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.

项目总结 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。在理解这种下降背后的机制方面的进展将使 有效干预并大幅减轻家庭以及政府和社会的负担 程序。随着婴儿潮一代的年龄增长，我们将面临50万亿美元的医疗保险成本，从而放大 关注的规模和我们提议的工作的意义。衰老是阿尔茨海默氏症的主要危险因素 疾病(AD)，目前影响着500多万美国人。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。建立疾病进程的早期指标 临床前阶段是生物医学研究的重要目标。我们的项目目标是确定神经 与淀粉样蛋白病理堆积相关的特征(即生物标记物)，并客观地确定 如何结合这些生物标记物来识别临床前AD患者。我们将结合国家- 最先进的高分辨率多模式磁共振成像工具，具有针对性、创新性的认知测试方法和 利用我们当地的UCI阿尔茨海默病研究中心(ADRC)(1)招募无症状 老年人(临床核心)，(2)基于载脂蛋白E基因的样本丰富(病理学核心)，(3)详细 所有参与者的认知评估(临床核心)，以及(4)开发统计模型以实现最优 结合成像和认知测量预测认知功能衰退(数据管理和统计 核心)。我们将从ADRC和当地招募没有症状的老年人(60-85岁，n=150) 并将通过ApoE基因来丰富样本的淀粉样阳性。我们将进行PET淀粉样蛋白 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 在整个样本中)。我们将进行高分辨率多模式磁共振成像和靶向认知 在基线时对所有参与者进行检查，并同时重复认知检查 ADRC每年一次和两年一次的后续访问。在目标1中，我们将使用一组新开发的认知测试 关注内侧颞叶(MTL)过程的记忆功能，特别是“模式分离”。 这些测试在物体、空间和时间域上有不同的助记干扰。在目标2中，我们将使用HIGH 分辨率静息状态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、功能和连通性。 在目标3中，我们将使用统计预测建模来确定下列措施的最佳组合 预测纵向认知/临床衰退。总体而言，拟议的研究将显著地为 我们对衰老大脑中存在和不存在淀粉样蛋白病变时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。