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Laureys, S.

Publications and source records attributed to Laureys, S..

At least 19 recordsLinked to original sources

Individualized Functional Connectivity-Guided TMS Targeting Theory of Mind Network for Autism Spectrum Disorder

Transcranial magnetic stimulation (TMS) shows promise in autism spectrum disorder (ASD), but variable outcomes may reflect suboptimal targeting. We developed a functional-connectivity (FC)-guided individualized TMS approach by identifying an ASD-relevant effective region and selecting superficial targets. In a multi-site mega-analysis of Autism Brain Imaging Data Exchange I data (298 ASD, 348 controls), the region with the greatest regional homogeneity (ReHo) abnormality was defined as the effective region. Individualized dorsolateral prefrontal cortex (DLPFC) and inferior parietal lobule (IPL) targets were localized as sites with strongest FC to this region. Group differences, symptom associations, and a six-patient case series were examined. The posterior cingulate cortex (PCC) showed the greatest ReHo abnormality and was implicated in theory-of-mind (ToM) circuitry. PCC-guided targets showed weaker FC in ASD in the right IPL, correlating with Autism Diagnostic Interview social scores; left DLPFC FC differences lacked symptom associations. In the case series, individualized PCC-IPL-guided TMS reduced ToM-related symptoms and Childhood Autism Rating Scale scores. PCC-IPL FC-guided TMS is a biologically informed intervention for modulating ToM circuitry in ASD.

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Individualised electrophysiological neural field models for the assessment of thalamocortical function in disorders of consciousness: a multicentre study

Understanding the neural mechanisms underlying disorders of consciousness (DoC) remains a major challenge, particularly in distinguishing limited awareness in minimally conscious state (MCS) and complete unawareness in unresponsive wakefulness syndrome, also coined vegetative state (UWS/VS). In this multicentre study, we fitted a biophysically informed corticothalamic neural field model to high-density EEG data from two large independent datasets, comprising 203 UWS patients, 270 MCS patients and 74 healthy controls. We then used the fitted parameters to simulate EEG time series on a per-subject basis and compared empirical and simulated complexity metrics. The model reliably captured the spectral features across different states of consciousness and revealed reduced corticothalamic integrity in DoC patients that was more pronounced in UWS than in MCS, supporting the mesocircuit hypothesis. Furthermore, the simulated EEG reproduced the complexity patterns of the empirical recordings, with permutation entropy emerging as a sensitive marker capable of distinguishing between MCS and UWS for both real and simulated time series.

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Comparison of Neural Tracking and Spectral Entropy in Patients with Disorders of Consciousness

ObjectivesThis study aims to explore the brain responses of patients with disorders of consciousness (DoC) to natural speech. Specifically, it focuses on a key characteristic of natural speech: the speech envelope. To achieve this, we employed two distinct measures. The first evaluates how effectively the patients brain activity "tracks" the speech envelope, called neural tracking. The second assesses the complexity of the brains responses to the speech stimulus, measured through spectral entropy. These two measures are then compared in their association with the patients clinical diagnosis and their level of behavioral responsiveness, both of which were assessed using the Coma Recovery Scale-Revised (CRS-R). DesignFour patients with DoC participated in this study, during which their brain activity was recorded using electroen-cephalography (EEG). At the same time, they listened to a narrated story in both Dutch and Swedish. Additionally, EEG baseline recordings were collected. We employed a backward modeling approach to evaluate the speech envelopes neural tracking. This technique involves training a model to map the relationship between EEG signals and the corresponding speech envelope. Once the model is trained, it can use unseen EEG data to reconstruct the speech envelope, which is then compared to the original speech envelope to assess how effectively the patient processed the auditory stimulus. For the behavioral assessment, we recalculated the CRS-R score of each patient into the CSR-R index, a more meaningful score that utilizes all the information contained in the CRS-R instead of only the highest scores on each subscale. ResultsOur findings revealed positive correlations between spectral entropy and the CRS-R index, which were more pronounced during the listening conditions than the baseline. While neural tracking of the speech envelope did not correlate with the CRS-R index, it did exhibit a positive association with CRS-R diagnoses, indicating that patients with better clinical diagnoses demonstrated higher levels of neural tracking. Additionally, we identified an interaction effect between spectral entropy and neural tracking. Specifically, higher levels of neural tracking were associated with a stronger positive relationship between spectral entropy and the CRS-R index. In contrast, when neural tracking was lower, this relationship disappeared. ConclusionThis study demonstrated the potential of neural tracking and spectral entropy as complementary tools to investigate patients with DoC. Spectral entropy proved valuable for assessing behavioral responsiveness, while neural tracking shows promise in assessing the DoC diagnosis. Terms: disorders of consciousness (DoC), neural tracking, speech envelope, spectral entropy

