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Bayesian modelling of fMRI time series

2000

Conference Paper

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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.

Author(s): , PADFR. and Rasmussen, CE. and Hansen, LK.
Pages: 754-760
Year: 2000
Day: 0
Editors: Sara A. Solla, Todd K. Leen and Klaus-Robert M{\"u}ller

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Digital: 0
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{2306,
  title = {Bayesian modelling of fMRI time series},
  author = {},
  pages = {754-760},
  editors = {Sara A. Solla,  Todd K. Leen and Klaus-Robert M{\"u}ller},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2000}
}