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    A scalable multi-resolution spatio-temporal model for brain activation and connectivity in fMRI data

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    Type
    Article
    Authors
    Castruccio, Stefano
    Ombao, Hernando cc
    Genton, Marc G. cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Applied Mathematics and Computational Science Program
    Statistics Program
    Date
    2018-01-22
    Online Publication Date
    2018-01-22
    Print Publication Date
    2018-09
    Permanent link to this record
    http://hdl.handle.net/10754/626948
    
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    Abstract
    Functional Magnetic Resonance Imaging (fMRI) is a primary modality for studying brain activity. Modeling spatial dependence of imaging data at different spatial scales is one of the main challenges of contemporary neuroimaging, and it could allow for accurate testing for significance in neural activity. The high dimensionality of this type of data (on the order of hundreds of thousands of voxels) poses serious modeling challenges and considerable computational constraints. For the sake of feasibility, standard models typically reduce dimensionality by modeling covariance among regions of interest (ROIs)—coarser or larger spatial units—rather than among voxels. However, ignoring spatial dependence at different scales could drastically reduce our ability to detect activation patterns in the brain and hence produce misleading results. We introduce a multi-resolution spatio-temporal model and a computationally efficient methodology to estimate cognitive control related activation and whole-brain connectivity. The proposed model allows for testing voxel-specific activation while accounting for non-stationary local spatial dependence within anatomically defined ROIs, as well as regional dependence (between-ROIs). The model is used in a motor-task fMRI study to investigate brain activation and connectivity patterns aimed at identifying associations between these patterns and regaining motor functionality following a stroke.
    Citation
    Castruccio S, Ombao H, Genton MG (2018) A scalable multi-resolution spatio-temporal model for brain activation and connectivity in fMRI data. Biometrics. Available: http://dx.doi.org/10.1111/biom.12844.
    Publisher
    Wiley
    Journal
    Biometrics
    DOI
    10.1111/biom.12844
    PubMed ID
    29359375
    arXiv
    arXiv:1602.02435
    Additional Links
    http://onlinelibrary.wiley.com/doi/10.1111/biom.12844/full
    ae974a485f413a2113503eed53cd6c53
    10.1111/biom.12844
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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