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.

翻译：理解人脑的结构与功能组织需要对皮层折叠模式进行细致考察，其中三铰回（3HG）已被确认为关键的结构标志。GyralNet作为皮层折叠的网络表征，将3HG建模为节点、回峰建模为边，凸显了其在皮层间连接中作为关键枢纽的作用。然而，现有分析3HG的方法面临重大挑战：包括典型神经影像分辨率下3HG的亚体素尺度问题、建立跨被试对应关系的计算复杂性，以及将3HG视为独立节点而忽略其群落层面关系的过度简化。为突破这些局限，我们提出了一种完全可微分的子网络划分框架，该框架采用谱模块度最大化优化策略对GyralNet内的3HG组织进行模块化划分。通过整合拓扑结构相似性和DTI衍生的连接模式作为属性特征，我们的方法提供了具有生物学意义的皮层组织表征。在人类连接组计划（HCP）数据集上的大量实验表明，本方法能在个体层面有效划分GyralNet，同时保持3HG在跨被试间的群落层面一致性，为理解脑连接提供了稳健的基础。