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Published in final edited form as: J Stroke Cerebrovasc Dis. 2017 Jan 4;26(4):779–786. doi: 10.1016/j.jstrokecerebrovasdis.2016.10.017

White Matter Hyperintensity Associations with Cerebral Blood Flow in Elder Subjects Stratified by Cerebrovascular Risk

Ahmed A Bahrani 1,2, David K Powell 3, Guoquiang Yu 1, Eleanor S Johnson 3, Gregory A Jicha 4, Charles D Smith 1,3,4
PMCID: PMC5473621  NIHMSID: NIHMS857588  PMID: 28063772

Abstract

Goal

Add clarity to the relationship between deep and periventricular brain white matter hyperintensities, cerebral blood flow and cerebrovascular risk in older persons.

Methods

Deep and periventricular WMH and regional grey and white matter blood flow from arterial spin labeling were quantified from magnetic resonance imaging scans of 26 cognitively normal elder subjects stratified by cerebrovascular disease risk. FLAIR images were acquired using a high-resolution 3D sequence that reduced partial volume effects seen with slice-based techniques.

Findings

Deep WMH (dWMH) but not periventricular WMH (pWMH) were increased in the high CVD risk patients; pWMH but not dWMH were associated with decreased regional cortical (GM) blood flow. We also found blood flow in white matter is decreased in regions of both pWMH and dWMH, with a greater degree of decrease in pWMH areas.

Conclusion

WMH are usefully divided into dWMH and pWMH regions because they demonstrate differential effects. Three-dimensional regional WMH volume is a potentially valuable marker for CVD based on associations with cortical CBF and with white matter CBF.

Keywords: white matter hyperintensities, small vessel disease, cerebral blood flow, arterial spin labeling image, segmentation, fluid attenuated inversion recovery, vascular risk

Introduction

Cerebrovascular disease (CVD) is a common medical morbidity in the United States, with high rates of disability through stroke and dementia [1, 2]. Detection and characterization of existing cerebrovascular disease, therefore, remains an important medical issue. An understudied aspect of cerebrovascular disease is the relationship between cerebral blood flow (CBF) and white matter hyperintensities (WMH, [3, 4]). WMH are periventricular and deep regions of abnormal signal commonly seen on MRI scans of elderly and high vascular risk patients [5, 6]. It has been found that WMH embody an ischemic component with functional consequences including cognitive decline and dementia, but due to the underlying complexity of these lesions a full formulation of their significance has not been achieved [7, 8]. In this study we analyzed the relationship between WMH and CBF in subjects stratified by cerebrovascular risk. The aim of this study was to find relationships between WMH, CBF and CVD risk, expecting that WMH would increase with increased CVD risk and with decreased CBF.

Materials and Methods

Subjects

Twenty-six healthy subjects were recruited by the Center for Clinical and Translational Science (CCTS) and the Sanders-Brown Center on Aging at the University of Kentucky. Written consent was obtained from each individual before participation according to an approved protocol from the Institutional Review Board (IRB) at the University of Kentucky.

A CVD risk score, based on Framingham risk estimation modified for stroke prediction[9], was assigned to each patient by study neurologists. The Framingham study risk score is based on several parameters, e.g., gender, age, history of diabetes, blood pressure, smoking history, and cholesterol level, and used to predict CVD over ten years for each subject. The Framingham scoring scale range is 1 – 30 points. Subjects were divided, based on their risk score, into two groups: high-risk (n = 12) and low-risk (n = 14) for CVD, based on a cutoff of 15 points. Image analysis was blind to age and CVD risk status.

