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