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. Author manuscript; available in PMC: 2016 Jun 1.
Published in final edited form as: J Vasc Surg. 2014 Mar 19;61(6):1511–1520.e1. doi: 10.1016/j.jvs.2014.02.021

Patterns of Chronic Venous Insufficiency in the Dural Sinuses and Extracranial Draining Veins and Their Relationship with White Matter Hyperintensities for Patients with Parkinson's Disease

Manju Liu 1, Haibo Xu 3, Yuhui Wang 3, Yi Zhong 4, Shuang Xia 5, David Utriainen 4, Tao Wang 6, E Mark Haacke 1,2,4,7,✉
PMCID: PMC4169367  NIHMSID: NIHMS569033  PMID: 24655749

Abstract

Background

Idiopathic Parkinson's disease (IPD) remains one of those neurodegenerative diseases where the cause remains unknown. Many clinically diagnosed cases of IPD are associated with cerebrovascular disease and white matter hyperintensities (WMH). The purpose of this study was to investigate the presence of transverse sinus and extracranial venous abnormalities in IPD patients and their relationship with brain WMH.

Methods

Twenty-three IPD patients and 23 age-matched normal controls were recruited in this study. They had conventional neurological MR structural, angiographic scans and, for blood flow, quantification of the extracranial vessels. Venous structures were evaluated with 2D-time-of-flight (TOF); flow was evaluated with 2D phase contrast (PC); and WMH volume was quantified with T2 weighted fluid attenuated inversion recovery. The IPD and normal subjects were classified using both the MR TOF and PC images into four categories: 1) complete or local missing transverse sinus and internal jugular veins (IJVs) on the TOF images; 2) low flow in the transverse sinus and stenotic IJVs; 3) reduced flow in the IJVs; and 4) normal flow and no stenosis.

Results

When broken into the above 4 categories with categories 1 through 3 combined, a significant difference in the distribution of the IPD patients and normal controls (χ2=7.7, p<.01) was observed. Venous abnormalities (categories 1, 2 and 3) were seen in 57% of IPD subjects and only in 30% of controls. In IPD subjects, category type correlated with both flow abnormalities and WMH.

Conclusion

From this preliminary study, we conclude that a major fraction of IPD patients appear to have abnormal venous anatomy and flow on the left side of the brain and neck and that the flow abnormalities appear to correlate with WMH volume. Studies with a larger sample size are still needed to confirm these findings.

Introduction

Idiopathic Parkinson's disease (IPD) is the second most common neurodegenerative disease after Alzheimer’s disease, and it affects roughly 0.1% to 0.3% of the population1. The main known risk factor is age. The etiology of IPD remains unknown. Generally, Parkinson’s patients (PD) show a loss of dopaminergic neurons in the substantia nigra pars compacta, a reduction of dopamine levels in the striatum over time2 and accumulation of intraneuronal inclusions called Lewy bodies and Lewy neurites3. Although clinically PD is a motor disorder and has a good response to dopaminergic therapy, in the advanced stages of PD most of the motor disability symptoms don’t respond to dopaminergic therapy anymore3. There are also many non-motor problems, such as cognitive impairment, autonomic dysfunction, neuropsychiatric symptoms4 and fatigue5 for patients in both early and advanced stages. These findings suggest the dopaminergic system may not be the only system involved in the PD process.

It has been shown that there is an increased presence of white matter hyperintensity (WMH) in IPD patients6. The suggested causes for these deficits include ischemia and venous insufficiency from periventricular venular collagenosis7–9. WMH might cause or exacerbate motor or cognitive features of PD10 especially in the presence of gray matter vascular lesions involving the substantia nigra or striatum11, corticostriatal-thalamocortical loop disruption, damage to the interhemispheric connections of the corpus callosum10, or disruption of the subcortical afferents . If typical late-onset PD patients have a history of minor stroke, ischemic heart disease, or diabetes mellitus, they show more severe clinical features12. One group found that the severity of WMH at baseline was the best predictor of PD progression13.

