Hemisphere-Separated Cross-Connectome Aggregating Learning via VAE-GAN for Brain Structural Connectivity Synthesis

Abstract

The brain network is an effective tool and has been widely used in the field of brain neurodegenerative disease analysis. Due to the high cost of accessing medical image data, efforts have been devoted to investigating data augmentation. However, the brain network containing topological characteristics of non-European space is different from the traditional image data, which makes it challenging to synthesize brain structural connectivity and limits the application of brain network analysis. In this paper, a Hemisphere-separated Cross-connectome Aggregating Learning (HCAL) model is proposed to synthesize realistic and diverse brain structural connectivities. Specifically, the latent representation is transformed from structural connectivity by the graph variational autoencoder (GVAE). To generate more diverse and high-quality structural connectivities, the hemisphere-separated generator with a cross-connectome aggregating mechanism is developed to first learn local topological patterns by splitting the whole brain into inter- and intra-hemispheres, then capture global topological characteristics among all the neighbors for each brain region. Also, the connectivity-aware discriminator is devised to make the adversarial training stable and enhance the disease diagnosis. Evaluations of the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that the proposed model generates brain structural networks with higher quality and more diversity than related methods. In addition, the classification performance using the augmented data achieved by our approach achieves an accuracy improvement of over 3% compared with the competing method. The proposed model provides an alternative way to synthesize brain networks for connectivity-based disease analysis.