An automated method for identifying artifact in independent component analysis of resting-state fMRI

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 M RI (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.