Integrative modeling and dynamic prediction of Alzheimer's disease

项目摘要/摘要 建议的R01拨款是直接响应PAR-18-352《 行为和社会科学(R01)“。阿尔茨海默病(AD)是一种进行性的神经退行性疾病 这会导致多个领域的损害(例如，认知、行为和生活质量)和进步 在时间上以及跨域和跨个体的异质性。没有一个单一的生物标志物能提供足够的 以获取整个光谱中潜在的疾病严重程度的信息。因此，AD研究收集 来自多个来源的数据(例如，临床、神经成像和遗传；多模式数据)。我们提议写一部小说 综合建模框架，提供统计原则性推理、准确的个性化预测 疾病进展，以及基于新的特定主题数据的动态预测更新。这种新颖的模式 发展对于确定阿尔茨海默病的风险和保护因素以及针对高危人群也很重要 以使管理、预后和治疗选择个性化。总体目标是：(1) 建立纵向临床综合建模的多元函数混合模型(MFMM) 数据；(2)使用这种模型来提供对目标未来结果轨迹和风险的个性化预测 事件；(3)结合高维神经成像和遗传学，提出综合模型 数据；(4)通过专业软件开发和网络使这一方法更容易获得 部署。我们的方法可以广泛应用于其他具有类似多模式数据的临床研究。 结构。

Project Summary/Abstract The proposed R01 grant is in direct response to PAR-18-352 “Methodology and Measurement in the Behavioral and Social Sciences (R01)”. Alzheimer's disease (AD) is a progressive, neurodegenerative disorder that causes impairment in multiple domains (e.g., cognition, behavior, and quality of life) and progresses heterogeneously in time and across domains and individuals. No single biomarker provides sufficient information to capture the underlying severity of disease across the entire spectrum. Hence, AD studies collect data from multiple sources (e.g., clinical, neuroimaging, and genetic; multi-modal data). We propose a novel integrative modeling framework to provide statistically-principled inference, accurate personalized prediction of disease progression, and dynamic prediction update, based on new subject-specific data. This novel model development is important to identify risk and protective factors for AD and target high risk individuals, as well as to personalize the management, prognosis, and treatment selections. The overall objectives are to: (1) develop a multivariate functional mixed model (MFMM) for the integrative modeling of the longitudinal clinical data; (2) use such model to provide personalized prediction of future outcome trajectories and risks of target events; (3) advance the integrative model by incorporating the high-dimensional neuroimaging and genetic data; (4) make this methodology easily accessible via professional software development and web deployment. Our methods can be broadly applied to other clinical studies with similar multi-modal data structure.