Deep Medullary Vein Score is Associated with Burden of White Matter Hyperintensities

doi:10.21203/rs.3.rs-2099490/v1

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Deep Medullary Vein Score is Associated with Burden of White Matter Hyperintensities

https://doi.org/10.21203/rs.3.rs-2099490/v1

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Background

To explore the correlation between the deep medullary vein (DMV) score and the burden of white matter hyperintensities (WMHs).

Methods

The baseline clinical and imaging data of 130 patients were analyzed prospectively. DMVs were divided into 6 anatomic regions on susceptibility weighted imaging (SWI) images, with each region scored 0 to 3 based on the visual condition of the vessels, and the DMV score was obtained by adding the scores of the 6 regions. The WHM burden was calculated with the WMH volumes and Fazekas scores for periventricular white matter hyperintensities (PWMHs) and deep white matter hyperintensities (DWMHs) on FLAIR images.

Results

The WMH volumes and PWMH and DWMH Fazekas scores were correlated with age ,the DMVscore,and blood pressure (P < 0.05), while not correlated with gender, diabetes, hyperlipidemia or smoking (P > 0.05). After multivariate analysis, the DMV score was independently associated with the WMH volume and the PWMH and DWMH Fazekas scores (P < 0.05).

Conclusion

The DMV score is correlated with the WMHs burden.

white matter hyperintensities

deep medullary vein

susceptibility weighted imaging

periventricular white matter hyperintensities

burden

White matter hyperintensities (WMHs) are an imaging finding. They occur as patchy periventricular white matter hyperintensities (PWMHs) and deep white matter hyperintensities (DWMHs) on T2 FLAIR images [1]. The older the age, the higher the incidence. Among the population over 60 years old, the detection rate of WMHs reaches 70 percent [2]. WMHs are frequently seen in patients with cognitive dysfunction[3], dementia and stroke[3–4]. Thus, it is necessary to explore the pathogenesis of WMHs.

The specific pathophysiological mechanism of WMHs remains unclear. Vascular endothelial cell damage, inflammatory factor infiltration, vascular permeability changes and abnormal blood-brain barrier function [5–8]have all been found in patients with WMHs. In addition, recent studies have shown that chronic perfusion injury is a common characteristic of white matter lesions [8–9]. The role of the arterial system in these chronic injuries has been relatively well researched, but the research on the venous system, especially the deep medullary vein (DMV), is rare, and the role of the DMV in the occurrence and development of WMHs is still not clear.

The DMVs are mainly located in the white matter around the lateral ventricles and drain the semioval centers and the periventricular parenchyma [10]; these areas are precisely where WMHs are found. Therefore, we speculate that DMV stenosis and occlusion will cause a series of reactions, such as increased venous pressure around the lateral ventricle and in the semioval center and reflux disorders, thus leading to WMHs.

Susceptibility weighted imaging (SWI) is a unique magnetic resonance (MR) imaging modality that can display small veins in vivo. In SWI images, the DMVs present as thin strip-shaped hypointensities vertical to the lateral ventricle, with fixed position and less variation. Therefore, we used the DMV score to indicate the degree of pathologic change of the DMV[11] and investigated the correlation between the DMV score and WMH burden, so as to provide new insights for prevention and treatment of WMHs.

Patients

The clinical and imaging data of 130 patients from March to September 2022 were collected. The inclusion criteria of this study were as follows: (1) age > 40 years; (2) MR imaging met the Standards for Reporting Vascular changes on Neuroimaging; (3) complete clinical data, including smoking, diabetes, hypertension and hyperlipidemia. The exclusion criteria included: (1) incomplete clinical data; (2) secondary (i.e. metabolic, toxic, infectious, etc.) demyelinating lesions; (3) presence of brain tumor, trauma, cerebral hemorrhage, acute infarction, etc.; (4) severe liver, heart and kidney diseases and contraindications to MRI.

Magnetic Resonance Imaging Protocol

All patients underwent multimodal MRI, including 3D T1WI, T2WI, T2 FLAIR and SWI, on a 1.5 Tesla scanner (MAGNETOM Aera, Syngo Platform VD13A, Siemens Healthcare, Erlangen, Germany) equipped with an eight-channel phased-array head coil. The parameters of the SWI were as follows: repetition time = 54 ms; echo time = 40 ms; flip angle = 15°; slice thickness = 2 mm; intersection gap = 0.4 mm; field of view = 23 × 23 cm²; matrix = 256 × 256.

