Research Article | Open Access
Francesco Sisini, Eleuterio Toro, Mauro Gambaccini, Paolo Zamboni, "The Oscillating Component of the Internal Jugular Vein Flow: The Overlooked Element of Cerebral Circulation", Behavioural Neurology, vol. 2015, Article ID 170756, 9 pages, 2015. https://doi.org/10.1155/2015/170756
The Oscillating Component of the Internal Jugular Vein Flow: The Overlooked Element of Cerebral Circulation
The jugular venous pulse (JVP) provides valuable information about cardiac haemodynamics and filling pressures and is an indirect estimate of the central venous pressure (CVP). Recently it has been proven that JVP can be obtained by measuring the cross-sectional area (CSA) of the IJV on each sonogram of an ultrasound B-mode sonogram sequence. It has also been proven that during its pulsation the IJV is distended and hence that the pressure gradient drives the IJV haemodynamics. If this is true, then it will imply the following: (i) the blood velocity in the IJV is a periodic function of the time with period equal to the cardiac period and (ii) the instantaneous blood velocity is given by a time function that can be derived from a flow-dynamics theory that uses the instantaneous pressure gradient as a parameter. The aim of the present study is to confirm the hypothesis that JVP regulates the IJV blood flow and that pressure waves are transmitted from the heart toward the brain through the IJV wall.
The evaluation of the jugular venous pulse (JVP), defined as the movement of expansion of the jugular vein due to changes in pressure in the right atrium, provides valuable information about cardiac haemodynamics and filling pressures , characteristic wave patterns pathognomic of cardiac diseases , and an indirect estimate of the central venous pressure (CVP). The JVP evaluation can be useful in the diagnosis and/or prognosis of many heart diseases . Such a pulse consists of three positive waves (, , and ) and two descents, defined, respectively, as and . Wave corresponds to the atrial contraction and is synchronized with the wave of the electrocardiogram (ECG). Descent corresponds to the lowering of the atrioventricular septum, interrupted by a small positive wave in relation to the closure of the tricuspid valve; the third wave corresponds to the cardiac systole and is followed by the descent which corresponds to the opening of the tricuspid valve.
In a recent paper  we proved that JVP can be obtained by a simple ultrasound (US) B-mode investigation that consists in measuring the cross-sectional area (CSA) of the IJV on each sonogram of video clip acquired in the transversal plane. In that paper, we acquired the time-dependent CSA datasets of three healthy subjects and then calculated the autocorrelation function of the datasets to show that they were periodic and finally we showed that their wave form presented the same , , and waves as the JVP. Our study gave a quantification of the IJV CSA variation during the cardiac cycle. We also have seen that the IJV perimeter, measured on each sonogram of the video clip, was correlating with the IJV CSA (). On this point, we believe that it is desirable to have a confirmation of this finding also using a different imaging modality. However, a direct explanation of this finding is that, when in supine position, the pulsation of the IJV is a distension of its wall. This result is very important because it means that there is a time varying transmural pressure whose effect is clearly visible and cannot be neglected; in fact, large changes in transmural pressure are required to induce CSA deformation accompanied by a stretching of the wall [5, 6]. If this is true then the following points are also true: (i) the blood velocity in the IJV is a periodic function of the time with period equal to the cardiac period and (ii) the instantaneous blood velocity is given by a time function that can be derived from a flow-dynamics theory that uses the instantaneous pressure gradient as a parameter, for example, the linear Womersley solution of the Navier-Stokes equations .
It is worth noting that, in an elastic medium as is the IJV wall, an impulse that gives rise to a wall distension will be propagated following the wave equation of d’Alembert. The time periodic variation of the CSA and hence of pressure measured for the IJV is hence related to the propagation of pressure waves propagating from the heart toward the brain. Thus, in supine position, when the IJV is distended, the blood flow is governed by an oscillating pressure gradient  and pressure waves generated by the cardiac revolution are then transmitted from the heart toward the brain (see Figure 1).