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Non-duality in brain and experience of advanced meditators - Key role for Intrinsic Neural Timescales

Distinguishing between self (internal) and environment (external) is fundamental to human experience, with ordinary waking consciousness structured around this duality. However, contemplative traditions describe non-dual states where this distinction dissolves. Despite its significance, the neural basis of non-duality remains underexplored. Using psychological questionnaires for non-duality experience and EEG-based intrinsic neural timescales as measured by the autocorrelation window (ACW), we studied non-duality in advanced meditators, novice meditators, and controls. All subjects underwent breath-watching meditation (internal attention) and a visual oddball cognitive task (external attention); this allowed us to conceptualize non-duality as a lack of distinction between internal and external attention. Our key findings include: (a) advanced meditators report greater experience of non-duality during breath-watching (psychological scales), (b) EEG-based ACW is longer during internal attention (breath watch) than external attention (oddball task) in all subjects taken together, (c) advanced meditators show no such distinction with equal duration of their ACW during both internal and external attention (we replicated this finding in another dataset of expert meditators); (d) the advanced meditators internal-external ACW difference correlated with their experience of the degree of non-duality (psychological scales) during internal attention. Together, these findings suggest that the brains intrinsic neural timescales during internal and external attention play a key role in mediating the experience of non-duality in advanced meditators.

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Time-to-onset and temporal dynamics of EEG during breath-watching meditation

IntroductionMind-body practices, such as meditation, enhance mental well-being. Research studies consistently demonstrate improved brain function and psychological well-being in meditation practitioners. A substantial body of neuroscientific evidence highlights changes in alpha and theta frequency bands during meditation among practitioners. Neurophysiological effects of meditation are reported as average power changes from resting to meditative states. However, there is a notable gap in research concerning the time-to-onset and temporal dynamics of these changes during meditation. MethodOur study addresses this gap by recording high-density 128-channel EEG data during breath- watching meditation in three groups: meditation-naive controls (n = 28), novice meditators (n = 33), and advanced meditators (n = 42). Meditators were trained in the Isha Yoga tradition. Real-time changes in brain power across different frequency bands were analyzed by segmenting the EEG data into 1-minute intervals. Using the first 30 seconds of breath- watching as the baseline, we calculated within-group power differences between this baseline and successive 1-minute segments (non-overlapping, non-sliding windows). For between- group comparisons, we assessed power differences among the three groups at 0.5, 3, 6, and 9 minutes. ResultsOur results indicate that time-to-onset of statistically significant increases in alpha, theta, and beta1 power, as well as decreases in delta and gamma1 power, occur around the 2-3 minute mark, with effects starting to peak between 7- and 10-minutes duration across all three groups. Statistically significant differences were observed between groups in the magnitude of these changes: advanced practitioners exhibited higher theta and theta-alpha power at all time points compared to the other groups. ConclusionOur findings suggest that neurophysiological changes begin around 2-3 minutes after starting meditation and peak around 7-10 minutes across all three groups. However, the magnitude of these effects is greater in the advanced meditator group. As long as meditation retreats are not possible for many individuals, brief meditation practices of 7 minutes or more, delivered through digital platforms, could offer accessible, effective, and scalable solutions to improve mental well-being. This suggests a broader application of meditation practices in daily life, encouraging even those with tight schedules to incorporate such beneficial practices.

