Pedro A. M. Mediano

This study uses multivariate information decomposition to analyze how five psychedelics alter information processing in the human brain, revealing new insights into neural reorganization.

This study investigates how psychoactive drugs alter the global workspace of consciousness by analyzing resting-state fMRI data, finding distinct changes in synergistic and redundant interactions across drug types.

This study investigates brain changes after a single high-dose psilocybin administration, finding anatomical and functional alterations linked to improved well-being and cognitive flexibility.

The paper uses neural complexity measures to show that perceptual clarity of auditory stimuli correlates with changes in time- and frequency-resolved information-theoretic measures of brain activity.

This paper uses information-theoretic measures to analyze brain dynamics in Alzheimer's disease and finds reduced synergy and increased redundancy in AD patients compared to cognitively normal individuals.

This study quantifies emergent population-level activity in neuronal systems using information-theoretic and network science methods, showing oscillations and avalanches exhibit emergent behavior at different timescales.

The paper uses EEG and Lempel-Ziv complexity to assess cognitive reserve in elderly participants, finding a significant interaction between arousal and performance.

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.

The paper investigates how ketamine affects hedonic experiences by analyzing neural dynamics during music-evoked peak experiences using fMRI and information-theoretical measures.

This paper introduces graphTRIP, a geometric deep learning model that predicts antidepressant treatment response using neuroimaging and clinical data, identifying biomarkers and causal mechanisms.

The paper introduces a computational engine to systematically discover cause-effect relationships between brain structure and function using in-silico brain models.

This study uses computational whole-brain modeling to show that competitive interactions in mammalian connectomes enhance computational capacity and fit brain dynamics better than cooperative-only models.

This study examines how 5-MeO-DMT alters human brain dynamics by reorganizing low-frequency neural activity patterns observed through EEG.

This paper proposes a synergistic global workspace model of consciousness using network science and information theory, showing that loss of consciousness correlates with reduced information integration in specific brain networks.

This study examines how neural complexity changes during prenatal development and finds it decreases with maturation, more rapidly in male fetuses.

This study shows that broadband EEG measures like aperiodic exponent and entropy predict corticospinal excitability better than traditional band-limited features.

This study examines how external stimuli modulate brain entropy during a psychedelic experience, finding that eye closure enhances entropy changes and that video stimuli disrupt the relationship between brain activity and subjective experience.

This study examines how transcranial ultrasound stimulation affects neural networks in macaques, showing changes in specific brain networks and network topology.

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

This paper introduces CSER, a new complexity estimator for neural signals that overcomes limitations of LZ by providing spectral decomposition and higher temporal resolution, revealing gamma band dominance and early entropy changes in consciousness states.

This study quantifies neural emergence in brain injury patients using functional MRI and shows reduced emergent dynamics correlate with structural connectome disruptions.

The paper identifies harmonic patterns of structure-function dependence in the human connectome that correlate with different states of consciousness.

This study compares neural dynamics changes in schizophrenia and psychedelic-induced states using M/EEG data, finding distinct patterns in information transfer and Bayesian inference alterations.

This paper builds a whole-brain computational model to show that age-related changes in high-order functional interactions are explained by variations in the connectome.

The paper evaluates phase-based dynamic functional connectivity metrics in resting-state fMRI to identify reliable and interpretable measures of brain dynamics and their relationships.

The paper introduces an information decomposition framework to identify synergistic and redundant neural interactions, showing synergistic activity underlies human complex cognition and evolutionary brain expansion.

The paper introduces FastDMF, a computationally efficient implementation of the Dynamic Mean Field model, enabling simulations of whole-brain dynamics with significantly faster performance and reduced memory usage.

This study examines how ketamine and sleep stages affect neural complexity in cats using Lempel-Ziv compression, finding dose-dependent changes and differences across brain regions.

This study examines fluctuations in neural complexity during wakefulness using MEG and fMRI, finding links to conscious level and cognitive performance.