Elsevier

NeuroImage

Volume 59, Issue 3, 1 February 2012, Pages 2142-2154
NeuroImage

Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

https://doi.org/10.1016/j.neuroimage.2011.10.018Get rights and content

Abstract

Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.

Highlights

► Large changes in rs-fcMRI timecourses coincide with motion despite motion regression. ► Motion increases short-distance correlations and decreases long-distance correlations. ► Motion artifacts do not arise from, and are not fully countered by, motion regressions. ► Framewise indices of data quality and methods to remove motion artifact are proposed.

Introduction

It is well established that head motion is undesirable in fMRI studies (Friston et al., 1996, Hutton et al., 2002, Jiang et al., 1995, Johnstone et al., 2006, Oakes et al., 2005, Wu et al., 1997). Blood oxygen level dependent (BOLD) signal acquisition depends upon precise spatial and temporal placement of magnetic gradients on scales of millimeters and milliseconds. Movement of the head during scans not only shifts the position of brain matter in space, it fundamentally disrupts the establishment of magnetic gradients and subsequent readout of the BOLD signal. To compensate for these effects, it is common practice to realign data (here, a part of fMRI preprocessing). Spatial realignment corrects motion-induced shifts in space but does not correct intensity changes resulting from disruption of the physical principles underlying MRI. Therefore additional measures are often taken (here, a part of functional connectivity processing), such as ICA decomposition or regression of motion estimates, to remove spurious motion-related signal from the data (Beckmann and Smith, 2005, Erhardt et al., 2010, Fox et al., 2009, Robinson et al., 2009a, Weissenbacher et al., 2009). In this paper, we demonstrate that at least some of these approaches to motion correction do not fully remove motion-related signal from the data. Critically, we also find that the inclusion of motion-contaminated data introduces colored noise into the study of functional brain organization via resting state functional connectivity MRI (rs-fcMRI).

Subject movement is often measured with summary statistics based upon head realignment parameters. Since subjects move during scans, it is standard practice to estimate the position of the head in space at each volume of the data and to realign all volumes using rigid body transforms. In a rigid body transform, head position is described at each timepoint by six parameters (translational displacements along X, Y, and Z axes and rotational displacements of pitch, yaw, and roll). These realignment parameters can be condensed into a single summary statistic, such as root mean squared head position change (RMS movement). Summary statistics are often used to describe subject motion and to make decisions about cohort formation or matching. When forming cohorts, scans with summary statistics above some threshold (e.g., RMS movement over half a voxel's width) are considered essentially unusable, and these scans are discarded from the analysis. When multiple cohorts are compared, motion matching is often accomplished using means or t-tests of summary statistics between cohorts.

Summary statistics of motion are quite useful, but they do not always distinguish between qualitatively different types of subject movement. Consider two subjects: Subject A who is perfectly still but moves suddenly once to arrive at a very different head position, and Subject B who has frequent small to moderate movements about the original head position. It is possible for these two subjects to have similar, or even identical, RMS movement estimates, despite the substantial qualitative differences in how they moved. Since head displacement disrupts the spin history assumptions upon which BOLD signal establishment and readout depend, all other things being equal, Subject A will have data of acceptable quality throughout the scan except during and immediately after head motion, whereas the data of Subject B will be somewhat compromised throughout much of the scan. In an effort to counter such effects, head realignment estimates or other indices of movement are often regressed from data (for example, within GLMs for task fMRI, or during functional connectivity processing for rs-fcMRI). Here, we demonstrate that clear artifacts remain in the data even after such regressions, and that these artifacts have systematic effects upon rs-fcMRI correlations.

Following a regression-based approach to motion correction (Fox et al., 2009), this report begins by demonstrating a correspondence between head displacement and large-amplitude changes in rs-fcMRI BOLD signal. These changes are evident in single rs-fcMRI timecourses, and occur throughout the brain and across subjects. Based upon the suspicious co-occurrence of head movement and changes in rs-fcMRI signal, two indices of data quality that operate on a frame-by-frame basis to flag suspect frames of MR data are proposed. In four cohorts, removal of flagged frames produces structured changes in patterns of correlation, such that some short-distance correlations are weakened, and some medium- to long-distance correlations are strengthened. Some similar effects related to motion in functional connectivity data have recently been described (Van Dijk et al., 2011). Control analyses demonstrate that the artifact does not arise from (and is not adequately countered by) regressions performed during functional connectivity processing, nor is it simply a product of frame removal from data. We conclude by demonstrating how this artifact has impacted our own data and how removing the artifact modifies our previous conclusions.

One of the principal attractions of rs-fcMRI is that the minimal burden upon subjects allows investigators to explore populations (especially pediatric and clinical) that are typically difficult to study. The present results indicate that systematic but spurious rs-fcMRI correlation structures are induced by subject motion in ways that are not always detected or dealt with in common approaches to rs-fcMRI analysis. These effects can obscure patterns of functional connectivity within single cohorts and create spurious differences between cohorts. This characterization suggests the need to critically revisit previous work that may not have adequately controlled for frame-by-frame head displacement, and the need for greater care when dealing with subject movement.

Before moving to the data we wish to clarify the intent of this paper. Although this paper suggests a method to dampen or remove the influences of movement on rs-fcMRI analyses, this paper is intended to be descriptive rather than proscriptive. Also note that the basis for the artifactual effects described herein is not specific to rs-fcMRI, but is a general feature of fMRI, and should also be present in diffusion imaging or task fMRI studies. The approach described in this paper may be adapted to these modalities.

Section snippets

Subjects

Subjects were recruited from the Washington University in St. Louis campus and the surrounding community. Individuals were excluded if there was a history of metal implants or other contraindications to the MRI environment, or a history of developmental delay, neurological or psychiatric illness, including the use of psychotropic medications. All subjects were native English speakers. All adult subjects gave informed consent, and all children gave assent with parental consent, in accordance

Results

Single timecourses in a single subject demonstrate a relationship between head motion and changes in the BOLD signal, even after data realignment and regression of realignment estimates and their derivatives from the data. Fig. 1A shows rs-fcMRI timecourses at 3 left occipital regions of interest (ROIs) in a single subject. These data are from a single child with RMS movement of 0.50 mm (processed data are 3 mm isotropic voxels derived from 3.75 × 3.75 × 4 mm acquisition voxels). The extent of

Discussion

We have demonstrated that small movements produce colored noise in rs-fcMRI networks. Evidence for this artifact was first observed in timecourses, where movement of the head visibly coincided with large changes in the rs-fcMRI signal. This relationship was found in ROIs throughout the brain, and in all subjects studied. To explore the effects of this motion-containing data on rs-fcMRI analyses, two semi-independent indices of data quality were proposed that operate on a frame-by-frame basis to

Acknowledgments

We thank Steve Nelson and two of our anonymous reviewers for helpful comments during the development of this manuscript. This work was supported by NIH R21NS061144 (SP), NIH R01NS32979 (SP), a McDonnell Foundation Collaborative Action Award (SP), NIH R01HD057076 (BLS), NIH U54MH091657 (David Van Essen), and NSF IGERT DGE-0548890 (Kurt Thoroughman).

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