Neural Decoding of Visual Imagery During Sleep

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Science  03 May 2013:
Vol. 340, Issue 6132, pp. 639-642
DOI: 10.1126/science.1234330

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Reading Dreams

How specific visual dream contents are represented by brain activity is unclear. Machine-learning–based analyses can decode the stimulus- and task-induced brain activity patterns that represent specific visual contents. Horikawa et al. (p. 639, published online 4 April) examined patterns of brain activity during dreaming and compared these to waking responses to visual stimuli. The findings suggest that the visual content of dreams is represented by the same neural substrate as observed during awake perception.


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.

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