Gabriele Arnulfo

This study examines how phase synchronization and phase-amplitude coupling change across sleep and wakefulness in the human brain, identifying state-specific patterns and differences between epileptogenic and non-epileptogenic regions.

This study investigates the relationship between delta-band synchrony and beta-gamma bistability in sleep-related epilepsy, finding they synergistically contribute to seizure generation.

The paper examines how REM sleep affects large-scale brain network dynamics and links this to its reduced epileptogenicity by analyzing SEEG recordings from epilepsy patients.

CROCOpy is a Python toolbox for analyzing neuronal oscillations and functional connectivity metrics in electrophysiological data.

This study uses advanced EEG analysis to assess cortical maturation in preterm infants by examining oscillatory dynamics during different vigilance states.

The paper develops a probabilistic model to identify epileptogenic networks using interictal SEEG data, achieving high classification accuracy with a reduced feature space.

This study compares Biophysical Network Models (BNMs) with different delay specifications to determine their role in generating alpha-band phase synchronization networks in MEG data.

The paper uses SEEG data to identify stable, frequency-specific modules in phase-synchronization connectomes that are anatomically contiguous and functionally related.

The paper shows that individual differences in brain synchronization levels correlate with power-law long-range temporal correlations, with variations in healthy and epileptic brain regions.

This paper identifies critical-like bistable dynamics in the resting-state human brain, showing their functional and clinical significance across frequency bands.

This study investigates how biophysical network models can replicate MEG-derived phase-synchronization networks, finding that conduction delays do not improve the match but structural connections and Wilson-Cowan dynamics do.

This study shows that high-gamma activity (HGA) in human cortex can be synchronized across distant brain regions, revealing a new form of neuronal communication distinct from lower-frequency synchronization.

The paper identifies genuine cross-frequency coupling networks in human resting-state brain activity using MEG and SEEG data, showing distinct mechanisms for coupling oscillations across frequencies.