Cerebroarterial pulsatility and resistivity indices are associated with cognitive impairment and white matter hyperintensity in elderly subjects: A phase-contrast MRI study

Abstract

Increased cerebroarterial pulsations are thought to be contributing factors in microvascular damage and cognitive impairment. In this study, we assessed the utility of two-dimensional (2D) phase-contrast MRI (PC-MRI) in quantifying cerebroarterial pulsations and evaluated the associations of pulsatile and non-pulsatile hemodynamic measures with cognitive performance and white matter hyperintensities (WMH). Neurocognitive assessments on 50 elderly subjects were performed using clinical dementia rating (CDR) and Montreal cognitive assessment (MoCA). An electrocardiogram-gated 2D PC-MRI sequence was used to calculate mean flow rate, pulsatility index (PI), and resistivity index (RI) of the internal carotid artery. For each subject, whole brain global cerebral blood flow (gCBF) and relative WMH volume were also quantified. Elevated RI was significantly associated with reduced cognitive performance quantified using MoCA (p = 0.04) and global CDR (p = 0.02). PI and RI were both significantly associated with relative WMH volume (p = 0.01, p < 0.01, respectively). However, non-pulsatile hemodynamic measures were not associated with cognitive impairment or relative WMH volume. This study showed that the cerebroarterial pulsatile measures obtained using PC-MRI have stronger association with the measures of cognitive impairment compared to global blood flow measurement and as such, might be useful as potential biomarkers of cerebrovascular dysfunction in preclinical populations.

Keywords

Cerebroarterial pulsations cognitive impairment phase-contrast MRI pulsatility index resistivity index white matter hyperintensity

Introduction

More than two decades ago, De La Torre hypothesized that impaired cerebral microcirculation may lead to microvascular and metabolic damage to the brain and in turn, can detrimentally affect cognitive performance.¹ Since then, emerging evidence has suggested that cardiovascular comorbidities and cerebral microangiopathy play an important role in the pathogenesis of neurodegenerative disorders such as dementia and Alzheimer’s disease (AD).^2–7 Previous studies have evaluated the impact of total cerebral blood flow (CBF) alterations on cognitive performance, suggesting impaired CBF is associated with cognitive decline.^8–11 Recently, much attention has been given to the study of cerebroarterial pulsatility and its potential role in mediating intracranial hemodynamics and cognition. Increased pulsatility of cerebral arteries has been found to be associated with cognitive decline in AD patients and has been suggested to be involved in conversion to AD in subjects with mild cognitive impairment (MCI).^12,13 Additionally, cerebroarterial pulsatility has been reported to be significantly increased in patients with small vessel disease (SVD), which may be involved in the pathophysiology of AD.¹⁴ While the mechanistic link between arterial pulsation and cognitive impairment is not clear yet, it has been suggested that excessive arterial pulsatility can impair cerebral microcirculation by inducing elevated pulsatile stress and hypoperfusion.^15,16

Cerebroarterial pulsation has been typically characterized using pulsatility index (PI) and resistivity index (RI). PI and RI are quantified as the peak temporal variation of blood flow waveform normalized by the mean and maximum flow rates, respectively. Elevation in these parameters has been suggested to reflect increased arterial wall stiffness as well as elevated vascular impedance against the flow distal to the measurement site.^15,17,18 PI of major cerebral arteries has been evaluated in multiple studies in the presence of various neurological disorders.^12,19–24 However, few studies have investigated the association of RI with cognitive performance.

Transcranial Doppler (TCD) sonography has been widely used to quantify cerebrovascular pulsatility and its association with markers of neurological pathologies including SVD, AD, and vascular dementia.^21,25,26 Recent developments in TCD have made this technique less prone to the artifacts and limitations of earlier implementations.^27,28 However, TCD results could still be operator-dependent and the efficient application of this technique depends on the availability of an acoustic window through the skull, which is known to be limited in older subjects.²⁹ Phase-contrast magnetic resonance imaging (PC-MRI) allows for direct quantification of cerebroarterial blood velocity across the cardiac cycle by utilizing bipolar gradient to encode velocity through flow-induced phase shift along the gradient direction. Hemodynamic parameters including mean arterial flow as well as PI and RI can be derived from PC-MRI data. Recent studies have applied time-resolved three-directional PC-MRI (4D Flow) to quantify cerebroarterial PI in patients with acute ischemic stroke³⁰ and AD, and MCI.³¹ However, the utility of 4D Flow could be hampered in cerebrovascular studies by prolonged acquisition time and the need for sophisticated post-processing. By contrast, two-dimensional (2D) PC-MRI, which is available on all commercial scanners, offers much shorter scan time and straightforward processing.

