Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping.

By Rainer Goebel, Alard Roebroeck, Dae-Shik Kim, Elia Formisano. Magnetic Resonance Imaging, 2003

728 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/462bc37f9329f7a8a3d3c128303799ecafd772ad

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In Cognitive Neuroscience

Citations in the corpus

Neural correlates of cognitive efficiency (2006, methodology): “Thus, it permitted characterization of the strength and direction of influence between discrete brain regions (Goebel et al., 2003).”
Mapping directed influence over the brain using Granger causality and fMRI (2005, methodology): “Download date: 14 Sep. 2023 www.elsevier.com/locate/ynimg NeuroImage 25 (2005) 230–242 Mapping directed influence over the brain using Granger causality and fMRI Alard Roebroeck,* Elia Formisano, and Rainer Goebel”
Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation (2011, methodology): “Other approaches such as psychophysiological interactions or Granger causalitymapping are used to identify regions, showing a context-dependent change in their coupling with a particular seed region (Friston et al., 1997; Goebel et al., 2003) under the assumptions of the employed model.”
A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks (2008, methodology): “Briefly, GCA detects causal interactions between brain regions by assessing the 12570 www.pnas.org cgi doi 10.1073 pnas.0800005105 Sridharan et al. predictability of signal changes in one brain region based on the time-course of responses in another brain region (28).”
Saliency, switching, attention and control: a network model of insula function (2010, neutral): “Granger causal analyses (GCA) enable the detection of causal interactions between brain regions by assessing the extent to which signal changes in one brain region can predict signal changes in another brain region (Goebel et al. 2003).”
Assessing functional connectivity in the human brain by fMRI. (2007, methodology): “The first category of methods includes seed–voxel correlation maps, Granger causality maps derived from bivariate autoregressive models [3] and psychophysiological interaction (PPI) maps [4].”
Functional and Effective Connectivity: A Review (2011, neutral): “One can convert any dynamic causal model into a linear state-space or vector autoregression model (Goebel et al., 2003; Harrison et al., 2003; see Rogers et al., 2010 for review) by solving (integrating) the Taylor approximation to Equation (3) over the intervals between data samples, D , using the…”
Dissociable controlled retrieval and generalized selection mechanisms in ventrolateral prefrontal cortex. (2005, background): “…et al., 1998; Wagner et al., 2001; Zhang et al., 2004), though the functional character and topographic organization of processing within left VLPFC remains highly controversial (Badre and Wagner, 2002; Dobbins and Wagner, 2005; Gold et al., 2005; Moss et al., 2005; Thompson-Schill, 2003).”
Analysing connectivity with Granger causality and dynamic causal modelling (2013, methodology): “Granger causality (GC) is first applied to fMRI time-series using the method of ‘Granger causality mapping’ (GCM) from a seed voxel to other voxels [43] Important extension of the GCM approach, examining the influence of confounding hemodynamic response functions [31]”
Network modelling methods for FMRI (2011, neutral): “Early use of Granger causality for neuroimaging data can be found in Goebel et al. (2003) and Roebroeck et al. (2005).”
Comparing hemodynamic models with DCM (2007, neutral): “, 2007), or multivariate autoregression (Harrison et al., 2003; Goebel et al., 2003), coupling parameters are estimated directly from the measured time-series.”
Learning Graphs From Data: A Signal Representation Perspective (2018, neutral): “Similar ideas can also be found in the Granger causality or vector autoregressive models (VARMs) [58], [59].”
A DCM for resting state fMRI (2014, background): “In other words, the cross correlation functions or cross spectra represent generalised measures of functional connectivity that retain a temporal aspect—and preserve information on directed functional connectivity, which is exploited using temporal precedence representations; for example, multivaria”
Schizophrenia, neuroimaging and connectomics (2012, methodology): “, 2003) and granger causality analysis (Goebel et al., 2003) are two examples of methods designed to model effective connectivity between regions, though inferring causal interactions from BOLD measurements is often computationally intensive and”