To understand collective network behavior in the complex human brain, pairwise correlation networks alone are insufficient for capturing the high-order interactions that extend beyond pairwise interactions and play a crucial role in brain network dynamics. These interactions often reveal intricate relationships among multiple brain networks, significantly influencing cognitive processes. In this study, we explored the correlation of correlation networks and topological network analysis with resting-state fMRI to gain deeper insights into these higher-order interactions and their impact on the topology of brain networks, ultimately enhancing our understanding of brain function. We observed that the correlation of correlation networks highlighted network connections while preserving the topological structure of correlation networks. Our findings suggested that the correlation of correlation networks surpassed traditional correlation networks, showcasing considerable potential for applications in various areas of network science. Moreover, after applying topological network analysis to the correlation of correlation networks, we observed that some high-order interaction hubs predominantly occurred in primary and high-level cognitive areas, such as the visual and fronto-parietal regions. These high-order hubs played a crucial role in information integration within the human brain.

翻译：为了理解复杂人脑中的集体网络行为，仅凭成对相关性网络不足以捕捉超越成对交互的高阶交互作用，这些高阶交互在脑网络动力学中扮演着关键角色。这些交互通常揭示了多个脑网络之间错综复杂的关系，显著影响认知过程。在本研究中，我们通过静息态功能磁共振成像探索了相关性网络的相关性与拓扑网络分析，以更深入地理解这些高阶交互及其对脑网络拓扑结构的影响，最终增进我们对脑功能的理解。我们观察到，相关性网络的相关性在保持相关性网络拓扑结构的同时，突出了网络连接。我们的研究结果表明，相关性网络的相关性超越了传统的相关性网络，在网络科学的各个领域展现出巨大的应用潜力。此外，在对相关性网络的相关性应用拓扑网络分析后，我们观察到一些高阶交互枢纽主要出现在初级和高级认知区域，如视觉区和额顶叶区域。这些高阶枢纽在人脑信息整合中发挥着至关重要的作用。