Bayesian Modelling of fMRI Time Series

Pedro Højen-Sørensen, Lars Kai Hansen, Carl Edward Rasmussen

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    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.
    Original languageEnglish
    Title of host publicationProceedings of NIPS 99
    Place of PublicationDenver
    Publication date2000
    Pages754-760
    Publication statusPublished - 2000
    EventNeural Information Processing Systems 1999 - Denver, United States
    Duration: 29 Nov 19994 Dec 1999

    Conference

    ConferenceNeural Information Processing Systems 1999
    Country/TerritoryUnited States
    CityDenver
    Period29/11/199904/12/1999

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