Meta-analysis in human brain mapping

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成像社区提供经过精选的数据 支持基于坐标的元分析的集合、元数据、计算工具和相关资源 (CBMA)、元分析连通性建模(MACM)、元数据知情解释(“解码”) 成像结果，以及挖掘(包括机器学习)的元分析先验(按受试者) 神经成像数据。到目前为止，BrainMap项目已经设计并填充了两个基于坐标的 数据库：1)任务激活库(TA DB)；以及2)基于体素的形态测量库(VBM DB)。 助教数据库包含17,200个实验，总共代表78,000个受试者和110个任务- 激活范例。VBM数据库包含&gt；3,100个实验，总共代表&gt；81,000个受试者 80种精神、神经和发育障碍，ICD-10编码。BrainMap项目有 创建、优化和验证多平台(Java脚本)、开放访问工具的集成管道，以 整理(Scribe)、过滤和检索(Sleuth)、分析(GingerALE)、可视化(Mango)和解释分析 输出(用于芒果的BrainMap元数据插件)。几种网络建模方法已被应用于 脑图数据--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)和基于连接的分割(CBP)--但均未实现 仍然是流水线组件。这些CBMA资源的利用率很高：BrainMap软件、数据和 元数据被用于825种同行评议的出版物。其中，&gt；350在 当前资助期(2015年4月至2019年3月；brainmap.org/pubs)。在这场竞争性更新中，有四个工具- 提出了发展目标，每个目标都扩展了这一高影响力的研究资源。 目标1.数据库扩容。BrainMap数据存储库将得到扩展。 目的2.元分析网络建模。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 将执行验证。 目标4.元数据推理工具。用于挖掘BrainMap与位置相关的元数据的工具将得到扩展。 数据共享计划。整个数据库创建的BrainMap数据、元数据、管道工具和模板 建模(例如，ICA和ATM网络掩码)在BrainMap.org上共享。在所有新数据条目中，超过 一半由BrainMap用户贡献，即通过BrainMap.org共享社区数据。用于社区编码 对于参赛作品，BrainMap团队提供管理和质量控制。全面的数据库映像(数据库 转储)可通过协作使用协议向工具开发人员提供。

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.