van Diessen E, Numan T, van Dellen E, van der Kooi A W, Boersma M, Hofman D, van Lutterveld R, van Dijk B W, van Straaten E C W, Hillebrand A, Stam C J
Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, The Netherlands.
Department of Intensive Care, University Medical Center Utrecht, The Netherlands.
Clin Neurophysiol. 2015 Aug;126(8):1468-81. doi: 10.1016/j.clinph.2014.11.018. Epub 2014 Nov 28.
Electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings during resting state are increasingly used to study functional connectivity and network topology. Moreover, the number of different analysis approaches is expanding along with the rising interest in this research area. The comparison between studies can therefore be challenging and discussion is needed to underscore methodological opportunities and pitfalls in functional connectivity and network studies. In this overview we discuss methodological considerations throughout the analysis pipeline of recording and analyzing resting state EEG and MEG data, with a focus on functional connectivity and network analysis. We summarize current common practices with their advantages and disadvantages; provide practical tips, and suggestions for future research. Finally, we discuss how methodological choices in resting state research can affect the construction of functional networks. When taking advantage of current best practices and avoid the most obvious pitfalls, functional connectivity and network studies can be improved and enable a more accurate interpretation and comparison between studies.
静息状态下的脑电图（EEG）和脑磁图（MEG）记录越来越多地用于研究功能连接性和网络拓扑结构。此外，随着该研究领域兴趣的增加，不同分析方法的数量也在不断扩大。因此，研究之间的比较可能具有挑战性，需要进行讨论以强调功能连接性和网络研究中的方法学机遇与陷阱。在本综述中，我们讨论了记录和分析静息状态EEG和MEG数据的整个分析流程中的方法学考量，重点是功能连接性和网络分析。我们总结了当前常见做法及其优缺点；提供实用技巧和对未来研究的建议。最后，我们讨论静息状态研究中的方法选择如何影响功能网络的构建。当利用当前的最佳实践并避免最明显的陷阱时，功能连接性和网络研究可以得到改进，并能够在研究之间进行更准确的解释和比较。