White matter lesion load or white matter hyperintensity (WMH) characterized on T2-weighted MRI images has been interpreted as a surrogate marker for age-related cerebrovascular damage and cerebral SVD.^32–34 The prevalence of WMH has shown a strong association with vascular risk factors.^35,36 For example, total and periventricular WMHs were found to be associated with Framingham score (r = 0.32, 95% confidence interval: [0.16, 0.47]), an index which summarizes various vascular risk factors (e.g. systolic blood pressure, diabetes).³⁷ Recent evidence indicates that WMH progression could be a consequence of impaired drainage of the interstitial fluid from the white matter, which is mainly driven by the pulsations of the penetrating arteries.³⁸ However, the association of WMH with cerebrovascular pulsatility has not been fully elucidated.

The purpose of the present study was to evaluate the feasibility of 2D PC-MRI to assess the association of cerebroarterial pulsatility and resistivity measures with cognitive performance and White matter lesion load. Internal carotid arteries (ICAs) are the main conduits of the blood to the brain through which cardiovascular pulsations are transferred to cerebral microvasculature. As such, we evaluated the cross-sectional association of PC-MRI-derived PI, RI, and mean flow rate in ICA with cognitive performance in a cohort of elderly adults. We also assessed the association of whole brain global cerebral blood flow (gCBF), as a non-pulsatile hemodynamic parameter, with cognitive function. We hypothesized that a stronger negative association should exist between ICA pulsatility measures and cognitive performance, compared to that between CBF measures and cognitive performance. Furthermore, to determine if the PC-MRI-derived pulsatility measures are indicative of WMH severity, we examined the association between cerebroarterial pulsations and WMH volume in the same cohort.

Methods

Participants and clinical assessments

A total of 50 (36 females, all Latinos) individuals aged between 60 and 92 years (69.1 ± 6.9) were recruited from the Los Angeles Latino Eye Study cohort for the MarkVCID study (www.markvcid.org) at the University of Southern California (USC). Exclusion criteria were contraindication for MRI and history of major psychiatric illness (e.g. schizophrenia, bipolar disorder). None of the participants evaluated in this study had severe ICA stenosis. The study protocol was approved by the Institutional Review Board at USC (IRB# HS-16-00673) conforming with the World Medical Association Declaration of Helsinki. After submitting the written informed consent, each participant underwent medical evaluation and cognitive assessments including the clinical dementia rating (CDR)³⁹ and the Montreal cognitive assessment (MoCA).⁴⁰

Table 1.

Demographic and clinical characteristics of participants.

Characteristics (n = 50)		M/V
Age, yr	69.1 ± 6.9	–
Female	36 (72%)	–
Years of education, yd	6.9 ± 3.6	15
Systolic blood pressure, mmHg	143 ± 17	2
Diastolic blood pressure, mmHg	80 ± 11	2
Hypertension, n	28 (65%)	7
HbA1c, %	6.4 ± 1.2	1
Hypercholesterolemia, n	32 (74%)	7
Diabetes, n	16 (37%)	7
Total cholesterol, mg/dL	178 ± 40	1
Global CDR score = 0.5, n	14 (33%)	8
MoCA score	21.4 ± 4.4	14
WMH volume, mL	3.96 ± 4.10	4

M/V: number of missing values; CDR: clinical dementia rating; MoCA: Montreal cognitive assessment.

Note: Data are presented as mean ± standard deviation or number (percentage).

Imaging protocol

MRI measurements were carried out on a Siemens Prisma 3-Tesla Scanner using a 20-channel head coil (Siemens Medical Solutions, Erlangen, Germany). All MRI measures were blinded to any prior information. Following a rapid “scout” localizer scan, three-dimensional T2‐weighted fluid‐attenuated inversion recovery (FLAIR) images were acquired with the following imaging parameters: resolution = 1 × 1 × 1 mm³, field of view (FOV) = 256 × 256 × 176 mm, inversion time/TE/TR = 1800/388/5000 ms, echo spacing = 3.66 ms, echo train duration = 900 ms, Bandwidth = 651 Hz/Px, acquisition time ∼6.5 min.

