摘要： 重度抑郁症作为一种高发且危害严重的精神障碍，早期精准诊断对治疗干预至关重要。功能性磁共振成像作为一种非侵入性的神经影像学技术，为抑郁症诊断提供了无创的神经影像依据，有助于构建详细的脑功能连接。但传统深度学习方法在处理脑功能连接数据时，存在忽视全局时间动态特征和难以建模多脑区高阶交互的缺陷。为解决上述问题，提出一种基于时空交叉注意力超图神经网络的抑郁症辅助诊断方法。该方法以功能性磁共振数据构建的脑功能连接图为研究对象，通过时间分支捕捉脑区信号的时序动态特征，空间分支建模脑区之间的高阶关联，利用时空交叉注意力模块实现两类特征的深度融合。在大规模多中心数据集上进行实验验证，结果表明，提出的模型平均准确率达83.74%、灵敏度达73.76%、特异性达93.39%，相较其他方法提升明显。消融实验验证了空间分支、时间分支、时空交叉注意力模块的有效性，为抑郁症的临床辅助诊断提供了一种新的技术方案。

Abstract: Major Depressive Disorder is a prevalent and severe mental disorder, and early accurate diagnosis is crucial for treatment intervention. Functional Magnetic Resonance Imaging, as a non-invasive neuroimaging technique, provides non-invasive neuroimaging evidence for depression diagnosis and helps construct detailed brain functional connectivity for diagnosing. However, traditional deep learning methods have limitations in neglecting global temporal dynamic features and difficulty in modeling high-order interactions among multiple brain regions when processing brain functional connectivity data. To address these issues, an auxiliary diagnosis method for depression based on a Spatio-Temporal Cross-Attention HyperGraph Neural Network is proposed. This method takes brain functional connectivity graphs constructed from functional magnetic resonance imaging data as the research object, captures the temporal dynamic features of brain region signals through a temporal branch, models the high-order correlations between brain regions via a spatial branch, achieves deep fusion of the two types of features using a spatio-temporal cross-attention module. Experimental verification on a large-scale multi-center dataset shows that the proposed model achieves an average accuracy of 83.74%, sensitivity of 73.76%, and specificity of 93.39%, showing significant improvements compared with other methods. Ablation experiments verify the effectiveness of the spatial branch, the temporal branch and the spatio-temporal cross-attention module, providing a new technical solution for the clinical auxiliary diagnosis of depression.