Marcel M. Breeuwer

This paper presents a benchmark challenge to compare machine learning models for GABA-edited MRS reconstruction using reduced transients, finding that deep learning models achieve comparable metrics to conventional methods.

This study evaluates three multi-echo fMRI combination methods on resting-state networks, finding optimal combination provides the most consistent temporal and spatial results compared to tSNR and tCNR methods.

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.