Abstract
Understanding the relationship between brain functional connectivity and structural connectivity is important in the field of brain imaging, and it can help us better comprehend the working mechanisms of the brain. Much effort has been made on this issue, but it is still far from satisfactory. The brain transmits information through a network architecture, which means that the regions and connections of the brain are significant. The main difficulties with this issue are currently at least two aspects. On the one hand, the importance of different brain regions in structural and functional integration has not been fully addressed; on the other hand, the connectome skeleton of the brain, plays the role in common and key connections in the brain network, has not been clearly studied. To alleviate the above problems, this paper proposes a transformer-based self-supervised graph reconstruction framework (TSGR). The framework uses the graph neural network (GNN) to fuse functional and structural information of the brain, reconstructs the brain graph through a self-supervised model and identifies the regions that are important to the reconstruction task. These regions are considered as key connectome regions which play an essential role in the communication connectivity of the brain network. Based on key brain regions, the connectome skeleton can be obtained. Experimental results demonstrate the effectiveness of the proposed method, which obtains key regions and connectome skeleton in the brain network. This provides a new angle of view to explore the relationship between brain function and structure. Our code is available at https://github.com/kang105/TSGR.
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This work was supported by the National Natural Science Foundation of China (62006194).
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Kang, Y., Wang, R., Shi, E., Wu, J., Yu, S., Zhang, S. (2023). Exploring Brain Function-Structure Connectome Skeleton via Self-supervised Graph-Transformer Approach. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14227. Springer, Cham. https://doi.org/10.1007/978-3-031-43993-3_30
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