Cerebral arteries were localized using a fast “vessel scout” gradient-echo sequence. ICAs were identified and the blood flow waveform over a cardiac cycle was measured using an ECG-triggered single-slice 2D PC-MRI with the following parameters: resolution = 0.5 × 0.5 × 5.0 mm³, velocity encoding value (VENC) = 100 cm/s, TE/TR = 4.86/49.25 ms, flip angle = 15°, number of phases = 24, acquisition time ∼45 s. PC-MRI acquisitions were carried out at the level between the second and third cervical vertebrae on a plane perpendicular to the ICA outline in the vessel scout maximum intensity projection image (Figure 1(a)).

Figure 1.

2D PC-MRI-based measurement of left ICA flow rate on a representative subject. Single slice perpendicular to the ICA between C1 and C2 vertebral levels was selected on the sagittal maximum intensity projection of the vessel scout acquisition (a). The structural image (b) was used to select the ICA with better orthogonality to the acquisition plane (Note the difference between selected and discarded ICAs depicted by the blue and red arrows, respectively). The dynamic vessel mask was generated by thresholding the time-resolved magnitude images. The thresholded cross-sectional area is shown as a green mask on the phase image (c). Vessel size and phase images were used to calculate ICA flow rate and cross-sectional area waveforms (d).

To minimize the scan time, a single through-plane velocity encoding was applied in the PC-MRI sequence. The signal-to-noise ratio (SNR) of the PC-MRI encoded velocity highly depends on the flow direction, which achieves maximum when the blood vessel is perpendicular to the imaging slice. As such, to ensure good image quality and accuracy of flow measurement, for each subject in the current study, PC-MRI measurements on the left and right ICAs were visually compared and only the one with better orthogonality to the acquisition plane (more circular outline) was retained for the final analysis (Figure 1(b)). In order to assess the repeatability of PI and RI, in 24 subjects, PC-MRI measurements were carried out twice, approximately six weeks apart. For subjects who underwent repeated measurements, clinical assessment was carried out only once during the first visit.

Whole brain global CBF (gCBF, mL/100g.min) measurements were carried out using a pseudo-continuous arterial spin labeling (pCASL) sequence with the following imaging parameters: three-dimensional gradient and spin echo (GRASE) readout, TR/TE = 4300/36.76 ms, labeling duration = 1.5s, post labeling delay = 2000 ms, number of slices = 48, resolution = 2.5 × 2.5 × 2.5 mm, label/control pairs = 8 and one M0 image. CBF maps were generated based on a standard single-compartment ASL perfusion model recommended by Alsop et al.⁴¹

Data analysis

PC-MRI data were processed using an internally developed software written in MATLAB (The MathWorks Inc., Natick, MA), which provided toolsets for phase unwrapping, region of interest (ROI) selection, and waveform analysis. For each PC-MRI dataset, an ROI encompassing the ICA was generated semi-automatically from the magnitude image using the following procedure. First, a rectangular region around the ICA was selected manually using a Marquee tool and the signal intensity of the magnitude image in that region was normalized by the maximum signal value in the ICA. Next, the ICA mask was generated by thresholding the normalized intensity map. In order to exclude pixels with partial volume effects, a conservative threshold value of 0.55 was used in all subjects and the corresponding phase images were used to obtain ICA blood flow waveform (Q, mL/s). Subsequently, PI and RI were calculated as

P I = \frac{Q_{\max} - Q_{\min}}{Q_{mean}}

(1)

R I = \frac{Q_{\max} - Q_{\min}}{Q_{\max}}

(2)

where mean flow rate Q_mean is the average of blood flow (averaged velocity × area) over the cardiac cycle. Additionally, the size of the thresholded ICA region throughout the cardiac cycle was used to calculate ICA cross-sectional area (A_ICA) and quantify ICA area distensibility (D_ICA) as

D_{ICA} = \frac{{A_{ICA}}_{\max} - {A_{ICA}}_{\min}}{{A_{ICA}}_{mean}}

(3)

where A_ICAmean (mm²) denotes the averaged ICA area over the cardiac cycle. ICA blood flow rate and cross-sectional area waveforms in a representative subject are shown in Figure 1(d).

