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Title: An automated method for identifying artifact in independent component analysis of resting-state FMRI.
Austin Authors: Bhaganagarapu, Kaushik;Jackson, Graeme D ;Abbott, David F 
Affiliation: The 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 , Australia
Issue Date: 10-Jul-2013
Publication information: Frontiers in Human Neuroscience 2013; 7(): 343
Abstract: An 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.
Gov't Doc #: 23847511
DOI: 10.3389/fnhum.2013.00343
Journal: Frontiers in human neuroscience
Type: Journal Article
Subjects: ICA
automated classification
functional magnetic resonance imaging
independent component analysis
independent component labeling
Appears in Collections:Journal articles

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