MRI acquisition

A Siemens 3T TIM Trio MRI scanner at the University of Kentucky Magnetic Resonance Imaging and Spectroscopy Center (MRISC) equipped with a 32-channel head coil was used to scan subjects. Acquisition sequences were: (1) T1-weighted Magnetization-Prepared Rapid Acquisition Gradient Echo (MPRAGE), TE 2.3 ms, TR 2530 ms, IR 1100 ms, FA is 7°, 1 × 1 × 1 mm resolution full brain coverage (2) Fluid Attenuated Inversion Recovery (FLAIR), TE 388 ms, TR 6000 ms, IR is 2200 ms, 3D 1 × 1 × 1 mm with no gap between slices, and (3) Pulsed Arterial-Spin-Labeling (PASL) TE 12 ms, TR 3400 ms, IR1 700 ms, IR2 1900 ms, 4 × 4 × 4 mm resolution with 1 mm gap between slices, full brain coverage excluding the cerebellum.

Image Processing

MRI image processing used semi-automated methods to quantitate regional WMH volume and CBF flow values. These methods are summarized in Figure 1. Two images were registered and averaged to increase the signal-to-noise ratio. Scalp and bone tissue were stripped using the FSL FMRIB software library (FSL v5.0.8) Brain Extraction Tool (BET) (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET). Slice by slice cleanup of unwanted tissue was performed using the Medical Image Processing Analysis and Visualization (MIPAV v7.2.0) application (http://mipav.cit.nih.gov). The stripped brain image was converted to a mask by thresholding (brain mask). FLAIR and PASL images were registered to the image in SPM12 (http://www.fil.ion.ucl.ac.uk/spm) and stripped of extraneous tissue using the brain mask.

Figure 1.

Figure 1

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Diagrammatic overview of the processing workflow for quantitating WMH and CBF using, FLAIR and ASL images. Segments: C1: GM; C2: WM tissue class 1; C3: CSF; C7: WM tissue class 2.

Multimodal segmentation was performed on masked, FLAIR and ASL brain images in the SPM12 unified regime to create separate native-space images representing grey matter (GM; Figure 2A), white matter (WM) and cerebrospinal fluid (CSF) using an inhouse segmentation template created from 145 images of normal subjects. WM was modeled as two separate tissue classes to represent variations due to WM intensities [10]. The International Consortium for Brain Mapping (ICBM) atlas reference was registered to the segmentation template, and atlas regions combined to create masks representing the frontal, temporal, parietal and occipital brain regions for both GM (Figure 2B) and WM (Figure 2C).

Figure 2.

Figure 2

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A. Native-space GM segmented image. Panels B and C show ICBM template masks of WM and GM transferred to the standard space of the subject (compare with Panel A). Panel D shows the grey matter template masks applied to the registered ASL image.

CBF quantification

The ICBM lobar masks were registered to each subject’s native-space ASL image using the inverse transform generated during segmentation. Median CBF values were extracted from each grey matter template region (Figure 2D).

White Matter Hyperintensity Quantification

The two WM segmentation images (Figures 3A and 3B) were summed (Figure 3C) and converted to a binary WM mask in native space (Figure 3D). In a few instances WM voxels were misclassified in an extraneous tissue segment, likely due to partial volume effects; in these cases, these voxels were added to the two WM segments before binary conversion. The unitary WM mask isolated WM in the FLAIR image (Figure 3E) by multiplication, (Figure 3F). The Gaussian fit to the histogram of WM voxels was used to set the threshold for WMH (Figure 3G), as the mean plus 3 × S.D., corresponding to a p-value of 0.01. The threshold values were applied to each FLAIR image and 1 mm Gaussian filtered to remove noise, creating a WMH image for each subject (Figure 3H).

Figure 3.

Figure 3

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Sequential steps of the segmentation process to quantitate WMH. A, B: segmented images representing two tissue classes of WM; C: total WM (A+B); D: WM mask from thresholding of D; E: FLAIR image; F: FLAIR image masked by D (FLAIR WM voxels); G: histogram of E showing Gaussian-fit and threshold of 3.0 S.D. (arowhead); H: WMH image obtained by applying threshold to F.