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A unified central thalamus mechanism underlying diverse recoveries in disorders of consciousness

Disorders of consciousness (DoC) encompass a range of states characterized by prolonged altered awareness due to heterogeneous brain damage and are associated with highly diverse prognoses. However, the neural mechanisms underlying such diverse recoveries in DoC remain unclear. To address this issue, we analysed neuronal spiking activities recorded from the central thalamus (CT), a key hub in arousal regulation, in a cohort of 23 DoC patients receiving deep brain stimulation treatment. Using machine learning techniques, we identified a core set of electrophysiological features of the CT, particularly the theta rhythm, that could account for individual recovery outcomes across highly varied etiologies (trauma, brainstem hemorrhage, and anoxia), clinical baselines and patient ages. These features also correctly identified one subgroup of patients who exhibited poor initial clinical manifestations but recovered unexpectedly. Simulating a conductance-based model further revealed the neurodynamics of the theta rhythm in the CT during different stages of consciousness recovery. Taken together, these findings uncover a previously unknown, unified CT mechanism that governs the recoveries in DoC.

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A virtual clinical trial of psychedelics to treat patients with disorders of consciousness

Disorders of consciousness (DoC), including the unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), have limited treatment options. Recent research suggests that psychedelic drugs, known for their complexity-enhancing properties, could be promising treatments for DoC. This study uses whole-brain computational models to explore this potential. We created individualised models for DoC patients, optimised with empirical fMRI and diffusion-weighted imaging (DWI) data, and simulated the administration of LSD and psilocybin. We used an in-silico perturbation protocol to distinguish between different states of consciousness, including DoC, anaesthesia, and the psychedelic state, and assess the dynamical stability of the brains of DoC patients pre- and post-psychedelic simulation. Our findings indicate that LSD and psilocybin shift DoC patients brains closer to criticality, with a greater effect in MCS patients. In UWS patients, the treatment response correlates with structural connectivity, while in MCS patients, it aligns with baseline functional connectivity. This virtual clinical trial lays a computational foundation for using psychedelics in DoC treatment and highlights the future role of computational modelling in drug discovery and personalised medicine.

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Individual trajectories for recovery of neocortical activity in disorders of consciousness

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 responsible for 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 fosters physiological brain rhythms in the selection of patients with DoC. 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 that might be potentially receptive to treatment and undergo plasticity.

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Criticality of resting-state EEG predicts perturbational complexity and level of consciousness during anesthesia

1Consciousness has been proposed to be supported by electrophysiological patterns poised at criticality, a dynamical regime which exhibits adaptive computational properties, maximally complex patterns and divergent sensitivity to perturbation. Here, we investigated dynamical properties of the resting-state electroencephalogram of healthy subjects undergoing general anesthesia with propofol, xenon or ketamine. We then studied the relation of these dynamic properties with the perturbational complexity index (PCI), which has shown remarkably high sensitivity in detecting consciousness independent of behavior. All participants were unresponsive under anesthesia, while consciousness was retained only during ketamine anesthesia (in the form of vivid dreams)., enabling an experimental dissociation between unresponsiveness and unconsciousness. We estimated (i) avalanche criticality, (ii) chaoticity, and (iii) criticality-related measures, and found that states of unconsciousness were characterized by a distancing from both the edge of activity propagation and the edge of chaos. We were then able to predict individual subjects PCI (i.e., PCImax) with a mean absolute error below 7%. Our results establish a firm link between the PCI and criticality and provide further evidence for the role of criticality in the emergence of consciousness. 2 Significance StatementComplexity has long been of interest in consciousness science and had a fundamental impact on many of todays theories of consciousness. The perturbational complexity index (PCI) uses the complexity of the brains response to cortical perturbations to quantify the presence of consciousness. We propose criticality as a unifying framework underlying maximal complexity and sensitivity to perturbation in the conscious brain. We demonstrate that criticality measures derived from resting-state electroencephalography can distinguish conscious from unconscious states, using propofol, xenon and ketamine anesthesia, and from these measures we were able to predict the PCI with a mean error below 7%. Our results support the hypothesis that critical brain dynamics are implicated in the emergence of consciousness and may provide new directions for the assessment of consciousness.

