A statistical model of the penetrating arterioles and venules in the human cerebral cortex

ObjectiveModels of the cerebral microvasculature are required at many different scales in order to understand the effects of microvascular topology on CBF. There are, however, no data-driven models at the arteriolar/venular scale. In this paper, we develop a data-driven algorithm based on available data to generate statistically accurate penetrating arterioles and venules. MethodsA novel order-based density-filling algorithm is developed based on the statistical data including bifurcating angles, LDRs, and area ratios. Three thousand simulations are presented, and the results validated against morphological data. These are combined with a previous capillary network in order to calculate full vascular network parameters. ResultsStatistically accurate penetrating trees were successfully generated. All properties provided a good fit to experimental data. The k exponent had a median of 2.5 and an interquartile range of 1.75-3.7. CBF showed a standard deviation ranging from andplusmn;18% to andplusmn;34% of the mean, depending on the penetrating vessel diameter. ConclusionsSmall CBF variations indicate that the topology of the penetrating vessels plays only a small part in the large regional variations of CBF seen in the brain. These results open up the possibility of efficient oxygen and blood flow simulations at MRI voxel scales which can be directly validated against MRI data.</p

Advancing treatment of retinal disease through in silico trials

Abstract Treating retinal diseases to prevent sight loss is an increasingly important challenge. Thanks to the configuration of the eye, the retina can be examined relatively easily in situ. Owing to recent technological development in scanning devices, much progress has been made in understanding the structure of the retina and characterising retinal biomarkers. However, treatment options remain limited and are often of low efficiency and efficacy.&amp;#xD;&amp;#xD;In recent years, the concept of in silico clinical trials has been adopted by many pharmaceutical companies to optimise and accelerate the development of therapeutics. In silico clinical trials rely on the use of mathematical models based on the physical and biochemical mechanisms underpinning a biological system. With appropriate simplifications and assumptions, one can generate computer simulations of various treatment regimens, new therapeutic molecules, delivery strategies and so forth, rapidly and at a fraction of the cost required for the equivalent experiments. Such simulations have the potential not only to hasten the development of therapies and strategies but also to&amp;#xD;optimise the use of existing therapeutics.&amp;#xD;&amp;#xD;In this paper, we review the state-of-the-art in in silico models of the retina for mathematicians, biomedical scientists and clinicians, highlighting the challenges to developing in silico clinical trials. Throughout this paper, we highlight key findings from in silico models about the physiology of the retina in health and disease. We describe the main building blocks of in silico clinical trials and identify challenges to developing in silico clinical trials of retinal diseases.</jats:p

The Ageing Brain: Investigating the Role of Age in Changes to the Human Cerebral Microvasculature With an in silico Model

Ageing causes extensive structural changes to the human cerebral microvasculature, which have a significant effect on capillary bed perfusion and oxygen transport. Current models of brain capillary networks in the literature focus on healthy adult brains and do not capture the effects of ageing, which is critical when studying neurodegenerative diseases. This study builds upon a statistically accurate model of the human cerebral microvasculature based on ex-vivo morphological data. This model is adapted for “healthy” ageing using in-vivo measurements from mice at three distinct age groups—young, middle-aged, and old. From this new model, blood and molecular exchange parameters are calculated such as permeability and surface-area-to-volume ratio, and compared across the three age groups. The ability to alter the model vessel-by-vessel is used to create a continuous gradient of ageing. It was found that surface-area-to-volume ratio reduced in old age by 6% and permeability by 24% from middle-age to old age, and variability within the networks also increased with age. The ageing gradient indicated a threshold in the ageing process around 75 years old, after which small changes have an amplified effect on blood flow properties. This gradient enables comparison of studies measuring cerebral properties at discrete points in time. The response of middle aged and old aged capillary beds to micro-emboli showed a lower robustness of the old age capillary bed to vessel occlusion. As the brain ages, there is thus increased vulnerability of the microvasculature—with a “tipping point” beyond which further remodeling of the microvasculature has exaggerated effects on the brain. When developing in-silico models of the brain, age is a very important consideration to accurately assess risk factors for cognitive decline and isolate early biomarkers of microvascular health.</jats:p

