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dc.contributor.authorBehroozi, M.en_US
dc.contributor.authorDaliri, M.R.en_US
dc.contributor.authorBoyaci H.en_US
dc.date.accessioned2016-02-08T09:49:45Z
dc.date.available2016-02-08T09:49:45Z
dc.date.issued2011en_US
dc.identifier.issn2008126X
dc.identifier.urihttp://hdl.handle.net/11693/21683
dc.description.abstractFunctional magnetic resonance imaging (fMRI) is a safe and non-invasive way to assess brain functions by using signal changes associated with brain activity. The technique has become a ubiquitous tool in basic, clinical and cognitive neuroscience. This method can measure little metabolism changes that occur in active part of the brain. We process the fMRI data to be able to find the parts of brain that are involve in a mechanism, or to determine the changes that occur in brain activities due to a brain lesion. In this study we will have an overview over the methods that are used for the analysis of fMRI data.en_US
dc.language.isoEnglishen_US
dc.source.titleBasic and Clinical Neuroscienceen_US
dc.subjectFmrien_US
dc.subjectGeneral Linear Model (GLM)en_US
dc.subjectIndependent Component Analysis (ICA)en_US
dc.subjectMachine learningen_US
dc.subjectMulti-voxel pattern analysis (mvpa)en_US
dc.subjectPrincipal Component Analysis (PCA)en_US
dc.subjectarticleen_US
dc.subjectblocked paradigmen_US
dc.subjectbrain mappingen_US
dc.subjectconnectivity analysisen_US
dc.subjectdata analysisen_US
dc.subjectdata pre processingen_US
dc.subjectevent related paradigmen_US
dc.subjectexperimental designen_US
dc.subjectfunctional magnetic resonance imagingen_US
dc.subjectgeneralized linear modelen_US
dc.subjectindependent component analysisen_US
dc.subjectinformation processingen_US
dc.subjectintensity normalizationen_US
dc.subjectmachine learningen_US
dc.subjectmixed paradigmen_US
dc.subjectmulti voxel pattern analysisen_US
dc.subjectmultivariate analysisen_US
dc.subjectneuroimagingen_US
dc.subjectprincipal component analysisen_US
dc.subjectsignal detectionen_US
dc.subjectsignal processingen_US
dc.subjectslice timing correctionen_US
dc.subjectstatistical analysisen_US
dc.subjectstatistical modelen_US
dc.subjecttemporal filteringen_US
dc.subjectunivariate analysisen_US
dc.titleStatistical analysis methods for the fMRI dataen_US
dc.typeArticleen_US
dc.departmentDepartment of Psychology
dc.citation.spage67en_US
dc.citation.epage74en_US
dc.citation.volumeNumber2en_US
dc.citation.issueNumber4en_US


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