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Re-awakening the brain: Forcing transitions in disorders of consciousness by external in silico perturbation

A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. The ability to promote a transition from one brain state to another by externally driven stimulation could significantly impact rehabilitation and treatments for patients suffering from complex brain injury cases. Thus, it is crucial to find therapeutic interventions able to re-balance the dynamics of brain disorders towards more healthy regimes. Here, we investigated resting-state fMRI data of patients suffering from disorders of consciousness (DoC) after coma (minimally conscious and unresponsive wakefulness states) and healthy controls. We applied model-free and model-based approaches to help elucidate the underlying brain mechanisms of patients with DoC. The model-free approach allowed us to characterize brain states in DoC and healthy controls as a probabilistic metastable substate (PMS) space. The PMS of each group was characterized by a repertoire of unique patterns (i.e., metastable substates) with different probabilities of occurrence. In the model-based approach, we adjusted the PMS of each DoC group to a causal whole-brain model. This allowed us to explore optimal strategies for promoting a transition to the PMS of the control group by applying off-line in silico probing. Furthermore, this approach enabled us to evaluate the impact of all possible local perturbations in terms of their global effects and sensitivity to stimulation, which is a biomarker providing a deeper understanding of the mechanisms underlying DoC. Our results show that transitions from DoC to more healthy regimes were obtained in a synchronous protocol, in which areas from the motor and subcortical networks were the most sensitive to perturbation. This motivates further work to continue understanding brain function and treatments of disorders of consciousness by external stimulation. Author summaryWe studied disorders of consciousness by defining a brain state as a repertoire of metastable substates with different probabilities of occurrence. We created whole-brain computational models of DoC to uncover the causal mechanisms underlying recovery. These models allowed us to transition from DoC to a control healthy state by studying the effects of artificial individual local perturbations under different protocol regimes. We demonstrated successful transitions in the synchronization protocol and showed that the most sensitive areas were located in the motor network and subcortical regions. We believe this could be very valuable for developing clinical treatments and has a great deal for future therapies.

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Lateral frontoparietal effective connectivity differentiates and predicts state of consciousness in traumatic disorders of consciousness

