What information is represented in distributed brain circuit interactions is unknown due to the lack of multivariate methods for decoding the representational content of interregional neural communication. Here we present Multi-Connection Pattern Analysis (MCPA), which probes 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 as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. 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 in realistic circumstances. Applying MCPA to fMRI data shows that interactions between visual cortex regions are sensitive to information that distinguishes individual natural images, suggesting that image individuation occurs through interactive computation across the visual processing network. MCPA-based representational similarity analyses (RSA) are used to test hypotheses regarding information transformation between regions of the visual processing network. These results support models of error coding in interactions among regions of the network. Further RSA analyses relate the non-linear information transformation operations between layers of a computational model (HMAX) of visual processing to the information transformation between regions of the visual processing network. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions.