ReviewRecent progress and outstanding issues in motion correction in resting state fMRI
Introduction
Over the last decade, studies of correlated fMRI signal, as opposed to task-evoked activity, have come to represent a considerable portion of published fMRI studies. Such studies, called functional connectivity studies, have been used to examine the functional organization of the brain and to describe changes in this organization over development, aging, and disease states. Functional connectivity studies often involve no explicit task (other than for subjects to lie in the scanner for some period of time) and are therefore called “resting state” studies of spontaneous (or “intrinsic”) fMRI signal.
One finding that spanned many of the first functional connectivity fMRI studies is that the young, the elderly, and the diseased often exhibited “underconnectivity” relative to healthy young adults. For example, between (distant) regions of the default mode network, children had weaker signal correlations than young adults (Fair et al., 2007), and these same (distant) correlations were weaker in older subjects than in young adults (Andrews-Hanna et al., 2007). These findings were part of a larger story whereby correlation values were modulated across the lifespan in a distance-dependent manner: in childhood, short-distance correlations were strong and longer-distance correlations were weaker, then as children aged into young adulthood, short-distance correlations weakened and longer-distance correlations strengthened, and then as subjects aged into later adulthood the short-distance correlations again became stronger and long-distance correlations became weaker. Interestingly, frameworks for understanding some diseases sometimes had similar aspects of distance dependence, such as “local overconnectivity but long-distance disconnection” in autism (Courchesne and Pierce, 2005).
In 2011, three groups reported a previously unrecognized aspect of motion artifact in functional connectivity MRI studies, which is that motion adds spurious variance that tends to be more similar at nearby voxels than at distant voxels, causing distance-dependent modulation of signal correlations (Power et al., 2012, Satterthwaite et al., 2012, Van Dijk et al., 2012). Troublingly, even relatively small movements that were traditionally not thought to be problematic could produce these effects. This meant that, all other things being equal, a higher-motion group would have relatively stronger short-distance correlations than a lower-motion group, and, depending on the processing strategy used, that long-distance correlations would often be weaker in the higher-motion group. The implications for functional connectivity studies of development, aging, and disease were clear, since children, the elderly, and patients tend to move more in the scanner than typical and/or young adult populations. Indeed, in the 3 initial studies, previously-described developmental (Power et al., 2012, Satterthwaite et al., 2012) and aging (Van Dijk et al., 2012) differences were reported to be at least partially explained by motion artifact.
The full impact of motion artifact on functional connectivity studies of development, aging, and disease is not yet clear, since methods to counteract motion artifact are still being developed and validated (Bright and Murphy, 2013, Jo et al., 2013, Kundu et al., 2013, Muschelli et al., 2014, Patel et al., 2014, Power et al., 2014, Satterthwaite et al., 2013a, Scheinost et al., 2014, Yan et al., 2013a, Yan et al., 2013b, Zeng et al., 2014). Studies that carefully control for motion artifact are beginning to determine which previous findings might relate to motion artifact, and which findings withstand newer and more stringent corrections for motion artifact (Fair et al., 2012, Satterthwaite et al., 2013b, Tyszka et al., 2014). On the one hand, some findings may be attenuated or overturned. On the other hand, improved removal of a biasing artifact may reveal effects that were previously obscured.
Artifact caused by small movements is not uniquely problematic to functional connectivity studies. Within the last year, scrutiny of small movements in diffusion weighted imaging (DWI) has determined that small movements also create previously unrecognized, spurious differences in this modality (Yendiki et al., 2013). This realization has prompted the re-evaluation of prior structural connectivity findings with newer and more stringent methods of motion correction. One of the first such re-evaluative studies now indicates that many differences in white matter tracts previously attributed to autism may be attributable to motion artifact, though differences in the inferior longitudinal fasciculus withstand the newer motion correction procedures (Koldewyn et al., 2014). This report has a parallel in the re-evaluative reports in functional connectivity studies: in data from control and high-functioning autistic subjects, binning subjects by motion creates much larger group differences than binning by diagnosis (Tyszka et al., 2014). These findings underscore the importance of identifying and removing the influences of motion in MRI studies.
Motion is unique among artifactual influences in fMRI in that it can be measured from the data itself via realignment parameters (unlike, for example, cardiac- or respiratory-related artifact, which require external recordings). Thus, all fMRI datasets contain the substrates needed for many motion-targeting denoising procedures. Over one dozen papers have been published specifically on motion artifact in functional connectivity fMRI since 2011. The aim of this review is to synthesize and consolidate the knowledge gained to date, and, in doing so, to point out directions for future research. Much of the literature on motion artifact is technical; we will attempt to communicate this literature to a broad audience. The first portions of the paper concern findings and methods that are applicable to most existing datasets. In the final portions of the paper we consider emerging methods that may be useful in future datasets.
The reader should note that the field of denoising, especially with respect to motion, is rapidly evolving. There is no consensus about which methods are most successful at removing motion artifact, or even how to measure success in removing motion artifact. Different groups would likely have different interpretations of the literature. We have tried to steer a neutral course in this article. Our figures present data from 11 studies (2 of our own) and we discuss nearly every paper on motion published after 2011 up until the time of submission (and some papers that emerged during review). Our intent is to collate most of the important new data regarding motion artifact and to help readers learn how to interpret various commonly-used analyses aimed at detecting or removing motion artifact. Our interpretation of the literature is unavoidably embedded in selecting and discussing certain data. Our intent is not to steer the field toward or away from a particular processing stream; we do not make processing recommendations. At the end of the article, we do suggest some data that, if reported, would help reviewers and readers assess motion-related effects in a given study.
Section snippets
Features of motion artifact
For over two decades, it has been known that motion creates strong disturbances in fMRI signal (Friston et al., 1996, Hajnal et al., 1994). In the mid-1990s, nearly all fMRI studies were task studies, which modeled evoked activity from many trials or blocks of trials. In these studies, any source of noise, including motion, would be suppressed via averaging in the modeling process, unless the noise systematically related to experimental timing. Most studies of motion at that time focused on
Methods to reduce or remove motion-related variance
Having covered many of the recent gains in understanding motion's impact on signals and correlations, we now turn to recent developments in removing motion artifact. We first review progress in post-hoc corrections that can be applied to any existing dataset. Progress in acquisition methods is mainly relevant to future datasets and will be discussed later.
Further measures of denoising success
Many different analyses have been used to measure motion artifact and the success of denoising strategies. We have discussed scrubbing, QC-RSFC correlations, and the formation and elimination of group differences in typical subjects binned by motion. Another type of outcome measure used in many studies is the reduction of correlations between motion traces and fMRI signal (or signal quality measures) across stages of processing. In general, early in processing, spikes in motion traces (e.g.,
Conclusions
Since the first reports in 2011 on motion artifact in resting state functional connectivity, much time and effort have been invested in understanding and removing effects of motion. Considerable progress has been made. Motion artifact is recognized to produce systematic decreases in signal, especially for larger movements, but it can also cause variable disruptions in fMRI signal. The duration of these disruptions can be brief or surprisingly long, impacting correlations for up to 10 s. Motion
Acknowledgments
The authors thank Tim Laumann and Caterina Gratton for comments on the manuscript. We also thank our reviewers for suggestions that improved the manuscript. This work was supported by NIHF30 MH940322 (JDP), NIHNS046424 (SEP), a McDonnell Collaborative Activity Award (SEP), and by the Intellectual and Developmental Disabilities Research Center at Washington University (NIH/NICHDP30 HD062171).
Conflict of interest statement
The authors have no conflicts of interest to report.
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