Graph‐Regularized Manifold‐Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation

作者：Yee Fan Tan, Fuad Noman, Raphaël C.‐W. Phan, Hernando Ombao, Chee‐Ming Ting · 发表于：Human Brain Mapping · 年份：2025 · DOI：10.1002/hbm.70322 · 被引用次数：2 · 研究领域：Functional Brain Connectivity Studies、Advanced Neuroimaging Techniques and Applications、Neural dynamics and brain function

Common measures of brain functional connectivity (FC) including covariance and correlation matrices are symmetry-positive definite (SPD) matrices residing on a cone-shaped Riemannian manifold. Despite its remarkable success for Euclidean-valued data generation, the use of standard generative adversarial networks (GANs) to generate manifold-valued FC data neglects its inherent SPD structure and hence the inter-relatedness of edges in real FC. We propose a novel graph-regularized manifold-aware conditional Wasserstein GAN (GR-SPD-GAN) for FC data generation on the SPD manifold that can preserve the global FC structure. Specifically, we optimize a generalized Wasserstein distance between the real and generated SPD data under adversarial training, conditioned on the class labels. The resulting generator can synthesize new SPD-valued FC matrices associated with different classes of brain networks, for example, brain disorder or healthy control. Furthermore, we introduce additional population graph-based regularization terms on both the SPD manifold and its tangent space to encourage the generator to respect the inter-subject similarity of FC patterns in the real data. This also helps in avoiding mode collapse and produces more stable GAN training. Evaluated on resting-state functional magnetic resonance imaging (fMRI) data of major depressive disorder (MDD), qualitative and quantitative results show that the proposed GR-SPD-GAN clearly outperforms several state-of-the-art GANs in ...