Neuroimaging studies have suggested an important role for the default mode network (DMN) in disorders of consciousness (DoC). However, the extent to which DMN connectivity can discriminate DoC states - unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) - is less evident. Particularly, it is unclear whether effective DMN connectivity, as measured indirectly with dynamic causal modelling (DCM) of resting EEG can disentangle UWS from healthy controls and from patients considered conscious (MCS+). Crucially, this extends to UWS patients with potentially "covert" awareness (minimally conscious star, MCS*) indexed by voluntary brain activity in conjunction with partially preserved frontoparietal metabolism as measured with positron emission tomography (PET+ diagnosis; in contrast to PET-diagnosis with complete frontoparietal hypometabolism). Here, we address this gap by using DCM of EEG data acquired from patients with traumatic brain injury in 11 UWS (6 PET- and 5 PET+) and in 12 MCS+ (11 PET+ and 1 PET-), alongside with 11 healthy controls. We provide evidence for a key difference in left frontoparietal connectivity when contrasting UWS PET- with MCS+ patients and healthy controls. Next, in a leave-one-subject-out cross-validation, we tested the classification performance of the DCM models demonstrating that connectivity between medial prefrontal and left parietal sources reliably discriminates UWS PET- from MCS+ patients and controls. Finally, we illustrate that these models generalize to an unseen dataset: models trained to discriminate UWS PET- from MCS+ and controls, classify MCS* patients as conscious subjects with high posterior probability (pp > .92). These results identify specific alterations in the DMN after severe brain injury and highlight the clinical utility of EEG- based effective connectivity for identifying patients with potential covert awareness. Author SummaryOur study investigates the role of the Default Mode Network (DMN) in individuals with disorders of consciousness (DoC), such as unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). Previous neuroimaging studies have suggested a role for the DMN in DoC, but its ability to differentiate between UWS and MCS remain unclear. Using advance brain imaging and modelling techniques, we analyzed data from DoC patients with traumatic brain injury and healthy controls. Our findings reveal a key difference in left frontoparietal connectivity when comparing UWS to MCS patients and healthy individuals. To validate our results, we employed a robust cross-validation approach, which demonstrated that the connectivity between frontal and left parietal brain regions reliably discriminates UWS patients from MCS patients and controls. Furthermore, we extended our analysis to include patients with potential covert awareness, showcasing the clinical utility of our findings. We successfully classified these patients as conscious with high accuracy. This research significantly contributes to our understanding of the DMN in DoC and highlights the potential use of electroencephalography-based connectivity analysis in clinical settings. By identifying specific alterations in the DMN after severe brain injury, our study may aid in the accurate diagnosis and management of individuals with disorders of consciousness, potentially improving their overall outcomes.

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Low-dimensional organization of global brain states of reduced consciousness

Brain states are frequently represented using a unidimensional scale measuring the richness of subjective experience (level of consciousness). This description assumes a mapping between the high-dimensional space of whole-brain configurations and the trajectories of brain states associated with changes in consciousness, yet this mapping and its properties remain unknown. We combined whole-brain modelling, data augmentation and deep learning for dimensionality reduction to determine a mapping representing states of consciousness in a low-dimensional space, where distances parallel similarities between states. An orderly trajectory from wakefulness to brain injured patients is revealed in a latent space whose coordinates represent metrics related to functional modularity and structure-function coupling, both increasing alongside loss of consciousness. Finally, we investigated the effects of model perturbations, providing geometrical interpretation for the stability and reversibility of states. We conclude that conscious awareness depends on functional patterns encoded as a low-dimensional trajectory within the vast space of brain configurations.

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Single session of rTMS enhances brain metastability and intrinsic ignition

BackgroundEmerging evidence support the view that brain stimulation might improve essential tremor (ET) by altering brain networks and facilitating plasticity. Yet, we are still missing a mechanistic explanation of the whole brain dynamics underlying these plasticity defining changes. MethodIn this study, we explored the effect of low-frequency repetitive transcranial magnetic stimulation (rTMS) over left primary motor cortex (L-M1) on functional connectivity dynamics (FCD) in patients with ET. Resting-state fMRI (RsfMRI) was acquired before and after a single session of rTMS in 30 patients with ET and compared with RsfMRI of 20 age and gender matched healthy controls (HCs). We have measured the effect of brain stimulation using network topological re-organization through whole brain integration and segregation, brain stability and capacity of neural propagation through metastability and intrinsic ignition. ResultsPatients with ET had altered FCD measures compared to controls. After a single session rTMS, the brain connectivity measures approached normality and patients with ET revealed significantly higher integration, lower segregation with higher metastability and increased intrinsic ignition. ConclusionBrain metastability and intrinsic ignition measures could be valuable tools in appreciating mechanisms of brain stimulation in ET and other neurological diseases.

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Disruption in structural-functional network repertoire and time resolved subcortical-frontoparietal connectivity in disorders of consciousness

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 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, prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, i.e. so-called eigenmodes, were supplemented as potential markers. By analysing time-resolved functional connectivity from fMRI 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 fronto-parietal subcortical 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 global neuronal workspace theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness.

