Subject-specific multi-poroelastic model for exploring the risk factors associated with the early stages of Alzheimer's disease

Abstract

There is emerging evidence suggesting that Alzheimer's disease is a vascular disorder, caused by impaired cerebral perfusion, which may be promoted by cardiovascular risk factors that are strongly influenced by lifestyle. In order to develop an understanding of the exact nature of such a hypothesis, a biomechanical understanding of the influence of lifestyle factors is pursued. An extended poroelastic model of perfused parenchymal tissue coupled with separate workflows concerning subject-specific meshes, permeability tensor maps and cerebral blood flow variability is used. The subject-specific datasets used in the modelling of this paper were collected as part of prospective data collection. Two cases were simulated involving male, non-smokers (control and mild cognitive impairment (MCI) case) during two states of activity (high and low). Results showed a marginally reduced clearance of cerebrospinal fluid (CSF)/interstitial fluid (ISF), elevated parenchymal tissue displacement and CSF/ISF accumulation and drainage in the MCI case. The peak perfusion remained at 8 mm s −1 between the two cases.

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

“…In the literature, several works have utilised a poroelastic approach in modelling parenchymal tissue within the realm of hydrocephalus and oedema formation in the brain and small intestine (Tully and Ventikos, 2011;Vardakis et al, 2013b;Guo et al, 2018;Vardakis et al, 2017Vardakis et al, , 2016Chou et al, 2016;Chou, 2016;Vardakis et al, 2013a;Kaczmarek et al, 1997;Levine, 1999Levine, , 2000Smillie et al, 2005;Sobey and Wirth, 2006;Shahim et al, 2012;Aldea et al, 2019;Thompson et al, 2019). The poroelastic modelling of parenchymal tissue for the purpose of investigating AD yields a narrower selection of relevant work (Guo et al, 2018;Thompson et al, 2019). Recently, Aldea and colleagues (Aldea et al, 2019) utilise poroelastic theory in a multiscale model of arteries in order to test the hypothesis that cerebrovascular smooth muscle cells drive intramural periarterial drainage.…”

“…Recently, Aldea and colleagues (Aldea et al, 2019) utilise poroelastic theory in a multiscale model of arteries in order to test the hypothesis that cerebrovascular smooth muscle cells drive intramural periarterial drainage. Recently, Guo and colleagues (Guo et al, 2018) introduce a pipeline that intertwines a general 3D multiple-network poroelastic model of perfused parenchymal tissue, an image-based modelling pipeline and a detailed subject-specific boundary condition model that can be used to model the influence of lifestyle and environmental factors in obtaining novel biomarkers during the MCI stage of AD.…”

“…This work uses a novel consolidated pipeline that integrates three important components: a three-dimensional multiple-network poroelastic theory (MPET)-based model of perfused cerebral parenchyma; an accurate, fully automated image-based model personalisation workflow (Guo et al, 2018); and a subject-specific boundary condition model that targets the driving compartment of the MPET model. Specifically, MPET allows for the detailed investigation of spatiotemporal transport of fluid between the cerebral blood (arteries, capillaries and veins), CSF and interstitial fluid (CSF/ISF) and brain parenchyma across multiple scales.…”

Uncertainty quantification of parenchymal tracer distribution using random diffusion and convective velocity fields

“…Even though computational models can distinguish between diffusion and bulk flow, a major challenge remains with regard to the unknown material parameters, boundary conditions and other model configurations needed to accurately predict the movement of ISF in the brain parenchyma. For instance, the permeability of brain tissue used in computational models varies from 10 −10 to 10 −17 m 2 [28,36]. Because the permeability is directly linked to the Darcy fluid velocity in these models, this parameter choice could result in a difference of 7 orders of magnitude in predicted ISF flow.…”

Virtual physiological human 2016: translating the virtual physiological human to the clinic

“…An example of using VPH models in prospective studies, to support analysis and shed light on emerging hypotheses, is provided by Guo et al [16], who use personalized modelling of fluid transport in the brain to help decipher underlying mechanisms regarding Alzheimer's disease. Using subject-specific data from 103 individuals as input for an extended model of perfused parenchymal tissue coupled to subject-specific meshes, permeability tensor maps and cerebral blood flow, the authors aim to build an understanding of the exact nature of emerging evidence, suggesting that Alzheimer's disease is a vascular disorder, caused by impaired cerebral perfusion, which in turn may be promoted by cardiovascular risk factors that are strongly influenced by lifestyle.…”