Hypergraph Overlapping Community Detection for Brain Networks
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913938906546176 |
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| author | Vu, Duc Aviyente, Selin |
| author_facet | Vu, Duc Aviyente, Selin |
| contents | Functional magnetic resonance imaging (fMRI) has been commonly used to construct functional connectivity networks (FCNs) of the human brain. TFCNs are primarily limited to quantifying pairwise relationships between ROIs ignoring higher order dependencies between multiple brain regions. Recently, hypergraph construction methods from fMRI time series data have been proposed to characterize the high-order relations among multiple ROIs. While there have been multiple methods for constructing hypergraphs from fMRI time series, the question of how to characterize the topology of these hypergraphs remains open. In this paper, we make two key contributions to the field of community detection in brain hypernetworks. First, we construct a hypergraph for each subject capturing high order dependencies between regions. Second, we introduce a spectral clustering based approach on hypergraphs to detect overlapping community structure. Finally, the proposed method is implemented to detect the consensus community structure across multiple subjects. The proposed method is applied to resting state fMRI data from Human Connectome Project to summarize the overlapping community structure across a group of healthy young adults. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08999 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hypergraph Overlapping Community Detection for Brain Networks Vu, Duc Aviyente, Selin Signal Processing I.5.3; I.2.6 Functional magnetic resonance imaging (fMRI) has been commonly used to construct functional connectivity networks (FCNs) of the human brain. TFCNs are primarily limited to quantifying pairwise relationships between ROIs ignoring higher order dependencies between multiple brain regions. Recently, hypergraph construction methods from fMRI time series data have been proposed to characterize the high-order relations among multiple ROIs. While there have been multiple methods for constructing hypergraphs from fMRI time series, the question of how to characterize the topology of these hypergraphs remains open. In this paper, we make two key contributions to the field of community detection in brain hypernetworks. First, we construct a hypergraph for each subject capturing high order dependencies between regions. Second, we introduce a spectral clustering based approach on hypergraphs to detect overlapping community structure. Finally, the proposed method is implemented to detect the consensus community structure across multiple subjects. The proposed method is applied to resting state fMRI data from Human Connectome Project to summarize the overlapping community structure across a group of healthy young adults. |
| title | Hypergraph Overlapping Community Detection for Brain Networks |
| topic | Signal Processing I.5.3; I.2.6 |
| url | https://arxiv.org/abs/2507.08999 |