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Causal Modelling and Brain Connectivity in Functional Magnetic Resonance Imaging

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  • Karl Friston

Abstract

Recent advances in data analysis and modeling allow the use of fMRI data to ask not just which brain regions are involved in various cognitive and perceptual tasks, but also how they communicate with each other. Karl Friston examines two different state-of-the-art approaches to modeling brain connectivity using neuroimaging.

Suggested Citation

  • Karl Friston, 2009. "Causal Modelling and Brain Connectivity in Functional Magnetic Resonance Imaging," PLOS Biology, Public Library of Science, vol. 7(2), pages 1-6, February.
    • Handle: RePEc:plo:pbio00:1000033
    DOI: 10.1371/journal.pbio.1000033
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    References listed on IDEAS

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    1. Odd O. Aalen & Arnoldo Frigessi, 2007. "What can Statistics Contribute to a Causal Understanding?," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 155-168, March.
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    Cited by:

    1. Wei Tang & Steven L Bressler & Chad M Sylvester & Gordon L Shulman & Maurizio Corbetta, 2012. "Measuring Granger Causality between Cortical Regions from Voxelwise fMRI BOLD Signals with LASSO," PLOS Computational Biology, Public Library of Science, vol. 8(5), pages 1-14, May.
    2. repec:jss:jstsof:44:i13 is not listed on IDEAS
    3. Liane Schmidt & Maël Lebreton & Marie-Laure Cléry-Melin & Jean Daunizeau & Mathias Pessiglione, 2012. "Neural Mechanisms Underlying Motivation of Mental Versus Physical Effort," PLOS Biology, Public Library of Science, vol. 10(2), pages 1-13, February.
    4. Vasilii V. Kuimov & Konstantin V. Simonov & Eva V. Shcherbenko & Liudmila V. Iushkova, 2023. "Ecosystems and Their Digital Models as Factors of Algocognitive Culture and Transition to New Technological Order," Social Sciences, MDPI, vol. 12(4), pages 1-11, March.
    5. Luca M. Possati, 2021. "Freud and the algorithm: neuropsychoanalysis as a framework to understand artificial general intelligence," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-19, December.
    6. Will D Penny & Klaas E Stephan & Jean Daunizeau & Maria J Rosa & Karl J Friston & Thomas M Schofield & Alex P Leff, 2010. "Comparing Families of Dynamic Causal Models," PLOS Computational Biology, Public Library of Science, vol. 6(3), pages 1-14, March.
    7. Oscar Miranda-Dominguez & Brian D Mills & Samuel D Carpenter & Kathleen A Grant & Christopher D Kroenke & Joel T Nigg & Damien A Fair, 2014. "Connectotyping: Model Based Fingerprinting of the Functional Connectome," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-16, November.
    8. Tian Ge & Keith M Kendrick & Jianfeng Feng, 2009. "A Novel Extended Granger Causal Model Approach Demonstrates Brain Hemispheric Differences during Face Recognition Learning," PLOS Computational Biology, Public Library of Science, vol. 5(11), pages 1-13, November.
    9. Qiang Luo & Tian Ge & Fabian Grabenhorst & Jianfeng Feng & Edmund T Rolls, 2013. "Attention-Dependent Modulation of Cortical Taste Circuits Revealed by Granger Causality with Signal-Dependent Noise," PLOS Computational Biology, Public Library of Science, vol. 9(10), pages 1-15, October.

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