  Human brain networks can be characterized at different temporal or spatial
scales given by the age of the subject or the spatial resolution of the
neuroimaging method. Integration of data across scales can only be successful
if the combined networks show a similar architecture. One way to compare
networks is to look at spatial features, based on fibre length, and topological
features of individual nodes where outlier nodes form single node motifs whose
frequency yields a fingerprint of the network. Here, we observe how
characteristic single node motifs change over age (12-23 years) and network
size (414, 813, and 1615 nodes) for diffusion tensor imaging (DTI) structural
connectivity in healthy human subjects. First, we find the number and diversity
of motifs in a network to be strongly correlated. Second, comparing different
scales, the number and diversity of motifs varied across the temporal (subject
age) and spatial (network resolution) scale: certain motifs might only occur at
one spatial scale or for a certain age range. Third, regions of interest which
show one motif at a lower resolution may show a range of motifs at a higher
resolution which may or may not include the original motif at the lower
resolution. Therefore, both the type and localisation of motifs differ for
different spatial resolutions. Our results also indicate that spatial
resolution has a higher effect on topological measures whereas spatial
measures, based on fibre lengths, remain more comparable between resolutions.
Therefore, spatial resolution is crucial when comparing characteristic node
fingerprints given by topological and spatial network features. As node motifs
are based on topological and spatial properties of brain connectivity networks,
these conclusions are also relevant to other studies using connectome analysis.
