GIFTwrap: a containerized and FAIR cloud-based implementation of the widely used GIFT toolbox: Request for supplemental funds for NIH 2R01EB006841 “Multivariate methods for identifying multi-task/mult

摘要 GIFT软件是实现fMRI数据和组ICA的第一个软件工具，自那以来 已被脑成像界广泛采用。多年来，这个工具一直在不断扩展，现在 提供对一大套工具的访问，包括数据驱动的功能连接、十几种不同的ICA 方法，独立向量分析，动态功能连通性，空间动力学，连接组可视化- 实例化，以及其他任何单一工具无法提供的更多功能。它还提供全自动ICA AP- 用于个体主题预测和分类的方法。尽管我们已经开始发展这一模式， Gift主要仍然基于独立的软件开发和分析模型。现代工具有 走向集中分析、可比性、分析互动和社区发展。在这篇文章中- 我们将专注于三个主要目标：1)建筑结构改进，以促进公平原则 并使工具现代化，2)将礼物工具发布为脑成像数据结构应用程序(BIDSapp)，以方便 使用并集成到现代分析框架中，并在几个广泛使用的分析平台中部署GIFT- 表单，以及3)为个人提供基于Web的界面以运行全自动ICA分析，这需要 一个简单的数据上传到工具。这一结果将产生重大影响，因为它将提供 一个已经拥有大量用户基础的工具的实用程序，将向社区中的更多人开放它的使用，包括 专注于现代集装箱环境中的大数据工具和部署，并将促进 自动化、复制、交叉研究比较，以及通过空间处理管道和噪声的稳健性 受限的ICA和内部网络的自动标记(不仅仅是伪像和信号)。

Abstract The GIFT software was the first software tool to implement ICA of fMRI data and group ICA which has since been widely adopted by the brain imaging community. This tool has continuously expanded over the years, now providing access to a large suite of tools including data-driven functional connectivity, over a dozen different ICA approaches, independent vector analysis, dynamic functional connectivity, spatial dynamics, connectome visu- alization, and much more that are not offered in any other single tool. It also offers fully automated ICA ap- proaches for use in individual subject prediction and classification. Though we have begun to evolve the model, GIFT is primarily still based on a standalone software development and analysis model. Modern tools have moved towards centralized analysis, comparability, analytic interaction and community development. In this sup- plement we will focus on three main goals: 1) Building of architecture improvements to facilitate FAIR principles and modernize the tools, 2) to release the GIFT tools as a brain imaging data structure app (BIDSapp) for easy use and integration into modern analysis frameworks, and to deploy GIFT in several widely used analytic plat- forms, and 3) to provide a web-based interface for individuals to run fully automated ICA analysis which requires a simple upload of data to the tools. The result will have significant impact as it will offer a large expansion of utility for a tool which already has a large user base, will open up its use to many more in the community including those focused on big data tools and deployment within a modern containerized environment, and will facilitate automation, replication, cross-study comparisons, and robustness to processing pipelines and noise via spatially constrained ICA and automated labelling of intrinsic networks (beyond just artifact versus signal).