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- W4387425110 abstract "Biomarkers extracted from brain functional connectivity (FC) can assist in diagnosing various psychiatric disorders. Recently, several deep learning-based methods are proposed to facilitate the development of biomarkers for auxiliary diagnosis of depression and promote automated depression identification. Although they achieved promising results, there are still existing deficiencies. Current methods overlook the subgraph of braingraph and have a rudimentary network framework, resulting in poor accuracy. Conducting FC analysis with poor accuracy model can render the results unreliable. In light of the current deficiencies, this paper designed a subgraph neural network-based model named SGMDD for analyzing FC signatures of depression and depression identification. Our model surpassed many state-of-the-art depression diagnosis methods with an accuracy of 73.95%. To the best of our knowledge, this study is the first attempt to apply subgraph neural network to the field of FC analysis in depression and depression identification, we visualize and analyze the FC networks of depression on the node, edge, motif, and functional brain region levels and discovered several novel FC feature on multi-level. The most prominent one shows that the hyperconnectivity of postcentral gyrus and thalamus could be the most crucial neurophysiological feature associated with depression, which may guide the development of biomarkers used for the clinical diagnosis of depression." @default.
- W4387425110 created "2023-10-08" @default.
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- W4387425110 date "2023-01-01" @default.
- W4387425110 modified "2023-10-18" @default.
- W4387425110 title "SGMDD: Subgraph Neural Network-Based Model for Analyzing Functional Connectivity Signatures of Major Depressive Disorder" @default.
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- W4387425110 doi "https://doi.org/10.1007/978-981-99-7074-2_28" @default.
- W4387425110 hasPublicationYear "2023" @default.
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