Enhancing Group-Wise Consistency in 3-Hinge Gyrus Matching Via Anatomical Embedding and Structural Connectivity Optimization

Recently, a novel cortical folding pattern known as the 3-hinge gyrus (3HG) has been identified. 3HGs are defined as the convergence of the gyri coming from three distinct directions on gyral crests. In contrast to cortical regions, 3HGs are defined at a finer scale and they widely exist across different individuals, representing both commonalities and individualities of cortical folding patterns. It is important to note that 3HGs are identified in individual spaces, lacking natural cross-subject correspondences. To address this issue, we have developed a learning-based method to encode anatomical features of 3HGs into a set of embedding vectors that can be compared across individuals. However, this method solely relies on anatomical features and can be suboptimal because it does not consider the related structural connectivity patterns, as many 3HGs have multiple potential matches using anatomical properties only. In this study, we leverage the multimodal imaging data (T1 MRI and DTI) which are complementary to each other in representing 3HGs, to enhance the precision when identifying one-to-one correspondence for 3HGs. Through extensive experiments, we have demonstrated the effectiveness of our approach in mitigating the one-to-many match issue associated with 3HGs, significantly improving the accuracy of 3HG correspondences. This accomplishment holds considerable implications for group-level analyses based on 3HGs and contributes to the broader utilization of 3HGs in brain studies.