Park Bo-Yong, Byeon Kyoungseob, Park Hyunjin
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea.
Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea.
Front Neuroinform. 2019 Feb 11;13:5. doi: 10.3389/fninf.2019.00005. eCollection 2019.
The preprocessing of functional magnetic resonance imaging (fMRI) data is necessary to remove unwanted artifacts and transform the data into a standard format. There are several neuroimaging data processing tools that are widely used, such as SPM, AFNI, FSL, FreeSurfer, Workbench, and fMRIPrep. Different data preprocessing pipelines yield differing results, which might reduce the reproducibility of neuroimaging studies. Here, we developed a preprocessing pipeline for T1-weighted structural MRI and fMRI data by combining components of well-known software packages to fully incorporate recent developments in MRI preprocessing into a single coherent software package. The developed software, called FuNP (Fusion of Neuroimaging Preprocessing) pipelines, is fully automatic and provides both volume- and surface-based preprocessing pipelines with a user-friendly graphical interface. The reliability of the software was assessed by comparing resting-state networks (RSNs) obtained using FuNP with pre-defined RSNs using open research data ( = 90). The obtained RSNs were well-matched with the pre-defined RSNs, suggesting that the pipelines in FuNP are reliable. In addition, image quality metrics (IQMs) were calculated from the results of three different software packages (i.e., FuNP, FSL, and fMRIPrep) to compare the quality of the preprocessed data. We found that our FuNP outperformed other software in terms of temporal characteristics and artifacts removal. We validated our pipeline with independent local data ( = 28) in terms of IQMs. The IQMs of our local data were similar to those obtained from the open research data. The codes for FuNP are available online to help researchers.
功能磁共振成像(fMRI)数据的预处理对于去除不需要的伪影并将数据转换为标准格式是必要的。有几种广泛使用的神经成像数据处理工具,如SPM、AFNI、FSL、FreeSurfer、Workbench和fMRIPrep。不同的数据预处理流程会产生不同的结果,这可能会降低神经成像研究的可重复性。在这里,我们通过组合知名软件包的组件,开发了一种用于T1加权结构MRI和fMRI数据的预处理流程,以将MRI预处理的最新进展充分整合到一个连贯的软件包中。开发的软件称为FuNP(神经成像预处理融合)流程,它是全自动的,并通过用户友好的图形界面提供基于体积和表面的预处理流程。通过将使用FuNP获得的静息态网络(RSN)与使用开放研究数据(n = 90)的预定义RSN进行比较,评估了该软件的可靠性。获得的RSN与预定义的RSN匹配良好,表明FuNP中的流程是可靠的。此外,从三个不同软件包(即FuNP、FSL和fMRIPrep)的结果中计算图像质量指标(IQM),以比较预处理数据的质量。我们发现,我们的FuNP在时间特征和伪影去除方面优于其他软件。我们使用独立的本地数据(n = 28)在IQM方面验证了我们的流程。我们本地数据的IQM与从开放研究数据中获得的IQM相似。FuNP的代码可在线获取,以帮助研究人员。