Biology subjects

Rognes, M. E.

Publications and source records attributed to Rognes, M. E..

At least 19 recordsLinked to original sources

bioRxiv2026

Low-rank tensor decompositions reveal coupled spatiotemporal patterns of CSF tracer transport in the human brain

Intrathecal contrast-enhanced longitudinal MRI, or glymphatic MRI (gMRI), provides a unique clinical window into human cerebrospinal fluid (CSF) transport, interstitial fluid (ISF) interaction, and the glymphatic system. However, standard analyses flatten these multi-subject longitudinal data into univariate comparisons across regions of interest, destroying the underlying multi-way structure and obscuring coupled spatiotemporal dynamics. Here we analyze gMRI data from 92 patients (43 diagnosed with idiopathic normal pressure hydrocephalus [iNPH] and 49 reference subjects [REF]) using unsupervised non-negative low-rank tensor decompositions that treat tracer signal in CSF and brain parenchyma simultaneously. The analysis is stratified by sex to avoid confounding diagnosis with the sex imbalance between cohorts. We recover four replicable components, three of which resolve distinct transport pathways capturing CSF--ISF interaction: distribution of tracer in the supratentorial subarachnoid space and cerebral gray and white matter at 24 hours; transient early influx in regions consistent with transport along major cerebral arteries; and tracer influx to ventricular CSF, also called ventricular reflux. The first two components are positively correlated and thus establish a direct quantitative link between early-stage influx and 24-hour tracer distribution. Moreover, the expression of the two influx pathways varies significantly between REF and iNPH cohorts, demonstrating how unsupervised tensor decompositions provide an automated, data-driven framework to stratify patient groups and derive quantitative markers of solute transport in the human brain.

neuroscience↗

bioRxiv2025

Mechanochemical modeling of exercise-induced skeletal muscle hypertrophy

Skeletal muscle displays remarkable plasticity, adapting its size and strength in response to mechanical loading, particularly, from exercise. This process, known as hypertrophy, is fundamental to athletic training and rehabilitation, but is challenging to quantitatively predict due to its multifactorial, multiscale nature. Specifically, skeletal muscle hypertrophy results from an integration of macroscopic mechanical stimuli with the intracellular signaling pathways that govern muscle growth. In this work, we present a multiscale computational model that mechanistically integrates these mechanical and biochemical stimuli and offers a framework for predicting the outcomes of different types of exercise on skeletal muscle growth. The framework couples a transversely isotropic hyperelastic model for tissue-level mechanics with a system of ordinary differential equations representing the IGF1-AKT-mTOR-FOXO signaling pathway, a key regulator of protein synthesis and degradation. We link these scales using a volumetric growth model, where the signaling dynamics inform a growth tensor that drives changes in muscle cross-sectional area. This approach enables the simulation of long-term muscle adaptation, providing a mechanistic tool to investigate how different exercise protocols lead to macroscopic hypertrophy. Simulations from our model capture the temporal dynamics of hypertrophy under varying load protocols and highlight how feedback between protein synthesis and muscle growth regulates the dose-response relationship to prevent unbounded growth. Using muscle geometries derived from the Visible Human dataset, we study how human variations in muscle geometry affect hypertrophy. Finally, we demonstrate that the mechanochemical coupling between muscle geometry and signaling not only predicts macroscopic shape changes but also provides buffering from local signaling heterogeneity. Ultimately, this framework offers a predictive computational tool for optimizing training regimens and understanding the multiscale determinants of muscle adaptations.

physiology↗

bioRxiv2025

Heterogeneity in human brain clearance adds resilience against tauopathy -- a computational model informed by glymphatic MRI

Neurotoxic protein fragments such as amyloid-beta and tau accumulate in characteristic staging patterns in Alzheimers disease (AD). The brain clears such metabolic substances via multiple different systems, including via the glymphatic (extracellular/extravascular) pathway. Here, we ask how the distinct features that characterize human glymphatic function would affect the prion-like cascade of protein invasion associated with AD. To address this question, we extract and analyze individual clearance rates from human glymphatic MRI (gMRI) data sets. These clearance rates define subject-specific maps of glymphatic clearance that vary both across cortical lobes and Braak staging regions. We apply these clearance maps as initial states in a computational network model linking misfolded proteins, tissue damage, and local clearance to simulate a series of individual proteinopathy trajectories. Our results show that the spatial heterogeneity in initial clearance induces characteristic propagation patterns, delaying and redirecting the disease progression. Moreover, reducing this spatial heterogeneity accelerates disease progression and induces staging patterns typically associated with AD. A comparison between well-rested subjects and subjects who underwent a single night of sleep deprivation did not reveal differences in initial clearance maps nor in simulated disease progression. These findings suggest that spatial heterogeneity in brain clearance may be a key factor for neurodegenerative resilience.

