What information is represented in the interactions between neural populations is unknown due to the lack of multivariate methods for decoding the representational content of neural communication. Here we present Multi-Connection Pattern Analysis (MCPA), which probes the involvement of distributed computational processing and probe the representational structure of neural interactions. MCPA learns mappings between the activity patterns as a factor of the information being processed. These maps are used to predict the multivariate activity pattern from one neural population based on the activity pattern from another population. 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. These results suggest that image individuation occurs through interactive computation across the visual processing network. Thus, MCPA can be used to assess the information processed in the coupled activity of interacting neural circuits.