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• W4285206647 abstract "The dynamic brain network can reflect time-varying changes of the BLOD signal fluctuations, and has been widely used in the research of brain diseases identification. This network consists of a set of connection matrixes, where each connection matrix represents the relationship between brain regions under a certain period. Researchers often convert these matrices into vectors, and then use the K-means clustering method to divide these matrices into different brain states according to their vector-based distances. Through analyzing these states, they can identify some brain abnormalities. However, simply using the vector-based distances may lead to two problems: 1) it ignores the topological properties and underlying mechanisms of brain networks, and 2) it never considers individual differences between subjects. Hence, to solve these two problems, we propose a novel method, called GK-BSC, for constructing brain states with dynamic brain networks. Specifically, we first uses the graph kernel rather than the vector-based distances to measure the similarities between connection matries of the dynamic brain network. Then, we aggregate these matrices to generate brain states based on these calculated similarities. This aggregation operation is sustained several times on one subject, whereby each subject is represented by a set of hierarchical brain states. Finally, we extract features from these states, and feed them into the multi-instance support vector machine (MI-SVM) for identifying patients. Experiments on a real schizophrenia dataset suggest that our method not only improves the performance of schizophrenia identification, but also accurately locates the brain abnormalities." @default.
• W4285206647 created "2022-07-14" @default.
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• W4285206647 date "2022-01-01" @default.
• W4285206647 modified "2023-09-25" @default.
• W4285206647 title "GK-BSC: Graph Kernel-Based Brain States Construction With Dynamic Brain Networks and Application to Schizophrenia Identification" @default.
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• W4285206647 doi "https://doi.org/10.1109/access.2022.3178748" @default.
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