Neuroimage-driven biophysical inverse problems for atrophy and tau propagation

项目摘要/摘要 阿尔茨海默病(AD)的早期发现和分层提供了大量的医学、 情感和经济利益。一个重要的研究方向是开发方法来 早期诊断和患者分类，希望开发治疗方法- 在认知损害出现之前。神经成像使推导出密钥成为可能 活体生物标记物：用磁共振成像(MRI)和 用正电子发射断层成像法研究错褶皱的A-β和Tau沉积 (宠物)。社区努力创建了具有数千个案例的高质量数据集，这些案例 包括多模式成像扫描、认知评估、实验室工作和基因 信息。然而，数据的异构性对传统的 统计方法。作为对现有定量分析技术的补充，我们 建议使用疾病进展的生物物理数学模型。 先前的工作表明，蛋白质在退化过程中错误折叠的数学模型 疾病可以捕捉疾病的时空动态，并可以增强图像 通过提供临床上有用的生物标志物来解释模型参数和 疾病的发展。 在这个项目中，我们将集成一个新的tau偏微分方程模型。 传播使用我们小组开发的最先进的参数校准方法。 我们的假设是，该模型将提供新的诊断和预后价值。 首先，我们将致力于开发一种新的tau传播模拟器。这款车 将解释tau的进展及其与萎缩的耦合。第二，我们将 开发使用多参数磁共振的模型校准算法 成像、扩散张量成像和tau PET。我们将进行一次回顾 使用阿尔茨海默病神经成像计划和 哈佛大学老龄化大脑研究数据集。

PROJECT SUMMARY/ABSTRACT Early detection and stratification of Alzheimer’s disease (AD) offers numerous medical, emotional and financial benefits. A critical research direction is to develop methods for earlier diagnosis and patient classification, with the hope of developing treatment— before cognitive damage sets in. Neuroimaging has made it possible to derive key biomarkers in vivo: measures of atrophy using Magnetic Resonance Imaging (MRI) and accumulation of misfolded Aβ and tau deposits using Positron Emission Tomography (PET). Community efforts have created high-quality datasets with 1000s of cases that comprise multimodal imaging scans, cognitive evaluations, lab work, and genetic information. However, the heterogeneity of the data is a challenge for traditional statistical methods. Complementary to existing quantitative analysis techniques, we propose to use biophysical mathematical models of disease progression. Prior work has shown that mathematical models of protein misfolding in degenerative disorders can capture spatiotemporal disease dynamics and can enhance image interpretation by providing clinically useful biomarkers in terms of model parameters and disease progression. In this project, we will integrate a novel partial differential equation model of tau propagation with state-of-the-art parameter calibration methods developed in our group. Our hypothesis is that this model will provide novel diagnostic and prognostic value. First, we will work on the development of a new tau propagation simulator. This model will account for the progression of tau and its coupling to atrophy. Second, we will develop model calibration algorithms that use multiparametric Magnetic Resonance Imaging, Diffusion Tensor Imaging, and tau PET. We will conduct a retrospective validation study using images from the Alzheimer’s Disease Neuroimaging Initiative and the Harvard Aging Brain Study datasets.