群体层面的沟回折叠模式建模以实现稳健且解剖学导向的个体化脑图谱
Community-Level Modeling of Gyral Folding Patterns for Robust and Anatomically Informed Individualized Brain Mapping
AI总结：
本文提出了一种基于群体层面折叠单元的建模方法，通过双profile表示和联合形态-几何匹配，提升个体化脑图谱的稳健性和解剖学准确性。
摘要：
皮层折叠表现出显著的个体间差异，同时保持稳定的解剖学地标，这些地标能够实现对皮层组织的精细表征。其中，三铰沟（3HG）作为一种关键的折叠基本单元，显示出一致的拓扑结构，但形态、连接性和功能上存在有意义的变化。现有的基于地标的方法通常独立建模每个3HG，忽略了3HGs形成更高阶折叠社区的事实，这些社区捕捉了中等尺度的结构。这种简化削弱了解剖学表示，并使一对一匹配对位置变化和噪声敏感。我们提出了一种谱图表示学习框架，该框架建模群体层面的折叠单元，而非孤立地标。每个3HG通过结合表面拓扑和结构连接的双profile表示进行编码。针对特定主体的谱聚类识别出连贯的折叠社区，随后进行拓扑细化以保持解剖学连续性。对于跨主体对应，我们引入了联合形态-几何匹配，联合优化几何和形态学相似性。在超过1000名人类连接组计划受试者中，所得到的社区显示出减少的形态学方差、更强的模块化组织、改进的半球一致性以及优于基于图谱和基于地标或嵌入基线的对齐效果。这些发现表明，群体层面的建模提供了一种稳健且解剖学导向的框架，用于个体化皮层表征和可靠的跨主体对应。
英文摘要：
Cortical folding exhibits substantial inter-individual variability while preserving stable anatomical landmarks that enable fine-scale characterization of cortical organization. Among these, the three-hinge gyrus (3HG) serves as a key folding primitive, showing consistent topology yet meaningful variations in morphology, connectivity, and function. Existing landmark-based methods typically model each 3HG independently, ignoring that 3HGs form higher-order folding communities that capture mesoscale structure. This simplification weakens anatomical representation and makes one-to-one matching sensitive to positional variability and noise. We propose a spectral graph representation learning framework that models community-level folding units rather than isolated landmarks. Each 3HG is encoded using a dual-profile representation combining surface topology and structural connectivity. Subject-specific spectral clustering identifies coherent folding communities, followed by topological refinement to preserve anatomical continuity. For cross-subject correspondence, we introduce Joint Morphological-Geometric Matching, jointly optimizing geometric and morphometric similarity. Across over 1000 Human Connectome Project subjects, the resulting communities show reduced morphometric variance, stronger modular organization, improved hemispheric consistency, and superior alignment compared with atlas-based and landmark-based or embedding-based baselines. These findings demonstrate that community-level modeling provides a robust and anatomically grounded framework for individualized cortical characterization and reliable cross-subject correspondence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
