http://www.nitrc.org/forum/forum.php?forum_id=4934 <table border="0" width="100%"><tr><td align="left"/></tr></table> <p><b>ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data.</b></p> <p>Neuroimage. 2015 Mar 11;</p> <p>Authors: Pruim RH, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF</p> <p>Abstract<br/> 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; [42]). 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.<br/> </p><p>PMID: 25770991 [PubMed - as supplied by publisher]</p> en-us Copyright 2000-2026 NITRC OSI Sat, 02 May 2026 4:14:49 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator