网络二值化中EEG基于相位的功能连接的复杂层次拓扑
Accounting for the Complex Hierarchical Topology of EEG Phase-Based Functional Connectivity in Network Binarisation
摘要：
研究大脑功能的二进制网络分析面临着一个方法上的挑战，选择一个适当的阈值二进制边缘权重。对于基于EEG相位的功能连接，我们测试的假设，这种二进制化应考虑到复杂的层次结构中发现的功能连接。我们探索适合这种结构的密度范围，并提供了一个比较国家的最先进的二进制化技术，最近提出的跨跨度阈值（CST），最小生成树，效率-成本优化和工会的最短路径图，任意比例阈值和加权网络。我们测试这些技术加权复杂的层次结构模型，通过对比模型实现小的参数差异。我们还测试了这些技术对随机和有针对性的拓扑攻击的鲁棒性，我们发现CST在最先进的EEG网络拓扑建模、对拓扑网络攻击的鲁棒性和三个真实数据集上表现出良好的一致性，与我们的分层复杂性假设一致。这提供了有趣的新的证据到相关的考虑大量的边缘在EEG功能连接研究，以提供信息密度的拓扑结构。
英文摘要：
Research into binary network analysis of brain function faces a methodological challenge in selecting an appropriate threshold to binarise edge weights. For EEG phase-based functional connectivity, we test the hypothesis that such binarisation should take into account the complex hierarchical structure found in functional connectivity. We explore the density range suitable for such structure and provide a comparison of state-of-the-art binarisation techniques, the recently proposed Cluster-Span Threshold (CST), minimum spanning trees, efficiency-cost optimisation and union of shortest path graphs, with arbitrary proportional thresholds and weighted networks. We test these techniques on weighted complex hierarchy models by contrasting model realisations with small parametric differences. We also test the robustness of these techniques to random and targeted topological attacks.We find that the CST performs consistenty well in state-of-the-art modelling of EEG network topology, robustness to topological network attacks, and in three real datasets, agreeing with our hypothesis of hierarchical complexity. This provides interesting new evidence into the relevance of considering a large number of edges in EEG functional connectivity research to provide informational density in the topology.
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