@article{pmid41072637,

title = {Altered electrophysiological meta-state dynamics in disorders of consciousness},

author = {Pablo Núñez and Prejaas Tewarie and Víctor Rodríguez-González and Naji L N Alnagger and Glenn J M van der Lande and Marie M Vitello and Paolo Cardone and Aurore Thibaut and Laouen Belloli and Steven Laureys and Jacobo D Sitt and Jitka Annen and Olivia Gosseries},

doi = {10.1016/j.neuroimage.2025.121519},

issn = {1095-9572},

year  = {2025},

date = {2025-12-01},

journal = {Neuroimage},

volume = {323},

pages = {121519},

abstract = {BACKGROUND: This multi-centric study aimed to explore differences in brain activity patterns in patients with disorders of consciousness (DoC), including unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS).nnMETHODS: Using high-density electroencephalographic (EEG) recordings from 368 DoC patients, 39 who emerged from MCS (eMCS), and 73 healthy controls, we examined instantaneous functional connectivity-based meta-states acting as attractors in a dynamical system, extracted by means of community detection algorithms and recurrence analysis. We analyzed data from two patient cohorts and included resting-state and auditory processing tasks in four frequency bands (delta, theta, alpha, beta) and from three perspectives, namely: (i) discrete activation of dominant states, (ii) a dynamical system composed of attractor states and (iii) the correlation and anticorrelation patterns of the active states.nnRESULTS: Findings revealed that while the overall structure of brain connectivity remained stable after injury, patients with DoC and those who emerged showed notable differences in the speed and consistency of how their brain states activated. Specifically, in higher frequencies, UWS patients exhibited faster, and less stable dynamics, shorter dwell times and decreased meta-state anticorrelation compared to those in MCS and eMCS. Moreover, a four-way combined learning classification analysis showed that the measures were able to distinguish the UWS and MCS subgroups.nnSIGNIFICANCE: These brain state dynamics could serve as valuable markers for assessing states of consciousness. Our results highlight the potential of using high-temporal resolution dynamic brain activity patterns to improve the understanding of altered consciousness and their application to clinical settings.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid41218082,

title = {Individual trajectories for recovery of neocortical activity in disorders of consciousness},

author = {Prejaas K B Tewarie and Romesh Abeysuriya and Rajanikant Panda and Pablo Nùñez and Marie M Vitello and Glenn van der Lande and Olivia Gosseries and Aurore Thibaut and Steven Laureys and Gustavo Deco and Jitka Annen},

doi = {10.1371/journal.pcbi.1013659},

issn = {1553-7358},

year  = {2025},

date = {2025-11-01},

journal = {PLoS Comput Biol},

volume = {21},

number = {11},

pages = {e1013659},

abstract = {The evolution from disturbed brain activity to physiological brain rhythms can precede recovery in patients with disorders of consciousness (DoC). Accordingly, intriguing questions arise: What are the pathophysiological factors associated to disrupted brain rhythms in patients with DoC, and are there potential pathways for individual patients with DoC to return to normal brain rhythms? We addressed these questions at the individual subject level using biophysical simulations based on electroencephalography (EEG). The main findings are that unconscious patients exhibit a loss of excitatory corticothalamic synaptic strength. Synaptic plasticity in this excitatory corticothalamic circuitry facilitates the return of physiological brain rhythms, characterized by the reappearance of spectral peaks and flattening of the aperiodic (1/f) component of the power spectrum, in the selection of patients with DoC, particularly in those who are minimally conscious. The extent to which this occurred was correlated with cerebral glucose uptake. The current findings emphasize the importance of excitatory thalamocortical activity in reestablishing normal brain rhythms after brain injury and show that biophysical modelling of the corticothalamic circuitry could help select patients who might be potentially receptive to treatment and undergo plasticity.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid40434895,

title = {Revisiting the Multilayer Network Framework for Electrophysiological Networks},

author = {Prejaas K B Tewarie and Steven Laureys and Rikkert Hindriks},

doi = {10.1089/brain.2025.0010},

issn = {2158-0022},

year  = {2025},

date = {2025-06-01},

journal = {Brain Connect},

volume = {15},

number = {5},

pages = {189--194},

abstract = { The multilayer network framework has emerged as an innovative approach for analyzing electrophysiological networks, providing insights into complex neuronal interactions by integrating connectivity across different frequency bands in electroencephalography (EEG) and magnetoencephalography (MEG) data.  Traditionally, multilayer networks have treated canonical frequency bands (e.g., delta, theta, alpha, beta, gamma) as distinct layers. Recent findings could raise potential concerns regarding this approach, emphasizing the need to incorporate the distinction between periodic (oscillatory) and aperiodic (broadband) signal components.  Aperiodic signals may reflect excitation-inhibition balance and scale-free dynamics, while periodic signals capture oscillatory rhythms, both contributing uniquely to brain network interactions. A multilayer network framework in the current context could be applicable in the case of genuine coupling between these components, termed "aperiodic-to-periodic coupling." This necessitates novel connectivity metrics and analytical methods that can handle broadband data. Furthermore, challenges remain in decomposing these components in the time domain and developing robust metrics for broadband connectivity that account for signal leakage.  Addressing these issues will enhance multilayer frameworks, enabling better insights into brain network integrity, cognitive dysfunction, and neurological conditions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38964240,

