Skip to main content
NCBI home page
Journal of Cerebral Blood Flow & Metabolism logoLink to Journal of Cerebral Blood Flow & Metabolism
. 2015 May 13;35(10):1610–1615. doi: 10.1038/jcbfm.2015.92

Reduced blood flow in normal white matter predicts development of leukoaraiosis

Manya Bernbaum 1,2,3, Bijoy K Menon 1,2,3,4,5,6,*, Gordon Fick 6, Eric E Smith 1,2,3,4,5,6, Mayank Goyal 2,3,4,5, Richard Frayne 1,2,3,4,5, Shelagh B Coutts 1,2,3,4,5
PMCID: PMC4640308  PMID: 25966951

Abstract

The purpose of this study was to investigate whether low cerebral blood flow (CBF) is associated with subsequent development of white matter hyperintensities (WMH). Patients were included from a longitudinal magnetic resonance (MR) imaging study of minor stroke/transient ischemic attack patients. Images were co-registered and new WMH at 18 months were identified by comparing follow-up imaging with baseline fluid-attenuated inversion recovery (FLAIR). Regions-of-interest (ROIs) were placed on FLAIR images in one of three categories: (1) WMH seen at both baseline and follow-up imaging, (2) new WMH seen only on follow-up imaging, and (3) regions of normal-appearing white matter at both time points. Registered CBF maps at baseline were used to measure CBF in the ROIs. A multivariable model was developed using mixed-effects logistic regression to determine the effect of baseline CBF on the development on new WMH. Forty patients were included. Mean age was 61±11 years, 30% were female. Low baseline CBF, female sex, and presence of diabetes were independently associated with the presence of new WMH on follow-up imaging. The odds of having new WMH on follow-up imaging reduces by 0.61 (95% confidence interval=0.57 to 0.65) for each 1 mL/100 g per minute increase in baseline CBF. We conclude that regions of white matter with low CBF develop new WMH on follow-up imaging.

Keywords: cerebral blood flow, leukoaraiosis, white matter hyperintensities

Introduction

Leukoaraiosis, visible on magnetic resonance (MR) imaging (MRI) as white matter hyperintensities (WMH) of presumed vascular origin on T2-weighted imaging, is a ubiquitous part of normal aging1 but, when present in large volumes, may be associated with cognitive decline and an elevated risk of stroke.2, 3, 4, 5 Leukoaraiosis is also associated with potentially modifiable risk factors that lead to stroke, such as hypertension, diabetes, and dyslipidemia.2 Preventing or slowing down leukoaraiosis may, therefore, have the potential to reduce disability from stroke and cognitive decline. Nonetheless, despite years of study, there still remain unanswered questions regarding its pathogenesis.

Pathologic studies have found small vessel damage and blood–brain barrier dysfunction in areas of leukoaraiosis. Consequently, leukoaraiosis is considered to be mainly vascular in origin.6 Positron emission tomography and MR perfusion have shown low cerebral blood flow (CBF) in regions of leukoaraiosis.7 A previous cross-sectional study demonstrated a reduction in CBF, not only within areas of abnormal white matter, but also throughout normal-appearing white matter (NAWM).8 If reduced CBF in NAWM precedes development of leukoaraiosis, this could provide a potential target for new therapies augmenting CBF in those brain regions. Longitudinal studies that assess the relationship between CBF at baseline and the subsequent development of leukoaraiosis are important first steps toward realizing this goal.

Using a longitudinal stroke-MRI cohort study with follow-up at 18 months, we sought to understand whether regions of low cerebral blood flow in NAWM develop into regions with WMH (leukoaraiosis) over time.

