ATR Computational Neuroscience Laboratories, Kyoto 619-0288, Japan.
Science. 2013 May 3;340(6132):639-42. doi: 10.1126/science.1234330. Epub 2013 Apr 4.
Visual imagery during sleep has long been a topic of persistent speculation, but its private nature has hampered objective analysis. Here we present a neural decoding approach in which machine-learning models predict the contents of visual imagery during the sleep-onset period, given measured brain activity, by discovering links between human functional magnetic resonance imaging patterns and verbal reports with the assistance of lexical and image databases. Decoding models trained on stimulus-induced brain activity in visual cortical areas showed accurate classification, detection, and identification of contents. Our findings demonstrate that specific visual experience during sleep is represented by brain activity patterns shared by stimulus perception, providing a means to uncover subjective contents of dreaming using objective neural measurement.
睡眠中的视觉意象一直是人们持续推测的话题，但由于其隐私性质，客观分析受到了阻碍。在这里，我们提出了一种神经解码方法，该方法通过借助词汇和图像数据库，利用人类功能磁共振成像模式和口头报告之间的联系，使用机器学习模型根据测量的大脑活动，预测睡眠起始期间视觉意象的内容。在视觉皮层区域的刺激诱导脑活动上训练的解码模型表现出了准确的分类、检测和内容识别。我们的研究结果表明，睡眠期间特定的视觉体验是由与刺激感知共享的大脑活动模式来表示的，这为使用客观神经测量来揭示梦境的主观内容提供了一种手段。
