Rapid and accurate diagnosis of mental disorders has long been an essential challenge in clinical medicine. Due to the advantage in addressing non-Euclidean structures, graph neural networks have been increasingly used to study brain networks. Among the existing methods, the population graph models have achieved high predictive accuracy by considering intersubject relationships but weak interpretability limits its clinical applicability. The individual graph approach models functional brain networks and identifies abnormal brain regions that cause diseases but has poor accuracy. To address these issues, we propose a heterogeneous graph neural network based on brain functional connectivity (FC-HGNN), which is an end-to-end model with a two-stage process. In the first phase, the brain connectomic graph is used to extract individual brain features. An integrated intrahemispheric and interhemispheric convolutional graph layer is used to learn brain region features, and a local–global dual-channel pooling layer is used to identify biomarkers. In the second stage, a heterogeneous population graph is constructed based on sex and the fusion of imaging and non-imaging data from subjects. The feature embeddings of same-sex and opposite-sex neighbours are learned separately according to a hierarchical feature aggregation approach. Subsequently, they are adaptively fused to generate the final node embedding, which is then utilized for obtaining classification predictions. The cross-validation and transduction learning results show that FC-HGNN achieves state-of-the-art performance in classification prediction experiments using two public datasets. Moreover, FC-HGNN identifies crucial biomarker regions relevant for disease classification, aligning with existing studies and exhibiting outstanding predictive performance on actual clinical data. The code is available at https://github.com/Guuuu11/FC-HGNN_Pytorch.

中文翻译：

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FC-HGNN：基于大脑功能连接的异构图神经网络，用于精神障碍识别


精神疾病的快速、准确诊断长期以来一直是临床医学面临的重要挑战。由于在解决非欧几里得结构方面的优势，图神经网络已越来越多地用于研究脑网络。现有方法中，群体图模型通过考虑主体间关系实现了较高的预测精度，但可解释性较弱限制了其临床适用性。个体图方法对功能性大脑网络进行建模，并识别导致疾病的异常大脑区域，但准确性较差。为了解决这些问题，我们提出了一种基于大脑功能连接的异构图神经网络（FC-HGNN），这是一个具有两阶段过程的端到端模型。在第一阶段，大脑连接图用于提取个体大脑特征。集成的半球内和半球间卷积图层用于学习大脑区域特征，局部-全局双通道池层用于识别生物标志物。在第二阶段，基于性别以及受试者的成像和非成像数据的融合构建异质群体图。根据分层特征聚合方法分别学习同性和异性邻居的特征嵌入。随后，它们被自适应地融合以生成最终的节点嵌入，然后用于获得分类预测。交叉验证和转导学习结果表明，FC-HGNN 在使用两个公共数据集的分类预测实验中实现了最先进的性能。 此外，FC-HGNN 确定了与疾病分类相关的关键生物标志物区域，与现有研究相一致，并对实际临床数据表现出出色的预测性能。代码可在 https://github.com/Guuuu11/FC-HGNN_Pytorch 获取。