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 淀粉样蛋白β和Tau是轻度认知障碍（MCI）/阿尔茨海默氏病（AD）的标志。这 体内淀粉样蛋白β，tau和神经变性与认知，临床和遗传标记的关系 不太了解。患有AD病理学的患者在其临床症状和疾病中暴露了异质性 课程。了解AD的潜在神经生物学异质性机制并改善 成果一直是中心目标。该提案利用了体内淀粉样蛋白的完整信息 β正电子发射断层扫描（淀粉样蛋白宠物），tau PET，结构磁共振成像（SMRI）， 通过先进的机器学习方法认知，临床和遗传测量，并研究 这些测量在MCI/AD患者中相对于正常对照的患者之间的关系。提案将 研究来自阿尔茨海默氏病神经成像倡议（ADNI; n = 898）和华盛顿的数据 大学的骑士阿尔茨海默氏病研究中心（骑士ADRC； n = 1,121）。这项研究将是第一个 检查区域性淀粉样宠物，Tau Pet和Smri标记及其与认知，临床和 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 提案将以分布量比（DVR）和SMRI为数量，将区域宠物结果量化为体积和 研究他们与认知[迷你精神状态检查（MMSE）]的关联，临床[临床痴呆症 盒子（CDR-SB）和CDR]的评分总和[多基因风险评分（PRS）和载脂蛋白E（APOE）] 测量。 AIM 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具有认知和临床表型，并检验了区域是否 基于大脑的成像测量暴露了多元预测关联与认知和临床 MCI/AD患者和对照组中的表型。 AIM 2将研究淀粉样宠物的区域异质性Tau PET和SMRI通过半监督的机器学习方法产生结果。该研究将比较成像 确定的患者或对照组与患者的每个亚组之间的结果 成像标记在患者亚组之间是否有所不同的假设。 AIM 3将检查 淀粉样宠物，tau宠物和SMRI异质性特征与认知和遗传学的关系 成像签名是否与MCI/AD亚组中的认知和遗传学有所不同 相对于控件。总体而言，该创新提案将产生有关广告异质性的关键信息 机制，并有助于诊断和AD治疗的精确医学。 1