ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data
Open accessAbstract
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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Motion artefact removal using ICA-AROMA was applied.
“Last, spatial smoothing (6 mm FWHM Gaussian kernel) and motion artefact removal using ICA-AROMA (Pruim et al., 2015) were applied.”
From Are prediction error modifications domain specific in autism but domain general in ADHD? (2025) · cites this paper as Pruim et al., 2015
Head motion is unlikely to underlie the effects we report because we excluded subjects having a high degree of head motion and repeated the analysis after regressing out motion-related components.
“Head motion is unlikely to underlie the effects we report because we excluded subjects having a high degree of head motion and repeated the analysis after regressing out motion-related components using a similar approach to Pruim et al. (48).”
From Understanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies (2016) · cites this paper as [48]
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“Last, spatial smoothing (6 mm FWHM Gaussian kernel) and motion artefact removal using ICA-AROMA ( Pruim et al., 2015) were applied.”
- Authors
- Pruim, R. H. R., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., & Beckmann, C. F.
- Affiliations
- Radboud University Nijmegen (NL)
- Corresponding author
- Pruim, Raimon H.R.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2015-05
- Volume
- 112
- Pages
- 267-277
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2047453615
- Cited by
- 1,684
- References
- 57
- Citations by year
- 2026: 154 · 2025: 201 · 2024: 185 · 2023: 203 · 2022: 197 · 2021: 207 · 2020: 193 · 2019: 126 · 2018: 94 · 2017: 66 · 2016: 50 · 2015: 8
- FWCI
- 58.83
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Funders
- Wellcome Trust (FP7/2007–2013, 433-09-242, 098369/Z/12/Z, 098369)
- European Commission (EU-FP7, 2007–2013, FP7 2007-2013, 278948, FP7/2007, 327340)
- ZonMw (60-60600-97-193, 056-13-015, 433-09-242)
- Radboud Universiteit
- Vrije Universiteit Amsterdam (433-09-242, FP7/2007-2013, 278948, 056-13-015)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (433‐09‐242, 175.010.2007.010, NWO-Vidi 864.12.003, FP7 2007-2013, 05613015, 864.12.003, NWO-Vidi 864-12, 60-60600-97-193)
- Accare
- Universitair Medisch Centrum Groningen
- Radboud Universitair Medisch Centrum
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Keywords
- independent component analysis, motion artifact removal, head movement, fMRI denoising, scrubbing, autocorrelation structure, resting-state fMRI, task-based fMRI
- MeSH terms
- Algorithms, Artificial Intelligence, Cerebrospinal Fluid, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Motion, Rest, Reproducibility of Results, Artifacts, Principal Component Analysis
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