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基于适应性超图神经网络的抑郁症诊断
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Abstract:
近年来的研究越来越关注基于深度学习的抑郁症自动诊断。然而，以往基于图神经网络(GNN)的抑郁症fMRI数据分类方法并未充分考虑脑区间的高级交互作用以及其动态变化。为了解决这一挑战，本研究提出了适应性超图神经网络(AHGNN)来进行抑郁症的诊断。该模型采用了超图神经网络(HGNN)来提取脑区间的高阶交互信息，并使用可学习策略适应性地在模型训练过程中更新大脑超图结构。我们在抑郁症脑影像大数据联盟(REST-meta-MDD)数据集上对该模型进行了训练和评估。实验结果显示，研究所提出的架构在抑郁症诊断上取得了显著效果，模型的分类准确率达到了71.41%，实验结果还表明超图神经网络和自适应超图构建模块的运用显著增强了模型的分类性能。
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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