Identifying Brain Networks of Multiple Time Scales via Deep Recurrent Neural Network

For decades, task-based functional magnetic resonance imaging (tfMRI) has been a powerful noninvasive tool to explore the organizational architecture of human brain function. Researchers have developed a variety of brain network analysis methods for tfMRI data, including the general linear model (GLM), independent component analysis (ICA) and sparse representation methods. However, these shallow models are limited in faithful reconstruction and modeling of the hierarchical and temporal structures of brain networks, as demonstrated in more and more studies. Recently, recurrent neural networks (RNNs) exhibit great ability of modeling hierarchical and temporal dependency features in the machine learning field, which might be suitable for tfMRI data modeling. To explore such possible advantages of RNNs for tfMRI data, we propose a novel framework of deep recurrent neural network (DRNN) to model the functional brain networks for tfMRI data. Experimental results on the motor task tfMRI data of Human Connectome Project 900 subjects data release demonstrated that the proposed DRNN can not only faithfully reconstruct functional brain networks, but also identify more meaningful brain networks with multiple time scales which are overlooked by traditional shallow models. In general, this work provides an effective and powerful approach to identifying functional brain networks of multiple time scales from tfMRI data.