Giovanni Rabuffo

This study decomposes gait cycles using a dynamic system framework to identify coordination patterns in human locomotion.

This paper identifies optimal brain stimulation strategies by mapping how responses vary with target location and brain state, reducing trial-to-trial variability.

This paper shows that pre-stimulus brain states predict and reduce variability in stimulation responses by timing interventions to optimal neural states.

This paper introduces a framework linking oscillatory and broadband activities to transient salient events in resting brain data, showing how they emerge from spectral properties and interactions.

The paper identifies criticality signatures in cortical networks during synchronization onset, showing both intra- and inter-hemispheric coordination patterns.

This paper develops a multiscale computational framework to study how brain criticality at local and global scales interacts through structural connectivity.

The paper quantifies consciousness using spatiotemporal brain complexity metrics from resting-state and stimulation data, showing agreement with perturbational complexity in a more accessible paradigm.

This paper introduces a topological fingerprinting method using homological scaffolds from resting-state fMRI data, achieving near-perfect individual identification accuracy.

This study uses PHATE to reduce the dimensionality of resting-state MEG data, revealing dynamic brain states and their transitions in a low-dimensional space.

This paper introduces Virtual Brain Twins (VBTs) as personalized digital brain models that integrate structural and functional data to improve understanding of brain function and support personalized treatment.

This paper uses computational modeling and dynamical systems analysis to show that symmetry breaking in brain connectivity generates the resting state manifold, producing key features observed in empirical data.

This study examines neuronal avalanches during speech and music listening versus rest, finding stimulus-driven coordination and distinct network dynamics.

This study shows how local brain manipulations affect global brain dynamics using simulations and experimental data, revealing specific network changes and localization of effects.

This study investigates how structural brain organization influences altered functional dynamics in temporal lobe epilepsy using MRI and hdEEG data.

The paper shows that brain timescales dynamically shift between back-to-front and front-to-back gradients based on non-local interactions.

This paper develops a mean-field model of neuronal populations incorporating ion-exchange mechanisms to simulate brain activity and validate its effects through in vitro experiments.

This paper identifies neuronal cascades as a mechanism underlying resting-state brain dynamics, showing they produce BOLD co-fluctuations and stable brain states in mice and humans.