Functional magnetic resonance imaging (fMRI) has recently been adopted as an investigational tool in the field of neuroscience. The signal changes induced by brain activations are small ( approximately 1-2%) at 1.5T. Therefore, the signal-to-noise ratio (SNR) of the time series used to calculate the functional maps is critical. In this study, the minimum SNR required to detect an expected MR signal change is determined using computer simulations for typical fMRI experimental designs. These SNR results are independent of manufacturer, site environment, field strength, coil type, or type of cognitive task used. Sensitivity maps depicting the minimum detectable signal change can be constructed. These sensitivity maps can be used as a mask of the activation map to help remove false positive activations as well as identify regions of the brain where it is not possible to confidently reject the null hypothesis due to a low SNR.