For each subject, the T2-weighted FLAIR images were used to quantify white matter lesion load by measuring WMH volumes around the ventricles and in deep white matter. WMH volumes were quantified from FLAIR images using ITK-SNAP⁴² and its semi-automatic segmentation tool for supervised classification based on random forests.^42,43 First, the original FLAIR image was imported into the program. Using the paintbrush tool, three tissue classes (WMH, non-hyperintense tissue, and CSF) were marked as training examples for the classifier. WMH was specified as the foreground, while CSF and non-hyperintense tissue formed the background. Then, seeds were placed in the observed WMH, and the active contour algorithm was used to iteratively expand them into the ROI based on the earlier classification. Lastly, the segmented regions were carefully inspected by clinical fellows and manually adjusted if necessary. The final segmentation was used to quantify the overall WMH severity using the total volume of WMH as well as deep white matter (DWM) and periventricular white matter (PWM) Fazekas scores.⁴⁴ Relative WMH volumes were calculated by normalizing the total WMH volume by the intracranial volume quantified using Statistical Parametric Mapping tool (SPM12) from T1-weighted structural images.

Statistical analysis

Repeatability of PC-MRI-derived pulsatility measurements was evaluated by performing the Bland–Altman analysis and calculating intraclass correlation coefficient (ICC). Generalized estimating equations (GEE) method was used to assess associations (i) between MRI-measured cerebral hemodynamics parameters (PI, RI, mean flow rate, and gCBF) and cognitive performance (MoCA and global CDR scores) and (ii) between cerebral hemodynamic parameters and WMH measures (relative volume and Fazekas scores). All regression models were adjusted for age and gender. Regression models involving cognitive performance scores were additionally adjusted for years of education, self-reported by the participants. Statistical analyses were conducted in SAS (version 9.4, SAS Institute Inc., Cary, NC) and a two-sided p-value less than 0.05 was considered as statistical significance.

Results

Demographic and clinical characteristics of participants are summarized in Table 1. Diabetes, hypertension, and hypercholesterolemia were present in 37, 65, and 74% of the participants, respectively. Fourteen subjects (33%) had mild cognitive impairment as defined by CDR score = 0.5.

The repeatability analysis was carried out for cerebroarterial pulsatile parameters using Bland–Altman plots as shown in Figure 2. Close correlation was observed from the linear regression corresponding to PI (r² = 0.86) and RI (r² = 0.83). The 95% confidence intervals were [−0.25 – +0.22] and [−0.07 – +0.06] for PI and RI measurements, respectively. Furthermore, ICC value was 0.92 for PI and 0.91 for RI, indicating an excellent test-retest reproducibility for these measurements.

Figure 2.

Bland–Altman plots showing the correlation and the confidence intervals for repeated measurements of RI obtained from two visits in the time span of approximately six weeks.

To evaluate the association between cerebral hemodynamic measures and cognitive performance, we carried out regression analysis between PC-MRI and ASL-based parameters and the MoCA score. Increased RI was significantly associated with lower MoCA scores, and the correlation remained significant after controlling for age, gender and years of education (β = −0.005, p = 0.04). PI also showed a significant negative correlation with MoCA (β = −0.022 p = 0.002), but this correlation was not retained after adjusting for the confounding factors (β = −0.013 p = 0.15, Figure 3). Neither mean ICA flow rate nor gCBF were significantly associated with MoCA (p = 0.75, 0.61 respectively) with and without adjusting for the confounding factors, indicating that PC-MRI-derived pulsatility and resistivity measures might reflect the cerebrovascular alterations leading to cognitive decline better compared to cerebral blood flow measures.

Figure 3.

Association of PC-MRI-measured flow parameters of the ICA and ASL-measured global CBF with cognitive performance quantified by the MoCA score. Increased pulsatility and resistivity in the ICA are associated with cognitive decline (lower MoCA score). p and β represent the GEE linear model p-value and the effect size, respectively. p_a and β_a represent the p-value and the effect size from the same model with adjustment for age, gender, and education level. Black lines and shaded areas show linear regression based on the unranked data and the standard deviation of each variable, respectively. Dotted lines indicate confidence bands of the linear fit.

To further assess the association of cognitive function with pulsatile and non-pulsatile cerebral hemodynamic parameters, we quantified the group differences between participants with global CDR scores of 0 and 0.5 for each parameter (Figure 4). While both PI and RI showed positive correlation with CDR, after adjusting for age, gender and years of education, only RI was significantly different between groups with different CDR scores (0.68 ± 0.07 vs. 0.73 ± 0.07, p = 0.02). Furthermore, similar to the results from MoCA regression analysis, none of the non-pulsatile flow measures including mean flow rate and gCBF were significantly different between groups with different CDR scores.

Figure 4.

