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Dynamic correction of artifacts due to susceptibility effects and time-varying eddy currents in diffusion tensor imaging., , и . NeuroImage, 57 (4): 1343-1347 (2011)Improved Delineation of Short Cortical Association Fibers and Gray/White Matter Boundary Using Whole-Brain Three-Dimensional Diffusion Tensor Imaging at Submillimeter Spatial Resolution., , , , и . Brain Connect., 4 (9): 636-640 (2014)Effect of Repetitive Transcranial Magnetic Stimulation on fMRI Resting-State Connectivity in Multiple System Atrophy., , , , , , и . Brain Connect., 5 (7): 451-459 (2015)Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis., , , , , , , , , и 38 other автор(ы). NeuroImage, (2021)A unified machine learning method for task-related and resting state fMRI data analysis., и . EMBC, стр. 6426-6429. IEEE, (2014)Application of k-space energy spectrum analysis to susceptibility field mapping and distortion correction in gradient-echo EPI., , и . NeuroImage, 31 (2): 609-622 (2006)Comparison of Acoustic Radiation Force Impulse Imaging Derived Carotid Plaque Stiffness With Spatially Registered MRI Determined Composition., , , , , , , , и . IEEE Trans. Medical Imaging, 34 (11): 2354-2365 (2015)Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity?, , , , , , , , , и 29 other автор(ы). Human Brain Mapping, 41 (14): 3839-3854 (Juli 2020)Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis, , , , , , , , , и 38 other автор(ы). NeuroImage, (August 2021)