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

阿尔茨海默病的纵向和横截面白质分析

基本信息

批准号：
7845567
负责人：
Kenichi Oishi
金额：
$ 16.81万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-06-01 至 2011-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7845567
关键词：
Affect Age Aging Alzheimer&apos s Disease Arts Atlases Atrophic Biological Markers Brain Brain Mapping Cells Cessation of life Clinical Clinical Trials Cross-Sectional Studies Data Databases Dementia Development Diffusion Diffusion Magnetic Resonance Imaging Disease Disease Progression Early Diagnosis Elderly Fiber Goals Grant Hand Image Image Analysis Impaired cognition International Limbic System Magnetic Resonance Imaging Maps Measures Memory Loss Methods Monitor Morphology Neuroanatomy Neurodegenerative Disorders Neurons Pathogenesis Pathology Patients Pharmaceutical Preparations Population Prevalence Process Research Sensitivity and Specificity Societies Structure Surveys System Techniques Technology Testing Time United States Universities aged aging brain base cerebral atrophy cost design diffusion anisotropy gray matter improved innovation longitudinal analysis longitudinal database mild neurocognitive impairment neuron loss normal aging public health relevance white matter

项目摘要

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.

描述（由申请人提供）：该应用的总体假设是白质（WM），尤其是边缘区，是阿尔茨海默氏病（AD）和扩散张量成像（DTI）的主要靶标之一，可以灵敏地检测这些WM区域的变化。目的是开发定量DTI图像分析技术，以检测AD中的WM异常。 AD是痴呆症的最常见原因。尽管主要的病理是皮质神经元细胞变性，但越来越多的证据表明，WM病理比灰质病理学有优势。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远小于其骨髓纤维。因此，WM似乎是早期诊断和监测疾病进展的良好重点。 MRI是一种非侵入性技术，在美国广泛使用，作为成像生物标志物具有很大的潜力。但是，常规MRI不能为区分各种WM结构提供对比，并且对于检测特定WM结构的变化具有低灵敏度和特异性。 DTI是一种有可能检测特定白质结构异常的方法。与常规MRI分析相比，使用这种方法可以提高检测WM异常的敏感性和特异性。但是，DTI数据的量化技术尚未得到充分发达，很难完全利用DTI揭示的WM解剖信息。该应用基于我们开发的两项技术创新：一个是一个白质大脑图集（Jhu-dti-Mni），在立体定位坐标中，其中包含基于扩散张量成像的详细的白质图；另一种是基于高度弹性的非线性大脑归一化方法（LDDMM）的最新计算神经解剖学技术，该方法可以在转化过程中保留WM光纤连接性。在AIM 1中，我们将把这项工作扩展到1）优化老年人群的白色物质地图集，以及2）测试LDDMM的高级成本功能以提高正常化质量。优化后，我们将通过横截面分析（AIM 2）和纵向分析（AIM 3）应用这些技术来检测AD和轻度认知障碍（MCI）患者的WM异常（MCI）。本研究可用于约翰霍普金斯大学获得的现有纵向MRI和临床数据库。每四个月从同一受试者获取每四个月的数据。在AIM 2中，将来自AD和MCI患者的归一化DTI数据与年龄匹配的对照进行比较，以检测形态和MR参数的特异性改变（扩散常数，扩散性各向异性和T2）。在AIM 3中，我们将通过纵向分析来表征疾病进展相关的形态和MR参数变化。总而言之，我们提出了一种新的图像分析方法，用于全面的WM调查，该方法将检测MCI和AD中疾病特异性变化和疾病进展特异性变化。公共卫生相关性：我们将为阿尔茨海默氏病开发新的成像生物标志物。我们的图像分析方法基于概率的白质图集和白质纤维方向的转化，使我们能够全面调查白质结构，以检测大脑的特定疾病和时间依赖性的变化。