An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data
Highlights
► We describe spatial, temporal, and spectral features of rsfc-MRI motion artifact. ► We show how these artifact features impact preprocessing choices. ► We systematically evaluate different confound regression and filtering techniques. ► Our optimized preprocessing approach minimizes rsfc-MRI motion artifact.
Introduction
Although it has long been known that in-scanner head motion can have profound effects on fMRI timeseries data (Bullmore et al., 1999, Friston et al., 1996), the specific importance of this artifact for the analysis of resting state functional connectivity MRI (rsfc-MRI; Biswal et al., 1995, Fox and Raichle, 2007) has only recently been appreciated. In particular, it has been demonstrated in three large independent samples (Power et al., 2011a, Power et al., 2011b, Satterthwaite et al., 2012a, Van Dijk et al., 2011) that even relatively small amounts of in-scanner head motion represent a substantial confound for rsfc-MRI data. All three studies concluded that motion in general tends to enhance short-range connectivity and diminish long-distance connectivity among network nodes. As rsfc-MRI has evolved to become an important tool for examining brain networks in health and disease (Biswal et al., 2010, Fox and Greicius, 2010, Glahn et al., 2010, Seeley et al., 2009, Yeo et al., 2011, Zhou et al., 2010), it is of critical importance to understand how best to model and account for this artifact.
Power et al., 2011a, Power et al., 2011b recently introduced a novel method, called “scrubbing,” that identifies motion-induced spikes in the rsfc-MRI timeseries and excises these data with a temporal mask; adjacent timepoints are then temporally concatenated. Subsequently, Carp (2011) proposed a modification of scrubbing where data were removed and interpolated prior to band-pass filtering in order to avoid propagation of the motion artifact during the application of the band-pass filter. Using simulated data, he demonstrated that this modified scrubbing procedure was able to recover the “ground truth” connectivity in this timeseries (Carp, 2011). In a reply to Carp, Power et al. (2012) note that this procedure may be of marginal benefit given the fact that motion often occurs in long epochs, and that the effect of motion may occur beyond one isolated volume.
Scrubbing is a preprocessing technique that can be implemented after (Power et al., 2011a, Power et al., 2011b) or as part of (Power et al., 2012) standard rsfc-MRI preprocessing, which usually includes image re-alignment, spatial smoothing, filtering, and confound regression (Van Dijk et al., 2010). Notably, no prior report has investigated whether these standard rsfc-MRI preprocessing steps can themselves be improved to control the artifacts induced by in-scanner head motion. Here, we focus on two of these steps – confound regression and filtering – and investigate whether improved methods can produce better control of motion artifact.
There is substantial variation regarding how motion is modeled during confound regression (Auer, 2008, Johnstone et al., 2006): some studies include only the six motion parameters themselves, while others include the temporal derivatives, or even the quadratic of both the raw parameters and derivatives (zu Eulenburg et al., 2012). Yet other studies have modeled motion-induced spikes in the timeseries data with individual regressors, effectively removing the effect of these data points on any subsequent analysis of the residual timeseries (Lemieux et al., 2007). Furthermore, while most studies apply a band-pass filter with a high-pass cutoff in the range of 0.008–0.01 Hz and a low-pass threshold of 0.08–0.1 Hz (Cordes et al., 2001, Niazy et al., 2011), it has not yet been specifically demonstrated how motion affects the magnitude spectra of rsfc-MRI data, nor is it known whether band-pass filtering can be tailored for better control of motion artifact.
This study investigates the effect of motion and the improvement of preprocessing procedures in a large sample of adolescents (n = 348) who completed an rsfc-MRI study of typical duration (6 minutes). We had two primary goals. First, we sought to describe the spatial, temporal, and spectral characteristics of motion artifact, and evaluated how typical preprocessing steps alter the manifestations of this artifact. Second, we systematically evaluated whether confound regression and filtering could be improved to provide better control of motion artifact. Results reveal that the effectiveness of preprocessing procedures on the control of motion artifact are quite variable, and that improved preprocessing provides a substantial benefit beyond typical procedures, allowing the attenuation but not complete removal of motion artifact from rsfc-MRI data.
Section snippets
Overall approach
Reflecting the two main goals of this study, the methods and results of this paper are described in two parts. In the first section we further describe how in-scanner motion affects rsfc-MRI data through use of both real data and simulations, and how different preprocessing strategies may alter the way motion artifact manifests. In order to evaluate the spatial distribution of motion artifact, we introduce a novel procedure for estimating motion on a voxelwise basis. In the second section, we
Discussion
Motion artifact is a primary obstacle impeding the application of rsfc-MRI to the study of individual and group differences. Therefore, understanding and mitigating the influence of this artifact are critical. Our results further describe the spatial, temporal, and spectral characteristics of motion artifact in rsfc-MRI data, providing information that can guide efforts to minimize its effect. Systematic analyses indicate that improved confound regression and filtering can substantially
Disclosures
Authors report no disclosures.
Financial support
This study was supported by grants from the National Institute of Mental Health RC2MH089983 and RC2MH089924. Dr. Satterthwaite was supported by NIMH T32 MH019112, APIRE, and The Brain & Behavior Research Foundation through the Marc Rapport Family Investigator Grant. Dr. Wolf was supported by NIMH MH085096, APIRE, and The Brain & Behavior Research Foundation through the Sidney R. Baer, Jr. Foundation. Dr. Eickhoff was supported by the Human Brain Project (R01‐MH074457‐01A1) and the Helmholtz
Acknowledgments
Many thanks to the acquisition and recruitment team: Karthik Prabhakaran, Jeff Valdez, Marisa Riley, Rosetta Chiavacci, Ryan Hopson, Jack Keefe, and Nick DeLeo. James Dickson, Chad T. Jackson, Mark Griffin, and Tianyi Du provided data management and systems support. Thanks to Aaron Alexander Bloch for discussion of the permutation analysis, and to Jonathan Power, Mika Rubinov, and our anonymous reviewers for many helpful suggestions and discussion.
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