Large-scale automated synthesis of human functional neuroimaging data.

Large-scale automated synthesis of human functional neuroimaging data.
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DOI:
10.1038/nmeth.1635
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发表时间:
2011-06-26
期刊:
影响因子:
48
通讯作者:
Wager, Tor D.
Wager, Tor D.
中科院分区:
生物学1区
文献类型:
--
作者:
Yarkoni, Tal;Poldrack, Russell A.;Nichols, Thomas E.;Van Essen, David C.;Wager, Tor D.

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人类神经影像学文献的爆炸性增长导致了对人类大脑功能的理解的重大进展,但也使得神经影像学发现的聚集和合成变得越来越困难。在这里,我们描述并验证了一个自动化的大脑映射框架,该框架使用文本挖掘,元分析和机器学习技术来生成神经和认知状态之间映射的大型数据库。我们证明了我们的方法能够自动进行大规模,高质量的神经成像荟萃分析,解决长期存在的推理问题,在神经成像文献,并支持准确的“解码”广泛的认知状态从整个研究和个人的大脑活动。总的来说,我们的研究结果验证了一个强大的生成框架,用于以前所未有的规模合成人类神经成像数据。
The explosive growth of the human neuroimaging literature has led to major advances in understanding of human brain function, but has also made aggregation and synthesis of neuroimaging findings increasingly difficult. 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 demonstrate the capacity of our approach 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 validate a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.
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