Effective connectivity: Influence, causality and biophysical modeling

P. A. Valdes-Sosa*, A.F. Roebroeck, J. Daunizeau, K. Friston

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

249 Citations (Web of Science)
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This is the final paper in a Comments and Controversies series dedicated to "The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution". We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener-Akaike-Granger-Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments.
Original languageEnglish
Pages (from-to)339-361
Issue number2
Publication statusPublished - 1 Jan 2011

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