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Relaxation parameter estimation and brain activation detection are two main study areas in magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI). Relaxation parameters can be used to distinguish voxels containing different types of tissue while activation determines voxels that are associated with neuronal activity. In nearly all fMRI studies, the first few scans are discarded from the data before computing brain activation to avoid magnetic saturation effects. However, these first images have important information on the relaxation decay parameters for the type of tissue contained in voxels, which could provide pathological tissue discrimination [3]. It is also well-known that the voxels located in gray matter contain neurons that are to be active while the subject is performing a task and thus voxel relaxivities that are different for different tissue types should be included in a model. In this study, we develop a model to obtain both relaxation parameters and determine brain activation simultaneously. This model is examined with realistically simulated fMRI data, which includes the first few scans as well the remainder of the time series. To compute the activation statistics, the estimated T1 and T2 * of gray matter are incorporated into the hypothesis testing setting. Nonlinear least squares estimation is used for estimating the parameters and the general procedure of generalized likelihood ratio test is performed to calculate activation statistics