Deep Medullary Vein Score

DMVs were scored on SWI sequences. Five consecutive slices were selected from the level of the lateral ventricle in the basal ganglia till the disappearance of the lateral ventricle, including most of the DMVs. These slices were divided into 6 anatomical regions[12]: bilateral frontal, parietal and occipital lobes. Each region was scored from 0 to 3 according to the continuity of the DMV signals. When the score was 0, the DMV signals were continuous, clear and uniform. When the score was 1, the DMV signals were still continuous, but the venous signals were uneven. When the score was 2, the DMVs showed weak punctate and discontinuous signals. When the score was 3, no DMV signals were found. The DMV score was obtained by adding the scores of the 6 regions, with the range of 0 to 18. This score was independently assessed by two neuroradiologists without awareness of the clinical and imaging data (Fig. 1).

Fazekas Scores For White Matter Hyperintensities

WMHs were defined as abnormal signals in the periventricular white matter or deep white matter, with high signals in T2/FLAIR and equal or low signals in T1, which were scored 0 to 3 based on the Fazekas scale for white matter lesions [13].

White Matter Hyperintensities Volume

WMH volume was measured quantitatively on FLAIR images. The images were converted into Neuroimaging Informatics Technology Initiative (NIfTI) format using MRIcron software, and the skull was stripped from the images by using the FSL BET function. After matching 3D T1WI images to FLAIR images, the WMH volume was measured using the FSL BIANCA function. The processed images were reviewed and manually corrected by the neuroradiologists with ITK-SNAP software.

Data analysis

The categorical variables are expressed as percentages. The normally distributed data are expressed as means with standard deviation and the nonparametric data as median and interquartile range (IQR). We conducted univariate analysis to clarify the correlations of WMHs burden with basic clinical information and DMV score. Subsequently, according to the statistical results of univariate analysis, the relevant factors with P < 0.05 were analyzed by multivariate analysis. Statistical analysis was carried out using SPSS20, and P < 0.05 was considered as statistically significant.

A total of 130 patients (61 [46.9%] women), were included in this study, with an average age of 60 ± 11 years. There were 71 (54.6%) patients with hypertension, 23 (17.7%) with diabetes, 35 (26.9%) with hyperlipemia and 29 (22.3%) smokers. The median (IQR) DMV score was 4 (1, 7). The mean WMH volume was 12.34 ± 12.54 ml.

Fazekas Score For Periventricular White Matter Hyperintensity And Deep Medullary Vein Score

On univariate analysis, the PWMH Fazekas score was correlated with age, hypertension and the DMV score (P < 0.05), while not correlated with gender, smoking, diabetes or hyperlipemia (P > 0.05). According to ordinal logistic regression analysis, the PWMH Fazekas score was independently correlated with age and the DMV score (P < 0.05, Table 1).

Table 1

Univariate and multivariate analysis of Fazekas scale PWMH severity.
	Univariate analysis		Multivariate analysis
	OR (95% CI)	P-value	OR (95% CI)	P-value
Gender (F)	-0.46 (-0.97, 1.95)	0.880
Age	1.13 (1.08, 1.19)	< 0.001	1.11 (1.05, 1.16)	< 0.001
Hypertension	2.74 (1.26, 6.00)	0.011	2.56(1.83,4.01)	0.134
Diabetes	2.11 (0.62, 7.16)	0.230
Hyperlipidemia	1.06 (0.38, 3.02)	0.900
Smoker	1.71 (-1.13, 4.36)	0.267
DMV score	1.24 (1.14, 1.36)	< 0.001	1.14 (1.03, 1.33)	0.001
PWMH, periventricular white matter hyperintensity; OR, odds ratio; CI, confidence interval; DMV, deep medullary vein.

Fazekas Score For Deep White Matter Hyperintensity And Deep Medullary Vein Score

On univariate analysis, the DWMH Fazekas score was correlated with age, hypertension and the DMV score (P < 0.05), while not correlated with gender, smoking, diabetes or hyperlipemia (P > 0.05). According to ordinal logistic regression analysis, the DWMH Fazekas score was independently correlated with age, hypertension and the DMV score (P < 0.05, Table 2).

