A Multidimensional Alzheimer's Disease Brain Atlas

多维阿尔茨海默病大脑图谱

基本信息

  • 批准号:
    7493972
  • 负责人:
  • 金额:
    $ 34.49万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2007
  • 资助国家:
    美国
  • 起止时间:
    2007-09-05 至 2011-07-31
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): This is a competitive renewal of a 10-year project that has provided enormous insight into Alzheimer's Disease (AD), a disease that costs $113 billion/yr in the U.S. alone, with no known cure. The project develops a multidimensional, computational atlas of AD. Our 5-year plan of work provides the most powerful computational tools to track AD emerging and spreading in the living brain - years before symptoms begin. We will correlate 3 perspectives on AD in an atlas coordinate framework - serial MRI, novel PET tracers, and 3D pathology. This will provide scientists will the most sensitive approach ever created to gauge which factors affect disease progression (drug treatment, genetic risk, etc.), and how effectively treatments slow the transition to AD. First, we chart the anatomic trajectory of AD with novel analyses of serial MRI (Aims 1,2). Our tools to detect brain changes (cortical thickness mapping, tensor-based morphometry) provided the first time-lapse maps of the disease spreading in the living brain. Here we apply them to MCI subjects (who are at five-fold higher risk of converting to AD in any given year) to identify the best predictors of imminent disease onset, and to predict changes in specific cognitive domains. This will greatly advance drug trials by better identifying candidates for early treatment, who can benefit most before irreversible damage sets in. Next, we will use novel PET tracer molecules to reconstruct the dynamic sequence of AD pathology as it builds up and spreads in the living brain (Aim 3). Hailed as a breakthrough in the AD community, our newly-developed PET (positron emission tomography) tracer compound, [18F]-FDDNP, visualizes amyloid plaques and neurofibrillary tangles (NFTs) - hallmarks of AD previously only detectable at autopsy. Our sensitive surface-based 3D analytic techniques will map the spatio-temporal trajectory of plaque and tangle build-up in aging, mild cognitive impairment, and AD, comparing groups to identify brain changes that predict imminent cognitive deterioration or transition to AD; we will compare and correlate these signals with MRI measures of atrophic rates and cortical degeneration to create joint MR-PET measures of disease burden. We will pioneer 3D cryosection imaging (in 3 subjects/year) to establish how 3D reconstructed maps of tangle density and betaamyloid distribution throughout the brain correlate with imaging measures from living patients. This groundtruth data will be a resource to the AD community, revealing the cellular correlates of imaging signals whose physiological meaning is poorly understood. We will map individuals and populations, revealing group patterns of cortical thinning and plaque and tangle pathology that predict outcomes. Identifying predictors of imminent decline and disease onset, our atlas will identify candidates with emerging pathology for early drug treatment, and will store statistical data to quantify how well treatments resist AD in those at risk. We will share all images, protocols, and algorithms with our 100+ collaborating laboratories.
描述(申请人提供):这是一个为期10年的项目的竞争性续签,该项目为阿尔茨海默病(AD)提供了巨大的洞察力,这种疾病仅在美国就花费了1130亿美元,目前还没有已知的治疗方法。该项目开发了一个多维的AD计算地图集。我们的五年工作计划提供了最强大的计算工具来跟踪AD在活体大脑中的出现和传播--在症状开始之前的几年。我们将在一个地图集的坐标框架中将AD的三个角度联系起来--序列MRI、新型PET示踪剂和3D病理学。这将为科学家提供有史以来最敏感的方法来衡量哪些因素影响疾病进展(药物治疗、遗传风险等),以及治疗方法如何有效地减缓向AD的过渡。首先,我们用新的序列MRI分析(AIMS 1,2)绘制了AD的解剖轨迹。我们检测大脑变化的工具(皮质厚度图、基于张量的形态计量学)提供了疾病在活脑中传播的第一张时间推移图。在这里,我们将它们应用于MCI受试者(他们在任何给定年份转换为AD的风险高出五倍),以确定即将发生疾病的最佳预测因素,并预测特定认知领域的变化。这将通过更好地确定早期治疗的候选者来极大地推进药物试验,这些候选者可以在不可逆转的损害开始之前受益最大。接下来,我们将使用新型的PET示踪分子来重建AD病理的动态序列,因为它在活的大脑中建立和传播(目标3)。我们新开发的PET(正电子发射断层扫描)示踪化合物[18F]-FDDNP被誉为AD领域的一项突破,它可以显示淀粉样斑块和神经原纤维缠结(NFT)--这是AD以前只有在尸检中才能发现的特征。我们敏感的基于表面的3D分析技术将绘制衰老、轻度认知障碍和AD中斑块和缠绕堆积的时空轨迹,比较各组以识别预测认知即将恶化或过渡到AD的大脑变化;我们将这些信号与萎缩率和皮质变性的MRI测量进行比较和关联,以创建疾病负担的联合MR-PET测量。我们将开创3D冷冻成像的先河(每年在3名受试者中),以建立3D重建的大脑中缠结密度和β淀粉样蛋白分布的地图如何与活着的患者的成像措施相关联。这些基本事实数据将成为AD社区的资源,揭示成像信号的细胞关联,其生理意义尚不清楚。我们将绘制个人和人群的地图,揭示预测结果的皮质变薄和斑块的群体模式和缠绕的病理。通过识别即将出现的衰退和疾病发病的预测因素,我们的图集将确定早期药物治疗的新出现病理的候选对象,并将存储统计数据,以量化那些有风险的人的治疗抵抗AD的效果。我们将与100多个协作实验室共享所有图像、协议和算法。

