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
    EventProceedings of NIPS 99, - Denver
    Duration: 1 Jan 1999 → …

    Conference

    ConferenceProceedings of NIPS 99,
    CityDenver
    Period01/01/1999 → …

    Cite this

    Højen-Sørensen, P., Hansen, L. K., & Rasmussen, C. E. (2000). Bayesian Modelling of fMRI Time Series. In Proceedings of NIPS 99 (pp. 754-760).