Barrier dysfunction or drainage reduction: differentiating causes of CSF protein increase

BACKGROUND Cerebrospinal fluid (CSF) protein analysis is an important element in the diagnostic chain for various central nervous system (CNS) pathologies. Among multiple existing approaches to interpreting measured protein levels, the Reiber diagram is particularly robust with respect to physiologic inter-individual variability, as it uses multiple subject-specific anchoring values. Beyond reliable identification of abnormal protein levels, the Reiber diagram has the potential to elucidate their pathophysiologic origin. In particular, both reduction of CSF drainage from the cranio-spinal space as well as blood-CNS barrier dysfunction have been suggested ρas possible causes of increased concentration of blood-derived proteins. However, there is disagreement on which of the two is the true cause. METHODS We designed two computational models to investigate the mechanisms governing protein distribution in the spinal CSF. With a one-dimensional model, we evaluated the distribution of albumin and immunoglobulin G (IgG), accounting for protein transport rates across blood-CNS barriers, CSF dynamics (including both dispersion induced by CSF pulsations and advection by mean CSF flow) and CSF drainage. Dispersion coefficients were determined a priori by computing the axisymmetric three-dimensional CSF dynamics and solute transport in a representative segment of the spinal canal. RESULTS Our models reproduce the empirically determined hyperbolic relation between albumin and IgG quotients. They indicate that variation in CSF drainage would yield a linear rather than the expected hyperbolic profile. In contrast, modelled barrier dysfunction reproduces the experimentally observed relation. CONCLUSIONS High levels of albumin identified in the Reiber diagram are more likely to originate from a barrier dysfunction than from a reduction in CSF drainage. Our in silico experiments further support the hypothesis of decreasing spinal CSF drainage in rostro-caudal direction and emphasize the physiological importance of pulsation-driven dispersion for the transport of large molecules in the CSF

Turnover rate of cerebrospinal fluid in female sheep: changes related to different light-dark cycles

<p>Abstract</p> <p>Background</p> <p>Sheep are seasonal breeders. The key factor governing seasonal changes in the reproductive activity of the ewe is increased negative feedback of estradiol at the level of the hypothalamus under long-day conditions. It has previously been demonstrated that when gonadotropin secretions are inhibited during long days, there is a higher concentration of estradiol in the cerebrospinal fluid (CSF) than during short days. This suggests an involvement of the CSF and choroid plexus in the neuroendocrine regulatory loop, but the mechanisms underlying this phenomenon remain unknown. One possible explanation of this difference in hormonal content is an effect of concentration or dilution caused by variations in CSF secretion rate. The aim of this study was thus to investigate changes in the CSF turnover rate related to light-dark cycles.</p> <p>Methods</p> <p>The turnover rate of the CSF was estimated by measuring the time taken for the recovery of intraventricular pressure (IVP) after removal of a moderate volume (0.5 to 2 ml) of CSF (slope in mmHg/min). The turnover rate was estimated three times in the same group of sheep: during a natural period of decreasing day-length corresponding to the initial period when gonadotropin activity is stimulated (SG1), during a long-day inhibitory period (IG), and finally during a short-day stimulatory period (SG2).</p> <p>Results</p> <p>The time taken and the speed of recovery of initial IVP differed between groups: 8 min 30 sec, 0.63 ± 0.07 mmHg/min(SG1), 11 min 1 sec, 0.38 ± 0.06 mmHg/min (IG) and 9 min 0 sec, 0.72 ± 0.15 mmHg/min (SG2). Time changes of IVP differed between groups (ANOVA, p < 0.005, SG1 different from IG, <it>p </it>< 0.05). The turnover rate in SG2: 183.16 ± 23.82 μl/min was not significantly different from SG1: 169. 23 ± 51.58 μl/min (Mann-Whitney test, <it>p </it>= 0.41), but was significantly different from IG: 71.33 ± 16.59 μl/min (<it>p </it>= 0.016).</p> <p>Conclusion</p> <p>This study shows that the turnover rate of CSF in ewes changes according to the light-dark cycle; it is increased during short day periods and reduced in long day periods. This phenomenon could account for differences in hormonal concentrations in the CSF in this seasonal species.</p

