Abstract
Understanding the relationship between brain function and structure is vital important in the field of brain image analysis. It elucidates the working mechanism of the brain, which will contribute to better understand the brain and simulate the brain-like system. Extensive efforts have been made on this topic, but still far from the satisfactory. The major difficulties are at least two aspects. One is the huge individual difference among the subjects, which makes it hard to obtain stable results at groupwise level, e.g., noise signals can significantly affect the exploring process. The other one is the huge difference between functional and structural features of the brain, both in their pattern and size, which are very different. To alleviate the above problems, in this paper, we propose a two-stage multi-view low-rank sparse subspace clustering (Two-stage MLRSSC) method to jointly study the relationship between brain function and structure and identify the common regions of brain function and structure. The major innovation of proposed Two-stage MLRSSC is that comparable features of brain function and structure can be effectively extracted from low-rank sparse representation, and results are further improved the stability by two-stage strategy. Finally, groupwise-based stable functional and structural common regions are identified for better understanding the relationship. Experimental results shed new ways to explore the brain function and structure, new insights are observed and discussed.
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Zhang, S. et al. (2022). A Novel Two-Stage Multi-view Low-Rank Sparse Subspace Clustering Approach to Explore the Relationship Between Brain Function and Structure. In: Lian, C., Cao, X., Rekik, I., Xu, X., Cui, Z. (eds) Machine Learning in Medical Imaging. MLMI 2022. Lecture Notes in Computer Science, vol 13583. Springer, Cham. https://doi.org/10.1007/978-3-031-21014-3_20
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