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 7547 neuroscience terms across 13459 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.
All the data that we can share without violating copyright (including word counts of publications) have been shared on https://github.com/neuroquery/ alongside with the analysis scripts. Everything is readily downloadable without any authorization or login required.
- Jérôme Dockès
- Tal Yarkoni
- Fabian Suchanek
- Bertrand Thirion
- Bertrand Thirion
- Gael Varoquaux
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Thomas Yeo, National University of Singapore, Singapore
© 2020, Dockès et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.