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:
  • 金额:
    $ 16.81万
  • 依托单位:
  • 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

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):本申请的总体假设是,白色物质(WM),特别是边缘束,是阿尔茨海默病(AD)的主要靶点之一,并且扩散张量成像(DTI)可以灵敏地检测这些WM束的变化。目的是发展定量DTI图像分析技术来检测AD患者的WM异常。AD是痴呆症最常见的原因。虽然主要病理是皮质神经元细胞变性,越来越多的证据表明,优势WM病理灰质病理。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似乎是早期诊断和监测疾病进展的一个很好的焦点。MRI是一种在美国广泛使用的非侵入性技术,作为成像生物标志物具有很大的潜力。然而,常规MRI不能提供对比以区分各种WM结构,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是一种有潜力检测特定白色物质结构异常的方法。与常规MRI分析相比,使用这种方法可以增加检测WM异常的灵敏度和特异性。然而,DTI数据的量化技术还没有得到很好的发展,很难充分利用DTI所揭示的WM解剖信息。该应用基于我们开发的两项技术创新:一项是立体定位坐标中的白色物质脑图谱(JHU-DTI-MNI),其中包含基于扩散张量成像的详细白色物质图;另一项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留WM纤维连接。在目标1中,我们将扩展这项工作,以1)优化老年人群的白色物质图谱,2)测试我们的LDDMM的高级成本函数,以提高归一化质量。优化后,我们将应用这些技术检测AD和轻度认知障碍(MCI)患者的WM异常,通过横断面分析(目标2)和纵向分析(目标3)。在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。这些数据每四个月从相同的受试者那里获得一年。在目标2中,将AD和MCI患者的标准化DTI数据与年龄匹配的对照组进行比较,以检测形态学和MR参数(扩散常数,扩散各向异性和T2)的疾病特异性改变。在目标3中,我们将通过纵向分析来表征形态学和MR参数的疾病进展相关变化。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,该调查将检测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。我们的图像分析方法的基础上的概率白色物质图谱和白色物质纤维方向为导向的转换,使我们能够全面调查的白色物质结构,以检测疾病特异性和时间依赖性的改变的大脑。

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(1)
Advanced neonatal NeuroMRI.
先进的新生儿神经磁共振成像。
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Kenichi Oishi其他文献

Kenichi Oishi的其他文献

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{{ truncateString('Kenichi Oishi', 18)}}的其他基金

Precision Medicine for Neonatal Hypoxic-Ischemic Encephalopathy: Combined Neuroimaging Clinical Approach to Link Phenotypes to Prognosis
新生儿缺氧缺血性脑病的精准医学:将表型与预后联系起来的联合神经影像学临床方法
  • 批准号:
    10557147
  • 财政年份:
    2022
  • 资助金额:
    $ 16.81万
  • 项目类别:
Precision Medicine for Neonatal Hypoxic-Ischemic Encephalopathy: Combined Neuroimaging Clinical Approach to Link Phenotypes to Prognosis
新生儿缺氧缺血性脑病的精准医学:将表型与预后联系起来的联合神经影像学临床方法
  • 批准号:
    10417856
  • 财政年份:
    2022
  • 资助金额:
    $ 16.81万
  • 项目类别:
Development of quantitative MRI DTI analysis tool for preterm neonate
早产儿定量MRI DTI分析工具的开发
  • 批准号:
    8893110
  • 财政年份:
    2011
  • 资助金额:
    $ 16.81万
  • 项目类别:
Development of quantitative MRI DTI analysis tool for preterm neonate
早产儿定量MRI DTI分析工具的开发
  • 批准号:
    8107915
  • 财政年份:
    2011
  • 资助金额:
    $ 16.81万
  • 项目类别:
Development of quantitative MRI DTI analysis tool for preterm neonate
早产儿定量MRI DTI分析工具的开发
  • 批准号:
    8334037
  • 财政年份:
    2011
  • 资助金额:
    $ 16.81万
  • 项目类别:
Development of quantitative MRI DTI analysis tool for preterm neonate
早产儿定量MRI DTI分析工具的开发
  • 批准号:
    8700435
  • 财政年份:
    2011
  • 资助金额:
    $ 16.81万
  • 项目类别:
Development of quantitative MRI DTI analysis tool for preterm neonate
早产儿定量MRI DTI分析工具的开发
  • 批准号:
    8510698
  • 财政年份:
    2011
  • 资助金额:
    $ 16.81万
  • 项目类别:

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