Manifold-valued statistical models for longitudinal morphometic analysis in preclinical Alzheimer's disease (AD)

用于临床前阿尔茨海默病 (AD) 纵向形态分析的流形值统计模型

• 批准号: 9170619
• 负责人: Sterling C Johnson
• 金额: $ 33.15万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9170619
• 关键词: Address; Adult; Adult Children; Affect; Age; Algorithms; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s disease risk; Amyloid; Amyloid deposition; Area; Beryllium; Biological Markers; Brain; Brain imaging; Cerebrospinal Fluid; Clinical; Clinical Markers; Clinical Trials; Clinical Trials Design; Cloud Computing; Code; Cognitive; Communities; Complex; Computer software; Consensus; Data; Data Set; Dementia; Disease; Early Intervention; Elderly; Event; Evolution; Family; Health; Heart; Heterogeneity; Image; Image Analysis; Impaired cognition; Individual; Industry; Linear Models; Machine Learning; Measures; Methods; Modeling; Neurobehavioral Manifestations; Neurosciences; Outcome; Pathologic Processes; Pathology; Pattern; Procedures; Process; Recording of previous events; Registries; Research; Risk; Risk Factors; Running; Signal Transduction; Site; Staging; Statistical Data Interpretation; Statistical Models; System; Testing; Translations; Treatment Efficacy; Universities; Validation; Washington; Wisconsin; Work; abstracting; base; clinical practice; cohort; design; direct application; disease phenotype; image processing; imaging modality; insight; interest; learning strategy; middle age; mild cognitive impairment; morphometry; neuroimaging; novel; novel therapeutics; open source; pre-clinical; research study; response; sex; software development; statistics; tau Proteins; therapy design; user friendly software; web interface

Project Summary The ability to quantitatively characterize incipient Alzheimer's disease (AD) pathology in its preclinical stage is a critical step for early interventions involving disease modifying therapy and for designing efficient clinical trials to test therapy efficacy. This project focuses on deriving statistical image analysis methods for identifying the relationship of morphometric changes in this early stage with direct indicators of AD pathology (such as amyloid deposition) and various risk factors such as family history in late midlife adults who are cognitively healthy. The proposed analysis will be conducted on the largest preclinical AD cohort assembled to date and help elucidate how clinical-cognitive-imaging AD phenotypes emerge in asymptomatic individuals at risk for AD. The core of our analyses is a set of algorithms that allow operating directly on powerful “manifold-valued” representations of morphometric change and consequently yield high sensitivity in picking up real but statistically weak multi-modal patterns of the disease process. Hypothesis: 1) Detecting precise associations between morphometric changes in preclinical AD subjects with amyloid burden and various risk factors is possible using new algorithms that work directly with representations of the Jacobians of the deformation fields derived from longitudinal imaging data. 2) Conducting such analyses on a large multi-site cohort of asymptomatic at-risk preclinical AD individuals with identified AD pathology will a) reveal important insights into early disease processes when dementia is 15+ years away, b) provide preclinical AD biomarkers and c) provide frameworks for predicting clinical endpoints at the level of individual subjects. Specific Aims: 1) To develop new algorithms for performing statistical analysis of manifold representations of morphometric changes concurrently with multiple covariates representing risk factors, AD pathology markers, and clinical/cognitive measures. 2) Conducting an end-to-end analysis of two independent preclinical AD cohorts to identify the relationship of morphometric changes with various predictors as well as test/retest validation across sites. 3) Analyzing the largest preclinical AD cohort to date for characterizing the relationship of the entire spectrum of predictors to clinical endpoints for late midlife individuals. 4) Providing industry- strength open-source software implementing the full suite of models, integrated with existing software toolboxes, for deployments on a workstation, a high throughput cluster or the cloud. Significance: This project will have a significant impact across three distinct areas of brain imaging research: 1) characterization of how clinical-cognitive-imaging AD phenotypes emerge in asymptomatic individuals in the earliest stages of AD, 2) rigorous algorithms for morphometric change analysis in neuroimaging and neuroscience, 3) open-source end-to-end implementations of the algorithms for use within the community.

项目概要 在临床前阶段定量表征早期阿尔茨海默病 (AD) 病理学的能力是 涉及疾病修饰治疗的早期干预和设计有效的临床试验的关键一步 以测试治疗效果。该项目的重点是推导统计图像分析方法来识别 早期阶段形态变化与 AD 病理学直接指标的关系（例如 淀粉样蛋白沉积）和各种危险因素，例如认知能力低下的中年晚期成年人的家族史 健康。拟议的分析将针对迄今为止收集的最大的临床前 AD 队列进行 帮助阐明临床认知成像 AD 表型如何在有风险的无症状个体中出现 广告。我们分析的核心是一组算法，可以直接对强大的“流形值”进行操作 形态变化的表示，因此在拾取真实的但具有高灵敏度 疾病过程的多模态模式在统计学上较弱。 假设：1) 检测临床前 AD 受试者的形态变化与 使用直接处理表征的新算法可以检测淀粉样蛋白负担和各种风险因素 从纵向成像数据导出的变形场的雅可比行列式。 2）进行此类分析 对具有已确定的 AD 病理学特征的无症状高危临床前 AD 个体进行大型多中心队列研究 a) 揭示 15 年以上痴呆症发生时早期疾病过程的重要见解，b) 提供 临床前 AD 生物标志物和 c) 提供在个体水平上预测临床终点的框架 科目。 具体目标： 1）开发新算法来执行流形表示的统计分析 形态变化与代表危险因素、AD 病理标志物的多个协变量同时发生 和临床/认知测量。 2) 对两个独立的临床前 AD 进行端到端分析 队列以确定形态变化与各种预测因素以及测试/重新测试的关系 跨站点验证。 3) 分析迄今为止最大的临床前 AD 队列以描述这种关系 中年晚期个体临床终点的整个预测因子。 4）提供工业- 强大的开源软件，实现全套模型，与现有软件集成 工具箱，用于在工作站、高吞吐量集群或云上部署。 意义：该项目将对脑成像研究的三个不同领域产生重大影响： 1) 临床认知成像 AD 表型如何在无症状个体中出现的特征 AD 的最早阶段，2) 神经影像学中形态变化分析的严格算法和 神经科学，3) 算法的开源端到端实现，供社区内使用。