Age-based sensitivity analysis on cardiac hemodynamics using lumped-parameter modelling

Age is a major risk for heart failure, which is associated with the reduction in ventricular compliance, increase in arterial stiffening, and increase in systemic vascular resistance. In this study, a lumped-parameter model is used to investigate the effect of aging on the possibility of heart failure occurrence. Model parameters including the systemic and pulmonary arterial compliance and resistance, and the left ventricular elastance are calculated for different ages using a ratio-based method. These parameters are then used in the lumped-parameter model. Our findings show that as age increases, there is a leftward and a rightward shift in the left ventricle and right ventricle pressure-volume loops, respectively. For the left ventricle, there is a decrease in stroke volume and an increase in ventricular pressure as the age increases. This correlates with the occurrence of arterial hypertension in the older population. Meanwhile, the right ventricular pressure is maintained as the population gets older, despite the increase in the stroke volume. This is possibly due to the shift in intraventricular septum that causes an enlargement of the right ventricle as the age increases. This study provides understanding on the effect of age on the occurrence of heart failure.This study demonstrates the relationship of aging with cardiac hemodynamics, which provides the potential risk of heart failure occurrence. Although there are many risk factors that can cause heart failure, aging has been strongly associated with its occurrence. Understanding how age affects heart failure can help to differentiate them from other effects such as dietary, gender, and early cardiovascular diseases including arrhythmia and myocardial infarction

Mathematical modelling of haemorrhagic transformation within a multiscale microvasculature network

Abstract Objective. Haemorrhagic transformation (HT) is one of the most common complications after ischaemic stroke, caused by damage to the blood–brain barrier (BBB) that could be the result of stroke progression or a complication of stroke treatment with reperfusion therapy. The aim of this study is to develop further a previous simple HT mathematical model into an enlarged multiscale microvasculature model in order to investigate the effects of HT on the surrounding tissue and vasculature. In addition, this study investigates the relationship between tissue displacement and vascular geometry. Approach. By modelling tissue displacement, capillary compression, hydraulic conductivity in tissue and vascular permeability, we establish a mathematical model to describe the change of intracranial pressure (ICP) surrounding the damaged vascular bed after HT onset, applied to a 3D multiscale microvasculature. The use of a voxel-scale model then enables us to compare our HT simulation with available clinical imaging data for perfusion and cerebral blood volume ( C B V ) in the multiscale microvasculature network. Main results. We showed that the haematoma diameter and the maximum tissue displacement are approximately proportional to the diameter of the breakdown vessel. Based on the voxel-scale model, we found that perfusion reduces by approximately 13 – 17 % and C B V reduces by around 20 – 25 % after HT onset due to the effect of capillary compression caused by increased interstitial pressure. The results are in good agreement with the limited experimental data. Significance. This model, by enabling us to bridge the gap between the microvascular scale and clinically measurable parameters, providing a foundation for more detailed validation and understanding of HT in patients.</jats:p

Quantifying the contribution of intracranial pressure and arterial blood pressure to spontaneous tympanic membrane displacement

