Modeling brain diverse and complex hemodynamic response patterns via deep recurrent autoencoder

For decades, a variety of task-based functional MRI (tfMRI) data analysis approaches have been developed, including the general linear model (GLM), sparse representations, and independent component analysis (ICA). However, these methods are mainly shallow models and are limited in faithfully modeling the complex, diverse, and concurrent spatial-temporal functional brain activities. Recently, recurrent neural networks (RNNs) have demonstrated great superiority in modeling temporal dependency of signals, while autoencoder models have been proven to be effective in automatically estimating the optimal representations of the original data. These characteristics of RNNs and autoencoders naturally meet the requirement of modeling hemodynamic response patterns in tfMRI data. Thus, we propose a novel unsupervised framework of deep recurrent autoencoder (DRAE) for modeling hemodynamic response patterns in this article. The basic idea of the DRAE model is to combine the deep RNN and the autoencoder to automatically characterize the meaningful functional brain networks and corresponding diverse and complex hemodynamic response patterns simultaneously. The experimental results demonstrate the superiority of the proposed DRAE model in automatically estimating the diverse and complex hemodynamic response patterns.