Methods to detect, characterize, and remove motion artifact in resting state fMRI

By Jonathan D. Power, Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, Steven E. Petersen. NeuroImage, 2013

3,609 citations recorded by the source corpus.

Head motion systematically alters correlations in resting state functional connectivity fMRI (RSFC). In this report we examine impact of motion on signal intensity and RSFC correlations. We find that motion-induced signal changes (1) are often complex and variable waveforms, (2) are often shared across nearly all brain voxels, and (3) often persist more than 10 seconds after motion ceases. These signal changes, both during and after motion, increase observed RSFC correlations in a distance-dependent manner. Motion-related signal changes are not removed by a variety of motion-based regressors, but are effectively reduced by global signal regression. We link several measures of data quality to motion, changes in signal intensity, and changes in RSFC correlations. We demonstrate that improvements in data quality measures during processing may represent cosmetic improvements rather than true correction of the data. We demonstrate a within-subject, censoring-based artifact removal strategy based on volume censoring that reduces group differences due to motion to chance levels. We note conditions under which group-level regressions do and do not correct motion-related effects.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a3f132288f4797236098766cb2893b5d3afe69f6

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior (2019, positive): “Consistent with previous work (Power et al., 2014; Burgess et al., 2016; Ciric et al., 2017), the median absolute FC-QC correlations decreased from 0.”
The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites (2018, background): “Head motion is a significant concern for pediatric imaging and has received significant attention in the domain of rs-fMRI (Fair et al., 2012; Power et al., 2012, 2013; Satterthwaite et al., 2012; Yan et al., 2013a, 2013b; Van Dijk et al., 2012).”
Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality and Emotion (2017, methodology): “across censored frames using least squares spectral estimation (Power et al., 2014).”
Prospective Motion Correction in Functional MRI (2016, background): “Even after regressing out motion-relevant parameters, data censoring, commonly referred to as “scrubbing” is often necessary (Carp, 2013; Power et al., 2014).”
How Visual Is the Visual Cortex? Comparing Connectional and Functional Fingerprints between Congenitally Blind and Sighted Individuals (2015, background): “Head motion can exert a confounding effect on functional connectivity patterns (Power et al., 2012, 2014; Van Dijk et al., 2012; Satterthwaite et al., 2013).”
Functional system and areal organization of a highly sampled individual human brain (2015, methodology): “Additional RSFC preprocessing followed the procedures described in Power et al. (2014), including motion scrubbing; white matter, ventricle, and global signal regression; and temporal ﬁltering.”
Reduction of motion-related artifacts in resting state fMRI using aCompCor (2014, methodology): “, 2012) and that this modulation of motion effects by inter-node distance can be influenced by preprocessing (Power et al., 2014; Satterthwaite et al., 2013).”
The UNC/UMN Baby Connectome Project (BCP): An Overview of the Study Design and Protocol Development (2018, positive): “Functional data were also temporally and spatially filtered using recently defined strategies (Power et al., 2014).”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, methodology): “While no model completely abolished motion-related variance, 36-parameter models with censoring (Satterthwaite et al., 2013; Power et al., 2014a) performed well across a range of benchmarks, as did ICA-AROMA with GSR. Notably, in terms of limiting QC-FC relationships, the top six confound…”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, positive): “Censoring can be very effective in studies of static FC (e.g., Power et al., 2014), and recent work suggests that it may be similarly powerful in the context of TVFC (Laumann et al., 2017).”
Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses (2017, neutral): “(Satterthwaite et al., 2012; Van Dijk et al., 2012; Power et al., 2014, 2015).”
Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI (2015, methodology): “Previous evaluations of strategies for motion artifact removal focused on their potential to remove motion artifacts (Power et al., 2012, 2014; Satterthwaite et al., 2013a; Yan et al., 2013), while the importance of retaining signal of interest is often disregarded.”
The Global Signal in fMRI: Nuisance or Information? (2017, positive): “the minimally processed version is typically used and the global signal is included as an additional nuisance regressor term in a general linear model (GLM) analysis of the data (Power et al., 2014).”