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Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI
Abstract
We proposed ICA-AROMA as a strategy for the removal of motion-related artifacts from fMRI data (Pruim et al., 2015). ICA-AROMA automatically identifies and subsequently removes data-driven derived components that represent motion-related artifacts. Here we present an extensive evaluation of ICA-AROMA by comparing our strategy to a range of alternative strategies for motion-related artifact removal: (i) no secondary motion correction, (ii) extensive nuisance regression utilizing 6 or (iii) 24 realignment parameters, (iv) spike regression (Satterthwaite et al., 2013a), (v) motion scrubbing (Power et al., 2012), (vi) aCompCor (Behzadi et al., 2007; Muschelli et al., 2014), (vii) SOCK (Bhaganagarapu et al., 2013), and (viii) ICA-FIX (Griffanti et al., 2014; Salimi-Khorshidi et al., 2014), without re-training the classifier. Using three different functional connectivity analysis approaches and four different multi-subject resting-state fMRI datasets, we assessed all strategies regarding their potential to remove motion artifacts, ability to preserve signal of interest, and induced loss in temporal degrees of freedom (tDoF). Results demonstrated that ICA-AROMA, spike regression, scrubbing, and ICA-FIX similarly minimized the impact of motion on functional connectivity metrics. However, both ICA-AROMA and ICA-FIX resulted in significantly improved resting-state network reproducibility and decreased loss in tDoF compared to spike regression and scrubbing. In comparison to ICA-FIX, ICA-AROMA yielded improved preservation of signal of interest across all datasets. These results demonstrate that ICA-AROMA is an effective strategy for removing motion-related artifacts from rfMRI data. Our robust and generalizable strategy avoids the need for censoring fMRI data and reduces motion-induced signal variations in fMRI data, while preserving signal of interest and increasing the reproducibility of functional connectivity metrics. In addition, ICA-AROMA preserves the temporal non-artifactual time-series characteristics and limits the loss in tDoF, thereby increasing statistical power at both the subject- and the between-subject analysis level.
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“In addition, a state-of-the-art ICA-based denoising procedure (ICA-AROMA, Pruim et al., 2015) was used to remove motion-related artifacts in native space”
From Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders (2019) · cites this paper as Pruim et al., 2015
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ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data
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Metadata
- Authors
- Pruim, R. H. R., Mennes, M., 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
- 278-287
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2052615191
- Cited by
- 487
- References
- 62
- Citations by year
- 2026: 25 · 2025: 39 · 2024: 32 · 2023: 53 · 2022: 45 · 2021: 48 · 2020: 56 · 2019: 58 · 2018: 44 · 2017: 43 · 2016: 34 · 2015: 10
- FWCI
- 26.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- Wellcome Trust (098369/Z/12/Z)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- motion artifact removal, resting-state fMRI, functional connectivity, signal preservation
- MeSH terms
- Adult, Algorithms, Child, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Motion, Neural Pathways, Regression Analysis, Rest, Databases, Factual, Artifacts, Principal Component Analysis
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