【摘要】 目的 针对多站点抑郁症数据分类的泛化能力不强，以及使用三维原始图像作为深度学习分类模型的输入容易过拟合的问题，设计了一种卷积神经网络架构用于重度抑郁症（MDD）辅助诊断。方法 该模型基于静息态功能磁共振成像得到的低维功能连接矩阵作为输入，从中提取功能相关信息和高阶抽象特征从而分类。结果 将该模型在多站点REST-meta-MDD数据集上验证，分类准确率为70.39%。结论 通过遮挡分析描述了不同大脑区域对MDD辅助诊断的贡献，结果表明默认模式网络、视觉网络和额顶控制网络对MDD分类任务具有重要作用。更多还原

【Abstract】 Objective The generalization ability of multi-site depression data classification is not adequately strong, and the use of the 3D raw image as an input of deep learning classification model is prone to overfitting problems. Therefore, a convolutional neural network architecture was here proposed, which was designed for MDD auxiliary diagnosis. Methods In the model, the low-dimensional functional connection matrices based on resting-state fMRI were used as an input, from which functional information and higher-order abstract features were extracted for classification. Result The model was validated on multi-site REST-metaMDD datasets, and the classification accuracy was 70.39%. Besides, the contribution of different brain regions to MDD auxiliary diagnosis was described. Conclusion The results indicated that the default mode network,visual network and fronto-parietal control network could play an important role in MDD classification tasks.更多还原