neuroscience↗

bioRxiv2025

Stretch and flow at the gliovascular interface: high-fidelity modelling of the mechanics of astrocyte endfeet

Astrocyte endfeet form a near-continuous sheath around the brains vasculature, defining the perivascular spaces (PVS) that are crucial for brain fluid flow and solute transport. Yet, their precise physiological role remains poorly understood. Using 3D electron microscopy data, we created a high-fidelity poroelastic computational model of an arteriole segment with surrounding endfeet and parenchyma to investigate tissue displacement and fluid flow within the PVS, endfeet, and extracellular space (ECS) in response to blood vessel pulsations. Our model predicts that arteriole dilations compress the PVS while expanding the overall endfoot sheath volume due to tangential stretch. Moreover, fluid exchange primarily occurs through inter-endfoot gaps, driven by pressure differences, rather than across the aquaporin-4 (AQP4) rich endfoot membrane. PVS stiffness critically modulates these dynamics: increased stiffness of the PVS, for instance, due to vessel pathology or aging, would minimize or even reverse fluid exchange at the gliovascular interface. While AQP4 mediated water movement has a negligible impact on pulsation-driven mechanics, it significantly enhances osmotically driven fluid flow. Overall, our findings elucidate the complex balance of forces governing gliovascular mechanics and suggest that PVS composition strongly influences endfoot-parenchymal fluid exchange. SignificancePerivascular spaces, formed by astrocyte endfeet wrapping the vasculature, are high-conduit pathways for brain fluid flow and clearance. Vascular pulsations drive this flow, but the resulting mechanical interactions at the gliovascular interface remain largely unknown. We introduce a computational model of the solid and fluid mechanics here, using realistic geometries to capture intricate astrocyte morphology at the subcellular level. Our simulations reveal that changes in perivascular composition - associated with aging or neurodegenerative diseases - fundamentally alter mechanical coupling, potentially impeding fluid transport. This work provides a mechanistic framework for understanding brain clearance and constitutes a foundational model for computational studies of mechanical forces in the nervous system.

neuroscience↗

bioRxiv2025

Advection versus diffusion in brain ventricular transport

Cerebrospinal fluid (CSF) is integral to brain function. CSF provides mechanical support for the brain and helps distribute nutrients, neurotransmitters and metabolites throughout the central nervous system. CSF flow is driven by several processes, including the beating of motile cilia located on the walls of the brain ventricles. Despite the physiological importance of CSF, the underlying mechanisms of CSF flow and solute transport in the brain ventricles remain to be comprehensively resolved. This study analyzes and evaluates specifically the role of motile cilia in CSF flow and transport. We developed finite element methods for modeling flow and transport using the geometry of the zebrafish larval brain ventricles, for which we have detailed knowledge of cilia properties and CSF motion. The computational model is validated by in vivo experiments that monitor transport of a photoconvertible protein secreted in the brain ventricles. Our results show that while cilia contribute to advection of large particles, diffusion plays a significant role in the transport of small solutes. We also demonstrate how cilia location and the geometry of the ventricular system impact solute distribution. Altogether, this work presents a computational framework that can be applied to other ventricular systems, together with new concepts of how molecules are transported within the brain and its ventricles.

neuroscience↗

bioRxiv2025

In-silico molecular enrichment and clearance of the human intracranial space

The mechanisms of intracranial solute transport are fundamental to human brain health, with alterations often linked to disease and functional impairment, and with distinct opportunities for personalized diagnostics and treatment. However, our understanding of these mechanisms and their interplay remains incomplete, in part due to the complexity of integrating insights across scales, between species and from different modalities. Here, we combine mixed-dimensional modelling, multi-modal magnetic resonance images, and high performance computing to construct and explore a high-fidelity in-silico model of human intracranial molecular enrichment. This model predicts the temporo-spatial spreading of a solute within an image-derived geometric representation of the subarachnoid space, ventricular system and brain parenchyma, including networks of surface perivascular spaces (PVSs). Our findings highlight the significant impact of cerebrospinal fluid (CSF) production and intracranial pulsatility on molecular enrichment following intrathecal tracer injection. We demonstrate that low-frequency vasomotion induces moderate CSF flow in surface PVS networks which substantially enhances tracer enrichment, and that impaired enrichment is a direct natural consequence of enlarged PVSs. This openly available technology platform thus provides an opportunity for integrating separate observations on diffusion in neuropil, vascular dynamics, intracranial pulsatility, CSF production, and efflux, and for exploring drug delivery and clearance in the human brain.

