FuNP (Fusion of Neuroimaging Preprocessing) Pipelines: A Fully Automated Preprocessing Software for Functional Magnetic Resonance Imaging
FuNP (Fusion of Neuroimaging Preprocessing) Pipelines: A Fully Automated Preprocessing Software for Functional Magnetic Resonance Imaging
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DOI：
10.3389/fninf.2019.00005
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发表时间：
2019-02-11
影响因子：
3.5
通讯作者：
Park, Hyunjin
中科院分区：
文献类型：
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作者：
Park, Bo-Yong;Byeon, Kyoungseob;Park, Hyunjin
为了去除不需要的伪影并将数据转换为标准格式，需要对功能磁共振成像(FMRI)数据进行预处理。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fMRlPrep。不同的数据前处理管道会产生不同的结果，这可能会降低神经影像研究的重复性。在这里，我们开发了一条T1加权结构MRI和fMRI数据的预处理管道，通过结合知名软件包的组件，将MRI预处理的最新进展完全整合到单个连贯的软件包中。开发的软件称为FuNP(融合神经成像前处理)管道，是全自动的，并为基于体积和表面的前处理管道提供了用户友好的图形界面。通过比较FuNP获得的静息状态网络(RSNs)和使用开放研究数据的预定义RSNs(n=90)来评估软件的可靠性。得到的RSNs与预定义的RSNs匹配良好，表明FuNP中的管道是可靠的。此外，根据三个不同软件包(即FuNP、FSL和FMRlPrep)的结果计算图像质量指标(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。我们用独立的本地数据(n=28)在IQM方面验证了我们的管道。我们本地数据的IQM与从公开研究数据获得的IQM相似。FuNP的代码可以在网上获得，以帮助研究人员。
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 fMRlPrep. 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 (n = 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 fMRlPrep) 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 (n = 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.