Neuroimaging biomarkers for cognitive decline in elderly with amyloid pathology

项目摘要 由于60岁以上的人群中，与糖尿病相关的认知能力下降是一个重要的公共卫生问题， 继续大幅增长。在理解这种下降的机制方面取得的进展将允许 有效的干预措施，大大减轻家庭以及政府和社会的负担 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， 关注的规模和我们所提议的工作的重要性。衰老是老年痴呆症的主要危险因素 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方法， 到2050年，这一数字可能增加到1 600万。建立疾病进程的早期指标， 临床前阶段是生物医学研究的关键目标。我们的项目目标是确定 与淀粉样蛋白病理学积累相关的特征（即生物标志物），并客观地确定 如何将这些生物标志物联合收割机来鉴定临床前AD患者。我们将联合收割机的状态- 最先进的高分辨率多模态MRI工具，具有针对性的创新认知测试方法， 利用我们当地的UCI阿尔茨海默病研究中心（ADRC）（1）招募无症状的 老年人（临床核心），（2）基于ApoE基因型的样本富集（病理核心），（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，以最佳 结合联合收割机成像和认知测量来预测认知衰退（数据管理和统计 核心）。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基因型富集淀粉样蛋白阳性样本。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 整个样品）。我们将进行高分辨率的多模式MRI和有针对性的认知 在基线时对所有参与者进行认知检查，并在 ADRC每年和每两年进行一次随访。在目标1中，我们将使用一套新开发的认知测试， 集中在记忆功能归因于内侧颞叶（MTL）的过程，特别是“模式分离”。 这些测试在对象、空间和时间域上改变记忆干扰。在目标2中，我们将使用高 静息态fMRI（1.5mm）、高分辨微结构弥散张量成像（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测量组合， 预测纵向认知/临床衰退。总的来说，拟议的研究将大大告知 我们对存在和不存在淀粉样病变的衰老大脑认知能力下降的理解 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。

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.