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:
    7372602
  • 负责人:
  • 金额:
    $ 34.65万
  • 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(由申请人提供):这是一个为期10年的项目的竞争性更新,该项目为阿尔茨海默病(AD)提供了巨大的洞察力,仅在美国,这种疾病每年花费1130亿美元,没有已知的治疗方法。该项目开发了AD的多维计算图谱。我们的5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。我们将在一个图谱坐标框架中关联AD的3个视角-系列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),以及治疗如何有效地减缓向AD的转变。首先,我们通过对系列MRI的新分析绘制了AD的解剖轨迹(目标1,2)。我们检测大脑变化的工具(皮层厚度映射,基于张量的形态测量)提供了第一个疾病在活体大脑中传播的延时图。在这里,我们将它们应用于MCI受试者(在任何一年中转化为AD的风险高出五倍),以确定即将发生疾病的最佳预测因子,并预测特定认知领域的变化。这将通过更好地识别早期治疗的候选人来大大推进药物试验,这些候选人可以在不可逆转的损害发生之前受益最多。接下来,我们将使用新的PET示踪分子来重建AD病理学的动态序列,因为它在活体大脑中建立和传播(目标3)。我们新开发的PET(正电子发射断层扫描)示踪剂化合物[18 F]-FDDNP被誉为AD社区的突破,可使淀粉样斑块和神经纤维缠结(NFT)可视化-这是以前只能在尸检中检测到的AD标志。我们敏感的基于表面的3D分析技术将绘制斑块和缠结在衰老、轻度认知障碍和AD中积聚的时空轨迹,比较各组以识别预测即将发生的认知恶化或向AD过渡的大脑变化;我们将比较这些信号并将其与萎缩率和皮质变性的MRI测量结果相关联,以创建疾病负担的联合MR-PET测量结果。我们将开创3D冷冻切片成像(3例受试者/年),以确定整个大脑中缠结密度和β淀粉样蛋白分布的3D重建图如何与活体患者的成像测量相关。这些地面实况数据将成为AD社区的资源,揭示其生理意义知之甚少的成像信号的细胞相关性。我们将绘制个体和人群,揭示皮质变薄和斑块和缠结病理的群体模式,预测结果。识别即将下降和疾病发作的预测因子,我们的图谱将识别具有早期药物治疗的新兴病理学的候选者,并将存储统计数据以量化治疗在风险人群中抵抗AD的效果。我们将与100多个合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:
    $ 34.65万
  • 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:
    $ 34.65万
  • 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:
    $ 34.65万
  • 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:
    $ 34.65万
  • 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:
    $ 34.65万
  • 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:
    $ 34.65万
  • 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:
    $ 34.65万
  • 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:
    $ 34.65万
  • 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:
    $ 34.65万
  • 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:
    $ 34.65万
  • 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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