Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?
ContributorsHaugg, Amelie; Sladky, Ronald; Skouras, Stavros; McDonald, Amalia; Craddock, Cameron; Kirschner, Matthias; Herdener, Marcus; Koush, Yury; Papoutsi, Marina; Keynan, Jackob N; Hendler, Talma; Cohen Kadosh, Kathrin; Zich, Catharina; MacInnes, Jeff; Adcock, R Alison; Dickerson, Kathryn; Chen, Nan-Kuei; Young, Kymberly; Bodurka, Jerzy; Yao, Shuxia; Becker, Benjamin; Auer, Tibor; Schweizer, Renate; Pamplona, Gustavo; Emmert, Kirsten; Haller, Sven; Van De Ville, Dimitri ; Blefari, Maria-Laura; Kim, Dong-Youl; Lee, Jong-Hwan; Marins, Theo; Fukuda, Megumi; Sorger, Bettina; Kamp, Tabea; Liew, Sook-Lei; Veit, Ralf; Spetter, Maartje; Weiskopf, Nikolaus; Scharnowski, Frank
Published inHuman Brain Mapping, vol. 41, no. 14, p. 3839-3854
Publication date2020
Abstract
Keywords
- fMRI
- Functional neuroimaging
- Learning
- Meta-analysis
- Neurofeedback
- Real-time fMRI
Funding
- Swiss National Science Foundation - Enhancing functional connectivity in prefrontal networks to test and improve self-control mechanisms in decision-making [32003B_166566]
- Swiss National Science Foundation - 32003B_166566; BSSG10_155915;
- Swiss National Science Foundation - Closed-loop brain training [100014_178841]
Citation (ISO format)
HAUGG, Amelie et al. Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity? In: Human Brain Mapping, 2020, vol. 41, n° 14, p. 3839–3854. doi: 10.1002/hbm.25089
Main files (1)
Article (Published version)
Identifiers
- PID : unige:150379
- DOI : 10.1002/hbm.25089
- PMID : 32729652
Commercial URLhttp://www.ncbi.nlm.nih.gov/pmc/articles/pmc7469782/
ISSN of the journal1065-9471