neuroscience↗

bioRxiv2024

Spatial Modeling Algorithms for Reactions and Transport (SMART) in Biological Cells

Biological cells rely on precise spatiotemporal coordination of biochemical reactions to control their many functions. Such cell signaling networks have been a common focus for mathematical models, but they remain challenging to simulate, particularly in realistic cell geometries. Herein, we present our software, Spatial Modeling Algorithms for Reaction and Transport (SMART), a package that takes in high-level user specifications about cell signaling networks and molecular transport, and then assembles and solves the associated mathematical and computational systems. SMART uses state-of-the-art finite element analysis, via the FEniCS Project software, to efficiently and accurately resolve cell signaling events over discretized cellular and subcellular geometries. We demonstrate its application to several different biological systems, including YAP/TAZ mechanotransduction, calcium signaling in neurons and cardiomyocytes, and ATP generation in mitochondria. Throughout, we utilize experimentally-derived realistic cellular geometries represented by well-conditioned tetrahedral meshes. These scenarios demonstrate the applicability, flexibility, accuracy and efficiency of SMART across a range of temporal and spatial scales.

systems biology↗

bioRxiv2024

Modeling CSF circulation and the glymphatic system during infusion using subject specific intracranial pressures and brain geometries

BackgroundInfusion testing is an established method for assessing CSF resistance in patients with idiopathic normal pressure hydrocephalus (iNPH). To what extent the increased resistance is related to the glymphatic system is an open question. Here we introduce a computational model that includes the glymphatic system and enables us to determine the importance of 1) brain geometry, 2) intracranial pressure and 3) physiological parameters on the outcome of and response to an infusion test. MethodsWe implemented a seven-compartment multiple network porous medium model with subject specific geometries from MR images. The model consists of the arterial, capillary and venous blood vessels, their corresponding perivascular spaces, and the extracellular space (ECS). Both subject specific brain geometries and subject specific infusion tests were used in the modeling of both healthy adults and iNPH patients. Furthermore, we performed a systematic study of the effect of variations in model parameters. ResultsBoth the iNPH group and the control group reached a similar steady state solution when subject specific geometries under identical boundary conditions was used in simulation. The difference in terms of average fluid pressure and velocity between the iNPH and control groups, was found to be less than 6 % during all stages of infusion in all compartments. With subject specific boundary conditions, the largest computed difference was a 75 % greater fluid speed in the arterial perivascular space (PVS) in the iNPH group compared to the control group. Changes to material parameters changed fluid speeds by several orders of magnitude in some scenarios. A considerable amount of the CSF pass through the glymphatic pathway in our models during infusion, i.e., 28% and 38% in the healthy and iNPH patients, respectively. ConclusionsUsing computational models, we have found the relative importance of subject specific geometries to be less important than individual differences in terms of fluid pressure and flow rate during infusion. Model parameters such as permeabilities and inter-compartment transfer parameters are uncertain but important and have large impact on the simulation results. The computations predicts that a considerable amount of the infused volume pass through the brain either through the perivascular spaces or the extracellular space.

neuroscience↗

bioRxiv2023

Neural activity induces strongly coupled electro-chemo-mechanical interactions and fluid flow in astrocyte networks and extracellular space - a computational study

