Fernando E. Rosas

This study uses Bayesian heart-rate entropy to analyze autonomic dynamics during seizures, finding that seizure duration, not awareness, is the strongest correlate of entropy changes.

The paper uses complexity science to analyze brain dynamics during flow, boredom, and frustration induced by video games, finding distinct patterns in global entropy and network metrics.

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 introduces a voxel-wise O-information framework to analyze high-order functional interactions in neuroimaging data, revealing age-related changes in default mode network redundancy.

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.

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

This study investigates the coupling between structural connectivity and high-order functional interactions in the human brain, finding that structural networks are associated with both redundant and synergistic functional interactions.

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 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 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 paper uses Bayesian methods to analyze heart rate dynamics during psychedelic experiences, finding increased entropy and variability linked to subjective and neural effects.

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 paper develops a multivariate signal processing pipeline to build high-order brain networks from fMRI data and identifies high-order hubs associated with distinct brain systems.

This study uses Ising models and algorithmic complexity to analyze fMRI data and finds LSD increases brain complexity and moves it further from criticality.

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

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 uses O-Information to analyze how high-order statistical interdependencies in fMRI data change with age, finding increased redundancy in older adults across brain regions.

This study identifies common neural mechanisms underlying anaesthesia and disorders of consciousness by simulating brain dynamics with a whole-brain model incorporating GABA receptor density and structural connectomes.

This paper develops a mechanistic model explaining how 5HT2A-R activation increases neural entropy in the brain, showing non-uniform regional effects linked to anatomical connectivity.