Analysing connectivity with Granger causality and dynamic causal modelling

By Karl J. Friston, Rosalyn J. Moran, Anil K. Seth. Current Opinion in Neurobiology, 2013

651 citations recorded by the source corpus.

Highlights ► A brief introduction to the analysis of directed connectivity in brain networks. ► An overview of advances in Granger causality and dynamic causal modelling. ► A comparative evaluation of both approaches in terms of their pros and cons.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4d77b137c490ac2d24340b60f8bc7dab68ec699e

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

EEG and MEG: relevance to neuroscience. (2013, positive): “With the same general objective, some investigations apply dynamic causal models and Granger causality to estimate effective connectivity (Friston et al., 2013; David, 2011; Roebroeck et al., 2011), but such models have simplified parameter spaces, such that their constructive and predictive values…”
Advancing functional connectivity research from association to causation (2019, background): “Following Friston et al. 89 , approaches that use parameterized models to characterize causal interactions between neural entities.”
Understanding DCM: Ten simple rules for the clinician (2013, neutral): “For a comparison with methods that rest on similar principles, see Friston et al. (2013).”
Structural and Functional Brain Networks: From Connections to Cognition (2013, methodology): “The pros and cons of these methods have been discussed elsewhere (42, 43).”
Granger Causality Analysis in Neuroscience and Neuroimaging (2015, background): “The distinction between effective and functional connectivity clarifies why methods like G-causality and DCM are complementary rather than competitive—they make different assumptions, and they permit different interpretations (Friston et al., 2013).”
Exploring Causal Relationships in Visual Object Tracking (2015, methodology): “This approach has become known as Granger causality and has been successfully used in economics [18, 35], neurosciences [11, 16], and recently in computer vision [31, 39].”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, methodology): “Friston et al. pointed out that GC and DCM play complementary roles in analyzing the causal interactions (Friston et al., 2013).”
Causal Discovery with Attention-Based Convolutional Neural Networks (2019, background): “In addition, application areas such as neurobiology use dynamic causal modeling to estimate the connectivity of neuronal networks [17].”