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

使用机器学习表征阿尔茨海默病分子和解剖成像标记及其与认知和遗传学的关系

• 批准号: 10723499
• 负责人: Ganesh Chand
• 金额: $ 11.91万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10723499
• 关键词: Affect; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease patient; Amyloid; Amyloid beta-Protein; Apolipoprotein E; Behavior; Behavioral; Brain; Brain region; Clinical; Clinical Markers; Cognition; Cognitive; Complex; Computer Assisted; Data; Data Set; Dementia; Diagnosis; Diagnostic; Disease Outcome; Exhibits; Functional disorder; Future; Gene Expression Profile; Genetic; Genetic Markers; Goals; Heterogeneity; Image; Knowledge; Machine Learning; Magnetic Resonance Imaging; Measurement; Mental disorders; Methods; Multimodal Imaging; Nerve Degeneration; Neural Network Simulation; Neurobiology; Neurodegenerative Disorders; Neurofibrillary Tangles; Outcome; Participant; Patients; Pattern; Persons; Phenotype; Positron-Emission Tomography; Precision therapeutics; Preparation; Psychoses; Research; Research Personnel; Science; Senile Plaques; Subgroup; Sum; Symptoms; Techniques; Testing; Therapeutic; Translating; United States; Universities; Variant; Washington; anatomic imaging; behavioral phenotyping; brain based; clinical phenotype; cognitive performance; deep neural network; design; disease heterogeneity; imaging biomarker; imaging modality; improved; improved outcome; in vivo; innovation; machine learning method; machine learning model; machine learning prediction; magnetic resonance imaging biomarker; mental state; mild cognitive impairment; molecular imaging; neurobiological mechanism; neuroimaging; neuropathology; neuropsychiatric disorder; novel; patient subsets; polygenic risk score; pre-clinical; precision medicine; prognostic; public health relevance; research clinical testing; success; supervised learning; support vector machine; targeted treatment; tau Proteins; therapeutic biomarker; treatment response

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

项目摘要 淀粉样β蛋白和tau蛋白是轻度认知损害(MCI)/阿尔茨海默病(AD)的标志。这个 体内淀粉样β蛋白、tau蛋白和神经变性与认知、临床和遗传标记物的关系 都没有被很好地理解。阿尔茨海默病患者的临床症状和疾病表现出多样性 当然了。了解阿尔茨海默病潜在的神经生物学异质性机制并改善 结果一直是核心目标。这一建议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患者与正常对照组的这些测量之间的关系。这项提议将 研究阿尔茨海默病神经成像倡议(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)。这项研究将是第一个 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 在AD研究中使用机器学习、预测建模和异质性分析的遗传表型。这个 建议将区域PET结果量化为分布体积比(DVR)和sMRI作为体积和 探讨它们与认知[简易智力状态检查(MMSE)]、临床[临床痴呆]的关系 评价盒总和(CDR-SB和CDR)和遗传[多基因风险评分(PR)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关系，并检验是否存在区域性的假设 基于大脑的成像测量显示认知和临床的多变量预测性关联 MCI/AD患者和对照组的表型。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的pET和sMRI结果。这项研究将比较成像 确定的患者或对照亚组与每个患者亚组之间的结果，以测试 关于不同亚组患者的影像标志物是否不同的假设。目标3将研究 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 在MCI/AD亚组中，成像特征是否与认知和遗传学存在差异关联 相对于控件。总体而言，这一创新提案将提供有关AD异构性的关键信息 机制，有助于AD的精准医学诊断和治疗。 1