Models of communication and control for brain networks: distinctions, convergence, and future outlook

TLDR

Recent computational models of signal propagation and network control theory have highlighted white matter’s critical role in shaping brain activity, yet their relationship remains largely unexplored. This review explicitly bridges communication models and network control principles to synthesize current literature. The authors compare the two frameworks across abstraction level, dynamical complexity, network attribute dependence, and multiscale interactions, revealing both convergence and distinct features. The integrative perspective offers a unified view of brain network communication and control and identifies promising avenues for future research.

Abstract

Recent advances in computational models of signal propagation and routing in the human brain have underscored the critical role of white matter structure. A complementary approach has utilized the framework of network control theory to better understand how white matter constrains the manner in which a region or set of regions can direct or control the activity of other regions. Despite the potential for both of these approaches to enhance our understanding of the role of network structure in brain function, little work has sought to understand the relations between them. Here, we seek to explicitly bridge computational models of communication and principles of network control in a conceptual review of the current literature. By drawing comparisons between communication and control models in terms of the level of abstraction, the dynamical complexity, the dependence on network attributes, and the interplay of multiple spatiotemporal scales, we highlight the convergence of and distinctions between the two frameworks. Based on the understanding of the intertwined nature of communication and control in human brain networks, this work provides an integrative perspective for the field and outlines exciting directions for future work.