Characterizing Alzheimer's disease molecular and anatomical imaging markers and their relationships with cognition and genetics using machine learning

项目概要 β 淀粉样蛋白和 tau 蛋白是轻度认知障碍 (MCI)/阿尔茨海默病 (AD) 的标志。的 体内 β 淀粉样蛋白、tau 蛋白和神经变性与认知、临床和遗传标记的关系 没有被很好地理解。 AD 病理学患者的临床症状和疾病表现出异质性 当然。了解 AD 的潜在神经生物学异质性机制并改善 结果一直是中心目标。该提案利用了体内淀粉样蛋白的补充信息- β 正电子发射断层扫描（淀粉样蛋白 PET）、tau PET、结构磁共振成像（sMRI）、 通过先进的机器学习方法进行认知、临床和遗传测量，并研究 MCI/AD 患者相对于正常对照的这些测量值之间的关系。该提案将 研究来自阿尔茨海默病神经影像倡议 (ADNI; N = 898) 和华盛顿的数据 大学奈特阿尔茨海默病研究中心（奈特 ADRC；N = 1,121）。这项研究将首次 检查区域淀粉样蛋白 PET、tau PET 和 sMRI 标记物及其与认知、临床和认知的关系 在 AD 研究中使用机器学习预测模型和异质性分析来确定遗传表型。的 该提案将量化区域 PET 结果作为分布体积比 (DVR)，将 sMRI 量化为体积和 调查它们与认知[简易精神状态检查（MMSE）]、临床[临床痴呆 框的评分总和（CDR-SB）和CDR]，以及遗传[多基因风险评分（PRS）和载脂蛋白E（APOE）] 测量。目标 1 将开发机器学习建模方法来研究淀粉样蛋白的关系 PET、tau PET 和 sMRI 与认知和临床表型的比较，并检验区域性是否存在的假设 基于大脑的成像测量显示出与认知和临床的多变量预测关联 MCI/AD 患者和对照的表型。目标 2 将研究淀粉样蛋白 PET、tau 的区域异质性 通过半监督机器学习方法获得 PET 和 sMRI 结果。该研究将比较成像 确定的患者亚组或对照组与每个患者亚组之间的结果，以测试 患者亚组之间成像标记物是否存在差异的假设。目标 3 将检查 淀粉样蛋白 PET、tau PET 和 sMRI 异质性特征与认知和遗传学的关系待测试 MCI/AD 亚组中影像特征是否与认知和遗传学存在差异性关联 相对于控件。总体而言，这一创新提案将产生有关 AD 异质性的关键信息 机制，为AD诊治的精准医学做出贡献。 1

Project Summary Amyloid-beta and tau are hallmarks of mild cognitive impairment (MCI)/Alzheimer’s disease (AD). The relationships of in-vivo amyloid-beta, tau, and neurodegeneration with cognitive, clinical, and genetic markers are not well understood. Patients with AD pathology exhibit heterogeneity in their clinical symptoms and illness course. Understanding the underlying neurobiological heterogeneity mechanisms of AD and improving the outcomes have been the central goals. This proposal leverages complementary information of in-vivo amyloid- beta positron emission tomography (amyloid PET), tau PET, structural magnetic resonance imaging (sMRI), cognitive, clinical, and genetic measurements via advanced machine learning methods and investigates the relationships among these measurements in patients with MCI/AD relative to normal controls. The proposal will study the data from the Alzheimer Disease Neuroimaging Initiative (ADNI; N = 898) and the Washington University’s Knight Alzheimer Disease Research Center (Knight ADRC; N = 1,121). This study will be the first to examine regional amyloid PET, tau PET, and sMRI markers and their relationships with cognitive, clinical, and genetic phenotypes using machine learning predictive modeling and heterogeneity analytics in AD research. The proposal will quantify regional PET outcomes as distribution volume ratio (DVR) and sMRI as the volumes and investigate their associations with cognitive [Mini-mental state examination (MMSE)], clinical [clinical dementia rating sum of boxes (CDR-SB) and CDR], and genetic [polygenic risk scores (PRS) and apolipoprotein E (APOE)] measurements. Aim 1 will develop machine learning modeling methods to study the relationships of amyloid PET, tau PET, and sMRI with cognitive and clinical phenotypes and test the hypothesis of whether regional brain-based imaging measurements exhibit multivariate predictive associations with cognitive and clinical phenotypes in MCI/AD patients and controls. Aim 2 will study the regional heterogeneity of amyloid PET, tau PET, and sMRI outcomes via semi-supervised machine learning methods. The study will compare the imaging outcomes between identified subgroups of patients or controls vs. each subgroup of patients to test the hypothesis of whether imaging markers differ between subgroups of patients. Aim 3 will examine the relationships of amyloid PET, tau PET, and sMRI heterogeneity signatures with cognition and genetics to test whether imaging signatures associate differentially with cognition and genetics in the subgroups of MCI/AD relative to controls. Overall, this innovative proposal will yield critical information on AD heterogeneity mechanisms, and contribute to precision medicine of diagnosis and treatment of AD. 1