融合多源多视图脑连接数据，提升跨站点抑郁症识别准确率
Multi-Source Multi-View Graph Domain Adaptation with Hyperbolic Residual Encoding for Cross-Site MDD Identification from rs-fMRI
- 构建三种脑功能连接图，用图注意力网络分别编码不同视图
- 在7个目标站点上达到73.60%准确率和71.90% AUC
- 适合做跨中心医学影像分析或脑疾病诊断的研究者参考
从静息态功能磁共振（rs-fMRI）中跨站点识别重度抑郁症（MDD）受限于站点间分布偏移和异构功能连接（FC）视图。这些视图捕捉互补的神经关系，但存在显著站点偏差和图拓扑差异，导致对齐困难且易损失疾病相关特征或跨视图一致性。现有研究多将多视图连接组学习与跨站点适应分开处理。据我们所知，尚无工作在多源无监督域适应下联合建模多个FC视图用于跨站点rs-fMRI MDD分类。本文构建皮尔逊相关、稀疏表示和格兰杰因果三类图，每类由视图专用图注意力网络编码；通过双流自适应融合显式整合视图间交互，并引入轻量级双曲残差编码实现曲率感知表征优化；采用类别级柯西-施瓦茨对齐减少源间与源-目标差异，辅以对抗学习、信息最大化及置信度感知伪标签。在七个未标注目标域上，框架平均准确率达73.60%，AUC为71.90%，验证了在异质采集条件下的有效泛化能力。结果表明，统一建模异构视图、曲率感知精炼及多源域适应对跨站点MDD识别具有显著效果。代码已开源：https://github.com/OPUS-Lightphenexx/MM-HyperGDA
原文摘要 · Abstract (English)
Cross-site identification of major depressive disorder (MDD) from resting-state functional magnetic resonance imaging (rs-fMRI) is hindered by inter-site distribution shifts and heterogeneous functional connectivity (FC) views. These views capture complementary neural relationships but exhibit distinct site biases and graph topologies, complicating alignment without sacrificing disease-relevant information or cross-view consistency. Existing studies largely treat multi-view connectome learning and cross-site adaptation separately. To the best of our knowledge, few studies have jointly modeled multiple FC views under multi-source unsupervised domain adaptation for cross-site rs-fMRI-based MDD classification. We construct Pearson correlation, sparse representation, and Granger causality graphs, each encoded by a view-specific graph attention network. Dual-stream adaptive fusion explicitly integrates pairwise cross-view interactions, followed by lightweight hyperbolic residual encoding for curvature-aware representation refinement. Class-wise Cauchy--Schwarz alignment reduces inter-source and source-target discrepancies, complemented by adversarial learning, information maximization, and confidence-aware pseudo-labeling. Across seven unlabeled target domains, our framework achieves 73.60% mean accuracy and 71.90% AUC, demonstrating effective generalization under heterogeneous acquisition conditions. These results highlight the effectiveness of unified heterogeneous-view modeling, curvature-aware refinement, and multi-source domain adaptation for cross-site MDD identification.The source code is at https://github.com/OPUS-Lightphenexx/MM-HyperGDA
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