Geometric renormalization unravels self-similarity of the multiscale human connectome
UBICS papers 2020/10/05
Some members of the UBICS published a new paper in PNAS. The article titled "Geometric renormalization unravels self-similarity of the multiscale human connectome" was published by Muhua Zheng, Antoine Allard, Patric Hagmann, Yasser Alemán-Gómez, and M. Ángeles Serrano, the last August 2020.
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. The researchers of the study explored the multiscale organization of human connectomes using datasets of healthy subjects reconstructed at five different resolutions. They 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 resolution by coarse-graining and averaging over short similarity distances. These results suggest that simple organizing principles underlie the multiscale architecture of human structural brain networks, where the same connectivity law dictates short- and long-range connections between different brain regions over many resolutions. The implications are varied and can be substantial for fundamental debates, such as whether the brain is working near a critical point, as well as for applications including advanced tools to simplify the digital reconstruction and simulation of the brain.