Conclusions
Criticality has been proposed to characterize brain signals [3]. The critical state of an Ising model on a simulated brain network is characterized by having the maximal amount of total flow of information, with some units being close to be receiving the maximal amount of input information.
Recent works have simulated brain activity implementing several dynamical on the connectome structure, retrieving in some cases correlation-based networks similar to those observed from the analysis of neuroimaging data (mainly fMRI at rest). The present work extends the analysis to dynamical networks who take into account lagged and directional influences, concentrating for this abstract on the nodes that more prominently express this disparity between incoming and outgoing information.