fMRI hemodynamic response function estimation in autoregressive noise by avoiding the drift

Abstract

The measured functional magnetic resonance imaging (fMRI) time series is typically corrupted by instrumental drift and physiological noise due to respiration and heartbeat giving rise to temporal correlation in the signals. Most methods proposed so far for nonparametric hemodynamic response function (HRF) estimation in fMRI data do not account for these confounding effects, and thus produce biased and inefficient estimates. The aim of this paper is to address this issue by modeling the noise in fMRI time series using an autoregressive model of order p (AR(p)). Making use of a semiparametric model to characterize the fMRI time series and the AR(p) to model the temporally correlated noise, a g..