Effective connectivity: Influence, causality and biophysical modeling

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...

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Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Fla.), 2011-09, Vol.58 (2), p.339-361
Main Authors: Valdes-Sosa, Pedro A., Roebroeck, Alard, Daunizeau, Jean, Friston, Karl
Format: Article
Language:English
Subjects:
Algorithms
Bayes Theorem
Bayesian analysis
Biophysics
Causality
Comments and Controversies
Control theory
Data Interpretation, Statistical
Dynamic Causal Modeling
EEG
Effective connectivity
Electroencephalography
fMRI
Granger Causality
Image Processing, Computer-Assisted - methods
Image Processing, Computer-Assisted - statistics & numerical data
Independence
Magnetic Resonance Imaging
Markov Chains
Medical imaging
Models, Neurological
Nerve Net - anatomy & histology
Nerve Net - physiology
Neural Pathways - anatomy & histology
Neural Pathways - physiology
Probability distribution
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Summary: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.
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2011.03.058