Sackler Centre for Consciousness Science and School of Informatics, University of Sussex, Brighton, BN1 9QJ, UK.
J Neurosci Methods. 2010 Feb 15;186(2):262-73. doi: 10.1016/j.jneumeth.2009.11.020. Epub 2009 Dec 2.
Assessing directed functional connectivity from time series data is a key challenge in neuroscience. One approach to this problem leverages a combination of Granger causality analysis and network theory. This article describes a freely available MATLAB toolbox--'Granger causal connectivity analysis' (GCCA)--which provides a core set of methods for performing this analysis on a variety of neuroscience data types including neuroelectric, neuromagnetic, functional MRI, and other neural signals. The toolbox includes core functions for Granger causality analysis of multivariate steady-state and event-related data, functions to preprocess data, assess statistical significance and validate results, and to compute and display network-level indices of causal connectivity including 'causal density' and 'causal flow'. The toolbox is deliberately small, enabling its easy assimilation into the repertoire of researchers. It is however readily extensible given proficiency with the MATLAB language.
从时间序列数据评估有向功能连接是神经科学的一个关键挑战。解决这个问题的一种方法是利用格兰杰因果分析和网络理论的结合。本文描述了一个免费的 MATLAB 工具箱——“格兰杰因果连接分析”（GCCA）——它为各种神经科学数据类型（包括神经电、神经磁、功能磁共振成像和其他神经信号）提供了执行这种分析的核心方法集。该工具箱包括用于多变量稳态和事件相关数据的格兰杰因果分析的核心功能、用于预处理数据、评估统计显著性和验证结果的功能，以及用于计算和显示因果连接的网络级指标的功能，包括“因果密度”和“因果流”。该工具箱故意设计得很小，使其易于被研究人员采用。然而，由于熟练掌握 MATLAB 语言，它也很容易扩展。
