Major depressive disorder (MDD) is a common and severe psychiatric illness marked by loss of interest and low energy, which result in the highest burden of disability among all mental disorders. Clinical MDD diagnosis still utilizes the phenomenological approach of syndrome-based interview, which leads to a high rate of misdiagnosis. Therefore, it is highly imperative to explore effective biomarkers to enable precise personalized diagnosis. There still exist two main challenges due to complexity of MDD and individual differences. On the one hand, discriminative features need to be investigated to better reflect the characteristics of MDD. On the other hand, the performance from shallow and static learning models is still not satisfactory. To overcome these issues, we propose a novel Multi-Stage Graph Fusion Networks (MSGFN) for major depressive disorder diagnosis. At first, functional connectivity is calculated to better characterize interactions between white matter and gray matter. Second, multi-stage features are obtained by a deep subspace learning model, and a number of graphs are constructed under the self-expression constraints at each stage. Finally, a novel graph convolutional fusion module is proposed with graph convolutional operations to integrate features and graph at each stage. Extensive experiments demonstrate the superior performance of the proposed framework. Our source code is available on: https://github.com/LIST-KONG/MSGFN-master .

中文翻译：

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用于重度抑郁症诊断的多阶段图融合网络

重度抑郁症（MDD）是一种常见且严重的精神疾病，其特征是兴趣丧失和精力不足，导致残疾负担在所有精神障碍中最高。临床MDD诊断仍采用证候访谈的现象学方法，导致误诊率较高。因此，探索有效的生物标志物以实现精确的个性化诊断是非常必要的。由于 MDD 的复杂性和个体差异，仍然存在两个主要挑战。一方面，需要研究判别特征以更好地反映 MDD 的特征。另一方面，浅层和静态学习模型的表现仍然不尽如人意。为了克服这些问题，我们提出了一种用于重度抑郁症诊断的新型多阶段图融合网络 (MSGFN)。首先，计算功能连通性以更好地表征白质和灰质之间的相互作用。其次，通过深度子空间学习模型获得多阶段特征，并在每个阶段的自我表达约束下构建多个图。最后，提出了一种新的图卷积融合模块，使用图卷积运算在每个阶段集成特征和图。广泛的实验证明了所提出框架的优越性能。我们的源代码可在以下位置获得：通过深度子空间学习模型获得多阶段特征，并在每个阶段的自我表达约束下构建多个图。最后，提出了一种新的图卷积融合模块，使用图卷积运算在每个阶段集成特征和图。广泛的实验证明了所提出框架的优越性能。我们的源代码可在以下位置获得：通过深度子空间学习模型获得多阶段特征，并在每个阶段的自我表达约束下构建多个图。最后，提出了一种新的图卷积融合模块，使用图卷积运算在每个阶段集成特征和图。广泛的实验证明了所提出框架的优越性能。我们的源代码可在以下位置获得：https://github.com/LIST-KONG/MSGFN-master .