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.
Highlights
The propagation and transformation of signals among neuronal units that interact via structural connections can lead to emergent communication patterns at multiple spatial and temporal scales
Whereas the former is determined by the biophysical processes that act on the signals, the latter is dictated by the structural connectivity of brain networks
At scales larger than that of neuronal ensembles, brain dynamics can be modeled by coupling neural masses, Wilson-Cowan oscillators, or Kuramato oscillators according to the topology of structural connectivity (Breakspear, 2017; Muller et al, 2018; Palmigiano et al, 2017; Roberts et al, 2019; Sanz-Leon, Knock, Spiegler, & Jirsa, 2015)
Summary
Models of communication in brain networks have been essential in building a quantitative understanding of the relationship between structure and function. Control-theoretic models have been applied to brain networks to quantify the response of brain networks to exogenous and endogenous perturbations. Both of these frameworks investigate the role of interregional communication in determining the behavior and response of the brain. Both of these frameworks share common features, indicating the possibility of combining the two approaches. Models at the confluence of the two frameworks, with potential applications to various topics in neuroscience
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