Long-Term Tracking of Cerebral Microvascular Structural and Functional Alterations between Normal and Alzheimer's Aging

长期跟踪正常衰老和阿尔茨海默病衰老之间的脑微血管结构和功能变化

• 批准号: 10265356
• 负责人: Jonghwan Lee
• 金额: $ 36.83万
• 依托单位: BROWN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10265356
• 关键词: Address; Affect; Age; Aging; Alzheimer' s Disease; Alzheimer' s disease related dementia; Amyloid; Amyloid beta-Protein; Animal Husbandry; Behavioral; Biological Models; Biomedical Engineering; Blood Vessels; Blood capillaries; Blood flow; Caregivers; Cause of Death; Cerebrum; Clinical Research; Computer Models; Consensus; Correlation Studies; Data; Defect; Dementia; Development; Diagnosis; Dietary Intervention; Disease; Early Diagnosis; Early treatment; Economic Burden; Epidemic; Etiology; Failure; Functional disorder; Hyperemia; Image; Imaging technology; Impaired cognition; Individual; Knowledge; Laboratory Animals; Lead; Longevity; Measures; Medical History; Microcirculation; Modeling; Mus; Nerve Degeneration; Network-based; Neurodegenerative Disorders; Neurons; Neurosciences; Optical Coherence Tomography; Oxygen; Pathogenesis; Pathology; Pharmaceutical Preparations; Pharmacology; Play; Population; Process; Property; Public Health; Research Project Grants; Resolution; Risk Factors; Role; Senile Plaques; Structural defect; Structure; System; Techniques; Technology; Testing; Three-Dimensional Image; Time; United States; abeta accumulation; age related; base; cerebral microvasculature; clinically relevant; comorbidity; computer framework; cost; exercise intervention; gene therapy; human old age (65+); hypoperfusion; image processing; imaging biomarker; improved; in vivo; in vivo imaging; insight; interest; mouse model; neuron loss; neurovascular coupling; new technology; normal aging; psychologic; response; simulation; social; therapeutic target; tool; vascular factor

SUMMARY Alzheimer’s disease (AD), a progressive neurodegenerative disorder affecting millions of people worldwide, is currently incurable. As the population ages, AD and related dementia are becoming the biggest epidemic in medical history: the number of people aged 65 and older with AD is projected to increase between two- and three-fold by 2050. As shown by imaging and biomarker studies, age is a major risk factor for developing dementia, and the pathophysiological processes of AD begin more than a decade before the diagnosis of dementia. However, AD is a heterogeneous and multifactorial disease; thus, it is challenging to fully understand how the multiple etiologies and age-related prodromal processes contribute to its pathophysiology. Among other factors, deficits in cerebral microvascular structures and functions may play a key role in the onset and development of AD. Despite its importance for early diagnosis and as a therapeutic target, it is still unclear whether they are a causal factor for AD pathogenesis or an early consequence of multifactorial conditions that lead to AD at a later stage. Especially, two critical knowledge gaps exist: (1) Temporal relationships between vascular and other key factors during the onset and development of AD are not clear; (2) Little has been studied about how individual defects in various microvascular structural and functional properties distinctly correlate with and/or contribute to neuronal degeneration. Here, we will develop, optimize, and integrate experimental and computational technologies for the lifespan tracking and analysis of progressive microvascular alterations in AD versus normal aging in model mice. First, we will optimize our optical coherence tomography imaging and 3D image processing techniques to track the time-course of 32 vascular and non-vascular measures longitudinally over the mouse’s lifespan, including microvascular structure, microcirculation, functional reactivity, Aβ plaque accumulation, neuronal loss, and cognitive decline (Aim 1). These unprecedentedly comprehensive temporal dynamics data and advanced statistical/correlation analyses will enable us to determine whether the microvascular deficits precede neuronal loss or Aβ accumulation, and how those alterations are correlated, directly addressing the first knowledge gap. In Aim 2, we will improve our computational model of microvascular flow and functional hyperemia, and then combine the model with the experimental data of Aim 1 to investigate complicated cause- effect relationships. Our computational model will enable us to essentially “turn on” and “turn off” each microvascular deficit (e.g., thinner vessels, tortuous capillaries, hypoperfusion, capillary stalling) and test its effect on oxygen delivery to neurons, which is difficult and sometimes impossible to achieve experimentally. This combined approach will provide a powerful and unique strategy for testing the role of vascular deficits in neuronal degeneration, directly addressing the second knowledge gap, and informing future research for diagnosis and therapeutic target development.

概括 阿尔茨海默氏病（AD），一种影响数百万人的进行性神经退行性疾病 全球，目前无法治愈。随着人口的年龄，AD和相关痴呆症已成为最大的痴呆症 病史流行：预计65岁及65岁以上的人数将增加 到2050年，两倍和三倍。如成像和生物标志物研究所示，年龄是主要危险因素 发展痴呆症，AD的病理生理过程开始了十多年 诊断痴呆症。但是，AD是一种异质和多因素疾病。因此，这是具有挑战性的 充分了解多种病因和与年龄有关的前驱过程如何有助于其 病理生理学。除其他因素外，定义在脑微血管结构和功能中可能发挥作用 在AD的发作和发展中的关键作用。尽管它对早期诊断和作为治疗的重要性 目标，目前尚不清楚它们是AD发病机理的因素还是 多因素条件在以后导致广告。特别是存在两个关键知识差距：（1） 在AD发作和发展过程中血管与其他关键因素之间的时间关系是 不清楚； （2）几乎没有关于各种微血管结构和如何在各种微血管结构和 功能特性与和/或有助于神经元变性明显相关。 在这里，我们将开发，优化和整合实验和计算技术的 模型中AD与正常老化的进行性微血管变化的寿命跟踪和分析 老鼠。首先，我们将优化我们的光学相干断层扫描成像和3D图像处理技术 为了跟踪32个血管和非血管措施的时间顺序，在鼠标的寿命上纵向 包括微血管结构，微循环，功能反应性，Aβ斑块积累，神经元 损失和认知能力下降（目标1）。这些前所未有的全面临时动态数据和 高级统计/相关分析将使我们能够确定微血管缺陷是否存在 先于神经元丧失或Aβ积累，以及这些改变如何相关，直接解决 首先知识差距。在AIM 2中，我们将改善微血管流量和功能的计算模型 充血，然后将模型与AIM 1的实验数据相结合，以研究复杂的原因 - 影响关系。我们的计算模型将使我们能够本质上“打开”和“关闭”每个 微血管赤字（例如，较薄的血管，曲折毛细血管，灌注不足，毛细管停滞），并测试其 对氧气递送到神经元的影响，这很难实验，有时不可能实现。 这种合并的方法将为测试血管缺陷的作用提供强大而独特的策略 神经元变性，直接解决第二个知识差距，并向未来的研究告知 诊断和治疗目标发展。