The aim of the present study is to verify such a claim by comparing the blood velocity assessed by the current US Doppler method with the blood velocity calculated using the linear Womersley equations. The correspondence between them will confirm the hypothesis that JVP regulates the IJV blood flow and that theoretically pressure waves are transmitted from the heart toward the brain through the IJV wall.
2.1. Subjects Scanning and Protocol
We have chosen two subjects, labeled as , (a -year-old female and a -year-old male, resp., with no history of cardiovascular, hepatic, gastrointestinal, renal, and cerebral diseases. ECD screening for CCSVI was completely negative [8–10]), out of our database of US scan of the neck by using My-Lab 70 x-vision system (ESAOTE, Genoa, Italy) with a linear array probe 7.5–11 MHz and Vivid-q ultrasound system (GE Medical Systems Ultrasound, Horten, Norway) equipped with a linear probe (L11-812 MHz). They were chosen for this study because they were representative of a small and normal IJV CSA, respectively (with respect to the average diameter value of about 1.3 cm reported in ).
The assessment of the jugular CSA was performed using a B-mode scan in the transverse plane of the right IJV at c5/c6 level. Such region corresponds to the segment close to the junction of the IJV with the subclavian vein. For each subject we recorded first a transversal video clip of the CSA (i.e., a sonogram sequence) and immediately after a velocity spectral Doppler trace, both for at least four cardiac cycles. The trace was obtained selecting the “mean velocity mode,” which automatically produces a weighted average of the velocity over the whole US reflected spectrum. This results in a green trace (see Figure 3) that corresponds to the blood velocity averaged across the sample volume. The latter is selected to roughly correspond to the IJV lumen.
This study was conducted in accordance with the Ethical Standards of the Committee on Human Experimentation of the Azienda Ospedaliera Universitaria di Ferrara. All the volunteers signed an informed consent form.
2.2. Pulsed Flow-Dynamics Assumptions
Following the original paper of Womersley , we denote as the instantaneous blood velocity in direction and averaged over the IJV CSA. The velocity is calculated, at any time , using the Womersley equations, for which the time-dependent pressure gradient is required. The internal pressure was here assumed to be linearly related to the [6, 12, 13] as follows:where is a convenient additive constant and is the IJV compliance for unit of length defined as Given a pressure wave velocity , the wave equation states that
Substituting from (1) into (3), the pressure gradient is obtained. The minus sign on the right side of (3) represents a wave moving toward the positive direction of the -axis while the plus sign represents a wave moving toward the negative . In order to simplify the interpretation of the results, in this paper we chose to assume the positive direction of the from the head toward the heart (see Figure 1). The pressure waves are generated from the heart pulsation and propagate toward the jugular vein in the negative direction; for this reason they are represented by positive sign in (3). In this work we neglect the effects of reflected waves propagating from the brain toward the heart.
Moreover, the Womersley equations only give the instantaneous oscillating part of the blood velocity. The net instantaneous value of the blood velocity is given by summing the steady velocity with the oscillating velocity. The steady component of the velocity can be obtained by the Poiseuille law once the pressure gradient between the head and the heart is known. In this study we are not interested in a full assessment of the mean velocity so only the oscillating velocity is considered and analyzed.
Having assumed a positive from the head to the heart, the net blood velocity is positive (see Figure 1).
2.3. IJV Blood Velocity Assessment: The Oscillating Component
For each subject, the CSA dataset, representing the instantaneous value of the CSA during a cardiac cycle, was derived from the acquired video clip using the semiautomatic algorithm described in . The period of the IJV pulsation was obtained by its discrete Fourier transform calculated using the Grace software . Then we write so that runs from to during one pulse period. The mean CSA, denoted by , over a period is calculated. The Fourier coefficients of pressure gradient up to the tenth harmonic are calculated. These coefficients are then converted to modulus and phase . The blood velocity is then calculated from Equation in  by substituting the pressure gradient expressed as a Fourier series. The blood velocity is given by the summation of ten terms as in Equation in :where we have substituted the term in  by , which represents the time-dependent vein CSA. The parameter is the blood viscosity (0.04 Pa), that runs from 1 to 10 is the order of the Fourier coefficient, and and are derived from the Bessel function and tabulated in . Finally, is the well-known Womersley number given bywhere is the blood density (1.05 g/mL) and .
