High Throughput web-base Image Analysis of Mouse Brain MR Imaging Studies

小鼠脑 MR 成像研究的高通量网络图像分析

基本信息

  • 批准号:
    7272126
  • 负责人:
  • 金额:
    $ 19.72万
  • 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:
    2007-06-15 至 2009-05-30
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): In the last decade, MRI studies of human brain morphometry have been used to investigate a multitude of pathologies and drug-related effects in psychiatric research. The morphometric measures that differentiate patient populations or track longitudinal changes are often subtle and require a large number of subjects or repeated studies to detect and statistically model with significance. Cost, patient compliance, risks to the patients, and the rarity of certain diseases often limit traditional, clinical morphometric studies. These complications have motivated the use of model organisms such as of mice and rats. Animal studies are also very popular due to their small size and rapid development cycle, the wealth of genotype and phenotype data, as well as the maturity of the technology to manipulate their genetic information to induce disease. Brain morphometry models of rat and mice typically involve histological slides, behavioral data, genetic testing, and, increasingly, MRI scans. In particular, in addition to brain morphometry, MRI scans are being employed as a hypothesis generation method for focused histological and molecular examinations, and for strain comparisons. Effective methods have been developed for extracting brain morphometry from human MRI scans. We are leaders in the field for their development and their application. We have developed Legrendre polynomial methods for MRI bias correction methods, atlas-based methods for tissue classification, and spherical harmonics techniques for shape parameterization. We have applied these methods to correlate hippocampus shape variations that distinguish patients suffering from schizophrenia. By contrast, few automated quantitative analysis methods exist for small animal MRI. The standard is to manually outline brain features in MRI slices for a large number of animals, and such manual methods lack reproducibility and are extremely time consuming. The lack of automated MRI analysis methods is the limiting factor in many animal studies. We propose to develop automatic, reliable, high-throughput MR image analysis methods for small animal, brain morphometry studies. Additionally, we propose to develop an intuitive web-based interface for collecting and distributing the imaging data of small animal studies as well as initiating the processing of that data on a distributed processing network. The web-based data sharing and processing system also supports the inspection of the ongoing processing and the examination of the computed results. This web-based processing system is generic in nature and can be extended to host and process human MRI data as well as data from other modalities and other applications. To demonstrate and evaluate the data system, we will apply it to the study of the neuroanatomy of a fragile-X syndrome mouse model. This mouse model Is based on a knockout of the FMR1 mouse model, and it has shown behavioral deficits consistent with a Fragile X/autism human phenotype. The proposed software will advance murine MRI studies of morphometry and connectivity for neuro-developmental, and neuro-degenerative psychiatry diseases. The analysis of MR images of entire brain studies will become the matter of a few mouseclicks on a web-interface.
描述(申请人提供):在过去的十年里,人类大脑形态计量学的核磁共振研究已经被用于研究精神病学研究中的多种病理和药物相关的影响。区分患者群体或跟踪纵向变化的形态测量方法通常是微妙的,需要大量的受试者或重复研究来检测并建立有意义的统计模型。费用、患者依从性、对患者的风险以及某些疾病的罕见往往限制了传统的临床形态测量研究。这些并发症促使了小鼠和大鼠等模型生物的使用。动物研究也非常受欢迎,因为它们的规模小,发展周期快,丰富的基因和表型数据,以及成熟的技术,操纵它们的遗传信息来诱发疾病。大鼠和小鼠的大脑形态测量模型通常包括组织切片、行为数据、基因测试,以及越来越多的核磁共振扫描。特别是,除了脑形态测量外,MRI扫描还被用作一种假说生成方法,用于重点组织学和分子检查,以及进行应变比较。已经开发出从人类MRI扫描中提取脑形态计量的有效方法。我们在它们的开发和应用方面处于该领域的领先地位。我们开发了用于MRI偏差校正的Legrendre多项式方法、基于图谱的组织分类方法和用于形状参数化的球谐技术。我们已经应用这些方法来关联区分精神分裂症患者的海马体形状变化。相比之下,用于小动物核磁共振的自动化定量分析方法很少。标准是在大量动物的MRI切片上手动勾勒出大脑特征,这种手动方法缺乏重复性,而且极其耗时。缺乏自动化的MRI分析方法是许多动物研究的限制因素。我们建议开发自动的、可靠的、高通量的磁共振图像分析方法,用于小动物、脑形态计量研究。此外,我们建议开发一个基于Web的直观界面,用于收集和分发小动物研究的成像数据,并在分布式处理网络上启动对这些数据的处理。基于网络的数据共享和处理系统还支持对正在进行的处理的检查和对计算结果的检查。这种基于网络的处理系统本质上是通用的,可以扩展到托管和处理人类磁共振数据以及来自其他医疗设备和其他应用的数据。为了演示和评估数据系统,我们将把它应用于脆性X综合征小鼠模型的神经解剖学研究。这个小鼠模型是基于FMR1小鼠模型的敲除,它已经显示出与脆性X/自闭症人类表型一致的行为缺陷。拟议的软件将推进小鼠MRI对神经发育和神经退行性精神疾病的形态测量和连接性的研究。对整个大脑研究的磁共振图像的分析将变成在网络界面上点击几下鼠标的事情。

项目成果

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Martin Andreas Styner其他文献

Martin Andreas Styner的其他文献

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{{ truncateString('Martin Andreas Styner', 18)}}的其他基金

Longitudinal Analysis of the Dynamic Network Disruptions in Alzheimer's Disease
阿尔茨海默病动态网络中断的纵向分析
  • 批准号:
    9508126
  • 财政年份:
    2018
  • 资助金额:
    $ 19.72万
  • 项目类别:
International Conference on Information Processing in Medical Imaging (IPMI)
国际医学影像信息处理会议 (IPMI)
  • 批准号:
    9331007
  • 财政年份:
    2017
  • 资助金额:
    $ 19.72万
  • 项目类别:
Consortium with University of North Carolina
与北卡罗来纳大学联盟
  • 批准号:
    7995744
  • 财政年份:
    2008
  • 资助金额:
    $ 19.72万
  • 项目类别:
ADVANCED IMAGE ANALYSIS OF THE RODENT BRAIN
啮齿动物大脑的高级图像分析
  • 批准号:
    7601173
  • 财政年份:
    2007
  • 资助金额:
    $ 19.72万
  • 项目类别:
High Throughput web-base Image Analysis of Mouse Brain MR Imaging Studies
小鼠脑 MR 成像研究的高通量网络图像分析
  • 批准号:
    7446753
  • 财政年份:
    2007
  • 资助金额:
    $ 19.72万
  • 项目类别:
ADVANCED IMAGE ANALYSIS OF THE RODENT BRAIN
啮齿动物大脑的高级图像分析
  • 批准号:
    7358326
  • 财政年份:
    2006
  • 资助金额:
    $ 19.72万
  • 项目类别:
NEUROIMAGING CORE
神经影像核心
  • 批准号:
    7851414
  • 财政年份:
  • 资助金额:
    $ 19.72万
  • 项目类别:
NEUROIMAGING CORE
神经影像核心
  • 批准号:
    8089461
  • 财政年份:
  • 资助金额:
    $ 19.72万
  • 项目类别:
NEUROIMAGING CORE
神经影像核心
  • 批准号:
    8378841
  • 财政年份:
  • 资助金额:
    $ 19.72万
  • 项目类别:
NEUROIMAGING CORE
神经影像核心
  • 批准号:
    8268545
  • 财政年份:
  • 资助金额:
    $ 19.72万
  • 项目类别:

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