Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to Carp

By Jonathan D. Power, Kelly Anne Barnes, Abraham Z. Snyder, Bradley L. Schlaggar, Steven E. Petersen. NeuroImage, 2012

329 citations recorded by the source corpus.

We recently described a motion-related artifact in resting state functional connectivity MRI (rs-fcMRI) data that arises from subject head motion (Power et al. (2012), see also Satterthwaite et al. (2012); Van Dijk et al. (2012)). Head motion produces well-known disruptions in BOLD signal (Friston et al., 1996), and these artifactual modulations of BOLD signal, which are similar at nearby voxels, create spurious patterns in correlations in rs-fcMRI. Specifically, head motion augments short-distance correlations and weakens long-distance correlations. Thus, for instance, a higher-motion dataset would typically display weakened correlations between (distantly spaced) default mode regions but enhanced correlations between (closely spaced) visual regions in comparison to a low-motion data-set. We described this artifact in cohorts of children, adolescents, and adults, and its severity (magnitude) was related to the prevalence of motion within a cohort. Motion-related functional connectivity artifact is thus a substantial confound in the examination of single rs-fcMRI datasets and in comparisons of multiple rs-fcMRI datasets.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/81fc33897f0d1b5731095b938ab237c7b6f6465a

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Citations in the corpus

A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics (2013, neutral): “, 2012) or regressing out (Lemieux et al., 2007; Power et al., 2013; Satterthwaite et al., 2013) of single time points characterized by a sudden, sharp movement, or segments of motion-corrupted data in an otherwise usable time series.”
How Visual Is the Visual Cortex? Comparing Connectional and Functional Fingerprints between Congenitally Blind and Sighted Individuals (2015, methodology): “Specifically, for each subject, resting-state functional volumes were removed based on a threshold of framewise displacement (Power et al., 2012) 0.2 mm as well as the 1 back and 2 forward neighbors (Power et al., 2013).”
An Improved Framework for Confound Regression and Filtering for Control of Motion Artifact in the Preprocessing of Resting-State Functional Connectivity Data (2012, methodology): “As recently noted by Power et al. (2012) in their reply to Carp (2011), the criteria for de ﬁ ning what is a motion-related spike that should be removed (through scrubbing or spike regression) is of critical importance.”
Recent progress and outstanding issues in motion correction in resting state fMRI (2014, neutral): “…methods to counteract motion artifact are still being developed and validated (Bright and Murphy, 2013; Jo et al., 2013; Kundu et al., 2013; Muschelli et al., 2014; Patel et al., 2014; Power et al., 2014; Satterthwaite et al., 2013a; Scheinost et al., 2014; Yan et al., 2013a,b; Zeng et al., 2014).”
Methods to detect, characterize, and remove motion artifact in resting state fMRI (2013, methodology): “This approach sacrifices data but it is also effective in eliminating motion-related variance (Power et al., 2012; Power et al., 2013; Satterthwaite et al., 2013).”
The WU-Minn Human Connectome Project: An Overview (2013, background): “This may include motion artifacts (Power et al., 2012) that are not fully removed by the above processing steps, and which may artifactually influence correlation-based estimates of functional connectivity.”
A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series (2014, methodology): “…methods for dealing with subject motion Many of the most recent methods advocated for correcting movement artifacts have focused on scrubbing (Power et al., 2012, 2013), and combining different censoring operations with higher-order motion parameter regression models (Satterthwaite et al.,…”
The Nuisance of Nuisance Regression: Spectral Misspecification in a Common Approach to Resting-State fMRI Preprocessing Reintroduces Noise and Obscures Functional Connectivity (2013, background): “As such, computing DVARS prior to bandpass filtering and nuisance regressionmay providemore useful information that could be used to develop corresponding thresholds to identify motion contamination since large-amplitude motion-related fluctuations are often evident in data prior to these preprocess”