Using Structural Similarity and Kolmogorov-Arnold Networks for Anatomical Embedding of Cortical Folding Patterns
This work addresses a domain-specific challenge in neuroimaging for researchers studying brain networks, by enabling reliable node correspondence across individuals, though it appears incremental as it builds on existing methods like KANs.
The paper tackled the problem of establishing cross-subject correspondences for 3-hinge gyri (3HGs) in cortical folding patterns, which are difficult to align due to individual anatomical variations, and proposed a self-supervised framework using structural similarity and Kolmogorov-Arnold networks, resulting in effective establishment of robust correspondences as demonstrated in experiments.
The 3-hinge gyrus (3HG) is a newly defined folding pattern, which is the conjunction of gyri coming from three directions in cortical folding. Many studies demonstrated that 3HGs can be reliable nodes when constructing brain networks or connectome since they simultaneously possess commonality and individuality across different individual brains and populations. However, 3HGs are identified and validated within individual spaces, making it difficult to directly serve as the brain network nodes due to the absence of cross-subject correspondence. The 3HG correspondences represent the intrinsic regulation of brain organizational architecture, traditional image-based registration methods tend to fail because individual anatomical properties need to be fully respected. To address this challenge, we propose a novel self-supervised framework for anatomical feature embedding of the 3HGs to build the correspondences among different brains. The core component of this framework is to construct a structural similarity-enhanced multi-hop feature encoding strategy based on the recently developed Kolmogorov-Arnold network (KAN) for anatomical feature embedding. Extensive experiments suggest that our approach can effectively establish robust cross-subject correspondences when no one-to-one mapping exists.