Materials and Methods

Subjects

Consecutive patients >18 years presenting to the Foothills Medical Centre between May 2009 and September 2011 with high-risk transient ischemic attack (focal weakness or speech disturbance lasting >5 minutes), or a minor ischemic stroke (National Institute of Health Stroke Scale score ≤3) were recruited into the prospective CATCH (Computed tomography And MRI in the Triage of transient ischemic attack and minor Cerebrovascular events to identify High risk patients) study.9 Details are described in this paper,9 but briefly all patients had computed tomography/computed tomography angiography of the intracranial and extracranial vessels completed within 24 hours of symptom onset. Exclusion criteria for CATCH included a premorbid modified Rankin Scale ≥2, treatment with an acute thrombolytic drug, inability to complete imaging, and a serious comorbid illness that would make it unlikely for the patient to survive 3 months post event. Not all patients had an MRI completed, but patients who had baseline MRI completed including perfusion imaging were approached for enrollment into extended CATCH. Extended CATCH included follow-up imaging at 18 months. All patients had baseline demographics recorded including vascular risk factors, event details, and follow-up information.

Imaging

Baseline MRI was assessed for acute diffusion-weighted imaging-positive lesions with axial diffusion-weighted imaging, apparent diffusion coefficient, and fluid-attenuated inversion recovery (FLAIR). Both acute (within 48 hours) and follow-up (18 months) imaging were performed on a 3 T MR scanner (Signa, VH/i, General Electric Healthcare, Waukesha, WI, USA before December 2010 and Discovery 750, General Electric Healthcare after March 2011). MR sequences included axial diffusion tensor imaging (b=1,000 seconds/mm2), axial three-dimensional pre- and post-gadolinium time-of-flight MR angiography, axial T2, FLAIR, and spoiled gradient-recalled echo imaging, and axial dynamic susceptibility contrast perfusion-weighted imaging. Specific sequence details for FLAIR are: TR/TE=9,000 milliseconds/140 milliseconds, 40 slice with a slice thickness=3.5 mm (no gap), field-of-view=24 cm × 24 cm, 256 × 192 acquired matrix. The dynamic susceptibility contrast perfusion-weighted imaging sequence (echo planar imaging with flip angle/TR/TE=45°/2,000 milliseconds/30 milliseconds, 21 slices with slice thickness=5.0 mm (no gap), field-of-view=24 cm × 24 cm, and 144 × 144 acquired matrix) was obtained during the intravenous injection of a gadolinium-based paramagnetic contrast agent (Gadovist, Berlex, Wayne, NJ: 10 mL/second at 5 mL/second or Magnevist, Berlex: 20 mL/second at 5 mL/second) followed by a 20 mL physiologic saline flush at 5 mL/second.

Image Processing

Dynamic susceptibility contrast perfusion-weighted imaging was used to generate parametric perfusion maps (Olea Sphere; La Ciotat, France) from an established tracer kinetic model. An arterial input function (AIF) was manually selected in the middle cerebral artery (MCA) M1 segment of the normal unaffected hemisphere. Deconvolution of the AIF was performed using the delay-insensitive singular value decomposition.10 As the study is looking for brain regions that appear normal on FLAIR but have low CBF, the commonly used cross-calibration procedure (i.e., scaling to a fixed value of presumed normal white matter) can impair interpretability of perfusion maps. Instead, the software normalized individual perfusion parameter maps to the average value of that parameter in the region used to define the AIF. This technique generates quantitative maps of perfusion as described previously.11

Baseline FLAIR, baseline perfusion, and follow-up FLAIR images were co-registered with Olea Sphere. A single trained rater simultaneously compared baseline and follow-up FLAIR images to identify WMH of presumed vascular origin, as defined by recent consensus criteria.12 In each patient, multiple regions of interest (ROIs, ranging in number from 40 to 60) were placed on the co-registered follow-up FLAIR scans to capture information for three possible tissue outcome groups: (1) regions of WMH at baseline and at follow-up, i.e., old WMH (2) regions with new WMH present at follow-up that were not present at baseline, and (3) NAWM at both baseline and follow-up (Figures 1 and 2). Total ROI numbers were: 387 in old WMH, 294 in new WMH, and 1,099 in NAWM. The ROIs were standardized to be circular and were 6 pixels in area. If too large to be placed within a WMH, the size of the ROI was reduced but was never smaller than 2 pixels. In regions with NAWM at baseline and follow-up, the ROIs were placed according to a template that selected thirty-two a priori defined locations where WMH are common (Figures 1 and 2). If there was no NAWM in the pre-specified template locations, no ROIs were placed. Diffusion-weighted imaging-positive lesions were excluded from further analysis. All ROIs were placed masked to CBF information (Figure 3).