In the last few years, there has been an increasing interest in the role of veins in neurodegenerative diseases14, and more attention has been paid to the extracranial veins such as the internal jugular veins (IJVs) and the azygous veins as being potential sources of venous hypertension14–16. Obstructed venous outflow in the extracranial veins was reported to correlate significantly with hypoperfusion in the brain parenchyma which could contribute to hypoxia and axonal damage17, 18. Additionally, venous hypertension in the dural sinuses inhibits the absorption of CSF through arachnoid villi. Some studies showed an association between the venous outflow disturbances with low net CSF flow in patients with MS19, 20. An important aspect of the venous abnormalities is that they are potentially treatable with percutaneous transluminal angioplasty (PTA)21. The first application of PTA in the major cerebral veins was done by Zamboni et al in 2009 in MS patients with CCSVI22. Since then, a few studies have shown improvement in neurological outcomes and/or some quality-of-life parameters in MS patients who underwent PTA22–24.

The use of MR angiography (MRA) and phase contrast flow quantification (PCFQ) for the study of the vasculature in patients with PD is a novel concept spurred, in part, by our recent work in multiple sclerosis (MS) patients25. By applying flow encoding in the PC sequence, the intensity of the phase images is directly proportional to the speed26. The phase intensity in radians is then scaled to velocity using the relationship v=phase(Venc/π), in which Venc stands for velocity encoding. By positioning these 2D PC-MRI flow slices roughly perpendicular to the vessels in the neck, flow in all major vessels can be quantified. Using these morphologic and functional MRI techniques, we can not only obtain the anatomic vascular information but also the quantitative arterial and venous blood flow.

To this end, we proposed this preliminary study with a group of 23 IPD patients to see if some of these patients have abnormal structure or flow either intracranially and extracranially. The outcomes from this study have the potential to open new doors in studying the vascular etiology of IPD.

Methods

Recruitment of patients and controls

From May, 2011 to March, 2013, 40 IPD patients were recruited and scanned. Fifteen patients did not receive a complete set of scans due to motion or termination because of patient discomfort. Two IPD patients were excluded because they were scanned after taking medication. By excluding those cases, we finally included 23 IPD cases and for this reason 23 age-matched healthy subjects were included in this study. They were all recruited and imaged at Wuhan Union Hospital, China. The patients with clinically definite IPD were diagnosed by a neurologist at Wuhan Union Hospital based on the United Kingdom PD Society Brain Bank Criteria (UKPDSBB). The patients and controls were imaged under internal review board approved protocols.

Patients who fulfilled the UKPDSBB criteria were included. However, patients who had any of the following conditions were excluded from this study: any element of the exclusion criteria listed in UKPDSBB; patients with other neurological disorders such as Huntington's disease, multiple sclerosis, normal pressure hydrocephalus; drug-induced Parkinsonism; hypoxia; arteriosclerotic disease; hypertension or diabetes because excessive WMH may show in those patients. The following conditions were excluded for normal controls: history of cardiovascular, neurological or psychiatric conditions; head trauma; hypertension; diabetes; drug or alcohol problems.

All patients and controls consented to be subjects in this study. A 3T Siemens scanner with a 16 channel head/neck coil arrangement was used for acquiring the data (Siemens TRIO). Patients underwent conventional clinical imaging as well as angiographic (arterial and venous) and flow quantification imaging. The imaging parameters for each sequence are listed in Table 1. The MRI images of the patients were acquired before taking medication.

Table 1.

MR imaging parameters for different sequences. An arterial saturation band with a width of 40mm and a separation of 10mm from the excited slice was applied during 2D TOF-MRV acquisition. A maximum encoding velocity (VENC) of 50cm/sec was used for PCFQ.

