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@ARTICLE{Haugg:904428,
      author       = {Haugg, Amelie and Renz, Fabian M. and Nicholson, Andrew A.
                      and Lor, Cindy and Götzendorfer, Sebastian J. and Sladky,
                      Ronald and Skouras, Stavros and McDonald, Amalia and
                      Craddock, Cameron and Hellrung, Lydia and Kirschner,
                      Matthias and Herdener, Marcus and Koush, Yury and Papoutsi,
                      Marina and Keynan, Jackob and Hendler, Talma and Cohen
                      Kadosh, Kathrin and Zich, Catharina and Kohl, Simon H. and
                      Hallschmid, Manfred and MacInnes, Jeff and Adcock, R. Alison
                      and Dickerson, Kathryn C. and Chen, Nan-Kuei and Young,
                      Kymberly and Bodurka, Jerzy and Marxen, Michael and Yao,
                      Shuxia and Becker, Benjamin and Auer, Tibor and Schweizer,
                      Renate and Pamplona, Gustavo and Lanius, Ruth A. and Emmert,
                      Kirsten and Haller, Sven and Van De Ville, Dimitri and Kim,
                      Dong-Youl and Lee, Jong-Hwan and Marins, Theo and Megumi,
                      Fukuda and Sorger, Bettina and Kamp, Tabea and Liew,
                      Sook-Lei and Veit, Ralf and Spetter, Maartje and Weiskopf,
                      Nikolaus and Scharnowski, Frank and Steyrl, David},
      title        = {{P}redictors of real-time f{MRI} neurofeedback performance
                      and improvement – {A} machine learning mega-analysis},
      journal      = {NeuroImage},
      volume       = {237},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2021-05998},
      pages        = {118207 -},
      year         = {2021},
      abstract     = {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.},
      cin          = {INM-11},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-11-20170113},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:34048901},
      UT           = {WOS:000672584900003},
      doi          = {10.1016/j.neuroimage.2021.118207},
      url          = {https://juser.fz-juelich.de/record/904428},
}