Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, Colorado, USA.
Nat Methods. 2011 Jun 26;8(8):665-70. doi: 10.1038/nmeth.1635.
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.
神经影像学文献的快速增长推动了我们对人类大脑功能的理解取得重大进展，但也使得汇总和综合神经影像学研究结果变得愈发困难。在此，我们描述并验证了一种自动脑图谱绘制框架，该框架使用文本挖掘、元分析和机器学习技术生成一个大规模的神经与认知状态映射数据库。我们表明，该方法可用于自动进行大规模、高质量的神经影像学元分析，解决神经影像学文献中长期存在的推理问题，并支持从整个研究和个体被试的大脑活动中准确“解码”广泛的认知状态。总的来说，我们的研究结果验证了一种强大且具有生成性的框架，可用于以前所未有的规模对人类神经影像学数据进行综合。