Figure 1.

Figure 1

Open in a new tab

Template used to place regions of interest in normal-appearing white matter at both time points. Slices used were (from left to right): first slice superior to the ventricles, slice with the widest ventricles, and two slices inferior to the slice with the widest ventricles. In blue are regions in the centrum semiovale and in yellow are regions in the periventricular region. These indicated regions served as the template for CBF measurements in normal-appearing white matter (see text). CBF, cerebral blood flow.

Figure 2.

Figure 2

Open in a new tab

Co-registered baseline (left) and follow-up (right) FLAIR images. The anterior ROI is in NAWM at baseline that becomes WMH at follow-up. Posterior ROI is in a WMH at both time points. FLAIR, fluid-attenuated inversion recovery; NAWM, normal-appearing white matter; ROI, region of interest; WMH, white matter hyperintensity.

Figure 3.

Figure 3

Open in a new tab

Baseline FLAIR (upper left), follow-up FLAIR (upper right), DSC PWI (bottom left) and CBF (bottom right) showing ROI (blue circle in FLAIR and yellow circle on CBF map; expanded for clarity) placed in NAWM at baseline that becomes WMH at follow-up. CBF at baseline in ROI (yellow circle) was 12 mL/100 gm per minute. CBF, cerebral blood flow; DSC, dynamic susceptibility contrast; FLAIR, fluid-attenuated inversion recovery; NAWM, normal-appearing white matter; PWI, perfusion-weighted imaging; ROI, region of interest; WMH, white matter hyperintensity.

Statistical Analyses

Our analysis sought to understand the relationship between CBF at baseline and development of new WMH on follow-up imaging. Analyses were done at the level of each individual ROI, using mixed-effects logistic regression to account for within-patient clustering. For our primary analysis, we compared ROIs with new WMH at follow-up with ROIs without WMH at either baseline or follow-up (i.e., in NAWM). Development of new WMH was the dependent variable and CBF at baseline within each ROI was the main independent variable of interest. In addition, as we wanted to test whether age, sex, history of hypertension, and history of diabetes modified or confounded the relationship between baseline CBF and new WMH on follow-up imaging, these variables were included as fixed effects. Patient was included in the random effects part of the model. In addition, we tested for presence of multiplicative interactions between baseline CBF and these fixed effects variables. Using a combination of forward selection and backward elimination, we finally arrived at a parsimonious model that reports on main effects. We also performed a secondary analysis to determine the association between baseline CBF and presence of any WMH on follow-up imaging (i.e., including both new and old WMH). For this secondary analysis, we did not find any significant difference in baseline CBF between new and old WMH (P=0.32) and therefore collapsed the ROIs into a single group (i.e., regions of WMH on follow-up MRI) and compared it with ROIs without WMH (i.e., NAWM) at follow-up. The residuals in the final models were normally distributed. All hypothesis tests are two-sided, with P<0.05 considered statistically significant. Analyses were performed using Stata/SE 12.1 software (StataCorp LP, College Station, TX, USA).

Results

Between May 2009 and January 2012, 56 patients were enrolled in the Extended CATCH substudy. Eight patients who had baseline MR did not have follow-up imaging (three withdrew consent before follow-up imaging, two died, one became pregnant, one had a pacemaker inserted, and one was claustrophobic). Of the 48 remaining patients who had a baseline and 18-month follow-up imaging, 8 had dynamic susceptibility contrast perfusion-weighted imaging that was judged to be inadequate for analysis because of severe motion artifacts, leaving 40 patients for analysis. No patient had a clinical recurrent stroke between baseline and follow-up imaging.

Mean patient age at baseline was 61±11 years, 30% were female, 52.5% were hypertensive, and 12.5% had diabetes. Median National Institute of Health Stroke Scale at baseline was 1 (interquartile range: 1 to 3) Baseline characteristics are described in Table 1.

