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

Author:

Haugg 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,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

Funder

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Universität Zürich

Horizon 2020 Framework Programme

Seventh Framework Programme

Foundation for Research in Science and the Humanities

Deutsche Forschungsgemeinschaft

Publisher

Elsevier BV

Subject

Cognitive Neuroscience,Neurology

Reference93 articles.

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2. Open science challenges, benefits and tips in early acreer and beyond;Allen;PLoS Biol.,2019

3. Training efficiency and transfer success in an extended real-time functional MRI neurofeedback training of the somatomotor cortex of healthy subjects;Auer;Front. Hum. Neurosci.,2015

4. A network engineering perspective on probing and perturbing cognition with neurofeedback;Bassett;Ann. N. Y. Acad. Sci.,2017

5. Real-time fMRI neurofeedback reduces auditory hallucinations and modulates resting state connectivity of involved brain regions: part 2: default mode network -preliminary evidence;Bauer;Psychiatry Res.,2020

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