Session Z06: Cerebrospinal Fluid and Lymphatic Flow II

Rotational impacts induced cerebrospinal fluid pressurization

Traumatic brain injury (TBI) is a major cause of death and disability in the United States. While TBI has been recognized as a real issue, researchers are still trying to fully understand how the brain works. Especially, the critical role of the cerebrospinal fluid in transmitting and mitigating impacts on the head remains unclear. In this study, a lifelike head surrogate, including a soft, artificial brain matter, a transparent, rigid skull, and the CSF in between, has been developed. The surrogate was exposed to rotational impacts in the sagittal, horizontal, or corona planes. The localized deformation of the brain matter was characterized using the Digital Image Correlation approach. The head surrogate was equipped with multiple pressure sensors to measure the dynamic pressure response of the CSF.  The results show a strong correlation of CSF pressurization with the motion/deformation of the brain matter and the resulting changes in the gap thickness of the subarachnoid space. As the first study of its kind, the paper presents the first quantified measurements of the CSF pressurization during the concussion process. This study improves our understanding of the TBI mechanism due to rotational impacts and lays the foundation for further study on this topic.

    

-title- Effect of contraction wave shape in the secondary lymphatic vessel on pumping performance -/title-

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The lymphatic system plays an important role in interstitial fluid control of the body by transporting lymph and immune cells within a complex network of lymphatic valves and vessels. This system of valves and vessels allows unidirectional pumping without any centralized pump against an adverse pressure gradient, but many aspects of lymphatic pumping, especially the effect of contraction wave shape on intrinsic pumping remain understudied. The primary goal of present work is to investigate the effects on fluid pumping performance of contraction wave functions with different shapes using a fully coupled 3D fluid-structure interaction computational model. In particular, we probe the fluid pumping with a contraction wave function directly reduced from experimental data. Our results suggest that flow rate and pumping performance are strongly dependent on the contraction waveform of the vessel wall.

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MRI-based Estimation of Patient-specific Cerebrospinal Fluid Velocity across Breathing Techniques

Research has shown that disruptions of normal cerebrospinal fluid (CSF) flow may contribute to the progression of Alzheimer’s disease. The pulsatory nature of CSF flow has not been adequately characterized and is influenced by cardiac, respiratory, and low-frequency oscillations. To investigate the temporal behavior of CSF flow in the brain, we developed a novel estimation of CSF velocity using MRI-derived parameters. Using over 10-minutes of continuous functional MRI (fMRI) data coupled with breathing protocols, we measure the amplitude oscillations in a given voxel attributable to the inflow of fluid between time-points. A history of bi-directional flow-related oscillations in fMRI amplitude is obtained by placing a voxel at the centroid of the 4^th ventricle and performing the same scan for outflow. The maximum fMRI amplitude across all breathing protocols is used to estimate the maximum resolvable velocity corresponding to fresh fluid filling the voxel between time-points. We scale the temporal amplitude by the measured maximum amplitude to estimate patient-specific, temporally-resolved, axial flow speed. Velocities are compared across breathing protocols to estimate the effect of breathing on CSF flow as well as provide in-vivo estimations of velocity in the 4^th ventricle.   

In vivo particle tracking to parameterize simulations: insight into mechanisms of reduced glymphatic flow with age

The average human brain weighs only about 3 pounds, but accounts for approximately 20% of the human body’s metabolism. Despite this incredible metabolic rate, there are no lymphatic vessels in the brain, which is how the rest of the body removes waste. Rather, in vivo experiments performed in mice a decade ago demonstrated that cerebrospinal fluid (CSF) flows through the brain to remove metabolic wastes, such as amyloid-β, which form the plaque deposits in the brain that are a hallmark signature of neurological disorders like Alzheimer’s. Perivascular spaces (PVS) in the brain, which are annular channels surrounding blood vessels, provide the pathway for CSF to move through the brain and clear out these metabolic wastes. This pathway is known as the glymphatic system. Quantification of glymphatic flow has proven to be difficult due to the small length scales, need for adding a flow visualization tracer, challenges with gaining optical access to the skull’s interior, and the delicate nature of the brain’s environment. To visualize glymphatic flow in mice, we place a cranial window above the middle cerebral artery on the dorsal aspect of the skull and inject 1 µm fluorescent microspheres into the mouse’s CSF. We then perform particle tracking to quantify glymphatic flow velocities in the PVS of a live mouse. In this study, we report and compare preliminary measurements of PVS velocities of young and old mice (C57BL/6J). While it is known that CSF flow decreases with age, our measurements obtained from particle tracking enable parameterization and validation of simulations that provide insights into the specific mechanisms that reduce CSF flow with age.

    

Not Participating

Deposits of beta-amyloid (Aβ) proteins found in the brain are characteristics of neurological disorders such as Alzheimer’s disease and cerebral amyloid angiopathy. There is emerging evidence that the interstitial fluid (ISF) carrying soluble wastes such as Aβ proteins flows out of the brain through the basement membranes along the walls of cerebral arteries. The arterial wall is a porous medium consisting of muscle cells and extracellular matrix. The driving force for ISF flow within the arterial wall is hypothesized to come from arterial pulsations and low-frequency smooth muscle contraction waves, both of which lead to squeezing deformation of the porous medium. This results in the transport of the solutes in the porous basement membrane due to diffusion and forced advection. In our current work, we experimentally tested this hypothesis with a bench-top model to quantify the magnitudes of diffusion and directed advection in a porous microfluidic channel subjected to external squeezing forces. Type I collagen hydrogel was used as the porous medium to mimic the arterial basement membrane. Different squeezing conditions were achieved by varying the forcing wave magnitude, frequency, and direction. We present results for the transport velocities obtained through fluorescence imaging under the action of various forcing conditions.  

