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Bayesian modelling of fMRI time series

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84156

Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

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


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-E5C1-7
Zusammenfassung
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.