Morten L. Kringelbach

This paper identifies disrupted hierarchical organization in disorders of consciousness using fluctuation-dissipation theorem deviations from fMRI data.

This paper introduces BROAD-NESS to study auditory memory networks and finds dual-stream mechanisms and whole-brain coordination in predictive coding.

This study uses MEG to investigate short-term auditory recognition by analyzing neural responses to same versus different sounds, finding distinct patterns in early and late negative components.

The paper uses whole-brain modeling to analyze brain dynamics during advanced meditation states, finding increased functional integration and near-critical dynamics in later states associated with minimal phenomenal experience.

This review evaluates neurophysiological measures in healthy aging, MCI, and dementia using EEG, MEG, and combined methods, highlighting their potential as diagnostic tools and the need for standardization.

This study examines changes in brain network dynamics after 60 minutes of aerobic cycling, finding reduced striato-frontoparietal connectivity.

Inception simulates long-term recovery in Disorders of Consciousness using whole-brain models and fMRI data, showing improved patient classification and potential for capturing pharmacological effects.

This paper applies whole-brain models to study state transitions in depression following psilocybin and escitalopram treatments, showing differential effects on susceptibility and drivability to healthier states.

The paper investigates brain networks involved in predictive coding of temporal sequences using MEG, finding modality-specific and shared networks for auditory and visual processing.

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

This paper quantifies the energy cost of non-equilibrium brain computation using a new framework, showing the human brain is more energy-efficient than other mammals and cognitive tasks vary in their energy demands.

The paper investigates how non-equilibrium dynamics in the brain relate to complexity in response to stimuli, finding reduced asymmetry and irreversibility in states of reduced consciousness.

The paper introduces a whole-brain model called NEMO that uses neurotransmitter maps to simulate brain dynamics and shows it correlates with behavioral performance and intelligence.

This study examines how working memory (WM) relates to long-term memory (LTM) recognition of musical sequences, finding WM impacts novel sequences more in older adults than younger ones, and musical training improves memory performance.

FREQ-NESS estimates frequency-resolved brain networks using MEG data, revealing simultaneous networks and their interactions during resting state and auditory stimulation.

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 MEG data to show LSD reconfigures brain network frequencies and spatial distributions in specific frequency bands across different experimental conditions.

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

This paper introduces a framework to quantify irreversibility in brain network dynamics across multiple levels and identifies higher-order organizational patterns in human brain interactions.

This study uses artificial neural networks to analyze personality and cognitive traits of 328 soccer players, finding elite players have enhanced executive functions and specific personality traits.

This paper introduces CHARM, a framework that captures low-dimensional manifolds of critical brain dynamics using complex harmonics, revealing differences between wakefulness and sleep.

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 shows LSD reduces directed information hierarchy in the brain by balancing signal sending and receiving, as measured by MEG data.

This paper investigates the long-term effects of psilocybin on fronto-striatal-thalamic circuit dynamics and connectivity using longitudinal fMRI data and computational modeling.

This study investigates how the brain generalises abstract musical concepts over time using MEG, finding that neural responses to novel sequences differ significantly at faster paces.

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.

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.