variational bayesian causal connectivity analysis for fmri

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ID: 157336
2014
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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.
Reference Key
eluessi2014frontiersvariational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Martin eLuessi;Martin eLuessi;S. Derin Babacan;Rafael eMolina;James R. Booth;Aggelos K. Katsaggelos
Journal Nucleic Acids Research
Year 2014
DOI
10.3389/fninf.2014.00045
URL
http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00045/full
Keywords
connectivity fmri causality granger causality neuropsychiatry biological psychiatry

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