Integrating Machine Learning and Genomic Approaches to Understand Cerebral Small Vessel Disease Pathogenesis from White Matter Hyperintensity Patterns

整合机器学习和基因组方法从白质高信号模式了解脑小血管疾病的发病机制

• 批准号: 10468866
• 负责人: Chia-Ling Phuah
• 金额: $ 17.66万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-20 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10468866
• 关键词: 3-Dimensional; Address; Adult; Affect; Alzheimer' s Disease; Amyloid deposition; Archives; Arteries; Award; Bioinformatics; Biological; Biology; Blood Vessels; Brain; Categories; Cerebral Amyloid Angiopathy; Cerebral hemisphere hemorrhage; Cerebral small vessel disease; Cerebrovascular Disorders; Cerebrovascular Physiology; Clinical; Cluster Analysis; Complex; Computational Technique; Data; Data Set; Dementia; Detection; Development; Development Plans; Disease; Disease Progression; Distal; Elderly; Environment; Etiology; Failure; Functional disorder; Genes; Genetic; Genetic Determinism; Genetic Risk; Genetic Variation; Genomic approach; Genotype; Goals; Grant; Heritability; Heterogeneity; Hypertension; Image; Impaired cognition; Individual; Informatics; Intervention; Investigation; Ischemic Stroke; K-Series Research Career Programs; Knowledge; Lead; Lobar; Location; Longitudinal Studies; Machine Learning; Magnetic Resonance Imaging; Maps; Mentorship; Methods; Microvascular Dysfunction; Neurologic; Neurologist; Pathogenesis; Pathogenicity; Pathologic; Pathologic Processes; Pathology; Pathway interactions; Pattern; Phenotype; Play; Population; Prevention; Process; Research; Research Personnel; Research Proposals; Resources; Risk Factors; Role; Statistical Methods; Stroke; Stroke prevention; Testing; Training; Universities; Validation; Variant; Washington; White Matter Hyperintensity; arteriole; base; biobank; bioinformatics tool; brain parenchyma; career; cohort; design; disorder subtype; effective therapy; genetic analysis; genetic association; genetic variant; genome wide association study; genome-wide; hypertension control; imaging biomarker; imaging genetics; improved; innovation; insight; interdisciplinary approach; machine learning method; medical schools; multidisciplinary; nervous system disorder; neuroimaging; new therapeutic target; novel; patient population; population based; programs; racial and ethnic; risk variant; serial imaging; stroke therapy; success; therapeutic development; tool; treatment strategy; unsupervised learning

PROJECT SUMMARY As a neurointensivist and neurologist at Washington University School of Medicine in St. Louis (WUSM), my career goal is to develop an independent research program as a computational biologist capable of using advanced bioinformatics and statistical methods to integrate analysis of large-scale neuroimaging and genetic data, with the aim of deepening understanding of the biological mechanisms influencing cerebral small vessel disease (SVD) and identifying new targets for therapeutic development. As a first step towards this goal, I have designed an innovative proposal that combine machine-learning (ML) methods and integrated imaging genetic analyses of large-scale neuroimaging and genetic data to improve characterization of SVD disease mechanisms. The clinical, imaging, and etiologic heterogeneity of SVD have impeded efforts to uncover the pathophysiology of this common and debilitating neurological disease. White matter hyperintensities (WMH), a major imaging endpoint of SVD, are comprised of multiple SVD pathologic processes. Growing evidence suggests location-specific vulnerability of brain parenchyma to different underlying SVD pathologic processes, in which spatially localized WMH patterns may reflect distinct SVD etiologies. Characterizing WMH spatial pattern variations in SVD will not only provide insights into underlying pathogenesis, such as vascular amyloid deposition, arteriolosclerosis, and other less well defined or as-yet unknown disease mechanisms, but also lead to creation of novel imaging biomarkers of these SVD pathologic processes. This proposal addresses a key inadequacy, as existing WMH pattern definitions are determined empirically and cannot distinguish overlapping SVD etiologies and risk factors. In this proposal, I aim to capture WMH spatial pattern variations that reflect distinct SVD etiologies in an unbiased manner, by applying clustering analysis/ML methods to structural MRI data to create novel etiology-specific SVD imaging phenotypes. Moreover, given that genetics influence the variance of WMH, I will integrate genetic analyses of these WMH patterns to uncover novel mechanisms that influence SVD pathogenesis. My preliminary data demonstrate the feasibility of identifying data-driven WMH spatial pattern variations, which are specific to distinct SVD etiologies, and allow detection of genetic risk variants that may help inform SVD pathologic processes. My career plan leverages the extensive resources and exceptional environments at WUSM, under the guidance of a multidisciplinary mentorship team with expertise across diverse fields including cerebrovascular physiology, neuroimaging, informatics, genetics, and machine learning (Drs. Jin-Moo Lee, Daniel Marcus, Carlos Cruchaga and Yasheng Chen). In this Career Development Award, I propose to: 1) determine distinct WMH spatial patterns that can discriminate underlying SVD pathology and/or risk factors by applying pattern analysis ML methods to structural MRI data from three unique cohorts (n=2,710) enriched for different SVD pathologies (Aim 1a), and examining if the ML-defined WMH patterns segregate individuals by well-defined SVD risk factors as biologic validation (Aim 1b), and 2) identify genetic variants (Aim 2a) associated with WMH patterns that reflect diverse pathologic processes influencing SVD using genome wide association and gene-based analyses; replicate the top variants (Aim 2b) in an independent population-based cohort (n=21,708); and use advanced bioinformatics tools to uncover new biologic pathways associated with WMH spatial patterns (Aim 2c). This research proposal and accompanying development plan with focused training in machine learning, neuroimaging, and multivariate methods for integrated imaging genetics analysis, will build on my background in genetics towards a career investigating cerebrovascular disorders using translational bioinformatics. This Award will provide me with the necessary training to evolve into an independent investigator with a computational research program that can integrate large imaging and genetics datasets to derive results that are highly relevant to the prevention and treatment of cerebrovascular disease in my clinical patient population.

