Multi-scale Enhanced Graph Convolutional Network for Mild Cognitive Impairment Detection

•We design a MCI-graph framework which integrates both non-image information and image information. We use LWCC to extract the feature to avoid the high dimensional features.•We devise various parallel GCN layers using multiple inputs from the random walk embedding theory, which is able to identify the essential MCI information from the GCN graph embedding.•The random walk embeddings on the graph can explore the high order similarity of features.•We fuse the information from the functional networks and structural networks using a MSE-GCN model to improve prediction performance.