A challenging goal in neuroscience is to be able to read out, or decode, mental content from brain activity. Recent functional magnetic resonance imaging (fMRI) studies have decoded orientation1,2, position3 and object category4,5 from activity in visual cortex. However, these studies typically used relatively simple stimuli (for example, gratings) or images drawn from fixed categories (for example, faces, houses), and decoding was based on previous measurements of brain activity evoked by those same stimuli or categories. To overcome these limitations, here we develop a decoding method based on quantitative receptive-field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas. These models describe the tuning of individual voxels for space, orientation and spatial frequency, and are estimated directly from responses evoked by natural images. We show that these receptive-field models make it possible to identify, from a large set of completely novel natural images, which specific image was seen by an observer. Identification is not a mere consequence of the retinotopic organization of visual areas; simpler receptive-field models that describe only spatial tuning yield much poorer identification performance. Our results suggest that it may soon be possible to reconstruct a picture of a person’s visual experience from measurements of brain activity alone.
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Haynes, J. D. & Rees, G. Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nature Neurosci. 8, 686–691 (2005)
Kamitani, Y. & Tong, F. Decoding the visual and subjective contents of the human brain. Nature Neurosci. 8, 679–685 (2005)
Thirion, B. et al. Inverse retinotopy: inferring the visual content of images from brain activation patterns. Neuroimage 33, 1104–1116 (2006)
Cox, D. D. & Savoy, R. L. Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. Neuroimage 19, 261–270 (2003)
Haxby, J. V. et al. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430 (2001)
Haynes, J. D. & Rees, G. Decoding mental states from brain activity in humans. Nature Rev. Neurosci. 7, 523–534 (2006)
Hung, C. P., Kreiman, G., Poggio, T. & DiCarlo, J. J. Fast readout of object identity from macaque inferior temporal cortex. Science 310, 863–866 (2005)
Tsao, D. Y., Freiwald, W. A., Tootell, R. B. & Livingstone, M. S. A cortical region consisting entirely of face-selective cells. Science 311, 670–674 (2006)
Simoncelli, E. P. & Olshausen, B. A. Natural image statistics and neural representation. Annu. Rev. Neurosci. 24, 1193–1216 (2001)
Wu, M. C., David, S. V. & Gallant, J. L. Complete functional characterization of sensory neurons by system identification. Annu. Rev. Neurosci. 29, 477–505 (2006)
Daugman, J. G. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. A 2, 1160–1169 (1985)
Jones, J. P. & Palmer, L. A. An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. J. Neurophysiol. 58, 1233–1258 (1987)
Lee, T. S. Image representation using 2D Gabor wavelets. IEEE Trans. Pattern Anal. 18, 959–971 (1996)
DeYoe, E. A. et al. Mapping striate and extrastriate visual areas in human cerebral cortex. Proc. Natl Acad. Sci. USA 93, 2382–2386 (1996)
Dumoulin, S. O. & Wandell, B. A. Population receptive field estimates in human visual cortex. Neuroimage 39, 647–660 (2008)
Engel, S. A. et al. fMRI of human visual cortex. Nature 369, 525 (1994)
Hansen, K. A., David, S. V. & Gallant, J. L. Parametric reverse correlation reveals spatial linearity of retinotopic human V1 BOLD response. Neuroimage 23, 233–241 (2004)
Sereno, M. I. et al. Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging. Science 268, 889–893 (1995)
Smith, A. T., Singh, K. D., Williams, A. L. & Greenlee, M. W. Estimating receptive field size from fMRI data in human striate and extrastriate visual cortex. Cereb. Cortex 11, 1182–1190 (2001)
Sasaki, Y. et al. The radial bias: a different slant on visual orientation sensitivity in human and nonhuman primates. Neuron 51, 661–670 (2006)
Olman, C. A., Ugurbil, K., Schrater, P. & Kersten, D. BOLD fMRI and psychophysical measurements of contrast response to broadband images. Vision Res. 44, 669–683 (2004)
Singh, K. D., Smith, A. T. & Greenlee, M. W. Spatiotemporal frequency and direction sensitivities of human visual areas measured using fMRI. Neuroimage 12, 550–564 (2000)
Haynes, J. D. & Rees, G. Predicting the stream of consciousness from activity in human visual cortex. Curr. Biol. 15, 1301–1307 (2005)
Heeger, D. J. & Ress, D. What does fMRI tell us about neuronal activity? Nature Rev. Neurosci. 3, 142–151 (2002)
Logothetis, N. K. & Wandell, B. A. Interpreting the BOLD signal. Annu. Rev. Physiol. 66, 735–769 (2004)
Stanley, G. B., Li, F. F. & Dan, Y. Reconstruction of natural scenes from ensemble responses in the lateral geniculate nucleus. J. Neurosci. 19, 8036–8042 (1999)
Haynes, J. D., Lotto, R. B. & Rees, G. Responses of human visual cortex to uniform surfaces. Proc. Natl Acad. Sci. USA 101, 4286–4291 (2004)
Rainer, G., Augath, M., Trinath, T. & Logothetis, N. K. Nonmonotonic noise tuning of BOLD fMRI signal to natural images in the visual cortex of the anesthetized monkey. Curr. Biol. 11, 846–854 (2001)
Salinas, E. & Abbott, L. F. Vector reconstruction from firing rates. J. Comput. Neurosci. 1, 89–107 (1994)
Zhang, K., Ginzburg, I., McNaughton, B. L. & Sejnowski, T. J. Interpreting neuronal population activity by reconstruction: unified framework with application to hippocampal place cells. J. Neurophysiol. 79, 1017–1044 (1998)
This work was supported by a National Defense Science and Engineering Graduate fellowship (K.N.K.), the National Institutes of Health, and University of California, Berkeley intramural funds. We thank B. Inglis for assistance with MRI, K. Hansen for assistance with retinotopic mapping, D. Woods and X. Kang for acquisition of whole-brain anatomical data, and A. Rokem for assistance with scanner operation. We also thank C. Baker, M. D’Esposito, R. Ivry, A. Landau, M. Merolle and F. Theunissen for comments on the manuscript. Finally, we thank S. Nishimoto, R. Redfern, K. Schreiber, B. Willmore and B. Yu for their help in various aspects of this research.
Author Contributions K.N.K. designed and conducted the experiment and was first author on the paper. K.N.K. and T.N. analysed the data. R.J.P. provided mathematical ideas and assistance. J.L.G. provided guidance on all aspects of the project. All authors discussed the results and commented on the manuscript.
This file contains Supplementary Figures 1-11 with Legends, Supplementary Table 1, Supplementary Discussion, Supplementary Methods, and Supplementary Notes with additional references. (PDF 3551 kb)
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Kay, K., Naselaris, T., Prenger, R. et al. Identifying natural images from human brain activity. Nature 452, 352–355 (2008). https://doi.org/10.1038/nature06713
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