The complex interplay between chemical, electrical, and mechanical factors is fundamental to the function and homeostasis of the brain, but the effect of electrochemical gradients on brain interstitial fluid flow, solute transport, and clearance remains poorly quantified. Here, via in-silico experiments based on biophysical modeling, we estimate water movement across astrocyte cell membranes, within astrocyte networks, and within the extracellular space (ECS) induced by neuronal activity, and quantify the relative role of different forces (osmotic, hydrostatic, and electrical) on transport and fluid flow under such conditions. Our results demonstrate how neuronal activity in the form of extracellular ionic input fluxes may induce complex and strongly-coupled chemical-electrical-mechanical interactions in astrocytes and ECS. Furthermore, we observe that the fluid dynamics are crucially coupled to the spatial organization of the intracellular network, with convective and electrical drift dominating ionic diffusion in astrocyte syncytia. Author SummaryOver the last decades, the neuroscience community has paid increased attention to the astrocytes - star-shaped brain cells providing structural and functional support for neurons. Astrocyte networks are likely to be a crucial pathway for fluid flow through brain tissue, which is essential for the brains volume homeostasis and waste clearance. However, numerous questions related to the role of osmotic pressures and astrocytic membrane properties remain unanswered. There are also substantial gaps in our understanding of the driving forces underlying fluid flow through brain tissue. Answering these questions requires a better understanding of the interplay between electrical, chemical, and mechanical forces in brain tissue. Due to the complex nature of this interplay and experimental limitations, computational modeling can be a critical tool. Here, we present a high fidelity computational model of an astrocyte network and the extracellular space. The model predicts the evolution in time and distribution in space of intra- and extracellular volumes, ion concentrations, electrical potentials, and hydrostatic pressures following neural activity. Our findings show that neural activity induces strongly coupled chemical-mechanical-electrical interactions in the tissue and suggest that chemical gradients inside astrocyte syncytia strengthen fluid flow at the microscale.

neuroscience↗

bioRxiv2023

Human brain solute transport quantified by glymphatic MRI-informed biophysics during sleep and sleep deprivation

Whether you are reading, running or sleeping, your brain and its fluid environment continuously interacts to distribute nutrients and clear metabolic waste. Yet, the precise mechanisms for solute transport within the human brain have remained hard to quantify using imaging techniques alone. From multi-modal human brain MRI data sets in sleeping and sleep-deprived subjects, we identify and quantify CSF tracer transport parameters using forward and inverse subject-specific computational modelling. Our findings support the notion that extracellular diffusion alone is not sufficient as a brain-wide tracer transport mechanism. Instead, we show that human MRI observations align well with transport by either substantially enhanced (3.5x) extracellular diffusion in combination with local clearance rates corresponding to a tracer half-life of up to 5 hours, or by extracellular diffusion augmented by advection with brain-wide average flow speeds on the order of 1-9 {micro}m/min. Reduced advection fully explains reduced tracer clearance after sleep-deprivation, supporting the role of sleep and sleep deprivation on human brain clearance.

neuroscience↗

bioRxiv2022

Are brain displacements and pressures within the parenchyma induced by surface pressure differences? A computational modelling study

The intracranial pressure is implicated in many homeostatic processes in the brain and is a fundamental parameter in several diseases such as e.g. idiopathic normal pressure hydrocephalus (iNPH). The presence of a small but persistent pulsatile intracranial pulsatile transmantle pressure gradient (on the order of a few mmHg/m at peak) has recently been demonstrated in iNPH subjects. A key question is whether pulsatile ICP and displacements can be induced by a small pressure gradient originating from the brain surface e.g. pial arteries alone. In this study, we model the brain parenchyma as either a linearly elastic or a poroelastic medium and impose a pulsatile pressure gradient acting between the ventricular and the pial surfaces. Using this high-resolution physics-based model, we compute the effect of the pulsatile pressure gradient on parenchyma displacement, volume change, fluid pressure, and fluid flux. The resulting displacement field is pulsatile and in qualitatively and quantitatively good agreement with the literature, both with elastic and poroelastic models. However, the pulsatile forces on the boundaries are not sufficient for pressure pulse propagation through the brain parenchyma. Our results suggest that pressure differences originating over the brain surface via e.g. pial artery pulsatility are not sufficient to drive interstitial fluid (ISF) flow within the brain parenchyma and that potential pressure gradients found within the parenchyma rather arise from local pressure pulsations of blood vessels within the brain parenchyma itself.

bioengineering↗

bioRxiv2022

Human intracranial pulsatility during the cardiac cycle: a computational modelling framework

