Longitudinal predictive modeling for tau in Alzheimer's disease

项目摘要 阿尔茨海默病是老年痴呆症的最常见原因，其特征在于认知功能障碍。 无症状的临床前阶段，通过纵向跟踪病理生理学来识别和监测 生物标志物，例如，tau蛋白和淀粉样蛋白。由于tau蛋白缠结在内侧颞叶中的聚集是一种 记忆损伤的关键驱动因素，tau负荷的基于图像的准确纵向预测可以填补一个关键的 临床前阿尔茨海默病生物标志物开发的差距。Tau缠结表现出典型的 时空传播的神经解剖学模式，与疾病的进展密切相关， 神经变性动物模型的研究表明，tau蛋白的特征性传播模式 与阿尔茨海默氏症进展相关的神经连接，而不是物理接近 不同脑区之间的联系利用宏观结构连通性映射的图论方法 预测未来的tau蛋白负荷可能会成为阿尔茨海默病有价值的预后工具。的 这项R 01研究项目资助的首要研究目标是开发一种可解释的机器学习方法， 一种使用个体结构连接组学来个性化预测差异测量的模型， 来自多模态基线数据的τ。我们的方法依赖于纵向18F-Flortaucipir正电子发射 用于tau缠结成像的11 C-匹兹堡化合物B（PiB） 斑块和高角分辨率弥散磁共振（MR）成像， 连接组学的研究。我们将开发一个物理信息和可解释的图形神经网络， 根据多模式输入，包括基线tau、Aβ， 和一系列结构连接性度量。我们还将开发基于物理学的tau分析模型 进展，这将用于有效地指导机器学习框架。最后，我们将应用 机器学习模型研究tau聚集的最早皮质部位，检查连接组学 早期tau传播的基础，并利用我们模型的可解释性来发现和验证新的连接组学 生物标志物来表征临床前阿尔茨海默病。为了验证机器学习模型，我们将使用 来自哈佛衰老大脑研究的两个和三个时间点的连续tau PET数据， 临床前阿尔茨海默病的纵向成像资源。为了确保科学严谨性， 模型的验证将使用阿尔茨海默病神经成像计划的数据进行 数据库拟议的个性化预测模型可以显着影响临床前阿尔茨海默氏症 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 老年痴呆症

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.