Mathematical Models of Tau-PET Measures and Cognitive Decline in Alzheimer’s Disease Across the Lifespan

项目摘要/摘要 阿尔茨海默病(AD)病理过程的许多细节仍不清楚，如确切的， Tau蓄积与认知衰退之间的函数关系作为年龄的函数以及其他 可能改变这些关系的生物标记物。传统的统计方法不能轻易地回答 关于tau与认知的关系，由于它们的动态关系，未知的时间 滞后和复杂的测量误差结构。数学建模技术-常用于 传染病流行病学和计算生物学-是专门研究复杂的 生物变量之间的关系，同时纳入关于相关生理变量的先验知识 系统。这个被提议的项目利用了我从传染病的论文研究中获得的定量专业知识。 疾病，使用AD发病年龄跨度的数据来阐明tau-PET之间的关系 测量和认知。 随着更多的tau靶向药物通过流水线，确定最佳时机和 试验设计和批准后临床指南的治疗持续时间。以tau为靶点的理想时机 治疗方法可能取决于年龄、淀粉样蛋白或血管负荷等因素。现有的和新兴的血液- 基于生物标记物的可能提供有关tau如何在大脑中扩散和时间的重要信息 随后的纵向萎缩和认知能力下降。现在越来越多的研究进行tau-PET， 包括重复的神经成像，使人们有可能更好地理解tau的动力学 以及与其他生物标志物相关的认知。 我们提出了一种生物激励的数学建模方法来理解神经成像和 其他生物标记物可以用来更好地了解阿尔茨海默病的生物学。我们计划安装机械式的 AD诊断：阿尔茨海默病神经成像三个队列数据的模型 首创(ADNI)、纵向早发性阿尔茨海默病研究(LEADS)和90+研究。长的- 这项研究的目的是提高我们对阿尔茨海默病年龄相关病理生理学的理解。 确定tau和认知之间的确切关系，最终目标是指导治疗 阿尔茨海默病治疗的开发和试验。 拟议的培训活动包括实践研究经验，以及教学，高级 课程作业，指导阅读材料，并与主要导师M.玛丽亚·格拉莫教授和 共同导师吉尔·拉比诺维奇教授，医学博士。科学顾问María Corrada教授(MPI：90+研究； 加州大学欧文分校)、临床神经心理学家兼亚当·斯塔法罗尼教授和罗伊教授 安德森(伦敦帝国理工学院)也将贡献他们的专业知识。

PROJECT SUMMARY/ ABSTRACT Many specifics of the pathological process of Alzheimer’s disease (AD) remain unknown, such as the precise, functional relationship between tau accumulation and cognitive decline as a function of age, as well as other biomarkers that may modify these relationships. Conventional statistical approaches cannot easily answer questions about the relationship between tau and cognition, due to their dynamic relationship, unknown time lags, and complex measurement error structures. Mathematical modeling techniques—commonly used in infectious disease epidemiology and computational biology—are specialized for the study of complex relationships between biological variables, while incorporating prior knowledge about the relevant physiologic system. The proposed project leverages my quantitative expertise from dissertation research on infectious disease, using data from across the age span of AD onset to elucidate the relationship between tau-PET measures and cognition. As more tau-targeting drugs move through the pipeline, it is important to determine the optimal timing and duration of treatment for trial design and for post-approval clinical guidelines. The ideal timing for tau-targeting therapies may depend on factors such as age, amyloid, or vascular burden. Existing and emerging blood- based biomarkers may offer important information about how tau spreads in the brain and the timing of subsequent atrophy and cognitive decline longitudinally. A growing number of studies now perform tau-PET, and including repeated neuroimaging, making it possible for an improved understanding of the dynamics of tau and cognition in relation to other biomarkers. We propose a biologically motivated, mathematical modeling approach to understand how neuroimaging and other biomarkers can be used to better understand Alzheimer’s disease biology. We plan to fit mechanistic models to data from three cohorts across the age span of AD diagnosis: Alzheimer’s Disease Neuroimaging Initiative (ADNI), Longitudinal Early-onset Alzheimer's Disease Study (LEADS), and The 90+ Study. The long- term objective of this research is to improve our understanding of the age-specific pathophysiology of AD, determining the precise relationship between tau and cognition, with the ultimate goal of guiding therapeutic development and trials for AD treatment. The proposed training activities include hands-on research experience, as well as didactics, advanced coursework, and directed readings and mentorship with the primary mentor Professor M. Maria Glymour and co-mentor Professor Gil Rabinovici, MD. Scientific advisors Professors María Corrada (MPI: The 90+ Study; University of California, Irvine), clinical neuropsychologist and Professor Adam Staffaroni, and Professor Roy Anderson (Imperial College London) will also contribute their expertise.