Box plots of PC-MRI-measured flow parameters of the ICA and ASL-measured global CBF in subjects with normal cognition (CDR = 0) and subjects with impaired cognition (CDR = 0.5). Increased resistivity index was significantly associated with decreased cognitive performance. Horizontal lines within boxes represent group means and boxes represent the 25th and 75th percentile rank of group values. Error-bars indicate standard deviation across subjects in each group. p and β represent the GEE linear model p-value and the effect size, respectively. p_a and β_a represent the p-value and the effect size from the same model with adjustment for age, gender, and education level.

To determine if cerebral hemodynamic parameters are associated with WMH, we evaluated the association of these parameters with relative WMH volume and Fazekas scores. PI and RI were significantly correlated with the DWM Fazekas score (β = 0.17, p = 0.01, β = 0.05, p < 0.01, respectively, Figure 5(a)) after controlling for age and gender. However, PWM Fazekas score was not significantly associated with PI or RI. PI and RI were both significantly associated with relative WMH volume after correcting for age and gender (β = 32.3, p = 0.01, β = 8.32, p < 0.01, respectively, Figure 5(b)), which indicates that pulsatile measures of cerebral hemodynamics are associated with white matter lesion load. Relative WMH volume and Fazekas scores were not significantly associated with either mean flow rate or gCBF.

Figure 5.

(a) Box plots of PCMRI-measured flow parameters of the ICA and ASL-measured global CBF versus DWM and PWM Fazekas scores. Pulsatility and resistivity indices were significantly increased with the increase in the DWM Fazekas score. Horizontal lines within boxes represent group means and boxes represent the 25th and 75th percentile rank of group values. Error-bars indicate standard deviation across subjects in each group. p_a and β_a represent the p-value and the effect size from with adjustment for age and gender. To calculate the effect size, the Fazekas scores were considered as the independent variables and the overall change in each dependent variable (e.g. PI and RI) was quantified using the generalized estimating equations. (b) Association of PCMRI-measured flow parameters of the ICA and ASL-measured global CBF with relative WMH volume quantified through manual segmentation of FLAIR images. Increased pulsatility and resistivity of ICA blood flow are associated with larger relative WMH volume. p and β represent the GEE linear model p-value and the effect size, respectively. p_a and β_a represent the p-value and the effect size from the same model with adjustment for age and gender. Black lines and shaded areas show linear regression based on the unranked data and the standard deviation of each variable, respectively. Dotted lines indicate confidence bands of the linear fit.

We also quantified the association of ICA area parameters (A_ICAmean and D_ICA) with PI, cognitive performance scores, and WMH using regression and group difference analyses. A_ICA and Q waveforms had similar temporal variation patterns, indicating that alterations in vessel area were directly related to changes in the flow rate (Figure 1(d)). D_ICA was not significantly associated with PI (β = 0.09, p = 0.06) and ICA area parameters did not have significant association with any of the cognitive performance or WMH measures (Table 2).

Table 2.

Summary of regression and group analyses statistics between ICA hydrodynamics, ICA area parameters, cognitive performance scores, and WMH.

	MoCA Score	Global CDR score	DWM Fazekas score	PWM Fazekas score	Relative WMH volume	$P I$
$PI$	−0.013 (0.15)	0.12 (0.14)	0.17 (0.01)	0.08 (0.31)	32.3 (0.01)	n.a.
$RI$	−0.005 (0.04)	0.05 (0.02)	0.05 (<0.01)	0.03 (0.19)	8.32 (<0.01)	n.a.
$Q_{mean}$	0.011 (0.75)	-0.11 (0.61)	0.13 (0.49)	0.25 (0.19)	40.4 (0.17)	n.a.
$gCBG$	0.16 (0.61)	1.18 (0.61)	−2.87 (0.09)	1.59 (0.19)	−190 (0.38)	n.a.
$A_{IC A_{mean}}$	−1.19 (0.12)	0.52 (0.91)	2.08 (0.59)	−3.55 (0.37)	915.8 (0.25)	n.a.
$D_{ICA}$	0.001 (0.68)	0.05 (0.09)	0.02 (0.50)	0.027 (0.31)	0.95 (0.87)	0.09 (0.06)

Results are presented as: effect size (p-value). p-value and the effect size were calculated using GEE linear model with adjustment for age, gender, and education (if applicable).

Discussion

The present cross-sectional study investigated the utility of 2D PC-MRI in quantifying cerebroarterial pulsatility and its association with cognitive impairment and white matter lesion load in elderly subjects. Our findings suggest that PC-MRI-derived cerebroarterial pulsatility and resistivity indices are associated with cognitive performance and WMH. We found that compared to non-pulsatile hemodynamic measures including gCBF and mean flow rate, PI and RI were more sensitive indicators of the compromised cerebrovascular function which is thought to be linked to the neuropathology and cognitive impairment in elderly populations at risk of neurodegenerative disorders.

