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.

总结 阿尔茨海默病（AD）是一种影响数百万人的进行性神经退行性疾病 目前在世界范围内，都是无法治愈的。随着人口老龄化，AD和相关痴呆症正在成为最大的 病史中的流行病：预计65岁及以上AD患者的数量将在 到2050年翻两到三倍如成像和生物标志物研究所示，年龄是一个主要的风险因素， 发展中的痴呆症，和AD的病理生理过程开始前十多年开始， 痴呆症的诊断然而，AD是一种异质性和多因素疾病;因此， 充分了解多种病因和年龄相关的前驱过程如何有助于其 病理生理学在其他因素中，脑微血管结构和功能的缺陷可能在脑缺血中起作用。 在AD的发生和发展中起关键作用。尽管它对于早期诊断和作为一种治疗方法很重要， 目前尚不清楚它们是否是AD发病机制的致病因素或AD的早期后果。 多因素条件导致AD的后期阶段。特别是，存在两个关键的知识差距：（1） 在AD的发生和发展过程中，血管和其他关键因素之间的时间关系是 不清楚;（2）很少有人研究各种微血管结构和 功能特性明显地与神经元变性相关和/或促成神经元变性。 在这里，我们将开发，优化和整合实验和计算技术， AD与正常衰老模型中进行性微血管改变的寿命跟踪和分析 小鼠首先，我们将优化我们的光学相干层析成像和3D图像处理技术 为了在小鼠的寿命内纵向跟踪32个血管和非血管测量的时间过程， 包括微血管结构、微循环、功能反应性、Aβ斑块积聚、神经元 丧失和认知下降（目标1）。这些前所未有的全面的时间动态数据和 先进的统计/相关分析将使我们能够确定微血管缺陷是否 在神经元损失或Aβ积累之前，以及这些改变如何相互关联，直接解决了神经元损伤的问题。 第一个知识鸿沟。在目标2中，我们将改进微血管流动和功能的计算模型， 然后将模型与目标1的实验数据结合联合收割机，探讨复杂的原因。 影响关系。我们的计算模型将使我们能够基本上“打开”和“关闭”每个 微血管缺陷（例如，较薄的血管，曲折的毛细血管，灌注不足，毛细血管停滞），并测试其 这对向神经元的氧输送有影响，这在实验上很难实现，有时甚至不可能实现。 这种联合方法将提供一种强有力的和独特的策略，用于测试血管缺陷在以下方面的作用： 神经元变性，直接解决第二个知识差距，并为未来的研究提供信息， 诊断和治疗靶点开发。