Granger Causality Analysis in Neuroscience and Neuroimaging
By Anil K. Seth, Adam B. Barrett, Lionel C. Barnett. Journal of Neuroscience, 2015
850 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/83b8494bb20c3826152507bea1e7acad9e919060
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Bratislav Misic
- Waldemar Karwowski
- Farzad Vasheghani Farahani
- Nichole R. Lighthall
- Andrea Avena-Koenigsberger
- Olaf Sporns
- Floris P. de Lange
- Micha Heilbron
Citations in the corpus
- Communication dynamics in complex brain networks (2017, background): “More sophisticated measures have been proposed that are based on partial correlations 143,144 , coherence 145 or temporal precedence cues that enable estimation of directed interactions 74,146,147 .”
- Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, methodology): “To tackle this issue, GC analysis is typically performed by fitting a linear vector autoregressive (VAR) to the time series (Seth, 2010; Friston et al., 2013; Seth et al., 2015).”
- Granger Causality: A Review and Recent Advances (2021, methodology): “In spite of their limitations, bivariate tests of Granger causality have been widely used in many application areas, from economics (Chiou-Wei et al., 2008) and finance (Hong et al., 2009) to neuroscience (Seth et al., 2015) and meteorology (Mosedale et al., 2006).”
- Could a Neuroscientist Understand a Microprocessor? (2016, methodology): “Granger causality [37] has emerged as a method of assessing putative causal relationships between brain regions based on LFP data.”
- How Do Expectations Shape Perception? (2018, methodology): “The traditional way to achieve this is either by perturbing top-down connections, for example by using cooling or optogenetic (de)activation [152] or by resorting to causal modeling techniques such as dynamic causal modeling (DCM) [153] and Granger causality [154] which rely on various (contestable)”
- Neural mechanisms of time-forward predictions for naturalistic auditory tone sequences (2026, unknown): “In addition, although we used time-domain GC 29,42 , which is not subject to the spectral power confounds that might in fl uence frequency-domain GC 11 , other directed in fl uence measures such as nonlinear metrics could be explored in future work, e.g., refs.”