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

Haugg, A., Sladky, R., Skouras, S., McDonald, A., Craddock, C., Kirschner, M., et al. (2020). Can we predict real‐time fMRI neurofeedback learning success from pretraining brain activity? Human Brain Mapping, 41(14), 3839-3854. doi:10.1002/hbm.25089.

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 Creators:
Haugg, Amelie, Author
Sladky, Ronald, Author
Skouras, Stavros, Author
McDonald, Amalia, Author
Craddock, Cameron, Author
Kirschner, Matthias, Author
Herdener, Marcus, Author
Koush, Yury, Author
Papoutsi, Marina, Author
Keynan, Jackob N., Author
Hendler, Talma, Author
Cohen Kadosh, Kathrin, Author
Zich, Catharina, Author
MacInnes, Jeff, Author
Adcock, R. Alison, Author
Dickerson, Kathryn, Author
Chen, Nan‐Kuei, Author
Young, Kymberly, Author
Bodurka, Jerzy, Author
Yao, Shuxia, Author
Becker, Benjamin, AuthorAuer, Tibor, AuthorSchweizer, Renate, AuthorPamplona, Gustavo, AuthorEmmert, Kirsten, AuthorHaller, Sven, AuthorVan De Ville, Dimitri, AuthorBlefari, Maria‐Laura, AuthorKim, Dong‐Youl, AuthorLee, Jong‐Hwan, AuthorMarins, Theo, AuthorFukuda, Megumi, AuthorSorger, Bettina, AuthorKamp, Tabea, AuthorLiew, Sook‐Lei, AuthorVeit, Ralf, AuthorSpetter, Maartje, AuthorWeiskopf, Nikolaus1, Author           Scharnowski, Frank, Author more..
Affiliations:
1Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              

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Free keywords: fMRI; Functional neuroimaging; Learning; Meta-analysis; Neurofeedback; Real-time fMRI
 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.

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Language(s): eng - English
 Dates: 2020-05-182020-04-062020-05-262020-07-302020-10-01
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1002/hbm.25089
Other: epub 2020
PMID: 32729652
 Degree: -

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Project name : -
Grant ID : STWF‐17‐012
Funding program : -
Funding organization : Foundation for Research in Science and the Humanities at the University of Zurich
Project name : -
Grant ID : 32003B_166566; BSSG10_155915; 100014_178841
Funding program : -
Funding organization : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (SNF)
Project name : -
Grant ID : FK‐18‐030
Funding program : -
Funding organization : Forschungskredit of the University of Zurich

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Title: Human Brain Mapping
Source Genre: Journal
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Publ. Info: New York : Wiley-Liss
Pages: - Volume / Issue: 41 (14) Sequence Number: - Start / End Page: 3839 - 3854 Identifier: ISSN: 1065-9471
CoNE: https://pure.mpg.de/cone/journals/resource/954925601686