Rich club characteristics of dynamic brain functional networks in resting state

摘要: Conventional brain functional networks are constructed by extracting the entire time series from Magnetic Resonance Imaging (fMRI). Yet such a method is easy to ignore dynamic interaction patterns of regions that essentially change across time. In this study, we analyze connectivity characteristics Rich Club in resting-state networks, and study differences core at different periods. First, extracted fMRI construct network. Then, Clubs periods determined coefficients. particular, efficiency each calculated examine influences Connections, Feeder Connections Local Connections. Finally, node degree, clustering coefficient for nodes quantify processes Clubs, compared with those series. Experimental results demonstrate distribution network consistent series, while composition dynamically Moreover, connection show significant correlation Club, local global greater than These further illustrate viewpoint have influence on networks.