Neuroimaging biomarkers for cognitive decline in elderly with amyloid pathology

淀粉样蛋白病理老年人认知能力下降的神经影像生物标志物

• 批准号: 10160735
• 负责人: Daniel L Gillen
• 金额: $ 148.72万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10160735
• 关键词: Address; Affect; Age; Age-associated memory impairment; Aging; Alzheimer' s Disease; Alzheimer' s disease risk; American; Amyloid; Amyloid beta-Protein; Apolipoprotein E; Area; Biological Markers; Biomedical Research; Brain; Clinical; Cognitive; Communities; Complex; Dementia; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Elderly; Enrollment; Episodic memory; Evaluation; Functional Magnetic Resonance Imaging; Future; Genotype; Goals; Government; Head; Hippocampus (Brain); Image; Impaired cognition; Individual; Intervention Trial; Link; Magnetic Resonance Imaging; Measures; Medial; Medicare; Memory; Methods; Neurobiology; Neurocognitive; Neuropsychological Tests; Outcome; Outcome Measure; Participant; Pathologic; Pathology; Pattern; Perforant Pathway; Performance; Population; Positron-Emission Tomography; Prevalence; Prevention; Process; Public Health; Recommendation; Research; Research Personnel; Resolution; Rest; Sampling; Scanning; Sensitivity and Specificity; Statistical Models; Structure; Sum; System; Techniques; Temporal Lobe; Testing; Thick; Validation; Visit; Work; age related; aging brain; amyloid pathology; baby boomer; base; cognitive change; cognitive task; cognitive testing; cohort; cost; data management; effective intervention; family burden; follow-up; human old age (65+); human very old age (85+); improved; innovation; longitudinal analysis; multimodality; neuroimaging; neuroimaging marker; non-demented; power analysis; pre-clinical; predictive modeling; primary outcome; programs; recruit; relating to nervous system; secondary outcome; social; statistics; synergism; tool

PROJECT SUMMARY Age-related cognitive decline is a significant public health concern as the population over the age of 60 continues to grow sharply. Advances in understanding the mechanisms that underlie this decline will allow for effective interventions and substantially reduce the burden on families as well as government and social programs. We will be faced with 50 trillion dollars in Medicare costs as the baby boomers age, thus magnifying the size of the concern and the significance of our proposed work. Aging is a major risk factor for Alzheimer's disease (AD), which currently affects over five million Americans. If no prevention or treatment is discovered, this number could increase to 16 million by 2050. Establishing early indicators of the disease process during the preclinical stage is a critical goal of biomedical research. Our project goal is to determine the neural features (i.e. biomarkers) associated with amyloid pathology accumulation, and determine objectively how to combine these biomarkers to identify individuals with preclinical AD. We will combine state-of- the-art high-resolution multimodal MRI tools with targeted, innovative cognitive testing approaches and leverage our local UCI Alzheimer's Disease Research Center (ADRC) for (1) recruitment of asymptomatic older adults (Clinical Core), (2) sample enrichment based on ApoE genotype (Pathology Core), (3) detailed cognitive evaluation of all participants (Clinical Core), and (4) development of statistical models to optimally combine imaging and cognitive measures for prediction of cognitive decline (Data Management and Statistics Core). We will recruit asymptomatic older adults (60-85 years old, n=150) from the ADRC and from the local community and will enrich the sample for amyloid positivity via ApoE genotype. We will conduct PET amyloid scans with [18F] AV-45 (florbetapir) on all participants to determine amyloid status (targeting 50% positivity across the whole sample). We will conduct high-resolution multimodal MRI and targeted cognitive examinations in all participants at baseline, and repeat the cognitive examinations contemporaneously with ADRC annual and bi-annual follow-up visits. In Aim 1, we will use a set of newly developed cognitive tests that focus on memory function attributed to medial temporal lobe (MTL) processes, particularly “pattern separation”. These tests vary mnemonic interference in the object, spatial and temporal domains. In Aim 2, we will use high resolution resting state fMRI (1.5 mm), ultrahigh resolution microstructural diffusion tensor imaging (DTI, 0.66 mm), and high resolution structural MRI (0.55 mm), to assess structure, function and connectivity of the MTL. In Aim 3, we will use statistical prediction modeling to determine the optimal combination of measures that predicts longitudinal cognitive/clinical decline. Collectively, the proposed studies will significantly inform our understanding of cognitive decline in the aging brain in the presence and absence of amyloid pathology and allow us to better define preclinical AD and make recommendations for future intervention trials.

项目摘要 与年龄有关的认知下降是一个重大的公共卫生问题，因为60岁以上的人口 继续急剧增长。理解这一下降将允许的机制的进步 有效的干预措施，并大大减少对家庭以及政府和社会的烧毁 程序。随着婴儿潮一代的年龄，我们将面临50万亿美元的医疗保险费用，从而放大 关注的规模和我们提议的工作的重要性。衰老是阿尔茨海默氏症的主要危险因素 疾病（AD），目前影响超过500万美国人。如果没有发现预防或治疗， 到2050年，这个数字可能会增加到1600万。确定疾病过程的早期指标 临床前阶段是生物医学研究的关键目标。我们的项目目标是确定中性 与淀粉样病理积累相关的特征（即生物标志物），并客观地确定 如何结合这些生物标志物以识别临床前AD的个体。我们将结合最新 ART高分辨率多模式MRI工具具有针对性的创新认知测试方法和 利用我们当地的UCI阿尔茨海默氏病研究中心（ADRC）进行（1）招募无症状 老年人（临床核心），（2）基于APOE基因型（病理核心）的样品富集，（3）详细 对所有参与者（临床核心）的认知评估，以及（4）开发统计模型以最佳 结合预测认知下降的成像和认知测量值（数据管理和统计数据 核）。我们将从ADRC和本地招募无症状的老年人（60-85岁，n = 150） 社区，将通过APOE基因型丰富样品的淀粉样蛋白阳性。我们将进行宠物淀粉样蛋白 使用[18F] AV-45（Florbetapir）扫描所有参与者以确定淀粉样蛋白状态（针对50％的潜力 在整个样本中）。我们将进行高分辨率的多模式MRI并靶向认知 在基线的所有参与者中考试，并同时重复认知检查 ADRC年度和两年一次的随访。在AIM 1中，我们将使用一组新开发的认知测试 关注归因于内侧临时叶（MTL）过程的内存功能，尤其是“模式分离”。 这些测试在物体，空间和临时域中变化了助理干扰。在AIM 2中，我们将使用高 分辨率静止状态fMRI（1.5毫米），超高分辨率微结构扩散张量成像（DTI，0.66 MM）和高分辨率结构MRI（0.55 mm），以评估MTL的结构，功能和连通性。 在AIM 3中，我们将使用统计预测建模来确定措施的最佳组合 预测纵向认知/临床下降。总的来说，拟议的研究将大大告知 在存在和不存在淀粉样病理学的情况下，我们对衰老大脑认知能力下降的理解 并允许我们更好地定义临床前广告，并为将来的干预试验提出建议。