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

Amelie Haugg*, Fabian M Renz, Andrew A Nicholson, Cindy Lor, Sebastian J Götzendorfer, Ronald Sladky, Stavros Skouras, Amalia McDonald, Cameron Craddock, Lydia Hellrung, Matthias Kirschner, Marcus Herdener, Yury Koush, Marina Papoutsi, Jackob Keynan, Talma Hendler, Kathrin Cohen Kadosh, Catharina Zich, Simon H Kohl, Manfred HallschmidJeff MacInnes, R Alison Adcock, Kathryn C Dickerson, Nan-Kuei Chen, Kymberly Young, Jerzy Bodurka, Michael Marxen, Shuxia Yao, Benjamin Becker, Tibor Auer, Renate Schweizer, Gustavo 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 Spetter, Nikolaus Weiskopf, Frank Scharnowski, David Steyrl

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Original languageEnglish
Article number118207
Number of pages11
JournalNeuroimage
Volume237
Early online date25 May 2021
DOIs
Publication statusE-pub ahead of print - 25 May 2021

Keywords

  • Neurofeedback
  • Functional MRI
  • Mega-analysis
  • Machine learning
  • Real-time fMRI
  • Learning
  • RESONANCE-IMAGING NEUROFEEDBACK
  • CORTEX ACTIVITY
  • SELF-REGULATION
  • BRAIN ACTIVATION
  • MOTOR IMAGERY
  • REDUCTION
  • FEEDBACK
  • ATTENTION
  • EFFICACY
  • MEMORY

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