Using Structural Similarity and Kolmogorov-Arnold Networks for Anatomical Embedding of Cortical Folding Patterns
Minheng Chen, Chao Cao, Tong Chen, Yan Zhuang, Jing Zhang, Yanjun Lyu,, Xiaowei Yu, Lu Zhang, Tianming Liu, Dajiang Zhu

TL;DR
This paper introduces a self-supervised framework utilizing structural similarity and Kolmogorov-Arnold networks to embed and establish cross-subject correspondences of cortical folding patterns, specifically 3-hinge gyri, for improved brain network analysis.
Contribution
It presents a novel anatomical feature embedding method based on KAN and structural similarity, addressing the challenge of cross-subject correspondence without one-to-one mappings.
Findings
Effective in establishing robust cross-subject correspondences
Outperforms traditional registration methods in complex scenarios
Enhances brain network construction using 3HG patterns
Abstract
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…
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Taxonomy
TopicsInertial Sensor and Navigation · Neural Networks and Applications · Statistical and numerical algorithms
