Extracting Sequential Features from Dynamic Connectivity Network with rs-fMRI Data for AD Classification

Abstract

Dynamic functional connectivity (dFC) networks based on resting-state functional magnetic resonance imaging (rs-fMRI) can help us understand the function of brain better, and have been applied to brain disease identification, such as Alzheimer’s disease (AD) and its early stages (i.e., mild cognitive impairment, MCI). Deep learning (e.g., convolutional neural network, CNN) methods have been recently applied to dynamic FC network analysis, and achieve good performance compared to traditional machine learning methods. Existing studies usually ignore sequence information of temporal features from dynamic FC networks. To this end, in this paper, we propose a recurrent neural network-based learning framework to extract sequential features from dynamic FC networks with rs-fMRI data for brain disease classification. Experimental results on 174 subjects with baseline resting-state functional MRI (rs-fMRI) data from ADNI demonstrate the effectiveness of our proposed method in binary and multi-category classification tasks.