Functional magnetic resonance imaging (fMRI) intends to detect significant neural activity by means of statistical data processing. Commonly used statistical tests include the Student-t test, analysis of variance, and the generalized linear model test. A key assumption underlying these methods is that the data are Gaussian distributed. Moreover, although MR data are intrinsically complex valued, fMRI data analysis is usually performed on single valued magnitude data. Whereas complex MRI data are Gaussian distributed, magnitude data are Rician distributed. In this paper, we describe five Generalized Likelihood Ratio Tests (GLRTs) that fully exploit the knowledge of the distribution of the data: one is based on Rician distributed magnitude data and two are based on Gaussian distributed complex valued data. By means of Monte Carlo simulations, the performance of the GLRTs is compared with the classical statistical tests.