A Novel Population Graph Neural Network Based on Functional Connectivity for Mental Disorders Detection

Accurate and rapid clinical confirmation of psychiatric disorders based on imaging, symptom and scale data has long been difficult. Graph neural networks have received increasing attention in recent years due to their advantages in processing unstructured relational data, especially functional magnetic resonance imaging data. However, all existing methods have certain drawbacks. Individual graph methods are able to provide important biomarkers based on functional connectivity modelling, but their accuracy is low. Population graph methods, which improve the prediction performance by considering the similarity between patients, lack clinical interpretability. In this study, we propose a functional connectivity-based population graph (FCP-GNN) approach that possesses excellent classification capabilities while also providing significant biomarkers for clinical reference. The proposed method is divided into two phases. In the first phase, brain region features are learned hemispherically and used to identify biomarkers through a local-global dual-channel pooling layer. In the second phase, a heterogeneous population map is constructed based on gender. The feature information of same-sex and opposite-sex neighbours is learned separately using a hierarchical feature aggregation module to obtain the final embedding representation. The experiment results show that FCP-GNN achieves state-of-the-art performance in classification prediction work on two public datasets.