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  Predictors of real-time fMRI neurofeedback performance and improvement: A machine learning mega-analysis

Haugg, A., Renz, F. M., Nicholson, A. A., Lor, C., Götzendorfer, S. J., Sladky, R., et al. (2021). Predictors of real-time fMRI neurofeedback performance and improvement: A machine learning mega-analysis. NeuroImage, 237: 118207. doi:10.1016/j.neuroimage.2021.118207.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0008-ADE4-4 Version Permalink: https://hdl.handle.net/21.11116/0000-000D-159A-E
Genre: Journal Article

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 Creators:
Haugg, Amelie, Author
Renz, Fabian M., Author
Nicholson, Andrew A., Author
Lor, Cindy, Author
Götzendorfer, Sebastian J., Author
Sladky, Ronald, Author
Skouras, Stavros, Author
McDonald, Amalia, Author
Craddock, Cameron, Author
Hellrung, Lydia, Author
Kirschner, Matthias, Author
Herdener, Marcus, Author
Koush, Yury, Author
Papoutsi, Marina, Author
Keynan, Jackob, Author
Hendler, Talma, Author
Cohen Kadosh, Kathrin, Author
Zich, Catharina, Author
Kohl, Simon H., Author
Hallschmid, Manfred, Author
MacInnes, Jeff, AuthorAdcock, R. Alison, AuthorDickerson, Kathryn C., AuthorChen, Nan-Kuei, AuthorYoung, Kymberly, AuthorBodurka, Jerzy, AuthorMarxen, Michael, AuthorYao, Shuxia, AuthorBecker, Benjamin, AuthorAuer, Tibor, AuthorSchweizer, Renate, AuthorPamplona, Gustavo, AuthorLanius, Ruth A., AuthorEmmert, Kirsten, AuthorHaller, Sven, AuthorVan De Ville, Dimitri, AuthorKim, Dong-Youl, AuthorLee, Jong-Hwan, AuthorMarins, Theo, AuthorMegumi, Fukuda, AuthorSorger, Bettina, AuthorKamp, Tabea, AuthorLiew, Sook-Lei, AuthorVeit, Ralf, AuthorSpetter, Maartje, AuthorWeiskopf, Nikolaus1, Author           Scharnowski, Frank, AuthorSteyrl, David, Author more..
Affiliations:
1Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              

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Free keywords: Functional MRI; Learning; Machine learning; Mega-analysis; Neurofeedback; Real-time fMRI
 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.

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Language(s): eng - English
 Dates: 2021-05-142020-10-262021-05-242021-05-252021-08-15
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.neuroimage.2021.118207
Other: epub 2021
PMID: 34048901
 Degree: -

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Project name : -
Grant ID : FK‐18‐030
Funding program : -
Funding organization : Forschungskredit of the University of Zurich
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_166,566; BSSG10_155,915; 100,014_178,841
Funding program : -
Funding organization : Swiss National Science Foundation (SNSF)
Project name : -
Grant ID : 602186
Funding program : Seventh Framework Programme
Funding organization : European Union
Project name : -
Grant ID : 794395
Funding program : Horizon 2020
Funding organization : European Union
Project name : -
Grant ID : 178833530; 402170461
Funding program : -
Funding organization : Deutsche Forschungsgemeinschaft (DFG)

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Title: NeuroImage
Source Genre: Journal
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Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 237 Sequence Number: 118207 Start / End Page: - Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166