Our results showed that the increased cerebroarterial pulsations are negatively associated with cognitive performance in elderly subjects as quantified by the MoCA scores (Figure 3) and the global CDR scores (Figure 4). Elevated PI and RI have been suggested to reflect an increase in downstream microvascular resistance and a greater arterial rigidity at the measurement site.^15,17,18 The connection between excessive arterial pulsation and cognitive impairment was first highlighted in studies on the compliance of large extracerebral arteries.^45–47 The findings of these studies showed that aorta pulsatility and stiffness are cross-sectionally and longitudinally associated with cognitive decline. Subsequent TCD studies examined if the increased central arterial pulsations could transmit to the brain through cerebral arteries. Our PC-MRI results are in accordance with previous TCD and 4D Flow findings that demonstrated the positive correlation between pulsatility of cerebral arteries and the degree of cognitive impairment in AD and MCI patients.^{12,13,20,21,26,31} Furthermore, a previous longitudinal study reported a significant association between increased cerebroarterial pulsation and conversion of MCI to AD.¹³ The significant correlation observed in the current study between PC-MRI-measured RI and cognitive decline, further supports the notion that the increased cerebrovascular resistance and stiffness can lead to vascular and metabolic damages to the brain.

In the light of previously suggested connection between increased RI and elevated downstream microvascular resistance,^17,18 our findings, which showed that increased RI was associated with cognitive decline, support the hypothesis that cerebral hypoperfusion induced by the increased cerebromicrovascular resistance, is one possible mechanistic pathway that links cerebrovascular dysfunction to cognitive decline. Multiple lines of evidence suggest that neurodegenerative disorders, such as AD, are provoked, at least in part, by premorbid cerebrovascular risk factors that predate the neurodegenerative process.^48–50 A multitude of cerebrovascular risk factors including arteriosclerosis, amyloid angiopathy, and lipohyalinosis, that has been reported in large proportion of patients with neurodegenerative disorders, can feed into the pathophysiological cascade that promote chronic cerebral hypoperfusion.^4,51–54 A large body of data from histological and physiological studies point to the existence of a counterregulatory mechanism that is triggered in normal aging and AD brain to adapt to the progressive glucose deprivation resulting from sustained cerebral hypoperfusion.⁵⁵ It has been suggested that in the absence of sufficient glucose provision, an austerity program alters the cerebral metabolism from an almost exclusively glycolytic to a partial ketolytic regiment in order to preserve glucose for anabolic needs. Amyloid beta (Aβ) and other Aβ precursor protein proteolysis-derived peptides are thought to be mediators responsible for this metabolic switch. It is important to note that during the early stages of cerebrovascular pathogenesis, the impact of abnormal alterations of cerebral microvasculature on cerebral perfusion is not clear. Based on the elevated PC-MRI-derived RI and its association with cognitive impairment observed in the present study, we postulate that an association might exist between increased microvascular resistance and regional hypoperfusion. Further longitudinal studies are needed to investigate the association of cerebroarterial alterations with hypoperfusion and cognitive impairment at various stages of vascular compromise.

We also evaluated the association of cognitive impairment with cerebral blood flow including mean flow rate from PC-MRI and gCBF from ASL. Our results showed no significant association between cerebral blood flow and cognitive performance, suggesting that in elderly subjects not diagnosed with AD, global measures of cerebral blood flow, including gCBF and mean arterial flow, are not strong cerebrovascular-based indicators of cognitive dysfunction. In accordance with our results, in a large multi-ethnic study, no significant association was found between ASL CBF and global cognition in normal subjects.⁵⁶ Also, findings of a recent study using TCD have indicated that mean cerebroarterial flow was not significantly different between MCI and control subjects.⁵⁷ Unlike global measures of CBF, regional CBF alterations have been reported in the presence of neurodegenerative disorders. Multiple studies have found lower ASL CBF in patients with MCI and AD compared to healthy controls, especially in temporal-parietal and posterior cingulate cortices^58–62 and these changes have been often associated with cognitive decline.^61,63 Carrying out regional perfusion analysis, might reveal associations which were not detectable using gCBF quantified in the current study. However, the considerably more time and resource intensive postprocessing necessary to acquire regional CBF data might hinder its potential clinical utility.

