Convolutional bidirectional GRU for dynamic functional connectivity classification in brain diseases diagnosis

Abstract

Dynamic functional connectivity (DFC) classification is helpful for computer-aided diagnosis of brain diseases. In recent years, DFC classification based on deep learning has drawed increasing attention. However, how to effectively extract the deep spatio-temporal features of DFC to improve classification performance is still a very challenging research topic. To this end, this paper proposes a DFC classification method based on convolutional bidirectional gated recurrent unit, called DFC-CBGRU, which mainly includes three key operations: multi-scale topological features extraction, bidirectional spatio-temporal feature extraction, and feature fusion. Firstly, the proposed method uses convolutional neural network (CNN) to extract the multi-scale topological features composed of node-level, module-level, and graph-level features from functional connectivity network at each time point. Then, it employs bidirectional gated recurrent unit (GRU) to extract the bidirectionally dependent spatio-temporal features from the obtained time series of multi-scale topological features. Finally, it utilizes the one-dimensional CNN to fuse forward and backward spatio-temporal features to obtain the joint spatio-temporal features for classification. Experimental results on multiple brain diseases datasets show that the proposed method has a superior classification performance over other methods and is promising for extracting the discriminative FCs related to brain diseases accurately.