Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis
Contributeurs/tricesHaugg, 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![orcid](assets/images/orcid.png)
; 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
![orcid](assets/images/orcid.png)
Publié dansNeuroImage, vol. 237, 118207
Date de publication2021-08-15
Date de mise en ligne2021-05-25
Résumé
Mots-clés
- Functional MRI
- Learning
- Machine learning
- Mega-analysis
- Neurofeedback
- Real-time fMRI
- Adult
- Functional Neuroimaging
- Humans
- Machine Learning
- Magnetic Resonance Imaging
Structure d'affiliation
Groupe de recherche
Financement
- Swiss National Science Foundation - Enhancing functional connectivity in prefrontal networks to test and improve self-control mechanisms in decision-making [166566]
- Swiss National Science Foundation - Treatment of Human Brain Dysfunction with Neurofeedback. [155915]
- Swiss National Science Foundation - Closed-loop brain training [178841]
- European Commission - Taking imaging into the therapeutic domain: Self-regulation of brain systems for mental disorders [602186]
- European Commission - Dopaminergic midbrain modulations by (adaptive) neurofeedback [794395]
Citation (format ISO)
HAUGG, Amelie et al. Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis. In: NeuroImage, 2021, vol. 237, p. 118207. doi: 10.1016/j.neuroimage.2021.118207
Fichiers principaux (1)
Article (Published version)
Fichiers secondaires (1)
Identifiants
- PID : unige:168805
- DOI : 10.1016/j.neuroimage.2021.118207
- PMID : 34048901
ISSN du journal1053-8119