Predicting clinical outcomes of Alzheimer’s disease from complex brain networks

Abstract

Brain network modelling has been shown effective to study the brain connectivity in Alzheimer’s disease (AD). Although the topological features of AD affected brain networks have been widely investigated, combining hierarchical networks features for predicting AD receives little attention. In this study, we propose a spectral convolutional neural network (SCNN) framework to learn combinations of hierarchical network features for a reliable AD prediction outcomes. Due to the complex high-dimensional structure of brain networks, conventional convolutional neural networks (CNN) are not able to learn the complete geometrical information of brain networks. To address this limitation, our SCNN is spectrally designed to learn a complete set of network topological features. Specifically, we construct structural brain networks using magnetic resonance images (MRI) from 288 ADs, 272 mild cognitive impairments (MCI) and 272 normal controls (NC). Then, we deploy SCNN to classify ADs from MCIs and NCs. Experiment results show that SCNN is able to achieve the accuracy of 91.07% in AD/NC classification, 87.72 in AD/MCI classification and 85.45% in MCI/HC classification. In addition, we show that SCNN is able to predict clinical scores associated with AD with high precision.