Morten L. Kringelbach

This study uses off-equilibrium fluctuation-dissipation theorem to analyze brain dynamics in Alzheimer's disease, finding increased deviations in MCI and decreased deviations in AD, suggesting potential biomarkers for early detection.

This study uses connectome harmonic decomposition to show that ketamine-induced unresponsiveness increases localised harmonics, similar to psychedelic states, unlike traditional sedation which increases global harmonics.

This study shows that DMT alters connectome harmonics, increasing their repertoire entropy and correlating with subjective experience intensity over time.

This study uses MEG to investigate hierarchical brain mechanisms during auditory memory recognition and predictive coding, showing feedforward and backward connections and distinct power dynamics in different brain regions.

This study shows that hierarchical music syntax models predict theta power in brain regions associated with language and reward during music listening.

This paper uses computational models to simulate brain perturbations in disorders of consciousness, showing that transitions to healthy brain states can be achieved through targeted stimulation.

The paper shows that combining rare long-range connections with local connectivity improves the capture of functional brain dynamics compared to geometry alone.

This study examines how the pace of pubertal maturation affects brain organization by linking skeletal age stages to resting-state EEG entropy production.

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.

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.

The paper identifies brain regions and networks predictive of response to psilocybin therapy for treatment-resistant depression using whole-brain modeling and dynamic sensitivity analysis.

This study examines how mindfulness meditation affects brain dynamics by analyzing changes in harmonic complexity, revealing both immediate and long-term effects on brain state and trait characteristics.

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 uses a turbulent dynamics framework to analyze whole-brain fMRI data from TBI patients and healthy controls, showing reduced synchronization and reactivity during recovery.

This study examines how maternal prenatal distress affects neonatal brain network dynamics, finding reduced stability in the frontoparietal network.

HADES framework analyzes spacetime activity patterns in the brain, showing reduced low-frequency harmonic modes during DMT-induced states, indicating changes in functional hierarchy.

This study examines how age and musical training affect auditory short-term, long-term, and working memory in young and older adults, finding age-related declines and training benefits in specific tasks.

This study uses MEG to show that older adults have distinct neural responses to musical sequences compared to young adults, challenging the idea that aging uniformly reduces brain function.

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.

The paper uses computational modeling to identify key brain regions for personalized neurostimulation strategies to improve cognitive interventions in Alzheimer's disease.

This paper shows complex spatiotemporal brain dynamics emerge from transverse instabilities in large-scale neural models.

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 study uses EEG to examine age-related differences in short-term recognition of musical sequences, finding reduced neural responses in older adults despite no behavioral differences.

This study uses in-silico perturbation of a whole-brain model to identify critical brain areas involved in schizophrenia by analyzing resting-state fMRI data.

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

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

This study investigates how the complexity of auditory sequences affects neural activity during recognition memory, finding distinct brain network engagement for simple and complex stimuli.

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

The paper identifies integrated and segregated brain states during rest and tasks using graph theory and deep learning, finding integration correlates with information compression and segregation with specialization.

This paper shows that LSD and psilocybin reduce brain control energy and increase entropy by flattening the brain's energy landscape, using network control theory and PET data.