Geometric renormalization unravels self-similarity of the multiscale human connectome.

ABSTRACT: Structural connectivity in the brain is typically studied by reducing its observation to a single spatial resolution. However, the brain possesses a rich architecture organized over multiple scales linked to one another. We explored the multiscale organization of human connectomes using datasets of healthy subjects reconstructed at five different resolutions. We found that the structure of the human brain remains self-similar when the resolution of observation is progressively decreased by hierarchical coarse-graining of the anatomical regions. Strikingly, a geometric network model, where distances are not Euclidean, predicts the multiscale properties of connectomes, including self-similarity. The model relies on the application of a geometric renormalization protocol which decreases the r

SUBMITTER: Zheng M 

PROVIDER: S-EPMC7443937 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature