MSARAE: Multiscale adversarial regularized autoencoders for cortical network classification

Abstract

Due to privacy regulations and technical limitations, current research on the cerebral cortex frequently faces challenges, including limited data availability. The number of samples significantly influences the performance and generalization ability of deep learning models. In general, these models require sufficient training data to effectively learn underlying distributions and features, enabling strong performance on unseen samples. A limited sample size can lead to overfitting, thereby weakening the model’s generalizability. To address these challenges from a data augmentation perspective, we propose a Multi-Scale Adversarial Regularized Autoencoder (MSARAE) for augmenting and classifying cortical structural connectivity. The approach begins with data preprocessing and the construction of cortical structural connectivity networks. To better capture cortical features, the model leverages Laplacian eigenvectors to enhance topological information. Structural connectivity is then generated using variational autoencoders, with multi-scale graph convolutional layers serving as encoders to capture graph representations at different scales. An adversarial regularization mechanism is introduced to minimize the distribution discrepancy in the latent space. By training a discriminator, the model encourages the encoder to produce latent representations that closely match the distribution of real data, thereby improving its representational capacity. Finally, extensive experiments on the major depression disorder (MDD) dataset, the Human Connectome Project (HCP) dataset, and the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrated the superiority of the model.