Effective connectivity: influence, causality and biophysical modeling

Neuroimage. 2011 Sep 15;58(2):339-61. doi: 10.1016/j.neuroimage.2011.03.058. Epub 2011 Apr 6.


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.

MeSH terms

  • Algorithms
  • Bayes Theorem
  • Biophysics*
  • Causality*
  • Data Interpretation, Statistical
  • Electroencephalography
  • Image Processing, Computer-Assisted / methods*
  • Image Processing, Computer-Assisted / statistics & numerical data
  • Magnetic Resonance Imaging
  • Markov Chains
  • Models, Neurological*
  • Nerve Net / anatomy & histology
  • Nerve Net / physiology*
  • Neural Pathways / anatomy & histology
  • Neural Pathways / physiology*