Method and apparatus for predicting fluid flow through a subject conduit

A model-order reduction method is described for providing a fast fluid flow simulation model of fluid flow in a conduit such as a blood vessel with stenosis. A first method is described for generating a reduced generic numerical model (20) for predicting fluid flow characteristics of fluid flowing through a subject conduit (7'). In steps 11, 12 and 13, geometric and fluid-flow parameter data are derived from CT image scans for each sampled conduit 7 in a reference set. A 3D model is generated 13 for each sampled conduit 7. The geometric parameter data, the 3D model and the fluid flow parameter data are used to generate solutions to fluid dynamics (such as the Navier-Stokes) equations for each sampled conduit 7, and a full order model is created comprising the geometric parameters data, the fluid flow parameter data and the Navier Stokes solutions. A projection-reduction based, for example, on the Proper Orthogonal Decomposition - Discrete Empirical Interpolation Method (POD-DEIM) coupled with an offline/online splitting is used for the reduced-order description of geometric parameters, fluid parameters and fluid dynamic equations. The offline phase defines the constructors of the reduced-order model which are assembled based on the weights of the coefficients (reduced order parameters) identified in the offline phase. A second method is 30 described for using the reduced order model 20 to obtain solutions to Navier Stokes equations for the blood vessel 7' of a new patient (online phase)

Stabilized reduced-order models for unsteady incompressible flows in three-dimensional parametrized domains

In this work we derive a parametric reduced-order model (ROM) for the unsteady three-dimensional in-compressible Navier-Stokes equations without additional pre-processing on the reduced-order subspaces. Concerning the high-fidelity, full-order model, we start from a streamline-upwind Petrov-Galerkin stabilized finite element discretization of the equations using P-1-P-1 elements for velocity and pressure, respectively. We rely on Galerkin projection of the discretized equations onto reduced basis subspaces for the velocity and the pressure, respectively, obtained through Proper Orthogonal Decomposition on a dataset of snapshots of the full -order model. Both nonlinear and nonaffinely parametrized algebraic operators of the reduced-order system of nonlinear equations, including the projection of the stabilization terms, are efficiently assembled exploiting the Discrete Empirical Interpolation Method (DEIM), and its matrix version (MDEIM), thus obtaining an efficient offline-online computational splitting. We apply the proposed method to (i) a two-dimensional lid-driven cavity flow problem, considering the Reynolds number as parameter, and (ii) a three-dimensional pulsatile flow in stenotic vessels characterized by geometric and physiological parameter variations. We numerically show that the projection of the stabilization terms on the reduced basis subspace and their reconstruction using (M)DEIM allows to obtain a stable ROM with coupled velocity and pressure solutions, without any need for enriching the reduced velocity space, or further stabilizing the ROM. Additionally, we demonstrate that our implementation allows to compute the ROM solution about 20 times faster than the full order model

Modeling the interaction of microbubbles: Effects of proximity, confinement, and excitation amplitude

The interaction of closely spaced microbubbles (MBs) exposed to a transient external pressure field is relevant for a variety of industrial and medical applications. We present a computational framework employing an interface tracking approach to model the transient dynamics of multiple, interacting, insonated MBs in arbitrary settings. In particular, this technique allows studying the effects of mutual proximity, confinement, and variations in excitation amplitude on the translatory motion of pairs of differently sized MBs. Domains of mutual repulsion or attraction are observed for closely spaced MBs in the investigated range of excitation frequencies. The repulsion domain widens and shifts to lower frequencies with increasing excitation pressure amplitude. When the MBs are confined in rigid tubes of decreasing diameters, we observe a shift of the translatory patterns towards lower frequencies, accompanied by a change in relative strength of the two translation modes. This effect is correlated to a decrease of the resonance frequency due to confinement which causes changes in oscillation amplitude and phase shift between the bubble vibrations. Coupling to the viscous host liquid gives rise to phenomena such as collective MB drift, non-symmetric attraction or repulsion, and reversal of translation direction. A system comprising six MBs inside a narrow tube highlights the potential of the computational framework to treat complex setups with multiple bubbles

Three-dimensional computational modeling of subject-specific cerebrospinal fluid flow in the subarachnoid space