项目成果

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PAUL M THOMPSON其他文献

PAUL M THOMPSON的其他文献

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{{ truncateString('PAUL M THOMPSON', 18)}}的其他基金

CARE4Kids: Imaging Biomarker Core
CARE4Kids:成像生物标志物核心
  • 批准号:
    10203601
  • 财政年份:
    2021
  • 资助金额:
    $ 34.49万
  • 项目类别:
ENIGMA World Aging Center
ENIGMA世界老龄化中心
  • 批准号:
    10576402
  • 财政年份:
    2021
  • 资助金额:
    $ 34.49万
  • 项目类别:
ENIGMA World Aging Center
ENIGMA世界老龄化中心
  • 批准号:
    10328963
  • 财政年份:
    2021
  • 资助金额:
    $ 34.49万
  • 项目类别:
FiberNET: Deep learning to evaluate brain tract integrity worldwide and in AD
FiberNET:深度学习评估全球和 AD 脑道完整性
  • 批准号:
    10814696
  • 财政年份:
    2020
  • 资助金额:
    $ 34.49万
  • 项目类别:
Neuroimaging Core
神经影像核心
  • 批准号:
    10216924
  • 财政年份:
    2018
  • 资助金额:
    $ 34.49万
  • 项目类别:
ENIGMA-SD: Understanding Sex Differences in Global Mental Health through ENIGMA
ENIGMA-SD:通过 ENIGMA 了解全球心理健康中的性别差异
  • 批准号:
    9892045
  • 财政年份:
    2018
  • 资助金额:
    $ 34.49万
  • 项目类别:
Neuroimaging Core
神经影像核心
  • 批准号:
    10456750
  • 财政年份:
    2018
  • 资助金额:
    $ 34.49万
  • 项目类别:
Multi-Source Sparse Learning to Identify MCI and Predict Decline
多源稀疏学习识别 MCI 并预测衰退
  • 批准号:
    9008380
  • 财政年份:
    2016
  • 资助金额:
    $ 34.49万
  • 项目类别:
Data Science Research
数据科学研究
  • 批准号:
    9108711
  • 财政年份:
    2016
  • 资助金额:
    $ 34.49万
  • 项目类别:
ENIGMA Center for Worldwide Medicine, Imaging & Genomics
ENIGMA 全球医学影像中心
  • 批准号:
    9108710
  • 财政年份:
    2014
  • 资助金额:
    $ 34.49万
  • 项目类别:

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激素治疗、绝经年龄、既往产次和 APOE 基因型会影响老年人的认知。
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