2D TOF-MRV T2-FLAIR
(21 PD patients)		 T2-FLAIR
(2 PD patients,
15 normal
controls)		 SWI PCFQ
at C6/C7
Orientation Transverse Transverse Sagittal Transverse Transverse
TR (ms) 26 8500 6000 29 42.25
TE (ms) 5.02 93 396 20 4.13
Flip Angle 60° 130° 120° 15° 25°
Resolution (mm3) 0.5×0.5×2.5 0.43×0.43×5 0.5×0.5×1 0.5×0.5×2 0.57×0.57×4
Bandwidth (Hz/pixel) 217 287 781 119 531
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Data processing and analysis

Data processing was done using our in-house software Signal Processing in Nuclear Magnetic Resonance (SPIN, Detroit, Michigan). WMH were evaluated from the 2D FLAIR images. The total volume of WMH was calculated semi-automatically by SPIN. As the FLAIR data were collected with different resolutions, all the data were normalized to 1×1×5mm3 (transverse in-plane) before doing the volume quantification. In addition, the MRI visual rating scale proposed by Scheltens et al.27 was also used for evaluating the WMH level, which takes the number, size and location of the WMH into account. The modified visual rating criteria are presented in Appendix 1. The 2D TOF data were used for evaluating the venous structures in the head and neck. A saturation band was applied to suppress the arterial flow in the 2D TOF images. The maximum intensity projection (MIP) of the whole series was generated in the coronal view from the 2D TOF coverage. The major veins of interest for the structural analysis included the transverse sinuses and the extracranial veins in the neck.

The PCFQ images were used to analyze the through-plane blood flow in the lower neck (C6/C7). Thirty time points were collected for each cardiac cycle. Cardiac gating was achieved using pulse triggering. Background muscle was used to monitor the baseline to ensure there was no bulk drift over time. We chose 50cm/sec as the Venc because this gave much better SNR for venous flow than 100cm/sec, and flow in the arteries could still be unwrapped when aliasing occurred, which was done automatically with SPIN. The vessels of interest included (Figure 1): the internal and external jugular veins (IJVs, EJVs), vertebral veins, deep cervical veins, common carotid arteries and vertebral arteries. The vessel lumen was segmented from the magnitude images (Figures 1A and 1C) semi-automatically thanks to the presence of sufficiently high contrast and copied onto the phase images (Figures 1B and 1D).

Figure 1.

Figure 1

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PCFQ images without (A, B) and with (C, D) vessel contours used for blood flow quantification. A and C are magnitude images. B and D are phase images. Vessels of interest for this case include: internal jugular veins (solid down arrows), external jugular veins (up chevron arrows), common carotid arteries (notched down arrows), and vertebral arteries (up solid arrows). In this case, the right IJV has circulatory flow (blood flowing toward the brain) as shown by the black arrow in the phase image.

The MR flow technique we use gives a pixel by pixel flow for each vessel. From each pixel we get flow as a function of the cardiac cycle. From this data, for all vessels, we can obtain integrated flow over the entire cardiac cycle (ml), volume flow rate (ml/sec) and both positive and negative volume flow rates (ml/sec). The positive volume flow rate in the IJVs was used to establish the criteria for circulatory flow. Circulatory flow represents the situation where there is simultaneously positive and negative flow present within a vein. On the PC phase images, the vein with circulatory flow shows dark and bright signal areas (Figure 1D), and based on the positive volume flow rate plot, the vein has at least 4 time points with positive flow higher than 2ml/sec and positive flow (toward the brain) present for at least half of the cardiac circle. Example plots of the key parameters over a full cardiac cycle are shown in Figure 2. Figure 2A(3) shows the positive flow plot for an example with circulatory flow. The venous flow was normalized by the total arterial flow (tA), which was defined as the sum of the flow in the common carotid arteries and vertebral arteries. In addition, the ratio between the dominant and sub-dominant IJVs (Fd/Fsd) was calculated. Dominant IJV represents the IJV which carried the most outflow, and the sub-dominant IJV was the one with less outflow.

Figure 2.

Figure 2

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A: Plots of the integrated flow (A(1)), volume flow rate (A(2)) and positive volume flow rate (A(3)) of the vessels of interest over a full cardiac cycle at C6/C7 for the same case shown in Figure 1. B: The same types of plots as shown in A but for a case without circulatory flow. Please note that comparing the positive volume flow rate between Case A and Case B, the positive flow in the right IJV with circulatory flow (arrows in A(3)) is much higher IJV without circulatory flow.

