A spectrum of routing strategies for brain networks.

Details

Serval ID
serval:BIB_5C89FD4BBD2A
Type
Article: article from journal or magazin.
Collection
Publications
Title
A spectrum of routing strategies for brain networks.
Journal
PLoS computational biology
Author(s)
Avena-Koenigsberger A., Yan X., Kolchinsky A., van den Heuvel M.P., Hagmann P., Sporns O.
ISSN
1553-7358 (Electronic)
ISSN-L
1553-734X
Publication state
Published
Issued date
03/2019
Peer-reviewed
Oui
Volume
15
Number
3
Pages
e1006833
Language
english
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: epublish
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.
Keywords
Brain Mapping/methods, Cohort Studies, Communication, Connectome, Humans, Models, Neurological, Stochastic Processes
Pubmed
Web of science
Open Access
Yes
Create date
08/04/2019 17:53
Last modification date
20/08/2019 15:15
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