  Imaging neuroscience links brain activation maps to behavior and cognition
via correlational studies. Due to the nature of the individual experiments,
based on eliciting neural response from a small number of stimuli, this link is
incomplete, and unidirectional from the causal point of view. To come to
conclusions on the function implied by the activation of brain regions, it is
necessary to combine a wide exploration of the various brain functions and some
inversion of the statistical inference. Here we introduce a methodology for
accumulating knowledge towards a bidirectional link between observed brain
activity and the corresponding function. We rely on a large corpus of imaging
studies and a predictive engine. Technically, the challenges are to find
commonality between the studies without denaturing the richness of the corpus.
The key elements that we contribute are labeling the tasks performed with a
cognitive ontology, and modeling the long tail of rare paradigms in the corpus.
To our knowledge, our approach is the first demonstration of predicting the
cognitive content of completely new brain images. To that end, we propose a
method that predicts the experimental paradigms across different studies.