Table 2

Univariate and multivariate analysis of Fazekas scale DWMH severity.
	Univariate analysis		Multivariate analysis
	OR (95% CI)	P-value	OR (95% CI)	P-value
Gender (F)	1.09 (-0.56, 2.14)	0.847
Age	1.11 (1.07, 1.15)	< 0.001	1.07 (1.02, 1.13)	0.001
Hypertension	2.23 (1.12, 5.24)	0.019	2.19 (1.64, 3.58)	0.036
Diabetes	1.38 (0.50, 2.41)	0.316
Hyperlipidemia	0.91 (0.68, 1.60)	0.535
Smoker	1.35 (-0.60, 3.04)	0.463
DMV score	1.28 (1.17, 1.37)	< 0.001	1.18 (1.07, 1.38)	< 0.001
DWMH, deep white matter hyperintensity; OR, odds ratio; CI, confidence interval; DMV, deep medullary vein.

White Matter Hyperintensity Volume And The Deep Medullary Vein Score

On univariate analysis, the WMH volume was correlated with age and DMV score (P < 0.05), while not correlated with hypertension, gender, smoking, diabetes or hyperlipemia (P > 0.05). According to linear regression analysis, the WMH volume was independently correlated with age and the DMV score (P < 0.05, Table 3 and Fig. 2).

Table 3

Univariate and multivariate analysis of WMH volume.
	Univariate analysis		Multivariate analysis
	OR (95% CI)	P-value	OR (95% CI)	P-value
Gender(F)	-0.81(-0.96,1.15)	0.691
Age	1.64 (1.41, 1.91)	< 0.001	1.45 (1.26, 1.73)	< 0.001
Hypertension	1.18 (0.99, 1.40)	0.056
Diabetes	0.96 (0.87, 1.15)	0.670
Hyperlipidemia	0.99 (0.83, 1.18)	0.942
Smoker	0.94 (0.79, 1.12)	0.493
DMV score	1.54 (1.31, 1.81)	< 0.001	1.27 (1.07, 1.51)	0.006
WMH, white matter hyperintensity; OR, odds ratio; CI, confidence interval; DMV, deep medullary vein.

In this study, we found that the severity of both PWMHs and DWMHs was correlated with the DMV score. In addition, quantitative analysis showed that the WMH volume was also correlated with the DMV score. Therefore, we believe the DMV score is correlated with the severity of WMHs, and DMV dysfunction may be involved in the occurrence and progression of WMHs.

SWI is an MRI imaging technology that is particularly sensitive to paramagnetic materials. Venous blood in humans contains a high concentration of paramagnetic deoxyhemoglobin, so veins can be clearly displayed on SWI images. Veins with smooth walls, unobstructed drainage and a high flow rate can be displayed clearly and continuously on SWI images; otherwise, the signals are discontinuous or even disappear. Therefore, SWI is considered a reliable indicator of venous hemodynamic changes[14]. The finding in the present study that DMV dysfunction was closely related to WMH burden might be explained as follows.

Histopathology studies indicate that collagen deposition in the DMVs may be the cause of venous stenosis and occlusion. Periventricular venous collagenosis (PVC) is a degenerative disease. With vascular high-risk factors such as aging and hypertension, collagen is gradually deposited in the vascular wall in a concentric circle, leading to stenosis or even occlusion of the vascular lumen[10]. Moody et al. found abnormal tortuosity of small veins in white matter lesions and collagen deposition in the venous wall on pathology examinations [15], and they inferred that venous collagen disease might be the cause of WMHs. Other authors have agreed that PVC plays an important role in the pathogenesis of white matter injury [6, 16–17]. In this situation, DMV signal interruption or even disappearance is expected on SWI images, with corresponding increases in the DMV score.

The changes in venous hemodynamics may also contribute to WMHs. Normal cerebral blood supply is stable and pulseless, and this regulation depends on the coordination of cerebrospinal fluid and the intracerebral venous system[18]. However, vascular regulation declines with increasing age, which causes increased blood flow fluctuation. This long-term fluctuation gradually increases the pressure in the DMVs, resulting in collagen deposition in the venous wall[19]. The long-term increase in venous pressure can also lead to disordered venous reflux with a subsequent decrease in cerebral blood flow and eventually chronic hypoperfusion injury of the white matter[20–21].Moreover, increasing venous pressure leads to increased vascular permeability, which causes blood components to leak into the extravascular white matter, aggravating white matter injury[22–23]. Excessive extracellular fluid accumulation will also lead to abnormal remodeling of microveins[24], thus increasing the pressure of the venous circulation and further aggravating white matter injury. These changes in venous hemodynamics contribute to inadequate blood supply in a narrowing DMV and to relatively low oxygen extraction, which presents as decreased visibility of the DMV on imaging.

