2008
DOI: 10.1002/hbm.20606
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Multivariate Granger causality analysis of fMRI data

Abstract: This article describes the combination of multivariate Ganger causality analysis, temporal down-sampling of fMRI time series, and graph theoretic concepts for investigating causal brain networks and their dynamics. As a demonstration, this approach was applied to analyze epoch-to-epoch changes in a hand-gripping, muscle fatigue experiment. Causal influences between the activated regions were analyzed by applying the directed transfer function (DTF) analysis of multivariate Granger causality with the integrated… Show more

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Cited by 216 publications
(219 citation statements)
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“…All data showed covariance stationarity as indicated by the KPSS test, which is a test with the null hypothesis of no unit root. Finally, a model order of two was chosen based on previous studies with approximately half the sampling rate using a model order of one (Deshpande et al, 2009;Sato et al, 2010), and model validity was confirmed by the Durbin-Watson test. Significant directed connections based on F statistics between brain regions were determined at a threshold of p = 0.0001 (FDR corrected).…”
Section: Image Acquisition and Analysismentioning
confidence: 99%
“…All data showed covariance stationarity as indicated by the KPSS test, which is a test with the null hypothesis of no unit root. Finally, a model order of two was chosen based on previous studies with approximately half the sampling rate using a model order of one (Deshpande et al, 2009;Sato et al, 2010), and model validity was confirmed by the Durbin-Watson test. Significant directed connections based on F statistics between brain regions were determined at a threshold of p = 0.0001 (FDR corrected).…”
Section: Image Acquisition and Analysismentioning
confidence: 99%
“…In several instances fMRI signal change in the DMN started prior to the event onset on EEG. Another study (Szaflarski, et al 2010) found parietal (but not necessarily DMN) activation occurring prior to thalamic activation and also detected similar causal links using Granger causality measures of fMRI data (Deshpande, et al 2009, Goebel, et al 2003. Another causal methodology, dynamic causal modeling , can estimate the influence of one system on another.…”
Section: The Default-mode Networkmentioning
confidence: 79%
“…After voxel‐wise application of GC, the result can be averaged over all voxel‐wise GC scores between the two regions. This voxel‐wise application of GC can be performed in multiple ways: the GC score can be averaged with LASSO regularization (Tang, Bressler, Sylvester, Shulman, & Corbetta, 2012), through a multivoxel pattern‐based causality mapping as proposed by Kim, Kim, Ahmad, & Park (2013), or through hierarchical clustering as proposed by Deshpande, LaConte, James, Peltier, & Hu (2009). This strategic twist is already being used in some studies (Katwal, Gore, Gatenby, & Rogers, 2013; Zhao et al., 2016), however, it is not the most common approach in the field yet [voxel‐wise modeling is, however, increasingly popular for finding activation patterns in cognition from fMRI data (Huth, de Heer, Griffiths, Theunissen, & Gallant, 2016)].…”
Section: Discussionmentioning
confidence: 99%