扩散磁共振成像(dMRI)得出的大脑结构网络反映了大脑区域之间的白质连接,可以定量描述整个大脑的解剖连接模式。结构性脑连接体的发展导致了大量dMRI处理包和网络分析工具箱的出现。然而,基于dMRI数据的全自动网络分析仍然具有挑战性.在这项研究中,我们开发了一个名为“扩散连接体管道”(DCP)的跨平台MATLAB工具箱,用于自动构建脑结构网络并计算网络的拓扑属性。工具箱集成了几个开发的软件包,包括FSL,扩散工具包,SPM,Camino,MRtrix3和MRIcron.它可以处理从任何数量的参与者收集的原始dMRI数据,它还与来自公共数据集(如HCP和UKBiobank)的预处理文件兼容。此外,一个友好的图形用户界面允许用户配置他们的处理管道,而无需任何编程。为了证明DCP的能力和有效性,使用DCP进行了两个测试。结果表明,DCP可以重现我们以前的研究结果。然而,DCP有一些局限性,例如依赖MATLAB,无法修复基于指标的加权网络。尽管有这些限制,总的来说,DCP软件提供了一个标准化的,用于白质网络构建和分析的全自动计算工作流程,有利于推进未来人脑连接组学应用研究。
The brain structural network derived from diffusion magnetic resonance imaging (
dMRI) reflects the white matter connections between brain regions, which can quantitatively describe the anatomical connection pattern of the entire brain. The development of structural brain connectome leads to the emergence of a large number of
dMRI processing packages and network analysis toolboxes. However, the fully automated network analysis based on
dMRI data remains challenging. In this study, we developed a cross-platform MATLAB toolbox named \"Diffusion Connectome Pipeline\" (DCP) for automatically constructing brain structural networks and calculating topological attributes of the networks. The toolbox integrates a few developed packages, including FSL, Diffusion Toolkit, SPM, Camino, MRtrix3, and MRIcron. It can process raw
dMRI data collected from any number of participants, and it is also compatible with preprocessed files from public datasets such as HCP and UK Biobank. Moreover, a friendly graphical user interface allows users to configure their processing pipeline without any programming. To prove the capacity and validity of the DCP, two tests were conducted with using DCP. The results showed that DCP can reproduce the findings in our previous studies. However, there are some limitations of DCP, such as relying on MATLAB and being unable to fixel-based metrics weighted network. Despite these limitations, overall, the DCP software provides a standardized, fully automated computational workflow for white matter network construction and analysis, which is beneficial for advancing future human brain connectomics application research.