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Haugg A, Sladky R, Skouras S, et al. Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?. Hum Brain Mapp. 2020;41(14):3839-3854doi: 10.1002/hbm.25089.
Haugg, A., Sladky, R., Skouras, S., McDonald, A., Craddock, C., Kirschner, M., Herdener, M., Koush, Y., Papoutsi, M., Keynan, J. N., Hendler, T., Cohen Kadosh, K., Zich, C., MacInnes, J., Adcock, R. A., Dickerson, K., Chen, N. K., Young, K., Bodurka, J., Yao, S., Becker, B., Auer, T., Schweizer, R., Pamplona, G., Emmert, K., Haller, S., Van De Ville, D., Blefari, M. L., Kim, D. Y., Lee, J. H., Marins, T., Fukuda, M., Sorger, B., Kamp, T., Liew, S. L., Veit, R., Spetter, M., Weiskopf, N., & Scharnowski, F. (2020). Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?. Human brain mapping, 41(14), 3839-3854. https://doi.org/10.1002/hbm.25089
Haugg, Amelie, et al. "Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?." Human brain mapping vol. 41,14 (2020): 3839-3854. doi: https://doi.org/10.1002/hbm.25089
Haugg A, Sladky R, Skouras S, McDonald A, Craddock C, Kirschner M, Herdener M, Koush Y, Papoutsi M, Keynan JN, Hendler T, Cohen Kadosh K, Zich C, MacInnes J, Adcock RA, Dickerson K, Chen NK, Young K, Bodurka J, Yao S, Becker B, Auer T, Schweizer R, Pamplona G, Emmert K, Haller S, Van De Ville D, Blefari ML, Kim DY, Lee JH, Marins T, Fukuda M, Sorger B, Kamp T, Liew SL, Veit R, Spetter M, Weiskopf N, Scharnowski F. Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?. Hum Brain Mapp. 2020 Oct 01;41(14):3839-3854. doi: 10.1002/hbm.25089. Epub 2020 Jul 30. PMID: 32729652; PMCID: PMC7469782.
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Hum Brain Mapp. 2020 Oct 01;41(14):3839-3854. doi: 10.1002/hbm.25089. Epub 2020 Jul 30.

Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?.

Human brain mapping

Amelie Haugg, Ronald Sladky, Stavros Skouras, Amalia McDonald, Cameron Craddock, Matthias Kirschner, Marcus Herdener, Yury Koush, Marina Papoutsi, Jackob N Keynan, Talma Hendler, Kathrin Cohen Kadosh, Catharina Zich, Jeff MacInnes, R Alison Adcock, Kathryn Dickerson, Nan-Kuei Chen, Kymberly Young, Jerzy Bodurka, Shuxia Yao, Benjamin Becker, Tibor Auer, Renate Schweizer, Gustavo Pamplona, Kirsten Emmert, Sven Haller, Dimitri Van De Ville, Maria-Laura Blefari, Dong-Youl Kim, Jong-Hwan Lee, Theo Marins, Megumi Fukuda, Bettina Sorger, Tabea Kamp, Sook-Lei Liew, Ralf Veit, Maartje Spetter, Nikolaus Weiskopf, Frank Scharnowski

Affiliations

  1. Psychiatric University Hospital Zurich, University of Zurich, Zürich, Switzerland.
  2. Faculty of Psychology, University of Vienna, Vienna, Austria.
  3. Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.
  4. Department of Psychology, University of Virginia, Charlottesville, Virginia.
  5. Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, Austin, Texas.
  6. McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montreal, Canada.
  7. Magnetic Resonance Research Center, Department of Radiology & Biomedical Imaging, Yale University, New Haven, Connecticut.
  8. UCL Huntington's Disease Centre, Institute of Neurology, University College London, London, England.
  9. Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Tel Aviv, Israel.
  10. School of Psychology, University of Surrey, Guildford, England.
  11. Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, England.
  12. Institute for Learning and Brain Sciences, University of Washington, Seattle, Washington.
  13. Department of Psychiatry and Behavioral Sciences, Duke University, Durham, North Carolina.
  14. Department of Biomedical Engineering, University of Arizona, Tucson, Arizona.
  15. Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.
  16. Laureate Institute for Brain Research, Tulsa, Oklahoma.
  17. Clinical Hospital of Chengdu the Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
  18. Functional Imaging Laboratory, German Primate Center, Göttingen, Germany.
  19. Hôpital and Ophtalmique Jules Gonin, University of Lausanne, Lausanne, Switzerland.
  20. Department of Neurology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany.
  21. Radiology-Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
  22. Center for Neuroprosthetics, Ecole Polytechnique Féderale de Lausanne, Lausanne, Switzerland.
  23. Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
  24. Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea.
  25. D'Or Institute for Research and Education (IDOR), Rio de Janeiro, Brazil.
  26. School of Fundamental Science and Engineering, Waseda University, Tokyo, Japan.
  27. Department Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.
  28. Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, California.
  29. Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich, University of Tübingen, Tübingen, Germany.
  30. School of Psychology, University of Birmingham, Birmingham, England.
  31. Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.

PMID: 32729652 PMCID: PMC7469782 DOI: 10.1002/hbm.25089

Abstract

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

Keywords: fMRI; functional neuroimaging; learning; meta-analysis; neurofeedback; real-time fMRI

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