title = {Spatiotemporal dynamics of brain activity in cognition and consciousness: Comment on "Beyond task responsePre-stimulus activity modulates contents of consciousness" by Northoff, Zilio, and Zhang},

author = {Roxane S Hoyer and Prejaas K B Tewarie and Steven Laureys},

doi = {10.1016/j.plrev.2024.06.013},

issn = {1873-1457},

year  = {2024},

date = {2024-09-01},

journal = {Phys Life Rev},

volume = {50},

pages = {63--65},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid38520377,

title = {Higher-order functional connectivity analysis of resting-state functional magnetic resonance imaging data using multivariate cumulants},

author = {Rikkert Hindriks and Tommy A A Broeders and Menno M Schoonheim and Linda Douw and Fernando Santos and Wessel van Wieringen and Prejaas K B Tewarie},

doi = {10.1002/hbm.26663},

issn = {1097-0193},

year  = {2024},

date = {2024-04-01},

journal = {Hum Brain Mapp},

volume = {45},

number = {5},

pages = {e26663},

abstract = {Blood-level oxygenation-dependent (BOLD) functional magnetic resonance imaging (fMRI) is the most common modality to study functional connectivity in the human brain. Most research to date has focused on connectivity between pairs of brain regions. However, attention has recently turned towards connectivity involving more than two regions, that is, higher-order connectivity. It is not yet clear how higher-order connectivity can best be quantified. The measures that are currently in use cannot distinguish between pairwise (i.e., second-order) and higher-order connectivity. We show that genuine higher-order connectivity can be quantified by using multivariate cumulants. We explore the use of multivariate cumulants for quantifying higher-order connectivity and the performance of block bootstrapping for statistical inference. In particular, we formulate a generative model for fMRI signals exhibiting higher-order connectivity and use it to assess bias, standard errors, and detection probabilities. Application to resting-state fMRI data from the Human Connectome Project demonstrates that spontaneous fMRI signals are organized into higher-order networks that are distinct from second-order resting-state networks. Application to a clinical cohort of patients with multiple sclerosis further demonstrates that cumulants can be used to classify disease groups and explain behavioral variability. Hence, we present a novel framework to reliably estimate genuine higher-order connectivity in fMRI data which can be used for constructing hyperedges, and finally, which can readily be applied to fMRI data from populations with neuropsychiatric disease or cognitive neuroscientific experiments.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pmid35916363,

title = {Disruption in structural-functional network repertoire and time-resolved subcortical fronto-temporoparietal connectivity in disorders of consciousness},

author = {Rajanikant Panda and Aurore Thibaut and Ane Lopez-Gonzalez and Anira Escrichs and Mohamed Ali Bahri and Arjan Hillebrand and Gustavo Deco and Steven Laureys and Olivia Gosseries and Jitka Annen and Prejaas Tewarie},

doi = {10.7554/eLife.77462},

issn = {2050-084X},

year  = {2022},

date = {2022-08-01},

journal = {Elife},

volume = {11},

abstract = {Understanding recovery of consciousness and elucidating its underlying mechanism is believed to be crucial in the field of basic neuroscience and medicine. Ideas such as the global neuronal workspace (GNW) and the mesocircuit theory hypothesize that failure of recovery in conscious states coincide with loss of connectivity between subcortical and frontoparietal areas, a loss of the repertoire of functional networks states and metastable brain activation. We adopted a time-resolved functional connectivity framework to explore these ideas and assessed the repertoire of functional network states as a potential marker of consciousness and its potential ability to tell apart patients in the unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). In addition, the prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, that is so-called eigenmodes, was supplemented as potential markers. By analysing time-resolved functional connectivity from functional MRI data, we demonstrated a reduction of metastability and functional network repertoire in UWS compared to MCS patients. This was expressed in terms of diminished dwell times and loss of nonstationarity in the default mode network and subcortical fronto-temporoparietal network in UWS compared to MCS patients. We further demonstrated that these findings co-occurred with a loss of dynamic interplay between structural eigenmodes and emerging time-resolved functional connectivity in UWS. These results are, amongst others, in support of the GNW theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}