Functional magnetic reasonance imaging (fMRI) plays an important role in pre-surgical planning for patients with resectable brain lesions such as tumors. With appropriately designed tasks, the results of fMRI studies can guide resection, thereby preserving vital brain tissue. The mass univariate approach to fMRI data analysis consists of performing a statistical test in each voxel, which is used to classify voxels as either active or inactive — that is, related, or not, to the task of interest. In cognitive neuroscience, the focus is on controlling the rate of false positives while accounting for the severe multiple testing problem of searching the brain for activations. However, stringent control of false positives is accompanied by a risk of false negatives, which can be detrimental, particularly in clinical settings where false negatives may lead to surgical resection of vital brain tissue. Consequently, for clinical applications, we argue for a testing procedure with a stronger focus on preventing false negatives. We present a thresholding procedure that incorporates information on false positives and false negatives. We combine two measures of significance for each voxel: a classical p-value, which reflects evidence against the null hypothesis of no activation, and an alternative p-value, which reflects evidence against activation of a prespecified size. This results in a layered statistical map for the brain. One layer marks voxels exhibiting strong evidence against the traditional null hypothesis, while a second layer marks voxels where activation cannot be confidently excluded. The third layer marks voxels where the presence of activation can be rejected.