Objective: Although previous studies have shown associations between patient symptoms/outcomes and the spontaneous tympanic membrane displacement (spTMD) pulse amplitude, the contribution of the underlying intracranial pressure (ICP) signal to the spTMD pulse remains largely unknown. We have assessed the relative contributions of ICP and arterial blood pressure (ABP) on spTMD at different frequencies in order to determine whether spTMD contains information about the ICP above and beyond that contained in the ABP. Approach: Eleven patients, who all had invasive ICP and ABP measurements in situ, were recruited from our intensive care unit. Their spTMD was recorded and the power spectral densities of the three signals, as well as coherences between the signals, were calculated in the range 0.1–5 Hz. Simple and multiple coherences, coupled with statistical tests using surrogate data, were carried out to quantify the relative contributions of ABP and ICP to spTMD. Main results: Most power of the signals was found to predominate at respiration rate, heart rate, and their harmonics, with little outside of these frequencies. Analysis of the simple coherences found a slight preference for ICP transmission, beyond that from ABP, to the spTMD at lower frequencies (7/11 patients at respiration, 7/10 patients at respiration 1st harmonic) which is reversed at the higher frequencies (2/11 patients at heart rate and its 1st harmonic). Both ICP and ABP were found to independently contribute to the spTMD. The multiple coherence reinforced that ICP is preferentially being transmitted at respiration and respiration 1st harmonic. Significance: Both ABP and ICP contribute independently to the spTMD signal, with most power occurring at clear physiological frequencies—respiration and harmonics and heart rate and harmonics. There is information shared between the ICP and spTMD that is not present in ABP. This analysis has indicated that lower frequencies appear to favour ICP as the driver for spTMD

Coupling one-dimensional arterial blood flow to three-dimensional tissue perfusion models for in silico trials of acute ischaemic stroke

An acute ischaemic stroke is due to the sudden blockage of an intracranial blood vessel by an embolized thrombus. In the context of setting up in silico trials for the treatment of acute ischaemic stroke, the effect of a stroke on perfusion and metabolism of brain tissue should be modelled to predict final infarcted brain tissue. This requires coupling of blood flow and tissue perfusion models. A one-dimensional intracranial blood flow model and a method to couple this to a brain tissue perfusion model for patient-specific simulations is presented. Image-based patient-specific data on the anatomy of the circle of Willis are combined with literature data and models for vessel anatomy not visible in the images, to create an extended model for each patient from the larger vessels down to the pial surface. The coupling between arterial blood flow and tissue perfusion occurs at the pial surface through the estimation of perfusion territories. The coupling method is able to accurately estimate perfusion territories. Finally, we argue that blood flow can be approximated as steady-state flow at the interface between arterial blood flow and tissue perfusion to reduce the cost of organ-scale simulations

Prediction of early death after atrial fibrillation diagnosis using a machine learning approach: A French nationwide cohort study.

AimsAtrial fibrillation is associated with important mortality but the usual clinical risk factor based scores only modestly predict mortality. This study aimed to develop machine learning models for the prediction of death occurrence within the year following atrial fibrillation diagnosis and compare predictive ability against usual clinical risk scores.Methods and resultsWe used a nationwide cohort of 2,435,541 newly diagnosed atrial fibrillation patients seen in French hospitals from 2011 to 2019. Three machine learning models were trained to predict mortality within the first year using a training set (70% of the cohort). The best model was selected to be evaluate and compared with previously published scores on the validation set (30% of the cohort). Discrimination of the best model was evaluated using the C index. Within the first year following atrial fibrillation diagnosis, 342,005 patients (14.4%) died after a period of 83 (SD 98) days (median 37 [10-129]). The best machine learning model selected was a deep neural network with a C index of 0.785 (95% CI, 0.781-0.789) on the validation set. Compared to clinical risk scores, the selected model was superior to the CHA2DS2-VASc and HAS-BLED risk scores and superior to dedicated scores such as Charlson Comorbidity Index and Hospital Frailty Risk Score to predict death within the year following atrial fibrillation diagnosis (C indexes: 0.597; 0.562; 0.643; 0.626 respectively. PConclusionMachine learning algorithms predict early death after atrial fibrillation diagnosis and may help clinicians to better risk stratify atrial fibrillation patients at high risk of mortality.Translational perspectiveAtrial fibrillation is responsible for a substantial proportion of short-term mortality making futile, complex and expensive, cardiovascular procedures/devices or therapies that will not change overall prognosis due to competing risk between cardiovascular and non-cardiovascular death. Machine learning algorithms predict early mortality in atrial fibrillation patients with a better ability than previously developed traditional clinical risk scores. A Machine learning approach may help clinicians to better stratify atrial fibrillation patients at high risk of mortality and may assist physicians in decision-making when managing atrial fibrillation patients in a holistic and integrated care manner