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Connections between human brain regions have been extensively studied at a functional level. Much of those evidences suggested that functional interactions are mediated by synchronization of oscillations. For that reason, the use of synchronization likelihood (SL) has been one of the most suitable algorithms in highly nonlinear and non-stationary brain networks. In many EEG and MEG studies, the SL patterns was measured statistically. Therefore, this study used synchronization likelihood to establish small-world functional networks and a simple method for constructing quasi-dynamic graphs by dividing a long-term static graph into a sequence of subgraphs that each had a timescale of 1 s. Our results indicated that SL maps were not exactly the same between eyes-closed and eyes-open rest in each frequency band, especially for the widespread alpha oscillations which had high functional connectivity during eyes-closed condition. Parameters C and L indicate that graph properties could differ with brain frequency rhythms, with higher frequencies have a lower small-worldness. For further investigation, dynamic brain network might give more information about specific processing capabilities from several spatially separated units, where low frequencies represent the essential foundation and high frequencies represent cognitive processing. |
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