2.4. Spectral Doppler Averaged Velocity
Below we describe the procedure adopted to obtain a dataset sampled from the US Doppler velocity trace. Common commercial US systems do not allow the Doppler dataset to be exported; for this reason, in this work, the mean velocity dataset is obtained by digitally processing the image shot of the Doppler trace. The mean blood velocity is represented as a line overlapping with the spectral Doppler trace (Figure 3). The line is composed of pixels. An in-house developed procedure identifies the pixel belonging to the line by its RGB values. The coordinates and of each pixel were automatically recorded by the procedure, where is an index going from 1 to . The function has been obtained from and values using the following procedure:where is the index for the pixels and is the distance in pixels between two time divisions (separated by 1 s) of the time axes while is the distance in pixels between the 0 and 100 cm/s divisions, measured along the -axis. Combining the two expressions in (6) produces
The sampled Doppler velocity dataset is then plotted together with the calculated Womersley averaged velocity (). The instant of time corresponding to the maximum value of velocity was detected for both the Doppler and and it was used as reference marker to overlap the two plots.
2.4.1. Detailed Calculation
The detailed algorithm of all the calculations needed to produce the following results is reported in the appendix.
3.1. IJV Blood Velocity Assessment
Each result is produced for both subjects and and presented in the text following such order. Each figure starting from Figure 2 is divided into top and bottom for subjects and , respectively. The main numerical results are summarized in Table 1.
The US-JVP are plotted for both subjects in Figure 2; their repetition periods are 0.9 and 0.92 s for subjects and , respectively; the time averaged cross-sectional areas are 0.25 and 1.25 cm2. Both subjects show clearly visible and waves, while the one is barely detectable.
The time derivatives of the internal pressure and pressure gradient were obtained from the CSA dataset as described in the appendix.
The mean blood velocity was calculated following (4) and is plotted together with the CSA in Figure 4. It is worth noting that for both subjects the blood velocity changes inversely with respect to the IJV CSA; that is, when the IJV CSA goes toward its maximum the blood velocity goes toward its minimum and vice versa.
In Figure 4, is plotted together with the pressure gradient .
3.2. Spectral Doppler Averaged Velocity
Figure 3 depicts the spectral Doppler velocity traces. The time periods of such traces turned out to be 0.95 and 0.88 s for subjects and , respectively (see Table 1). Such values are very close to that of the US-JVP period; this is an expected result since the spectral Doppler trace was acquired as soon as the B-mode IJV investigation was completed so as to avoid changes in physiological conditions.
It is evident that the traces of both subjects and show a “two-wave” profile. Figure 5 depicts the blood velocity sampled from the spectral Doppler versus the calculated ones for subjects and , respectively. For both subjects, sampled and calculated velocity values show the same time dependence or, in other words, the same wave form. A reasonable agreement of their amplitudes was obtained by requiring a CVP up to 10 mmHg (see Appendix A.5 for details).
4.1. IJV Blood Velocity Assessment
The main result of the present study is the well apparent relationship between the blood velocity in the IJV measured by the current Doppler system and the same parameter derived from its pulse. For both subjects the presence of the two positive peak waves, “” and “” waves, respectively, in the US-JVP (the CSA diagram), generates two descents in the assessed velocity time diagram within the same cardiac period. Although the relationship between the JVP and the variation of pressure in the heart has been known since 1902 , until now, this finding has not been taken into consideration for assessing the brain outflow. Currently, the Doppler flow quantification is provided by the product of the time average velocity of flow and the CSA [16–19]. This quantitative approach currently provides an accepted objective criterion for assessing the flow rate. However, despite the pulsating variation during the cardiac cycle of the IJV CSA, the flow, according to this method, is calculated by using just a single CSA value. This raises the question as to whether overlooking the pulsatile variation of the IJV CSA may affect the US evaluation of cerebral venous return in the clinical setting. In addition, the results of the present study further corroborate the role of the mechanical propagation of the cardiac contraction in the upward direction. This wave propagation is not haemodynamically negligible because the present study clearly demonstrates how this phenomenon influences blood flow velocity. There are several studies which suggest that flow waves directed up towards the brain can contribute to neurodegenerative diseases by affecting cerebrospinal fluid absorption and/or brain perfusion, such as Alzheimer [8, 9, 20].
