Analysing connectivity with Granger causality and dynamic causal modelling
Open accessAbstract
This review considers state-of-the-art analyses of functional integration in neuronal macrocircuits. We focus on detecting and estimating directed connectivity in neuronal networks using Granger causality (GC) and dynamic causal modelling (DCM). These approaches are considered in the context of functional segregation and integration and--within functional integration--the distinction between functional and effective connectivity. We review recent developments that have enjoyed a rapid uptake in the discovery and quantification of functional brain architectures. GC and DCM have distinct and complementary ambitions that are usefully considered in relation to the detection of functional connectivity and the identification of models of effective connectivity. We highlight the basic ideas upon which they are grounded, provide a comparative evaluation and point to some outstanding issues.
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Dynamic causal models (DCMs) allow one to infer directed synaptic connections (effective connectivity) from neuroimaging or electrophysiological data.
“dynamic causal models (DCMs; for reviews see Daunizeau et al., 2011; Friston et al., 2013) allow one to infer directed synaptic connections (effective connectivity) from neuroimaging or electrophysiological data.”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Friston et al., 2013
Functional connectivity by itself does not disclose the mechanisms by which the measured signals were caused and may be vulnerable to confounds at the measurement level.
“Although advanced measures of functional connectivity can unearth directed influences (Friston et al., 2013; Seth et al., 2013), by itself functional connectivity does not disclose the mechanisms by which the measured signals were caused and may be vulnerable to confounds at the measurement level.”
From Allostatic Self-efficacy: A Metacognitive Theory of Dyshomeostasis-Induced Fatigue and Depression (2016) · cites this paper as Friston et al., 2013
Dynamic causal modeling and Granger causality algorithms are fundamentally different despite some similarities.
“While there are some similarities between DCM and Granger causality algorithms, they are fundamentally different (111).”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [111]
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- Authors
- Friston, K., Moran, R., & Seth, A. K.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- University of Sussex (GB)
- Corresponding author
- Friston, Karl
- Venue
- Current Opinion in Neurobiology
- Publisher
- Elsevier BV
- Published
- 2013-04
- Volume
- 23
- Issue
- 2
- Pages
- 172-178
- ISSN
- 0959-4388 (ISSN-L)
- PMC
- PMC3925802
- OpenAlex
- W2018305040
- Cited by
- 684
- References
- 55
- Citations by year
- 2026: 31 · 2025: 42 · 2024: 37 · 2023: 36 · 2022: 49 · 2021: 69 · 2020: 58 · 2019: 86 · 2018: 62 · 2017: 51 · 2016: 45 · 2015: 49 · 2014: 42 · 2013: 25
- FWCI
- 2.84
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Current Opinion in Neurobiology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Sussex Research Online (University of Sussex) (submittedVersion, cc-by)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion)
- Funders
- Wellcome Trust
- EPSRC
- Dr. Mortimer and Theresa Sackler Foundation
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- Granger causality, dynamic causal modeling, directed connectivity, functional connectivity, effective connectivity, functional segregation, functional integration, brain network architecture, causal inference
- MeSH terms
- Brain, Brain Mapping, Computer Simulation, Humans, Models, Neurological, Nerve Net, Neurons, Causality, Nonlinear Dynamics
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