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

Autor: Pragya Srivastava, Erfan Nozari, Jason Z. Kim, Harang Ju, Dale Zhou, Cassiano Becker, Fabio Pasqualetti, George J. Pappas, Danielle S. Bassett
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Network Neuroscience, Vol 4, Iss 4, Pp 1122-1159 (2020)
Druh dokumentu: article
ISSN: 2472-1751
DOI: 10.1162/netn_a_00158
Popis: AbstractRecent 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.
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