ArticlePLoS computational biology2019
A spectrum of routing strategies for brain networks.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Network-specific metabolic cost of functional connectivity in the human brain.bioRxiv : the preprint server for biology · 2026Article
- Trait-Relevant Tasks Improve Personality Prediction From Structural-Functional Brain Network Coupling.Human brain mapping · 2026Article
- Evaluating oscillatory mechanisms underlying flexible neural communication in the human brain.Network neuroscience (Cambridge, Mass.) · 2026Article
- Evaluating oscillatory mechanisms underlying flexible neural communication in the human brain.bioRxiv : the preprint server for biology · 2025Article
- Understanding the high-order network plasticity mechanisms of ultrasound neuromodulation.PLoS computational biology · 2025Article
- The role of structural connectivity on brain function through a Markov model of signal transmission.bioRxiv : the preprint server for biology · 2025Article
- Connectome-constrained ligand-receptor interaction analysis for understanding brain network communication.Nature communications · 2025Article
- Benchmarking methods for mapping functional connectivity in the brain.Nature methods · 2025Article
- Event-Marked Windowed Communication: Inferring Activity Propagation from Neural Time Series.Human brain mapping · 2025Article
- The overgrowth of structure-function coupling in premature brain during infancy.Developmental cognitive neuroscience · 2025Article
- Exploring the transmission of cognitive task information through optimal brain pathways.PLoS computational biology · 2025Article
- The role of structural connectivity on brain function through a Markov model of signal transmission.PloS one · 2025Article
- Structural connectome manifold changes related to body mass index and their associations with eating psychopathology.NeuroImage. Clinical · 2025Article
- The macroscale routing mechanism of structural brain connectivity related to body mass index.Human brain mapping · 2024Article
- Spatial and temporal pattern of structure-function coupling of human brain connectome with development.eLife · 2024Article
- Spatial and temporal pattern of structure-function coupling of human brain connectome with development.bioRxiv : the preprint server for biology · 2024Article
- Patterns of the left thalamus embedding into the connectome associated with reading skills in children with reading disabilities.Network neuroscience (Cambridge, Mass.) · 2024Article
- Effects of packetization on communication dynamics in brain networks.Network neuroscience (Cambridge, Mass.) · 2024Article
- A comprehensive survey of complex brain network representation.Meta-radiology · 2023Article
- Brain network communication: concepts, models and applications.Nature reviews. Neuroscience · 2023Review
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Authors and funding
6 authors.
Funding
Abstract
Communication of signals among nodes in a complex network poses fundamental problems of efficiency and cost. Routing of messages along shortest paths requires global information about the topology, while spreading by diffusion, which operates according to local topological features, is informationally "cheap" but inefficient. We introduce a stochastic model for network communication that combines local and global information about the network topology to generate biased random walks on the network. The model generates a continuous spectrum of dynamics that converge onto shortest-path and random-walk (diffusion) communication processes at the limiting extremes. We implement the model on two cohorts of human connectome networks and investigate the effects of varying the global information bias on the network's communication cost. We identify routing strategies that approach a (highly efficient) shortest-path communication process with a relatively small global information bias on the system's dynamics. Moreover, we show that the cost of routing messages from and to hub nodes varies as a function of the global information bias driving the system's dynamics. Finally, we implement the model to identify individual subject differences from a communication dynamics point of view. The present framework departs from the classical shortest paths vs. diffusion dichotomy, unifying both models under a single family of dynamical processes that differ by the extent to which global information about the network topology influences the routing patterns of neural signals traversing the network.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.