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: 9311342
• 负责人: Daniel L Gillen
• 金额: $ 75.63万
• 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 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9311342
• 关键词: Address; Affect; Age; Age-associated memory impairment; Aging; Aging-Related Process; Alzheimer' s Disease; 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; Family; Functional Magnetic Resonance Imaging; Future; Genotype; Goals; Government; Head; Hippocampus (Brain); Image; Impaired cognition; Individual; Intervention Trial; Link; Measures; Medial; Medicare; Memory; Methods; Modeling; Neurobiology; Neurocognitive; Neuropsychological Tests; Outcome; Outcome Measure; Participant; Pathologic; Pathology; Pattern; Perforant Pathway; Performance; Population; Positron-Emission Tomography; Prevalence; Prevention; Process; Public Health; Recommendation; Recruitment Activity; Research; Research Personnel; Resolution; Rest; Risk Factors; Sampling; Scanning; Sensitivity and Specificity; Statistical Models; Structure; Sum; System; Techniques; Temporal Lobe; Testing; Thick; Validation; Visit; Work; age related; aging brain; amyloid pathology; base; cognitive change; cognitive task; cognitive testing; cohort; cost; data management; effective intervention; follow-up; human very old age (85+); improved; innovation; longitudinal analysis; multimodality; neuroimaging; neuroimaging marker; power analysis; pre-clinical; primary outcome; programs; 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.

项目概要 与年龄相关的认知能力下降是一个重大的公共卫生问题，因为 60 岁以上的人口 继续大幅增长。对造成这种下降的机制的理解取得进展将有助于 有效干预，大幅减轻家庭、政府和社会的负担 程序。随着婴儿潮一代的老龄化，我们将面临 50 万亿美元的医疗保险费用，从而放大 关注的规模以及我们提议的工作的重要性。衰老是阿尔茨海默病的主要危险因素 疾病（AD），目前影响超过五百万美国人。如果没有发现预防或治疗方法， 到 2050 年，这一数字可能会增加到 1600 万。建立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神经 与淀粉样蛋白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来识别患有临床前 AD 的个体。我们将结合现状 最先进的高分辨率多模态 MRI 工具，具有针对性、创新的认知测试方法和 利用我们当地的 UCI 阿尔茨海默病研究中心 (ADRC) 进行 (1) 招募无症状患者 老年人（临床核心），(2) 基于 ApoE 基因型的样本富集（病理学核心），(3) 详细 对所有参与者进行认知评估（临床核心），以及 (4) 开发统计模型以优化 结合成像和认知测量来预测认知能力下降（数据管理和统计 核）。我们将从 ADRC 和当地招募无症状老年人（60-85 岁，n=150） 社区，并将通过 ApoE 基因型丰富淀粉样蛋白阳性样本。我们将进行 PET 淀粉样蛋白检查 使用 [18F] AV-45（florbetapir）对所有参与者进行扫描以确定淀粉样蛋白状态（目标为 50% 阳性 整个样本）。我们将进行高分辨率多模态 MRI 和有针对性的认知 在基线时对所有参与者进行检查，并同时重复认知检查 ADRC 每年和每两年进行一次随访。在目标 1 中，我们将使用一组新开发的认知测试 关注归因于内侧颞叶（MTL）过程的记忆功能，特别是“模式分离”。 这些测试改变了对象、空间和时间域中的助记干扰。在目标 2 中，我们将使用高 分辨率静息态 fMRI (1.5 mm)、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、功能和连接性。 在目标 3 中，我们将使用统计预测模型来确定最佳的措施组合： 预测纵向认知/临床衰退。总的来说，拟议的研究将显着提供信息 我们对在淀粉样蛋白病理存在或不存在的情况下衰老大脑认知能力下降的理解 并使我们能够更好地定义临床前 AD 并为未来的干预试验提出建议。