Pierpaolo Sorrentino

Data-driven gait cycle decomposition based on whole-body coordination dynamics

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

Structural topology shapes regional roles in neuronal avalanche initiation and termination

The paper examines how structural brain connectivity influences the initiation and termination of neuronal avalanches using MEG and dMRI data from healthy individuals.

FUSE: FUsing EEG-MEG in a Shared Embedding via self-supervised learning for BCI

This paper introduces a two-phase deep learning framework for EEG-MEG decoding in BCI, showing improved classification accuracy and transferability across participants.

Structure-Constrained Intrinsic Timescales Across Tasks

The paper maps intrinsic neural timescales across rest and tasks, showing task-induced changes while preserving temporal hierarchy and structural constraints.

Temporal persistence and structural organization of neuronal avalanche dynamics

The paper analyzes neuronal avalanche dynamics in resting-state MEG data to show temporal persistence and structural organization in brain activity.

Departure from OFF-State Microstate Dynamics Tracks Levodopa Response in Parkinson's Disease

This study shows that changes in whole-brain microstate dynamics after levodopa administration correlate with clinical improvement in Parkinson's disease.

Structural connectivity shapes a cross-modal gradient of brain flexibility

The paper introduces a new measure, flexibility gradient, to quantify regional contributions to brain flexibility and finds that structural properties correlate with regional flexibility contributions across multiple modalities.

A microcircuit model of astrocytic potassium buffering and neural synchronization

This study develops a microcircuit model to investigate how astrocytic potassium buffering and gap-junction coupling influence neuronal synchronization and prevent pathological states in the hippocampus.

Pre-stimulus Brain States Predict and Control Variability in Stimulation Responses

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

Artificial Intelligence for automatic movement recognition: a network-based approach

This paper introduces a network-based approach for movement recognition using kinematic data, showing that the kinectome outperforms PCA in classification accuracy and interpretability.

Modularity-dependent storage of dynamic spiking patterns: bridging micro- and mesoscopic representations

The paper studies how modular spiking neural networks store and retrieve dynamic spatiotemporal patterns by adjusting temporal overlap between modules.

Hormone-Dependent Microstates and the Reconfiguration of Resting-State Dynamics across the Menstrual Cycle

This study shows that hormone levels across the menstrual cycle affect brain activity patterns and predict psychological well-being.

A next-generation neural mass model with neuromodulation

This paper introduces a modular neural mass model incorporating neuromodulation, specifically dopamine dynamics, and analyzes its effects on oscillatory states.

EEG dynamics in Lewy body diseases is related to clinical fluctuations and degeneration of locus coeruleus

This study examines neuronal avalanches in Lewy body diseases, finding increased brain dynamics flexibility linked to clinical symptoms and degeneration of the locus coeruleus.

Linking local and large-scale salient events with oscillatory and broadband arrhythmic activities in the resting human brain

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.

Neuronal avalanches as a predictive biomarker of BCI performance- towards a tool to guide tailored training

This paper identifies neuronal avalanches as biomarkers to predict BCI performance and guide personalized training protocols.

Intra- and Interhemispheric Signatures of Criticality at the Onset of Synchronization

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

The Connectome Modulates Critical Brain Dynamics Across Local and Global Scales

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

Criticality explains structure-function relationships in the human brain

The paper investigates how structural brain connections enable diverse dynamic patterns by testing if neuronal activity near a critical regime explains structural-functional relationships.

Spatiotemporal brain complexity quantifies consciousness outside of perturbation paradigms

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.

Linking microscopic and mean-field descriptions ofdopaminergic modulation in neural circuits

This study compares a dopamine-modulated mean-field model with its corresponding spiking network to evaluate the model's accuracy in capturing network dynamics.

Magnetoencephalography dimensionality reduction informed by dynamic brain states

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.

Principles and Operation of Virtual Brain Twins

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.

Intermodal consistency of whole-brain connectivity and signal propagation delays

This paper compares non-invasive and invasive methods to estimate brain connectivity and signal propagation delays, finding they yield similar results.

Neuronal Avalanches in Naturalistic Speech and Music Listening

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

Measuring Neuronal Avalanches to inform Brain-Computer Interfaces

This study uses neuronal avalanches to improve brain-computer interfaces by showing their task-specific differences can be decoded better than current methods.

On Global Brain Reconfiguration after Local Manipulations

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

Altered spread of waves of activities at large scale is influenced by cortical thickness organization in temporal lobe epilepsy: a MRI-hdEEG study

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

Brain fingerprint is based on the aperiodic, scale-free, neuronal activity

This paper identifies subject-specific brain features by analyzing non-linear neuronal avalanches in magnetoencephalographic data, showing improved identifiability compared to linear methods.

Dynamical interactions reconfigure the gradient of cortical timescales

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