Patient specific computational modeling of fluid-structure interactions of cerebrospinal fluid for biomarkers in Alzheimer's disease

阿尔茨海默氏病已成为一个令人生畏的公共卫生问题，对老年人有着毁灭性的影响。 个人和社会层面。早期发现和诊断这种疾病将是非常有益的， 尽管我们正在努力降低这种疾病的发病率和死亡率，但仍然存在早期发现的可靠方法， 令人沮丧的难以捉摸最近的研究表明，脑内和周围的脑脊液（CSF）流动受损， 大脑，以及这种损伤如何与脑组织硬度联系在一起，在早期发展中起着至关重要的作用， 阿尔茨海默病的病理学。不幸的是，现有的测量脑脊液流量和脑组织的候选人， 刚度是高度侵入性或半定量分析，不能量化关键的机械性能 脑组织变形以及它如何与AD相关蛋白（如 淀粉样肽和磷酸化tau）。为了克服这些障碍，我们将制定一个 计算模型能够准确计算CSF速度和应力应变状态的 大脑皮层我们的方法将使用最先进的非侵入性MRI扫描， CSF和血流的计算流体动力学（CFD）建模，以确定生理状况 以及它们与脑组织的相互作用。为了解释脑组织的变形， 对流体传输的影响，流体模拟将与大脑的结构模型相结合 组织通过流体-结构相互作用（FSI）建模。这项技术的结果将允许 开发新的、非侵入性的和定量的生物标志物，如脑“硬度”（ 标志性组织病理学斑块和缠结），CSF在蛛网膜下腔的停留时间（与 蛋白质去除率）和其它流体动力学因素如颅内压脉动性。

Alzheimer's Disease has become a daunting public health concern with devastating effects on the individual and societal level. Early detection and diagnosis of the disease would be incredibly beneficial in the effort to reduce morbidity and mortality of the disease, yet reliable methods of early detection remain frustratingly elusive. Recent work has shown impairment of cerebrospinal fluid (CSF) flow in and around the brain, and how that impairment is linked to brain tissue stiffness, play a vital role in the early development of Alzheimer's Disease pathology. Unfortunately, existing candidates for measuring CSF flow and brain tissue stiffness are either highly invasive or semiquantitative analyses that cannot quantify key mechanical properties of brain tissue deformation and how it relates to the convective transport of AD-related proteins (such as amyloid -peptide and phosphorylated tau) via CSF flow. To overcome these obstacles, we will develop a computational model capable of accurately calculating CSF velocities and the stress-strain status of the brain cortex. Our approach will use state of the art, non-invasive MRI scans in concert with patient-specific computational fluid dynamic (CFD) modeling of CSF and blood flow to determine the physiological conditions of flow in the skull and their interactions with brain tissue. To account for the deformation of brain tissue and the resulting effects on fluid transport, the fluid simulations will be coupled with a structural model of the brain tissue via fluid-structure interaction (FSI) modeling. The results from this technique will allow for the development of novel, noninvasive and quantitative biomarkers such as brain "stiffness" (a surrogate for the hallmark histopathological plaques and tangles), CSF residence time in the subarachnoid space (related to protein-removal rates), and other hydrodynamic factors such as intracranial pressure pulsatility.