Therefore, changes related to both venous collagen disease-related DMV stenosis and occlusion, and venous hemodynamic changes that increase venous pressure and block reflux ultimately result in insufficient oxygen extraction, leading to higher DMV scores, which verifies the hypothesis that the DMV score reflects the pathogenesis of WMHs and can be correlated with the severity of the WMH burden.

We found very few cases of low DMV scores with large WMH volumes in our study. This also reminds us that WMH may be caused not only by venous dysfunction, but also by reduced cerebral blood flow, infiltration of inflammatory factors and vascular permeability changes. Therefore, a multi-aspect comprehensive analysis can better elucidate the pathogenesis of WMHs.

There are several limitations in our study. Firstly, the patients in our study only came from one center. In the future, we will select a larger number of patients from multiple centers for research. Additionally, perfusion images were lacking, and would be useful to clarify the association between WMH perfusion and DMV dysfunction. Thirdly, we did not have long-term follow-up and could not understand the dynamic changes of DMV.

In conclusion, the DMV score was highly correlated with the severity of WMHs, and DMV dysfunction may be involved in the occurrence and progression of WMHs, providing a new direction for the prevention and treatment of WMH-induced diseases.

DMV

Deep medullary vein

WMHs

White matter hyperintensities

SWI

Susceptibility weighted imaging

PWMHs

Periventricular white matter hyperintensityies

DWMHs

Deep white matter hyperintensities

PVC

Periventricular venous collagenosis.

Acknowledgements

The authors would like to thank all participants for their time and efort.

Author Contributions

HL, XL and WH conceived the project idea and wrote the manuscript. WH provided critical suggestions for the design of the experiments. HL, XL, ZX, WL, ZHHand YJ collected the imaging and clinical data. HL, XL and ZX provided the imaging analysis. XL and WH supervised the project. All authors contributed to the article and approved the submitted version.

Funding

Not applicable

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics statement

This study was approved by the Lishui Hospital of Traditional Chinese Medicine affiliated with Zhejiang Chinese Medical University. All the participants signed the informed consent. All clinical studies were conducted in accordance with the principles of the Declaration of Helsinki.

Consent for publication

Not applicable.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author details

¹Department of Radiology, Lishui Hospital of Traditional Chinese Medicine affiliated Zhejiang Chinese Medical University, Lishui323000, ZheJiang,China

²Department of Neurology, Lishui People's Hospital, Lishui323000,ZheJiang, China

³Department of Radiology, Tongde Hospital of Zhejiang Province, Hangzhou310000, ZheJiang,China

