Identification of non-linear models of neural activity in bold fmri

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    Abstract

    Non-linear hemodynamic models express the BOLD signal as a nonlinear, parametric functional of the temporal sequence of local neural activity. Several models have been proposed for this neural activity. We identify one such parametric model by estimating the distribution of its parameters. These distributions are themselves stochastic, therefore we estimate their variance by epoch based leave-one-out cross validation, using a Metropolis-Hastings algorithm for sampling of the posterior parameter distribution.
    Original languageEnglish
    Title of host publication3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano
    PublisherIEEE
    Publication date2006
    Pages952-955
    ISBN (Print)0-7803-9576-X
    DOIs
    Publication statusPublished - 2006
    Event3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006. -
    Duration: 1 Jan 2006 → …

    Conference

    Conference3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006.
    Period01/01/2006 → …

    Bibliographical note

    Copyright: 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

    Keywords

    • model comparison
    • BOLD fMRI
    • Bayes
    • learning
    • MCMC

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