Developing a Univariate Neurodegeneration Imaging Biomarker with Optimal Transportation

项目摘要/摘要 我们将开发并应用一种新型的单变量神经退行性变成像生物标志物来研究脑磁学 来自阿尔茨海默病神经成像倡议(ADNI)数据集的磁共振图像(MRI)和 亚利桑那州症状前个体的特征良好的APOE队列。我们最近的研究表明， 基于Wasserstein距离的脑成像指数优于其他几个单变量脑成像指数 在区分阿尔茨海默病(AD)患者和认知正常(CU)受试者时， 脑部断层MR和氟代脱氧葡萄糖正电子发射断层扫描(FDG-PET)图像。在当前 项目，我们将继续开发基于调和映射的新的结构磁共振分析方法和 变分原理。具体地说，我们将开发4D调和映射算法来计算正则成像 空间的纵向脑图像，并进一步计算4D沃瑟斯坦距离的单变量 具有有效变分框架的纵向神经退行性变指数。拟议的系统将 生成简单、客观和可靠的神经变性成像生物标记物来量化进展性 与阿尔茨海默病相关的症状前解剖改变和提供简明和信息量的单变量结果 随机临床试验措施(RCT)。为了调查我们方法的可靠性和实用性，我们 我将研究从ADNI和亚利桑那州APOE的症状前队列中获得的脑结构MRI扫描 研究对象。我们试图(1)将计算的神经变性成像指数与纵向 ADNI和亚利桑那州APOE独立队列中的认知轨迹；(2)评估其能力 通过区分β-淀粉样蛋白阳性的轻度认知障碍(MCI/CU)受试者和 ADNI队列中β淀粉样蛋白阴性的MCI/CU受试者；(3)研究其预测的可能性 ADNI及青壮年CU受试者遗忘性MCI临床进展率 亚利桑那州APOE队列的症状前个体；以及(4)验证其促进 通过减少所需的随机对照试验样本量对AD治疗进行评估。我们将进行正面交锋 提出的单变量神经退行性变生物标志物与其他最新技术的比较 具有这些任务的单变量结构MRI指标。我们还将开发和免费分发我们的软件 为研究界提供工具。

PROJECT SUMMARY / ABSTRACT We will develop and apply a novel univariate neurodegeneration imaging biomarker to brain magnetic resonance images (MRI) obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and the well-characterized Arizona APOE cohort of presymptomatic individuals. Our recent work has shown that Wasserstein distance-based brain imaging indices outperformed several other univariate brain imaging indices in discriminating Alzheimer’s disease (AD) patients from cognitively unimpaired (CU) subjects with cross- sectional brain MR and fluorodeoxyglucose positron emission tomography (FDG-PET) images. In the current project, we will continue developing novel structural MRI analysis methods based on harmonic maps and the variational principle. Specifically, we will develop 4D harmonic map algorithms to compute canonical imaging spaces of longitudinal brain images and further compute 4D Wasserstein distance-based univariate longitudinal neurodegeneration indices with an efficient variational framework. The proposed system will generate simple, objective, and reliable neurodegeneration imaging biomarkers to quantify progressive presymptomatic anatomical changes related to AD and provide concise and informative univariate outcome measures for randomized clinical trials (RCT). To investigate the reliability and practicality of our method, we will study brain structural MRI scans obtained from the ADNI and the Arizona APOE cohort of presymptomatic subjects. We seek to (1) correlate the computed neurodegeneration imaging indices with longitudinal cognitive trajectories in both ADNI and the independent Arizona APOE cohorts; (2) assess its ability to identify early AD by distinguishing beta-amyloid-positive mild cognitive impairment (MCI)/CU subjects from beta-amyloid-negative MCI/CU subjects in the ADNI cohort; (3) investigate its potential to predict progression rate to the clinical stage of amnestic MCI on CU subjects of the ADNI and the younger presymptomatic individuals of the Arizona APOE cohort; and (4) validate its potential to facilitate the evaluation of AD treatments by reducing the required RCT sample sizes. We will conduct head-to-head comparisons between the proposed univariate neurodegeneration biomarker and other state-of-the-art univariate structural MRI indices with these tasks. We will also develop and freely disseminate our software tools to the research community.