Real-time functional magnetic resonance imaging (rtfMRI) is an emerging approach for studying the functioning of the human brain. Computational challenges combined with high data velocity have to this point restricted rtfMRI analyses to studying regions of the brain independently. However, given that neural processing is accomplished via functional interactions among brain regions, neuroscience could stand to benefit from rtfMRI analyses of full-brain interactions. In this paper, we extend such an offline analysis method, full correlation matrix analysis (FCMA), to enable its use in rtfMRI studies. Specifically, we introduce algorithms capable of processing real-time data for all stages of the FCMA machine learning workflow: incremental feature selection, model updating, and real-time classification. We also present an actor-model based distributed system designed to support FCMA and other rtfMRI analysis methods. Experiments show that our system successfully analyzes a stream of brain volumes and returns neurofeedback with less than 180 ms of lag. Our real-time FCMA implementation provides the same accuracy as an optimized offline FCMA toolbox while running 3.6-6.2x faster.