Investigating Dementia via a multicompartmental poroelastic model of parenchymal tissue

In this paper, a workflow within the VPH-DARE@IT Clinical Research Platform is presented. This is used to model the biomechanical behaviour of perfused brain tissue. This workflow features a 3D multicompartmental poroelastic framework, patient-specific brain anatomy representations and continuous waveforms of internal carotid and vertebral arteries, which are used as a means of personalizing the boundary conditions that feed the arterial compartment of the in-house poroelastic solver. Results are shown comparing CSF/ISF clearance and accumulation in two males of similar age, both are non-smokers, however one is more active and is diagnosed with MCI and experiences less sleep

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

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 (CBF) variability is utilised. 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 MCI case) during two states of activity (high and low). Results showed a marginally reduced clearance of CSF/ISF, elevated parenchymal tissue displacement and CSF/ISF accumulation and drainage in the MCI case. Peak perfusion remained at 8 mm/s between the two cases

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

none14siThere 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.noneGuo L.; Vardakis J.C.; Lassila T.; Mitolo M.; Ravikumar N.; Chou D.; Lange M.; Sarrami-Foroushani A.; Tully B.J.; Taylor Z.A.; Varma S.; Venneri A.; Frangi A.F.; Ventikos Y.Guo L.; Vardakis J.C.; Lassila T.; Mitolo M.; Ravikumar N.; Chou D.; Lange M.; Sarrami-Foroushani A.; Tully B.J.; Taylor Z.A.; Varma S.; Venneri A.; Frangi A.F.; Ventikos Y

Highly integrated workflows for exploring cardiovascular conditions: Exemplars of precision medicine in Alzheimer's disease and aortic dissection = Processus à haut degré d’intégration pour l’étude de troubles cardiovasculaires : exemples de médecine de précision appliquée à la maladie d’Alzheimer et à la dissection aortique

For precision medicine to be implemented through the lens of in silico technology, it is imperative that biophysical research workflows offer insight into treatments that are specific to a particular illness and to a particular subject. The boundaries of precision medicine can be extended using multiscale, biophysics-centred workflows that consider the fundamental underpinnings of the constituents of cells and tissues and their dynamic environments. Utilising numerical techniques that can capture the broad spectrum of biological flows within complex, deformable and permeable organs and tissues is of paramount importance when considering the core prerequisites of any state-of-the-art precision medicine pipeline. In this work, a succinct breakdown of two precision medicine pipelines developed within two Virtual Physiological Human (VPH) projects are given. The first workflow is targeted on the trajectory of Alzheimer's Disease, and caters for novel hypothesis testing through a multicompartmental poroelastic model which is integrated with a high throughput imaging workflow and subject-specific blood flow variability model. The second workflow gives rise to the patient specific exploration of Aortic Dissections via a multi-scale and compliant model, harnessing imaging, computational fluid-dynamics (CFD) and dynamic boundary conditions. Results relating to the first workflow include some core outputs of the multiporoelastic modelling framework, and the representation of peri-arterial swelling and peri-venous drainage solution fields. The latter solution fields were statistically analysed for a cohort of thirty-five subjects (stratified with respect to disease status, gender and activity level). The second workflow allowed for a better understanding of complex aortic dissection cases utilising both a rigid-wall model informed by minimal and clinically common datasets as well as a moving-wall model informed by rich datasets. / Pour que la médecine actuelle puisse profiter de la technologie in silico, il est impératif que les flux de recherche biophysique offrent un aperçu précis des traitements spécifiques à une maladie particulière et à un sujet particulier. Les limites de la médecine peuvent être repoussées à l’aide de flux de travail multi-échelles, centrés sur la biophysique, qui tiennent compte des constituants fondamentaux des cellules et des tissus, et de leurs environnements dynamiques. L’utilisation de techniques numériques permettant de capter le large spectre des flux biologiques au sein d’organes et de tissus complexes, déformables et perméables est d’une importance capitale lorsqu’il s’agit d’examiner les conditions essentielles de tout pipeline médical de précision de pointe. Dans ce travail, une analyse succinte de deux pipelines de médecine de précision développés dans le cadre de deux projets VPH (Virtual Physiological Human) est donnée. Le premier flux de travail se concentre sur la trajectoire de la maladie d’Alzheimer et permet de tester de nouvelles hypothèses au moyen d’un modèle poroélastique à plusieurs compartiments qui est intégré à un flux de travail d’imagerie à haut débit et à un modèle de variabilité du débit sanguin spécifique au sujet. Le deuxième flux de travail donne lieu à l’exploration spécifique des dissections aortiques chez le patient par le biais d’un modèle multi-échelle conforme, exploitant l’imagerie, la dynamique des fluides computationnelle (CFD) et les conditions limites dynamiques. Les résultats relatifs au premier flux de travail comprennent certains des principaux extrants du cadre de modélisation multiporoélastique et la représentation des zones de gonflement péri-artériel et de solution de drainage périveineux. Ces dernières zones de solutions ont été analysées statistiquement sur une cohorte de trente-cinq sujets (stratifiés en fonction de l’état pathologique, du sexe et du niveau d’activité). Le deuxième flux de travail a permis de mieux comprendre les cas complexes de dissection aortique à l’aide d’un modèle à parois rigides fondé sur des ensembles de données minimales et cliniquement communes et d’un modèle à parois mobiles reposant sur de riches données

