  Communication processes within the human brain at different cognitive states
are neither well understood nor completely characterized. We assess
communication processes in the human connectome using ant colony-inspired
cooperative learning algorithm, starting from a source with no a priori
information about the network topology, and cooperatively searching for the
target through a pheromone-inspired model. This framework relies on two
parameters, namely pheromone perception and edge perception, to define the
cognizance and subsequent behaviour of the ants on the network and, overall,
the communication processes happening between source and target nodes.
Simulations obtained through different configurations allow the identification
of path-ensembles that are involved in the communication between node pairs.
These path-ensembles may contain different number of paths depending on the
perception parameters and the node pair. In order to assess the different
communication regimes displayed on the simulations and their associations with
functional connectivity, we introduce two network measurements, effective
path-length and arrival rate. These communication features are tested as
individual as well as combined predictors of functional connectivity during
different tasks. Finally, different communication regimes are found in
different specialized functional networks. Overall, this framework may be used
as a test-bed for different communication regimes on top of an underlaying
topology.