Table 1. Baseline characteristics in the study sample.

Age (mean±s.d.) 61±11
Female 30%
Hypertension 52.50%
NIHSS at baseline (median, IQR) 1 (1–3)
Diabetic 12.50%
WMH volume at baseline 9.21±11.87 mL
WMH volume at follow-up (18 months) 11.96±13.16 mL
   
Mean cerebral blood flow in mL/100 g per minute (mean±s.d.) (number of ROIs)
 WMH at baseline and follow-up (old WMH) 16.7±0.2 (387)
 New WMH 16.0±0.2 (294)
 Normal-appearing white matter (NAWM) 21.5±0.1 (1,099)
Open in a new tab

Abbreviations: IQR, interquartile range; NIHSS, National Institute of Health Stroke Scale; ROI, region of interest; WMH, white matter hyperintensity.

Using simple two-factor models that included baseline CBF and one of the variables (i.e., age, sex, hypertension, and diabetes) along with an interaction term between CBF and the second variable, we found that only baseline CBF (P<0.001) and the presence of diabetes (P=0.04) were independently associated with presence of new WMH at follow-up imaging. No significant two-way multiplicative interactions were observed (P>0.05). Two multi-factor models that included all possible multiplicative interactions between (1) CBF, sex, diabetes, and hypertension and (2) CBF, age, sex, and diabetes were built. We did not find any relevant interactions between these variables in these models. The final parsimonious model found CBF, sex, and diabetes as being independently associated with development of new WMH on follow-up imaging (Table 2). Age and hypertension were not associated with new WMH. All regions with baseline CBF value <10 mL/100 gm per minute had new WMH on follow-up imaging (Figure 4). Table 3 reports the age-adjusted odds of developing new WMH in regions with CBF 15 to 20 mL/100 g per minute, 20 to 25 mL/100 g per minute, and >25 mL/100 g per minute compared with regions with a baseline CBF <15 mL/100 g per minute. The odds of new WMH decreased with increasing CBF (Table 3 and Figure 4).

Table 2. Analysis showing effect of predictors (CBF value, diabetes, and sex at baseline) on development of new (primary) and any WMH (secondary) at follow-up imaging.

  Odds ratio 95% Confidence interval P-value
Primary analysis      
 CBF (per 1 mL/100 g per minute) 0.61 0.57–0.65 <0.001
 Diabetes (yes versus no) 4.13 1.25–13.6 0.02
 Sex (male versus female) 0.41 0.17–0.97 0.042
       
Secondary analysis
 CBF (per 1 mL/100 g per minute) 0.64 0.62–0.67 <0.001
 Diabetes (yes versus no) 3.28 1.37–7.88 0.008
 Sex (male versus female) 0.51 0.27–0.97 0.04
Open in a new tab

Abbreviations: CBF, cerebral blood flow; WMH, white matter hyperintensity.

Figure 4.

Figure 4

Open in a new tab

CBF distribution in ROIs with new WMH on follow-up versus those with old WMH (i.e., at both baseline and follow-up imaging) and those with NAWM on follow-up imaging. All regions with a baseline cerebral blood flow (CBF) <10 mL/100 g per minute had WMH on follow-up imaging. CBF, cerebral blood flow; NAWM, normal-appearing white matter; ROI, region of interest; WMH, white matter hyperintensity.

Table 3. Pre-specified CBF category specific odds (regarding first category) of developing new WMH in our study. In our study, all regions with CBF <10 mL/100 g per minute developed WMH at 18 months.

Cerebral blood flow (mL/100 g per minute) Odds ratio 95% Confidence interval
<15 1  
15–20 0.119 0.073–0.194
20–25 0.014 0.008–0.025
>25 0.004 0.001–0.011
Open in a new tab

Abbreviations: CBF, cerebral blood flow; WMH, white matter hyperintensity.

Secondary analyses of the association of baseline CBF with WMH on follow-up MRI, including both new WMH as well as WMH that was present on both baseline and follow-up scans, yielded similar findings: baseline CBF, diabetes, and sex were independently associated with the presence of WMH on follow-up imaging (Table 2). There were no interactions between age, sex, diabetes, hypertension, and baseline CBF.

