Informatics Meta-Spaces for the Exploration of Human Neuroanatomy

用于探索人类神经解剖学的信息学元空间

• 批准号: 7937028
• 负责人: John Darrell Van Horn
• 金额: $ 50万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7937028
• 关键词: Address; Advocate; American; Anatomy; Architecture; Archives; Area; Atlases; Automatic Data Processing; Behavior; Blast Cell; Brain; Brain Mapping; Brain imaging; Characteristics; Client; Clinical; Collection; Communities; Data; Data Set; Databases; Development; Diagnostic; Disease; Education; Elements; Environment; Genomics; Growth; Health; Human; Image; Imagery; Individual; Individual Differences; Informatics; Internet; Left; Link; Magnetic Resonance Imaging; Measurement; Measures; Mining; Neuroanatomy; Neurosciences; Neurosciences Research; Occupations; Outcome; Positioning Attribute; Process; Property; Protocols documentation; PubMed; Public Health; Relative (related person); Research; Research Personnel; Resources; Scanning; Scientist; Services; Shapes; Software Tools; Space Explorations; Structure; Surface; System; Technology; Text; TimeLine; base; bioimaging; brain shape; brain volume; cluster computing; computerized data processing; data mining; demographics; design; digital; empowered; insight; interest; neglect; neuroimaging; new technology; novel; regional difference; repository; statistics; tool; user-friendly; virtual; web page; web-enabled

DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (06) Enabling Technologies and specific Challenge Topic, 06-MH-103: New Technologies for Neuroscience Research. This project will provide a powerful, content-driven approach to the identification of brains having similar geometry and shape, the clustering of neuroanatomically similar cases, and the interactive 3D visualization of the large collections contained in neuroimaging archives. Beginning with the example of the LONI Image Data Archive (IDA), we will design automated data processing meta-workflows that will decompose the thousands of whole brain MRI volumes into constituent 3D neuroanatomical regions. We will characterize the geometric properties of these regional parcellations, store these measurements, and systematically assess pair-wise regional "distances" between brains, and decompose the resulting similarity matrix using multidimensional scaling and related approaches. These processes will be automated to accommodate the continuous growth of the archive and be able to include content obtained from other neuroimaging archives as well. We will graphically represent the derived space of brain similarity via an interactive and freely available 3D browser. Finally, we will develop means for users to upload their own MR anatomical volumes for automated processing via this same process using a large grid computational architecture; decompositions of the uploaded data will be compared against the shape statistics derived from the previously processed archival data; content- based search results will be returned to users via the web in the form of a rank ordered list of brain volumes having similar neuroanatomical characteristics; hyperlinks to additional meta-data and online information, as well as a depiction of the position of their data with respect to other derived brain data using the interactive 3D browser. Meta-data concerning each object in the display will be easily available describing subject demographics, diagnostic group, scanning parameters, etc. This project does not seek to advocate or support the development of any new centralized neuroimaging database but will provide an unprecedented service to the neuroscience community for interacting with existing digital brain archives. The tools developed here will be capable of accommodating that of other neuroimaging repositories as well as user's local archives, thus having utility beyond a single data resource. We expect the outcomes of this project to draw considerable interest and excitement from the neuroimaging community in a similar manner to which BLAST has had for the genomics community. Following a two year development timeline, we anticipate that these informatics-based approaches and tool deliverables will be instrumental for researchers as part of their neuroscientific enterprise, helping to guide research directions, enhance education, and will provide significant new insights concerning large-scale neuroimaging repositories of health and disease. H The outcomes of this novel project for informatics and dynamic visualization to are expected to draw considerable interest and excitement from the neuroimaging community. This project will empower content-driven searches in a similar manner to BLAST has provided for genomics and will provide significant new insights concerning large-scale neuroimaging repositories of health and disease. Over a two-year period of development, during which a number of new American jobs will be created, this project will deliver a robust, content-driven informatics approach to the identification of brains having similar geometry and shape, the clustering of neuroanatomically similar cases, and the interactive 3D visualization of the large collections contained in neuroimaging archives.

描述（由申请人提供）：此申请解决广泛的挑战领域（06）启用技术和特定挑战主题，06-MH-103：神经科学研究的新技术。 该项目将提供一种强大的，内容驱动的方法，用于识别具有相似几何形状和形状的大脑，神经解剖学上相似的情况的聚类以及神经图像档案中包含的大集合的交互式3D可视化。 以LONI图像数据存档（IDA）的示例开始，我们将设计自动数据处理元工作流，这些数据处理将将数千个大脑MRI量分解为组成3D神经洋解态区域。 我们将表征这些区域分层的几何特性，存储这些测量值，并系统地评估大脑之间的配对区域“距离”，并使用多维缩放和相关方法分解所得的相似性矩阵。 这些过程将自动化以适应档案的持续增长，并能够包括从其他神经影像档案中获得的内容。 我们将通过图形化表示大脑相似性的派生空间，并可以通过交互式可用的3D浏览器来表示。 最后，我们将开发用于用户使用大型网格计算体系结构的相同过程中的自动处理的MR解剖量上载的方法；上载数据的分解将与先前处理过的档案数据得出的形状统计数据进行比较；基于内容的搜索结果将通过Web以具有相似神经解剖学特征的大脑量列表的形式返回给用户；超链接到其他元数据和在线信息，以及使用Interactive 3D浏览器相对于其他派生的大脑数据的数据位置。 有关显示中每个对象的元数据将很容易地描述主题人口统计，诊断组，扫描参数等。本项目并不寻求主张或支持开发任何新的集中式神经成像数据库，但会为神经科学社区提供与现有数字大脑档案相互作用的前所未有的服务。 此处开发的工具将能够容纳其他神经成像存储库以及用户的本地档案，从而超过单个数据资源。 我们希望该项目的结果能以与BLAST对基因组学界的类似方式引起神经影像社区的极大兴趣和兴奋。 在开发两年的时间表之后，我们预计这些基于信息学的方法和工具可交付成果对研究人员来说将是其神经科学企业的一部分，有助于指导研究方向，增强教育，并提供有关健康和疾病的大规模神经模仿的新见解。 h这个新颖的信息学和动态可视化的新颖项目的结果有望引起神经影像社区的极大兴趣和兴奋。 该项目将以与BLAST相似的方式为基因组学提供的内容驱动的搜索能力，并将提供有关健康和疾病的大规模神经影像库的重要新见解。 在两年的开发期内，将创建许多新的美国工作岗位，该项目将提供一种强大的，内容驱动的信息学方法，以识别具有相似几何形状和形状的大脑，神经解剖学上相似的情况的聚类，以及在神经图像中包含的大型集合的交互式3D可视化。