovascular control and neuroscience are listed as examples of applications in prominent biomedical fields in which WGC analysis led to remarkable advancements in our knowledge. The review pays special attention to procedures for checking the reliability of the WGC approaches according to the statistical framework of hypothesis testing.
机译：自从C. W. J. Granger对N. Wiener所表达的想法进行操作定义以来，Wiener-Granger因果关系（WGC）已成为现代时间序列分析中最相关的概念之一。实际上，在由多个组件组成的网络中，根据隔离的概念进行工作并根据集成原理进行交互，推断因果关系为网络的有效连通性打开了一个窗口，并将实验证据与功能和机制联系在一起。本教程回顾了可预测性的改进，基于信息的方法和频域方法，这些方法可从多元实现中推断出生理过程中的WGC，并量化网络生理中因果关系的强度。与心血管控制和神经科学有关的研究被列为在重要生物医学领域中应用的实例，在这些领域中，WGC分析导致了我们知识的显着进步。审查特别重视根据假设检验的统计框架检查WGC方法可靠性的程序。

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