A robust signal detection method for fMRI data under correct Rice conditions

In this paper, we tackle the problem of signal detection in functional Magnetic Resonance Imaging (fMRI) data by means of a statistical analysis. The main problem of the commonly used statistical tests is that they are based on the assumption that the data are Gaussian distributed, which is only valid for high signal-to-noise ratios (SNRs). Hence, for low SNRs the classical statistical tests are inadequate due to the wrong normality assumption, since it is known from literature that fMRI data follow a Rice distribution. In order to handle both high and low SNRs, we present in this paper a correction for the simplest and most widely used t-test by incorporating the correct Rice conditions. The performance of the Rice-corrected statistical test is shown through simulations and compared with its uncorrected counterpart.