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Posterior integration and thalamo-frontotemporal broadcasting are impaired in disorders of consciousness

The study of the brains static and dynamical activity is opening a valuable source of assistance for the clinical assessment of patients with disorders of consciousness. For example, glucose uptake and dysfunctional spread of naturalistic and synthetic stimuli has proven useful to characterize hampered consciousness. However, understanding of the mechanisms behind loss of consciousness following brain injury is still missing. Here, we study the propagation of endogenous and in-silico exogenous perturbations in patients with disorders of consciousness, based upon directed and causal interactions estimated from resting-state fMRI. We found that patients with disorders of consciousness suffer decreased capacity for neural propagation and responsiveness to events, and that this can be related to glucose metabolism as measured with [18F]FDG-PET. In particular, we show that loss of consciousness is related to the malfunctioning of two neural circuits: the posterior cortical regions failing to convey information, in conjunction with reduced broadcasting of information from subcortical, temporal, parietal and frontal regions. These results seed light on the mechanisms behind disorders of consciousness, triangulating network function with basic measures of brain integrity and behavior. HighlightsO_LIPropagation of neural events and network responses are disrupted in patients with DoC. C_LIO_LILoss of consciousness is related to the malfunctioning of two neural circuits. C_LIO_LIPosterior cortical regions lack to integrate information in altered consciousness. C_LIO_LIBreakdown of information broadcasting of subcortical cortical areas in DoC. C_LIO_LILoss of network responses in DoC patients is related to glucose metabolism. C_LI

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Unifying turbulent dynamics framework distinguishes different brain states

Recently, significant advances have been made by identifying the levels of synchronicity of the underlying dynamics of a given brain state. This research has demonstrated that unconscious dynamics tend to be more synchronous than those found in conscious states, which are more asynchronous. Here we go beyond this dichotomy to demonstrate that the different brain states are always underpinned by spatiotemporal chaos but with dissociable turbulent dynamics. We investigated human neuroimaging data from different brain states (resting state, meditation, deep sleep, and disorders of consciousness after coma) and were able to distinguish between them using complementary model-free and model-based measures of turbulent information transmission. Our model-free approach used recent advances describing a measure of information cascade across spatial scales using tools from turbulence theory. Complementarily, our model-based approach used exhaustive in silico perturbations of whole-brain models fitted to the empirical neuroimaging data, which allowed us to study the information encoding capabilities of the brain states. Overall, the current framework demonstrates that different levels of turbulent dynamics are fundamental for describing and differentiating between brain states.

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Depth of sedation with dexmedetomidine modulates cortical excitability non-linearly

BackgroundCortical excitability changes across conscious states, being higher in unconsciousness compared to normal wakefulness. Anaesthesia offers controlled manipulation to investigate conscious processes and underlying brain dynamics. Among commonly used anaesthetic agents, dexmedetomidine (DEX) effects are not completely known. In this study, we investigated cortical excitability as a function of DEX sedation depth. MethodsTranscranial magnetic stimulation coupled with electroencephalography was recorded in 20 healthy subjects undergoing DEX sedation in four conditions (baseline, light sedation, deep sedation, recovery). Frontal and parietal cortices were stimulated using a neuronavigation system. Cortical excitability was inferred by slope, amplitude, positive and negative peak latencies of the first component (0-30 ms) of the TMS-evoked potential. Four Generalized Linear Mixed Models (GLMM) were used to test the effect of condition and brain region over cortical excitability. ResultsDexmedetomidine modulated amplitude (P<0.001), slope (P=0.0001) and positive peak (P=0.042), while the targeted brain region affected amplitude (P<0.001), slope (P<0.001), and negative peak (P=0.001). The interaction between dexmedetomidine and region had an effect over amplitude (P=0.004), and slope (P=0.009) such that cortical excitability was higher during all conditions where DEX was present as compared to the baseline. ConclusionsCortical excitability changes non-linearly as a function of the depth of DEX sedation, with a paradoxical non dose-dependent increase. The effect is region-specific, being present in the frontal but not in the parietal region. Future research should extend the current results with other anaesthetics to better understand the link between cortical excitability and depth of sedation.

