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（申请人提供）：本申请的总体假设是白质（WM），尤其是边缘束，是阿尔茨海默病（AD）的主要靶标之一，弥散张量成像（DTI）可以灵敏地检测白质（WM）的变化这些 WM 小册子。目标是开发定量 DTI 图像分析技术来检测 AD 中的 WM 异常。 AD 是痴呆症的最常见原因。尽管主要病理学是皮质神经元细胞变性，但越来越多的证据表明 WM 病理学优于灰质病理学。此外，WM 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有髓纤维小得多。因此，WM 似乎是早期诊断和疾病进展监测的一个很好的焦点。 MRI是一种在美国广泛应用的非侵入性技术，作为成像生物标志物具有巨大的潜力。然而，传统的MRI无法提供区分各种WM结构的对比，并且对于检测特定WM结构的变化的灵敏度和特异性较低。 DTI 是一种有可能检测特定白质结构异常的方法。与传统 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。然而，DTI 数据的量化技术尚未成熟，难以充分利用 DTI 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：一是立体坐标下的白质脑图谱（JHU-DTI-MNI），其中包含基于扩散张量成像的详细白质图；另一个是基于高弹性非线性脑归一化方法（LDDMM）的最先进的计算神经解剖学技术，可以在转换过程中保留WM纤维连接性。在目标 1 中，我们将把这项工作扩展到 1) 优化老年人群的白质图谱，2) 测试我们的 LDDMM 高级成本函数以提高标准化质量。优化后，我们将应用这些技术通过横断面分析（目标 2）和纵向分析（目标 3）来检测 AD 和轻度认知障碍（MCI）患者的 WM 异常。约翰·霍普金斯大学获得的现有纵向 MRI 和临床数据库可用于本研究。一年内每四个月从同一受试者处获取一次数据。在目标 2 中，将 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（扩散常数、扩散各向异性和 T2）的疾病特异性变化。在目标 3 中，我们将通过纵向分析来表征与疾病进展相关的形态学和 MR 参数的变化。总之，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 的疾病特异性变化和疾病进展特异性变化。公共健康相关性：我们将开发阿尔茨海默病的新成像生物标志物。我们基于概率白质图谱和白质纤维定向变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变化。