Letter

Fear reduction without fear through reinforcement of neural activity that bypasses conscious exposure

  • Nature Human Behaviour 1, Article number: 0006 (2016)
  • doi:10.1038/s41562-016-0006
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Abstract

Fear conditioning is a fundamentally important and preserved process across species1,2. In humans it is linked to fear-related disorders such as phobias and post-traumatic stress disorder (PTSD)3,4. Fear memories can be reduced by counter-conditioning, in which fear conditioned stimuli (CS+s) are repeatedly reinforced with reward5 or with novel non-threatening stimuli6. However, this procedure involves explicit presentations of CS+s, which is itself aversive before fear is successfully reduced. This aversiveness may be a problem when trying to translate such experimental paradigms into clinical settings7. It also raises the fundamental question as to whether explicit presentations of feared objects is necessary for fear reduction1,8. Although learning without explicit stimulus presentation has been previously demonstrated9,​10,​11,​12, whether fear can be reduced while avoiding explicit exposure to CS+s remains largely unknown. One recently developed approach employs an implicit method to induce learning by reinforcing stimulus-specific neural representations using real-time decoding of multivariate functional magnetic resonance imaging (fMRI) signals13,​14,​15 in the absence of stimulus presentation; that is, pairing rewards with the occurrences of multi-voxel brain activity patterns matching a specific stimulus (decoded fMRI neurofeedback (DecNef)13,15). It has been shown that participants exhibit perceptual learning for a specific visual stimulus feature through DecNef, without being given any strategy for the induction of specific neural representations, and without awareness of the content of reinforced neural representations13. Here we examined whether a similar approach could be applied to counter-conditioning of fear. We show that we can reduce fear towards CS+s by pairing rewards with the activation patterns in visual cortex representing a CS+, while participants remain unaware of the content and purpose of the procedure. This procedure may be an initial step towards novel treatments for fear-related disorders such as phobia and PTSD, via unconscious processing.

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Acknowledgements

We thank K. Nakamura for her help in scheduling and conducting the experiment, N. Hiroe for assistance with equipment, Y. Shimada and A. Nishikido for operating the fMRI scanner, H. Ban for technical advice, and M. Craske, M. Treanor, M. Sun, A. Izquierdo and F. Krasne for their comments on the manuscript. The study was conducted in the ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan). This work was partially supported by ‘Brain Machine Interface Development’ under the Strategic Research Program for Brain Sciences supported by the Japan Agency for Medical Research and Development (AMED), the ATR entrust research contract from the National Institute of Information and Communications Technology, and the US National Institute of Neurological Disorders and Stroke of the National Institutes of Health (grant no. R01NS088628 to H.L.). B.S. is funded by the Wellcome Trust, UK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Author notes

    • Ai Koizumi
    • , Kaoru Amano
    •  & Aurelio Cortese

    These authors contributed equally to this work

Affiliations

  1. Department of Decoded Neurofeedback, ATR Computational Neuroscience Laboratories, 2-2-2, Hikaridai, Seika-cho, Sorakugun, Kyoto, 619-0288, Japan

    • Ai Koizumi
    • , Aurelio Cortese
    • , Kazuhisa Shibata
    •  & Mitsuo Kawato
  2. Department of Psychology, Columbia University, 1190 Amsterdam Avenue 370 Schermerhorn Extension MC:5501, New York 10027, USA

    • Ai Koizumi
    •  & Hakwan Lau
  3. Center for Information and Neural Networks (CiNet), NICT, 1-4 Yamadaoka, Suita City, Osaka, 565-0871, Japan

    • Ai Koizumi
    • , Kaoru Amano
    • , Aurelio Cortese
    • , Wako Yoshida
    • , Ben Seymour
    •  & Mitsuo Kawato
  4. Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma Nara, 630-0192, Japan

    • Aurelio Cortese
    •  & Mitsuo Kawato
  5. Department of Psychology, UCLA, Box 951563, Los Angeles, California 90095-1563, USA

    • Aurelio Cortese
    •  & Hakwan Lau
  6. Department of Psychology, Graduate School of Environmental Studies, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan

    • Kazuhisa Shibata
  7. Department of Neural Computation for Decision-making, ATR Cognitive Mechanisms Laboratories, 2-2-2, Hikaridai, Seika-cho, Sorakugun, Kyoto, 619-0288, Japan

    • Wako Yoshida
    •  & Ben Seymour
  8. Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK.

    • Wako Yoshida
    •  & Ben Seymour
  9. Brain Research Institute, UCLA, Box 951761, Los Angeles, California 90095-1761, USA.

    • Hakwan Lau

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Contributions

A.K., H.L., B.S. and M.K. designed the study while actively discussing with other co-authors, A.K., K.A. and A.C. implemented the experiment, A.K. conducted the experiment, A.K., K.S., A.C., H.L. and M.K. analysed the results with the support of K.A. and W.Y. A.K., B.S., H.L. and M.K. wrote the manuscript.

Competing interests

K.S. and M.K. are the inventors of patents related to the DecNef method used in this study, and the original assignee of the patents is ATR, with which some of the authors are affiliated.

Corresponding authors

Correspondence to Ben Seymour or Mitsuo Kawato or Hakwan Lau.

Supplementary information

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  1. 1.

    Supplementary information

    Supplementary Figures 1–7, Supplementary Methods, Supplementary Table 1, Supplementary References.