Based on the anatomic information from the 2D TOF and the quantitative IJV flow, the IPD patients and normal controls were separated into 4 categories. Examples from each of these IJV abnormalities are shown in Figure 3A–D. In defining the category types from a venous anatomy and flow perspective, flow thresholds and cross section thresholds were defined. An Fd/Fsd ratio greater than 4 was taken to be abnormal. We determined the Fd/Fsd threshold based on the receiver operator characteristic (ROC) curve analysis of the patients and controls, and 4 represented the optimal cutoff to distinguish between the two populations. The work of Seoane et al28 suggested a factor of 3:1 for dominant to sub-dominant transverse sinus flow as a risk factor. IJVs are continuation of transverse sinus, so our result is not inconsistent with their findings. The threshold of normalized sub-dominant IJV flow (Fsd/tA) was calculated from the normal controls by subtracting the standard deviation from the mean (0.24 – 0.10 = 0.14). An IJV was called stenotic if the cross-sectional area was less than 12.5mm2 for the upper neck or less than 25mm2 for the lower neck25. Based on these quantitative data and our general observations, we considered the following four categories as representative of the data.

Figure 3.

Figure 3

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A–D: Example cases from each of four categories and different abnormal IJV conditions shown in the coronal projection of the 2D TOF images. A: two cases from Category 1, A(2) Fd/Fsd = 13.85. B: two cases from Category 2, B(1) Fd/Fsd = 21.1. C: A Category 3 case, Fd/Fsd = 4.39. D: A Category 4 case, Fd/Fsd = 2.1. A(1): missing left IJV. A(2): stenotic left IJV (white arrow in A(2)). B(1): banding artifact in the left IJV. The right IJV is big and the signal is uniform, so the banding artifact in the left IJV is caused by abnormal flow, not by swallowing or respiratory artifact. B(2): uneven signal contrast in the left IJV. Compared to the right IJV, the signal in the left IJV is not uniform and has a stenosis (white arrow in B(2)).

Category 1

All the following conditions must be met: 1) one or both transverse sinuses do not appear on the TOF; 2) one or both sigmoid sinuses are not shown on the TOF; 3) absence or local absence of IJVs on the TOF; 4) Fd/Fsd at C6/C7 is greater than 4 and Fsd/tA is less than 0.14, or circulatory flow in one or both of the IJVs. In the example case shown in Figure 3A(2), there is no left transverse sinus and no left sigmoid sinus on TOF. The upper left IJV is severely stenotic (left arrow) and the top part is absent, and some other small veins reconstitute the left IJV at the lower level. In this case, Fd/Fsd = 13.85.

Category 2

All the following conditions must be met: 1) one or both transverse sinuses do not appear on the TOF; 2) sigmoid sinuses appear on the TOF; 3) there is banding in the IJVs (this represents very slow blood flow) and/or the presence of stenosis along the IJVs; 4) same as Category1 criteria(4). In the examples shown in Figure 3B(1), the left transverse sinus is missing or invisible although the left sigmoid sinus is present. A banding artifact is evident in almost the entire left IJV, which may be caused by an oscillatory or slow flow. The left sigmoid sinus and the left IJV are much narrower than the right. The presence of a transverse sinus cannot be ruled out without the use of a contrast agent, but there may be reduced flow that makes it difficult to visualize with 2D TOF. Nevertheless, this slow flow may still be manifest in the reduced flow visualized here. In this case, Fd/Fsd = 21.1.

Category 3

All the following conditions must be met: 1) sigmoid sinuses appear on the TOF; 2) same as Category1 criteria(4). In the example case shown in Figure 3C, part of the left transverse sinus appears to be missing due to slow flow or just in-plane flow, the left sigmoid sinus is visible and Fd/Fsd = 4.39.

Category 4

All the following conditions must be met: 1) the transverse sinuses and sigmoid sinuses appear on the TOF; 2) the IJVs are not stenotic; and 3) Fd/Fsd at C6/C7 is less than 4 and and Fsd/tA is bigger than 0.14. The Fd/Fsd of the example shown in Figure 3D is 2.1. Except for category 4, all the other 3 categories contain some structural or flow abnormalities. Categories 1, 2 and 3 are ranked based on the severity of the vascular structural abnormalities with the abnormality in Category 1 being the most severe.

