Alba Xifra‐Porxas

This study examines the impact of global signal regression on resting-state fMRI connectivity, finding it reduces physiological artifacts while preserving electrophysiological connectivity patterns.

This paper models the hemodynamic response function using EEG-fMRI data during resting-state and motor tasks, finding frequency-specific and region-specific contributions to BOLD signal variance.

The paper investigates how physiological and motion processes affect functional connectivity and subject identifiability using fMRI data from the Human Connectome Project.

This study combines structural MRI and MEG data to predict brain age using dimensionality reduction techniques, finding that a stacked model outperforms individual modalities.

This paper proposes using simultaneous EEG-fMRI and convolutional sparse coding to model the hemodynamic response function, finding that lower amplitude BOLD peaks also contain important activity information.