Large-scale automated synthesis of human functional neuroimaging data

Tal Yarkoni, Russell A. Poldrack, Thomas E. Nichols, David C. Van Essen, Tor D. Wager

Research output: Contribution to journalArticlepeer-review

1932 Scopus citations


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.

Original languageEnglish
Pages (from-to)665-670
Number of pages6
JournalNature Methods
Issue number8
StatePublished - Aug 2011


Dive into the research topics of 'Large-scale automated synthesis of human functional neuroimaging data'. Together they form a unique fingerprint.

Cite this