Characterization of relationship between tau pathology and neurodegeneration in Alzheimer's disease using multimodal imaging

项目摘要 阿尔茨海默病(AD)的病理生理学特征是淀粉样蛋白(Aβ)斑块堆积 和tau神经原纤维缠结(NFT)。而斑块(A+)和缠结(T+)的存在对于 最近在ATN研究分类框架中编码的AD的生物学定义tau(T)被认为 是下游神经退行性变(N)和由此导致的认知障碍的主要驱动因素。然而， T-N关系有很大的变异性--表现为比预期的萎缩更高或更低 给定大脑区域中tau的水平，即使在精心策划的研究队列中也是如此。这种可变性是什么？ 代表？在这项研究中，我们探索了一种对规范中的变异性的定量测量的想法 T和N之间的关系本身就是一个可以帮助描述不同底层的不匹配度量 表型和调节因子。我们将通过对所获得的T与N的区域测量进行建模来检验这一点 在A+有症状的人群中进行体内成像。Tau-PET超视场成像与皮质厚度的关系 从结构磁共振成像将分别作为T和N的区域性测量。然后，我们将使用数据驱动 基于模型残差的表型发现聚类。区域模型残差捕捉空间 T-N关系的变化，概念上从二分的T/N+/-扩展ATN框架 命名为更丰富的描述，可能反映潜在共同病理的不同空间地形图。 将评估T-N失配度量的概念及其识别潜在表型的能力 在多个公开可用的和机构的数据集中，每个数据集都将提供不同的 表型。我们还将在A+个体的体外样本数据集中进行评估。我们将获得 从体外MRI定量测量N作为半自动皮质厚度估计，并使用T 数字组织病理学技术，在多个大脑区域。金标准组织病理学指标(例如TDP- 43，α-突触核蛋白，非AD tau，血管疾病)将有助于评估T-N 错配指标可以帮助识别与非AD共同病理的表型。最后，我们将评估T-N是否 失配指标可以预测未来的认知衰退以及纵向神经退行性变的比率 大脑的变化。

Project Summary The pathophysiology of Alzheimer’s disease (AD) is characterized by the accumulation of Amyloid (Aβ) plaques and tau neurofibrillary tangles (NFT). While the presence of both plaques (A+) and tangles (T+) are essential to the biological definition of AD as recently codified in the ATN research classification framework, tau (T) is thought to be the primary driver of downstream neurodegeneration (N) and the resulting cognitive impairment. However, there is substantial variability in the T-N relationship – manifested in higher or lower atrophy than expected for the level of tau in a given brain region, even in carefully curated research cohorts. What does this variability represent? In this study, we explore the idea that a quantitative measure of the variability in the canonical relationship between T and N is itself a “mismatch metric” that can help characterize different underlying phenotypes and modulatory factors. We will examine this by modeling region-wise measures of T vs. N obtained from in-vivo imaging in a cohort A+ symptomatic individuals. SUVR from tau-PET imaging and cortical thickness from structural MRI will serve as regional measures of T and N respectively. We will then use data-driven clustering for phenotype discovery based on the model residuals. Region-wise model residuals capture spatial variation in the T-N relationship, conceptually extending the ATN framework from the dichotomous T/N +/- designations to a richer description that may reflect differing spatial topography of underlying co-pathologies. The concept of the T-N mismatch metric and its ability to identify underlying phenotypes will be evaluated in multiple publicly available and institutional datasets, each of which will provide a diverse collection of phenotypes. We will also perform evaluation in a dataset of ex-vivo specimens of A+ individuals. We will obtain quantitative measures of N from ex-vivo MRI as a semi-automated cortical thickness estimate, and of T using digital histopathology techniques, in multiple brain regions. Gold standard histopathology measures (e.g. TDP- 43, alpha-synuclein, non-AD tau, vascular disease) obtained in these samples will help evaluate whether T-N mismatch metric can help identify phenotypes with non-AD co-pathology. Finally, we will evaluate if the T-N mismatch metric is predictive of future cognitive decline as well as rates of longitudinal neurodegenerative changes in the brain.