Our data clearly demonstrates how the assumption of the Poiseuille theorem in the case of pulsatile flow may expose the flow assessment to error. We focused our study on US but of course the MRV methodology used for IJV flow assessment could also be affected by the same incorrect assumption. We know that the distal segment of the IJV is characterized by significant CSA variation over time, an effect linked with the transmission of pressure waves from the right atrium. This phenomenon, very well known as JVP, is strongly connected with important regulators of the cardiovascular function: degree of filling, heart pulsation, and capacity of emptying of the IJV.
Moreover, the positive waves “,” “,” and “” clearly show a brain directed propagation of pressure waves with significance never investigated, as yet. We know that propagation is greater when the degree of filling of the venous system is higher and/or when the heart function is compromised. Acute symptoms in the latter rapidly appear in the pulmonary function but we have never investigated if this may have significance for cognitive or neurological decline already described in this particular category of patients. We speculate that cardiac, carotid, and jugular signals should be synchronized and used to assess the circulatory axis between the heart and brain, with significant changes in current clinical practice, in neuroimaging as well.
The results reported here can be reproduced using MRV because CSA and blood velocity can be measured at each instant of time as we did.
4.2. Pulsed Flow-Dynamics Assumptions
The Womersley approach presented in  derives the blood velocity from the pressure gradient assuming a rigid tube as a model. Such a model accounts for the pressure gradient time dependence but neglects the movement of the tube wall in the longitudinal and lateral direction. In later papers, Womersley made corrections accounting for such variability [13, 21] resulting in a new equation having the same form as the former (see Equation in ) and producing a flow about 10% higher in magnitude. In this paper we choose to follow the simpler approach presented in  for three reasons: the cited paper is widely known and widely adopted, the presented analytical model can be easily implemented making our results easily reproducible, and such a model results in an easily treatable linear equation and its results also hold for pulsing flow in an elastic tube [13, 21].
However, we encourage the adoption of more sophisticated models when the blood flow has to be calculated for clinical reasons (see, e.g., ).
The assumption of simple linear or quadratic relationships between CSA and pressure seems to be correct in the present case. In fact, considering the well-known “tube-law” , the CSA/perimeter correlation means that IJV pulsation lies in the curve region (i) where the transmural pressure is greater than the buckling pressure (see Figure in ); hence a linear relationship between pressure and CSA is straightforward. On the other hand, the elliptical shape of the IJV during its pulsation could suggest that the transmural pressure is below the buckling value; hence region (ii) could also be affected by the pulsation. Of course this finding needs to be further investigated.
4.3. Implication for Pressure Wave
The oscillating nature of the IJV is potentially relevant in the understanding of the relationship between brain drainage and several neurological disorders [22–29]. In particular, the effect of pulsatile flow has been recently hypothesized to be associated with retrograde hypertension transmitted from the IJV which, presumably, underlies transient global amnesia  and perhaps other neurological disorders, as recently hypothesized [30–32].
