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Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis., , , , , , , , , and 38 other author(s). NeuroImage, (2021)Voxelwise nonlinear regression toolbox for neuroimage analysis: Application to aging and neurodegenerative disease modeling., , , , , , , , , and 1 other author(s). CoRR, (2016)Superficial amygdala and hippocampal activity during affective music listening observed at 3 T but not 1.5 T fMRI., , , and . NeuroImage, (2014)Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?, , , , , , , , , and 29 other author(s). Human Brain Mapping, 41 (14): 3839-3854 (July 2020)The effects of psychiatric history and age on self-regulation of the default mode network., and . NeuroImage, (2019)Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis, , , , , , , , , and 38 other author(s). NeuroImage, (August 2021)The roles of superficial amygdala and auditory cortex in music-evoked fear and joy., , , , , , and . NeuroImage, (2013)