The influence of head motion on intrinsic functional connectivity MRI
Highlights
► We explored the influence of head motion on functional connectivity MRI estimates. ► Head motion had significant, systematic effects on several network measures. ► Majority of network variance across subjects was not related to motion. ► Head motion was associated with both decreased and increased metrics. ► These differences in another context could be mistaken for neuronal effects.
Introduction
Resting-state functional connectivity MRI (fcMRI) is widely used to explore the architecture of brain systems. Studies of differences across the lifespan, between individuals with clinical diagnoses, and across varied personality traits have become common (for recent reviews, see Fornito and Bullmore, 2010, Fox and Greicius, 2010, Vogel et al., 2010, Zhang and Raichle, 2010). The technique is robust and yields reliable measures within individuals (e.g., Honey et al., 2009, Meindl et al., 2010, Shehzad et al., 2009, Van Dijk et al., 2010, Zuo et al., 2010). Recent family and twin studies suggest functional connectivity estimates are heritable and thus may offer insight into how genetic variation affects brain function (Fornito et al., 2011, Glahn et al., 2010). However, there is general awareness that resting state fcMRI is sensitive to confounding factors including head motion even after common data preprocessing steps (Buckner, 2010, Cole et al., 2010). Head motion has long been known to be a confound in task-based functional MRI studies, but has become a particularly challenging problem in recent studies using fcMRI. Effects of interest are often between groups of subjects where differences in motion are expected such as between children and young adults, between young and old adults, and between patients and controls. The present paper explores how head motion affects measures of functional connectivity.
Section snippets
Overview
The primary focus of the paper is to explore how between-subject differences in head motion affect MRI measures of intrinsic functional connectivity. A large sample of data from typical, healthy control subjects ages 18 to 30 were selected (n = 1110). All subjects were imaged on matched MRI scanners using the same MRI sequence. Subjects with artifacts or abnormally low temporal signal-to-noise (tSNR) were eliminated but otherwise the sample represents a typical convenience sample of good to
Estimates of head motion
We explored the distribution of head motion across all 1088 usable subjects by plotting the frequency histogram of Mean Motion (the mean displacement of each brain volume as compared to the previous volume). A few observations are notable (Fig. 1A). First, there is substantial inter-subject variability. Second, a minority of subjects displayed disproportionately high levels of Mean Motion. Although this skewed distribution is expected from a distance measure it is worth noting that 8.5% of the
Discussion
The present study examined the influence of head motion on functional connectivity MRI. The primary result is that head motion has systematic effects on functional connectivity estimates that could easily be misinterpreted as neuronal effects. High levels of head motion were associated with reduced functional connectivity in large-scale distributed networks (e.g., the default network and the frontoparietal control network; Figs. 3A and B) and increased local functional connectivity (Figs. 3C
Conclusions
Head motion significantly affects measures of functional connectivity MRI even within the range of motion exhibited by typical, healthy young adults. The effects are dependent on the specific measure and include decreased functional coupling for distributed networks and increased functional coupling for local networks. Since motion was found to be a stable property within subjects – behaving as a trait – studies of genetic associations, heritability, and relations to behavior and personality
Acknowledgments
We thank the Harvard Center for Brain Science Neuroimaging Core, the Athinoula A. Martinos Center for imaging support, and the Harvard Neuroinformatics Research Group (Gabriele Fariello, Timothy O'Keefe, and Victor Petrov). The data analyzed were collected as part of the Genomics Superstruct Project. We thank Marisa Hollinshead, Elizabeth Hemphill, Leah Bakst, Angela Castellanos, and Sara Rubenstein for assistance in collecting the data. Avram Holmes and Jorge Sepulcre assisted in constructing
References (44)
- et al.
Disruption of large-scale brain systems in advanced aging
Neuron
(2007) - et al.
Isolating physiologic noise sources with independently determined spatial measures
NeuroImage
(2007) - et al.
The non-separability of physiologic noise in functional connectivity MRI with spatial ICA at 3T
J. Neurosci. Methods
(2010) - et al.
The respiration response function: the temporal dynamics of fMRI signal fluctuations related to changes in respiration
NeuroImage
(2008) - et al.
Influence of heart rate on the BOLD signal: the cardiac response function
NeuroImage
(2009) - et al.
Improved optimization for the robust and accurate linear registration and motion correction of brain images
NeuroImage
(2002) - et al.
Mapping sources of correlation in resting state FMRI, with artifact detection and removal
NeuroImage
(2010) - et al.
Complex network measures of brain connectivity: uses and interpretations
NeuroImage
(2010) - et al.
Quantifying head motion associated with motor tasks used in fMRI
NeuroImage
(2001) - et al.
On-line automatic slice positioning for brain MR imaging
NeuroImage
(2005)
Brain morphometry with multiecho MPRAGE
NeuroImage
Head motion suppression using real-time feedback of motion information and its effects on task performance in fMRI
NeuroImage
The oscillating brain: complex and reliable
NeuroImage
The effect of respiration variations on independent component analysis results of resting state functional connectivity
Hum. Brain Mapp.
Functional connectivity in the motor cortex of resting human brain using echo-planar MRI
Magn. Reson. Med.
Human functional connectivity: new tools, unresolved questions
Proc. Natl. Acad. Sci. U. S. A.
Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease
J. Neurosci.
Advances and pitfalls in the analysis and interpretation of resting-state FMRI data
Front. Syst. Neurosci.
Brain motion: measurement with phase-contrast MR imaging
Radiology
3D statistical neuroanatomical models from 305 MRI volumes
What can spontaneous fluctuations of the blood oxygenation-level-dependent signal tell us about psychiatric disorders?
Curr. Opin. Psychiatry
Genetic influences on cost-efficient organization of human cortical functional networks
J. Neurosci.
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