Small-world characterization is well-suited for analyzing functional brain networks at the systems level because these networks are complex and optimally connected to minimize information processing costs [36],[52]. Functional connectivity networks of the human brain constructed from EEG as well as MEG data have also been shown to have small-world architecture [38],[39]. Salvador et al. [53] examined connectivity in task-free functional MRI data with the same 90 ROI parcellation scheme used in our study and they reported small-world architecture in this network. This finding was replicated by Achard et al., who also reported that small-world properties were salient in the low frequency interval 0.03–0.06 Hz [35] in adults (ages 25–35 y), and by Supekar et al. in older adults (ages 37–77 y) [34]. These findings, primarily derived from functional data obtained from middle-age to older adults, suggest that the functional organization of the brain has a small-world architecture, a characteristic that may assist in robust and dynamic information processing.