This study aims at investigating three-dimensional subject-specific cerebrospinal fluid (CSF) dynamics in the inferior cranial space, the superior spinal subarachnoid space (SAS), and the fourth cerebral ventricle using a combination of a finite-volume computational fluid dynamics (CFD) approach and magnetic resonance imaging (MRI) experiments. An anatomically accurate 3D model of the entire SAS of a healthy volunteer was reconstructed from high resolution T2 weighted MRI data. Subject-specific pulsatile velocity boundary conditions were imposed at planes in the pontine cistern, cerebellomedullary cistern, and in the spinal subarachnoid space. Velocimetric MRI was used to measure the velocity field at these boundaries. A constant pressure boundary condition was imposed at the interface between the aqueduct of Sylvius and the fourth ventricle. The morphology of the SAS with its complex trabecula structures was taken into account through a novel porous media model with anisotropic permeability. The governing equations were solved using finite-volume CFD. We observed a total pressure variation from -42 Pa to 40 Pa within one cardiac cycle in the investigated domain. Maximum CSF velocities of about 15 cm/s occurred in the inferior section of the aqueduct, 14 cm/s in the left foramen of Luschka, and 9 cm/s in the foramen of Magendie. Flow velocities in the right foramen of Luschka were found to be significantly lower than in the left, indicating three-dimensional brain asymmetries. The flow in the cerebellomedullary cistern was found to be relatively diffusive with a peak Reynolds number (Re) = 72, while the flow in the pontine cistern was primarily convective with a peak Re =386. The net volumetric flow rate in the spinal canal was found to be negligible despite CSF oscillation with substantial amplitude with a maximum volumetric flow rate of 109 ml/min. The observed transient flow patterns indicate a compliant behavior of the cranial subarachnoid space. Still, the estimated deformations were small owing to the large parenchymal surface. We have integrated anatomic and velocimetric MRI data with computational fluid dynamics incorporating the porous SAS morphology for the subject-specific reconstruction of cerebrospinal fluid flow in the subarachnoid space. This model can be used as a basis for the development of computational tools, e.g., for the optimization of intrathecal drug delivery and computer-aided evaluation of cerebral pathologies such as syrinx development in syringomelia

Patient-specific three-dimensional simulation of LDL accumulation in a human left coronary artery in its healthy and atherosclerotic states

We calculate low-density lipoprotein (LDL) transport from blood into arterial walls in a three-dimensional patient-specific model of a human left coronary artery. The in-vivo anatomy data are obtained from computed tomography (CT) images of a patient with coronary artery disease. Models of the artery anatomy in its healthy and diseased states are derived after segmentation of the vessel lumen with and without the detected plaque, respectively. Spatial shear stress distribution at the endothelium is determined through the reconstruction of the arterial blood flow field using computational fluid dynamics (CFD). The arterial endothelium is represented by a shear-stress dependent three-pore model taking into account blood plasma and LDL passage through normal junctions, leaky junctions and the vesicular pathway. 70 mmHg and 120 mmHg of intraluminal pressures are employed as the normal and hypertensive operating pressures. By applying our model to both the healthy and diseased states, we show that the location of the plaque in the diseased state corresponds to one of the two sites with predicted high LDL concentration in the healthy state. We further show that in the diseased state, the site with high LDL concentration has shifted distal to the plaque, which is in agreement with the clinical observation that plaques generally grow in the downstream direction. We also demonstrate that hypertension leads to increased number of regions with high LDL concentration, elucidating one of the ways in which hypertension may promote atherosclerosis. Key words: LDL transport, Lipid Accumulation, Patient-Specific Simulations, Atherosclerosis

Reduced-order modeling of blood flow for noninvasive functional evaluation of coronary artery disease

We present a novel computational approach, based on a parametrized reduced-order model, for accelerating the calculation of pressure drop along blood vessels. Vessel lumina are defined by a geometric parametrization using the discrete empirical interpolation method on control points located on the surface of the vessel. Hemodynamics are then computed using a reduced-order representation of the parametrized three-dimensional unsteady Navier–Stokes and continuity equations. The reduced-order model is based on an offline–online splitting of the solution process, and on the projection of a finite volume full-order model on a low-dimensionality subspace generated by proper orthogonal decomposition of pressure and velocity fields. The algebraic operators of the hemodynamic equations are assembled efficiently during the online phase using the discrete empirical interpolation method. Our results show that with this approach calculations can be sped up by a factor of about 25 compared to the conventional full-order model, while maintaining prediction errors within the uncertainty limits of invasive clinical measurement of pressure drop. This is of importance for a clinically viable implementation of noninvasive, medical imaging-based computation of fractional flow reserve