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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Vydáno v:	NeuroImage Ročník 237; s. 118207
Hlavní autoři:	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
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier Inc 15.08.2021 Elsevier BV Elsevier Limited Elsevier
Témata:	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
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Shrnutí:	•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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1053-8119 1095-9572 1095-9572
DOI:	10.1016/j.neuroimage.2021.118207