Heterogeneous impact of motion on fundamental patterns of developmental changes in functional connectivity during youth

Abstract

Several independent studies have demonstrated that small amounts of in-scanner motion systematically bias estimates of resting-state functional connectivity. This confound is of particular importance for studies of neurodevelopment in youth because motion is strongly related to subject age during this period. Critically, the effects of motion on connectivity mimic major findings in neurodevelopmental research, specifically an age-related strengthening of distant connections and weakening of short-range connections. Here, in a sample of 780 subjects ages 8-22, we re-evaluate patterns of change in functional connectivity during adolescent development after rigorously controlling for the confounding influences of motion at both the subject and group level. We find that motion artifact inflates both overall estimates of age-related change as well as specific distance-related changes in connectivity. When motion is more fully accounted for, the prevalence of age-related change as well as the strength of distance-related effects is substantially reduced. However, age-related changes remain highly significant. In contrast, motion artifact tends to obscure age-related changes in connectivity associated with segregation of functional brain modules; improved preprocessing techniques allow greater sensitivity to detect increased withinmodule connectivity occurring with development. Finally, we show that subject's age can still be © 2013 Elsevier Inc. All rights reserved.Please address correspondence to: Theodore Satterthwaite, M.D., M.A, Brain Behavior Laboratory, 10 th Floor, Gates Building, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, sattertt@upenn.edu. * Drs. Satterthwaite and Wolf contributed equally to this manuscript. DISCLOSURES: Authors report no disclosures.Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. NIH Public Access

Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement

“…Negative correlations of non-convolved large motion shown in the supporting material ( Figure S4C) reveal similar effects as after convolution in typical motion artifact prone regions. Similar widespread artifactual negative correlations between head motion and fMRI have consistently been found (Satterthwaite et al, 2013;Yan et al, 2013), and motion can induce artifacts lasting up to 10 sec . Convolving power time series therefore does not prevent motion driven spurious correlations.…”

The Influence of Physiological Noise Correction on Test–Retest Reliability of Resting-State Functional Connectivity

“…Resting-state functional connectivity measures can be significantly affected by subject motion ( Jo et al, 2013;Power et al, 2012Power et al, , 2014Satterthwaite et al, 2012Satterthwaite et al, , 2013Van Dijk et al, 2012;Yan et al, 2013). Therefore, the volume-tovolume displacement (Satterthwaite et al, 2012;Van Dijk et al, 2012) was examined.…”

Using Edge Voxel Information to Improve Motion Regression for rs-fMRI Connectivity Studies

“…The lower the number the higher the data quality. This measure has been used by recent studies (Power et al, 2012(Power et al, , 2014Saad et al, 2012;Satterthwaite et al, 2013a). While it is not known what the whole-brain DVARS should be (as this can also contain neuronal blood oxygenation-level-dependent (BOLD) signal changes to a small extent), comparing the results of different models head-to-head gives us an idea of how they perform relative to one another in reducing motion effects, since the only difference between all the models resides in the regression of the different motion regressors.…”

“…Head motion has always be known to be an issue in the field of fMRI (Friston, 1996;Jiang, 1995;Oakes et al, 2005;Stillman et al, 1995), but it has recently received even more attention due to the discovery that even very small amounts of shot-to-shot motion or micromovements can significantly distort functional connectivity estimates derived from rs-fMRI data (Christodoulou et al, 2013;Jiang et al, 2013;Jo et al, 2013;Muschelli et al, 2014;Power et al, 2012Power et al, , 2014Power et al, , 2015Satterthwaite et al, 2012Satterthwaite et al, , 2013aSatterthwaite et al, , 2013bVan Dijk et al, 2012;Yan et al, 2013aYan et al, , 2013b. While it is possible to prevent some in-scanner motion through the use of new MRI pulse sequences (Bright and Murphy, 2013;Brown et al, 2010;Kundu et al, 2013;Kuperman et al, 2011;Maclaren et al, 2013;Ooi et al, 2011;White et al, 2010), training on MRI simulators before scanning (Lueken et al, 2012;Raschle et al, 2009), or even the use of head restraints and other bite bars, most data collection either do not or cannot make use of these techniques and motion artifacts are still detectable in these rs-fMRI data.…”