  In this paper, the brain functional networks derived from high-resolution
synchronous EEG time series during visual task are generated by calculating the
phase synchronization among the time series. The hierarchical modular
organizations of these networks are systematically investigated by the fast
Girvan-Newman algorithm. At the same time, the spatially adjacent electrodes
(corresponding to EEG channels) are clustered into functional groups based on
anatomical parcellation of brain cortex, and this clustering information are
compared to that of the functional network. The results show that the modular
architectures of brain functional network are in coincidence with that from the
anatomical structures over different levels of hierarchy, which suggests that
population of neurons performing the same function excite and inhibit in
identical rhythms. The structure-function relationship further reveals that the
correlations among EEG time series in the same functional group are much
stronger than those in different ones and that the hierarchical organization of
brain functional network may be a consequence of functional segmentation of
brain cortex.
