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000904428 1001_ $$0P:(DE-HGF)0$$aHaugg, Amelie$$b0$$eCorresponding author
000904428 245__ $$aPredictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis
000904428 260__ $$aOrlando, Fla.$$bAcademic Press$$c2021
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000904428 520__ $$aReal-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments.
000904428 536__ $$0G:(DE-HGF)POF4-5252$$a5252 - Brain Dysfunction and Plasticity (POF4-525)$$cPOF4-525$$fPOF IV$$x0
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000904428 7001_ $$0P:(DE-HGF)0$$aRenz, Fabian M.$$b1
000904428 7001_ $$0P:(DE-HGF)0$$aNicholson, Andrew A.$$b2
000904428 7001_ $$0P:(DE-HGF)0$$aLor, Cindy$$b3
000904428 7001_ $$0P:(DE-HGF)0$$aGötzendorfer, Sebastian J.$$b4
000904428 7001_ $$0P:(DE-HGF)0$$aSladky, Ronald$$b5
000904428 7001_ $$0P:(DE-HGF)0$$aSkouras, Stavros$$b6
000904428 7001_ $$0P:(DE-HGF)0$$aMcDonald, Amalia$$b7
000904428 7001_ $$0P:(DE-HGF)0$$aCraddock, Cameron$$b8
000904428 7001_ $$0P:(DE-HGF)0$$aHellrung, Lydia$$b9
000904428 7001_ $$0P:(DE-HGF)0$$aKirschner, Matthias$$b10
000904428 7001_ $$0P:(DE-HGF)0$$aHerdener, Marcus$$b11
000904428 7001_ $$0P:(DE-HGF)0$$aKoush, Yury$$b12
000904428 7001_ $$0P:(DE-HGF)0$$aPapoutsi, Marina$$b13
000904428 7001_ $$0P:(DE-HGF)0$$aKeynan, Jackob$$b14
000904428 7001_ $$0P:(DE-HGF)0$$aHendler, Talma$$b15
000904428 7001_ $$0P:(DE-HGF)0$$aCohen Kadosh, Kathrin$$b16
000904428 7001_ $$0P:(DE-HGF)0$$aZich, Catharina$$b17
000904428 7001_ $$0P:(DE-Juel1)173070$$aKohl, Simon H.$$b18$$ufzj
000904428 7001_ $$0P:(DE-HGF)0$$aHallschmid, Manfred$$b19
000904428 7001_ $$0P:(DE-HGF)0$$aMacInnes, Jeff$$b20
000904428 7001_ $$0P:(DE-HGF)0$$aAdcock, R. Alison$$b21
000904428 7001_ $$0P:(DE-HGF)0$$aDickerson, Kathryn C.$$b22
000904428 7001_ $$0P:(DE-HGF)0$$aChen, Nan-Kuei$$b23
000904428 7001_ $$0P:(DE-HGF)0$$aYoung, Kymberly$$b24
000904428 7001_ $$0P:(DE-HGF)0$$aBodurka, Jerzy$$b25
000904428 7001_ $$0P:(DE-HGF)0$$aMarxen, Michael$$b26
000904428 7001_ $$0P:(DE-HGF)0$$aYao, Shuxia$$b27
000904428 7001_ $$0P:(DE-HGF)0$$aBecker, Benjamin$$b28
000904428 7001_ $$0P:(DE-HGF)0$$aAuer, Tibor$$b29
000904428 7001_ $$0P:(DE-HGF)0$$aSchweizer, Renate$$b30
000904428 7001_ $$0P:(DE-HGF)0$$aPamplona, Gustavo$$b31
000904428 7001_ $$0P:(DE-HGF)0$$aLanius, Ruth A.$$b32
000904428 7001_ $$0P:(DE-HGF)0$$aEmmert, Kirsten$$b33
000904428 7001_ $$0P:(DE-HGF)0$$aHaller, Sven$$b34
000904428 7001_ $$0P:(DE-HGF)0$$aVan De Ville, Dimitri$$b35
000904428 7001_ $$0P:(DE-HGF)0$$aKim, Dong-Youl$$b36
000904428 7001_ $$0P:(DE-HGF)0$$aLee, Jong-Hwan$$b37
000904428 7001_ $$0P:(DE-HGF)0$$aMarins, Theo$$b38
000904428 7001_ $$0P:(DE-HGF)0$$aMegumi, Fukuda$$b39
000904428 7001_ $$0P:(DE-HGF)0$$aSorger, Bettina$$b40
000904428 7001_ $$0P:(DE-HGF)0$$aKamp, Tabea$$b41
000904428 7001_ $$0P:(DE-HGF)0$$aLiew, Sook-Lei$$b42
000904428 7001_ $$0P:(DE-HGF)0$$aVeit, Ralf$$b43
000904428 7001_ $$0P:(DE-HGF)0$$aSpetter, Maartje$$b44
000904428 7001_ $$0P:(DE-HGF)0$$aWeiskopf, Nikolaus$$b45
000904428 7001_ $$0P:(DE-HGF)0$$aScharnowski, Frank$$b46
000904428 7001_ $$0P:(DE-HGF)0$$aSteyrl, David$$b47
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