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）斑块的积聚 和tau神经元缠结（NFT）。虽然斑块（A+）和缠结（T+）的存在对于 最近在ATN研究分类框架中编纂的AD的生物学定义tau（T）被认为是 是下游神经变性（N）和由此产生的认知障碍的主要驱动力。然而，在这方面， 在T-N关系中存在实质性的变化-表现为比预期的更高或更低的萎缩， 特定大脑区域的tau蛋白水平，即使是在精心策划的研究队列中。这种变异性 代表？在这项研究中，我们探讨的想法，定量测量的变异性，在典型的 T和N之间的关系本身是一个“失配度量”，可以帮助表征不同的基础 表型和调节因子。我们将通过对T与N的区域测量进行建模来检查这一点， 来自A+症状个体队列中的体内成像。来自tau-PET成像和皮质厚度的SUVR 将分别作为T和N的区域测量值。然后我们将使用数据驱动 基于模型残差的表型发现的聚类。区域模型残差捕获空间 T-N关系的变化，从概念上扩展了ATN框架，从二分的T/N +/- 指定到更丰富的描述，其可以反映潜在共同病理学的不同空间形貌。 将评估T-N错配指标的概念及其识别潜在表型的能力 在多个公开的和机构的数据集，其中每一个将提供一个不同的收集， 表型我们还将在A+个体的离体标本数据集中进行评价。我们将获得 来自离体MRI的N的定量测量作为半自动皮质厚度估计，以及使用 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 错配度量可以帮助鉴定具有非AD共病的表型。最后，我们将评估T-N是否 不匹配度量可以预测未来的认知下降以及纵向神经退行性疾病的发生率。 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.