BrainFC-CGAN: A Conditional Generative Adversarial Network for Brain Functional Connectivity Augmentation and Aging Synthesis

Brain functional connectivity (FC) changes are associated with neuropsychiatric disorders and other underlying factors, such as age and gender. Due to small training sample, data augmentation has been increasingly used for deep learning-based classification of brain FC. Although deep generative models could generate brain FCs to enhance downstream classification, most existing methods neglect the underlying factors involved in the generation process and fail to preserve the subject identity. We propose a novel brain FC conditional Generative Adversarial Network (GAN) called BrainFC-CGAN with specialized layers and filters to preserve the symmetry property and topological structure of brain FCs. We design a FC generator that captures the complex variations between brain FCs, ages, and health statuses to generate synthetic FCs that preserve the subject identity. We categorized true brain FCs into different age groups; an augmented age-specific dataset generated from BrainFC-CGAN is combined with the training set for classification. Experimental results on major depressive disorder (MDD) resting-state functional magnetic resonance imaging data show that the proposed method synthesizes realistic brain FCs of different target age groups, significantly improving downstream classification performance over baseline without augmentation, and also outperforming several state-of-the-art GANs.