Impact of In-Scanner Head Motion on Multiple Measures of Functional Connectivity: Relevance for Studies of Neurodevelopment in Youth

By Theodore D. Satterthwaite, Daniel H. Wolf, James Loughead, Kosha Ruparel, Mark A. Elliott, Hakon Hakonarson, Ruben C. Gur, Raquel E. Gur. NeuroImage, 2012

1,136 citations recorded by the source corpus.

It has recently been reported (Van Dijk et al., 2011) that in-scanner head motion can have a substantial impact on MRI measurements of resting-state functional connectivity. This finding may be of particular relevance for studies of neurodevelopment in youth, confounding analyses to the extent that motion and subject age are related. Furthermore, while Van Dijk et al. demonstrated the effect of motion on seed-based connectivity analyses, it is not known how motion impacts other common measures of connectivity. Here we expand on the findings of Van Dijk et al. by examining the effect of motion on multiple types of resting-state connectivity analyses in a large sample of children and adolescents (n=456). Following replication of the effect of motion on seed-based analyses, we examine the influence of motion on graphical measures of network modularity, dual-regression of independent component analysis, as well as the amplitude and fractional amplitude of low frequency fluctuation. In the entire sample, subject age was highly related to motion. Using a subsample where age and motion were unrelated, we demonstrate that motion has marked effects on connectivity in every analysis examined. While subject age was associated with increased within-network connectivity even when motion was accounted for, controlling for motion substantially attenuated the strength of this relationship. The results demonstrate the pervasive influence of motion on multiple types functional connectivity analysis, and underline the importance of accounting for motion in studies of neurodevelopment.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4412d76b8bd046ee9bb36ff77166266131b91d48

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Citations in the corpus

Dynamic functional connectivity: Promise, issues, and interpretations (2013, background): “Head motion is also known to produce spurious, spatially structured artifacts in FC, and has been a topic of much controversy (Power et al., 2012; Satterthwaite et al., 2012; Van Dijk et al., 2012; Yan et al., 2013).”
A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series (2014, methodology): “S5 and S6, and Table 1, where results from all three cohorts were used.”
Resting state fMRI: A review of methods and clinical applications (2012, positive): “correction for head motion can result in spurious correlations in RS-fMRI analysis.(30-32) Although head motion is less concerning”
The Philadelphia Neurodevelopmental Cohort: A Publicly Available Resource for the Study of Normal and Abnormal Brain Development in Youth (2015, neutral): “…information regarding clinical assessment, cognitive testing, and imaging procedures is provided in PNC manuscripts that have used these data types (Satterthwaite et al., 2012a; Satterthwaite et al., 2013a; Satterthwaite et al., in press; Satterthwaite et al., 2014b; Satterthwaite et al.,…”
Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks (2012, neutral): “In addition to physiological artifacts, head motion artifacts have been shown to signiﬁcantly inﬂuence intrinsic functional connectivity measurements (Satterthwaite et al., 2012; Van Dijk et al., 2012).”
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).”
ICA-based artefact and accelerated fMRI acquisition for improved Resting State Network imaging (2014, background): “For example, it is well known that even relatively small amounts of headmotion represent a significant confound for rfMRI network identification (Power et al., 2012; Satterthwaite et al., 2012; Van Dijk et al., 2012).”
Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI (2015, background): “Corroborating previous reports we observed that head motion during rfMRI scanning has a large impact on functional connectivity estimates (Van Dijk et al., 2012; Power et al., 2012; Satterthwaite et al., 2012; Yan et al., 2013).”
Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses (2017, background): “Many studies have shown that motion is a major source of variability in functional connectivity studies that can lead to erroneous results when comparing groups of subjects (Power et al., 2012; Satterthwaite et al., 2012; Van Dijk et al., 2012).”
An Improved Framework for Confound Regression and Filtering for Control of Motion Artifact in the Preprocessing of Resting-State Functional Connectivity Data (2012, methodology): “Finally, as previously (Satterthwaite et al., 2012a), mean relative displacement was correlated across subjects with the connectivity calculated for every node pair.”