  Reaching a global view of brain organization requires assembling evidence on
widely different mental processes and mechanisms. The variety of human
neuroscience concepts and terminology poses a fundamental challenge to relating
brain imaging results across the scientific literature. Existing meta-analysis
methods perform statistical tests on sets of publications associated with a
particular concept. Thus, large-scale meta-analyses only tackle single terms
that occur frequently. We propose a new paradigm, focusing on prediction rather
than inference. Our multivariate model predicts the spatial distribution of
neurological observations, given text describing an experiment, cognitive
process, or disease. This approach handles text of arbitrary length and terms
that are too rare for standard meta-analysis. We capture the relationships and
neural correlates of 7 547 neuroscience terms across 13 459 neuroimaging
publications. The resulting meta-analytic tool, neuroquery.org, can ground
hypothesis generation and data-analysis priors on a comprehensive view of
published findings on the brain.
