Functional magnetic resonance imaging studies often involve the acquisition of data from multiple sessions and/or multiple subjects. A hierarchical approach can be taken to modelling such data with a General Linear Model at each level of the hierarchy introducing different random effects variance components. Inferring on these models is non-trivial with frequentist solutions being unavailable. A solution is to use a Bayesian framework. One important ingredient in this is the choice of prior on the variance components and top-level regression parameters. Due to the typically small numbers of sessions or subjects in neuro-imaging the choice of prior is critical. To alleviate this problem we introduce to neuro-image modelling the approach of reference priors, which drives the choice of prior such that it is non-informative in an information-theoretic sense. We propose two inference techniques at the top-level for multi-level hierarchies (a fast approach and a slower more accurate approach). We also demonstrate that we can infer on the top-level of multi-level hierarchies by inferring on the levels of the hierarchy separately and passing summary statistics of a non-central multivariate t-distribution between them.