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Adaptive Hypergraph Neural Network for the Diagnosis of Depression

DOI: 10.12677/ISL.2024.81001, PP. 1-8

Keywords: 脑网络,超图,抑郁诊断,图神经网络
Brain Network
, Hypergraph, Depression Diagnosis, Graph Neural Network

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Depression classification has emerged as a popular research topic in recent years. Previous graph neural network (GNN) based methods for Depression fMRI data classification have given less con-sideration to the high-order interactions among brain regions and the dynamic variations in func-tional connectivity. To address this issue, we propose an Adaptive Hypergraph Neural Network (AHGNN). We utilize HGNN, coupled with the learnable adaptive hypergraph construction (AHC) module, to extract intricate interplay among brain regions. The model is trained and evaluated on the REST-meta-MDD dataset. Our proposed architecture achieves a commendable depression diag-nostic performance, realizing a classification accuracy rate of 71.41%. The employment of HGNN and the AHC module significantly enhances the classification efficacy of the model.


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