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首页> 外文期刊>PLoS Computational Biology >Modeling of Large-Scale Functional Brain Networks Based on Structural Connectivity from DTI: Comparison with EEG Derived Phase Coupling Networks and Evaluation of Alternative Methods along the Modeling Path
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Modeling of Large-Scale Functional Brain Networks Based on Structural Connectivity from DTI: Comparison with EEG Derived Phase Coupling Networks and Evaluation of Alternative Methods along the Modeling Path

机译：基于DTI的结构连通性的大规模功能性脑网络建模：与EEG衍生相耦合网络的比较以及沿建模路径的替代方法的评估

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Author Summary Brain imaging techniques are broadly divided into the two categories of structural and functional imaging. Structural imaging provides information about the static physical connectivity within the brain, while functional imaging provides data about the dynamic ongoing activation of brain areas. Computational models allow to bridge the gap between these two modalities and allow to gain new insights. Specifically, in this study, we use structural data from diffusion tractography recordings to model functional brain connectivity obtained from fast EEG dynamics occurring at the alpha frequency. First, we present a simple reference procedure which consists of several steps to link the structural to the functional empirical data. Second, we systematically compare several alternative methods along the modeling path in order to assess their impact on the overall fit between simulations and empirical data. We explore preprocessing steps of the structural connectivity and different levels of complexity of the computational model. We highlight the importance of source reconstruction and compare commonly used source reconstruction algorithms and metrics to assess functional connectivity. Our results serve as an important orienting frame for the emerging field of brain network modeling.
机译：作者摘要脑成像技术大致分为结构成像和功能成像两大类。结构成像提供有关大脑内部静态物理连接的信息，而功能成像提供有关大脑区域动态持续激活的数据。计算模型可以弥合这两种模式之间的差距，并获得新的见解。具体来说，在这项研究中，我们使用来自扩散束摄影记录的结构数据来模拟从以α频率发生的快速EEG动态获得的功能性大脑连通性。首先，我们提出了一个简单的参考程序，该程序由几个步骤组成，以将结构与功能经验数据链接起来。其次，我们沿建模路径系统地比较了几种替代方法，以评估它们对模拟和经验数据之间总体拟合的影响。我们探索结构连接和计算模型的不同复杂程度的预处理步骤。我们强调了源重构的重要性，并比较了常用的源重构算法和指标来评估功能连通性。我们的结果为新兴的脑网络建模领域提供了重要的指导框架。

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