A. Velocity Calculation Detailed Procedure
A.1. IJV Elasticity Model
In this paragraph we present an expression for the vein volumetric compliance , that is, the ratio of volume variation with respect to pressure variation , and consequently for the compliance per unit of length . We assumethat implies the idea that the vein may change in radius but not in length. From the definition of volumetric compliance it follows thatThe coefficient of functional elasticity is defined asthen, using the definitions given in (A.1), we haveFrom the two definitions of given in (A.2) and (A.4) it follows thatFor cylindrical geometry, the relationship between the Young Modulus and can be assumed to be where is the vein wall thickness. The pressure wave velocity , that is, the velocity of propagation of the JVP in the jugular vein, is calculated following the Moens-Korteweg equation:which for the assumed relationship between and is dependent only on the parameters and :The central venous pressure (CVP), measured close to the IJV, is known to vary up to 5 mmHg during the cardiac cycle. Since during a cardiac cycle is given bythenEquation (A.10) can also be used with (A.5) to determine which is given as
A.2. Parameter Data
A.3. Input Data
From the US transversal scan of the IJV the following data represents the input for the velocity calculation procedure:(i)The US-JVP, consisting in a dataset with running from 1 to , where is the number of sonograms in the US video clip with each element of the dataset representing the IJV area measurement on the th sonogram, in pixels.(ii)The US video clip duration () in seconds.(iii)The US sonogram pixel dimension () in cm.
From the longitudinal US Doppler trace, consider the following:(i)The space averaged velocity time diagram, consisting in a dataset in pixels.(ii)The distance () between two divisions in the time axis of the Doppler trace.
A.4. Cross-Sectional Area
(1) dataset discrete Fourier transform (DFT) is operated (e.g., using Grace software).(2)The sonogram repetition period () of the dataset is obtained by the maximum of the DFT produced at .(3)The sonogram time interval () is calculated as .(4)The dataset time repetition period is then calculated as .(5)The dataset is multiplied by the pixel area () in order for each element of the dataset to represent the IJV area measurement on the th sonogram, in cm2.(6)The differential CSA dataset is obtained as .(7)The discrete partial time derivative dataset is obtained dividing the by .(8)Average CSA is calculated as .(9)The average vein diameter is calculated as .
The following steps focus on the pressure dataset calculation:(1)The compliance for unit of length is calculated as , where is a convenient parameter representing the maximum value reached by the CVP during the cardiac cycle. A typical value is 5 mmHg.(2)The pulse wave velocity (, lower case) is calculated as .(3)Time derivative pressure dataset, in unit of mmHg, is given by .(4)It is then converted into Pa by multiplying the dataset by 133.322 Pa/mmHg.(5)The pressure gradient along the -axis direction is calculated as .(6)The parameter is defined as .(7)The pressure gradient is then defined as .
A.6. Pressure Gradient Fourier Series
The pressure gradient is then decomposed in its Fourier terms as
The series coefficients are calculated as follows:(1).(2).(3).(4).(5).
A.7. Blood Velocity Fourier Series
The blood velocity expression is obtained by the pressure gradient expression as(1)The parameter for each harmonic is calculated as .(2)The coefficients and are tabulated in  for each value from 0 to 10. For the asymptotic expression was used .
|CCSVI:||Chronic venous cerebrospinal insufficiency|
|CVP:||Central venous pressure|
|DFT:||Discrete Fourier transform|
|ECD:||Echo color Doppler|
|IJV:||Internal jugular vein|
|JVP:||Jugular venous pulse|
|MRV:||Magnetic resonance venography|
|:||Distance in pixels between two time divisions|
|:||Distance in pixels between the 0 and 100 cm/s divisions|
|RGB:||Red Green Blue|
|:||Sonogram repetition period|
|:||Pressure waves velocity|
|:||Compliance for unit of length|
|:||Sonogram time interval|
|:||Coefficient of functional elasticity|
|:||Vein wall thickness|
|:||Blood flow velocity averaged over the IJV CSA.|
Conflict of Interests
The authors declare that they have no conflict of interests.
Francesco Sisini conceived the study, developed the algorithm for the data analysis, analyzed the US studies, performed the data analysis, and wrote the paper from the scratch. Eleuterio Toro wrote the paper and supervised all the mathematical aspects of the research. Mauro Gambaccini wrote the paper and critically revised it. Paolo Zamboni analyzed the jugular diagram, wrote the paper, and supervised all the medical aspects of the research.
