Unsupervised Joint Alignment Framework for Cross-Site Depression Classification Using fMRI

Major depressive disorder (MDD) is a prevalent and debilitating mental illness, affecting over 264 million people worldwide and imposing substantial social and economic burdens. Identifying MDD using resting-state functional MRI (rs-fMRI) is a promising direction for early diagnosis and intervention. However, inter-site heterogeneity-arising from differences in scanners and acquisition protocols-poses a major obstacle to building generalizable models across imaging sites. To address this challenge, we propose the Unsupervised Joint Alignment (UJA) framework for cross-site MDD classification. To the best of our knowledge, this is one of the first attempts to explore unsupervised rs-fMRI adaptation using an adversarial learning-based approach that jointly aligns both domain-level distributions and class-level structures. Specifically, UJA employs a multi-head self-attention module to extract informative representations from rs-fMRI data, followed by a unified alignment scheme that integrates adversarial domain-wise alignment with class-wise alignment based on dual classifiers and the sliced Wasserstein distance. Extensive experiments on the REST-meta-MDD dataset demonstrate that UJA consistently outperforms existing comparative methods across multiple cross-site scenarios. Ablation studies further highlight the complementary benefits of the dual alignment strategy. These findings highlight the potential of UJA to serve as a robust and generalizable tool for future clinical decision support in MDD diagnosis.