Multi-Source Multi-View Graph Domain Adaptation with Hyperbolic Residual Encoding for Cross-Site MDD Identification from rs-fMRI

2026-07-31

AI Summary

This work addresses the challenge of identifying major depressive disorder (MDD) from cross-site resting-state fMRI data, where distribution shifts and heterogeneous functional connectivity views hinder generalization. To tackle this, we propose a multi-source unsupervised domain adaptation framework that uniquely integrates multi-view graph domain adaptation with hyperbolic representation learning. Our approach employs view-specific graph attention networks to extract features, combined with a dual-stream adaptive fusion mechanism, hyperbolic residual encoding, and Cauchy–Schwarz class alignment. It further incorporates adversarial learning, information maximization, and confidence-aware pseudo-labeling to jointly optimize cross-view consistency and cross-site alignment. Evaluated on seven unlabeled target domains, the method achieves an average accuracy of 73.60% and an AUC of 71.90%, significantly enhancing the generalization performance of cross-site MDD recognition.

Abstract

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

Problem

• cross-site MDD identification
• multi-source domain adaptation
• multi-view functional connectivity
• distribution shift
• heterogeneous graph views

Innovation

• multi-source domain adaptation
• multi-view graph learning
• hyperbolic residual encoding
• cross-site MDD identification
• functional connectivity