Märtens Marcus, Meier Jil, Hillebrand Arjan, Tewarie Prejaas, Van Mieghem Piet
1Delft University of Technology, Faculty of Electrical Engineering, Mathematics and Computer Science, P.O Box 5031, Delft, The Netherlands.
2Department of Clinical Neurophysiology and Magnetoencephalography Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
Appl Netw Sci. 2017;2(1):25. doi: 10.1007/s41109-017-0046-z. Epub 2017 Aug 3.
Recent work has revealed frequency-dependent global patterns of information flow by a network analysis of magnetoencephalography data of the human brain. However, it is unknown which properties on a small subgraph-scale of those functional brain networks are dominant at different frequencies bands. Motifs are the building blocks of networks on this level and have previously been identified as important features for healthy and abnormal brain function. In this study, we present a network construction that enables us to search and analyze motifs in different frequency bands. We give evidence that the bi-directional two-hop path is the most important motif for the information flow in functional brain networks. A clustering based on this motif exposes a spatially coherent yet frequency-dependent sub-division between the posterior, occipital and frontal brain regions.
最近的研究工作通过对人类大脑的脑磁图数据进行网络分析，揭示了信息流的频率依赖性全局模式。然而，尚不清楚在这些功能性脑网络的小子图尺度上，哪些属性在不同频段占主导地位。基序是这个层面网络的构建单元，此前已被确定为健康和异常脑功能的重要特征。在本研究中，我们提出了一种网络构建方法，使我们能够在不同频段搜索和分析基序。我们证明，双向两跳路径是功能性脑网络中信息流最重要的基序。基于这一基序的聚类揭示了后脑、枕叶和额叶区域之间在空间上连贯但频率依赖的细分。
