Visual inspection of independent components: Defining a procedure for artifact removal from fMRI data

By Robert Emmett Kelly Jr., George S. Alexopoulos, Zhishun Wang, F. Gunning, Christopher F. Murphy, Sarah Shizuko Morimoto, Dora Kanellopoulos, Zhiru Jia, Kelvin O. Lim, Matthew J. Hoptman. Journal of Neuroscience Methods, 2010

364 citations recorded by the source corpus.

Artifacts in fMRI data, primarily those related to motion and physiological sources, negatively impact the functional signal-to-noise ratio in fMRI studies, even after conventional fMRI preprocessing. Independent component analysis’ demonstrated capacity to separate sources of neural signal, structured noise, and random noise into separate components might be utilized in improved procedures to remove artifacts from fMRI data. Such procedures require a method for labeling independent components (ICs) as representing artifacts to be removed or neural signals of interest to be spared. Visual inspection is often considered an accurate method for such labeling as well as a standard to which automated labeling methods are compared. However, detailed descriptions of methods for visual inspection of ICs are lacking in the literature. Here we describe the details of, and the rationale for, an operationalized fMRI data denoising procedure that involves visual inspection of ICs (96% inter-rater agreement). We estimate that dozens of subjects/sessions can be processed within a few hours using the described method of visual inspection. Our hope is that continued scientific discussion of and testing of visual inspection methods will lead to the development of improved, cost-effective fMRI denoising procedures.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/289bd4d315dcaa7d5642671369d6db44a62d9ef3

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Citations in the corpus

ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data (2015, positive): “Components representing noise can be regressed out from the data, implying that ICA can be used to remove noise from fMRI data in a data-driven fashion (Thomas et al., 2002; Kelly et al., 2010; Kundu et al., 2012).”
Building a Science of Individual Differences from fMRI. (2016, background): “In decomposition-basedmethods, identifying noise components is not always trivial [111]; some components may be a mixture of signal and noise, and there is a risk of ‘throwing the baby out with the bathwater’.”
Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis. (2012, methodology): “The time courses of noise components extracted by using group independent component analysis were also utilized for artefact removal for each subject (Liu et al., 2008a; Kelly et al., 2010).”
Hand classification of fMRI ICA noise components (2017, neutral): “A detailed description of the possible range of artefactual components is more difficult to find in the literature and very few papers report descriptions or examples of artefact-related components (Beckmann, 2012; Kelly et al., 2010; Rummel et al., 2013; Salimi-Khorshidi et al., 2014; Smith et al.,”