BRAIN project (Cox)

在过去的一年里，我们广泛地开发了Afni的测试，以防止代码库中的回归，并为Afni的功能提供更好的保证。这一在集装箱化和云环境中实施的测试基础设施已经与神经成像社区和Afni团队成员的反馈进行了迭代，以便成为一个健壮的解决方案，便于团队成员使用，通过Testkraken软件包与社区共享测试的任何共同方面。 我们继续开发打包Afni的方法，以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。 我们正在继续致力于增加R中神经成像分析的统计模型规范的灵活性。为了使这一壮举更容易处理，我们已经开始重构代码，使软件更易于使用、安装、维护和开发。这些努力的结果将是赠款提案中详细说明的模型规格的改进，以及CRAN储存库上托管的R包。 我们为实现投标统计模型规范的开发做出了重大贡献。我们没有扩展我们自己的界面来适应这种情况，而是直接在Fitlins代码库中工作(部分资金也来自Brain Initiative)。这条路线为标准JSON模型符号中指定的模型提供了AFNI独特的单一主题分析的更完整的实现。一个额外的好处是减少了工作中的冗余。虽然Fitlins为神经成像中的统计模型拟合创建了一个通用界面，但我们在幕后的工作为Afni的3dREMLfit工具创建了一座桥梁，提供了神经成像统计建模中的所有谨慎决策，以及随之而来的算法效率改进。这种协作的另一个好处是，Fitlins使用的久经沙场的工作流管理器nipype允许我们使用Afni代码以一种可重现、跟踪来源和可扩展的方式来拟合统计模型。 关于网络可视化，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需要考虑的许多质量控制细节。

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.