Longitudinal and Cross-sectional White Matter Analysis of Alzheimer's Disease

描述（由申请人提供）：本申请的总体假设是白质（WM），特别是边缘束，是阿尔茨海默病（AD）的主要目标之一，弥散张量成像（DTI）可以敏感地检测这些WM束的变化。目标是发展定量的DTI图像分析技术来检测AD中的WM异常。阿尔茨海默病是痴呆症最常见的原因。虽然主要病理为皮质神经元细胞变性，但越来越多的证据表明，WM病理优于灰质病理。此外，WM的改变可能是神经细胞损失的间接指标，因为神经细胞的体积比其髓鞘纤维小得多。因此，WM似乎是早期诊断和疾病进展监测的一个很好的重点。MRI是一种非侵入性技术，在美国广泛应用，作为一种成像生物标志物具有很大的潜力。然而，常规MRI不能提供区分各种WM结构的对比，对于检测特定WM结构变化的敏感性和特异性较低。DTI是一种有潜力检测特定白质结构异常的方法。与传统的MRI分析相比，使用该方法可以提高检测WM异常的敏感性和特异性。然而，DTI数据的量化技术还不发达，难以充分利用DTI所揭示的WM解剖信息。该应用程序基于我们开发的两项技术创新：一个是立体坐标中的白质脑图谱（JHU-DTI-MNI），其中包含基于扩散张量成像的详细白质图；另一种是基于高弹性非线性脑归一化方法（LDDMM）的最新计算神经解剖学技术，该技术可以在转换过程中保持WM纤维的连通性。在目标1中，我们将扩展这一努力，1)优化老年人群的白质图谱，2)测试我们先进的LDDMM成本函数，以提高归一化质量。优化后，我们将通过横断面分析（Aim 2）和纵向分析（Aim 3）将这些技术应用于AD和轻度认知障碍（MCI）患者的WM异常检测。约翰霍普金斯大学现有的纵向MRI和临床数据库可用于本研究。这些数据每四个月采集一次，持续一年。在Aim 2中，将AD和MCI患者的归一化DTI数据与年龄匹配的对照组进行比较，以检测形态和MR参数（扩散常数、扩散各向异性和T2）的疾病特异性改变。在Aim 3中，我们将通过纵向分析表征疾病进展相关的形态学和MR参数变化。综上所述，我们提出了一种新的图像分析方法，用于全面的WM调查，以检测MCI和AD的疾病特异性变化和疾病进展特异性变化。公共卫生相关性：我们将开发新的阿尔茨海默病成像生物标志物。我们基于概率白质图谱和白质纤维定向转换的图像分析方法使我们能够全面调查白质结构，以检测大脑的疾病特异性和时间依赖性改变。

DESCRIPTION (provided by applicant): The overall hypothesis of this application is that white matter (WM), especially the limbic tracts, is one of the primary targets of Alzheimer's disease (AD) and diffusion tensor imaging (DTI) can sensitively detect changes in these WM tracts. The goal is to develop quantitative DTI image analysis techniques to detect WM abnormalities in AD. AD is the most common cause of dementia. Although the primary pathology is cortical neuronal cell degeneration, increasing evidence indicates a preponderance of WM pathology over gray matter pathology. Moreover, WM alterations could be an indirect indicator of nerve cell loss, since the volume of a nerve cell is much smaller than its myelinated fiber. Therefore, WM seems to be a good focus for both early diagnosis and monitoring of disease progression. MRI is a non-invasive technique that is widely available in the United States, which has great potential as an imaging biomarker. However, conventional MRI cannot provide contrasts to differentiate various WM structures and has low sensitivity and specificity for detecting changes in specific WM structures. DTI is a method that has the potential to detect abnormalities in specific white matter structures. Using this method can increase the sensitivity and specificity to detect WM abnormalities, compared to conventional MRI analysis. However, quantification techniques for DTI data have not been well-developed and it has been difficult to fully exploit the WM anatomical information revealed by DTI. This application is based on two technical innovations we have developed: one is a white matter brain atlas (JHU-DTI-MNI) in stereotaxic coordinates that contains detailed white matter maps based on diffusion tensor imaging; and the other is the state-of-the-art computational neuroanatomy technology based on the highly-elastic non-linear brain normalization method (LDDMM), which can preserve WM fiber connectivity in the transformation process. In Aim 1, we will extend this effort to 1) optimize the white matter atlas for the elderly population, and 2) test our advanced cost functions of LDDMM to improve the normalization quality. After the optimization, we will apply these techniques to detect WM abnormalities in AD and Mild Cognitive Impairment (MCI) patients by cross-sectional analysis (Aim 2) and longitudinal analysis (Aim 3). An existing longitudinal MRI and clinical database acquired at Johns Hopkins University is available for this study. The data were acquired every four months for one year from the same subjects. In Aim 2, normalized DTI data from AD and MCI patients were compared to age-matched controls to detect disease-specific alterations in morphology and MR parameters (diffusion constant, diffusion anisotropy, and T2). In Aim 3, we will characterize disease progression-related changes in morphology and MR parameters by a longitudinal analysis. In summary, we propose a new image-analysis method for a comprehensive WM survey that will detect disease-specific changes and disease progression-specific changes in MCI and AD. PUBLIC HEALTH RELEVANCE: We will develop new imaging biomarkers for Alzheimer's disease. Our image-analysis method based on probabilistic white matter atlas and white matter fiber direction-oriented transformation enable us to comprehensively survey the white matter structures to detect disease specific and time-dependent alteration of the brain.