Data from: Validation of network communicability metrics for the analysis of brain structural networks.

Computational network analysis provides new methods to analyze the
brain's structural organization based on diffusion imaging
tractography data. Networks are characterized by global and local metrics
that have recently given promising insights into diagnosis and the further
understanding of psychiatric and neurologic disorders. Most of these
metrics are based on the idea that information in a network flows along
the shortest paths. In contrast to this notion, communicability is a
broader measure of connectivity which assumes that information could flow
along all possible paths between two nodes. In our work, the features of
network metrics related to communicability were explored for the first
time in the healthy structural brain network. In addition, the sensitivity
of such metrics was analysed using simulated lesions to specific nodes and
network connections. Results showed advantages of communicability over
conventional metrics in detecting densely connected nodes as well as
subsets of nodes vulnerable to lesions. In addition, communicability
centrality was shown to be widely affected by the lesions and the changes
were negatively correlated with the distance from lesion site. In summary,
our analysis suggests that communicability metrics that may provide an
insight into the integrative properties of the structural brain network
and that these metrics may be useful for the analysis of brain networks in
the presence of lesions. Nevertheless, the interpretation of
communicability is not straightforward; hence these metrics should be used
as a supplement to the more standard connectivity network metrics.