GyralNet Subnetwork Partitioning via Differentiable Spectral Modularity Optimization

• URL: http://arxiv.org/abs/2503.19823v2
• Date: Mon, 31 Mar 2025 21:17:19 GMT
• Title: GyralNet Subnetwork Partitioning via Differentiable Spectral Modularity Optimization
• Authors: Yan Zhuang, Minheng Chen, Chao Cao, Tong Chen, Jing Zhang, Xiaowei Yu, Yanjun Lyu, Lu Zhang, Tianming Liu, Dajiang Zhu,
• Abstract summary: We propose a differentiable subnetwork framework to modularize the organization of 3HGs within GyralNet.<n>By incorporating topological structural similarity and DTI-derived connectivity patterns as attribute features, our approach provides a biologically meaningful representation of cortical organization.
• Score: 30.044545011553172
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding the structural and functional organization of the human brain requires a detailed examination of cortical folding patterns, among which the three-hinge gyrus (3HG) has been identified as a key structural landmark. GyralNet, a network representation of cortical folding, models 3HGs as nodes and gyral crests as edges, highlighting their role as critical hubs in cortico-cortical connectivity. However, existing methods for analyzing 3HGs face significant challenges, including the sub-voxel scale of 3HGs at typical neuroimaging resolutions, the computational complexity of establishing cross-subject correspondences, and the oversimplification of treating 3HGs as independent nodes without considering their community-level relationships. To address these limitations, we propose a fully differentiable subnetwork partitioning framework that employs a spectral modularity maximization optimization strategy to modularize the organization of 3HGs within GyralNet. By incorporating topological structural similarity and DTI-derived connectivity patterns as attribute features, our approach provides a biologically meaningful representation of cortical organization. Extensive experiments on the Human Connectome Project (HCP) dataset demonstrate that our method effectively partitions GyralNet at the individual level while preserving the community-level consistency of 3HGs across subjects, offering a robust foundation for understanding brain connectivity.

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