Over the last two decades, Functional Magnetic Resonance Imaging (fMRI) has revolutionized the study of the brain. This non-invasive technique produces snapshots of brain activity over time, allowing researchers to literally peer into the mind as it performs everyday tasks like reading or viewing images. Gradually the need has emerged for fMRI analysis techniques that model activity occurring at numerous locations throughout the brain simultaneously, and make predictions about what a person is doing or thinking solely from his or her brain activity, or ”mind read. ” Machine learning techniques can accomplish both these goals, and thus have become a popular modeling choice; however, most standard machine learning algorithms were designed for problems in which there are relatively few candidate predictor variables, and the modeling objective is to make accurate predictions. In fMRI data, the number of predictor variables can be very large, while the likely number of relevant predictors may be quite small. Furthermore, machine learning algorithms are increasingly being employed in the natural sciences, and while accurate predictions can serve to validate scientific models, the end goal of such modeling is