Discussion

This study reports on the relationship between CBF at baseline and the presence of WMH at follow-up. Our results show that the odds of having new WMH on follow-up imaging reduces by 0.61 for each 1 mL/100 g per minute increase in CBF. These results provide evidence that low CBF at baseline in NAWM precedes the development of new WMH on follow-up imaging.

Our findings are consistent with previous cross-sectional studies demonstrating that hypoperfusion has a role in the pathogenesis of white matter damage.7, 13, 14, 15, 16 Our data suggests causality between reduced CBF and WMH (leukoaraiosis) by showing that reduced baseline CBF predicts new WMH at 18 months. Low CBF within regions of WMH, as seen in previous cross-sectional studies, cannot be solely a consequence of WMH because we show that decreased CBF precedes WMH formation. To our knowledge, this is the first longitudinal study linking low CBF to subsequent WMH development.

White matter hyperintensities are associated with an increased risk of stroke, dementia, and cognitive decline, so it is beneficial to find ways to reduce WMH in patients.1 Demonstrating a possible mechanism of WMH development opens the possibility of a new target for therapeutic trials aimed at improving CBF to slow or halt WMH development.

Pathologic studies have demonstrated vascular abnormalities in WMH that could account for the observed hypoperfusion.17, 18, 19 Van Swieten et al found a significant reduction in the diameter of arterioles in WMH compared with arterioles in normal white matter.19 In regions with WMH, blood vessels invariably show alterations in their structure. The severity of these changes vary from hyaline thickening to lipohyalinosis.20 These vascular abnormalities may begin as micro-structural vascular changes before any visible changes are detected on FLAIR imaging, causing the regional reduction in CBF in NAWM that we found in areas that developed WMH at 18 months.

Using diffusion tensor imaging, De Groot et al described micro-structural white matter changes, specifically reduced fractional anisotropy, at baseline with WMH development on follow-up imaging.6 These changes were observed in seemingly ‘normal' white matter on FLAIR imaging. Diffusion tensor imaging may provide supplementary information and further stratify risk of development of WMH.21 Vascular abnormalities and reduction in CBF observed on perfusion imaging, and the damage to the myelin observed on diffusion tensor imaging are not necessarily mutually exclusive.22 The decreased blood flow and the demyelination could be because of a broader failure of endothelium in blood vessels and glia.

Our results also suggest that diabetes is an independent predictor of development of new WMH presence at 18 months. Recent imaging studies in patients with type 2 diabetes reveal similar findings: a higher incidence of WMH and higher prevalence of lacunar infarcts compared with control subjects.23, 24 Diabetes is related to degenerative neuronal changes, affects blood–brain barrier permeability, and is associated with endothelial dysfunction.25 Although this pathology is consistent with pathology found in leukoaraiosis, studies are inconclusive as to whether diabetes is associated with white matter disease. Contrary to our findings, two large population-based studies have reported no association between diabetes and WMH presence.26, 27 The small number of diabetics (5 out of 40) in our study is a limitation; further studies are needed.

Our results suggest that women have a higher likelihood of developing new WMH on follow-up imaging than men. This was an unexpected finding, although previous studies have shown a higher density of WMH in women in both periventricular and deep white matter areas.28, 29 The Rotterdam Scan Study reported that women tended to have a higher prevalence and severity of WMH than men.29, 30 The female sex hormone estrogen has important functions in the brain including increasing CBF, protecting the brain from oxidative stress and prevention of neuronal atrophy.31, 32, 33 During and after menopause, depletion of estrogen might make the female brain more vulnerable to reduction of blood flow and damage from oxidative stress.

