Enzo Tagliazucchi

This study shows serotonergic psychedelics like LSD and DMT delay resting state neurovascular coupling, affecting hemodynamic delays and functional connectivity in brain regions with serotonin receptors.

This study tracks dose-dependent affective dynamics during DMT's acute effects using multimodal physiological signals and phenomenological reports.

This study uses intersubject correlation of naturalistic fMRI data to show reduced emotional neural synchrony in schizophrenia, particularly in the amygdala, and increased synchrony in visual and frontal regions.

This study uses whole-brain modeling to show that structural brain hubs influence dynamical state transitions in disorders of consciousness.

This paper introduces a modified neural mass model with inhibitory synaptic plasticity to better simulate EEG and fMRI recordings during NREM sleep and capture the spectral richness of brain activity.

This study uses large language models to annotate spontaneous thoughts from verbal reports and decodes their emotional valence from EEG data.

This study investigates the synergistic effects of psilocybin and compassion imagery on cognitive absorption, self-compassion, and brain network activity using fMRI and self-report measures in 105 participants.

The paper uses a whole-brain model of an anesthetized macaque to simulate and restore consciousness through thalamic stimulation, showing how brain states transition during loss and recovery of consciousness.

This study investigates how noradrenaline and acetylcholine influence functional connectivity during NREM sleep stages using a whole brain model and empirical data.

This paper develops a whole-brain model to study how transcranial ultrasound stimulation (TUS) induces functional changes in the brain, identifying two mechanisms that predict plasticity-driven effects.

This study analyzes EEG microstate dynamics in disorders of consciousness to identify differences in brain activity patterns between patients and healthy controls.

The paper examines how dimensionality reduction techniques preserve synergistic interactions in brain dynamics using resting-state fMRI data from 100 healthy participants.

The paper examines the temporal alignment between EEG responses and neural network activations, finding that early EEG components match low-level features and later components match semantic representations.

This paper applies an off-equilibrium thermodynamical framework based on the fluctuation-dissipation theorem to differentiate brain states like wakefulness and deep sleep.

This study examines the neural and subjective effects of medium- and high-dose DMT using EEG and experience tracking, finding that higher doses induce more intense experiences but less strong neural complexity correlations.

This study examines how psilocybin affects visual salience processing by analyzing eye-tracking and EEG data during natural scene perception.

This paper models low-dimensional brain networks to show they organize human cognition through flexible reconfiguration during tasks.

This study examines how classical psychedelics alter brain hierarchy and complexity, finding they reduce hierarchy while increasing neural complexity.

This study identifies dynamic structure-function correlations as robust markers of consciousness in humans across different unconscious states.

The paper uses whole-brain models to simulate the effects of serotonergic psychedelics on disorders of consciousness, showing they can shift brain dynamics toward conscious states.

The paper investigates how breathwork-induced psychedelic experiences affect neural dynamics using EEG and subjective reporting.

This study links cardiometabolic state, measured by an allostatic load index, with neurovascular burden and brain structure/function changes across age using EEG and MRI data.

This study uses a whole-brain model to show that video game expertise is linked to meso-scale functional connectivity changes and improved noise filtering in brain networks.

This paper models the transient brain state changes induced by DMT using a time-dependent whole-brain model and fMRI data, showing heightened reactivity in specific brain regions correlated with serotonin receptor density.

The paper decodes semantic content from EEG data during rapid serial visual presentation by distinguishing between image statistics and semantics, finding simpler models are more robust to statistical biases.

The paper shows that during decreased alertness, active task engagement increases frontoparietal and long-range brain synchronization, while passive listening increases thalamocortical synchronization.

The paper maps brain states of reduced consciousness into a low-dimensional space using whole-brain modeling and deep learning, revealing an orderly trajectory from wakefulness to brain injury.

This paper uses deep learning models to analyze whole-brain dynamics in neurodegenerative diseases, identifying key regions for inducing transitions from pathological to healthy states.

This paper uses strength-dependent perturbations to show the human brain operates at a balance between fluctuation and oscillation, enhancing flexibility.

This study identifies canonical brain dynamics by optimizing computational models to match empirical data from functional MRI, revealing stable spiral attractors and bifurcation-related dynamics.