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Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis, , , , , , , , , and 38 other author(s). NeuroImage, (August 2021)Real-time and Recursive Estimators for Functional MRI Quality Assessment., , , , , , , and . Neuroinformatics, 20 (4): 897-917 (2022)BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods., , , , , , , , , and 19 other author(s). PLoS Comput. Biol., (2017)Automatic analysis (aa): efficient neuroimaging workflows and parallel processing using Matlab and XML., , , , , , and . Frontiers Neuroinformatics, (2014)Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis., , , , , , , , , and 38 other author(s). NeuroImage, (2021)The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample., , , , , , , , and . NeuroImage, (2017)Predicting Body Mass Index From Structural MRI Brain Images Using a Deep Convolutional Neural Network., , , and . Frontiers Neuroinformatics, (2020)Predicting Cognitive Recovery of Stroke Patients from the Structural MRI Connectome Using a Naïve Bayesian Tree Classifier., , , , , , , and . CBMS, page 413-418. IEEE Computer Society, (2017)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)