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But compiling an answer to a specific question from this impressive number of results is a daunting task.

Finding consistent trends in the knowledge acquired across these studies is crucial, as individual studies by themselves seldom have enough statistical power to establish fully trustworthy results ( Button et al., 2013 Poldrack et al., 2017). IntroductionĮach year, thousands of brain-imaging studies explore the links between brain and behavior: more than 6000 publications a year contain the term ‘neuroimaging’ on PubMed. The resulting meta-analytic tool,, can ground hypothesis generation and data-analysis priors on a comprehensive view of published findings on the brain. We capture the relationships and neural correlates of 7547 neuroscience terms across 13 459 neuroimaging publications. This approach handles text of arbitrary length and terms that are too rare for standard meta-analysis. Our multivariate model predicts the spatial distribution of neurological observations, given text describing an experiment, cognitive process, or disease. We propose a new paradigm, focusing on prediction rather than inference.

Thus, large-scale meta-analyses only tackle single terms that occur frequently. Existing meta-analysis methods perform statistical tests on sets of publications associated with a particular concept.

The variety of human neuroscience concepts and terminology poses a fundamental challenge to relating brain imaging results across the scientific literature. Reaching a global view of brain organization requires assembling evidence on widely different mental processes and mechanisms.