Estimation of Hemodynamic States Based on Stacked Recurrent Neural Network in fMRI Time Series
The estimation of underlying hemodynamic states from the fMRI data can provide an objective representation of the brain activity at the neuronal level, which can contribute to the understanding of the brain operation mechanism and the development of the brain cognition research. So far, many methods have been proposed for estimating hemodynamic states from the fMRI data. However, most of these methods are limited to the further consideration of the temporal characteristic in the hemodynamic model. In addition, they require the prior knowledge on hemodynamic model parameter values, which are not measurable in the actual situation. Therefore, this paper presents a new approach based on recurrent neural network (RNN) to carry out the estimation of hemodynamic states, which employs RNN to extract the temporal features inherent in fMRI time series. Firstly, the inversion process has been constructed by the inversions of nonlinear functions in the hemodynamic model, which map the BOLD signal to hemodynamic states. Then, a novel neural network architecture called stacked recurrent neural network (SRNN) is used for estimating hemodynamic states with BOLD signals by approximating the mapping relations. Finally, the experimental results on the simulated data have shown that the new approach can not only capture the temporal characteristic in the fMRI data, but also can model the nonlinear relationship between hemodynamic states dynamically.