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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Veröffentlicht in:NeuroImage Jg. 237; S. 118207
Hauptverfasser: 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
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier Inc 15.08.2021
Elsevier BV
Elsevier Limited
Elsevier
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ISSN:1053-8119, 1095-9572, 1095-9572
Online-Zugang:Volltext
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