Carregant...
Miniatura

Tipus de document

Article

Versió

Versió publicada

Data de publicació

Llicència de publicació

cc-by (c) Allard, Antoine et al., 2020
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/170233

Navigable maps of structural brain networks across species

Títol de la revista

Director/Tutor

ISSN de la revista

Títol del volum

Resum

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.

Citació

Citació

ALLARD, Antoine, SERRANO MORAL, Ma. ángeles (maría ángeles). Navigable maps of structural brain networks across species. _PLoS Computational Biology_. 2020. Vol. 16, núm. 2, pàgs. e1007584. [consulta: 20 de gener de 2026]. ISSN: 1553-734X. [Disponible a: https://hdl.handle.net/2445/170233]

Exportar metadades

JSON - METS

Compartir registre