Fluid–structure interaction for highly complex, statistically defined, biological media: Homogenisation and a 3D multi-compartmental poroelastic model for brain biomechanics

Vardakis, John C.; Guo, Liwei; Peach, Thomas; Lassila, Toni; Mitolo, Micaela; Chou, Dean; Taylor, Zeike A.; Varma, Susheel; Venneri, Annalena; Frangi, Alejandro F.; Ventikos, Yiannis

doi:10.1016/j.jfluidstructs.2019.04.008

Cited by 31 publications

(56 citation statements)

References 75 publications

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“…47,48 The VA profiles are identical for both hemispheres, in line with previous work. 47,48 This implies a greater degree of morphological symmetry to the developing solution fields (as the arterial compartment is the driving compartment of the system) and allows the compartmental interrogation of the 6-MPET model to be conducted according to the aforementioned extensions to the high precision workflow. Figure 3 depicts the 28 regions (spanning the two hemispheres, so a total of 56 separate regions) that this precision medicine pipeline can stratify results for (like the hippocampus, brainstem and thalamus), in addition to the outputs of the 6-MPET system for the chosen Table 2.…”

Section: The 6-mpet Numerical Templatesupporting

confidence: 87%

“…55 In line with previous work, grid-independence of the 6-MPET system was conducted, and converged solutions fell within a specified percentage band of numerical error estimates. For details regarding the discretisation of the conservation equations, the reader is referred to Guo et al and Vardakis et al 47,48,54,55 Finally, the discretised form of the 6-MPET system is solved using the PETSc library. 59,60 Subject-specific dataset, 6-MPET parameters and boundary conditions Subject-specific dataset.…”

Section: The 6-mpet Numerical Templatementioning

confidence: 99%

“…The remaining intercompartmental flux terms (v ac , v cv ) are the same as in previous studies (see for instance Vardakis et al and Guo et al). 3,47,48,54,55 Boundary conditions of the 6-MPET numerical template. In the work conducted here, the outer/inner boundary of the cerebral tissue represents the cortical surface/cerebroventricular wall respectively.…”

Section: The 6-mpet Numerical Templatementioning

confidence: 99%

“…Importantly, the work presented here will form part of a broader study, which will involve the assessment of the consolidated pipeline utilising the entire Lido dataset. 3,47,48 Important limitations include the omission of crossstorage effects from the MPET formulation. 49,83 The Table 3.…”

Section: -Mpet Variablementioning

confidence: 99%

See 3 more Smart Citations

Exploring neurodegenerative disorders using a novel integrated model of cerebral transport: Initial results

Vardakis

Chou

Guo

et al. 2020

Proc Inst Mech Eng H

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The neurovascular unit (NVU) underlines the complex and symbiotic relationship between brain cells and the cerebral vasculature, and dictates the need to consider both neurodegenerative and cerebrovascular diseases under the same mechanistic umbrella. Importantly, unlike peripheral organs, the brain was thought not to contain a dedicated lymphatics system. The glymphatic system concept (a portmanteau of glia and lymphatic) has further emphasized the importance of cerebrospinal fluid transport and emphasized its role as a mechanism for waste removal from the central nervous system. In this work, we outline a novel multiporoelastic solver which is embedded within a high precision, subject specific workflow that allows for the co-existence of a multitude of interconnected compartments with varying properties (multiple-network poroelastic theory, or MPET), that allow for the physiologically accurate representation of perfused brain tissue. This novel numerical template is based on a six-compartment MPET system (6-MPET) and is implemented through an in-house finite element code. The latter utilises the specificity of a high throughput imaging pipeline (which has been extended to incorporate the regional variation of mechanical properties) and blood flow variability model developed as part of the VPH-DARE@IT research platform. To exemplify the capability of this large-scale consolidated pipeline, a cognitively healthy subject is used to acquire novel, biomechanistically inspired biomarkers relating to primary and derivative variables of the 6-MPET system. These biomarkers are shown to capture the sophisticated nature of the NVU and the glymphatic system, paving the way for a potential route in deconvoluting the complexity associated with the likely interdependence of neurodegenerative and cerebrovascular diseases. The present study is the first, to the best of our knowledge, that casts and implements the 6-MPET equations in a 3D anatomically accurate brain geometry.

