Self-supervised Learning with Adaptive Graph Structure and Function Representation for Cross-Dataset Brain Disorder Diagnosis

Resting-state functional magnetic resonance imaging (rs-fMRI) helps characterize the regional neural activity of the human brain. Currently, supervised deep learning methods that rely on a large amount of fMRI data have shown good performance in diagnosing specific brain diseases. However, there are significant differences in the structure and function of brain connectivity networks among patients with different brain diseases. This makes it difficult for the model to achieve satisfactory diagnostic performance when facing new diseases with limited data, thus severely hindering their application in clinical practice. In this work, we propose a self-supervised learning framework based on graph contrastive learning for cross-dataset brain disorder diagnosis. Specifically, we develop a graph structure learner that adaptively characterizes general brain connectivity networks for various brain disorders. We further develop a multi-state brain network encoder that can effectively enhance the representation of brain networks with functional information related to different brain diseases. We finally evaluate our model on different brain disorders and demonstrate advantages compared to other state-of-the-art methods.