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

Haugg, A., Renz, F., Nicholson, A., Lor, C., Götzendorfer, S., Sladky, R., et al. (2021). Predictors of real-time fMRI neurofeedback performance and improvement: A machine learning mega-analysis. NeuroImage, 237: 118207, pp. 1-11. doi:10.1016/j.neuroimage.2021.118207.

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Haugg, A, Author
Renz, FM, Author
Nicholson, AA, Author
Lor, C, Author
Götzendorfer, SJ, Author
Sladky, R, Author
Skouras, S, Author
McDonald, A, Author
Craddock, C, Author
Hellrung, L, Author
Kirschner, M, Author
Herdener, M, Author              
Koush, Y, Author
Papoutsi, M, Author
Keynan, J, Author
Hendler, T, Author
Kadosh, KC, Author
Zich, C, Author
Kohl, SH, Author
Hallschmid, M, Author
MacInnes, J, AuthorHadcock, RA, AuthorDickerson, K, AuthorChen, N-K, AuthorYoung, K, AuthorBodurka, J, AuthorMarxen, M, AuthorYao, S, AuthorBecker, B, AuthorAuer, T, AuthorSchweizer, R, AuthorPamplona, G, AuthorLanius, RA, AuthorEmmert, K, AuthorHaller, S, AuthorVan De Ville, D, AuthorKim, DY, AuthorLee, J-H, AuthorMarins, T, AuthorMegumi, F, AuthorSorger, B, AuthorKamp, T, AuthorLiew, S-L, AuthorVeit, R1, 2, Author              Spetter, M, AuthorWeiskopf, N, AuthorScharnowski, F, AuthorSteyrl, D, Author more..
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              


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 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.


 Dates: 2021-08
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
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Title: NeuroImage
Source Genre: Journal
Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 237 Sequence Number: 118207 Start / End Page: 1 - 11 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166