This study investigated the dynamic connectivity patterns between EEG and fMRI modalities, contributing to our understanding of brain network interactions. By employing a comprehensive approach that integrated static and dynamic analyses of EEG-fMRI data, we were able to uncover distinct connectivity states and characterize their temporal fluctuations. The results revealed modular organization within the intrinsic connectivity networks (ICNs) of the brain, highlighting the significant roles of sensory systems and the default mode network. The use of a sliding window technique allowed us to assess how functional connectivity varies over time, further elucidating the transient nature of brain connectivity. Additionally, our findings align with previous literature, reinforcing the notion that cognitive states can be effectively identified through short-duration data, specifically within the 30-60 second timeframe. The established relationships between connectivity strength and cognitive processes, particularly during different visual states, underscore the relevance of our approach for future research into brain dynamics. Overall, this study not only enhances our understanding of the interplay between EEG and fMRI signals but also paves the way for further exploration into the neural correlates of cognitive functions and their implications in clinical settings. Future research should focus on refining these methodologies and exploring their applications in various cognitive and clinical contexts.

翻译：本研究探讨了脑电图与功能磁共振成像模态之间的动态连接模式，增进了我们对大脑网络相互作用的理解。通过采用一种整合了EEG-fMRI数据静态与动态分析的综合方法，我们得以揭示不同的连接状态并刻画其时间波动特征。结果显示，在大脑内在连接网络中存在模块化组织，突显了感觉系统和默认模式网络的重要作用。滑动窗口技术的应用使我们能够评估功能连接如何随时间变化，进一步阐明了大脑连接的瞬时特性。此外，我们的发现与先前文献一致，强化了通过短时数据（特别是在30-60秒时间范围内）可以有效识别认知状态的观点。所建立的连接强度与认知过程（尤其是在不同视觉状态下）之间的关系，凸显了我们的方法对未来大脑动力学研究的相关性。总体而言，本研究不仅加深了我们对EEG与fMRI信号之间相互作用的理解，也为进一步探索认知功能的神经关联及其在临床环境中的意义铺平了道路。未来的研究应侧重于完善这些方法学，并探索其在各种认知和临床背景下的应用。