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 的竞争性更新，它支持 BrainMap 项目 (www.brainmap.org)。 BrainMap 项目的总体目标是为人类神经影像学界提供精选数据 支持基于坐标的荟萃分析的集合、元数据、计算工具和相关资源 （CBMA）、元分析连接模型（MACM）、元数据知情解释（“解码”） 成像结果，以及用于挖掘（包括机器学习）初级（每个主题）的元分析先验 迄今为止，BrainMap 项目已经设计并填充了两个基于坐标的数据。 数据库：1）任务激活存储库（TA DB）；2）基于体素的形态测量存储库（VBM DB）。 TA DB 包含 > 17,200 个实验，总共代表 > 78,000 个受试者和 > 110 个任务 VBM DB 包含超过 3,100 个实验，总共代表超过 81,000 个受试者。 BrainMap 项目已对超过 80 种精神、神经和发育障碍进行了 ICD-10 编码。 创建、优化和验证了多平台（Javascript）开放访问工具的集成管道 策划 (Scribe)、过滤和检索 (Sleuth)、分析 (GingerALE)、可视化 (Mango) 和解释分析 输出（Mango 的 BrainMap 元数据插件）已应用于多种网络建模方法。 BrainMap 数据——MACM、独立成分分析 (ICA)、图论建模 (GTM)、作者主题 建模 (ATM)、结构方程建模 (SEM) 和基于连通性的分区 (CBP) – 但没有 这些 CBMA 资源的利用率仍然很高：BrainMap 软件、数据和 元数据已在超过 825 种同行评审出版物中使用，其中超过 350 种是在 当前资助期（2015 年 4 月至 2019 年 3 月；brainmap.org/pubs）。 提出了发展目标，每一个目标都扩展了这一高影响力的研究资源。 目标 1. 数据库扩展。 BrainMap 数据存储库将得到扩展。 目标 2. 元分析网络建模将添加到 BrainMap 管道中。 目标 3：大规模模拟、比较和验证数据模拟、表征和验证。 将执行验证。 目标 4. 元数据推理工具。用于挖掘 BrainMap 位置相关元数据的工具将得到扩展。 数据共享计划。脑图数据、元数据、管道工具和全数据库创建的模板。 建模（例如 ICA 和 ATM 网络掩码）在 BrainMap.org 上共享，超过所有新数据条目。 一半由 BrainMap 用户贡献，即通过 BrainMap.org 进行社区数据共享。 BrainMap 团队提供参赛作品的策划和质量控制。 dumps）可通过协作使用协议提供给工具开发人员。

项目成果

Sex classification from functional brain connectivity: Generalization to multiple datasets.

根据功能性大脑连接进行性别分类：泛化到多个数据集。

• 发表时间: 2024-04-15
• 影响因子: 4.8
• 作者: Wiersch, Lisa;Friedrich, Patrick;Hamdan, Sami;Komeyer, Vera;Hoffstaedter, Feli;Patil, Kaustubh R;Eickhoff, Simon B;Weis, Susanne
• 通讯作者: Weis, Susanne