Lydia Hellrung

This paper analyzes fMRI neurofeedback data to identify brain regions and networks involved in feedback processing and regulation recalibration during training.

This study compares six multi-echo fMRI combination methods for their impact on BOLD sensitivity in offline and real-time settings, finding T2*FIT-weighted provides the best temporal signal-to-noise ratio.

The paper introduces a multi-echo fMRI dataset with task and resting state runs for developing and validating real-time analysis methods in neuroimaging.

This study uses machine learning to analyze 608 participants and identifies two factors improving neurofeedback performance: pre-training no-feedback runs and training patients over healthy participants.

This study examines how individual differences in self-regulation of the dopaminergic midbrain relate to cognitive control and reward sensitivity during neurofeedback training.

This study investigates how motion and physiological noise affect amygdala real-time fMRI neurofeedback learning and finds that these factors do not drive self-regulation learning.