    

Quantifying the relationship between spreading depolarization and the glymphatic system

Spreading depolarization (SD) is an electro-chemical wave that propagates through the brain cortex due to ionic imbalances in the neurons following stroke, migraine, and traumatic brain injury (TBI). It has been recently discovered that SD leads to a large increase in cerebrospinal fluid (CSF) influx into the brain, which contributes to edema following stroke and TBI. We develop a novel computational model that couples SD and CSF fluid flow through perivascular spaces (PVSs) in the brain. PVSs are CSF-filled annular channels that line the brain's vasculature. We first use high order numerical simulations to solve a system of physiologically realistic reaction-diffusion equations which govern the spatiotemporal dynamics of K⁺ and Na⁺ ions in the extracellular and intracellular spaces of the brain cortex during SD. We then couple the SD wave with a 1D CSF flow model that captures the change of volume flow rate, pressure, and cross-sectional area of the PVSs. The coupling is modelled using an empirical relationship between K⁺ concentration in the extracellular space (which increases following SD) and vessel radius (which forms the inner boundary of the PVSs). We find that the CSF volume flow rate depends on the wavelength and wave speed of SD, as well as the domain size and width of the PVS. We also quantify the peak pressures and volume flow rates obtained when two SD waves collide. Our numerical approach is very general and offers novel, quantitative insights into pathological conditions which involve different forms of coupling between SD and CSF flow in the brain.   

Simulation of fluid transport in a porous media model of the brain interstitium using the Lattice Boltzmann Method

Brain parenchyma is a complex porous medium consisting of numerous cell types, surrounded by interstitial fluid that composes 20 percent of the total brain volume. Like other parts of the human body, these cells produce waste that must be cleared from the interstitial fluid. Recent experiments demonstrate that the glymphatic system, a fluid circulation pathway analogous to the lymphatic system found in the body, contributes to the removal of such waste. Cerebrospinal fluid (CSF) surrounds the brain and flows into the brain along perivascular spaces (channels surrounding the brain's vasculature) to exchange with interstitial fluid and remove waste via advection and diffusion. Failure in glymphatic system function is hypothesized to cause waste accumulation, leading to neurodegenerative diseases such as Alzheimer's. In this work, we developed a Lattice Boltzmann Method (LBM) code which is a powerful tool for solving fluid flow in complicated geometries, such as porous media. We simulate advective-diffusive transport in an idealized porous media model of the brain tissue. Our approach is very general and allows us to rapidly test different hypothesized geometries and driving pressures, all of which are very difficult to measure in vivo. We will present preliminary simulations that offer novel insight into solute exchange between perivascular and interstitial spaces in the brain, with the goal of developing new hypotheses that can be tested against experiments. Our simulations provide an innovative approach to resolving fundamental details of mass transport through the interstitial spaces of the brain, which has been called the final frontier of neuroscience. Gaining a deeper understanding of solute exchange has tremendous implications for the prevention and treatment of neurological disorders like Alzheimer's disease.   

Modeling of Periodical Shearing Flow in a Fibrous Space

In this paper, a theoretical model is developed to describe the fluid flow in a thin fibrous gap between two parallel plates, in response to an oscillating shearing impact imposed on one of the boundaries. The fluid velocity field is determined by an effective medium approach, while the fiber deformation is solved by treating each fiber as a soft string. It has been found that the shearing behavior is mainly influenced by the oscillation frequency, fiber stiffness, and porous resistance. The key dimensionless parameters governing the process are the Womersley number, Brinkman number, and Bingham number. The fluid velocity, fiber displacement, shear stress, and porous resistance have been examined through parametric studies. The theory is applied to estimate the interaction between the arachnoid trabecula and the cerebral spinal fluid flow in the subarachnoid space, as the head is exposed to shaking impacts. The results indicate that the shear stress could penetrate through the porous gap and reach the brain matter, and the shear stress acting on the brain surface (Pia Mater) could be even higher than that on the inner surface of the skull (Dura Mater). For the first time, the transmission of the shearing impact from the skull to the soft brain matter is quantitatively determined.   

Dynamics of waste proteins in brain tissue: numerical insights into Alzheimer's risk factors

Over the last few decades, research has indicated that the build-up of waste proteins, like amyloid-β (Aβ), in the brain's interstitial spaces are linked to neurodegenerative diseases, such as Alzheimer's, but the details of how such proteins are removed from the brain is actively debated. One major component of the challenge in predicting and treating neurodegenerative diseases is the complexity of Aβ dynamics: Aβ monomers are produced as a natural consequence of metabolism, but they readily aggregate into heavier species, eventually forming plaques, which are a hallmark signature of many neurodegenerative diseases. We have developed a numerical model to investigate the aggregation and clearance mechanisms of Aβ in the interstitial spaces of the brain. The model describes the volume-averaged fluid flow between the perivascular spaces (fluid-filled spaces surrounding certain blood vessels) of the penetrating arterioles and venules in a segment of brain interstitium. Our numerical approach solves N coupled advection-diffusion-aggregation equations to model the production, aggregation, and fragmentation of N species of Aβ, where N ≥ 50. We simulate 50 Aβ species in order to investigate the protein-length dependence of clearance and aggregation. We then quantify the importance of several factors, including initial concentration, boundary conditions, Péclet number, and aggregation and fragmentation rates, to determine which factors promote or inhibit protein clearance. Our preliminary results provide novel insight into several known risk factors for Alzheimer's disease and cognitive decline.