Before revision:  In this paper, we pool data from large fMRI repositories to predict psychological conditions from statistical brain maps across different studies and subjects. We leverage advances in deep learning, intermediate representations and multi-task learning to learn universal interpretable low-dimensional representations of brain images, usable for predicting psychological stimuli in all input studies. The method improves decoding performance for 80\% of studies, by permitting cognitive information to flow from every study to the others: it notably gives a strong performance boost when decoding studies of small size.
After revision:  Our approach does not assume any prior knowledge of the commonalities shared by the studies in the corpus; those are inferred during model training. The method uses deep-learning techniques to extract representations - task-optimized networks - that form a set of basis cognitive dimensions relevant to the psychological manipulations. In this sense, it forms a novel kind of functional atlas, optimized to capture mental state across many functional-imaging experiments. As it bridges information on the neural support of mental processes, this representation improves decoding performance for 80\% of studies, by permitting cognitive information to flow from every study to the others: it notably gives a strong performance boost when decoding studies of small size.