Global-Local Brain Network based on Functional Connectivity for Individualized Prediction

Functional connectivity (FC) derived from fMRI reflects the interactions between brain regions of interest (ROIs). It has become one of the important features of individualized prediction. However, in studies using FC as input, some studies have extracted global features directly from the entire brain FC, lacking focus on local critical information. On the other hand, some studies have extracted local critical features from selected ROIs or connections, but lack access to global contextual information. In this paper, we propose a novel method, namely, Global-Local Brain Network (GLBN), focusing on both global contextual information and critical local information. We validate our proposed method on the largescale public dataset, the Cambridge Centre for Ageing and Neuroscience (Cam-CAN). For the prediction of age and fluid intelligence, GLBN achieves the mean absolute errors of 6.084, and 4.160, with Pearson’s correlations of 0.908, and 0.641, respectively. Our method demonstrates superior prediction accuracy compared to existing studies. Additionally, we visualize the brain ROIs that play crucial roles in the prediction tasks, affirming the biological interpretability of GLBN.