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

By Raimon H. R. Pruim, Maarten Mennes, Daan van Rooij, Alberto Llera, Jan K. Buitelaar, Christian F. Beckmann. NeuroImage, 2015

1,649 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b72d029c909f560d46a8f09b262bf698f5f58926

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Citations in the corpus

Don't fear ‘fear conditioning’: Methodological considerations for the design and analysis of studies on human fear acquisition, extinction, and return of fear (2017, neutral): “…1994). ince adding motion covariates (e.g. obtained from realignment) o the first-level model may reduce sensitivity (while enhancing pecificity) (Johnstone et al., 2006), alternative approaches such as ndependent component analysis might be considered (Kochiyama t al., 2005; Pruim et al., 2015).”
Inscapes: a movie paradigm to improve compliance in functional magnetic resonance imaging (2015, methodology): “…approach that would include the use of optimized MRIacquisition sequences to decrease the effects of headmovement on signal (Bright and Murphy, 2013; Kundu et al., 2013, 2015) and advanced post-processing techniques for motion correction (Murphy et al., 2013; Yan et al., 2013; Pruim et al., 2015).”
Building a Science of Individual Differences from fMRI. (2016, methodology): “Some of these methods have been shown to improve reliability [110].”
Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI (2015, methodology): “This suggestion is corroborated by evidence showing that fMRI data contained substantially higher levels of motion-related and generic noise, relative to signal of interest, after extensive nuisance regression and spike regression compared to ICA-AROMA (Pruim et al., 2015).”
Hand classification of fMRI ICA noise components (2017, positive): “ICA-AROMA, developed by Pruim et al. 2015a, 2015b), focuses instead on the identification and removal of artefacts due to head motion using four spatial and temporal features.”
Frequency-specific maturation of cortical organization and social-cognitive links from childhood to adolescence (2025, unknown): “Steps included removal of the initial 10 s (4 TRs), head-motion correction, slice timing correction, de-spiking, estimation of head-motion parameters, registration of functional images to high-resolution T1 structural images via boundary-based registration, denoising using ICA-AROMA (Pruim et al., 2”