Diffusion transformer-augmented fMRI functional connectivity for enhanced autism spectrum disorder diagnosis

Abstract Objective. Functional magnetic resonance imaging (fMRI) is often modeled as networks of Regions of Interest and their functional connectivity to study brain functions and mental disorders. Limited fMRI data due to high acquisition costs hampers recognition model performance. We aim to address this issue using generative diffusion models for data augmentation. Approach. We propose Brain-Net-Diffusion, a transformer-based latent diffusion model to generate realistic functional connectivity for augmenting fMRI datasets and evaluate its impact on classification tasks. Main results. The Brain-Net-Diffusion effectively generates connectivity patterns resembling real data and significantly enhances classification performance. Augmentation using Brain-Net-Diffusion increased downstream autism spectrum disorder classification accuracy by 4.3% compared to no augmentation. It also outperformed other augmentation methods, with accuracy improvements ranging from 1.3% to 2.2%. Significance. Our approach demonstrates the effectiveness of diffusion models for fMRI data augmentation, providing a robust solution for overcoming data scarcity in functional connectivity analysis. To facilitate further research, we have made our code publicly available at https://github.com/JoeZhao527/brain-net-diffusion .