Variational Bayesian causal connectivity analysis for fMRI Luessi, Martin Babacan, S. Derin Molina Soriano, Rafael Booth, James R. Katsaggelos, Aggelos K. fMRI Causality Connectivity Variational Bayesian method Granger causality 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. 2015-02-16T11:57:47Z 2015-02-16T11:57:47Z 2014 info:eu-repo/semantics/article Luessi, M.; et al. Variational Bayesian causal connectivity analysis for fMRI. Frontiers in Neuroinformatics, 8: 45 (2014). [http://hdl.handle.net/10481/34823] 1662-5196 http://hdl.handle.net/10481/34823 10.3389/fninf.2014.00045 eng http://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License Frontiers Research Foundation