Institute of Systems Neuroscience, Heinrich-Heine University, Düsseldorf, Germany.
Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
Brain Struct Funct. 2020 May;225(4):1261-1275. doi: 10.1007/s00429-020-02046-1. Epub 2020 Mar 6.
Regional connectivity-based parcellation (rCBP) is a widely used procedure for investigating the structural and functional differentiation within a region of interest (ROI) based on its long-range connectivity. No standardized software or guidelines currently exist for applying rCBP, making the method only accessible to those who develop their own tools. As such, there exists a discrepancy between the laboratories applying the procedure each with their own software solutions, making it difficult to compare and interpret the results. Here, we outline an rCBP procedure accompanied by an open source software package called CBPtools. CBPtools is a Python (version 3.5+) package that allows users to run an extensively evaluated rCBP analysis workflow on a given ROI. It currently supports two modalities: resting-state functional connectivity and structural connectivity based on diffusion-weighted imaging, along with support for custom connectivity matrices. Analysis parameters are customizable and the workflow can be scaled to a large number of subjects using a parallel processing environment. Parcellation results with corresponding validity metrics are provided as textual and graphical output. Thus, CBPtools provides a simple plug-and-play, yet customizable way to conduct rCBP analyses. By providing an open-source software we hope to promote reproducible and comparable rCBP analyses and, importantly, make the rCBP procedure readily available. Here, we demonstrate the utility of CBPtools using a voluminous data set on an average compute-cluster infrastructure by performing rCBP on three ROIs prominently featured in parcellation literature.
基于区域连通性的分区（rCBP）是一种广泛用于研究基于感兴趣区域（ROI）的长程连通性的结构和功能分化的方法。目前，尚无应用 rCBP 的标准化软件或指南，因此只有那些开发自己工具的人才能够使用该方法。因此，应用该方法的实验室之间存在差异，每个实验室都有自己的软件解决方案，这使得比较和解释结果变得困难。在这里，我们概述了一种 rCBP 程序，并提供了一个名为 CBPtools 的开源软件包。CBPtools 是一个 Python（版本 3.5+）包，允许用户在给定的 ROI 上运行经过广泛评估的 rCBP 分析工作流程。它目前支持两种模态：静息态功能连通性和基于扩散加权成像的结构连通性，同时支持自定义连通性矩阵。分析参数是可定制的，工作流程可以使用并行处理环境扩展到大量的主题。分区结果及其相应的有效性指标以文本和图形输出的形式提供。因此，CBPtools 提供了一种简单的即插即用且可定制的方法来进行 rCBP 分析。通过提供开源软件，我们希望促进可重复和可比较的 rCBP 分析，并且重要的是，使 rCBP 程序易于使用。在这里，我们使用平均计算集群基础设施上的大量数据集演示了 CBPtools 的实用性，通过对三个在分区文献中突出显示的 ROI 进行 rCBP 来展示其功能。
