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- Jérôme Dockès
- Inria, CEA, Université Paris-Saclay, Essonne, France
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- Russell A Poldrack
- Stanford University, Stanford, United States
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- Romain Primet
- Inria, Paris, France
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- Hande Gözükan
- Inria, Paris, France
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- Tal Yarkoni
- University of Texas at Austin, Austin, United States
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- Fabian Suchanek
- Télécom Paris University, Palaiseau, France
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- Bertrand Thirion
- Inria, CEA, Université Paris-Saclay, Essonne, France
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- Gaël Varoquaux
- Inria, CEA, Université Paris-Saclay, Essonne, France
説明
<jats:p>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 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.</jats:p>
収録刊行物
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- eLife
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eLife 9 1-, 2020-03-04
eLife Sciences Publications, Ltd
