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