Neuroimage-driven biophysical inverse problems for atrophy and tau propagation

神经图像驱动的萎缩和 tau 传播的生物物理逆问题

基本信息

批准号：
10302105
负责人：
George Biros
金额：
$ 18.35万
依托单位：
UNIVERSITY OF TEXAS AT AUSTIN
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10302105
关键词：
Address Affect Algorithm Design Algorithmic Software Algorithms Alzheimer&apos s Disease Alzheimer&apos s disease model Alzheimer&apos s disease patient Amyloid beta-Protein Area Atrophic Attention Biological Markers Biophysics Calibration Classification Clinical Clinical Research Cognitive Communities Computer Architectures Couples Coupling Data Data Set Degenerative Disorder Deposition Development Diagnosis Differential Equation Diffusion Magnetic Resonance Imaging Disease Disease Progression Early Diagnosis Early treatment Emotional Evaluation Evolution Family Feasibility Studies Fiber Future Goals Heterogeneity Image Image Analysis Image Enhancement Individual Longitudinal cohort Magnetic Resonance Imaging Mathematics Measures Medical Medical Imaging Methods Modeling Multimodal Imaging Neurodegenerative Disorders Organ Outcome Patients Positron-Emission Tomography Process Prognosis Research Retrospective Studies Scanning Series Societies Staging Statistical Methods Stratification Structure Techniques Testing Texas Time Validation Visit Work aging brain base biophysical model clinical biomarkers clinical imaging cognitive testing design distributed memory experimental study falls genetic information imaging biomarker improved in vivo mathematical model multimodality multithreading neuro-oncology neuroimaging novel novel diagnostics patient stratification pre-clinical preclinical study prognostic value protein misfolding spatiotemporal tau Proteins theories tool validation studies white matter

项目摘要

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.

项目摘要/摘要阿尔茨海默氏病（AD）的早期发现和分层提供了许多医学，情感和经济利益。一个关键的研究方向是开发用于较早的诊断和患者分类，希望开发治疗 - 在设定认知损害之前。神经影像学使得得出钥匙成为可能体内生物标志物：使用磁共振成像（MRI）和使用正电子发射断层扫描堆积错误折叠的Aβ和TAU沉积物（宠物）。社区努力创建了高质量的数据集，其中有1000种情况包括多模式成像扫描，认知评估，实验室工作和遗传信息。但是，数据的异质性是传统的挑战统计方法。与现有的定量分析技术互补，我们使用疾病进展的生物物理数学模型的提议。先前的工作表明，蛋白质的数学模型在退化性中折叠错误疾病可以捕获时空疾病动力学并可以增强图像通过在模型参数方面提供临床上有用的生物标志物来解释疾病进展。在这个项目中，我们将整合一个新型的tau的偏微分方程模型我们组开发的最新参数校准方法的传播。我们的假设是，该模型将提供新颖的诊断和预后价值。首先，我们将致力于开发新的tau繁殖模拟器。这个模型将解释tau的进展及其与萎缩的耦合。第二，我们会的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宠物。我们将进行回顾验证研究使用来自阿尔茨海默氏病神经影像倡议的图像和哈佛大学老化的大脑研究数据集。