Haugg, Amelie ;
Renz, Fabian M ;
Nicholson, Andrew A...
Haugg , A , Renz , F M , Nicholson , A A , Lor , C , Götzendorfer , S J , Sladky , R , Skouras , S , McDonald , A , Craddock , C , Hellrung , L , Kirschner , M , Herdener , M , Koush , Y , Papoutsi , M , Keynan , J , Hendler , T , Cohen Kadosh , K , Zich , C , Kohl , S H , Hallschmid , M , MacInnes , J , Adcock , R A , Dickerson , K C , Chen , N-K , Young , K , Bodurka , J , Marxen , M , Yao , S , Becker , B , Auer , T , Schweizer , R , Pamplona , G , Lanius , R A , Emmert , K , Haller , S , Van De Ville , D , Kim , D-Y , Lee , J-H , Marins , T , Megumi , F , Sorger , B , Kamp , T , Liew , S-L , Veit , R , Spetter , M , Weiskopf , N , Scharnowski , F & Steyrl , D 2021 , ' Predictors of real-time fMRI neurofeedback performance and improvement : A machine learning mega-analysis ' , Neuroimage , vol. 237 , 118207 . https://doi.org/10.1016/j.neuroimage.2021.118207.
,
2021
