Abstract：
The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.
摘要：
神经网络的动态描述引起了研究者的注意,因为与静息态网络相比,动态网络可以承载更多的信息。作为一种具有高时空分辨率的非侵入性电生理数据,脑磁图(MEG)可以为动态功能脑网络分析提供丰富的信息。在这次审查中,综述了MEG脑网络的发展。几种分析方法，如滑动窗口，隐马尔可夫模型,讨论了基于时间频率的方法在MEG动态脑网络研究中的应用。最后,多模态脑网络分析及其在MEG神经生理学中的应用研究现状，展望了未来的研究方向之一,已结束。
