基于双曲残差编码的多源多视图图域适应用于跨站点静息态fMRI的重度抑郁症识别

中文摘要 AI 辅助

从静息态功能磁共振成像（rs-fMRI）中进行跨站点重度抑郁症（MDD）识别，面临站点间分布偏移和异质功能连接（FC）视图的阻碍。这些视图捕捉互补的神经关系，但呈现出不同的站点偏差和图拓扑结构，使得在不牺牲疾病相关信息或跨视图一致性的情况下进行对齐变得复杂。现有研究大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，很少有研究在多源无监督域适应框架下联合建模多个FC视图用于基于rs-fMRI的跨站点MDD分类。我们构建皮尔逊相关、稀疏表示和格兰杰因果图，每个图由特定视图的图注意力网络编码。双流自适应融合明确整合成对的跨视图交互，随后采用轻量级双曲残差编码进行曲率感知的表示优化。类别级柯西-施瓦茨对齐减少源间及源-目标差异，辅以对抗学习、信息最大化和置信度感知伪标签。在七个未标记目标域上，我们的框架达到73.60%的平均准确率和71.90%的AUC，证明在异质采集条件下的有效泛化。这些结果凸显了统一异质视图建模、曲率感知优化及多源域适应对跨站点MDD识别的有效性。源代码可在指定地址获取。

英文摘要

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

发表机构

• School of Computer and Artificial Intelligence, Shandong Jianzhu University（山东建筑大学计算机与人工智能学院）
• Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China（电子科技大学基础与前沿研究院）
• School of Mathematics and Statistics, Chongqing Jiaotong University（重庆交通大学数学与统计学院）
• State Key Laboratory of Brain-machine Intelligence, Zhejiang University（浙江大学脑机智能国家重点实验室）
• Institute of Computer Vision and Traffic Image Understanding, School of Information Science and Engineering, Chongqing Jiaotong University（重庆交通大学信息科学与工程学院计算机视觉与交通图像理解研究所）
• School of Life Sciences, Westlake University（西湖大学生命科学学院）
• School of Computer and Artificial Intelligence, Nanjing University of Finance and Economics（南京财经大学计算机与人工智能学院）

机构由 AI 辅助整理，请以论文原文为准。