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How hot is the hot zone? Computational modelling clarifies the role of parietal and frontoparietal connectivity during anaesthetic-induced loss of consciousness

In recent years, specific cortical networks have been proposed to be crucial for sustaining consciousness, including the posterior hot zone and frontoparietal resting state networks (RSN). Here, we computationally evaluate the relative contributions of three RSNs - the default mode network (DMN), the salience network (SAL), and the central executive network (CEN) - to consciousness and its loss during propofol anaesthesia. Specifically, we use dynamic causal modelling (DCM) of 10 minutes of high-density EEG recordings (N = 10, 4 males) obtained during behavioural responsiveness, unconsciousness and post-anaesthetic recovery to characterise differences in effective connectivity within frontal areas, the posterior "hot zone", frontoparietal connections, and between-RSN connections. We estimate - for the first time - a large DCM model (LAR) of resting EEG, combining the three RSNs into a rich club of interconnectivity. Consistent with the hot zone theory, our findings demonstrate reductions in inter-RSN connectivity in the parietal cortex. Within the DMN itself, the strongest reductions are in feed-forward frontoparietal and parietal connections at the precuneus node. Within the SAL and CEN, loss of consciousness generates small increases in bidirectional connectivity. Using novel DCM leave-one-out cross-validation, we show that the most consistent out-of-sample predictions of the state of consciousness come from a key set of frontoparietal connections. This finding also generalises to unseen data collected during post-anaesthetic recovery. Our findings provide new, computational evidence for the importance of the posterior hot zone in explaining the loss of consciousness, highlighting also the distinct role of frontoparietal connectivity in underpinning conscious responsiveness, and consequently, suggest a dissociation between the mechanisms most prominently associated with explaining the contrast between conscious awareness and unconsciousness, and those maintaining consciousness. HighlightsO_LIModelling shows that connectivity within hot zone tracks change of conscious state C_LIO_LISeparately, frontoparietal connections support maintenance of conscious state C_LIO_LIStrength of frontoparietal connections predicts conscious state in unseen data C_LIO_LIBoth parietal hot zone and frontoparietal connectivity important for consciousness C_LI FundingThis work was supported by the UK Engineering and Physical Sciences Research Council (EP/P033199/1), Belgian National Funds for Scientific Research (FRS-FNRS), the University and University Hospital of Liege, the Fund Generet, the King Baudouin Foundation, the AstraZeneca Foundation, the European Unions Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3), DOCMA project (EU-H2020-MSCA-RISE-778234), the BIAL Foundation, the European Space Agency (ESA) and the Belgian Federal Science Policy Office (BELSPO) in the framework of the PRODEX Programme, the Center-TBI project (FP7-HEALTH-602150), the Public Utility Foundation Universite Europeenne du Travail, "Fondazione Europea di Ricerca Biomedica", the Mind Science Foundation, the European Commission, and the Special Research Fund of Ghent University. O.G. is research associate and S.L. is research director at the F.R.S-FNRS. Declaration of interestNone. Significance StatementVarious connectivity studies have suggested multiple network-level mechanisms driving changes in the state of consciousness, such as the posterior hot zone, frontal-, and large-scale frontoparietal networks. Here, we computationally evaluate evidence for these mechanisms using dynamic causal modeling for resting EEG recorded before and during propofol-anaesthesia, and demonstrate that, particularly, connectivity in the posterior hot zone is impaired during propofol-induced unconsciousness. With a robust cross-validation paradigm, we show that connectivity in the large-scale frontoparietal networks can consistently predict the state of consciousness and further generalise these findings to an unseen state of recovery. These results suggest a dissociation between the mechanisms most prominently associated with explaining the contrast between conscious awareness and unconsciousness, and those maintaining consciousness.

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