Brain Networks

The architecture of the human brain underlies human behavior and is extremely complex with multiple scales interacting with one another. However, research efforts are typically focused on simplified representations and a single spatial scale.

The architecture of the brain is intimately related with it spatial embedding, typically assumed to be the Euclidean space in which it has evolved and develops. Recent advances in network science, however, include the discovery that complex networks have a hidden geometry and that this geometry is hyperbolic. We showed that this methodology can also be used to infer high-quality maps of connectomes, where brain regions are given coordinates in hyperbolic space such that the closer they are the more likely that they are connected. Even if Euclidean space is assumed as the natural geometry of the brain, distances in hyperbolic space offer a more accurate interpretation of the structure of connectomes, which suggests a new perspective for the mapping of the organization of the brain’s neuroanatomical regions.

We are also interested in the multiscale spatial organization of the brain. Using two high-quality datasets with connectomes of 84 healthy human subjects with five anatomical resolutions for each, we found that the zoomed-out layers remain self-similar and that the geometric network model, where distances are not Euclidean but hyperbolic, predicts the observations by application of a renormalization protocol. Our results prove that the same principle explains brain connectivity, within the rank of length scales that cover the used datasets, and leads to efficient decentralized communication.