BRAIN project (Cox)

大脑项目（考克斯）

• 批准号: 10043763
• 负责人: Robert Cox
• 金额: $ 20.84万
• 依托单位: NATIONAL INSTITUTE OF MENTAL HEALTH
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10043763
• 关键词: Algorithms; Applications Grants; BRAIN initiative; Brain imaging; Code; Collaborations; Communities; Computer Simulation; Computer software; Data Analyses; Data Collection; Dependence; Development; Environment; Feedback; Funding; Goals; Imagery; Infrastructure; Internet; Modeling; Online Systems; Output; Quality Control; Reproducibility; Route; Specific qualifier value; Statistical Models; Testing; To specify; Work; cloud based; flexibility; member; neuroimaging; prevent; repository; tool; web page

Over the last year, we have developed AFNI's tests extensively to prevent regression in our codebase and provide a better guarantee of AFNI's functionality. This test infrastructure, implemented in a containerized- and cloud-environment has undergone iteration with feedback from members of the neuroimaging community and the AFNI team in order to be a robust solution, convenient for use by team members, that shares any common aspects of testing with the community via the testkraken software package. We continue to develop the way in which we package AFNI to make it easier to install across all platforms, including container and cloud contexts. One significant part of this effort has been to use the Cmake (cross-platform make) build tool and to specify the projects dependencies using the conda package manager. We are continuing to work on increasing the flexibility of statistical model specification for neuroimaging analysis in R. In order to make this a more tractable feat we have begun to refactor the code to make the software easier to use, install, maintain, and develop. The output of these efforts will be the improvements in model specification detailed in the grant proposal and an R package hosted on the CRAN repository. We have made substantial contributions toward the development of an implementation of the BIDS Statsmodels specification. Rather than extend our own interface to accommodate this, we worked directly in the Fitlins codebase (also partly funded by the BRAIN Initiative). This route provides a more integrated implementation of AFNI's unique single subject analysis for models specified in the standards JSON model notation. One added benefit is to reduce redundancy in efforts. While fitlins creates a general interface for fitting statistical models in neuroimaging, our work under the hood creates a bridge to AFNI's 3dREMLfit tool, providing all the careful decisions in neuroimaging statistical modelling and improvements to algorithmic efficiency that come along with it. An added benefit to this collaboration is that the battle-hardened workflow manager, nipype, used by fitlins allows us to use AFNI code to fit the statistical models in a manner that is reproducible, tracks provenance, and scalable. Regarding web visualization we have developed a HTML output for afni_proc.py that allows a user to assess many of the quality control details that need to be considered all in the convenient interface of a single webpage.

在过去的一年里，我们广泛地开发了AFNI的测试，以防止我们的代码库中的回归，并为AFNI的功能提供更好的保证。在容器化和云环境中实施的这个测试基础设施已经根据神经成像社区和AFNI团队成员的反馈进行了迭代，以便成为一个强大的解决方案，方便团队成员使用，通过testkraken软件包与社区共享测试的任何共同方面。 我们继续开发AFNI的打包方式，使其更容易在所有平台上安装，包括容器和云环境。这项工作的一个重要部分是使用Cmake（跨平台make）构建工具，并使用conda包管理器指定项目依赖项。 我们将继续致力于提高R中神经影像分析的统计模型规范的灵活性。为了使这成为一个更容易处理的壮举，我们已经开始重构代码，使软件更容易使用，安装，维护和开发。这些努力的结果将是在资助提案中详细描述的模型规范的改进和CRAN存储库上托管的R包。 我们对BIDS Statmodels规范的实现的发展做出了实质性的贡献。我们没有扩展自己的界面来适应这一点，而是直接在Fitlins代码库中工作（也部分由BRAIN Initiative资助）。这条路线为AFNI独特的单主题分析提供了一个更集成的实现，用于标准JSON模型符号中指定的模型。一个额外的好处是减少工作中的冗余。虽然fitlins为神经成像中的拟合统计模型创建了一个通用界面，但我们在幕后的工作创建了一个通往AFNI 3dREMLfit工具的桥梁，为神经成像统计建模提供了所有谨慎的决策，并沿着了算法效率的改进。这种合作的额外好处是，久经沙场的工作流管理器nipype，fitlins使用的方法允许我们使用AFNI代码以可重复、跟踪出处和可扩展的方式拟合统计模型。 关于Web可视化，我们已经为afni_proc.py开发了一个HTML输出，它允许用户在单个网页的方便界面中评估许多需要考虑的质量控制细节。