GyralNet Subnetwork Partitioning via Differentiable Spectral Modularity Optimization

2025-03-25

AI Summary

Current analyses of three-hinge gyral (3HG) patterns face three key bottlenecks: sub-voxel localization inaccuracy, complex cross-subject registration, and the oversimplified assumption of node independence—ignoring intrinsic community structure. To address these, we propose a differentiable spectral modularity optimization framework that, for the first time, embeds modularity maximization into an end-to-end differentiable pipeline to drive individualized cortical parcellation via the GyralNet subnetwork. Our method integrates cortical topological similarity with DTI-derived white-matter connectivity to construct multimodal graph features, and leverages differentiable graph neural networks grounded in spectral graph theory to achieve biologically interpretable 3HG community detection. Evaluated on the Human Connectome Project (HCP) dataset, our approach achieves a 92.7% cross-subject 3HG community structure preservation rate—significantly outperforming conventional graph-cut and clustering methods. This work establishes a novel paradigm for high-fidelity, individualized brain structural modeling.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Computer Vision: Multi-modal Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Analyzing 3HGs at sub-voxel scale in neuroimaging
• Reducing computational complexity in cross-subject 3HG correspondence
• Modeling 3HG community relationships in GyralNet

Innovation

• Differentiable spectral modularity optimization framework
• Topological and DTI-based attribute features
• Individual-level GyralNet partitioning with cross-subject consistency