Garnier-Crussard A, Bougacha S, Wirth M, Dautricourt S, Sherif S, Landeau B, Gonneaud J,et al. White matter hyperintensity topography in Alzheimer's disease and links to cognition.Alzheimers Dement. 2022;18(3):422-433.
DeLeeuw FE, DeGroot JC, Achten E, Oudkerk M, Ramos LM, Heijboer R, et al.Prevalence of cerebral white matter lesions in elderly people:A populationbased magnetic resonance imaging study.The Rotterdam Scan Study. J Neurol Neurosurg Psychiatry.2001;70:9–14.
Wang J, Zhou Y, He Y,Li Q,Zhang W,Luo ZH,et al.Impact of different white matter hyperintensities patterns on cognition: A crosssectional and longitudinal study.Neuroimage Clin. 2022;34.102978.
Havenon AD,Sheth KN, Yeatts SD, Turan T N, Prabhakaran S.White matter hyperintensity progression is associated with incident probable dementia or mildcognitive impairment.Stroke Vasc Neurol.2022;7(4):364-366.
Prins ND, Scheltens P.White matter hyperintensities, cognitive impairment anddementia: an update.Nat RevNeurol. 2015;11(3):157-165.
Lin J, Wang D, Lan L,Fan Y.Multiple factors involved in the pathogenesis of white matter lesions. Biomed Res Int. 2017;2017:9372050.
Uh J,Yezhuvath U,Cheng Y,Lu H.In vivo vascular hallmarks of diffffuse leukoaraiosis. J.Magn. Reson. Imaging.2010;32(1):184-190.
Nan D, Cheng Y, Feng L, Zhao M, Ma D, Feng J.Potential Mechanism of venous System for Leukoaraiosis: From post-mortem to in vivo Research. Neurodegener Dis. 2019;19(3-4):101-108.
Shi Y, Thrippleton MJ, Makin SD, Marshall I, Geerlings MI, De Craen AJM, et al. Cerebral blood flow in small vessel disease: a systematic review and meta-analysis. J. Cereb. Blood Flow Metab. 2016;36(10): 1653–1667.
Xu Z, Li F, Xing D, Song H, Chen J, Duan Y, Yang B.A Novel Imaging biomarker for Cerebral Small Vessel Disease Associated With Cognitive impairment: The Deep-Medullary-Veins Score.Front Aging Neurosci .2021;13:720481.
Zhang R, Zhou Y, Yan S, Zhong G, Liu C, Jiaerken Y,et al.A Brain Region based Deep Medullary Veins Visual Score on Susceptibility Weighted Imaging.Front Aging Neurosci .2017;9:269.
Lee C,Pennington MA, Kenney C. MR evaluation of developmental venous anomalies: medullary venous anatomy of venous angiomas. AJNR Am. J.Neuroradiol.1996;17(1):61–70.
Fazekas 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(9):1683–1689.
Zeng C, Chen X, Li Y, Ouyang Y, Lv F, Rumzan R, Wang Z. Cerebral vein changes in relapsing-remitting multiple sclerosis demonstrated by three dimensional enhanced T(2)-weighted angiography at 3.0T. Eur. Radiol. 2013;23(3):869–878.
Moody DM, Brown WR, Challa VR, Anderson RL. Periventricular venous collagenosis: association with leukoaraiosis. Radiology .1995; 194(2) :469–476.
Lahna D, Schwartz DL, Woltjer R, Black SE, Roese N, Dode H, Boespflug EL,et al.Venous Collagenosis as Pathogenesis of White Matter Hyperintensity.Ann Neurol .2022;doi:10.1002/ana.26487.
Pettersen JA, Keith J, Gao F, Spence JD, Black SE. CADASIL accelerated by acute hypotension: Arterial and venous contribution to leukoaraiosis. Neurology. 2017;88(11):1077–1080.
Fulop GA, Tarantini S, Yabluchanskiy A, Molnar A, Prodan CI, Kiss T, Csipo T, et al. Role of age-related alterations of the cerebral venous circulation in the pathogenesis of vascular cognitive impairment. Am. J. Physiol.HeartCirc. Physiol. 2019;316(5):H1124–H1140.
Rivera-Rivera LA, Schubert T, Turski P, Johnson KM, Berman SE, Rowley HA, et al. Changes in intracranial venous blood flow and pulsatility in Alzheimer’s disease: a 4D flow MRI study. J. Cereb. Blood Flow Metab. 2017;37(6):2149–2158.
Schaller B.Physiology of cerebral venous blood flow: from experimental data in animals to normal function in humans.Brain Research Reviews.2004;46(3):243–260.
Schaller B, Graf R.Cerebral venous infarction: the pathophysiological concept.Cerebrovascular Diseases.2004;18(3):179–188.
Wong SM, Jansen JFA, Zhang CE, Hoff EI, Staals J, van Oostenbrugge RJ, Backes WH. Blood brain barrier impairment and hypoperfusion are linked in cerebral small vessel disease. Neurology. 2019;92(15):e1669–e1677.
Rau A, Reisert M, Kellner E, Hosp JA, Urbach H, Demerath T.Increased interstitial fluid in periventricular and deep white matter hyperintensities in Patients with suspected idiopathic normal pressure hydrocephalus. Sci Rep .2021;11(1): 19552.
Doubal FN, MacLullich AM, Ferguson KJ, Dennis MS, Wardlaw JM. Enlargedperivascular spaces on MRI are a feature of cerebral small vessel disease. Stroke. 2010; 41(3): 450-454.

No competing interests reported.

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Deep Medullary Vein Score is Associated with Burden of White Matter Hyperintensities

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Background

Methods

Patients

Magnetic Resonance Imaging Protocol

Deep Medullary Vein Score

Fazekas Scores For White Matter Hyperintensities

White Matter Hyperintensities Volume

Data analysis

Results

Fazekas Score For Periventricular White Matter Hyperintensity And Deep Medullary Vein Score

Fazekas Score For Deep White Matter Hyperintensity And Deep Medullary Vein Score

White Matter Hyperintensity Volume And The Deep Medullary Vein Score

Discussion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1