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

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...

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Published inHuman brain mapping Vol. 41; no. 14; pp. 3839 - 3854
Main Authors Haugg, Amelie, Sladky, Ronald, Skouras, Stavros, McDonald, Amalia, Craddock, Cameron, Kirschner, Matthias, Herdener, Marcus, Koush, Yury, Papoutsi, Marina, Keynan, Jackob N., Hendler, Talma, Cohen Kadosh, Kathrin, Zich, Catharina, MacInnes, Jeff, Adcock, R. Alison, Dickerson, Kathryn, Chen, Nan‐Kuei, Young, Kymberly, Bodurka, Jerzy, Yao, Shuxia, Becker, Benjamin, Auer, Tibor, Schweizer, Renate, Pamplona, Gustavo, Emmert, Kirsten, Haller, Sven, Van De Ville, Dimitri, Blefari, Maria‐Laura, Kim, Dong‐Youl, Lee, Jong‐Hwan, Marins, Theo, Fukuda, Megumi, Sorger, Bettina, Kamp, Tabea, Liew, Sook‐Lei, Veit, Ralf, Spetter, Maartje, Weiskopf, Nikolaus, Scharnowski, Frank
Format Journal Article
LanguageEnglish
Published Hoboken, USA John Wiley & Sons, Inc 01.10.2020
Subjects
Adult
Biofeedback
Biofeedback training
Brain
Brain - diagnostic imaging
Brain - physiology
Brain Mapping
Electroencephalography
Feedback
fMRI
Functional magnetic resonance imaging
functional neuroimaging
Humans
Laws, regulations and rules
Learning
Magnetic Resonance Imaging
Meta-analysis
neurofeedback
Neurofeedback - physiology
Observational learning
Patients
Practice, Psychological
Prognosis
real-time fMRI
Success
meta-analysis
real-time fMRI
fMRI
learning
functional neuroimaging
neurofeedback
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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. Many real‐time fMRI neurofeedback studies report large inter‐individual differences in learning success, but the factors that cause this vast variability between participants remain unknown. Here, we used a meta‐analytic approach including data from 24 different neurofeedback studies with a total of 401 participants to determine whether levels of activity in target brain regions during pretraining functional localizer or no‐feedback runs could predict neurofeedback learning success. We were not able to identify common brain‐based success predictors across our diverse cohort of studies.
AbstractList 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.
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. Many real‐time fMRI neurofeedback studies report large inter‐individual differences in learning success, but the factors that cause this vast variability between participants remain unknown. Here, we used a meta‐analytic approach including data from 24 different neurofeedback studies with a total of 401 participants to determine whether levels of activity in target brain regions during pretraining functional localizer or no‐feedback runs could predict neurofeedback learning success. We were not able to identify common brain‐based success predictors across our diverse cohort of studies.
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.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.
Audience Academic
Author Hendler, Talma
Bodurka, Jerzy
Sorger, Bettina
Pamplona, Gustavo
Blefari, Maria‐Laura
Haugg, Amelie
Lee, Jong‐Hwan
Fukuda, Megumi
Papoutsi, Marina
Cohen Kadosh, Kathrin
Weiskopf, Nikolaus
MacInnes, Jeff
Dickerson, Kathryn
Sladky, Ronald
Kamp, Tabea
Chen, Nan‐Kuei
Kirschner, Matthias
Herdener, Marcus
Auer, Tibor
Kim, Dong‐Youl
Scharnowski, Frank
Liew, Sook‐Lei
Marins, Theo
Craddock, Cameron
Koush, Yury
Adcock, R. Alison
Schweizer, Renate
Veit, Ralf
Young, Kymberly
Spetter, Maartje
Zich, Catharina
Keynan, Jackob N.
Van De Ville, Dimitri
Haller, Sven
McDonald, Amalia
Becker, Benjamin
Skouras, Stavros
Yao, Shuxia
Emmert, Kirsten
AuthorAffiliation 27 Department Cognitive Neuroscience, Faculty of Psychology and Neuroscience Maastricht University Maastricht The Netherlands
3 Department of Biological and Medical Psychology University of Bergen Bergen Norway
14 Department of Biomedical Engineering University of Arizona Tucson Arizona
12 Institute for Learning and Brain Sciences University of Washington Seattle Washington
1 Psychiatric University Hospital Zurich University of Zurich Zürich Switzerland
7 Magnetic Resonance Research Center, Department of Radiology & Biomedical Imaging Yale University New Haven Connecticut
28 Division of Occupational Science and Occupational Therapy University of Southern California Los Angeles California
16 Laureate Institute for Brain Research Tulsa Oklahoma
6 McConnell Brain Imaging Centre Montréal Neurological Institute, McGill University Montreal Canada
18 Functional Imaging Laboratory German Primate Center Göttingen Germany
2 Faculty of Psychology University of Vienna Vienna Austria
22 Center for Neuroprosth
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Issue 14
Keywords meta-analysis
real-time fMRI
fMRI
learning
functional neuroimaging
neurofeedback
Language English
License Attribution
2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Foundation for Research in Science and the Humanities at the University of Zurich, Grant/Award Number: STWF‐17‐012; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Grant/Award Numbers: 32003B_166566, BSSG10_155915, 100014_178841; Forschungskredit of the University of Zurich, Grant/Award Number: FK‐18‐030
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Funding information Foundation for Research in Science and the Humanities at the University of Zurich, Grant/Award Number: STWF‐17‐012; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Grant/Award Numbers: 32003B_166566, BSSG10_155915, 100014_178841; Forschungskredit of the University of Zurich, Grant/Award Number: FK‐18‐030
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SecondaryResourceType review_article
Snippet Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic,...
SourceID swepub
pubmedcentral
proquest
gale
pubmed
crossref
wiley
SourceType Open Access Repository
Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 3839
SubjectTerms Adult
Biofeedback
Biofeedback training
Brain
Brain - diagnostic imaging
Brain - physiology
Brain Mapping
Electroencephalography
Feedback
fMRI
Functional magnetic resonance imaging
functional neuroimaging
Humans
Laws, regulations and rules
Learning
Magnetic Resonance Imaging
Meta-analysis
neurofeedback
Neurofeedback - physiology
Observational learning
Patients
Practice, Psychological
Prognosis
real-time fMRI
Success
Title Can we predict real‐time fMRI neurofeedback learning success from pretraining brain activity?
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fhbm.25089
https://www.ncbi.nlm.nih.gov/pubmed/32729652
https://www.proquest.com/docview/2439596766
https://www.proquest.com/docview/2429056989
https://pubmed.ncbi.nlm.nih.gov/PMC7469782
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417506
Volume 41
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