Depression is a serious mental disorder with complex etiology, exhibiting strong heterogeneity in clinical manifestations such as various subtypes. Research on depression subtypes may deepen the understanding of the disease, contributing to the diagnosis and prognosis. While brain functional network and graph neural networks (GNNs) provide such a means, the task is still challenged by limited feature encoding from the informative fMRI data, ineffective information fusion of brain functional network, and small size of the recruited subjects. Therefore, we propose a hierarchical encoding and fusion framework of brain functions. First, we pre-train a model to extract the features from individual brain regions, which signify nodes in the brain functional network. Then, distinct graphs are constructed to link the nodes within each subject, resulting in multi-view graphs of the brain functional network. We further develop a graph fusion strategy to integrate the multi-view information, by referring to the local encoding of the nodes and their interactions across multiple graph instances. Finally, we attain the classification of depression subtypes based on the fused graph representation. The experimental results demonstrate that our method can superiorly distinguish major depression subtypes and outperform the state-of-the-art methods.

中文翻译：

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抑郁症亚型分类的脑功能分层编码和融合


抑郁症是一种严重的精神障碍，病因复杂，各种亚型等临床表现表现出很强的异质性。对抑郁症亚型的研究可能会加深对该疾病的了解，有助于诊断和预后。虽然脑功能网络和图神经网络（GNN）提供了这样的手段，但该任务仍然面临着信息丰富的功能磁共振成像数据特征编码有限、脑功能网络信息融合不力以及招募受试者规模较小等挑战。因此，我们提出了大脑功能的分层编码和融合框架。首先，我们预训练一个模型，从各个大脑区域提取特征，这些区域代表大脑功能网络中的节点。然后，构建不同的图来链接每个受试者内的节点，从而形成大脑功能网络的多视图图。我们通过参考节点的本地编码及其跨多个图实例的交互，进一步开发了一种图融合策略来集成多视图信息。最后，我们基于融合图表示实现了抑郁症亚型的分类。实验结果表明，我们的方法可以更好地区分重度抑郁症亚型，并且优于最先进的方法。