Information transfer of an Ising model on a brain network

We implement the Ising model on an anatomical connectivity matrix describing the brain at a coarse scale. We find that the critical state of the model is also characterized by the maximal amount of total information transfer among variables and exhibits signature of the law of diminishing marginal returns, some nodes showing disparity between incoming and outgoing information. Our results extend the recent analysis of dynamical oscillators models on the connectome structure, taking into account lagged and directional influences, concentrating on the nodes that are more prone to became bottlenecks of information.