Haemodynamic Response Function (HRF) Model Selection in fMRI using Kalman Filtering

This paper proposes a Kalman-based multiple-model approach for the selection of a biophysical model describing the Haemodynamic Response Function (HRF) measured in BOLD-fMRI data. It is shown, both theoretically and through simulation, that the proposed method is able to successfully distinguish the correct HRF model among a set of physiologically plausible alternatives. Moreover, the feasibility of the technique is demonstrated by its application to an empirical dataset. In summary, the results obtained clearly indicate that the proposed methodology is potentially well-suited to be used in the modeling of BOLD-fMRI data.