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

• 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
• 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' s Disease; Alzheimer' s disease model; Alzheimer' 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

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.

项目总结/摘要 阿尔茨海默病（AD）的早期检测和分层提供了许多医疗， 情感和经济利益。一个关键的研究方向是开发方法， 早期诊断和患者分类，希望开发治疗方法- 在认知损伤出现之前神经成像技术使我们有可能 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技术的错误折叠Aβ和tau沉积物的累积 （PET）。社区的努力已经创建了高质量的数据集，其中包含1000个案例， 包括多模态成像扫描、认知评估、实验室工作和遗传学检查。 信息.然而，数据的异构性对传统的 统计方法作为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，在退行性疾病中蛋白质错误折叠的数学模型 疾病可以捕捉时空疾病动态并增强图像 通过在模型参数方面提供临床上有用的生物标志物进行解释， 疾病进展。 在这个项目中，我们将整合一个新的偏微分方程模型的τ 传播与国家的最先进的参数校准方法在我们的小组。 我们的假设是，该模型将提供新的诊断和预后价值。 首先，我们将致力于开发新的tau传播模拟器。该模型 将解释tau蛋白的进展及其与萎缩的耦合。二是 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。我们将进行回顾 使用阿尔茨海默病神经成像倡议的图像进行验证研究， 哈佛大脑老化研究数据集。