Navigable maps of structural brain networks across species

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 connectomes 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 navigability properties of mammalian and non-mammalian species, which implies the inability of Euclidean distances to fully explain the structural organization of their connectomes. In contrast, 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 framework to study decentralized communication processes in connectomes of different species and at different scales on an equal footing.

20 pages, 5 figures, 2 supp. tables, 3 supp. appendices, 10 supp. figures

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Cited by in corpus (16)

• Network Geometry
• Navigation of brain networks
• Geometric renormalization unravels self-similarity of the multiscale human connectome
• Detecting the ultra low dimensionality of real networks
• Diffusion geometry of multiplex and interdependent systems
• Link persistence and conditional distances in multiplex networks
• Emergent entanglement structures and self-similarity in quantum spin chains
• Modelling on the very large-scale connectome
• Random graphs and real networks with weak geometric coupling
• Hyperbolic Mapping of Human Proximity Networks
• Geometric detection of hierarchical backbones in real networks
• Optimal navigability of weighted human brain connectomes in physical space
• The multiscale self-similarity of the weighted human brain connectome
• Geometric representations of brain networks can predict the surgery outcome in temporal lobe epilepsy
• Dynamics of cold random hyperbolic graphs with link persistence
• Hyperbolic embedding of brain networks as a tool for epileptic seizures forecasting