"Predictors of real-time fMRI neurofeedback performance and improvement - A ..."

Amelie Haugg et al. (2021)

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DOI: 10.1016/J.NEUROIMAGE.2021.118207

access: open

type: Journal Article

metadata version: 2024-10-06

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Amelie Haugg, Fabian M. Renz, Andrew A. Nicholson, Cindy Lor, Sebastian J. Götzendorfer, Ronald Sladky, Stavros Skouras, Amalia McDonald, R. Cameron Craddock, Lydia Hellrung, Matthias Kirschner, Marcus Herdener, Yury Koush, Marina Papoutsi, Nimrod Jakob Keynan, Talma Hendler, Kathrin Cohen Kadosh, Catharina Zich, Simon H. Kohl, Manfred Hallschmid, Jeff MacInnes, R. Alison Adcock, Kathryn C. Dickerson, Nan-kuei Chen, Kymberly D. Young, Jerzy Bodurka, Michael Marxen, Shuxia Yao, Benjamin Becker, Tibor Auer, Renate Schweizer, Gustavo S. P. Pamplona, Ruth A. Lanius, Kirsten Emmert, Sven Haller, Dimitri Van De Ville, Dong-Youl Kim, Jong-Hwan Lee, Theo Marins, Fukuda Megumi, Bettina Sorger, Tabea Kamp, Sook-Lei Liew, Ralf Veit, Maartje S. Spetter, Nikolaus Weiskopf, Frank Scharnowski, David Steyrl:
Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis. NeuroImage 237: 118207 (2021)

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