ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data

• Published in: NeuroImage (Orlando, Fla.) Vol. 112; pp. 267 - 277
• Main Authors: Pruim, Raimon H R, Mennes, Maarten, van Rooij, Daan, Llera, Alberto, Buitelaar, Jan K, Beckmann, Christian F
• Format: Journal Article
• Language: English
• Published: United States Elsevier Limited 15.05.2015
• Subjects: Algorithms; Artificial Intelligence; Brain research; Cerebrospinal Fluid - physiology; Conflicts of interest; Data processing; Functional magnetic resonance imaging; Grants; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - statistics & numerical data; Motion; Noise; Principal Component Analysis; Reproducibility of Results; Rest; Motion; Connectivity; Functional MRI; Resting state; Artifact; Independent component analysis
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2015.02.064

Head motion during functional MRI (fMRI) scanning can induce spurious findings and/or harm detection of true effects. Solutions have been proposed, including deleting ('scrubbing') or regressing out ('spike regression') motion volumes from fMRI time-series. These strategies remove motion-induced signal variations at the cost of destroying the autocorrelation structure of the fMRI time-series and reducing temporal degrees of freedom. ICA-based fMRI denoising strategies overcome these drawbacks but typically require re-training of a classifier, needing manual labeling of derived components (e.g. ICA-FIX; Salimi-Khorshidi et al. (2014)). Here, we propose an ICA-based strategy for Automatic Removal of Motion Artifacts (ICA-AROMA) that uses a small (n=4), but robust set of theoretically motivated temporal and spatial features. Our strategy does not require classifier re-training, retains the data's autocorrelation structure and largely preserves temporal degrees of freedom. We describe ICA-AROMA, its implementation, and initial validation. ICA-AROMA identified motion components with high accuracy and robustness as illustrated by leave-N-out cross-validation. We additionally validated ICA-AROMA in resting-state (100 participants) and task-based fMRI data (118 participants). Our approach removed (motion-related) spurious noise from both rfMRI and task-based fMRI data to larger extent than regression using 24 motion parameters or spike regression. Furthermore, ICA-AROMA increased sensitivity to group-level activation. Our results show that ICA-AROMA effectively reduces motion-induced signal variations in fMRI data, is applicable across datasets without requiring classifier re-training, and preserves the temporal characteristics of the fMRI data.