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.

本研究探讨了脑电图（EEG）与功能磁共振成像（fMRI）之间的动态连接模式，弥补了对脑网络相互作用理解的空白。通过整合静态与动态的EEG-fMRI数据分析，我们发现了独特的连接状态及其时间波动，强调了感知系统和默认模式网络的重要性。研究结果揭示了连接强度与认知过程之间的关系，尤其是在不同视觉状态下，为进一步的脑动力学研究提供了基础。

动态脑电图-功能磁共振成像映射：揭示脑连接与认知状态之间的关系