Longitudinal predictive modeling for tau in Alzheimer's disease

项目总结 阿尔茨海默病是老年人痴呆症最常见的原因，其特征是认知功能障碍 无症状的临床前阶段，通过对病理生理的纵向跟踪来识别和监测 生物标志物，如tau和淀粉样蛋白。由于tau蛋白在内侧颞叶的聚集是一种 记忆受损的关键驱动因素，基于图像的准确纵向预测tau负荷可以填补关键的 临床前阿尔茨海默病生物标记物开发方面的差距。牛仔缠结表现出刻板印象 时空扩散的神经解剖学模式与血管病变的进展密切相关 神经退行性变。在动物模型中的研究表明，tau的特征模式传播 与阿尔茨海默病进展相关的是由神经连接而不是身体接近决定的 在不同的大脑区域之间。利用宏观结构连通性映射的图论方法 在人类中预测未来tau的负担可能会导致有价值的阿尔茨海默病预后工具。这个 这个R01研究项目的主要研究目标是开发一种可解释的机器学习 使用单独的结构连接学对不同度量进行个性化预测的模型 来自多模式基线数据的Tau。我们的方法依赖于纵向18F-Flortaucipir正电子发射 11C-匹兹堡化合物B(PIB)用于淀粉样蛋白成像的断层扫描(PET) 斑块，以及用于个性化结构的高角分辨率扩散磁共振(MR)成像 人类受试者的连接学。我们将开发一种物理信息和可解释的图形神经网络来 根据多模式输入预测区域tau负担的年变化率，包括基线tau、Aβ、 以及一系列结构连通性度量。我们还将开发新的基于物理的tau分析模型。 这将被用来有效地指导机器学习框架。最后，我们将应用 机器学习模型调查最早的皮质部位的tau聚集，以检查连接 在早期tau扩散的基础上，并利用我们模型的可解释性来发现和验证新的连接 用于表征临床前阿尔茨海默病的生物标志物。为了验证机器学习模型，我们将使用 哈佛大学老龄化大脑研究的两个和三个时间点的连续tau PET数据，这是最大的研究之一 临床前阿尔茨海默病的纵向影像资源。为确保科学严谨，次要 模型的验证将使用阿尔茨海默病神经成像倡议的数据进行 数据库。所提出的个性化预测模型可以显著影响临床前阿尔茨海默病 预后，促进正在进行的临床试验，并阐明神经连接和生物学基础 阿尔茨海默氏症。

PROJECT SUMMARY Alzheimer’s disease, the most common cause of dementia in the elderly, is characterized by a cognitively asymptomatic preclinical stage which is identified and monitored via longitudinal tracking of pathophysiological biomarkers, e.g., tau and amyloid. Since the aggregation of tau protein tangles in the medial temporal lobe is a key driver of memory impairment, accurate image-based longitudinal prediction of tau burden could fill a critical gap in biomarker development for preclinical Alzheimer’s disease. Tau tangles exhibit stereotypical neuroanatomical patterns of spatiotemporal spread that correlate strongly with the progression of neurodegeneration. Studies in animal models have suggested that the characteristic patterns of tau spread associated with Alzheimer’s progression are determined by neural connectivity rather than physical proximity between different brain regions. Graph-theoretic methods that utilize macroscale structural connectivity mapping in humans to predict future tau burden could lead to valuable prognostic tools for Alzheimer’s disease. The overarching research goal of this R01 Research Project Grant is to develop an interpretable machine learning model that uses individual structural connectomics to make personalized predictions of differential measures of tau from multimodal baseline data. Our approach relies on longitudinal 18F-Flortaucipir positron emission tomography (PET) for the imaging of tau tangles, 11C-Pittsburgh Compound B (PiB) for the imaging of amyloid plaques, and high-angular-resolution diffusion magnetic resonance (MR) imaging for individualized structural connectomics in human subjects. We will develop a physics-informed and interpretable graph neural network to predict the annual rate of change of the regional tau burden from multimodal inputs, including baseline tau, Aβ, and an array of structural connectivity metrics. We will also develop novel physics-based analytic models for tau progression, which will be used to effectively guide the machine learning framework. Finally, we will apply the machine learning model to investigate the earliest cortical site of tau aggregation, to examine the connectomic basis of early tau spread, and to leverage our model’s interpretability to discover and validate novel connectomic biomarkers to characterize preclinical Alzheimer’s disease. To validate the machine learning model, we will use serial tau PET data at two and three timepoints from the Harvard Aging Brain Study, one of the largest longitudinal imaging resources for preclinical Alzheimer’s disease. To ensure scientific rigor, secondary validation of the models will be performed using data from the Alzheimer’s Disease Neuroimaging Initiative database. The proposed personalized predictive model could significantly impact preclinical Alzheimer’s prognosis, facilitate ongoing clinical trials, and shed light on the neuroconnectomic and biological underpinnings of Alzheimer’s disease.