Yi-Tien Li1,2, Chun-Yuan Chang1, Yi-Cheng Hsu1, Jong-Ling Fuh3, and Fa-Hsuan Lin1,4
1Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan, 2Department of Medical Imaging, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan, 3Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan, Taipei, Taiwan, 4Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
Synopsis
This study quantified
the impact of physiological noise correction in characterizing the
resting-state fMRI in Alzheimer’s disease (AD) patients with age- and
gender-matched 17 healthy subjects and 15 AD patients. Using a seed-based
correlation method with seeds at posterior cingular cortex and medial
prefrontal cortex, we found that the difference in the functional connectivity
between AD patients and healthy controls was significantly reduced when
physiological noise was suppressed.
Purpose
Studies have reported that the strength of the default mode network
(DMN) is significantly reduced in Alzheimer’s disease (AD) patients1-6. However, few studies have considered the
contamination of physiological noise in DMN characterization. It has been reported that the patterns of
physiological noise caused by cardiac and breathing rhythms are not the same in
young and elderly subjects7-9. The contribution of cardiac and respiratory activity
to fMRI signal fluctuations can also depends on subject’s healthy and disease
state. Here, we estimate the physiological response functions and study the
impact of spontaneous physiology on characterizing the DMN of AD patients and
age-matched healthy subjects. The goal is to test how the difference in DMN
characteristics may change after appropriately controlling physiological response due to spontaneous cardiac and
breathing rhythms.Methods
Age- and gender-matched 17 healthy elderly and 15 AD patients
participated this study (Table 1) with written informed consents approved by
the Insitute Reviewing Board. MRI was performed on a 3T scanner (Magnetom Tim
Trio, Siemens) with a 32-channel head coil array. T1-weighted anatomical images were acquired by MPRAGE (TR/TE
= 2530/3.03ms, FOV = 256mm, flip angle = 7o, Matrix size = 224x256,
voxel size = 1.0mm3; GRAPPA, acceleration = 2). Resting-state
fMRI scans used T2*-weigthed
echo-planar imaging (TR/TE = 2000/30ms, FOV = 220mm, flip angle = 90o,
Matrix size = 384x384, slice thickness = 3.5mm, duration: 6’40”). Cardiac and
respiratory cycles were recorded using the pulse oxymeter placed on the index
finger tip and a respiration belt strapped around the upper abdomen. We used
RETROICOR10 to remove cardiac
and respiratory artifacts. We further removed the low-frequency respiratory and
heart rate effects by RVHRCOR11, where the
“respiratory variations” (RV) time series was generated by convolving the
root-mean-square amplitude of the respiration waveform across a 6-s sliding
window and a “respiration response function (RRF)”12; the heart rate (HR)
time series by convolving between the inverse of the average beat-to-beat
interval in a 6-s sliding window and a “cardiac response function (CRF)”13. Functional
connectivity was estimated by a seed-based correlation method14 with seeds at
posterior cingulate cortex (PCC; MNI [0, -53, 26]15) and medial
pre-frontal cortex (mPFC; MNI [0, 52, -6]6). We also analyzed group-specific
RRF and CRF for healthy subjects and AD patients. Specifically, brain image
voxels showing significant contribution to RV and HR fluctuations were pooled
together for RRF and CRF estimation by using a linear combination of the
previously published RRF12 and CRF13 and their first- and
second-order temporal derivatives.Results
The RV and HR regression
coefficient maps (Figure 1) in the general linear modeling show significant
differences between healthy controls and AD patients, especially at DMN
regions. We found that, for AD patients, the RV and HR factors both did not explain as much variance as for healthy subjects (Figure 1-C, F). Figure 2 shows DMN maps calculated by using PCC/mPFC as the seed
regions. Before physiological noise correction, the functional connectivity
with PCC showed significant difference in the mPFC (Figure 3-A). It became
insignificant after the physiological noise suppression (Figure 3-B).
Similarly, taking the mPFC as the seed region, the functional connectivity of
the healthy group differed signficantly from the AD group at PCC (Figure 3-D).
Again, this difference became insignificant after physiological noise suppression (Figure 3-E). Compared to healthy controls,
we found that AD patients had a delayed physiological response (Figure 4) with
a later arrival time in both respiration and cardiac response functions. By applying
the group-specific RRF and CRF in the RVHRCOR
approach, we found that the difference between healthy subjects and AD patients in DMN was
still reduced after physiological noise suppression (Figure 3-C,F).Discussion & Conclusion
The impact of the spontaneous physiology to two groups
were not the same. Specifically, RV and HR were both low frequency
physiological factors with prominent effects near the DMN nodes. While AD patients have
different DMN characteristics from normal controls before physiological noise
suppression as reported previously1-6,
however, this difference became insignificant after physiological noise
suppression. Note that we suppressed physiological noise by population-specific
physiological response functions, which had different physiological response
delays between healthy subjects and AD patients. Our results suggest the
importance of controlling noise due to spontaneous cardiac and respiratory
cycles in order to achieve more sensitive and specific differentiation between
healthy subjects and AD patients based on features in the DMN.Acknowledgements
This work was partially supported by Ministry of Science and Technology, Taiwan (103-2628-B-002-002-MY3, 105-2221-E-002-104), and the Academy of Finland (No. 298131).
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