Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI

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Building a Science of Individual Differences from fMRI. (2016, background): “the removal of independent components related to motion [91], or the complete removal of motion-affected frames known as scrubbing [86] (see [92] for a comprehensive review).”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, methodology): “…(ICA; Beckmann et al., 2005) of single-subject time series has increasingly been used for denoising, with noise components selected either by a trained classifier (ICA-FIX; Griffanti et al., 2014; Salimi-Khorshidi et al., 2014) or using a priori heuristics (ICA-AROMA; Pruim et al., 2015a, 2015b).”
Hand classification of fMRI ICA noise components (2017, unknown): “ICA-AROMA, developed by Pruim et al. 2015a, 2015b), focuses instead on the identification and removal of artefacts due to head motion using four spatial and temporal features.”
Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses (2017, methodology): “While these approaches have been shown to eﬀectively remove motion, ICA-based denoising profoundly improves the reproducibility of group-level RSN spatial maps relative to either scrubbing or nuisance regression (Pruim et al., 2015).”