Statistical analysis

The optimal cut-off values for Fd/Fsd, WMH volume and WMH visual score to separate IPD patients and controls were determined using a ROC analysis. An unpaired t-test was used to test the difference between the two populations for age, tA, common carotid arterial flow, vertebral arterial flow, normalized total IJV flow (tIJV/tA), normalized left IJV flow (LIJV/tA), Fd/Fsd, normalized collateral venous flow, and WMH visual score. A paired t-test was applied to test the difference between LIJV and RIJV. The categorization distribution difference between IPD patients and the normal controls was analyzed by Chi-squared test. A Wilcoxon Rank-sum test was used to test the WMH volume difference between patients and controls because of outliers.

Results

The age range of the IPD patients was 43 to 74 yr with a mean age of 62.6 yr and standard deviation of 8.3 yr. For the normal subjects, the age range was 56 to 81 yr and the age mean was 64.1 yr with a standard deviation of 6.7 yr. There is no statistical age difference between the two populations (p=0.48).

Category Analysis

The categorization distribution of the IPD patients along with the normal subjects using the criteria defined above is shown in Figure 4A. Based on the venous anatomic characteristics and asymmetric flow in the IJVs, there were clearly 4 categories with different severity of venous vasculature. However, with a small number of subjects in some categories, the distribution comparison between the patients and controls may not be applicable. We summed up the subject number in categories 1 to 3 which represent the ones with both anatomic and/or flow abnormalities and then did the comparison. For normal controls, 30% were in categories 1 to 3, and 70% in category 4; for the PD patients, 57% were in categories 1 to 3 and 43% belonged to category 4. According to the Chi-squared analysis, these two populations are significantly different (χ2=7.70, p < 0.01).

Figure 4.

Figure 4

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A. Distribution of the IPD patients and normal controls according to the defined categories. The normal population lies predominantly in category 4. Most IPD patients are distributed in categories 1to 3 which have venous structural or flow abnormalities. B. Scatter plot of dominant IJV vs. sub-dominant IJV flow at C6/C7 neck level in IPD patients (circle) and normal controls (cross). The higher black line represents the Fd/Fsd=4:1. Comparing to the normal subjects (5 out of 23 cases lie below the 4:1 ratio line), a higher percentage of the IPD patients (11 out of 23 cases: 48%) showed Fd/Fsd greater than 4:1. Moreover, all the patients with stenosis in the IJVs (solid circles) lie below the line indicating that they have more severe asymmetric flow in the IJVs. The lower black line represents Fd/Fsd=10:1. In this case, all but only one stenotic PD case falls below the 10:1 line and only one normal falls below this line. C: Scatterplot of category vs. Fd/Fsd for the 23 patients. They have good correlation (R2=0.72), which indicates that patients with higher Fd/Fsd tend to be in the category with more severe venous structural and flow problems.

Quantitative Flow Analysis

The tA for the IPD patients and normal subjects were 14.67±2.49 ml/sec and 16.25±2.1 ml/sec, respectively, and there was significant difference between them (p=0.04). The average flow in the common carotid arteries was 5.98±1.26ml/sec in IPD patients and 6.51±1.12ml/sec in controls. The blood flow in the common carotid artery was significantly larger (p=0.04) in the normal people than in the IPD patients. The average flow in the vertebral arteries was 1.39±0.65ml/sec and 1.57±0.76 in the IPD patients and normal subjects, respectively, and there was no significant between them. The tIJV and tIJV/tA for the normal controls was statistically higher than the patients (IPD: 10.85±3.29ml/s, normal controls: 13.54±2.14ml/s, p<0.01; IPD: 0.74±0.17, normal controls: 0.84±0.14, p=0.03).

The normalized flow of the right IJV was found to be significantly larger than the left IJV for both IPD patients (0.56±0.16 vs. 0.18±0.16, p<.0001) and the normal population (0.52±0.2 vs. 0.32±0.19, p=.01). The LIJV/tA was significantly higher in the normal controls (IPD: 0.18±0.16, normal controls: 0.32±0.19, p=0.01). Another measure of flow variation between the left and right IJVs is the ratio of the two IJVs, Fd/Fsd. Since Fsd can become quite small in some cases, we restricted the ratio to be 10:1 when performing the average values so as not to skew the data (this ratio was set to 10:1 for 8 PD patients and only 1 normal control). This ratio is significantly larger in IPD patients than in normal controls (p =.04). Figure 4B shows the scatterplot of dominant vs. sub-dominant IJV flow for the normal controls and IPD subjects.