This study was partially supported by the Italian Ministry of Education, University and Research (MIUR Programme PRIN 2010-2011), Grant no. 2010XE5L2R, and thanks are due to Jennifer Cowd, Xpress Translations Ltd. (email@example.com) for the proofreading of the paper.
- G. Naveen and G. Nitish, “Jugular venous pulse: an appraisal,” Journal, Indian Academy of Clinical Medicine, vol. 1, pp. 260–269, 2000.
- J. M. S. Chua Chiaco, N. I. Parikh, and D. J. Fergusson, “The jugular venous pressure revisited,” Cleveland Clinic Journal of Medicine, vol. 80, no. 10, pp. 638–644, 2013.
- M. H. Drazner, J. E. Rame, L. W. Stevenson, and D. L. Dries, “Prognostic importance of elevated jugular venous pressure and a third heart sound in patients with heart failure,” The New England Journal of Medicine, vol. 345, no. 8, pp. 574–581, 2001.
- F. Sisini, M. Tessari, G. Gadda et al., “An ultrasonographic technique to assess the jugular venous pulse: a proof of concept,” Ultrasound in Medicine and Biology, vol. 41, no. 5, pp. 1334–1341, 2015.
- M. Heil and O. E. Jensen, “Flows in deformable tubes and channels,” in Flow Past Highly Compliant Boundaries and in Collapsible Tubes, P. W. Carpenter and T. Pedley, Eds., vol. 72 of Fluid Mechanics and Its Applications, pp. 15–49, Springer, Dordrecht, The Netherlands, 2003.
- Y. C. Fung, Biomechanics: Circulation, Springer, New York, NY, USA, 1997.
- J. R. Womersley, “Method for the calculation of velocity, rate of flow and viscous drag in arteries when the pressure gradient is known,” The Journal of Physiology, vol. 127, no. 3, pp. 553–563, 1955.
- P. Zamboni, S. Morovic, E. Menegatti et al., “Screening for chronic cerebrospinal venous insufficiency (CCSVI) using ultrasound—recommendations for a protocol,” International Angiology, vol. 30, pp. 571–597, 2011.
- P. Zamboni, E. Menegatti, B. Weinstock-Guttman et al., “Hypoperfusion of brain parenchyma is associated with the severity of chronic cerebrospinal venous insufficiency in patients with multiple sclerosis: a cross-sectional preliminary report,” BMC Medicine, vol. 9, article 22, 2011.
- R. Zivadinov, S. Bastianello, M. D. Dake et al., “Recommendations for multimodal noninvasive and invasive screening for detection of extracranial venous abnormalities indicative of chronic cerebrospinal venous insufficiency: a position statement of the International Society for Neurovascular Disease,” Journal of Vascular and Interventional Radiology, vol. 25, no. 11, pp. 1785.e17–1794.e17, 2014.
- S. Furukawa, T. Nakagawa, I. Sakaguchi, and K. Nishi, “The diameter of the internal jugular vein studied by autopsy,” Romanian Journal of Legal Medicine, vol. 18, no. 2, pp. 125–128, 2010.
- D. Bessems, M. Rutten, and F. Van De Vosse, “A wave propagation model of blood flow in large vessels using an approximate velocity profile function,” Journal of Fluid Mechanics, vol. 580, pp. 145–168, 2007.
- J. R. Womersley, “Oscillatory flow in arteries: the constrained elastic tube as a model of arterial flow and pulse transmission,” Physics in Medicine and Biology, vol. 2, no. 2, pp. 178–187, 1957.
- Plasma Gate, “Plasma Gate developers community,” Grace, 2014, http://plasma-gate.weizmann.ac.il/Grace/.
- J. Mackenzie, The Study of the Pulse, Arterial, Venous, and Hepatic and of the Movements of the Heart, Young J. Pentland, Edinburgh, Scotland, 1902.
- P. Zamboni, F. Sisini, E. Menegatti et al., “An ultrasound model to calculate the brain blood outflow through collateral vessels: a pilot study,” BMC Neurology, vol. 13, article 81, 2013.
