It is unknown what information is represented in distributed brain circuit interactions because we lack methods for decoding the representational content of interregional neural communication. Here we present Multi-Connection Pattern Analysis (MCPA), which is designed to probe the nature of the representational space contained in the multivariate functional connectivity pattern between neural populations. MCPA works by learning mappings between the activity patterns of the populations separately for each condition, stimulus, or brain state in training data. These maps are used to predict the activity from one neural population based on the activity from the other population as a factor of the information being processed in test data. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA for decoding distributed information processing across a set of realistic circumstances and show that MCPA is insensitive to local information processing. Furthermore, applying MCPA to human intracranial electrophysiological data demonstrates that the interaction between occipital face area and fusiform face area contains information about individual faces. Representational analysis indicates that the OFA-FFA interaction codes face information differently than either the OFA or FFA individually. These results support the hypothesis that face individuation occurs not in a single region, but through interactive computation and distributed information representation across the face processing network. Thus, MCPA, can be used to assess the information processed the coupled activity of distributed, interacting neural circuits.