Time Course Based Artifact Identification for Independent Components of Resting-State fMRI

Abstract

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.

Methods for cleaning the BOLD fMRI signal

“…Therefore, several procedures have been proposed for assisting automated classification, which mainly differ in the algorithms used for supervised classification (and if necessary feature selection), the number and definitions of the spatial and temporal features used in the classification, as well as the type of fMRI data they are optimized to work with, either task-based, resting state, or both (Beall and Lowe, 2007; Bhaganagarapu et al, 2013; De Martino et al, 2007; Douglas et al, 2011; Formisano et al, 2002; Griffanti et al, 2014; Kochiyama et al, 2005; Liao et al, 2006; Perlbarg et al, 2007; Pruim et al, 2015a; 2015b; Rummel et al, 2013; Salimi-Khorshidi et al, 2014; Sochat et al, 2014; Soldati et al, 2009; Storti et al, 2013; Sui et al, 2009; Thomas et al, 2002; Tohka, et al, 2008; Wang and Li, 2015, Xu et al, 2014). …”

Hand classification of fMRI ICA noise components

“…Manual classification is typically used to test the efficacy of newly developed approaches (Bhaganagarapu et al, 2013, Perlbarg et al, 2007, Rummel et al, 2013, Storti et al, 2013) and/or to create training datasets for supervised algorithms (De Martino et al, 2007, Salimi-Khorshidi et al, 2014, Tohka et al, 2008). Moreover, small sample size or unusual characteristics of a given dataset might require full manual labelling for effective artefact removal.…”

Denoising the speaking brain: Toward a robust technique for correcting artifact-contaminated fMRI data under severe motion

“…First, due to the lack of a ground truth, the accuracies of these methods were either completely untested (Kochiyama et al, 2005; Kundu et al, 2012; Thomas et al, 2002) or only tested against the subjective classification scores of one or two human experts whose operational criteria or inter-rater reliability were often not reported (Bhaganagarapu et al, 2013; De Martino et al, 2007; Perlbarg et al, 2007; Tohka et al, 2008). Second, the quantitative measures (i.e., features) used for classification, which are usually based on the temporal (Kochiyama et al, 2005; Perlbarg et al, 2007; Rummel et al, 2013), spectral (Thomas et al, 2002), spatial or combined (Bhaganagarapu et al, 2013; De Martino et al, 2007; Tohka et al, 2008) properties of each component (as an exception, see Kundu et al, 2012), either were arbitrarily selected or had limited applicability due to an uncommon experimental setup (Kochiyama et al, 2005; Kundu et al, 2012; Thomas et al, 2002). A systematic method for individual feature selection is still lacking for the binary classification of sICA components.…”

“…A systematic method for individual feature selection is still lacking for the binary classification of sICA components. Third, the thresholds of these classification features were determined by arbitrary tuning (Kundu et al, 2012; Perlbarg et al, 2007; Rummel et al, 2013) or supervised learning (De Martino et al, 2007; Tohka et al, 2008) based only on a few pre-labeled datasets, thus the generalizability of these methods may be unreliable due to the variation of ICA components across datasets.…”