Integrating Similarity Awareness and Adaptive Calibration in Graph Convolution Network to Predict Disease

Significant memory concern (SMC) is the earlier stage of mild cognitive impairment (MCI), and its early treatment is quite vital to delay further disease-induced deterioration. To predict the deterioration, graph convolution network (GCN) with current adjacency matrix still suffers from limited prediction performance due to their subtle difference and obscure features. For this reason, we propose a similarity-aware adaptive calibrated GCN (SAC-GCN), which can combine functional and structural information to predict SMC and MCI. We utilize an adaptive calibration mechanism to construct a data-driven adjacency matrix. Specifically, we first design a similarity-aware graph using different receptive fields to consider the disease statuses. Namely, the labeled subjects are only connected with those subjects who have the same status in the convolution operation. Then we compute more accurate weights in graph edges from functional and structural scores. Current edge weights are used to construct an initial graph and pre-train the GCN. Based on the pre-trained GCN, the differences between scores replace the traditional correlation distances to evaluate edge weights. Lastly, we devise a calibration technique to fuse functional and structural information for edge weighting. The proposed method is tested on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The experimental results demonstrate that our proposed method is effective to predict disease-induced deterioration and superior over other related algorithm.