title: Large-scale automated synthesis of human functional neuroimaging data.
abstract: The rapid growth of the literature on neuroimaging in humans has led to major advances in our understanding of human brain function but has also made it increasingly difficult to aggregate and synthesize neuroimaging findings. Here we describe and validate an automated brain-mapping framework that uses text-mining, meta-analysis and machine-learning techniques to generate a large database of mappings between neural and cognitive states. We show that our approach can be used to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature and support accurate 'decoding' of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results have validated a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.
authors.affiliation: Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, Colorado, USA. tal.yarkoni@colorado.edu
authors.forename: Tal
authors.initials: T
authors.lastname: Yarkoni
authors.forename: Russell A
authors.initials: RA
authors.lastname: Poldrack
authors.forename: Thomas E
authors.initials: TE
authors.lastname: Nichols
authors.forename: David C
authors.initials: DC
authors.lastname: Van Essen
authors.forename: Tor D
authors.initials: TD
authors.lastname: Wager
mesh_terms: D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D006801:Humans; D020407:Internet; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009323:Natural Language Processing; D010506:Periodicals as Topic; D012984:Software