A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics

By Chaogan Yan, Brian Cheung, Clare Kelly, Stan Colcombe, R. Craddock, A. di Martino, Qingyang Li, Xi-Nian Zuo, F. X. Castellanos, Michael P. Milham. NeuroImage, 2013

1,450 citations recorded by the source corpus.

Functional connectomics is one of the most rapidly expanding areas of neuroimaging research. Yet, concerns remain regarding the use of resting-state fMRI (R-fMRI) to characterize inter-individual variation in the functional connectome. In particular, recent findings that “micro” head movements can introduce artifactual inter-individual and group-related differences in R-fMRI metrics have raised concerns. Here, we first build on prior demonstrations of regional variation in the magnitude of framewise displacements associated with a given head movement, by providing a comprehensive voxel-based examination of the impact of motion on the BOLD signal (i.e., motion-BOLD relationships). Positive motion-BOLD relationships were detected in primary and supplementary motor areas, particularly in low motion datasets. Negative motion-BOLD relationships were most prominent in prefrontal regions, and expanded throughout the brain in high motion datasets (e.g., children). Scrubbing of volumes with FD > 0.2 effectively removed negative but not positive correlations; these findings suggest that positive relationships may reflect neural origins of motion while negative relationships are likely to originate from motion artifact. We also examined the ability of motion correction strategies to eliminate artifactual differences related to motion among individuals and between groups for a broad array of voxel-wise R-fMRI metrics. Residual relationships between motion and the examined R-fMRI metrics remained for all correction approaches, underscoring the need to covary motion effects at the group-level. Notably, global signal regression reduced relationships between motion and inter-individual differences in correlation-based R-fMRI metrics; Z-standardization (mean-centering and variance normalization) of subject-level maps for R-fMRI metrics prior to group-level analyses demonstrated similar advantages. Finally, our test-retest (TRT) analyses revealed significant motion effects on TRT reliability for R-fMRI metrics. Generally, motion compromised reliability of R-fMRI metrics, with the exception of those based on frequency characteristics – particularly, amplitude of low frequency fluctuations (ALFF). The implications of our findings for decision-making regarding the assessment and correction of motion are discussed, as are insights into potential differences among volume-based metrics of motion.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Neurology

View Paper: https://www.semanticscholar.org/paper/8935522a06065d38f204caaa4e4de42ebab62b97

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Citations in the corpus

Methods for cleaning the BOLD fMRI signal (2016, neutral): “Censoring is also known as scrubbing (Carp, 2013; Jones et al., 2010; Kennedy and Courchesne, 2008; Power et al., 2012, 2013, 2014; Siegel et al., 2014), and is equivalent to adding scan nulling regressors in the model (Lemieux et al., 2007; Satterthwaite et al., 2013; Yan et al., 2013a).”
ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data (2015, neutral): “Typical nuisance regression models include 6 to 24 motion-related covariates derived from volume-realignment parameters (Friston et al., 1996; Yan et al., 2013a; Satterthwaite et al., 2013a).”
Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “In particular, it has been revealed that motion increases non-specific local coupling and decreases long-range coupling in ways that are similar to differences found between children and adults (Fair et al., 2012b; Power et al., 2012; Satterthwaite et al., 2012; Yan et al., 2013).”
The Neuro Bureau ADHD-200 Preprocessed Repository (2016, neutral): “characterizes individual motion (such as root mean square deviation [85]) should be included as a nuisance regressor in the group-level model [78, 80].”
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, methodology): “Please see Yan et al. (2013b) for more details.”
Reduction of motion-related artifacts in resting state fMRI using aCompCor (2014, methodology): “To date, all of the research examining the effects of motion on seedbased rs-FC has included mean signals from WM and CSF as nuisance regressors (Hallquist et al., 2013; Jo et al., 2013; Power et al., 2012; Satterthwaite et al., 2012, 2013; Yan et al., 2013); in some cases, the first derivative of e”
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).”
Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia (2014, background): “Subjects had their eyes closed during the resting state scan.”
Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia (2015, neutral): “1 mm) have been significantly associated with regional BOLD signals identified using rsfMRI data (Van Dijk et al., 2012; Yan et al., 2013).”
Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI (2015, methodology): “Motion-related fMRI artifacts can impact a wide range of FC measures (Satterthwaite et al., 2012; Yan et al., 2013) and can induce increases in local functional connectivity and decreases in long-distancefunctional connectivity(VanDijketal.,2012;Poweretal.,2012;Satterthwaiteetal., 2012).”
How Visual Is the Visual Cortex? Comparing Connectional and Functional Fingerprints between Congenitally Blind and Sighted Individuals (2015, methodology): “To further exclude any potential effects of head motion, we repreprocessed the resting-state functional images using a scrubbing method (Power et al., 2012; Yan et al., 2013).”
The Development of Human Amygdala Functional Connectivity at Rest from 4 to 23 Years: a cross-sectional study (2014, neutral): “…and a global signal regressor that has been shown to ameliorate sub-millimeter motion confounds as well as white-matter and ventricle regressors were included (for a total of 15 nuisance regressors) in all regressions to correct for head motion artifacts (Van Dijk et al., 2010; Yan et al., 2013).”
Quantitative Assessment of Structural Image Quality (2017, background): “This approach is based on the observation that participant motion tends to be highly correlated across acquisitions: individuals with high motion in one sequence tend to have high motion in other sequences (Pardoe et al., 2016; Yan et al., 2013).”
Methods to detect, characterize, and remove motion artifact in resting state fMRI (2013, methodology): “7) and the recent reports on its utility for removing motion artifact (Satterthwaite et al., 2013; Yan et al., 2013).”
Prospective Motion Correction in Functional MRI (2016, background): “able to effectively remove motion-induced brain networks differences (Yan et al., 2013).”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, methodology): “…(2017), http://dx.doi.org/10.1016/j.neuroimage.2017.03.020 In response to this challenge, there has been a recent proliferation of participant-level confound regression and censoring methods aimed at mitigating the impact of motion on functional connectivity (Yan et al., 2013a; Power et al., 2015).”