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

Handle URI:
http://hdl.handle.net/10754/626948
Title:
A scalable multi-resolution spatio-temporal model for brain activation and connectivity in fMRI data
Authors:
Castruccio, Stefano ( 0000-0002-6728-965X ) ; Ombao, Hernando ( 0000-0001-7020-8091 ) ; Genton, Marc G. ( 0000-0001-6467-2998 )
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program; Statistics Program
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-Blackwell
Journal:
Biometrics
Issue Date:
23-Jan-2018
DOI:
10.1111/biom.12844
ARXIV:
arXiv:1602.02435
Type:
Article
ISSN:
0006-341X
Additional Links:
http://onlinelibrary.wiley.com/doi/10.1111/biom.12844/full
Appears in Collections:
Articles; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Statistics Program

Full metadata record

DC FieldValue Language
dc.contributor.authorCastruccio, Stefanoen
dc.contributor.authorOmbao, Hernandoen
dc.contributor.authorGenton, Marc G.en
dc.date.accessioned2018-01-30T06:48:49Z-
dc.date.available2018-01-30T06:48:49Z-
dc.date.issued2018-01-23en
dc.identifier.citationCastruccio 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.en
dc.identifier.issn0006-341Xen
dc.identifier.doi10.1111/biom.12844en
dc.identifier.urihttp://hdl.handle.net/10754/626948-
dc.description.abstractFunctional 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.en
dc.publisherWiley-Blackwellen
dc.relation.urlhttp://onlinelibrary.wiley.com/doi/10.1111/biom.12844/fullen
dc.rightsThe Biometrics Copyright Transfer Agreement allows authors to post the final Accepted Author Manuscript of an accepted paper on their personal websites and on not for profit repositories such as arXiv, PubMedCentral, etc as soon as the final definitive publisher's version of the paper (the Version of Record) appears online in the Early View section of Wiley Online Library. In other words, the "embargo period" for Biometrics following online publication of the Version of Record in the Early View section is ZERO (0) MONTHS.en
dc.subjectBig dataen
dc.subjectBrain imagingen
dc.subjectFunctional magnetic resonance imageen
dc.subjectGaussian processesen
dc.subjectMulti-resolution modelen
dc.subjectSpace-time statisticsen
dc.titleA scalable multi-resolution spatio-temporal model for brain activation and connectivity in fMRI dataen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.contributor.departmentStatistics Programen
dc.identifier.journalBiometricsen
dc.eprint.versionPost-printen
dc.contributor.institutionDepartment of Applied and Computational Mathematics and Statistics; University of Notre Dame; 153 Hurley Hall, Notre Dame Indiana 46556 U.S.A.en
dc.identifier.arxividarXiv:1602.02435-
kaust.authorOmbao, Hernandoen
kaust.authorGenton, Marc G.en
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