We did not find that hypertension had an independent effect on the development of new WMH. The lack of association could be because of careful control of hypertension, as all patients in our study were followed by a stroke neurologist and had treatment of vascular risk factors. Unfortunately, we do not have follow-up blood pressure information on these patients to know whether this is the case or not. In the 3C study, WMH progression in hypertensive patients was limited to the patients with poorly controlled hypertension despite medication, with no significant WMH increase in patients with well-controlled hypertension.34 We did not find an independent effect of age on development of new WMH. It is possible that the effect of age seen in other studies could be mediated by age-related reductions in CBF. Previous studies have found that increasing age results in worsening of leptomeningeal collateral arteries and reduction in CBF in white matter.35

Our study has some limitations. We cannot rule out the possibility of the bolus of contrast agent being delayed or dispersed before entering the tissue bed, thus affecting the AIF and perfusion estimates. We also used a previously published technique to derive quantitative perfusion estimates; this technique would need further validation in more studies.11 The normalization method used in this technique assumes that the CBF value of the AIF is identical across subjects, however, this might not be accurate in patients with vascular disease. Our study, showing proof of a biologic hypothesis, is one such validation. In addition, the small number of patients is a potential limitation, we placed a large number of ROIs per patient that increased our statistical power to detect differences in CBF between white matter regions that did or did not progress to WMH. Mixed models allowed us to adjust for clustering of ROIs within patients. Also, we included a real-life selection of stroke patients including some with large artery disease and others with potential cardioembolic sources of stroke. There is the potential that different etiologies of WMH have different blood flow and this is a potential limitation.

Conclusion

In patients with minor stroke or transient ischemic attack, reduced cerebral blood flow precedes development of new WMH. Larger studies with longer follow-up intervals are needed to corroborate our findings. Only then can studies to determine a pharmacological treatment effective at restoring perfusion in NAWM be attempted.

Footnotes

The CATCH study was funded by grants from Canadian Institute of Health Research (CIHR), Alberta-Innovates Health Solutions (AI-HS), and a Pfizer Cardiovascular research award program. BKM holds a Heart and Stroke Foundation of Canada Professorship in Stroke Imaging and has received grant funding from the CIHR, AI-HS/Pfizer, and the Faculty of Medicine, University of Calgary. MG has received honoraria from Penumbra and Covidien. RF is the Hopewell Professor of Brain Imaging. SBC receives salary support from the AI-HS and the Heart and Stroke Foundation of Canada's Distinguished Clinician Scientist award, supported in partnership with the CIHR Institute of Circulatory and Respiratory Health and AstraZeneca Canada. EES receives salary support from CIHR and AI-HS, holds the Kathy Taylor Chair in Vascular Dementia, and has received grant funding from CIHR, National Institute of Neurological Disorders, Heart and Stroke Foundation of Alberta, and the Alzheimer Society of Canada.

