BrainNet-GAN: Generative Adversarial Graph Convolutional Network for Functional Brain Network Synthesis from Routine...

Functional brain network (FBN) derived from functional Magnetic Resonance Imaging (fMRI) has promising prospects in clinical research, but fMRI is not a routine acquisition data, which limits its popularity in clinical applications. Therefore, it is imperative to generate FBN based on routine clinical structural MRI brain network. In this study, a BrainNet-GAN model was proposed for generating FBN from radiomics-based morphological brain network (radMBN) derived from routinely acquired T1-weighted image (T1WI). BrainNet-GAN integrated two Multi-Channel Multi-Scale Adaptive (Multi²Ada) generators and two (Local_to_Global) discriminators. In the generator, Graph Convolutional Network (GCN) was used inside each channel to aggregate multi-scale information between direct or indirect neighbors of nodes, and the output of each channel was adaptively fused through several sets of learnable coefficients; In the discriminator, Multi-channel GCN was used to aggregate local nodes information, and a feature selection module was designed to establish correlations between feature maps at different channels. Additionally, a Multi-Angle Multi-Constraint (MAMC) loss function was proposed, which could guide the learning process of the model from different aspects. Experiments with 2116 subjects in two publicly available datasets showed that BrainNet-GAN model exhibited promising performance on the task of generating FBN. Meanwhile, the individual-level brain network visualization was displayed with high consistency in generated FBN and target FBN. Further, the Top 10 brain regions identified by four graph-theory analysis metrics also exhibited with consistency. The proposed BrainNet-GAN model demonstrated superior performance in generating FBN based on radMBN, which could facilitate the application of FBN in clinical practice.