According to the four vascular categories we defined, Category 1 and Category 2 contain the conditions of: IJV stenosis or missing IJVs, so we refer to patients in Category 1 and 2 as stenotic patients and patients in Category 3 and 4 as non-stenotic patients. According to Figure 4B, the stenotic patients showed much higher Fd/Fsd than the non-stenotic patients. For the tIJV and tA, there is no significant difference found between the stenotic and non-stenotic patients (tIJV: 10.2±4.4ml/s vs. 11.3±2.4ml/s, p=0.52; tA: 14.7±4.4ml/s vs. 14.6±1.7ml/s, p=0.96). The category type appears to correlate well with Fd/Fsd (Figure 4C), which indicates that the higher the Fd/Fsd ratio, the more likely the patient sits in the severe abnormal structural and flow categories.

Correlation between FLAIR WMH and categorization

Of the 23 IPD cases, 21 showed WMH. The mean, median and standard deviation of WMH volume in IPD patients were 5827mm3, 502mm3 and 10755mm3 with a range of 0 mm3 to 42177mm3. The mean, median and standard deviation of the WMH volume in the normal controls were 776mm3, 265mm3 and 1358mm3 with range of 0mm3 to 6147 mm3. There is a significant difference between the two populations according to the Wilcoxon rank-sum test (p=.04). The IPD patients and normal controls were further divided into a high WMH volume group and a low WMH volume group. The threshold for this separation was set to 500mm3, which was determined by an ROC analysis between the normal controls and PD patients. The distribution of the patients and normal controls with high and low WMH volume along with the category type is shown in Figure 5A.

Figure 5.

Figure 5

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A: The distribution of IPD patients and normal controls with low WMH volume (<500mm3) and high WMH volume (>=500mm3) for different categories. Majority of Category 4 patients and normal controls contain low WMH volume, while more of the patients in Categories 1 to 3 show high WMH volume. B: The distribution of IPD patients and normal controls with low WMH visual score (<=3) and high WMH visual score (>=4) for different categories. All the patients in Category 1 have high WMH visual scores. For patients in Categories 2 and 3, more of the patients show high WMH scores.

The WMH visual score for the IPD patients was 5.8 ± 5.3, while the score of the age-matched normal controls was 3.1 ± 3.0. There is significant difference between the two populations for the WMH visual rating (p=0.05). Moreover, all the patients in Category 1 showed a WMH visual score greater than or equal to 4. The distribution of patients with low WMH score (<=3) and high WMH score (>=4) according to their categorization is shown in Figure 5B. We chose a score of 3 as the threshold because it could best separate the patients’ categorization distribution.

Discussion

This paper is the first to study venous abnormalities on patients with IPD. The categorization defined in this work could help distinguish IPD patients from normal controls: 70% of the normal controls appeared in Category 4, while 57% of the IPD patients belonged to Category 1 through 3 (those containing venous structural and/or venous flow abnormalities). In addition, those patients with higher WMH scores tended to be in Category 1 through 3, which indicates a likely correlation between the vascular abnormalities and brain WMH. Moreover, 3 cases with Fd/Fsd less than 4 and a WMH score higher than 4 showed circulatory flow in at least one of the IJVs. The combination of structural abnormalities, asymmetric IJV flow, and abnormal hemodynamics such as circulatory flow could together be a major risk factor for developing WMH and perhaps even the disease itself.

