Slice-acceleration Related Biases in Multiband-EPI Resting State Functional Connectivity

Zahra Faraji-Dana^{1,2}, Ali Golestani^{3}, Yasha Khatamian^{3}, Simon Graham^{1,2}, and J. Jean Chen^{1,3}

All
images were acquired using a Siemens TIM Trio 3 T MRI System (Siemens,
Erlangen, Germany) employing the 32-channel phased-array head coil receiver.
Twelve subjects were each scanned with a functional single-shot MB
gradient-echo (GE) echo-planar imaging (EPI) ^{1} sequence (TR=323ms, TE=30 ms,
flip angle=40°, 15 slices, 3.44 mm × 3.44 mm × 4.6 mm, 2230 volumes,
acceleration factor=3, phase encoding shift factor=2), and a T1-weighted
anatomical sequence (MPRAGE, TR = 2400 ms, TE = 2.43 ms, FOV = 256 mm, TI =
1000 ms, readout bandwidth = 180 Hz/px, voxel size = 1 mm × 1 mm × 1 mm). In the MB-EPI scan, the 15 slices were grouped
as 1-6-11, 2-7-12, 3-8-13, 4-9-14, 5-10-15, and acquired in this order. For each subject, a pneumatic belt
(BioPac, Goleta, USA) and the vendor supplied pulse oximeter were used to
record the respiratory and cardiac signals, respectively. Subjects were
instructed to close their eyes and relax.
The
rs-fcfMRI processing was carried out using the FMRIB Software
Library v5 (FSL5) (Analysis Group, FMRIB, Oxford, UK)
^{7}.

The preprocessing pipeline included motion correction, brain extraction,
spatial smoothing, and regression of six motion parameters. Five different physiological
noise correction schemes were performed on the data of each subject using MATLAB
(MathWorks, Natick, MA): a) no correction; b) image-based retrospective
correction of cardiac and respiratory waveforms (RETROICOR) ^{8}; c) RETROICOR+RVTcor; d) RETROICOR+CRVcor; and e)
RETROICOR+RVTcor+CRVcor, where RVTcor and CRVcor refer to the inclusion of
these physiological noise signals in the retrospective correction algorithm.
Thus, five post-processed fMRI datasets were considered using a
multi-regression model, focusing on rs-fcMRI maps of the default mode network
(DMN), as it has extensive brain coverage. A 4 mm-radius spherical seed was
generated over the posterior cingulate cortex (PCC) using the coordinates reported by reported by Van
Dijk et al ^{9}. For each of the five corrected datasets, the spatial average of
the signal from the seed was correlated with all other voxels to generate the connectivity
maps. All statistical connectivity maps were then corrected using mixture
modelling ^{10} and the resulting Z-maps were then thresholded at Z>2.3 (p<0.01).
Comparing the rs-fcMRI maps to an appropriate connectivity template for the DMN
^{11}, the number of false positive and false negative activations were separately
obtained for each slice, each physiological noise correction scheme, and each
subject. For each subject and correction scheme, the mean number of false
positives and false negatives per slice was then determined. Two repeated-measures
analyses of variance (rANOVAs) were subsequently performed using MATLAB to compare
the effect of the correction schemes on the mean number of false positives and
false negatives. Using the known MB-EPI slice-grouping, the slice-group effect on
the number of false positives and false negatives of each slice was independently
studied via linear mixed effect (LME) analysis in R (R Foundation for Statistical Computing, Vienna, Austria).

Fig. 1 illustrates the rs-fcMRI maps for a
representative subject, corresponding to the five physiological noise
correction schemes (a-e), and the DMN template (f) ^{11}. Using the template
as reference, the number of false positives gradually reduces in progression
from correction scheme a) to c), with little difference observed between
schemes (c-e). Similar results were
observed across the cohort, except that the number of false positives continues
to decrease across all five correction schemes. The rANOVA results showed a strong
trend of physiological correction reducing the number of false positives, that
approached statistical significance (p=0.09).
No effects were found for false negatives (p=0.81).

Table 1 shows the p-values of the slice-group LME modeling for different physiological noise correction schemes. According to the LME model without physiological noise correction, the number of false positives demonstrates a significant slice-group effect, whereas the number of false negatives does not. Additionally, the slice-group effect (both false positives and false negatives) incrementally diminishes (i.e. p-value increases) progressing from correction scheme a) to scheme e). Specifically, for false positives, RETROICOR was effective at suppressing the slice-group effect so that it was no longer statistically significant.

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Fig. 1: rs-fcMRI maps for a representative subject after applying a) no
correction b) RETROICOR, c) RETROICOR+RVTcor, d) RETROICOR+CRVcor, e)
RETROICOR+ RVTcor+CRVcor, (f) the DMN resting-state connectivity template in
the subject coordinates. The arrows point at false positive activations.

Table1: p-values of
the slice-group LME analysis after applying different physiological noise
correction schemes (a-e).

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)

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