Estimating Neural Response Functions from fMRI - Kumar, Sukhbinder; Penny, William D. (Frontiers in Neuroinformatics, 2014)
This paper proposes a methodology for estimating Neural Response Functions (NRFs) from fMRI data. These NRFs describe nonlinear relationships between experimental stimuli and neuronal population responses. The method is based on a two-stage model comprising an NRF and a Hemodynamic Response Function (HRF) that are simultaneously fitted to fMRI data using a Bayesian optimisation algorithm. This algorithm also produces a model evidence score, providing a formal model comparison method for evaluating alternative NRFs. The HRF is characterised using previously established `Balloon' and BOLD signal models. We illustrate the method with two example applications based on fMRI studies of the auditory system. In the first, we estimate the time constants of repetition suppression and facilitation, and in the second we estimate the parameters of population receptive fields in a tonotopic mapping study.