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Large-scale automated synthesis of human functional neuroimaging data

Nature Methods volume 8, pages 665670 (2011) | Download Citation

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.

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Acknowledgements

We thank T. Braver (Washington University), J. Gray (Yale University) and K. Ochsner (Columbia University) for data; E. Reid for help with validation analyses; members of the Wager lab for manually coding the pain database; members of the Neuroimaging and Data Access Group (http://nidag.org/), and particularly R. Mar, for suggestions; and R. Bilder, R. Raizada and J. Andrews-Hanna for comments on a draft of this paper. This work was supported by awards from US National Institute of Nursing Research (F32NR012081 to T.Y.), National Institute of Mental Health (R01MH082795 to R.A.P. and R01MH076136 to T.D.W.), US National Institutes of Health (R01MH60974 to D.C.V.E.) and National Institute on Drug Abuse (R01DA027794 and 1RC1DA028608 to T.D.W.).

Author information

Affiliations

  1. Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, Colorado, USA.

    • Tal Yarkoni
    •  & Tor D Wager
  2. Imaging Research Center, University of Texas at Austin, Austin, Texas, USA.

    • Russell A Poldrack
  3. Department of Psychology, University of Texas at Austin, Austin, Texas, USA.

    • Russell A Poldrack
  4. Department of Neurobiology, University of Texas at Austin, Austin, Texas, USA.

    • Russell A Poldrack
  5. Department of Statistics, University of Warwick, Coventry, UK.

    • Thomas E Nichols
  6. Warwick Manufacturing Group, University of Warwick, Coventry, UK.

    • Thomas E Nichols
  7. Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis, Missouri, USA.

    • David C Van Essen

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Contributions

T.Y. conceived the project and carried out most of the software implementation, data analysis and writing. R.A.P. provided data and performed analyses. T.E.N. provided statistical advice, reviewed all statistical procedures and contributed to the implementation of the naive Bayes classifier. D.C.V.E. provided data, contributed to automated data extraction and coordinated data validation. T.D.W. conceived the classification analyses, wrote part of the software, provided data and suggested and performed analyses. All authors contributed to writing and editing the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Tal Yarkoni.

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DOI

https://doi.org/10.1038/nmeth.1635

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