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Variational Bayesian causal connectivity analysis for fMRI

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 autoregressiv...

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Detalles Bibliográficos
Autores principales: Luessi, Martin, Babacan, S. Derin, Molina, Rafael, Booth, James R., Katsaggelos, Aggelos K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4017144/
https://www.ncbi.nlm.nih.gov/pubmed/24847244
http://dx.doi.org/10.3389/fninf.2014.00045
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author Luessi, Martin
Babacan, S. Derin
Molina, Rafael
Booth, James R.
Katsaggelos, Aggelos K.
author_facet Luessi, Martin
Babacan, S. Derin
Molina, Rafael
Booth, James R.
Katsaggelos, Aggelos K.
author_sort Luessi, Martin
collection PubMed
description 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.
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spelling pubmed-40171442014-05-20 Variational Bayesian causal connectivity analysis for fMRI Luessi, Martin Babacan, S. Derin Molina, Rafael Booth, James R. Katsaggelos, Aggelos K. Front Neuroinform Neuroscience 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. Frontiers Media S.A. 2014-05-05 /pmc/articles/PMC4017144/ /pubmed/24847244 http://dx.doi.org/10.3389/fninf.2014.00045 Text en Copyright © 2014 Luessi, Babacan, Molina, Booth and Katsaggelos. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Luessi, Martin
Babacan, S. Derin
Molina, Rafael
Booth, James R.
Katsaggelos, Aggelos K.
Variational Bayesian causal connectivity analysis for fMRI
title Variational Bayesian causal connectivity analysis for fMRI
title_full Variational Bayesian causal connectivity analysis for fMRI
title_fullStr Variational Bayesian causal connectivity analysis for fMRI
title_full_unstemmed Variational Bayesian causal connectivity analysis for fMRI
title_short Variational Bayesian causal connectivity analysis for fMRI
title_sort variational bayesian causal connectivity analysis for fmri
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4017144/
https://www.ncbi.nlm.nih.gov/pubmed/24847244
http://dx.doi.org/10.3389/fninf.2014.00045
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