Variational Bayesian causal connectivity analysis for fMRI

Martin Luessi*, S. Derin Babacan, Rafael Molina, James R. Booth, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


The ability to accurately estimate effective connectivity among brain regions from neuroimaging data could help answering many open questions in neuroscience. We propose a method which uses causality to obtain a measure of effective connectivity from fMRI data. The method uses a vector autoregressive model for the latent variables describing neuronal activity in combination with a linear observation model based on a convolution with a hemodynamic response function. Due to the employed modeling, it is possible to efficiently estimate all latent variables of the model using a variational Bayesian inference algorithm. The computational efficiency of the method enables us to apply it to large scale problems with high sampling rates and several hundred regions of interest. We use a comprehensive empirical evaluation with synthetic and real fMRI data to evaluate the performance of our method under various conditions.

Original languageEnglish (US)
Article number45
JournalFrontiers in Neuroinformatics
Issue numberMAY
StatePublished - May 5 2014


  • Causality
  • Connectivity
  • Granger causality
  • Variational Bayesian method
  • fMRI

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Biomedical Engineering
  • Computer Science Applications


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