The advent of advanced multivariate time-series analysis methods to capture directional information flow in the brain, such as large-scale Granger causality (lsGC), has already yielded insights into healthy and diseased brain states and holds promise for continued discoveries in neuroscience. Here, a set of functional brain networks was generated by applying lsGC to a resting-state functional MRI dataset comprised of 20 healthy individuals in order to characterize network properties across a range of spatial scales and network densities. Network vertex definition was performed using brain parcellation templates at three spatial resolutions (90, 231, and 438 regions of interest) and eight network densities. The small-worldness parameter σ and degree-distribution were computed for each network. Over all thresholds and spatial resolutions considered, the networks were determined to have small-world properties, as indicated by mean σ » 1. Moreover, all networks were determined to have an exponentially truncated degree distribution, indicating a network structure highly resilient to both hub and random vertex attack. In addition, k-nearest neighbors thresholding and Louvain community detection was performed on the mean network from all subjects to extract functional modules. Eight functional modules were discovered, three of which corresponded to known resting-state networks, including the default mode, visual, and sensorimotor networks. The results of these analyses show that lsGC has the ability to capture important aspects of brain connectivity, including small-worldness, resilience to attack, and functional modularity.