Time course based artifact identification for independent components of resting-state FMRI

Rummel, Christian; Verma, Rajeev Kumar; Schöpf, Veronika; Abela, Eugenio; Hauf, Martinus; Berruecos, José Fernando Zapata; Wiest, Roland (2013). Time course based artifact identification for independent components of resting-state FMRI. Frontiers in human neuroscience, 7, p. 214. Lausanne: Frontiers Research Foundation 10.3389/fnhum.2013.00214

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In functional magnetic resonance imaging (fMRI) coherent oscillations of the blood oxygen level-dependent (BOLD) signal can be detected. These arise when brain regions respond to external stimuli or are activated by tasks. The same networks have been characterized during wakeful rest when functional connectivity of the human brain is organized in generic resting-state networks (RSN). Alterations of RSN emerge as neurobiological markers of pathological conditions such as altered mental state. In single-subject fMRI data the coherent components can be identified by blind source separation of the pre-processed BOLD data using spatial independent component analysis (ICA) and related approaches. The resulting maps may represent physiological RSNs or may be due to various artifacts. In this methodological study, we propose a conceptually simple and fully automatic time course based filtering procedure to detect obvious artifacts in the ICA output for resting-state fMRI. The filter is trained on six and tested on 29 healthy subjects, yielding mean filter accuracy, sensitivity and specificity of 0.80, 0.82, and 0.75 in out-of-sample tests. To estimate the impact of clearly artifactual single-subject components on group resting-state studies we analyze unfiltered and filtered output with a second level ICA procedure. Although the automated filter does not reach performance values of visual analysis by human raters, we propose that resting-state compatible analysis of ICA time courses could be very useful to complement the existing map or task/event oriented artifact classification algorithms.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology

UniBE Contributor:

Rummel, Christian, Verma, Rajeev Kumar, Abela, Eugenio, Hauf, Martinus, Wiest, Roland Gerhard Rudi


600 Technology > 610 Medicine & health




Frontiers Research Foundation




Factscience Import

Date Deposited:

04 Oct 2013 14:39

Last Modified:

02 Mar 2023 23:21

Publisher DOI:


PubMed ID:


Web of Science ID:





https://boris.unibe.ch/id/eprint/15725 (FactScience: 223154)

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