References

  1. 1Smith EE. Leukoaraiosis and stroke. Stroke 2010; 41: S139–S143. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. 2Vermeer SE, Hollander M, van Dijk EJ, Hofman A, Koudstaal PJ, Breteler MM et al. Silent brain infarcts and white matter lesions increase stroke risk in the general population: the Rotterdam Scan Study. Stroke 2003; 34: 1126–1129. [DOI] [PubMed] [Google Scholar]
  3. 3Vermeer SE, Prins ND, den Heijer T, Hofman A, Koudstaal PJ, Breteler MM. Silent brain infarcts and the risk of dementia and cognitive decline. N Engl J Med 2003; 348: 1215–1222. [DOI] [PubMed] [Google Scholar]
  4. 4Kuller LH, Longstreth WT, Jr., Arnold AM, Bernick C, Bryan RN, Beauchamp NJ, Jr. et al. White matter hyperintensity on cranial magnetic resonance imaging: a predictor of stroke. Stroke 2004; 35: 1821–1825. [DOI] [PubMed] [Google Scholar]
  5. 5Maillard P, Carmichael O, Harvey D, Fletcher E, Reed B, Mungas D et al. FLAIR and diffusion MRI signals are independent predictors of white matter hyperintensities. AJNR Am J Neuroradiol 2013; 34: 54–61. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. 6de Groot M, Verhaaren BF, de Boer R, Klein S, Hofman A, van der Lugt A et al. Changes in normal-appearing white matter precede development of white matter lesions. Stroke 2013; 44: 1037–1042. [DOI] [PubMed] [Google Scholar]
  7. 7Yao H, Sadoshima S, Kuwabara Y, Ichiya Y, Fujishima M. Cerebral blood flow and oxygen metabolism in patients with vascular dementia of the Binswanger type. Stroke 1990; 21: 1694–1699. [DOI] [PubMed] [Google Scholar]
  8. 8O'Sullivan M, Lythgoe DJ, Pereira AC, Summers PE, Jarosz JM, Williams SC et al. Patterns of cerebral blood flow reduction in patients with ischemic leukoaraiosis. Neurology 2002; 59: 321–326. [DOI] [PubMed] [Google Scholar]
  9. 9Coutts SB, Modi J, Patel SK, Demchuk AM, Goyal M, Hill MD et al. CT/CT angiography and MRI findings predict recurrent stroke after transient ischemic attack and minor stroke: results of the prospective CATCH study. Stroke 2012; 43: 1013–1017. [DOI] [PubMed] [Google Scholar]
  10. 10Wu O, Ostergaard L, Weisskoff RM, Benner T, Rosen BR, Sorensen AG. Tracer arrival timing-insensitive technique for estimating flow in MR perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix. Magn Reson Med 2003; 50: 164–174. [DOI] [PubMed] [Google Scholar]
  11. 11Kane I, Carpenter T, Chappell F, Rivers C, Armitage P, Sandercock P et al. Comparison of 10 different magnetic resonance perfusion imaging processing methods in acute ischemic stroke: effect on lesion size, proportion of patients with diffusion/perfusion mismatch, clinical scores, and radiologic outcomes. Stroke 2007; 38: 3158–3164. [DOI] [PubMed] [Google Scholar]
  12. 12Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013; 12: 822–838. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. 13Oishi M, Mochizuki Y, Hara M, Takasu T. Central motor conduction time in patients with periventricular lucencies. J Neurol Sci 1996; 142: 30–35. [DOI] [PubMed] [Google Scholar]
  14. 14Miyazawa N, Satoh T, Hashizume K, Fukamachi A. Xenon contrast CT-CBF measurements in high-intensity foci on T2-weighted MR images in centrum semiovale of asymptomatic individuals. Stroke 1997; 28: 984–987. [DOI] [PubMed] [Google Scholar]
  15. 15Yao H, Sadoshima S, Ibayashi S, Kuwabara Y, Ichiya Y, Fujishima M. Leukoaraiosis and dementia in hypertensive patients. Stroke 1992; 23: 1673–1677. [DOI] [PubMed] [Google Scholar]
  16. 16Hatazawa J, Shimosegawa E, Satoh T, Toyoshima H, Okudera T. Subcortical hypoperfusion associated with asymptomatic white matter lesions on magnetic resonance imaging. Stroke 1997; 28: 1944–1947. [DOI] [PubMed] [Google Scholar]
  17. 17Matsusue E, Sugihara S, Fujii S, Ohama E, Kinoshita T, Ogawa T. White matter changes in elderly people: MR-pathologic correlations. Magn Reson Med Sci 2006; 5: 99–104. [DOI] [PubMed] [Google Scholar]
  18. 18Fazekas F, Kleinert R, Offenbacher H, Schmidt R, Kleinert G, Payer F et al. Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology 1993; 43: 1683–1689. [DOI] [PubMed] [Google Scholar]