Our results from the group and regression analysis showed that only RI was significantly associated with cognitive performance quantified by the MoCA and the CDR scores. Neither the global blood flow parameters (gCBF and mean arterial flow) nor the A_ICAmean was significantly associated with the measures of cognitive performance. It has been speculated that the increased arterial stiffness and the resulting increase in cerebrovascular pulsatility might act as a protective mechanism to maintain global perfusion, as greater cerebroarterial pulsatility allows higher mean flow rate to be generated for an unchanged mean arterial blood pressure.⁶⁴ Thus, gCBF and mean arterial flow might not be sensitive markers of disease progression and severity in early stages of the neurodegeneration process, a notion which has been supported by the results of a recent study indicating that the mechanisms responsible for gCBF autoregulation are not impaired in AD or MCI.⁵⁷ Given the similar definitions, RI and PI are correlated with each other. However, our results indicated weaker association of PI with MoCA and CDR scores compared to the associations observed between RI and these scores. This difference may be partly attributed to the existence of mean arterial flow in PI equation that showed no association with cognitive performance. Future studies evaluating the association between RI, PI, and regional and global CBF can shed more light on the possible connections between cerebrovascular pulsatility and changes in cerebral perfusion.

Our finding that PC-MRI-measured PI and RI are associated with the measures of WMH severity indicates that cerebroarterial pulsatility might contribute to cerebral microvascular and brain tissue damage. Consistent with our results, several studies have shown that elevated cerebral pulsations, quantified using TCD, are positively associated with WMH volume.^48,65–67 The etiology of DWM and PWM alterations differs. DMW changes are related to chronic small vessel ischemia, whereas PWM changes reflect the demyelination, ependymitis granularis, and subependymal gliosis, as well as small vessel ischemia.^68–70 Our results which showed significant association between measures of cerebroarterial pulsatility and DWM Fazekas score further support the possible association between arterial pulsatility and cerebrovascular dysfunction. It has been suggested that the arterial stiffening can impair the dampening of the blood flow waveform and lead to excessive transmission of blood pressure to the cerebral microvasculature.^45,71 Also, the increased cerebroarterial pulsation has been suggested to cause perivascular shear stress and damage to oligodendrocytes⁴⁸ causing dysfunction of perivascular glymphatic system. The clinical manifestation of these structural damages has been demonstrated in reports that associated WMH with cognitive impairment^33,72,73 and neuropsychiatric symptoms.³⁴ As such, PC-MRI-measured cerebroarterial pulsatility measures, quantified in the current study, might be useful as potential biomechanical markers of white matter lesion load.

The results of the ICA cross-sectional area analysis showed no significant association between D_ICA and PI, indicating that the increase in these two parameters do not reflect the same physiological alteration. D_ICA was not significantly associated with any of the cognitive performance scores or WMH measures. Area distensibility indicates vessel stiffness at the site of measurement, whereas arterial pulsatility could additionally reflect the downstream vascular impedance against the flow.^17,18 As such, our results suggest that cognitive impairment and white matter lesion load might have greater association with vasculature resistance distal to ICA compared to ICA stiffness. The results of the ICA area analysis in the present study should be interpreted with caution, as the accuracy of vessel cross-sectional area calculations was limited by the spatial resolution of the MRI sequence and the resulting partial volume effects.

Our results showed no significant association between WMH severity and both gCBF and ICA arterial flow, indicating that in our cohort, WMH was not accompanied by a large alteration in global cerebral blood flow. WMHs are primarily provoked in physiologically low-perfused areas of the brain (i.e. the periventricular and deep white matter).⁷⁴ As such, even a small CBF deficit can induce ischemic areas in regions associated with WMH and the physiological variability of gCBF might be too large to detect such small differences among subjects with different degrees of WMH. Our results are consistent with a recent report that changes in cerebrovascular resistance might precede cerebral hypoperfusion in AD.⁷⁵ Based on our observations that PI and RI are the only parameters significantly associated with the WMH severity, one can postulate that the structural damages induced by elevated cerebroarterial pulsations might precede the alterations in global cerebral perfusion. As such, PC-MRI-derived cerebral pulsatile measures could reflect microvascular and neuronal damages with more sensitivity compared to gCBF. In contrast to our observations, some previous findings suggest that lower gCBF may relate to WMH severity in elderly subjects and AD patients.^32,76,77 However, a recent study reported that the association between WMH and perfusion was significant only for the regional CBF corresponding to the hyperintensity regions.⁷⁸ The discrepancy between previous reports could be caused by interpopulation variations and the difference in WMH quantification techniques. Further research using larger cohorts may lead to a better understanding of the possible mechanisms linking WMH and perfusion.

