Mamba-Based Residual Generative Adversarial Network for Functional Connectivity Harmonization During Infancy

How to harmonize site effects is a fundamental challenge in modern multi-site neuroimaging studies. Although many statistical models and deep learning methods have been proposed to mitigate site effects while preserving biological characteristics, harmonization schemes for multi-site resting-state functional magnetic resonance imaging (rs-fMRI), particularly for functional connectivity (FC), remain undeveloped. Moreover, statistical models, though effective for region-level data, are inherently unsuitable for capturing complex, nonlinear mappings required for FC harmonization. To address these issues, we develop a novel, flexible deep learning method, Mamba-based Residual Generative adversarial network (MR-GAN), to harmonize multi-site functional connectivities. Our method leverages the Mamba Block, which has been proven effective in traditional visual tasks, to define FC-specified sequential patterns and integrate them with a multi-task residual GAN to harmonize multi-site FC data. Experiments on 939 infant rs-fMRI scans from four sites demonstrate the superior performance of the proposed method in harmonization compared to other approaches.