项目摘要 作为圣路易斯华盛顿大学医学院（WUSM）的神经强化专家和神经学家，我 职业目标是发展一个独立的研究计划，作为一个计算生物学家能够使用 先进的生物信息学和统计学方法，以整合大规模神经成像和遗传分析， 数据，旨在加深对影响脑小血管的生物学机制的理解 疾病（SVD）和确定新的治疗发展的目标。为了实现这一目标，我 设计了一个创新的建议，结合联合收割机机器学习（ML）方法和集成成像遗传 分析大规模神经影像学和遗传学数据，以改善SVD疾病机制的表征。 SVD的临床、影像学和病因学异质性阻碍了揭示 这种常见的神经衰弱疾病的病理生理学。白色高信号（WMH），a SVD的主要成像终点由多种SVD病理过程组成。越来越多的证据 提示脑实质对不同潜在SVD病理过程的位置特异性脆弱性， 其中空间定位的WMH模式可以反映不同的SVD病因。表征WMH空间 SVD的模式变化不仅可以提供对潜在发病机制的见解，如血管淀粉样蛋白 沉积，小动脉硬化和其他不太明确或未知的疾病机制，但也导致 创造这些SVD病理过程的新的成像生物标志物。这项提案涉及一个关键问题， 不足，因为现有的WMH模式定义是根据经验确定的，无法区分重叠 SVD病因和风险因素。在这个提议中，我的目标是捕捉WMH空间模式的变化， 通过将聚类分析/ML方法应用于结构MRI， 数据，以创建新的病因特异性SVD成像表型。此外，考虑到遗传因素对 WMH的变异，我将整合这些WMH模式的遗传分析，以揭示新的机制， 影响SVD发病机制。我的初步数据证明了识别数据驱动WMH的可行性 空间模式变异，其特异于不同的SVD病因，并允许检测遗传风险变体 这可能有助于了解SVD的病理过程。 我的职业规划利用了WUSM的广泛资源和特殊环境， 多学科导师团队的指导，具有包括脑血管在内的不同领域的专业知识 生理学、神经影像学、信息学、遗传学和机器学习（Jin-Moo Lee、丹尼尔马库斯、卡洛斯博士 Cruchaga和Yasheng Chen）。在这个职业发展奖，我建议：1）确定不同的WMH 通过应用模式分析可以区分潜在SVD病理和/或风险因素的空间模式 ML方法对来自3个独特队列（n= 2，710）的结构MRI数据进行了不同SVD病理学富集 (Aim检查ML定义的WMH模式是否通过明确定义的SVD风险因素将个体隔离 作为生物学验证（目标1b），以及2）鉴定与WMH模式相关的遗传变异（目标2a）， 使用全基因组关联和基于基因的分析反映影响SVD的不同病理过程; 在一个独立的基于人群的队列（n= 21，708）中复制最常见的变异（Aim 2b）;并使用高级 生物信息学工具，以发现与WMH空间模式相关的新生物途径（目标2c）。 这项研究提案和附带的开发计划，重点是机器学习培训， 神经影像学和多元方法的综合成像遗传学分析，将建立在我的背景 在遗传学方面，他的职业是利用翻译生物信息学研究脑血管疾病。这 奖励将为我提供必要的培训，使我成为一名独立的调查员， 一项研究计划，可以整合大型成像和遗传学数据集，以获得高度相关的结果 脑血管疾病的预防和治疗在我的临床患者人群。