Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/170233
Title: | Navigable maps of structural brain networks across species |
Author: | Allard, Antoine Serrano Moral, Ma. Ángeles (María Ángeles) |
Keywords: | Mapatge del cervell Geometria euclidiana Xarxes neuronals (Neurobiologia) Brain mapping Euclidean geometry Neural networks (Neurobiology) |
Issue Date: | 3-Feb-2020 |
Publisher: | Public Library of Science (PLoS) |
Abstract: | Connectomes are spatially embedded networks whose architecture has been shaped by physical constraints and communication needs throughout evolution. Using a decentralized navigation protocol, we investigate the relationship between the structure of the connec- tomes of different species and their spatial layout. As a navigation strategy, we use greedy routing where nearest neighbors, in terms of geometric distance, are visited. We measure the fraction of successful greedy paths and their length as compared to shortest paths in the topology of connectomes. In Euclidean space, we find a striking difference between the nav- igability properties of mammalian and non-mammalian species, which implies the inability of Euclidean distances to fully explain the structural organization of their connectomes. In con- trast, we find that hyperbolic space, the effective geometry of complex networks, provides almost perfectly navigable maps of connectomes for all species, meaning that hyperbolic distances are exceptionally congruent with the structure of connectomes. Hyperbolic maps therefore offer a quantitative meaningful representation of connectomes that suggests a new cartography of the brain based on the combination of its connectivity with its effective geometry rather than on its anatomy only. Hyperbolic maps also provide a universal frame- work to study decentralized communication processes in connectomes of different species and at different scales on an equal footing. |
Note: | Reproducció del document publicat a: https://doi.org/10.1371/journal.pcbi.1007584 |
It is part of: | PLoS Computational Biology, 2020, vol. 16, num. 2, p. e1007584 |
URI: | http://hdl.handle.net/2445/170233 |
Related resource: | https://doi.org/10.1371/journal.pcbi.1007584 |
ISSN: | 1553-734X |
Appears in Collections: | Articles publicats en revistes (Institut de Recerca en Sistemes Complexos (UBICS)) Articles publicats en revistes (Física de la Matèria Condensada) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
701614.pdf | 1.64 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License