To increase the power of neuroimaging analyses, it is common practice to reduce the whole-brain search space to a subset of hypothesis-driven regions-of-interest (ROIs). Rather than strictly constrain analyses, we propose to incorporate prior knowledge using probabilistic ROIs (pROIs) using a hierarchical Bayesian framework. Each voxel prior probability of being "of-interest" or "of-non-interest" is used to perform a weighted fit of a mixture model. We demonstrate the utility of this approach through simulations with various pROIs, and the applicability using a prior based on the NeuroSynth database search term "emotion" for thresholding the fMRI results of an emotion processing task. The modular structure of pROI correction facilitates the inclusion of other innovations in Bayesian mixture modeling, and offers a foundation for balancing between exploratory analyses without neglecting prior knowledge.