Astrocytic Ion Dynamics: Implications for Potassium Buffering and Liquid Flow

We review modeling of astrocyte ion dynamics with a specific focus on the implications of so-called spatial potassium buffering, where excess potassium in the extracellular space (ECS) is transported away to prevent pathological neural spiking. The recently introduced Kirchoff-Nernst-Planck (KNP) scheme for modeling ion dynamics in astrocytes (and brain tissue in general) is outlined and used to study such spatial buffering. We next describe how the ion dynamics of astrocytes may regulate microscopic liquid flow by osmotic effects and how such microscopic flow can be linked to whole-brain macroscopic flow. We thus include the key elements in a putative multiscale theory with astrocytes linking neural activity on a microscopic scale to macroscopic fluid flow.Comment: 27 pages, 7 figure

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

Numerous problems of relevance in physiology and biomechanics, have at their core, the presence of a deformable solid matrix which experiences flow-induced strain. Often, this fluid-structure interaction (FSI) is directed the opposite way, i.e. it is solid deformation that creates flow, with the heart being the most prominent example. In many cases, this interaction of fluid and solid is genuinely bidirectional and strongly coupled, with solid deformation inducing flow and fluid pressure deforming the solid. Although an FSI problem, numerous cases in biomechanics are not tractable via the traditional FSI methodologies: in the internal flows that are of interest to use, the number and range of fluid passages is so vast that the direct approach of a deterministically defined boundary between fluid and solid is impossible to apply. In these cases, homogenisation and statistical treatment of the material-fluid system is possibly the only way forward. Such homogenisation, quite common to flow-only systems through porous media considerations, is also possible for FSI systems, where the loading is effectively internal to the material. A prominent technique of this type is that of poroelasticity. In this paper, we discuss a class of poroelastic theory techniques that allow for the co-existence of a multitude of – always statistically treated –channels and passages of widely different properties: termed multiple-network poroelasticity (or multicompartmental poroelasticity). This paradigm is particularly suitable for the study of living tissue, that is invariably permeated – perfused – by fluids, often different in nature and across a wide range of scales. Multicompartmental poroelasticity is capable of accounting for a full bidirectional coupling between the fluids and the solid matrix and allows us to track transport of a multitude of substances together with the deformation of the solid material that this transport gives rise to or is caused by, or both. For the purposes of demonstration, we utilise a complex and physiologically very important system, the human brain (specifically, we target the hippocampus), to exemplify the qualities and efficacy of this methodology during the course of Alzheimer’s Disease. The methodology we present has been implemented through the Finite Element Method, in a general manner, allowing for the co-existence of an arbitrary number of compartments. For the applications used in this paper to exemplify the method, a four-compartment implementation is used. A unified pipeline is used on a cohort of 35 subjects to provide statistically meaningful insight into the underlying mechanisms of the neurovascular unit (NVU) in the hippocampus, and to ascertain whether physical activity would have an influence in both swelling and drainage by taking into account both the scaled strain field and the proportion of perfused blood injected into the brain tissue. A key result garnered from his study is the statistically significant differences in right hemisphere hippocampal NVU swelling between males in the control group and females with mild cognitive impairment during high and low activity states

Highly integrated workflows for exploring cardiovascular conditions: Exemplars of precision medicine in Alzheimer's disease and aortic dissection

For precision medicine to be implemented through the lens of in silico technology, it is imperative that biophysical research workflows offer insight into treatments that are specific to a particular illness and to a particular subject. The boundaries of precision medicine can be extended using multiscale, biophysics-centred workflows that consider the fundamental underpinnings of the constituents of cells and tissues and their dynamic environments. Utilising numerical techniques that can capture the broad spectrum of biological flows within complex, deformable and permeable organs and tissues is of paramount importance when considering the core prerequisites of any state-of-the-art precision medicine pipeline. In this work, a succinct breakdown of two precision medicine pipelines developed within two Virtual Physiological Human (VPH) projects are given. The first workflow is targeted on the trajectory of Alzheimer's Disease, and caters for novel hypothesis testing through a multicompartmental poroelastic model which is integrated with a high throughput imaging workflow and subject-specific blood flow variability model. The second workflow gives rise to the patient specific exploration of Aortic Dissections via a multi-scale and compliant model, harnessing imaging, computational fluid-dynamics (CFD) and dynamic boundary conditions. Results relating to the first workflow include some core outputs of the multiporoelastic modelling framework, and the representation of peri-arterial swelling and peri-venous drainage solution fields. The latter solution fields were statistically analysed for a cohort of thirty-five subjects (stratified with respect to disease status, gender and activity level). The second workflow allowed for a better understanding of complex aortic dissection cases utilising both a rigid-wall model informed by minimal and clinically common datasets as well as a moving-wall model informed by rich datasets