  The properties of functional brain networks strongly depend on how their
nodes are chosen. Commonly, nodes are defined by Regions of Interest (ROIs),
pre-determined groupings of fMRI measurement voxels. Earlier, we have
demonstrated that the functional homogeneity of ROIs, captured by their spatial
consistency, varies widely across ROIs in commonly-used brain atlases. Here, we
ask how ROIs behave as nodes of dynamic brain networks. To this end, we use two
measures: spatiotemporal consistency measures changes in spatial consistency
across time and network turnover quantifies the changes in the local network
structure around a ROI. We find that spatial consistency varies non-uniformly
in space and time, which is reflected in the variation of spatiotemporal
consistency across ROIs. Further, we see time-dependent changes in the network
neighborhoods of the ROIs, reflected in high network turnover. Network turnover
is nonuniformly distributed across ROIs: ROIs with high spatiotemporal
consistency have low network turnover. Finally, we reveal that there is rich
voxel-level correlation structure inside ROIs. Because the internal structure
and the connectivity of ROIs vary in time, the common approach of using static
node definitions may be surprisingly inaccurate. Therefore, network
neuroscience would greatly benefit from node definition strategies tailored for
dynamical networks.
