Effect of remote signal propagation in an empirical brain network

Increasing evidence has shown that brain functions are seriously influenced by the heterogeneous structure of a brain network, but little attention has been paid to the aspect of signal propagation. We here study how a signal is propagated from a source node to other nodes on an empirical brain network by a model of bistable oscillators. We find that the unique structure of the brain network favors signal propagation in contrast to other heterogeneous networks and homogeneous random networks. Surprisingly, we find an effect of remote propagation where a signal is not successfully propagated to the neighbors of the source node but to its neighbors' neighbors. To reveal its underlying mechanism, we simplify the heterogeneous brain network into a heterogeneous chain model and find that the accumulation of weak signals from multiple channels makes a strong input signal to the next node, resulting in remote propagation. Furthermore, a theoretical analysis is presented to explain these findings.