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Please use this identifier to cite or link to this item: https://olympias.lib.uoi.gr/jspui/handle/123456789/11087
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dc.contributor.authorOikonomou, V. P.en
dc.contributor.authorBlekas, K.en
dc.contributor.authorAstrakas, L.en
dc.date.accessioned2015-11-24T17:02:42Z-
dc.date.available2015-11-24T17:02:42Z-
dc.identifier.issn0018-9294-
dc.identifier.urihttps://olympias.lib.uoi.gr/jspui/handle/123456789/11087-
dc.rightsDefault Licence-
dc.subjectexpectation maximization (em) algorithmen
dc.subjectfunctional magnetic resonance imaging (fmri) analysisen
dc.subjectgeneral linear regression model (glm)en
dc.subjectmarkov random field (mrf)en
dc.subjectrelevance vector machine (rvm)en
dc.subjecttime-seriesen
dc.subjectbayesian-inferenceen
dc.subjectlinear-modelen
dc.subjectpriorsen
dc.subjectalgorithmen
dc.subjectdesignen
dc.subjectimagesen
dc.subjectbrainen
dc.titleA Sparse and Spatially Constrained Generative Regression Model for fMRI Data Analysisen
heal.typejournalArticle-
heal.type.enJournal articleen
heal.type.elΆρθρο Περιοδικούel
heal.identifier.primaryDoi 10.1109/Tbme.2010.2104321-
heal.languageen-
heal.accesscampus-
heal.recordProviderΠανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικήςel
heal.publicationDate2012-
heal.abstractIn this study, we present an advanced Bayesian framework for the analysis of functional magnetic resonance imaging (fMRI) data that simultaneously employs both spatial and sparse properties. The basic building block of our method is the general linear regression model that constitutes a well-known probabilistic approach. By treating regression coefficients as random variables, we can apply an enhanced Gibbs distribution function that captures spatial constrains and at the same time allows sparse representation of fMRI time series. The proposed scheme is described as a maximum a posteriori approach, where the known expectation maximization algorithm is applied offering closed-form update equations for the model parameters. We have demonstrated that our method produces improved performance and functional activation detection capabilities in both simulated data and real applications.en
heal.journalNameIeee Transactions on Biomedical Engineeringen
heal.journalTypepeer reviewed-
heal.fullTextAvailabilityTRUE-
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