The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Wiener–Granger Causality: A well established methodology
Open accessAbstract
For decades, the main ways to study the effect of one part of the nervous system upon another have been either to stimulate or lesion the first part and investigate the outcome in the second. This article describes a fundamentally different approach to identifying causal connectivity in neuroscience: a focus on the predictability of ongoing activity in one part from that in another. This approach was made possible by a new method that comes from the pioneering work of Wiener (1956) and Granger (1969). The Wiener-Granger method, unlike stimulation and ablation, does not require direct intervention in the nervous system. Rather, it relies on the estimation of causal statistical influences between simultaneously recorded neural time series data, either in the absence of identifiable behavioral events or in the context of task performance. Causality in the Wiener-Granger sense is based on the statistical predictability of one time series that derives from knowledge of one or more others. This article defines Wiener-Granger Causality, discusses its merits and limitations in neuroscience, and outlines recent developments in its implementation.
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Cites
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Self-citationIdentifying neural drivers with functional MRI: an electrophysiological validation
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Cited by
A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls
Bastos, A. M., & Schoffelen, J.-M. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2015.00175 · Cited by 1,313
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Analysing connectivity with Granger causality and dynamic causal modelling
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Self-citationvan Diessen, E., Numan, T., van Dellen, E., van der Kooi, A. W., Boersma, M., Hofman, D., van Lutterveld, R., van Dijk, B. W., van Straaten, E. C. W., Hillebrand, A., et al. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2014.11.018 · Cited by 417
Dynamic causal modelling: A critical review of the biophysical and statistical foundations
Daunizeau, J., David, O., & Stephan, K. E. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.062 · Cited by 331
Metadata
- Authors
- Bressler, S. L., & Seth, A. K.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2011-09
- Volume
- 58
- Issue
- 2
- Pages
- 323-329
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2113762408
- Cited by
- 847
- References
- 10
- FWCI
- 17.92
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
