Michalis Kassinopoulos

This study examines how physiological response functions (PRFs) vary across individuals and brain regions in fMRI data and shows that allowing spatial and subject-specific variability improves PRF model performance.

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 study examines brain connectivity patterns in epilepsy patients with and without SUDEP risk, linking cardiorespiratory rhythms to neural activity and SUDEP risk.

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 evaluates fMRI preprocessing strategies using a multi-measure framework to assess their impact on data quality, finding that removing global signal and 17% of white matter components improves QC metrics most.

This study introduces a new cardiac pulsatility model (CPM) for fMRI noise correction, showing it outperforms existing methods in explaining fMRI 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 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.

The paper shows that different analysis workflows by 70 teams on the same fMRI dataset led to varied results, but meta-analysis found consensus in activated regions.

This paper develops a framework to estimate physiological response functions (PRF) for resting-state fMRI, showing they vary across subjects and sessions and are linked to heart rate and breathing patterns.