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  Bayesian modelling of fMRI time series

PADFR, Rasmussen, C., & Hansen, L. (2000). Bayesian modelling of fMRI time series.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E5C1-7 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E5C2-5
Genre: Conference Paper

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 Creators:
PADFR, Author
Rasmussen, CE1, Author              
Hansen, LK, Author
Solla, Sara A., Editor
Leen, Todd K., Editor
Müller, Klaus-Robert, Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, escidoc:1497795              

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 Abstract: We present a Hidden Markov Model (HMM) for inferring the hidden psychological state (or neural activity) during single trial fMRI activation experiments with blocked task paradigms. Inference is based on Bayesian methodology, using a combination of analytical and a variety of Markov Chain Monte Carlo (MCMC) sampling techniques. The advantage of this method is that detection of short time learning effects between repeated trials is possible since inference is based only on single trial experiments.

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 Dates: 2000
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: 2306
 Degree: -

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