TY  - JOUR
AU  - Haugg, Amelie
AU  - Renz, Fabian M.
AU  - Nicholson, Andrew A.
AU  - Lor, Cindy
AU  - Götzendorfer, Sebastian J.
AU  - Sladky, Ronald
AU  - Skouras, Stavros
AU  - McDonald, Amalia
AU  - Craddock, Cameron
AU  - Hellrung, Lydia
AU  - Kirschner, Matthias
AU  - Herdener, Marcus
AU  - Koush, Yury
AU  - Papoutsi, Marina
AU  - Keynan, Jackob
AU  - Hendler, Talma
AU  - Cohen Kadosh, Kathrin
AU  - Zich, Catharina
AU  - Kohl, Simon H.
AU  - Hallschmid, Manfred
AU  - MacInnes, Jeff
AU  - Adcock, R. Alison
AU  - Dickerson, Kathryn C.
AU  - Chen, Nan-Kuei
AU  - Young, Kymberly
AU  - Bodurka, Jerzy
AU  - Marxen, Michael
AU  - Yao, Shuxia
AU  - Becker, Benjamin
AU  - Auer, Tibor
AU  - Schweizer, Renate
AU  - Pamplona, Gustavo
AU  - Lanius, Ruth A.
AU  - Emmert, Kirsten
AU  - Haller, Sven
AU  - Van De Ville, Dimitri
AU  - Kim, Dong-Youl
AU  - Lee, Jong-Hwan
AU  - Marins, Theo
AU  - Megumi, Fukuda
AU  - Sorger, Bettina
AU  - Kamp, Tabea
AU  - Liew, Sook-Lei
AU  - Veit, Ralf
AU  - Spetter, Maartje
AU  - Weiskopf, Nikolaus
AU  - Scharnowski, Frank
AU  - Steyrl, David
TI  - Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis
JO  - NeuroImage
VL  - 237
SN  - 1053-8119
CY  - Orlando, Fla.
PB  - Academic Press
M1  - FZJ-2021-05998
SP  - 118207 -
PY  - 2021
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:34048901
UR  - <Go to ISI:>//WOS:000672584900003
DO  - DOI:10.1016/j.neuroimage.2021.118207
UR  - https://juser.fz-juelich.de/record/904428
ER  -