Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/11810
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dc.contributor.authorBhaganagarapu, Kaushiken
dc.contributor.authorJackson, Graeme Den
dc.contributor.authorAbbott, David Fen
dc.date.accessioned2015-05-16T01:26:18Z
dc.date.available2015-05-16T01:26:18Z
dc.date.issued2013-07-10en
dc.identifier.citationFrontiers in Human Neuroscience 2013; 7(): 343en
dc.identifier.govdoc23847511en
dc.identifier.otherPUBMEDen
dc.identifier.urihttp://ahro.austin.org.au/austinjspui/handle/1/11810en
dc.description.abstractAn enduring issue with data-driven analysis and filtering methods is the interpretation of results. To assist, we present an automatic method for identification of artifact in independent components (ICs) derived from functional MRI (fMRI). The method was designed with the following features: does not require temporal information about an fMRI paradigm; does not require the user to train the algorithm; requires only the fMRI images (additional acquisition of anatomical imaging not required); is able to identify a high proportion of artifact-related ICs without removing components that are likely to be of neuronal origin; can be applied to resting-state fMRI; is automated, requiring minimal or no human intervention. We applied the method to a MELODIC probabilistic ICA of resting-state functional connectivity data acquired in 50 healthy control subjects, and compared the results to a blinded expert manual classification. The method identified between 26 and 72% of the components as artifact (mean 55%). About 0.3% of components identified as artifact were discordant with the manual classification; retrospective examination of these ICs suggested the automated method had correctly identified these as artifact. We have developed an effective automated method which removes a substantial number of unwanted noisy components in ICA analyses of resting-state fMRI data. Source code of our implementation of the method is available.en
dc.language.isoenen
dc.subject.otherICAen
dc.subject.otherartifactsen
dc.subject.otherautomated classificationen
dc.subject.otherautomaticen
dc.subject.otherfMRIen
dc.subject.otherfunctional magnetic resonance imagingen
dc.subject.otherindependent component analysisen
dc.subject.otherindependent component labelingen
dc.titleAn automated method for identifying artifact in independent component analysis of resting-state FMRI.en
dc.typeJournal Articleen
dc.identifier.journaltitleFrontiers in human neuroscienceen
dc.identifier.affiliationThe Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Austin Hospital , Melbourne, VIC , Australia ; Department of Medicine, The University of Melbourne , Melbourne, VIC , Australiaen
dc.identifier.doi10.3389/fnhum.2013.00343en
dc.description.pages343en
dc.relation.urlhttps://pubmed.ncbi.nlm.nih.gov/23847511en
dc.type.austinJournal Articleen
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.languageiso639-1en-
item.openairetypeJournal Article-
crisitem.author.deptNeurology-
crisitem.author.deptThe Florey Institute of Neuroscience and Mental Health-
crisitem.author.deptThe Florey Institute of Neuroscience and Mental Health-
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