BackgroundTodays availability of medical imaging and computational resources set the scene for high-fidelity computational modelling of brain biomechanics. The brain and its environment feature a dynamic and complex interplay between the tissue, blood, cerebrospinal fluid (CSF) and interstitial fluid (ISF). Here, we design a computational platform for modelling and simulation of intracranial dynamics, and assess the models validity in terms of clinically relevant indicators of brain pulsatility. MethodsWe develop finite element models of fully coupled cardiac-induced pulsatile CSF flow and tissue motion in the human brain environment. The three-dimensional model geometry is derived from magnetic resonance images (MRI), features a high level of detail including the brain tissue, the ventricular system, and the cranial subarachnoid space (SAS). We model the brain parenchyma at the organ-scale as an elastic medium permeated by an extracellular fluid network and describe flow of CSF in the SAS and ventricles as viscous fluid movement. Representing vascular expansion during the cardiac cycle, a pulsatile net blood flow distributed over the brain parenchyma acts as the driver of motion. Additionally, we investigate the effect of model variations on a set of clinically relevant quantities of interest. ResultsOur model predicts a complex interplay between the CSF-filled spaces and poroelastic parenchyma in terms of ICP, CSF flow, and parenchymal displacements. Variations in the ICP are dominated by their temporal amplitude, but with small spatial variations in both the CSF-filled spaces and the parenchyma. Induced by ICP differences, we find substantial ventricular and cranial-spinal CSF flow, some flow in the cranial SAS, and small pulsatile ISF velocities in the brain parenchyma. Moreover, the model predicts a funnel-shaped deformation of parenchymal tissue in dorsal direction at the beginning of the cardiac cycle. ConclusionsOur model accurately depicts the complex interplay of ICP, CSF flow and brain tissue movement and is well-aligned with clinical observations. It offers a qualitative and quantitative platform for detailed investigation of coupled intracranial dynamics and interplay, both under physiological and pathophysiological conditions.

biophysics↗

bioRxiv2022

CSF circulation and dispersion yield rapid clearancefrom intracranial compartments

In this paper we used a computational model to estimate the clearance of tracer driven by circulation of cere-brospinal fluid (CSF) produced in the choroid plexus (CP) located within the lateral ventricles. CSF was assumed to exit the subarachnoid space (SAS) via different outflow routes such as the parasagittal dura, cribriform plate and/or meningeal lymphatics. We also modelled a reverse case where fluid was produced within the spinal canal and absorbed in the CP in line with observation on certain iNPH patients. No directional interstitial fluid flow was assumed within the brain parenchyma. Tracers were injected into the foramen magnum. The models demonstrate that convection in the SAS yield rapid clearance from both the SAS and the brain interstitial fluid (ISF) and can speed up intracranial clearance from years, as would be the case for purely diffusive flow, to days.

neuroscience↗

bioRxiv2022

The role of clearance in neurodegenerative diseases

Alzheimers disease, the most common form of dementia, is a systemic neurological disorder associated with the formation of toxic, pathological aggregates of proteins within the brain that lead to severe cognitive decline, and eventually, death. In normal physiological conditions, the brain rids itself of toxic proteins using various clearance mechanisms. The efficacy of brain clearance can be adversely affected by the presence of toxic proteins and is also known to decline with age. Motivated by recent findings, such as the connection between brain cerebrospinal fluid clearance and sleep, we propose a mathematical model coupling the progression of toxic proteins over the brains structural network and protein clearance. The model is used to study the interplay between clearance in the brain, toxic seeding, brain network connectivity, aging, and progression in neurodegenerative diseases such as Alzheimers disease. Our findings provide a theoretical framework for the growing body of medical research showing that clearance plays an important role in the etiology, progression and treatment of Alzheimers disease.

neuroscience↗

bioRxiv2021

Validating a computational framework for ionicelectrodiffusion with cortical spreading depression as a case study

Cortical spreading depression (CSD) is a wave of pronounced depolarization of brain tissue accompanied by substantial shifts in ionic concentrations and cellular swelling. Here, we validate a computational framework for modelling electrical potentials, ionic movement, and cellular swelling in brain tissue during CSD. We consider different model variations representing wild type or knock-out/knock-down mice and systematically compare the numerical results with reports from a selection of experimental studies. We find that the data for several CSD hallmarks obtained computationally, including wave propagation speed, direct current shift duration, peak in extracellular K+ concentration as well as a pronounced shrinkage of extracellular space, are well in line with what has previously been observed experimentally. Further, we assess how key model parameters including cellular diffusivity, structural ratios, membrane water and/or K+ permeabilities affect the set of CSD characteristics. Significance StatementMovement of ions and molecules in and between cellular compartments is fundamental for brain function. Cortical spreading depression (CSD) is associated with dramatic failure of brain ion homeostasis. Better understanding the sequence of events in CSD could thus provide new insight into physiological processes in the brain. Despite extensive experimental research over the last decades, even basic questions related to mechanisms underlying CSD remain unanswered. Computational modelling can play an important role going forward, since simulation studies can address hypotheses that are difficult to target experimentally. Here, we assess the physiological validity of a novel mathematical framework for detailed modelling of brain electrodiffusion and osmosis - and provide a platform for in silico studies of CSD and other cerebral electro-mechanical phenomena.

