Functional Hypergraph Uncovers Novel Covariant Structures over Neurodevelopment
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Abstract (arXiv)
Brain development during adolescence is marked by substantial changes in brain structure and function, leading to a stable network topology in adulthood. However, most prior work has examined the data through the lens of brain areas connected to one another in large-scale functional networks. Here, we apply a recently-developed hypergraph approach that treats network connections (edges) rather than brain regions as the unit of interest, allowing us to describe functional network topology from a fundamentally different perspective. Capitalizing on a sample of 780 youth imaged as part of the Philadelphia Neurodevelopmental Cohort, this hypergraph representation of resting-state functional MRI data reveals three distinct classes of sub-networks (hyperedges): clusters, bridges, and stars, which represent spatially distributed, bipartite, and focal architectures, respectively. Cluster hyperedges show a strong resemblance to the functional modules of the brain including somatomotor, visual, default mode, and salience systems. In contrast, star hyperedges represent highly localized subnetworks centered on a small set of regions, and are distributed across the entire cortex. Finally, bridge hyperedges link clusters and stars in a core-periphery organization. Notably, developmental changes within hyperedges are ordered in a similar core-periphery fashion, with the greatest developmental effects occurring in networked hyperedges within the functional core. Taken together, these results emphasize that the network organization of human brain emerges across multiple scales and evolves substantially through the adolescent period.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Dec 2016.
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Citing papers per year: 2021: 1; 2022: 1.
2 papers.
- 2022Equivariant Hypergraph Neural Networkscited by 14
- 2021Adaptive Neural Message Passing for Inductive Learning on Hypergraphscited by 10
- 2022Equivariant Hypergraph Neural Networks22 Aug 2022
- 2021Adaptive Neural Message Passing for Inductive Learning on Hypergraphs22 Sep 2021