NeuroImage (Aug 2021)
Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis
- Amelie Haugg,
- Fabian M. Renz,
- Andrew A. Nicholson,
- Cindy Lor,
- Sebastian J. Götzendorfer,
- Ronald Sladky,
- Stavros Skouras,
- Amalia McDonald,
- Cameron Craddock,
- Lydia Hellrung,
- Matthias Kirschner,
- Marcus Herdener,
- Yury Koush,
- Marina Papoutsi,
- Jackob Keynan,
- Talma Hendler,
- Kathrin Cohen Kadosh,
- Catharina Zich,
- Simon H. Kohl,
- Manfred Hallschmid,
- Jeff MacInnes,
- R. Alison Adcock,
- Kathryn C. Dickerson,
- Nan-Kuei Chen,
- Kymberly Young,
- Jerzy Bodurka,
- Michael Marxen,
- Shuxia Yao,
- Benjamin Becker,
- Tibor Auer,
- Renate Schweizer,
- Gustavo Pamplona,
- Ruth A. Lanius,
- Kirsten Emmert,
- Sven Haller,
- Dimitri Van De Ville,
- Dong-Youl Kim,
- Jong-Hwan Lee,
- Theo Marins,
- Fukuda Megumi,
- Bettina Sorger,
- Tabea Kamp,
- Sook-Lei Liew,
- Ralf Veit,
- Maartje Spetter,
- Nikolaus Weiskopf,
- Frank Scharnowski,
- David Steyrl
Affiliations
- Amelie Haugg
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland; Faculty of Psychology, University of Vienna, Austria; Corresponding author.
- Fabian M. Renz
- Faculty of Psychology, University of Vienna, Austria
- Andrew A. Nicholson
- Faculty of Psychology, University of Vienna, Austria
- Cindy Lor
- Faculty of Psychology, University of Vienna, Austria
- Sebastian J. Götzendorfer
- Faculty of Psychology, University of Vienna, Austria
- Ronald Sladky
- Faculty of Psychology, University of Vienna, Austria
- Stavros Skouras
- Department of Biological and Medical Psychology, University of Bergen, Norway
- Amalia McDonald
- Department of Psychology, University of Virginia, United States
- Cameron Craddock
- Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, United States
- Lydia Hellrung
- Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Switzerland
- Matthias Kirschner
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland; McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Canada
- Marcus Herdener
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland
- Yury Koush
- Department of Radiology and Biomedical Imaging, Yale University, United States
- Marina Papoutsi
- UCL Huntington's Disease Centre, Institute of Neurology, University College London, United Kingdom; IXICO plc, United Kingdom
- Jackob Keynan
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Israel
- Talma Hendler
- Functional Brain Center, Wohl Institute for Advanced Imaging, Tel-Aviv Sourasky Medical Center, Tel-Aviv University, Israel
- Kathrin Cohen Kadosh
- School of Psychology, University of Surrey, United Kingdom
- Catharina Zich
- Nuffiled Department of Clinical Neurosciences, University of Oxford, United Kingdom
- Simon H. Kohl
- JARA-Institute Molecular Neuroscience and Neuroimaging (INM-11), Jülich Research Centre, Germany
- Manfred Hallschmid
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Germany; German Center for Diabetes Research (DZD), Germany
- Jeff MacInnes
- Institute for Learning and Brain Sciences, University of Washington, United States
- R. Alison Adcock
- Duke Institute for Brain Sciences, Duke University, United States; Department of Psychiatry and Behavioral Sciences, Duke University, United States
- Kathryn C. Dickerson
- Department of Psychiatry and Behavioral Sciences, Duke University, United States
- Nan-Kuei Chen
- Department of Biomedical Engineering, University of Arizona, United States
- Kymberly Young
- Department of Psychiatry, School of Medicine, University of Pittsburgh, United States
- Jerzy Bodurka
- Laureate Institute for Brain Research, United States
- Michael Marxen
- Department of Psychiatry, Technische Universität Dresden, Germany
- Shuxia Yao
- Clinical Hospital of the Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, China
- Benjamin Becker
- Clinical Hospital of the Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, China
- Tibor Auer
- School of Psychology, University of Surrey, United Kingdom
- Renate Schweizer
- Functional Imaging Laboratory, German Primate Center, Germany
- Gustavo Pamplona
- Department of Ophthalmology, University of Lausanne and Fondation Asile des Aveugles, Switzerland
- Ruth A. Lanius
- Department of Psychiatry, University of Western Ontario, Canada
- Kirsten Emmert
- Department of Neurology, University Medical Center Schleswig-Holstein, Kiel University, Germany
- Sven Haller
- Department of Surgical Sciences, Radiology, Uppsala University, Sweden
- Dimitri Van De Ville
- Center for Neuroprosthetics, Ecole polytechnique féderale de Lausanne, Switzerland; Faculty of Medicine, University of Geneva, Switzerland
- Dong-Youl Kim
- Department of Brain and Cognitive Engineering, Korea University, Korea
- Jong-Hwan Lee
- Department of Brain and Cognitive Engineering, Korea University, Korea
- Theo Marins
- D'Or Institute for Research and Education, Brazil
- Fukuda Megumi
- Center for Brain Science, RIKEN, Japan
- Bettina Sorger
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, The Netherlands
- Tabea Kamp
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, The Netherlands
- Sook-Lei Liew
- University of Southern California, United States
- Ralf Veit
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, Germany; Institute for Diabetes Research and Metabolic Diseases of the Helmholtz Center Munich at the University of Tübingen, Germany; German Center for Diabetes Research (DZD), Germany; High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Germany
- Maartje Spetter
- School of Psychology, University of Birmingham, United Kingdom
- Nikolaus Weiskopf
- Max Planck Institute for Human Cognitive and Brain Sciences, Germany
- Frank Scharnowski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland; Faculty of Psychology, University of Vienna, Austria
- David Steyrl
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital, University of Zurich, Switzerland; Faculty of Psychology, University of Vienna, Austria
- Journal volume & issue
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Vol. 237
p. 118207
Abstract
Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments.With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.
