ArticleNetwork neuroscience (Cambridge, Mass.)2020
Models of communication and control for brain networks: distinctions, convergence, and future outlook.
Article in Network neuroscience (Cambridge, Mass.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Longitudinal changes in control energy of brain networks in older adults at familial risk for Alzheimer's disease.bioRxiv : the preprint server for biology · 2026Article
- Hierarchical whole-brain modeling of critical synchronization dynamics in the human brain.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- High-frequency bursts facilitate fast communication for human spatial attention.Nature neuroscience · 2026Article
- Phase-dependent stimulation response is shaped by the brain's dynamic functional connectivity.Network neuroscience (Cambridge, Mass.) · 2026Article
- Designing Neural Dynamics: From Digital Twin Modeling to Regeneration.International journal of molecular sciences · 2025Review
- Effects of tDCS of the DLPFC on brain networks: A hybrid brain modeling study.PLoS computational biology · 2025Article
- Exploring the suitability of piecewise-linear dynamical system models for cognitive neural dynamics.Frontiers in neuroscience · 2025Article
- Topological Biomarkers of Alzheimer's Disease from Functional Brain Network Analysis.Current Alzheimer research · 2025Article
- Low-dimensional controllability of brain networks.PLoS computational biology · 2025Article
- The control costs of human brain dynamics.Network neuroscience (Cambridge, Mass.) · 2025Article
- The topology and geometry of neural representations.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Shaping dynamical neural computations using spatiotemporal constraints.Biochemical and biophysical research communications · 2024Review
- Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies.Nature biomedical engineering · 2024Article
- An Agent-Based Model to Reproduce the Boolean Logic Behaviour of Neuronal Self-Organised Communities through Pulse Delay Modulation and Generation of Logic Gates.Biomimetics (Basel, Switzerland) · 2024Article
- Causation in neuroscience: keeping mechanism meaningful.Nature reviews. Neuroscience · 2024Review
- Brain dynamics predictive of response to psilocybin for treatment-resistant depression.Brain communications · 2024Article
- Article
- A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network.Brain structure & function · 2023Article
- Brain network communication: concepts, models and applications.Nature reviews. Neuroscience · 2023Review
- Reduced emergent character of neural dynamics in patients with a disrupted connectome.NeuroImage · 2023Article
Corrections and comments
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
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.
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