Carotid stiffness and pulsatility associated with cognitive impairment: A phase-contrast MRI study
Poster No:
244
Submission Type:
Abstract Submission
Authors:
Lirong Yan1, Jianing Tang1, Elizabeth Joe2, Helena Chui2
Institutions:
1Northwestern University, Chicago, IL, 2University of Southern California, Los Angeles, CA
First Author:
Co-Author(s):
Introduction:
Arterial stiffening and increased pulsatility serve as important markers of vascular dysfunction, which lead to transmission of excessive pulse energy to downstream vasculature resulting in microvascular dysfunction. Separate MRI scans at different carotid segments are typically performed to assess carotid stiffness by measuring pulse wave velocity (PWV), which is prone to cardiac variations. Recently, a fast single-slice oblique-sagittal phase-contrast MRI (OS PC-MRI) technique has been introduced to simultaneously image multiple arterial velocity waveforms along the common carotid artery (CCA) and the internal carotid artery (ICA), which allows for multiple vascular metric measurements, including carotid PWV (cPWV), arterial pulsatility of both ICA and CCA quantified by pulsatility index (PI), and CCA-ICA damping factor (cDF). This study aims to investigate the associations of these vascular metrics with cognitive measurements in aged subjects.
Methods:
Participants and clinical assessments:
Forty elderly participants (22 female,73.3 ± 7.7 years) were enrolled in the study after providing written informed consent. Among them, 29 participants had Clinical Dementia Rating (CDR), 29 received Mini-Mental State Exam (MMSE), and 40 received Montreal Cognitive Assessment (MoCA).
MRI experiments:
The MRI experiments were conducted on a Siemens Prisma 3T MRI scanner using a 20-channel head/neck coil. A single-slice retrospectively gated 2D OS PC-MRI with a single in-plane velocity encoding (CCA to ICA) was performed on each participant to acquire blood velocity waveforms along the CCA-ICA segment simultaneously (Figures 1a & b). Imaging parameters include spatial resolution=1x1x1mm3, VENC=80cm/s, TE/TR=4.32/14.22ms, flip angle=10°, real temporal resolution=14.22ms, 70-90 phases across a cardiac cycle, scan time was 1 to 2min depending on the heart rate.
Image processing and statistical analysis
A reference waveform was calculated as the average of velocity waveforms obtained from all axial locations along ICA-CCA. The transit time between each waveform and the reference waveform was calculated using the time-to-foot (TTF) method. cPWV was calculated as the inverse slope of the line fitted to the transit time versus distance along the vessel (Figure 1c). PI was calculated from the average velocity waveforms along each segment i.g., CCA, ICA (Figure 1e). cDF was calculated as the ratio of the average PI values between CCA and ICA (Figure 1f).
According to the normality test on the MRI and cognitive data, the correlations of carotid vascular metrics with cognitive measures were calculated across subjects using Pearson or Spearman's correlation coefficients, respectively. Age, gender, and education were considered as covariances using partial correlation.
Forty elderly participants (22 female,73.3 ± 7.7 years) were enrolled in the study after providing written informed consent. Among them, 29 participants had Clinical Dementia Rating (CDR), 29 received Mini-Mental State Exam (MMSE), and 40 received Montreal Cognitive Assessment (MoCA).
MRI experiments:
The MRI experiments were conducted on a Siemens Prisma 3T MRI scanner using a 20-channel head/neck coil. A single-slice retrospectively gated 2D OS PC-MRI with a single in-plane velocity encoding (CCA to ICA) was performed on each participant to acquire blood velocity waveforms along the CCA-ICA segment simultaneously (Figures 1a & b). Imaging parameters include spatial resolution=1x1x1mm3, VENC=80cm/s, TE/TR=4.32/14.22ms, flip angle=10°, real temporal resolution=14.22ms, 70-90 phases across a cardiac cycle, scan time was 1 to 2min depending on the heart rate.
Image processing and statistical analysis
A reference waveform was calculated as the average of velocity waveforms obtained from all axial locations along ICA-CCA. The transit time between each waveform and the reference waveform was calculated using the time-to-foot (TTF) method. cPWV was calculated as the inverse slope of the line fitted to the transit time versus distance along the vessel (Figure 1c). PI was calculated from the average velocity waveforms along each segment i.g., CCA, ICA (Figure 1e). cDF was calculated as the ratio of the average PI values between CCA and ICA (Figure 1f).
According to the normality test on the MRI and cognitive data, the correlations of carotid vascular metrics with cognitive measures were calculated across subjects using Pearson or Spearman's correlation coefficients, respectively. Age, gender, and education were considered as covariances using partial correlation.
·Figure 1. Illustration of OS PC-MRI data acquisition and processing
Results:
cPWV showed significant negative correlations with both MoCA and MMSE (cPWV vs. MoCA: r = -0.4, p = 0.01; cPWV vs. MMSE: r = -0.57, p = 0.008;), and the correlations remained significant after controlling for age, gender, years of education (cPWV vs. MoCA: r = -0.36, p = 0.03; cPWV vs. MMSE: r = -0.53, p = 0.005;) (Figure 2). Furthermore, the participants with CDR>0 (n=19) showed higher cPWV values compared to those with CDR=0 (n=21) (p=0.0045). These results provide convergent evidence that elevated cPWV is strongly associated with cognitive decline.
No significant correlations were found between the average PI values at both ICA and CCA with cognitive measures, which may be caused by the variations along vessel segments during the PI measurements. However, a significant negative correlation between cDF and MoCA was observed (p = 0.036). This finding indicates that the damping factor of arteries could be a sensitive vascular marker for cognitive impairment.
No significant correlations were found between the average PI values at both ICA and CCA with cognitive measures, which may be caused by the variations along vessel segments during the PI measurements. However, a significant negative correlation between cDF and MoCA was observed (p = 0.036). This finding indicates that the damping factor of arteries could be a sensitive vascular marker for cognitive impairment.
·Figure 2. Associations of PWV with cognitive functions
Conclusions:
This study demonstrates that OS PC-MRI is a promising imaging tool to assess intracranial arterial stiffness and pulsatility, which can serve as sensitive imaging markers for cognitive impairment.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Physiology, Metabolism and Neurotransmission :
Cerebral Metabolism and Hemodynamics 2
Keywords:
Acquisition
Aging
MRI
1|2Indicates the priority used for review
Provide references using author date format
2. Lau, Kui Kai, et al. "Age and sex-specific associations of carotid pulsatility with small vessel disease burden in transient ischemic attack and ischemic stroke." International Journal of Stroke 13.8 (2018): 832-839.
3. Heidari Pahlavian S, Cen SY, Bi X, Wang DJJ, Chui HC, Yan L. Assessment of carotid stiffness by measuring carotid pulse wave velocity using a single-slice oblique-sagittal phase-contrast MRI. Magn Reson Med. 2021;86(1):442-455. doi:10.1002/mrm.28677