  19. 19van Swieten JC, van den Hout JH, van Ketel BA, Hijdra A, Wokke JH, van Gijn J. Periventricular lesions in the white matter on magnetic resonance imaging in the elderly. A morphometric correlation with arteriolosclerosis and dilated perivascular spaces. Brain 1991; 114: 761–774. [DOI] [PubMed] [Google Scholar]
  20. 20O'Sullivan M. Leukoaraiosis. Pract Neurol 2008; 8: 26–38. [DOI] [PubMed] [Google Scholar]
  21. 21O'Sullivan M, Summers PE, Jones DK, Jarosz JM, Williams SC, Markus HS. Normal-appearing white matter in ischemic leukoaraiosis: a diffusion tensor MRI study. Neurology 2001; 57: 2307–2310. [DOI] [PubMed] [Google Scholar]
  22. 22Hassan A, Hunt BJ, O'Sullivan M, Parmar K, Bamford JM, Briley D et al. Markers of endothelial dysfunction in lacunar infarction and ischaemic leukoaraiosis. Brain 2003; 126: 424–432. [DOI] [PubMed] [Google Scholar]
  23. 23Korf ES, van Straaten EC, de Leeuw FE, van der Flier WM, Barkhof F, Pantoni L et al. Diabetes mellitus, hypertension and medial temporal lobe atrophy: the LADIS study. Diabet Med 2007; 24: 166–171. [DOI] [PubMed] [Google Scholar]
  24. 24van Harten B, Oosterman JM, Potter van Loon BJ, Scheltens P, Weinstein HC. Brain lesions on MRI in elderly patients with type 2 diabetes mellitus. Eur Neurol 2007; 57: 70–74. [DOI] [PubMed] [Google Scholar]
  25. 25Arvanitakis Z, Schneider JA, Wilson RS, Li Y, Arnold SE, Wang Z et al. Diabetes is related to cerebral infarction but not to AD pathology in older persons. Neurology 2006; 67: 1960–1965. [DOI] [PubMed] [Google Scholar]
  26. 26Longstreth WT, Jr, Manolio TA, Arnold A, Burke GL, Bryan N, Jungreis CA et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The Cardiovascular Health Study. Stroke 1996; 27: 1274–1282. [DOI] [PubMed] [Google Scholar]
  27. 27Schmidt R, Launer LJ, Nilsson LG, Pajak A, Sans S, Berger K et al. Magnetic resonance imaging of the brain in diabetes: the Cardiovascular Determinants of Dementia (CASCADE) Study. Diabetes 2004; 53: 687–692. [DOI] [PubMed] [Google Scholar]
  28. 28Sachdev PS, Wen W, Christensen H, Jorm AF. White matter hyperintensities are related to physical disability and poor motor function. J Neurol Neurosurg Psychiatry 2005; 76: 362–367. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. 29de Leeuw FE, de Groot JC, Achten E, Oudkerk M, Ramos LM, Heijboer R et al. Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry 2001; 70: 9–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. 30Breteler MM, van Swieten JC, Bots ML, Grobbee DE, Claus JJ, van den Hout JH et al. Cerebral white matter lesions, vascular risk factors, and cognitive function in a population-based study: the Rotterdam Study. Neurology 1994; 44: 1246–1252. [DOI] [PubMed] [Google Scholar]
  31. 31Burns A, Murphy D. Protection against Alzheimer's disease? Lancet 1996; 348: 420–421. [DOI] [PubMed] [Google Scholar]
  32. 32Goodman Y, Bruce AJ, Cheng B, Mattson MP. Estrogens attenuate and corticosterone exacerbates excitotoxicity, oxidative injury, and amyloid beta-peptide toxicity in hippocampal neurons. J Neurochem 1996; 66: 1836–1844. [DOI] [PubMed] [Google Scholar]
  33. 33McEwen BS, Alves SE, Bulloch K, Weiland NG. Ovarian steroids and the brain: implications for cognition and aging. Neurology 1997; 48: S8–15. [DOI] [PubMed] [Google Scholar]
  34. 34Godin O, Tzourio C, Maillard P, Mazoyer B, Dufouil C. Antihypertensive treatment and change in blood pressure are associated with the progression of white matter lesion volumes: the Three-City (3C)-Dijon Magnetic Resonance Imaging Study. Circulation 2011; 123: 266–273. [DOI] [PubMed] [Google Scholar]
  35. 35Menon BK, Smith EE, Coutts SB, Welsh DG, Faber JE, Goyal M et al. Leptomeningeal collaterals are associated with modifiable metabolic risk factors. Ann Neurol 2013; 74: 241–248. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Journal of Cerebral Blood Flow & Metabolism are provided here courtesy of SAGE Publications

RESOURCES