Zhengshi Yang^{1}, Xiaowei Zhuang^{1}, Karthik Sreenivasan^{1}, Virendra Mishra^{1}, and Dietmar Cordes^{1,2}

^{1}Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States, ^{2}University of Colorado, Boulder, CO, United States

### Synopsis

The
fluctuation introduced by head motion considerably confounds the interpretation
of resting-state fMRI data. Specifying motion regressors without taking fMRI
data itself into consideration may not be sufficient to model the impact of
head motion. We proposed a robust and automated deep neural network (DNN) to
derive motion regressors with both fMRI data and estimated realignment
parameters considered. The results show that DNN-derived regressors outperform
traditional regressors based on several quality control measurements.

### Introduction

The
fluctuation introduced by head motion considerably confounds the interpretation
of resting-state functional magnetic resonance imaging (rs-fMRI) data [1].
Estimated rigid-body motion parameters and their variances were commonly used
as nuisance regressors to reduce motion-related artifacts [2]. These regressors
were generated without taking fMRI data itself into consideration and thus may
not be sufficient to model the impact of head motion because of the complexity
of motion artifacts. To better model how in-scanner motion affects rs-fMRI
data, we have developed a robust and automated deep neural network model to
derive motion regressors with both fMRI data and motion parameters considered.### Methods

Subjects: The
structural MRI and rs-fMRI data used in this study were downloaded from the
publicly available ADNI database (http://adni.loni.usc.edu/).
76 subjects identified as normal controls by site investigators were used in
this study. The rs-fMRI data were acquired from an echo-planar imaging sequence
with parameters: 140 time points; TR/TE=3000/30 ms; flip angle=80 degrees; 48
slices; spatial resolution=3.3 mm x 3.3 mm x 3.3mm and imaging matrix=64 x 64. Before
motion regression, slice timing correction, rigid-body head motion correction, co-registration,
normalization and detrending were applied on fMRI data. cnn12 neural network architecture: As shown in Fig.1, the cnn12
network is constructed with two temporal convolutional layers in the sequential
order. Both layers have filter size f=5, stride length s=1 and same padding so that the output has the
same length as the original input. In these two convolutional layers, 32
temporal filters are specified for the first one, and 12 temporal filters are
specified for the second one to match the number of traditional motion
regressors used in this study. The 6 estimated rigid-body motion parameters R=[X Y Z yaw pitch roll] are replicated
to generate samples matching the number of non-gray matter (non-GM) voxels.
These duplicate samples become unique and meaningful when they are linked to
different non-GM time series. The voxels are limited to non-GM voxels because
white matter or ventricle voxels share similar motion-related artifacts as gray
matter voxels but do not contain neural signal. The cnn12 network is trained for each subject separately and thus each
subject has a unique set of model parameters. The correlation between non-GM
time series and the 12 output regressors is defined as the loss function and
maximized to train the cnn12 network.
The optimal output regressors are applied on the same subject for reducing
motion-related fluctuation. The neural network converges in less than 40 epochs
for the fMRI data with 135 time points. The computational time for each subject
is less than 2 minutes on a Tesla K40c GPU with 2,880 cores.### Results

We have compared the fMRI data processed with only general preprocessing
steps (raw), traditional motion regression with motion parameters and their
derivatives (mot12) and cnn12-derived motion regression. Figure 2 shows the
remaining variance (in %) of regional time series after motion regressing using
cnn12 or mot12. The proposed cnn12 network significantly reduces more variance
than mot12 (two-tailed ttest: t=129.8, p=0). 98.1% of mot12-regressed time
series have higher remaining variance than the corresponding time series
regressed by cnn12. The median percentages of variance retained for cnn12 and
mot12 were 50.2% and 73.1%, respectively. For the raw fMRI data, the mean of
the standard deviation across whole brain is significantly linearly correlated
with quality control (QC) measurements including FD and rmsFD (see blue curve
in Fig.3, p<10-8). The mot12-denoised fMRI data (red curve) has
reduced the linear relationship with QC measurements but not significantly. In
contrast, cnn12 (green curve) has significantly reduced the linear relationship
with p<0.05. The fMRI data processed by different techniques from a single
subject is shown in Fig.4, visually cnn12 has a better performance than mot12
in alleviating marked band effects introduced by head motion, as pointed out by
blue arrows. The FD (red), sum of absolute translational parameters (blue) and
sum of absolute rotational parameters (black) are presented in the top panel.### Discussion and Conclusion

In this study, a deep neural network is proposed to derive
motion-related artifacts using rigid-body motion parameters and fMRI data
itself. With the same number of regressors, cnn12-derived regressors explain
more variance than traditional regressors. The proposed cnn12 network but not
mot12 significantly reduces the linear trend between mean whole-brain standard
deviation and QC measurements, indicating improved data quality. To the best of
our knowledge, this is the first study where a deep neural network is designed
for denoising resting-state functional MRI data.### Acknowledgements

This research project was supported by the NIH (grant 1R01EB014284 and
COBRE grant 5P20GM109025) and a private grant from Peter and Angela Dal Pezzo. Data
collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging
Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD
ADNI (Department of Defense award number W81XWH-12-2-0012). ### References

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K.J., Williams, S., Howard, R., Frackowiak, R.S., Turner, R., 1996. Movement‐related effects in fMRI time‐series. Magnetic resonance in medicine 35, 346-355.