Seyhmus Guler1, Burak Erem2, Onur Afacan1, Alexander L. Cohen1, and Simon K. Warfield1
1Boston Children's Hospital and Harvard Medical School, Boston, MA, United States, 2TrueMotion, Inc., Boston, MA, United States
Synopsis
Subject movement during fMRI acquisition creates motion artifact.
“Scrubbing” removes motion-corrupted volumes and is performed after
temporal filtering since it creates temporal discontinuities. Thus, it does not
prevent the spread of corrupted time samples from high motion volumes to their
neighbors during temporal filtering. To mitigate this “leakage”, we propose a
novel method, Dynamic Missing-data Completion (DMC), that replaces motion
corrupted volumes with synthetic data matching the temporal dynamics of the
uncorrupted no-motion volumes. We analyzed six rsfMRI scans with different motion levels and found that DMC provides
added benefit in further reduction of motion contamination that remains after
scrubbing.
INTRODUCTION:
Subject movement during
an fMRI acquisition introduces correlated noise that alters the timeseries of
brain regions of interest (ROIs) and remains a significant issue in pediatric
and clinical populations. “Scrubbing”, one method that aims to reduce the
effect of subject motion, is the removal of motion-corrupted data volumes
before functional connectivity analysis1. Since removing affected
volumes creates temporal discontinuities in the data, scrubbing must be
performed after temporal filtering. As such, scrubbing does not prevent the
leakage of motion artifact from high motion volumes into neighboring volumes
during temporal filtering. To address this issue, we propose a novel method,
Dynamic Missing-data Completion (DMC), that replaces motion-corrupted data
volumes with synthetic data that matches the temporal dynamics of the
surrounding uncorrupted no-motion volumes prior to temporal filtering and excision.
Specifically, DMC fills in missing data (corrupted volumes) by solving a convex
relaxation of a Hankel-structured matrix rank minimization problem, which has
less computational cost than the tensor factorization approach proposed by
Yokota and colleagues2. Here, we compare DMC+Scrubbing to Scrubbing Alone
and a Baseline pipeline that does not perform motion-based excision.METHODS:
We acquired six rsfMRI
runs with different levels of subject motion from a healthy volunteer to test
our proposed method. Each run was analyzed using three pipelines (Baseline,
Scrubbing, and DMC+Scrubbing), as shown in Figure 1. The Baseline pipeline was
implemented using fMRIPrep3 and consisted of: registration to MNI
space, intra-run re-alignment, and non-aggressive AROMA denoising4,
followed by nuisance signal regression of white matter, cerebrospinal
fluid, global signal average timeseries, and 6 motion parameters. A
bandpass temporal filter with cut-off frequencies of 0.01-0.1Hz was applied
before analysis. The Scrubbing pipeline added a step after temporal filtering that
removed volumes with Framewise Displacement (FD) greater than 0.5 mm, along
with 1 preceding and 2 succeeding volumes. Finally, the DMC pipeline also
included the Scrubbing step, but first replaced high-motion volumes with
synthetic data that matched the temporal dynamics of uncorrupted volumes prior
to temporal filtering and subsequent Scrubbing. As such, DMC specifically addresses
motion contamination, or ‘leakage’, caused by temporal filtering that is not
captured by Scrubbing alone. To quantify the effect of leakage of motion
artifact from high motion volumes to their neighbors during temporal filtering,
we separated all data volumes in six rsfMRI runs into 3 categories: 1)
uncorrupted no-motion volumes that were at least five volumes further (outside
5-neighborhood) from scrubbed data, 2) high-motion volumes that are scrubbed, and 3) volumes in
5-neighborhood of scrubbed data. We compared the mean and standard deviation of
brain signal, defined as vectorized BOLD values of all brain voxels in the
corresponding volume, in these 3 categories. We then used a published
post-cingulate (PCC) ROI5 to create exemplary ROI-based functional
connectivity timeseries and voxel-wise correlation maps.RESULTS:
Figure 2 demonstrates the FD across a BOLD
run (upper panel) and the associated demeaned and normalized ROI timeseries
after temporal filtering for both Baseline and DMC pipelines, shown both prior
to scrubbing (middle panel) and after scrubbing (lower panel). It can be seen
that these timecourses diverge considerably at time points adjacent to high
motion data volumes, highlighting the leakage of motion contamination caused by
temporal filtering that DMC addresses.
Figure 3 shows the scatter plot of mean and
standard deviation of brain signal in all data volumes of six rsfMRI scans. The
standard deviation in high-motion volumes is significantly higher than that of
no-motion volumes (p<10-10). In addition, the standard deviation
for volumes in the 5-neighborhood of scrubbed data in Scrubbing case is
significantly higher than that of no-motion volumes (p=1.5x10-7).
On the other hand, there was no statistically significant difference between
standard deviation of brain signal in volumes in the 5-neighborhood of scrubbed
data in DMC case and that of no-motion volumes.
Seed-based correlation maps of the PCC ROI
from a “large motion” run using each pipeline and from a “no motion” run are
shown in Figure 4. Figure 5 shows the histograms of these four correlation
maps. There is a subtle decrease in non-specific positive correlation in the
DMC case compared to the Scrubbing pipeline, however the more dramatic
comparison is between the Baseline vs either the Scrubbing or DMC. While
subtle, the differences between the DMC and Scrubbing correlation maps are likely
to become more dramatic with increased motion and/or reduction in total amount
of data; i.e., the typical situation for clinical application.DISCUSSION:
DMC provides an
additional level of motion mitigation when combined with Scrubbing by removing
motion corrupted data volumes before temporal filtering and replacing
them with customized synthetic data. This reduces the spread of motion
corrupted signal, “leakage”, to neighboring volumes during temporal filtering.
Our findings suggest that this leakage effect is sufficient to artifactually
increase positive correlations in a broad fashion. As such, incorporation of
DMC or similar approaches provides added benefit without requiring
reacquisition or extraneous motion measurement in obtaining functional
connectivity measures from populations where motion is a persistent confound
and may play an even more prominent role in reduced duration datasets.CONCLUSION:
DMC removes additional
effect of motion contamination caused by temporal filtering, an issue that
cannot be addressed by Scrubbing alone without excessive censoring. Acknowledgements
This research was supported in part by the following grants: NIH-5R01EB019483, NIH-4R01NS079788 and NIH-R44MH086984.References
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