ArticleProceedings of the National Academy of Sciences of the United States of America2020
Geometric renormalization unravels self-similarity of the multiscale human connectome.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Prediction, syntax and semantic grounding in the brain and large language models.Scientific reports · 2026Article
- Interaction of Functional Brain Networks Is Associated With k-Clique Percolation in the Human Structural Connectome.Human brain mapping · 2025Article
- Article
- The multiscale self-similarity of the weighted human brain connectome.PLoS computational biology · 2025Article
- Exploring the transmission of cognitive task information through optimal brain pathways.PLoS computational biology · 2025Article
- Brain connectome from neuronal morphology.Network neuroscience (Cambridge, Mass.) · 2025Article
- A Novel and Powerful Dual-Stream Multi-Level Graph Convolution Network for Emotion Recognition.Sensors (Basel, Switzerland) · 2024Article
- Article
- Emergence of metastability in frustrated oscillatory networks: the key role of hierarchical modularity.Frontiers in network physiology · 2024Article
- A Self-Similarity Logic May Shape the Organization of the Nervous System.Advances in neurobiology · 2024Article
- The D-Mercator method for the multidimensional hyperbolic embedding of real networks.Nature communications · 2023Article
- Brain network communication: concepts, models and applications.Nature reviews. Neuroscience · 2023Review
- Hierarchical overlapping modular structure in the human cerebral cortex improves individual identification.iScience · 2023Article
- Geometrical congruence, greedy navigability and myopic transfer in complex networks and brain connectomes.Nature communications · 2022Article
- Detecting the ultra low dimensionality of real networks.Nature communications · 2022Article
- Characteristic functional cores revealed by hyperbolic disc embedding and k-core percolation on resting-state fMRI.Scientific reports · 2022Article
- A survey of brain network analysis by electroencephalographic signals.Cognitive neurodynamics · 2022Article
- High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns.Nature communications · 2021Article
- Scaling up real networks by geometric branching growth.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Persistence of information flow: A multiscale characterization of human brain.Network neuroscience (Cambridge, Mass.) · 2021Article
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5 authors.
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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 resolution by coarse-graining and averaging over short similarity distances. Our 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.
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