Not all PD patients showed these flow abnormalities. However, PD is not a disease but a syndrome and there may be many sources for its development. In this light, one may consider CCSVI to be just a co-morbidity associated with PD. As for the normal controls, there is definitely an overlap with categories 1 through 3. If one used this to determine who might get PD or neurodegenerative disease, normal controls seen these three categories would be incorrectly assessed. But we have no way of telling today where they will sit 20 or 30 years from now. Our finding of the asymmetric pattern of the IJV flow is consistent with many other research publications since a majority of individuals are right dominant29. However, the flow ratio between the two IJVs (Fd/Fsd) of IPD patients is significantly higher than the normal controls. Moreover, patients with vascular stenosis have significantly higher Fd/Fsd than the non-stenotic patients.

Recently, more attention has been paid to venous collaterals30, 31. By developing a model to calculate the venous collateral flow, which is normalized to the tA, a collateral flow index (CFI) has been compared between normal controls and MS patients with CCSVI. In the supine position, the patient's CFI was reported to be significantly higher than the normal controls (61%, p<0.0002)30. As our data was also collected in the supine position with quantitative flow in the common carotids, vertebral arteries, IJVs and vertebral veins, a similar type of comparison can be made between the IPD patients and the normal controls. In fact, we too found that the normalized collateral flow in the IPD patients was statistically higher than in the normal controls (0.26±0.17 vs. 0.16±0.14, p=0.03). One might well ask, “If the flow in the stenotic IJV and/or transverse sinus can be recovered, could PTA be a potential therapy for some of these patients?”32, 33

The observation of the original criteria established by Zamboni et al. 34 are not directly observable by the methods which we have employed. While the original five CCSVI criteria mainly concern flow and stenosis patterns at the lower neck level in the IJV and vertebral veins as well as the azygous vein, the observations which we have made constitute a new criteria which has only been shown in MRI thus far29. Whether or not similar findings could be made on ultrasound is unclear and needs further study, however given that a lack of venous outflow and structural abnormalities have been observed using MRI in the transverse sinus and IJV in our group of PD subjects the authors would expect that a similar observation would be possible with ultrasound.

There are several different MRI visual rating scales for WMHs27. We chose Scheltens et al. for this study because it is quite commonly used35. In addition, according to Pantoni et al’s study36, the four MRI rating scales referenced in their paper are well correlated. Although having WMH is not a standard clinical diagnostic criterion for IPD, in this study cohort, 21 patients showed WMH, 5 had very high WMH volume (more than 10,000mm3) and of these 4 had a high visual score (greater than 9). A study did by Piccini et al.6 found a positive relationship between the WMH volume and PD symptom severity.

According to many in vivo imaging studies, 30% to 55% of PD patients show WMHs37, and some studies have suggested that WMH are more frequently present in patients with PD than in normal elderly individuals and hypertension patients6, 38. Our finding that IPD patients have higher WMH volume and visual score than normal controls is consistent with those studies. WMHs may represent either lacunar infarcts or other tissue damage in the thalamocortial motor system, a key area in the pathogenesis of Parkinsonian symptoms2. WMH are thought to relate to cardiovascular disease8. Young et al.9 reported the association between WMH and loss of vascular integrity in a neuropathological study, which suggested a vascular origin for these lesions. An MRI-pathological correlation study by Gao et al.7 demonstrated that focal and periventricular hyperintensities (PVH) often relate to venules and can increase or decrease over time. They proposed that venous collagenosis dilates the veins, making them macroscopic and causing venous insufficiency with consequent vessel leakage and vasogenic edema7. These data support our findings where patients with venous structural and/or venous flow problems are more likely to have more WMHs. Other factors could affect the WMH scores besides venous abnormalities. However, it is believed that WMH especially in aging can be caused by reductions in perfusion to the brain39.

Limitations and future directions

There are a number of limitations to this study. First, the sample size is small. Nevertheless, even with this small number, we found clear abnormal venous vascular characteristics in IPD patients with significant differences from the normal subjects. This suggests that there is a prevalence of vascular abnormalities in patients with IPD. With a larger number of patients, it would be possible to study the correlation between the vascular problems with IPD sub-types. The second limitation is the lack of 3D time-resolved contrast-enhanced arteriovenography (3D CE MRAV) scans which could offer the advantage of acquiring separate arterial and venous phases and better delineate the dural sinuses. Even though 2D TOF causes reduced signal from in-plane flow, for patients with fast enough flow in the transverse sinuses (such as seen in many cases on the right hand side), we still expect some signal representing the anatomy of the vessels. Third, collecting flow in the transverse sinuses would also be useful to understand the fluid dynamics of the evidently abnormal venous flow on the left side.