neuroscience↗

bioRxiv2021

Brain solute transport is more rapid in periarterial than perivenous spaces

BackgroundPerivascular fluid flow, of cerebrospinal or interstitial fluid in spaces surrounding brain blood vessels, is recognized as a key component underlying brain transport and clearance. An important open question is how and to what extent differences in vessel type or geometry affect perivascular fluid flow and transport. MethodsUsing computational modelling in both idealized and image-based geometries, we study and compare fluid flow and solute transport in pial (surface) periarterial and perivenous spaces. ResultsOur findings demonstrate that differences in geometry between arterial and venous pial perivascular spaces (PVSs) lead to higher net CSF flow, more rapid tracer transport and earlier arrival times of injected tracers in periarterial spaces compared to perivenous spaces. ConclusionsThese findings can explain the experimentally observed rapid appearance of tracers around arteries, and the delayed appearance around veins without the need of a circulation through the parenchyma, but rather by direct transport along the PVSs.

bioengineering↗

bioRxiv2020

The mechanisms behind perivascular fluid flow

Flow of cerebrospinal fluid (CSF) in perivascular spaces (PVS) is one of the key concepts involved in theories concerning clearance from the brain. Experimental studies have demonstrated both net and oscillatory movement of microspheres in PVS (Mestre et al. (2018), Bedussi et al. (2018)). The oscillatory particle movement has a clear cardiac component, while the mechanisms involved in net movement remain disputed. Using computational fluid dynamics, we computed the CSF velocity and pressure in a PVS surrounding a cerebral artery subject to different forces, representing arterial wall expansion, systemic CSF pressure changes and rigid motions of the artery. The arterial wall expansion generated velocity amplitudes of 60–260 µm/s, which is in the upper range of previously observed values. In the absence of a static pressure gradient, predicted net flow velocities were small (<0.5 µm/s), though reaching up to 7 µm/s for non-physiological PVS lengths. In realistic geometries, a static systemic pressure increase of physiologically plausible magnitude was sufficient to induce net flow velocities of 20–30 µm/s. Moreover, rigid motions of the artery added to the complexity of flow patterns in the PVS. Our study demonstrates that the combination of arterial wall expansion, rigid motions and a static CSF pressure gradient generates net and oscillatory PVS flow, quantitatively comparable with experimental findings. The static CSF pressure gradient required for net flow is small, suggesting that its origin is yet to be determined.Significance Statement Cerebrospinal fluid flow along perivascular spaces is hypothesized to be instrumental for clearance of metabolic waste from the brain, such as e.g. clearance of amyloid-beta, a protein known to accumulate as plaque within the brain in Alzheimer’s patients. Arterial pulsations have been proposed as the main driving mechanism for perivascular fluid flow, but it is unclear whether this mechanism alone is sufficient. Our results show that arterial pulsations drive oscillatory movement in perivascular spaces, but also indicate that a pressure gradient is required for net flow. However, the required pressure gradient is relatively small, thus suggesting that its origins can be associated with physiological processes within the brain and/or experimental procedures.Competing Interest StatementThe authors have declared no competing interest.View Full Text

physiology↗

bioRxiv2019

Intracranial pressure elevation alters CSF clearance pathways

BackgroundInfusion testing is a common procedure to determine whether shunting will be beneficial in patients with normal pressure hydrocephalus. The method has a well-developed theoretical foundation and corresponding mathematical models that describe the CSF circulation from the choroid plexus to the arachnoid granulations. Here, we investigate to what extent the proposed glymphatic or paravascular pathway (or similar pathways) modifies the results of the traditional mathematical models.\n\nMethodsWe used a two-compartment model consisting of the subarachnoid space and the paravascular spaces. For the arachnoid granulations, the cribriform plate, capillaries and paravascular spaces, resistances were calculated and used to estimate flow before and during an infusion test. Next, pressure in the subarachnoid space and paravascular spaces were computed. Finally, different variations to the model were tested to evaluate the sensitivity of selected parameters.\n\nResultsAt baseline, we found a very small paravascular flow directed into the subarachnoid space, while 60% of the fluid left through the arachnoid granulations and 40% left through the cribriform plate. However, during the infusion, paravascular flow reversed and 25% of the fluid left through these spaces, while 60% went through the arachnoid granulations and only 15% through the cribriform plate.\n\nConclusionsThe relative distribution of CSF flow to different clearance pathways depends on intracranial pressure (ICP), with the arachnoid granulations as the main contributor to outflow. As such, ICP increase is an important factor that should be addressed when determining the pathways of injected substances in the subarachnoid space.

bioengineering↗