Rice Univesrity Logo
    • FAQ
    • Deposit your work
    • Login
    View Item 
    •   Rice Scholarship Home
    • Faculty & Staff Research
    • Faculty Publications
    • View Item
    •   Rice Scholarship Home
    • Faculty & Staff Research
    • Faculty Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Bayesian models for functional magnetic resonance imaging data analysis

    Thumbnail
    Name:
    Data-Analysis.pdf
    Size:
    2.355Mb
    Format:
    PDF
    View/Open
    Author
    Zhang, Linlin; Guindani, Michele; Vannucci, Marina
    Date
    2015
    Abstract
    Functional magnetic resonance imaging (fMRI), a noninvasive neuroimaging method that provides an indirect measure of neuronal activity by detecting blood flow changes, has experienced an explosive growth in the past years. Statistical methods play a crucial role in understanding and analyzing fMRI data. Bayesian approaches, in particular, have shown great promise in applications. A remarkable feature of fully Bayesian approaches is that they allow a flexible modeling of spatial and temporal correlations in the data. This article provides a review of the most relevant models developed in recent years. We divide methods according to the objective of the analysis. We start from spatiotemporal models for fMRI data that detect task-related activation patterns. We then address the very important problem of estimating brain connectivity. We also touch upon methods that focus on making predictions of an individual's brain activity or a clinical or behavioral response. We conclude with a discussion of recent integrative models that aim at combining fMRI data with other imaging modalities, such as electroencephalography/magnetoencephalography (EEG/MEG) and diffusion tensor imaging (DTI) data, measured on the same subjects. We also briefly discuss the emerging field of imaging genetics. 
    Citation
    Zhang, Linlin, Guindani, Michele and Vannucci, Marina. "Bayesian models for functional magnetic resonance imaging data analysis." Wiley Interdisciplinary Reviews: Computational Statistics, 7, no. 1 (2015) Wiley: 21-41. https://doi.org/10.1002/wics.1339.
    Published Version
    https://doi.org/10.1002/wics.1339
    Keyword
    Bayesian Statistics; Brain Connectivity; Classification and Prediction; Spatio-Temporal Activation Models; fMRI
    Type
    Journal article
    Publisher
    Wiley
    Citable link to this page
    https://hdl.handle.net/1911/94844
    Rights
    This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Wiley.
    Metadata
    Show full item record
    Collections
    • Faculty Publications [4976]
    • Statistics Publications [137]

    Home | FAQ | Contact Us | Privacy Notice | Accessibility Statement
    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892
    Site Map

     

    Searching scope

    Browse

    Entire ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsTypeThis CollectionBy Issue DateAuthorsTitlesSubjectsType

    My Account

    Login

    Statistics

    View Usage Statistics

    Home | FAQ | Contact Us | Privacy Notice | Accessibility Statement
    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892
    Site Map