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A Bayesian model selection approach to fMRI activation detection

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dc.contributor.authorSeghouane, Abd-Krim
dc.contributor.authorOng, Ju
dc.coverage.spatialHong Kong China
dc.date.accessioned2015-12-10T23:05:40Z
dc.date.createdSeptember 26-29 2010
dc.date.issued2010
dc.date.updated2016-02-24T11:02:45Z
dc.description.abstractA fundamental question in functional MRI (fMRI) data analysis is to declare pixels either activated or non-activated with respect to the experimental design. A new statistical test for detecting activated pixels in fMRI data is proposed. The test is based on comparing the dimension of the parametric models fitted to the voxels fMRI time series data with and without controlled activation-baseline pattern. The Bayesian information criterion, is used for this comparison. This test has the advantage of not requiring any user-specified threshold to be estimated. The effectiveness of the proposed fMRI activation detection method is illustrated on real experimental data.
dc.identifier.urihttp://hdl.handle.net/1885/62463
dc.publisherIEEE Signal Processing Society
dc.relation.ispartofseriesIEEE International Conference on Image Processing 2010
dc.sourceProceedings of IEEE International Conference on Image Processing 2010
dc.subjectKeywords: Activation detection; Bayesian information criterion; Bayesian model selection; Data analysis; Experimental data; Experimental design; fMRI data; Functional MRI; Functional MRI (fMRI); Parametric models; Time-series data; Image processing; Imaging systems Activation detection; Bayesian information criterion; Functional MRI
dc.titleA Bayesian model selection approach to fMRI activation detection
dc.typeConference paper
local.bibliographicCitation.lastpage4404
local.bibliographicCitation.startpage4401
local.contributor.affiliationSeghouane, Abd-Krim, College of Engineering and Computer Science, ANU
local.contributor.affiliationOng, Ju, College of Engineering and Computer Science, ANU
local.contributor.authoruidSeghouane, Abd-Krim, u4593707
local.contributor.authoruidOng, Ju, u3936566
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080106 - Image Processing
local.identifier.absseo970109 - Expanding Knowledge in Engineering
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationu4334215xPUB701
local.identifier.doi10.1109/ICIP.2010.5653354
local.identifier.scopusID2-s2.0-78651092932
local.type.statusPublished Version

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