A final quality-control procedure used a high-contrast display of the FLAIR image at the Gaussian-fit mean center and window value of 10 × S.D., side by side with the WMH image, comparing slice by slice for correspondence between isolated WMH and FLAIR hyperintensities. Manual editing of extraneous pixels due to pulsation and flow artifacts was needed occasionally, resulting in the final WMH image (Figure 3H), quantitated by calculating total voxels and total WMH voxel volume [10].

Voxels in the WMH image were divided into pWMH and dWMH using the following method designed to produce a reproducible and consistent definition of pWMH and dWMH: (1) the CSF segment (Figure 4A) was used to generate a mask by thresholding at 0.33 (Figure 4B). Connected voxels were morphologically identified as belonging to ventricles. (Figure 4C) and isolated (Figure 4D). The ventricular volume was intensity-inverted (Figure 4E) and multiplied by the FLAIR image (Figure 4F) to exclude the CSF (Figure 4G), (2) the border of the ventricular mask was morphologically identified (Figure 4H) and dilated (53 voxels; Figure 4I), then multiplied by the CSF-excluded FLAIR image to identify periventricular tissue (Figure 4J). Ten percent of the mean value from periventricular tissue voxel histogram was set as minimum threshold level to obtain the periventricular binary mask (Figure 4K), and (3) this mask was multiplied by the total WMH mask (Figure 4L; cf. Figure 3H) to obtain pWMH (Figure 4M), and the negative of this periventricular binary mask multiplied by the total WMH mask to obtain dWMH (Figure 4N).

Figure 4.

Figure 4

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Sequential steps of obtaining deep WM and pre-ventricular WM binary masks. A – the CSF isolated from other tissue, panel B. C – identifying tissue. D – ventricular tissue voxels. E – reversing the intensity of (D) and multiply by FLAIR image, F, to remove the CSF, image G. H – is the edge of (D) that is dilated by 5×5×5 voxels, image I. (I and G) are multiplied to get the periventricular tissue, J, which is converted to binary image, K. L, WMH image, multiply by (K) once and the reverse intensity of (K) once to get pWMH and dWMH respectively images M and N.

Statistical Methods

Multivariate analysis of variance (MANOVA) models used log-transformed measured WMH volumes as repeated independent variables because of their typical skewed distributions (JMP v9, SAS Institute). Dependent variables were risk group (high versus low vascular risk) and total intracranial volume (TIV) to control for head size. Post hoc contrasts of dWMH and pWMH versus risk group relationships were performed. A similar model was used for ASL cerebral blood flow in three defined WM regions: the two containing either dWMH or pWMH, and one without WMH. Age, risk group and TIV were used as covariates in this model.

To test relationships between dWMH and pWMH, a standard least squares model was constructed with log dWMH as the dependent variable, and risk group nested within log pWMH as independent variables, with TIV as control for head size. A p-value of 0.05 was considered significant; a p-value <0.10 marginally significant.

Results

The 26 subjects had a mean age of 77.8 ± 6.8 years (range 66 – 88). There were twenty-three females (mean age 77.8 ± 6.9) and three males (mean age 77.0 ± 7.9). There was a significant difference in age between risk groups: the low-risk group had a mean age of 72.7 ±4.2 and the high-risk group had a mean age of 83 ± 4.6 (p < 0.0001).

White Matter Hyperintensities

Total WMH volume positively correlated with independent visual ratings using the Longstreth scale[11], confirming that volumes correspond to what a trained neurologist interprets as WMH (Figure 5A). There was a significant correlation between pWMH and dWMH in the regression model, but the relationship between pWMH and dWMH was different for high and low-risk subjects: the slope of regression was higher in the high-risk group (1.26 ± 0.22 SEM; t = 5.5, p < 0.0001) compared to low-risk (0.86 ± 0.34, t=2.5, p = 0.02). The mean dWMH volume was higher in the high-risk group compared to low risk (t = 2.3, p = 0.03; Figure 5B).

Figure 5.

