Variational Bayesian causal connectivity analysis for fMRI. Luessi, M., Babacan, S. D., Molina, R., Booth, J. R., & Katsaggelos, A. K. Frontiers in Neuroinformatics, 8(MAY):45 , publisher = Frontiers Media SA, may, 2014. ![Variational Bayesian causal connectivity analysis for fMRI [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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. © 2014 Luessi, Babacan, Molina, Booth and Katsaggelos.
@article{Martin2014,
abstract = {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. {\textcopyright} 2014 Luessi, Babacan, Molina, Booth and Katsaggelos.},
author = {Luessi, Martin and Babacan, S. Derin and Molina, Rafael and Booth, James R. and Katsaggelos, Aggelos K.},
doi = {10.3389/fninf.2014.00045},
issn = {1662-5196},
journal = {Frontiers in Neuroinformatics},
keywords = {Causality,Connectivity,Granger causality,Variational Bayesian method,fMRI},
month = {may},
number = {MAY},
pages = {45 , publisher = Frontiers Media SA},
title = {{Variational Bayesian causal connectivity analysis for fMRI}},
url = {http://journal.frontiersin.org/article/10.3389/fninf.2014.00045/abstract},
volume = {8},
year = {2014}
}
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