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Section: The 6-mpet Numerical Templatesupporting

confidence: 87%

Section: The 6-mpet Numerical Templatementioning

confidence: 99%

Section: The 6-mpet Numerical Templatementioning

confidence: 99%

Section: -Mpet Variablementioning

confidence: 99%

See 2 more Smart Citations

Exploring neurodegenerative disorders using a novel integrated model of cerebral transport: Initial results

Vardakis

Chou

Guo

et al. 2020

Proc Inst Mech Eng H

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show abstract

“…The MPET theory has been successfully used in the modeling of biomechanical problems, e.g., hydrocephalus (Levine, 2008; Tully and Ventikos, 2011; Sobey et al, 2012), cerebral oedema (Vardakis et al, 2016), and Alzheimer's disease (Guo et al, 2018; Vardakis et al, 2019). However, there still lacks thorough and rigorous validation using experimental and clinical data.…”

Section: Introductionmentioning

confidence: 99%

On the Validation of a Multiple-Network Poroelastic Model Using Arterial Spin Labeling MRI Data

Guo

Lyu

et al. 2019

Front. Comput. Neurosci.

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The Multiple-Network Poroelastic Theory (MPET) is a numerical model to characterize the transport of multiple fluid networks in the brain, which overcomes the problem of conducting separate analyses on individual fluid compartments and losing the interactions between tissue and fluids, in addition to the interaction between the different fluids themselves. In this paper, the blood perfusion results from MPET modeling are partially validated using cerebral blood flow (CBF) data obtained from arterial spin labeling (ASL) magnetic resonance imaging (MRI), which uses arterial blood water as an endogenous tracer to measure CBF. Two subjects—one healthy control and one patient with unilateral middle cerebral artery (MCA) stenosis are included in the validation test. The comparison shows several similarities between CBF data from ASL and blood perfusion results from MPET modeling, such as higher blood perfusion in the gray matter than in the white matter, higher perfusion in the periventricular region for both the healthy control and the patient, and asymmetric distribution of blood perfusion for the patient. Although the partial validation is mainly conducted in a qualitative way, it is one important step toward the full validation of the MPET model, which has the potential to be used as a testing bed for hypotheses and new theories in neuroscience research.

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Physiological and Pathological Factors Affecting Drug Delivery to the Brain by Nanoparticles

Islam

Leach

Smith³

et al. 2021

Advanced Science

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The prevalence of neurological/neurodegenerative diseases, such as Alzheimer's disease is known to be increasing due to an aging population and is anticipated to further grow in the decades ahead. The treatment of brain diseases is challenging partly due to the inaccessibility of therapeutic agents to the brain. An increasingly important observation is that the physiology of the brain alters during many brain diseases, and aging adds even more to the complexity of the disease. There is a notion that the permeability of the blood–brain barrier (BBB) increases with aging or disease, however, the body has a defense mechanism that still retains the separation of the brain from harmful chemicals in the blood. This makes drug delivery to the diseased brain, even more challenging and complex task. Here, the physiological changes to the diseased brain and aged brain are covered in the context of drug delivery to the brain using nanoparticles. Also, recent and novel approaches are discussed for the delivery of therapeutic agents to the diseased brain using nanoparticle based or magnetic resonance imaging guided systems. Furthermore, the complement activation, toxicity, and immunogenicity of brain targeting nanoparticles as well as novel in vitro BBB models are discussed.

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Fluid–structure interaction for highly complex, statistically defined, biological media: Homogenisation and a 3D multi-compartmental poroelastic model for brain biomechanics

Cited by 31 publications

References 75 publications

Exploring neurodegenerative disorders using a novel integrated model of cerebral transport: Initial results

Exploring neurodegenerative disorders using a novel integrated model of cerebral transport: Initial results

On the Validation of a Multiple-Network Poroelastic Model Using Arterial Spin Labeling MRI Data

Physiological and Pathological Factors Affecting Drug Delivery to the Brain by Nanoparticles

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