Community-Level Modeling of Gyral Folding Patterns for Robust and Anatomically Informed Individualized Brain Mapping

2026-02-01

AI Summary

This work addresses the limitations of existing sulcal landmark–based methods, which exhibit weak anatomical representation and are sensitive to positional variability and noise due to their neglect of higher-order folding community structure. To overcome this, we propose a spectral graph representation learning framework that elevates gyral modeling from isolated landmarks to folding communities composed of three-hinge gyri (3HG). By integrating dual-profile encoding of surface topology and structural connectivity, combined with subject-specific spectral clustering and topological refinement, our approach enables coherent identification of folding communities. Furthermore, we introduce a morphology–geometry joint matching mechanism for cross-subject alignment. Evaluated on over a thousand Human Connectome Project (HCP) subjects, our method significantly improves community modularity, hemispheric consistency, and morphological stability, outperforming atlas-, landmark-, and embedding-based baselines in alignment performance.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Community question answering

Abstract

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.

Problem

• cortical folding
• three-hinge gyrus
• individualized brain mapping
• anatomical correspondence
• inter-individual variability

Innovation

• spectral graph representation
• folding communities
• three-hinge gyrus
• joint morphological-geometric matching
• individualized brain mapping