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Perceptual Learning Incepted by Decoded fMRI Neurofeedback Without Stimulus Presentation

Science
9 Dec 2011
Vol 334, Issue 6061
pp. 1413-1415

Abstract

It is controversial whether the adult primate early visual cortex is sufficiently plastic to cause visual perceptual learning (VPL). The controversy occurs partially because most VPL studies have examined correlations between behavioral and neural activity changes rather than cause-and-effect relationships. With an online-feedback method that uses decoded functional magnetic resonance imaging (fMRI) signals, we induced activity patterns only in early visual cortex corresponding to an orientation without stimulus presentation or participants’ awareness of what was to be learned. The induced activation caused VPL specific to the orientation. These results suggest that early visual areas are so plastic that mere inductions of activity patterns are sufficient to cause VPL. This technique can induce plasticity in a highly selective manner, potentially leading to powerful training and rehabilitative protocols.

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References and Notes

1
Schoups A., Vogels R., Qian N., Orban G., Practising orientation identification improves orientation coding in V1 neurons. Nature 412, 549 (2001).
2
Yotsumoto Y., Watanabe T., Sasaki Y., Different dynamics of performance and brain activation in the time course of perceptual learning. Neuron 57, 827 (2008).
3
Hua T., et al.., Perceptual learning improves contrast sensitivity of V1 neurons in cats. Curr. Biol. 20, 887 (2010).
4
Censor N., Bonneh Y., Arieli A., Sagi D., Early-vision brain responses which predict human visual segmentation and learning. J. Vision 9, 1 (2009).
5
Karni A., Sagi D., The time course of learning a visual skill. Nature 365, 250 (1993).
6
Law C. T., Gold J. I., Neural correlates of perceptual learning in a sensory-motor, but not a sensory, cortical area. Nat. Neurosci. 11, 505 (2008).
7
Yang T., Maunsell J. H., The effect of perceptual learning on neuronal responses in monkey visual area V4. J. Neurosci. 24, 1617 (2004).
8
Lewis C. M., Baldassarre A., Committeri G., Romani G. L., Corbetta M., Learning sculpts the spontaneous activity of the resting human brain. Proc. Natl. Acad. Sci. U.S.A. 106, 17558 (2009).
9
Yamashita O., Sato M. A., Yoshioka T., Tong F., Kamitani Y., Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns. Neuroimage 42, 1414 (2008).
10
Bray S., Shimojo S., O’Doherty J. P., Direct instrumental conditioning of neural activity using functional magnetic resonance imaging-derived reward feedback. J. Neurosci. 27, 7498 (2007).
11
Caria A., et al.., Regulation of anterior insular cortex activity using real-time fMRI. Neuroimage 35, 1238 (2007).
12
deCharms R. C., et al.., Learned regulation of spatially localized brain activation using real-time fMRI. Neuroimage 21, 436 (2004).
13
Weiskopf N., et al.., Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): Methodology and exemplary data. Neuroimage 19, 577 (2003).
14
Toda A., Imamizu H., Kawato M., Sato M. A., Reconstruction of two-dimensional movement trajectories from selected magnetoencephalography cortical currents by combined sparse Bayesian methods. Neuroimage 54, 892 (2011).
15
Huxlin K. R., et al.., Perceptual relearning of complex visual motion after V1 damage in humans. J. Neurosci. 29, 3981 (2009).
16
Corthout E., Uttl B., Walsh V., Hallet M., Cowey A., Plasticity revealed by transcranial magnetic stimulation of early visual cortex. Neuroreport 11, 1565 (2000).
17
Giovannelli F., et al.., Involvement of the parietal cortex in perceptual learning (Eureka effect): An interference approach using rTMS. Neuropsychologia 48, 1807 (2010).
18
Dinse H. R., Ragert P., Pleger B., Schwenkreis P., Tegenthoff M., Pharmacological modulation of perceptual learning and associated cortical reorganization. Science 301, 91 (2003).
19
Miyashita Y., Cognitive memory: Cellular and network machineries and their top-down control. Science 306, 435 (2004).
20
Kawato M., From ‘understanding the brain by creating the brain’ towards manipulative neuroscience. Philos. Trans. R. Soc. London Ser. B 363, 2201 (2008).
21
Seitz A. R., Kim D., Watanabe T., Rewards evoke learning of unconsciously processed visual stimuli in adult humans. Neuron 61, 700 (2009).
22
Engel S. A., Glover G. H., Wandell B. A., Retinotopic organization in human visual cortex and the spatial precision of functional MRI. Cereb. Cortex 7, 181 (1997).
23
Fize D., et al.., The retinotopic organization of primate dorsal V4 and surrounding areas: A functional magnetic resonance imaging study in awake monkeys. J. Neurosci. 23, 7395 (2003).
24
Yotsumoto Y., et al.., Location-specific cortical activation changes during sleep after training for perceptual learning. Curr. Biol. 19, 1278 (2009).
25
Kamitani Y., Tong F., Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8, 679 (2005).
26
Nishida S., Sasaki Y., Murakami I., Watanabe T., Tootell R. B., Neuroimaging of direction-selective mechanisms for second-order motion. J. Neurophysiol. 90, 3242 (2003).
27
Miyawaki Y., et al.., Visual image reconstruction from human brain activity using a combination of multiscale local image decoders. Neuron 60, 915 (2008).
28
Yamashita O., Sato M. A., Yoshioka T., Tong F., Kamitani Y., Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns. Neuroimage 42, 1414 (2008).
29
Brainard D. H., The psychophysics toolbox. Spat. Vision 10, 433 (1997).
30
Fischl B., et al.., Automatically parcellating the human cerebral cortex. Cereb. Cortex 14, 11 (2004).
31
Toda A., Imamizu H., Kawato M., Sato M. A., Reconstruction of two-dimensional movement trajectories from selected magnetoencephalography cortical currents by combined sparse Bayesian methods. Neuroimage 54, 892 (2011).

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Information & Authors

Information

Published In

Science
Volume 334 | Issue 6061
9 December 2011

Submission history

Received: 1 August 2011
Accepted: 26 October 2011
Published in print: 9 December 2011

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Acknowledgments

This work was conducted in “Brain Machine Interface Development” under the Strategic Research Program for Brain Sciences by the Ministry of Education, Culture, Sports, Science and Technology of Japan. T.W. was partially supported by NIH grants R01 AG031941 and R01 EY015980 and Y.S. by grants R01 MH091801 and NSF 0964776. We thank J. Dobres, M. Fukuda, G. Ganesh, H. Imamizu, A. R. Seitz, and K. Tanaka for their comments on a draft of this manuscript and M. Fukuda, Y. Furukawa, S. Hirose, M. Sato, and ATR BAIC for technical assistances.

Authors

Affiliations

Kazuhisa Shibata
Advanced Telecommunications Research Institute International Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan.
Present address: Department of Psychology, Boston University, 64 Cummington Street, Boston, MA 02215, USA.
Takeo Watanabe [email protected]
Advanced Telecommunications Research Institute International Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan.
Present address: Department of Psychology, Boston University, 64 Cummington Street, Boston, MA 02215, USA.
Yuka Sasaki
Advanced Telecommunications Research Institute International Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan.
Present address: Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, 149 Thirteenth Street, Charlestown, MA 02129, USA; and Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.
Mitsuo Kawato
Advanced Telecommunications Research Institute International Computational Neuroscience Laboratories, 2-2-2 Hikaridai, Keihanna Science City, Kyoto 619-0288, Japan.

Notes

To whom correspondence should be addressed. E-mail: [email protected]

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