Large-scale automated synthesis of human functional neuroimaging data

• Published in: Nature methods Vol. 8; no. 8; pp. 665 - 670
• Main Authors: Yarkoni, Tal, Poldrack, Russell A, Nichols, Thomas E, Van Essen, David C, Wager, Tor D
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.08.2011; Nature Publishing Group
• Subjects: 631/1647/245; 631/1647/48; 631/378; Analysis; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Brain; Brain - physiology; Brain Mapping - methods; Cognition & reasoning; Data mining; Data Mining - methods; Humans; Internet; Life Sciences; Magnetic Resonance Imaging - methods; Medical imaging; Medical technology; Natural Language Processing; Neuroimaging; Neurology; Periodicals as Topic; Proteomics; Software; Technology application; United States; United Kingdom
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/nmeth.1635

A framework and web interface for the large-scale and automated synthesis of human neuroimaging data extracted from the literature is presented. It is used to generate a large database of mappings between neural and cognitive states and to address long-standing inferential problems in the neuroimaging literature. 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.