An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data
An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data
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DOI：
10.1016/j.neuroimage.2015.03.055
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发表时间：
2015-08
期刊：
影响因子：
5.7
通讯作者：
M. Schirner;Simon Rothmeier;Viktor Jirsa;A. Mcintosh;P. Ritter
中科院分区：
文献类型：
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作者：
M. Schirner;Simon Rothmeier;Viktor Jirsa;A. Mcintosh;P. Ritter
全球每年都会获取大量多模态神经影像数据。为了提取用于计算神经科学应用的高维信息，需要标准化数据融合和有效简化为综合数据结构。这种自洽的多模态数据集可用于计算大脑建模，以约束具有大脑个体可测量特征的模型，例如虚拟大脑（TVB）所做的。 TVB 是一个模拟平台，它使用经验结构和功能数据来构建人类个体的完整大脑模型。为了方便模型构建，我们开发了用于结构、功能和扩散加权磁共振成像 (MRI) 以及可选脑电图 (EEG) 数据的处理管道。该管道结合了多种最先进的神经信息学工具，可生成特定主题的皮质和皮质下分区、表面镶嵌、结构和功能连接组、导联场矩阵、电源活动估计和区域聚合血氧水平依赖 (BOLD) 功能 MRI (fMRI) 时间序列。该管道的输出文件可以直接上传到TVB，以创建和模拟个性化的大规模网络模型，该模型在皮质表面三角测量和白质纤维束描记的基础上结合了皮质内和皮质间的相互作用。我们详细介绍了各个处理流的陷阱并讨论了验证方法。通过该管道，我们还引入了估计全脑结构连接（SC）网络中纤维束传输强度的新方法，并比较了不同纤维束成像或分割方法的结果。我们在 50 个多模式数据集上测试了管道的功能。为了量化管道连接体提取部分的稳健性，我们计算了几个量化其重新扫描可靠性的指标，并将它们与其他纤维束成像方法进行比较。我们与管道一起提出了几项原则来指导未来标准化大脑模型构建的工作。管道的代码和完全处理的数据集通过虚拟大脑网站 (thevirtualbrain.org) 和 github (https://github.com/BrainModes/TVB-empirical-data-pipeline) 向公众开放。此外，该管道可以通过方便的 Web 界面直接与神经科学网关门户 (http://www.nsgportal.org) 上的高性能计算 (HPC) 资源一起使用。
Large amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high-dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network models that incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain.org) and via github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface.