GMAC: A Matlab toolbox for spectral Granger causality analysis of fMRI data

Abstract

Investigation of causal interactions within brain networks using Granger causality analysis (GCA) is a key challenge in studying neural activity on the basis of functional magnetic resonance imaging (fMRI). The article describes an open-source software toolbox GMAC (Granger multivariate autoregressive connectivity) implementing multivariate spectral GCA. Available features are: fMRI data importing/exporting, network nodes definition, time series preprocessing, multivariate autoregressive modeling, spectral Granger causality indexes estimation, statistical significance assessment using surrogate data, network analysis and visualization of connectivity results. All functions have been integrated into a user-friendly graphical interface developed in the Matlab environment, easily accessible to both technical and clinical users. (C) 2012 Elsevier Ltd. All rights reserved.