Meta-analysis in human brain mapping

人脑图谱的荟萃分析

• 批准号: 10056029
• 负责人: PETER Thornton FOX
• 金额: $ 54.34万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCIENCE CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10056029
• 关键词: Agreement; Area; Brain; Brain Mapping; Code; Collaborations; Communities; Computer software; Data; Data Set; Databases; Disease; Educational workshop; Equation; Functional disorder; Funding; Goals; Guidelines; Human; Image; Institution; International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10); Internet; Java; Link; Location; Machine Learning; Mango - dietary; Masks; Mental disorders; Meta-Analysis; Metadata; Methods; Mining; Modeling; Online Systems; Output; Peer Review; Plug-in; Publications; Publishing; Quality Control; Research Domain Criteria; Resources; Rest; Site; Software Framework; Specificity; Structure; Training; Universities; Validation; base; candidate marker; computerized tools; data pipeline; data sharing; data warehouse; design; developmental disease; experimental study; graph theory; independent component analysis; interest; large scale simulation; morphometry; nervous system disorder; network architecture; network models; neuroimaging; neuropsychiatric disorder; repository; simulation; tool; tool development

This is the competing renewal of R01MH074457-13, which sustains the BrainMap Project (www.brainmap.org). The overall goal of the BrainMap Project is to provide the human neuroimaging community with curated data sets, metadata, computational tools, and related resources that enable coordinate-based meta-analyses (CBMA), meta-analytic connectivity modeling (MACM), meta-data informed interpretation (“decoding”) of imaging results, and meta-analytic priors for mining (including machine learning) primary (per-subject) neuroimaging data. To date, the BrainMap Project has designed and populated two coordinate-based databases: 1) a task-activation repository (TA DB); and, 2) a voxel-based morphometry repository (VBM DB). The TA DB contains >17,200 experiments, collectively representing > 78,000 subjects and > 110 task- activation paradigms. The VBM DB contains > 3,100 experiments, collectively representing > 81,000 subjects with > 80 psychiatric, neurologic and developmental disorders with ICD-10 coding. The BrainMap Project has created, optimized and validated an integrated pipeline of multi-platform (Javascript), open-access tools to curate (Scribe), filter and retrieve (Sleuth), analyze (GingerALE), visualize (Mango) and interpret analysis output (BrainMap meta-data plugins for Mango). Several network-modeling approaches have been applied to BrainMap data -- MACM, independent components analysis (ICA), graph theory modeling (GTM), author-topic modeling (ATM), structural equation modeling (SEM), and connectivity-based parcellation (CBP) – but none are yet pipeline components. Utilization of these CBMA resources is substantial: BrainMap software, data and meta-data have been used in > 825 peer-reviewed publications. Of these, > 350 were published within the current funding period (April 2015-March 2019; brainmap.org/pubs). In this competing renewal, four tool- development aims are proposed, each of which extends this high-impact research resource. Aim 1. Database Expansion. BrainMap data repositories will be expanded. Aim 2. Meta-analytic Network Modeling. Network modeling will be added to the BrainMap pipeline. Aim 3. Large-Scale Simulations, Comparisons and Validations. Data simulations, characterizations and validations will be performed. Aim 4. Meta-data Inferential tools. Tools for mining BrainMap’s location-linked meta-data will be expanded. Data Sharing Plan. BrainMap data, meta-data, pipeline tools, and templates created by whole-database modeling (e.g., ICA and ATM network masks) are shared at BrainMap.org. Of all new data entries, more than half are contributed by BrainMap users, i.e., community data sharing via BrainMap.org. For community-coded entries, the BrainMap team provides curation and quality control. Comprehensive database images (database dumps) are available to tool developers through Collaborative Use Agreements.

这是R01MH074457-13的竞争续约，该续约维持了脑贴图项目（www.brainmap.org）。 Brainmap项目的总体目标是为人类的神经影像社区提供精选的数据 集合，元数据，计算工具和相关资源，可启用基于坐标的荟萃分析 （CBMA），元分析连接建模（MACM），元数据知情解释（“解码”） 成像结果和用于采矿（包括机器学习）的荟萃分析先验（每项受试者） 神经影像数据。迄今为止，Brainmap项目已经设计并填充了两个基于坐标的项目 数据库：1）任务激活存储库（TA DB）； 2）基于体素的形态计量库（VBM DB）。 TA DB包含> 17,200个实验，统称为78,000名受试者和> 110个任务 - 激活范例。 VBM DB包含> 3,100个实验，统称为81,000名受试者 具有ICD-10编码的80种精神病，神经系统和发育障碍。 Brainmap项目有 创建，优化和验证了多平台（JavaScript）的集成管道，开放访问工具 策展人（抄写员），滤波器和检索（侦探），分析（姜饼），可视化（芒果）和解释分析 输出（芒果的脑图元数据插件）。已经应用了几种网络建模方法 Brainmap数据 - MACM，独立组件分析（ICA），图理论建模（GTM），作者主题 建模（ATM），结构方程建模（SEM）和基于连接性的拟合（CBP） - 但没有 仍是管道组件。这些CBMA资源的利用是实质的：脑图软件，数据和 元数据已在> 825个同行评审的出版物中使用。其中，> 350在 当前的融资期（2015年4月2019年3月； brainmap.org/pubs）。在这种竞争的续约中，四个工具 - 提出了开发目标，每种目标都扩展了这种高影响力的研究资源。 目标1。数据库扩展。 Brainmap数据存储库将扩展。 目标2。荟萃分析网络建模。网络建模将添加到Brainmap管道中。 目标3。大规模的模拟，比较和验证。数据模拟，字符和 将执行验证。 AIM4。META-DATA推论工具。开采BrainMap与位置链接的元数据的工具将扩展。 数据共享计划。脑图数据，元数据，管道工具和模板由全数据库创建 建模（例如ICA和ATM网络掩码）在BrainMap.org共享。在所有新数据条目中，不仅仅是 一半是由Brainmap用户贡献的，即通过BrainMap.org共享社区数据。用于社区编码 条目，Brainmap团队提供策划和质量控制。综合数据库图像（数据库 垃圾场）可以通过协作使用协议可用于工具开发人员。