Predicting functional connectivity network from routinely acquired T1-weighted imaging-based brain network by generative U-GCNet

Predicting the function magnetic resonance imaging (fMRI)-based brain network (fMRI-BN) from structure MRI-based brain network is imperative in clinical practice because fMRIs are not routinely acquired in a vast majority of hospitals. In this study, a generative U-GCNet (U-shaped graph convolutional Network) was proposed to predict fMRI-BN from radiomics-based morphological brain network (radMBN) derived from routinely acquired T1-WI image. Specifically, the U-GCNet consisted of a graph convolutional network (GCN) encoder module (En-GCN), a deep feature connectivity construction module (DF2C), and a GCN decoder module (De-GCN). Both En-GCN and De-GCN employed mixed local-and-long distance node feature aggregation strategy to enhance the graph encoding and decoding ability, and the DF2C reshaped the deep feature matrix into the connectivity matrix for outputting the brain network prediction. Additionally, a multi-scale network similarity loss function was conducted on full values, upper triangular values, and each row values of connectivity matrix. Experiments on 3169 subjects from three publicly available databases demonstrated that the U-GCNet could predict the fMRI-BN from radMBN with a promising performance (MSE [0.0002 0.0025], PCC [0.956 0.991]) over eight alternative methods under comparison. The results exhibited a significant correlation (PCC [0.796, 0.897], P<0.05) between the estimated and real radiomics-function coupling values. The individual-level and group-level brain network visualization was displayed with high consistency. The TOP brain regions identified by four graph-based metrics also exhibited with consistency. These results demonstrated that the proposed U-GCNet could achieve promising prediction of fMRI-BN from radMBN which could alleviate the limited availability of fMRI and boost its usage in clinical practice.