Conclusions

In this paper, we have shown several key findings. First, that WMH correlate with venous jugular flow. And second, there are venous vascular abnormalities in patients with IPD that tend not to be present in the normal population. More specifically, we found that 90% of the patients showed WMHs and that 57% of the patients had structural and/or venous flow abnormalities in the transverse sinus, sigmoid sinus and IJVs. Nevertheless, this is a preliminary work and further studies are required to confirm these conclusions. These findings may prove to be an important imaging means to sub-classify IPD patients, to enhance our understanding of the etiology of IPD and, perhaps, even lead to the development of new treatment regimens.

Clinical Relevance.

MRI is a powerful tool to study the venous structural abnormalities, quantify blood flow and quantify the number and volume of brain white matter lesions. Correlations between the flow abnormalities and white matter hyperintensities with patient’s symptoms were found in this study. These findings may prove to be an important means to sub-classify IPD patients. Studying those IPD patients with venous structural and flow abnormalities may help understand more about the etiology or progression of the disease.

Acknowledgements

This work was supported, in part, by National Natural Science Foundation of China (grant number: 81171386 and 30770623) and the National Heart, Lung, And Blood Institute of the National Institutes of Health (award number R42HL112580). We would like to thank Jing Jiang, Ying Wang and Wei Feng for their software development supports; Yanwei Miao and Jie Yang for the data analysis discussion; Hong Li, Chenjun Zhu and Xin Sun for helping collect MRI data and data processing.

APPENDIX 1

Modified visual rating scale for WMH proposed by Scheltens et al. [40, 49]

WMH in periventricular and deep white matter are scored separately as are the scores for different lobes (the basal ganglia are also included in this assessment). The maximum score for the former is 6 and the latter is 30 as outlined below. Note that the latter has five separate regions, the frontal lobe, parietal lobe, occipital lobe, temporal lobe and basal ganglia.

Periventricular Hyperintensities (Minimum, 0; Maximum 6)

  • No caps in the occipital lobe ---- 0

  • Caps in the occipital lobe less or equal than 5mm ---- 1

  • Caps in the occipital lobe greater or equal than 6mm and less than 10mm ---- 2

  • No caps in the frontal lobe ---- 0

  • Caps in the frontal lobe less or equal than 5mm ---- 1

  • Caps in the frontal lobe greater or equal than 6mm and less than 10mm ---- 2

  • No bands at the lateral ventricles ---- 0

  • Bands at the lateral ventricles less or equal than 5mm ---- 1

  • Bands at the lateral ventricles greater or equal than 6mm and less than 10mm ---- 2

The same criteria is applied for the frontal lobe, parietal lobe, occipital lobe, temporal lobe and basal ganglia

Deep White Matter Hyperintensities (Minimum, 0; Maximum, 30)

  • No lesion in the region being studied ---- 0

  • Number of the lesions less/equal than 5, size of the lesions less/equal than 3mm ---- 1

  • Number of the lesions more/equal than 6, size of the lesions less/equal than 3mm ---- 2

  • Number of the lesions less/equal than 5, size of the lesions greater/equal than 4mm and less/equal than 10mm ---- 3

  • Number of the lesions more/equal than 6, size of the lesions greater/equal than 4mm and less/equal than 10mm ---- 4

  • At least one lesion larger than 11mm ---- 5

    Lesions are confluent ---- 6

Footnotes

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Contributor Information

Manju Liu, Email: manjuliu2008@gmail.com.

Haibo Xu, Email: xuhaibo1120@hotmail.com.

Yuhui Wang, Email: wangyuhui1983@163.com.

Yi Zhong, Email: neuzhong@gmail.com.

Shuang Xia, Email: xiashuangimaging@gmail.com.

David Utriainen, Email: davidutriainen@gmail.com.

Tao Wang, Email: wangtaowh@hust.edu.cn.

E. Mark Haacke, Email: nmrimaging@aol.com.

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