Morten L. Kringelbach

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.

This study uses fMRI to analyze functional brain dynamics during music listening in adolescents, finding increased engagement of the orbitofrontal cortex reward system.

The paper examines the relationship between working memory abilities and brain activity during long-term recognition of auditory sequences, finding positive correlations in specific brain regions.

The paper shows that metastable oscillatory modes in the brain emerge from synchronization in the spacetime connectome, matching MEG data from healthy individuals.

This paper introduces a framework using turbulent dynamics to differentiate brain states by analyzing neuroimaging data with model-free and model-based measures.

This study uses MEG to show that weaker mother-infant bonding in first-time mothers is associated with reduced brain responses to infant cries in specific regions.

The paper shows that neural activity during conscious wakefulness has a preferred temporal direction, distinguishable from inverted signals, while this asymmetry is reduced during sleep and anesthesia.

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.

The paper examines associations between stock market indices and human mood, alcohol intake, blood pressure, and brain volume using UK Biobank data and replicates findings in US studies.

The paper uses in silico stimulation on a resting-state fMRI dataset to identify brain regions that can shift aging brain states to middle-aged states.

This study uses whole-brain network dynamics to differentiate four gender groups, finding distinct patterns in neural activity propagation and metastability.

This study identifies optimal spatiotemporal scales for whole-brain dynamics by analyzing fMRI data and quantifying network activity entropy across different spatial and temporal resolutions.

The paper identifies a dual simultaneous processing mechanism in the human brain for temporal pattern recognition using MEG and MRI.

This study compares structural and functional brain connectivity in individuals with high versus average fluid intelligence using DTI and MEG, finding differences in network organization and frequency-specific patterns.

This study examines how brain dynamics change during low-level states of consciousness, finding reduced stability in brain hubs and decreased heterogeneity in synchronization patterns.

The paper models human brain connectivity with and without long-range exceptions to show they improve information processing.

The paper identifies functional harmonic modes as a governing principle for brain organization, showing they can reconstruct task-related activity and delineate cortical areas from resting-state fMRI data.

The paper develops a framework to distinguish brain states by their stability and reversibility, using whole-brain models to analyze consciousness levels and their response to perturbations.

This study shows that regional transcriptional heterogeneity in excitatory and inhibitory receptor gene expression improves the accuracy of models of brain functional connectivity and supports complex neuronal dynamics.

This study identifies a low-dimensional intrinsic manifold underlying human brain dynamics across wakefulness and sleep, achieving 96% accuracy in decoding brain states.

This paper uses a deep learning framework to quantify non-equilibrium in brain activity, showing higher levels during tasks than rest and distinguishing between health and neuropsychiatric diseases.

This study uses fMRI to analyze brain activity in monkeys during wakefulness and anesthesia, identifying macroscopic properties and network changes associated with loss of consciousness.

The paper compares noise-driven multistability and deterministic chaos models to understand brain dynamics, finding chaotic models better reproduce multiple observables across parameter ranges.

This paper uses a whole-brain computational model to simulate perturbations under LSD and finds increased dynamical complexity and recovery time, with strongest effects in resting state networks.

This paper introduces graph neural fields, a framework for modeling brain activity on the human connectome, and demonstrates its ability to reproduce BOLD fMRI harmonic power spectra.

The paper investigates rapid brain mechanisms for encoding and predicting complex temporal sound sequences using MEG data from 68 participants.

This study shows that disruption of cortical hierarchy, measured by Intrinsic Ignition, is a new signature of consciousness loss during anesthesia.

The paper demonstrates amplitude turbulence in human brain dynamics using neuroimaging data and a whole-brain model, linking turbulence to optimal brain function and information processing.

The paper investigates how autistic and typically developing individuals process familiar music using fMRI, finding differences in brain network activity during familiar music listening.

The paper investigates brain dynamics during musical pattern recognition using MEG, finding increased activity and connectivity in a widespread network over time.