- F. Doepp, S. J. Schreiber, T. von Münster, J. Rademacher, R. Klingebiel, and J. M. Valdueza, “How does the blood leave the brain? A systematic ultrasound analysis of cerebral venous drainage patterns,” Neuroradiology, vol. 46, no. 7, pp. 565–570, 2004.
- B. Chambers, J. Chambers, L. Churilov, H. Cameron, and R. Macdonell, “Internal jugular and vertebral vein volume flow in patients with clinically isolated syndrome or mild multiple sclerosis and healthy controls: results from a prospective sonographer-blinded study,” Phlebology, vol. 29, no. 8, pp. 528–535, 2014.
- F. Kantarci, S. Albayram, N. O. Demirci et al., “Chronic cerebrospinal venous insufficiency: does ultrasound really distinguish multiple sclerosis subjects from healthy controls?” European Radiology, vol. 22, no. 5, pp. 970–979, 2012.
- C. Beggs, C.-P. Chung, N. Bergsland et al., “Jugular venous reflux and brain parenchyma volumes in elderly patients with mild cognitive impairment and Alzheimer's disease,” BMC Neurology, vol. 13, article 157, 2013.
- J. R. Womersley, “XXIV. Oscillatory motion of a viscous liquid in a thin-walled elastic tube—I: the linear approximation for long waves,” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science Series 7, vol. 46, no. 373, pp. 199–221, 1955.
- C.-P. Chung, A.-C. Chao, H.-Y. Hsu, S.-J. Lin, and H.-H. Hu, “Decreased jugular venous distensibility in migraine,” Ultrasound in Medicine and Biology, vol. 36, no. 1, pp. 11–16, 2010.
- C.-H. Chou, A.-C. Chao, S.-R. Lu, H.-H. Hu, and S.-J. Wang, “Cephalic venous congestion aggravates only migraine-type headaches,” Cephalalgia, vol. 24, no. 11, pp. 973–979, 2004.
- F. Doepp, S. J. Schreiber, J. P. Dreier, K. M. Einhäupl, and J. M. Valdueza, “Migraine aggravation caused by cephalic venous congestion,” Headache, vol. 43, no. 2, pp. 96–98, 2003.
- C.-P. Chung, H.-Y. Hsu, A.-C. Chao, W.-J. Wong, W.-Y. Sheng, and H.-H. Hu, “Flow volume in the jugular vein and related hemodynamics in the branches of the jugular vein,” Ultrasound in Medicine and Biology, vol. 33, no. 4, pp. 500–505, 2007.
- M. Liu, H. Xu, Y. Wang et al., “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,” Journal of Vascular Surgery, vol. 61, no. 6, pp. 1511.e1–1520.e1, 2015.
- R. Zivadinov and C.-P. Chung, “Potential involvement of the extracranial venous system in central nervous system disorders and aging,” BMC Medicine, vol. 11, article 260, 2013.
- R. Zivadinov, “Is there a link between the extracranial venous system and central nervous system pathology?” BMC Medicine, vol. 11, article 259, 2013.
- Y.-C. Tsao, C.-P. Chung, H.-Y. Hsu et al., “Collapsed Jugular Vein and abnormal cerebral blood flow changes in patients of Panic Disorder,” Journal of Psychiatric Research, vol. 58, pp. 155–160, 2014.
- B. H. J. Juurlink, “Is there a pulse wave encephalopathy component to multiple sclerosis?” Current Neurovascular Research, vol. 12, no. 2, pp. 199–209, 2015.
- T. W. Tucker, “A physics link between venous stenosis and multiple sclerosis,” Medical Hypotheses, vol. 77, no. 6, pp. 1074–1078, 2011.
- E. F. Toro, “Brain venous haemodynamics, neurological diseases and mathematical modelling. A review,” Applied Mathematics and Computation, vol. 272, part 2, pp. 542–579, 2016.
- J. H. Clark, “The elasticity of veins,” American Journal of Physiology, vol. 105, pp. 418–427, 1933.
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