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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 publication

DOI

10.1038/nmeth.1635

Type

Journal article

Journal

Nature methods

Publication Date

26/06/2011

Volume

8

Pages

665 - 670

Addresses

Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, Colorado, USA. tal.yarkoni@colorado.edu

Keywords

Brain, Humans, Magnetic Resonance Imaging, Brain Mapping, Natural Language Processing, Internet, Software, Periodicals as Topic, Data Mining