Steven Laureys

This study uses neural field models to assess thalamocortical function in disorders of consciousness, finding reduced corticothalamic integrity in UWS compared to MCS.

The paper develops an individualized TMS targeting approach for ASD based on functional connectivity to the posterior cingulate cortex, showing symptom reduction in a case series.

This study uses computational models to simulate psychedelic treatments for disorders of consciousness, finding they shift brains closer to criticality, with varying effects based on patient state.

The paper uses biophysical simulations to study recovery of neocortical activity in disorders of consciousness, finding that excitatory corticothalamic synaptic strength loss correlates with cerebral glucose uptake and potential for recovery.

This study develops a personalized fMRI analysis pipeline for disorders of consciousness, identifying connectivity and local parameters as biomarkers for diagnosis and prognosis.

This study examines non-duality in advanced meditators using EEG-based intrinsic neural timescales and psychological scales, finding that advanced meditators show reduced distinction between internal and external attention.

This study compares neural tracking and spectral entropy in DoC patients to assess their brain responses to speech, finding correlations with clinical diagnosis and behavioral responsiveness.

This study examines the time-to-onset and temporal dynamics of EEG changes during breath-watching meditation across three groups, finding significant differences in power changes between groups.

The paper shows that negative-valence visual stimulation at 5 Hz enhances subcortical theta oscillations and improves visual search and memory in humans.

This study applies neural field theory to EEG data to identify correlations between model parameters and consciousness states, highlighting potential biomarkers and physiological bases.

The paper identifies a unified central thalamus mechanism that explains diverse recovery outcomes in disorders of consciousness using electrophysiological data and machine learning.

The paper introduces an AI framework to simulate and predict mechanisms of consciousness and disorders, validated with neurophysiological data.

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 investigates how criticality in resting-state EEG correlates with perturbational complexity and consciousness during anesthesia, finding that unconscious states show reduced criticality.

This study uses dynamic causal modelling of EEG to differentiate and predict consciousness states in traumatic disorders of consciousness, showing left frontoparietal connectivity differences between UWS and MCS+ patients.

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.

A single session of rTMS improves brain metastability and intrinsic ignition in patients with essential tremor, suggesting potential therapeutic mechanisms.

The paper examines functional connectivity and network dynamics in disorders of consciousness, finding reduced metastability and network repertoire in unresponsive wakefulness syndrome compared to minimally conscious state.

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 identifies impaired neural propagation and responsiveness in disorders of consciousness using resting-state fMRI and links these deficits to glucose metabolism and specific circuit failures.

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 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 examines how dexmedetomidine sedation depth affects cortical excitability, finding non-linear changes and region-specific effects.

This paper uses computational modeling to show that changes in posterior hot zone and frontoparietal connectivity during propofol anaesthesia correlate with loss of consciousness.

This study shows that focal brain injuries lead to local sleep-like dynamics in the surrounding cortex, disrupting signal complexity while coexisting with wakefulness patterns.

StarNEt, a deep learning model, improves MRI segmentation for transcranial electrical stimulation by providing faster and more accurate white and gray matter masks compared to FreeSurfer.

This study evaluates an attention-based EEG paradigm to detect volitional brain responses in disorders of consciousness, finding responses in some patients without behavioral responses.

This study examines functional reorganization in children after hemispherotomy, finding altered perfusion and connectivity patterns in the remaining hemisphere.

The paper investigates how spontaneous hemodynamic response changes with different levels of consciousness using resting state fMRI, finding common dysfunction in precuneus and posterior cingulate across conditions.

The paper introduces PCIST, a fast method to estimate perturbational complexity of brain signals, validated on TMS/hd-EEG and SEEG data, showing comparable accuracy to the original PCI with improved speed and applicability.