Communication dynamics in complex brain networks

Key Points The topology of structural brain networks shapes patterns of interaction and signalling among neurons and brain regions, and the resulting communication dynamics is important for brain function. Different aspects of network topology imply different communication mechanisms, from routing of information through shortest paths to alternative models that involve spreading, diffusion and broadcasting. Different topological attributes promote different types of communication mechanisms. Communication dynamics are subject to competing constraints and demands (trade-offs) among efficiency, cost, versatility and resilience. One aspect of cost is the amount of information needed to implement network communication. This cost is high for routing and low for diffusion, and is likely to be an important factor for determining the biological feasibility of a given communication model. The brain comprises complex structural and functional networks, but much remains to be determined regarding how these networks support the communication processes that underlie neuronal computation. In this Review, Avena-Koenigsberger, Misic and Sporns discuss the network basis of communication dynamics in the brain. Neuronal signalling and communication underpin virtually all aspects of brain activity and function. Network science approaches to modelling and analysing the dynamics of communication on networks have proved useful for simulating functional brain connectivity and predicting emergent network states. This Review surveys important aspects of communication dynamics in brain networks. We begin by sketching a conceptual framework that views communication dynamics as a necessary link between the empirical domains of structural and functional connectivity. We then consider how different local and global topological attributes of structural networks support potential patterns of network communication, and how the interactions between network topology and dynamic models can provide additional insights and constraints. We end by proposing that communication dynamics may act as potential generative models of effective connectivity and can offer insight into the mechanisms by which brain networks transform and process information.