Multi-dimensional network framework for AD detection and progression

项目摘要 阿尔茨海默病（Alzheimer's disease，AD）是最常见的一种痴呆症，对患者、家庭和精神健康都有重要影响 和公共卫生系统。据估计，2018年有570万美国人患有阿尔茨海默氏症。 在疾病的临床表现时，已经存在显著的不可逆脑损伤， 使得在疾病的早期阶段诊断AD成为潜在治疗的迫切先决条件， 延迟或防止疾病2.据估计，早期和准确的AD检测可以节省高达7.9万亿美元的费用。 医疗和护理费用1.此外，早期AD检测和进展对于监测AD治疗的效果至关重要。 实验性治疗以及为开发有效治疗提供信息。这是一个迫切的临床需要， 改善早期AD检测和进展。随着AD神经生物学的最新进展，我们的理解 这种疾病已经从一个基于临床症状的生物结构转变为一个多因素的生物结构， 并且是异质的，并且不能由任何单一可用的生物标志物来解释。NIH投入了数十亿美元 在过去的几十年里，有300万美元用于资助几个中心和大型老年人群体的数据计划; 从而产生大量的多模态神经成像、认知、临床、生物样本和遗传数据。然而，在这方面， 在采用创新的综合方法汇总各种模式的数据方面所作的努力较少 来捕捉AD的异质性。为了填补多模态AD数据分析范式中的差距， 拟议研究的总体目标是测试和验证多维网络框架， 在单个模型中跨模态聚合数据，以捕获AD的异质性，并进一步 增强AD检测和进展。我们的中心假设-由以前的证据和初步的支持 数据-是拟议的框架将提高AD检测和进展的能力， 检测跨多个数据类型的公共以及互补信号，并通过减少 不同的规模，收集偏见和噪音在每一个模态。我们将整合行为，临床，MR 来自ADNI和斯坦福大学ADRC数据的成像、Aβ和Tau标记物以及神经变性标记物，以测试 并验证了所提出的多维网络框架，以集成不同的数据类型， 检测AD（Aim 1）以及通过应用多维网络表征AD进展 在各种测量中纵向变化的框架（目标2）。据我们所知，这是第一次研究 将各种AD数据集成到多维网络模型中，以表征AD，从而进一步增强AD 检测和进展。如果被证明是成功的，这个高风险高回报的提议将对 AD表征、早期发现和进展具有显著的健康和经济影响。此外，委员会认为， 这项研究的成功完成将为多模态数据的综合分析提供关键工具， 帮助改变现有数据集和临床试验的分析范式，主要集中在 独立分析单一数据类型。

PROJECT SUMMARY Alzheimer's disease (AD) is the most common form of dementia with significant impact on patients, families and the public health system. An estimated 5.7 million Americans have Alzheimer's in 2018 1. At the time of clinical manifestation of the disease, significant irreversible brain damage is already present, rendering the diagnosis of AD at early stages of the disease an urgent prerequisite for potential therapies to delay or prevent symptoms2. It is estimated that early and accurate AD detection could save up to $7.9 trillion in medical and care costs1. Further, early AD detection and progression is crucial for monitoring the effect of experimental treatments as well as for informing developing efficient treatments. It is a pressing clinical need to improve early AD detection and progression. With recent advances in neurobiology of AD, our understanding of the disease has moved from one based on clinical symptoms to a biological construct that is multifactorial and heterogeneous and that cannot be explained by any single available biomarkers. NIH has devoted billions of dollars in the past decades to fund several centers and data initiatives on large cohorts of older adults; resulting in a wealth of multi-modal neuroimaging, cognitive, clinical, biospecimen, and genetic data. However, less effort has been made to implement innovative integrative methods for aggregating data across modalities to capture the heterogeneity of AD. To fill the gap in the analysis paradigm of multi-modal AD data, the overarching goals of the proposed study are to test and validate a multi-dimensional network framework for aggregating data across modalities in a single model to capture the heterogeneity of AD and to further enhance AD detection and progression. Our central hypothesis – backed by previous evidence and preliminary data – is that the proposed framework will enhance AD detection and progression by improving the ability to detect common as well as complementary signals across multiple data types and by reducing the effect of differences in scale, collection bias and noise in each modality. We will integrate behavioral, clinical, MR imaging, Aβ and Tau markers, and neurodegeneration markers from ADNI and Stanford ADRC data to test and validate the proposed multi-dimensional network framework for integration of different data types for early detection of AD (Aim1) as well as to characterize AD progression by applying multi-dimensional network framework to longitudinal changes in various measurements (Aim 2). To our knowledge, this is the first study that integrates various AD data in a multi-dimensional network model to characterize AD to further enhance AD detection and progression. If proven successful, this high-risk high-reward proposal will have a large impact on AD characterization, early detection and progression with significant health and economical impact. Moreover, successful completion of this study will provide critical tools for integrative analysis of multimodal data and will help shift the current analysis paradigm of available datasets and clinical trials which mainly focuses on independent analysis of single data types.