Figure 5

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(A) Linear regression of Longstreth visual rating scale (0–9) for all patients on log WMH total volume, demonstrating face validity of the WMH volume measurement (adjusted r2= 0.88, p<0.0001), (B) log WMH volume in low and high risk groups; overall volume is higher in high-risk patients (p=0.02), but only the dWMH volume is significant (p = 0.04; error bars = SD, asterisk *).

MANOVA analysis confirmed that WMH volume was higher in the high-risk group (F = 5.8, p = 0.02). Further analysis demonstrates that dWMH volume was higher in the high-risk group (t = 2.2, p =0.04), but pWMH volume was not significantly different (t = 1.0, p = 0.34) between groups.

Age correlated with total WMH (p = 0.04) and dWMH (p = 0.03) but not pWMH volume (p= 0.10). There was a strong correlation seen between age and risk score (r2 = 0.56; p < 0.0001).

Cerebral Blood Flow (CBF)

There were no differences in global or in regional cortical GM CBF between risk groups. There was no association between CBF and age. We analyzed associations between WMH volume and regional CBF. Results are shown in Table 1. Correlations were significant between parietal WMH volume and posterior frontal, parietal, temporal, and occipital CBF, and marginally significant with total CBF. Occipital pWMH volume was significantly correlated with CBF in the medial temporal GM (Table 1).

Table 1.

Spearman rank correlations for the pWMH and CBF for different brain regions. There were no significant correlations between dWMH or total WMH volumes with CBF.

Total Frontal (ant) Frontal (post) Occipital Parietal Temporal Temporal (medial)
WMH
Total −0.28 (0.15) −0.16 (0.41) −0.33 (0.09) −0.37 (0.06) −0.37 (0.06) −0.37 (0.06) −0.31 (0.11)
Frontal −0.16 (0.41) −0.16 (0.41) −0.24 (0.23) −0.21 (0.29) −0.17 (0.37) −0.23 (0.23) −0.25 (0.20)
Occipital −0.06 (0.73) 0.13 (0.50) −0.006 (0.97) −0.08 (0.66) −0.07 (0.72) −0.25 (0.19) −0.42 (0.03)§
Parietal −0.34 (0.08) −0.28 (0.15) −0.39 (0.04)§ −0.39 (0.04)§ −0.44 (0.02)§ −0.38 (0.05)§ −0.30 (0.13)
Temporal −0.13 (0.49) 0.004 (0.98) −0.09 (0.63) −0.23 (0.24) −0.18 (0.36) −0.23 (0.24) −0.29 (0.14)
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§

denotes significant p-value.

Average white matter CBF was calculated for three WM regions containing dWMH, pWMH or neither lesion (F = 53.5, p < 0.0001; Figure 6). Age, risk group and TIV did not significantly affect CBF. Post-hoc paired comparisons showed significantly decreased CBF in dWMH (t = 5.7, p < 0.001) and pWMH (t = 11.0, p < 0.0001) regions compared to normal appearing white matter. CBF in pWMH was less than in dWMH areas (t = 3.1, p = 0 003)

Figure 6.

Figure 6

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ASL blood flow in white matter in order left to right: total WM including WMH, total WM but excluding WMH, dWMH regions, and pWMH regions. Paired comparisons are significantly different as shown. Lowest CBF is in pWMH regions. Bar height: mean CBF; error bars: standard deviation.

Discussion

The aim of this study was to find relationships between WMH, CBF and CVD risk in elder patients. CVD risk was assessed by the Framingham CVD stroke risk score assigned to each patient. Fourteen subjects with low-risk were compared to twelve subjects with high-risk. We used a 3-D FLAIR WMH imaging technique that improved resolution and registration, and reduced partial volume effects compared to slice-based techniques. The main findings are that dWMH but not pWMH are increased in high-risk patients, and that pWMH but not dWMH are associated with decreased regional cortical (GM) blood flow. We also found blood flow in white matter is decreased in regions of both pWMH and dWMH, with a greater degree of decrease in pWMH areas.

