GyralNet Subnetwork Partitioning via Differentiable Spectral Modularity Optimization
This work addresses the problem of understanding cortical organization for neuroscientists, but it is incremental as it builds on existing GyralNet methods by adding community-level analysis.
The paper tackled the challenge of analyzing three-hinge gyri (3HGs) in brain networks by proposing a differentiable spectral modularity optimization framework for subnetwork partitioning in GyralNet, demonstrating effective individual-level partitioning with cross-subject consistency on the HCP dataset.
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.