This study had several limitations. First, the cross-sectional design of our analysis prevents us from making inferences about any causal relation between cerebroarterial pulsatility, cognitive performance, and the severity of WMH. Our findings could not elucidate whether the increased cerebroarterial pulsatility, which highlights elevated vascular resistance and stiffness, is a consequence of neuronal loss and lower metabolic demand or a primary factor leading to the neuronal damage. Future longitudinal studies on larger cohorts are needed to determine the direction of the causalities corresponding to the observations made in the current study.

Second, the PC-MRI-based quantification of PI and RI in the current study was carried out only based on the ICA flow waveform. ICA is the main vessel responsible for the brain perfusion, and recent studies have shown similar hemodynamic patterns among most intracranial arteries.⁷⁹ Nevertheless, there might be regional alterations in cerebral hemodynamics not detectible by measuring ICA flow characteristics alone. Also, ICA hydrodynamics and area parameters quantified in this study were from a single ICA selected in each participant based on the signal quality of the vessel. As such, possible regional variations between left and right ICAs were not considered in the analysis performed in the current study. Further investigations focusing on PC-MRI measurements of cerebroarterial pulsatility in vessels more downstream than ICA (e.g. MCA) and small cerebral perforating arteries can provide stronger basis for interpretation of the observations made in the present study and lead to a deeper understanding of the link between cerebrovascular pathologies and cognitive impairment. Future studies can implement 7 T PC-MRI technique, the application of which has been recently demonstrated for reproducible quantification of PI in small cerebral perforating and lenticulostriate arteries.^80,81

Third, compared to TCD, PC-MRI has much lower temporal resolution which could adversely impact the accuracy of PI and RI calculations by underestimating the peak-to-peak flow difference. This underestimation can depend on the waveform shape and can vary in different individuals. Further studies are necessary to investigate the application of more improved PC-MRI sequences with higher temporal resolution^82,83 in quantifying cerebroarterial pulsatility measures.

Fourth, the relatively small size and the heterogenous nature of the cohort used in the current study precluded us from clustering our participants into MCI or AD groups, as well as evaluating the possible residual confounding effect of various cardiovascular risk factors, such as hypertension and diabetes. Also, due to the limited resolution of the available structural images, we checked only for the presence of severe ICA stenosis and were not able to accurately grade milder cases. Moreover, genetic data, such as APOE genotype, were unavailable, we were not able to evaluate the potential effect of genetic risk factors. Analysis of these risk factors along with neurodegenerative biomarkers related to Aβ accumulation and clearance can help to better elucidate the mechanisms linking cognitive impairment and cerebroarterial pulsations.

In conclusion, our work showed that pulsatile measures of ICA obtained using PC-MRI are associated with cognitive impairment and the severity of WMH. Compared to ASL-measured global CBF, cerebroarterial pulsatility parameters (RI and PI) were found to be more sensitive indicators of the potential mechanisms linking cognitive impairment and cerebrovascular dysfunction. The PC-MRI-measured pulsatility parameters investigated in the current study can be acquired using a fast and widely available sequence involving minimal postprocessing. Future investigations using these parameters may lead to a more profound comprehension of the mechanisms by which cerebrovascular pulsatile injury triggers or contributes to the neurodegenerative process.

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by National Institute of Health (NIH) grants UH2-NS100614, K25-AG056594 and R01-EB028297. This work was also supported by American Heart Association (AHA) grant 16SDG29630013 and Alzheimer’s disease research center (ADRC) grant NIA AG P50-AG05142.

Acknowledgements

The authors thank Katherin Martin and Kai Wang for assistance with data collection, Dr. Kay Jann for assistance with ASL processing, and Giuseppe Barisano for evaluation of WMH.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Authors’ contributions

SHP carried out the experiments, developed the postprocessing pipeline, conducted data analysis, and wrote the manuscript. XW and HZ conducted data analysis and manuscript revision. SM and XS carried out experiments and manuscript revision. MC recruited patients. LD, JR, HC, and DW interpreted the cognitive data, discussed results, and revised manuscript. LR designed the study, guided experiments and data analysis, and revised the manuscript. All authors edited and revised the manuscript and approved final submission.

ORCID iDs

Soroush H Pahlavian

Danny JJ Wang

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