The underlying origin of WMH remains controversial, perhaps because different mechanisms responsible for their appearance and pathologies can be found at autopsy that so far are not distinguishable using current MRI methods[2, 7, 12–18]. The strongest and most consistent associations with WMH are age and certain cerebrovascular risk factors including diabetes, hypertension, hypercholesterolemia, and cigarette smoking[8, 9, 19–26]. These factors are included in vascular risk measures such as the Framingham score used in this study. A genetic component may be involved in cases where evidence of CVD is lacking[27–31].

Age associations with WMH are likely explained by the accumulation of vascular disease over time together with other changes in cerebral white matter that may be unrelated to small vessel cerebrovascular disease per se, such as cerebral amyloid angiopathy or AD-related changes[32, 33]. Nonetheless, WMH are taken with much supporting evidence to represent a marker of classical small vessel cerebrovascular disease in white matter. Small vessel CVD is increasingly recognized as an important pathological contributor to clinical decline in our aging US population, showing strong associations with both gait disorders and vascular dementia [34–36]. Vascular dementia can occur as a pure form, or often together with more common dementias including Alzheimer’s disease (where it is termed mixed vascular dementia)[37].

We divided WMH into dWMH and pWMH for several reasons[10, 38–42]. Firstly, the white matter tracts adjacent to the ventricles encompassed by pWMH are limbic (cingulum) and motor (corticospinal and related tracts), whereas dWMH are more likely to involve long association tracts and thus long-range inter-regional connectivity. The possibility that clinical correlates of dWMH and pWMH may be different is also supported by the association of dWMH but not pWMH with vascular dementia [10, 43]. Finally, the small-vessel circulation in white matter may be different between deep and periventricular zones, with a greater vulnerability of the periventricular region to large- vessel disease [44]. This is consistent with our finding that low cortical CBF is associated with greater pWMH.

We found that the relationship between pWMH and dWMH was different depending on risk group: the increase in dWMH per given change in pWMH was higher in the high-risk group, even though pWMH volume was the same. This suggests that vascular risk factors are associated with more widespread small vessel disease in white matter, whether directly or indirectly via large vessel flow compromise. Other evidence of small vessel disease in our data was decreased white matter CBF in both dWMH and pWMH regions. However, these differences were not related to age or to CVD risk status.

There are several caveats to the interpretation of our data. The first is the association of age with risk status in our patients. Age is strongly associated with CVD, but there may be other independent effects of age on WMH we aren’t able to discern in our analysis. Better selection of subjects and larger recruitment would have helped overcome this limitation. Another caution is the relatively low proportion of patients with high grades of WMH. We were able to offset this problem by normalizing distributions of WMH and using non-parametric correlations for CBF, still achieving significant results, but these findings should be confirmed in future studies. Additional methodologic analysis considerations include our planned use of a 3D ASL acquisition for better comparison with 3D WMH volumes.

Conclusions

We find evidence that WMH are usefully divided into dWMH and pWMH regions because they demonstrate differential associations with CVD and demographic risk factors. 3D WMH volume is a potentially valuable marker for CVD that is able to differentiate associations of pWMH and dWMH with risk factor profiles as well as cortical and white matter CBF. Further studies using such methods in distinct and larger cohorts are needed to conform and extend the present findings.

Acknowledgments

We wish to thank The Higher Committee for Education Development in Iraq for their support, assistance from Weikai Kong, Yu Shang, Chong Huang, and Abner Rayapati, and the help we received from the University of Kentucky Alzheimers Center, particularly from Dr Fred Schmitt of the ADC. This study was supported by a pilot award (GY) from the National Institutes of Health (NIH) P30 #AG028383 and a Grant-In-Aid (GY) from the American Heart Association (AHA) #16GRNT30820006.

This study was supported by a pilot award (GY) from the National Institutes of Health (NIH) P30 #AG028383 and a Grant-In-Aid (GY) from the American Heart Association (AHA) #16GRNT30820006

Footnotes

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