神经影像学
计算机科学
认知
神经功能成像
人脑
人工智能
比例(比率)
神经科学
心理学
量子力学
物理
作者
Tal Yarkoni,Russell A. Poldrack,Thomas E. Nichols,David C. Van Essen,Tor D. Wager
出处
期刊:Nature Methods
[Springer Nature]
日期:2011-06-26
卷期号:8 (8): 665-670
被引量:2922
摘要
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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