A nonlinear model of fMRI BOLD signal including the trend component

This paper presents a nonlinear model of the human brain activity response to visual stimuli according to Blood-Oxygen-Level-Dependent (BOLD) signals scanned by functional Magnetic Resonance Imaging (fMRI). A BOLD signal usually contains a low frequency signal component (trend), which is often ignored by the existing models or removed by approximation methods. However, such detrending could also destroy the dynamics of the BOLD signal and miss an important response. This paper shows a model that, in the absence of detrending, can predict the BOLD signal with smaller errors than existing models. For detrending, the presented model has also a lower Schwarz information criterion than existing models, which implies that the presented model will be less likely to overfit the experimental data.