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

•First machine learning mega-analysis to investigate predictors of real-time fMRI neurofeedback success.•Inclusion of a pre-training no feedback was associated with higher neurofeedback performance.•Patients were associated with higher neurofeedback performance than healthy individuals.•More data (s...

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Bibliographic Details
Published in:NeuroImage Vol. 237; p. 118207
Main Authors: Haugg, Amelie, Renz, Fabian M., Nicholson, Andrew A., Lor, Cindy, Götzendorfer, Sebastian J., Sladky, Ronald, Skouras, Stavros, McDonald, Amalia, Craddock, Cameron, Hellrung, Lydia, Kirschner, Matthias, Herdener, Marcus, Koush, Yury, Papoutsi, Marina, Keynan, Jackob, Hendler, Talma, Cohen Kadosh, Kathrin, Zich, Catharina, Kohl, Simon H., Hallschmid, Manfred, MacInnes, Jeff, Adcock, R. Alison, Dickerson, Kathryn C., Chen, Nan-Kuei, Young, Kymberly, Bodurka, Jerzy, Marxen, Michael, Yao, Shuxia, Becker, Benjamin, Auer, Tibor, Schweizer, Renate, Pamplona, Gustavo, Lanius, Ruth A., Emmert, Kirsten, Haller, Sven, Van De Ville, Dimitri, Kim, Dong-Youl, Lee, Jong-Hwan, Marins, Theo, Megumi, Fukuda, Sorger, Bettina, Kamp, Tabea, Liew, Sook-Lei, Veit, Ralf, Spetter, Maartje, Weiskopf, Nikolaus, Scharnowski, Frank, Steyrl, David
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Inc 15.08.2021
Elsevier BV
Elsevier Limited
Elsevier
Subjects:
501011 Cognitive psychology
501011 Kognitionspsychologie
Adult
Adult; Functional Neuroimaging; Humans; Machine Learning; Magnetic Resonance Imaging; Neurofeedback; Functional MRI; Learning; Machine learning; Mega-analysis; Real-time fMRI
ATTENTION
Big Data
Biofeedback
BRAIN ACTIVATION
Brain mapping
CORTEX ACTIVITY
efficacy
Electroencephalography
Feedback
Functional magnetic resonance imaging
Functional MRI
Functional Mri ; Learning ; Machine Learning ; Mega-analysis ; Neurofeedback ; Real-time Fmri
Functional Neuroimaging
Humans
Independent study
info:eu-repo/classification/ddc/610
info:eu-repo/classification/ddc/616.0757
Learning
Learning algorithms
Machine Learning
Magnetic Resonance Imaging
Medical imaging
Mega-analysis
memory
Motivation
MOTOR IMAGERY
Neural networks
Neurofeedback
Neuroimaging
Neurosciences. Biological psychiatry. Neuropsychiatry
Questionnaires
RC321-571
Real-time fMRI
reduction
resonance-imaging neurofeedback
self-regulation
Subpopulations
Success
Learning
Mega-analysis
Real-time fMRI
Functional MRI
Neurofeedback
Machine learning
ISSN:1053-8119, 1095-9572, 1095-9572
Online Access:Get full text
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Summary:•First machine learning mega-analysis to investigate predictors of real-time fMRI neurofeedback success.•Inclusion of a pre-training no feedback was associated with higher neurofeedback performance.•Patients were associated with higher neurofeedback performance than healthy individuals.•More data (sharing) in the future will allow for design optimization and a better understanding of neurofeedback learning. 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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ISSN:1053-8119
1095-9572
1095-9572
DOI:10.1016/j.neuroimage.2021.118207