NARX Neural Networks for Dynamical Modelling of fMRI Data

Functional magnetic resonance imaging (fMRI) is an important technique to study the human brain (the most complex biological dynamical system) functions which are often described by the hemodynamic responses (HDR). It measures the changes of the blood oxygenation level dependent (BOLD) signals due to the neural activities. The measured fMRI data is the response of the human brain to a particular processing task. In this paper, the nonlinear autoregressive with exogenous inputs (NARX) neural networks are investigated as a method to model the dynamics underlying the fMRI data. Studies on both simulated as well as real event-related fMRI data show that the proposed scheme can capture the underlying dynamics of the brain and reconstruct the BOLD signals from the measured noisy fMRI data. In addition, a good estimate of the HDR of the brain is also obtained.