A Cascaded Multi-modality Analysis in Mild Cognitive Impairment

Abstract

Though reversing the pathology of Alzheimer’s disease (AD) has so far not been possible, a more tractable goal may be the prevention or slowing of the disease when diagnosed in its earliest stage, such as mild cognitive impairment (MCI). Recent advances in deep modeling approaches trigger a new era for AD/MCI classification. However, it is still difficult to integrate multi-modal imaging data into a single deep model, to gain benefit from complementary datasets as much as possible. To address this challenge, we propose a cascaded deep model to capture both brain structural and functional characteristic for MCI classification. With diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) data, a graph convolution network (GCN) is constructed based on brain structural connectome and it works with a one-layer recurrent neural network (RNN) which is responsible for inferring the temporal features from brain functional activities. We named this cascaded deep model as Graph Convolutional Recurrent Neural Network (GCRNN). Using Alzheimer’s Disease Neuroimaging Initiative (ADNI-3) dataset as a test-bed, our method can achieve 97.3% accuracy between normal controls (NC) and MCI patients.