Mastering the “arcana of neuroimaging analysis”, the obscure knowledge required to apply an appropriate combination of software tools and parameters to analyse a given neuroimaging dataset, is a time consuming process. Therefore, it is not typically feasible to invest the additional effort required generalise workflow implementations to accommodate for the various acquisition parameters, data storage conventions and computing environments in use at different research sites, limiting the reusability of published workflows. We present a novel software framework, Abstraction of Repository-Centric ANAlysis (Arcana), which enables the development of complex, “end-to-end” workflows that are adaptable to new analyses and portable to a wide range of computing infrastructures. Analysis templates for specific image types (e.g. MRI contrast) are implemented as Python classes, which define a range of potential derivatives and analysis methods. Arcana retrieves data from imaging repositories, which can be BIDS datasets, XNAT instances or plain directories, and stores selected derivatives and associated provenance back into a repository for reuse by subsequent analyses. Workflows are constructed using Nipype and can be executed on local workstations or in high performance computing environments. Generic analysis methods can be consolidated within common base classes to facilitate code-reuse and collaborative development, which can be specialised for study-specific requirements via class inheritance. Arcana provides a framework in which to develop unified neuroimaging workflows that can be reused across a wide range of research studies and sites.

中文翻译：

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捕获神经影像分析奥秘的综合框架。

掌握“神经影像分析的奥卡纳”，运用软件工具和参数的适当组合来分析给定的神经影像数据集所需的晦涩知识是一个耗时的过程。因此，通常不可行的是花费额外的精力来概括工作流实施以适应不同研究站点使用的各种采集参数，数据存储约定和计算环境，从而限制了已发布工作流的可重用性。我们提出了一个新颖的软件框架，以存储库为中心的分析的抽象（Arcana），可以开发复杂的“端到端”工作流程，这些工作流程适用于新的分析并可以移植到各种计算基础架构中。用于特定图像类型（例如MRI对比）的分析模板被实现为Python类，该类定义了一系列潜在的导数和分析方法。Arcana从成像存储库（可以是BIDS数据集，XNAT实例或简单目录）中检索数据，并将选定的衍生产品和相关物源存储回存储库中，以供后续分析使用。工作流程是使用Nipype构建的，可以在本地工作站或高性能计算环境中执行。通用分析方法可以合并到通用基类中，以促进代码重用和协作开发，可以通过类继承专门用于特定于研究的要求。Arcana提供了一个框架，可在其中开发统一的神经成像工作流程，这些工作流程可在广泛的研究和站点中重复使用。