Search

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ihalainen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

High-order interactions are required across brain regions to accomplish specific cognitive functions. These functional interdependencies are reflected by synergistic information that can be obtained by combining the information from all the sources considered and redundant information (i.e., common information provided by all the sources). However, electroencephalogram (EEG) functional connectivity is limited to pairwise interactions thereby precluding the estimation of high-order interactions. In this multicentric study, we used measures of synergistic and redundant information to study in parallel the high-order interactions between five EEG electrodes during three non-ordinary states of consciousness (NSCs): Rajyoga meditation (RM), hypnosis, and auto-induced cognitive trance (AICT). We analyzed EEG data from 22 long-term Rajyoga meditators, nine volunteers undergoing hypnosis, and 21 practitioners of AICT. We here report the within-group changes in synergy and redundancy for each NSC in comparison with their respective baseline. During RM, synergy increased at the whole brain level in the delta and theta bands. Redundancy decreased in frontal, right central, and posterior electrodes in delta, and frontal, central, and posterior electrodes in beta1 and beta2 bands. During hypnosis, synergy decreased in mid-frontal, temporal, and mid-centro-parietal electrodes in the delta band. The decrease was also observed in the beta2 band in the left frontal and right parietal electrodes. During AICT, synergy decreased in delta and theta bands in left-frontal, right-frontocentral, and posterior electrodes. The decrease was also observed at the whole brain level in the alpha band. However, redundancy changes during hypnosis and AICT were not significant. The subjective reports of absorption and dissociation during hypnosis and AICT, as well as the mystical experience questionnaires during AICT, showed no correlation with the high-order measures. The proposed study is the first exploratory attempt to utilize the concepts of synergy and redundancy in NSCs. The differences in synergy and redundancy during different NSCs warrant further studies to relate the extracted measures with the phenomenology of the NSCs., Competing Interests: Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. C.S. is the founder of TranceScience Research Institute in Paris., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Graph signal processing (GSP) is a novel approach to analyse multi-dimensional neuroimaging data, constraining functional measures by structural characteristics in a single framework (i.e. graph signals). In this approach, functional time series are assigned to the vertices of the underlying weighted graph and GSP analysis is performed in each time point of the signal. Here we used GSP to study local brain connectivity changes in patients with disorders of consciousness based on resting state high density electroencephalography (hdEEG) recordings. Total variation of the graph signals is a measure of signal smoothness over the underlying graph. In this study, we constructed the underlying graph based on the geometrical distances between each electrode pairs in such a way that local smoothness of the signal can be studied. Total variation analysis in α-band showed that in the pathological states of altered consciousness, local short range communication of brain regions in this frequency band is stronger than in healthy states which shows that information is segregated in local regions in patients with disorders of consciousness.

Increasing evidence links disorders of consciousness (DOC) with disruptions in functional connectivity between distant brain areas. However, to which extent the balance of brain network segregation and integration is modified in DOC patients remains unclear. Using high-density electroencephalography (EEG), the objective of our study was to characterize the local and global topological changes of DOC patients' functional brain networks. Resting state high-density-EEG data were collected and analyzed from 82 participants: 61 DOC patients recovering from coma with various levels of consciousness (EMCS (n = 6), MCS+ (n = 29), MCS- (n = 17) and UWS (n = 9)), and 21 healthy subjects (i.e., controls). Functional brain networks in five different EEG frequency bands and the broadband signal were estimated using an EEG connectivity approach at the source level. Graph theory-based analyses were used to evaluate their relationship with decreasing levels of consciousness as well as group differences between healthy volunteers and DOC patient groups. Results showed that networks in DOC patients are characterized by impaired global information processing (network integration) and increased local information processing (network segregation) as compared to controls. The large-scale functional brain networks had integration decreasing with lower level of consciousness., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice. However, it is unknown how dependable these EEG markers are in the face of signal variability because of different EEG configurations, EEG protocols and subpopulations from different centres encountered in practice. In this study we analysed 327 recordings of patients with disorders of consciousness (148 unresponsive wakefulness syndrome and 179 minimally conscious state) and 66 healthy controls obtained in two independent research centres (Paris Pitié-Salpêtrière and Liège). We first show that a non-parametric classifier based on ensembles of decision trees provides robust out-of-sample performance on unseen data with a predictive area under the curve (AUC) of ~0.77 that was only marginally affected when using alternative EEG configurations (different numbers and positions of sensors, numbers of epochs, average AUC = 0.750 ± 0.014). In a second step, we observed that classifiers based on multiple as well as single EEG features generalize to recordings obtained from different patient cohorts, EEG protocols and different centres. However, the multivariate model always performed best with a predictive AUC of 0.73 for generalization from Paris 1 to Paris 2 datasets, and an AUC of 0.78 from Paris to Liège datasets. Using simulations, we subsequently demonstrate that multivariate pattern classification has a decisive performance advantage over univariate classification as the stability of EEG features decreases, as different EEG configurations are used for feature-extraction or as noise is added. Moreover, we show that the generalization performance from Paris to Liège remains stable even if up to 20% of the diagnostic labels are randomly flipped. Finally, consistent with recent literature, analysis of the learned decision rules of our classifier suggested that markers related to dynamic fluctuations in theta and alpha frequency bands carried independent information and were most influential. Our findings demonstrate that EEG markers of consciousness can be reliably, economically and automatically identified with machine learning in various clinical and acquisition contexts.

Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness metrics able to disentangle between these components have not been reported. Here, we propose an explainable consciousness indicator (ECI) using deep learning to disentangle the components of consciousness. We employ electroencephalographic (EEG) responses to transcranial magnetic stimulation under various conditions, including sleep (n = 6), general anesthesia (n = 16), and severe brain injury (n = 34). We also test our framework using resting-state EEG under general anesthesia (n = 15) and severe brain injury (n = 34). ECI simultaneously quantifies arousal and awareness under physiological, pharmacological, and pathological conditions. Particularly, ketamine-induced anesthesia and rapid eye movement sleep with low arousal and high awareness are clearly distinguished from other states. In addition, parietal regions appear most relevant for quantifying arousal and awareness. This indicator provides insights into the neural correlates of altered states of consciousness. The authors propose an explainable consciousness indicator using deep learning to quantify arousal and awareness under sleep, anesthesia, and in patients with disorders of consciousness. [ABSTRACT FROM AUTHOR]

Background and Objectives: Persistent post-concussive symptoms (PCS) consist of neurologic and psychological complaints persisting after a mild traumatic brain injury (mTBI). It affects up to 50% of mTBI patients, may cause long-term disability, and reduce patients' quality of life. The aim of this review was to examine the possible use of different neuroimaging modalities in PCS. Methods: Articles from Pubmed database were screened to extract studies that investigated the relationship between any neuroimaging features and symptoms of PCS. Descriptive statistics were applied to report the results. Results: A total of 80 out of 939 papers were included in the final review. Ten examined conventional MRI (30% positive finding), 24 examined diffusion weighted imaging (54.17% positive finding), 23 examined functional MRI (82.61% positive finding), nine examined electro(magneto)encephalography (77.78% positive finding), and 14 examined other techniques (71% positive finding). Conclusion: MRI was the most widely used technique, while functional techniques seem to be the most sensitive tools to evaluate PCS. The common functional patterns associated with symptoms of PCS were a decreased anti-correlation between the default mode network and the task positive network and reduced brain activity in specific areas (most often in the prefrontal cortex). Significance: Our findings highlight the importance to use functional approaches which demonstrated a functional alteration in brain connectivity and activity in most studies assessing PCS. [ABSTRACT FROM AUTHOR]

Due to life-saving medical advances, the diagnosis and treatment of disorders of consciousness (DOC) has become a more commonly occurring clinical issue. One recently developed intervention option has been non-invasive transcranial direct current stimulation. This dichotomy of patient responders may be better understood by investigating the mechanism behind the transcranial direct current stimulation (tDCS) intervention. The combination of transcranial magnetic stimulation and electroencephalography (TMS-EEG) has been an important diagnostic tool in DOC patients. We therefore examined the neural response using TMS-EEG both before and after tDCS in seven DOC patients (four diagnosed as in a minimally conscious state and three with unresponsive wakefulness syndrome). tDCS was applied over the dorsolateral prefrontal cortex, while TMS pulses were applied to the premotor cortex. None of the seven patients showed relevant behavioral change after tDCS. We did, however, find that the overall evoked slow activity was reduced following tDCS intervention. We also found a positive correlation between the strength of the slow activity and the amount of high-frequency suppression. However, there was no significant pre-post tDCS difference in high frequencies. In the resting-state EEG, we observed that both the incidence of slow waves and the positive slope of the wave were affected by tDCS. Taken together, these results suggest that the tDCS intervention can reduce the slow-wave activity component of bistability, but this may not directly affect high-frequency activity. We hypothesize that while reduced slow activity may be necessary for the recovery of neural function, especially consciousness, this alone is insufficient. [ABSTRACT FROM AUTHOR]

Determining the state of consciousness in patients with disorders of consciousness is a challenging practical and theoretical problem. Recent findings suggest that multiple markers of brain activity extracted from the EEG may index the state of consciousness in the human brain. Furthermore, machine learning has been found to optimize their capacity to discriminate different states of consciousness in clinical practice. However, it is unknown how dependable these EEG markers are in the face of signal variability because of different EEG configurations, EEG protocols and subpopulations from different centres encountered in practice. In this study we analysed 327 recordings of patients with disorders of consciousness (148 unresponsive wakefulness syndrome and 179 minimally conscious state) and 66 healthy controls obtained in two independent research centres (Paris Pitié-Salpêtrière and Liège). We first show that a non-parametric classifier based on ensembles of decision trees provides robust out-of-sample performance on unseen data with a predictive area under the curve (AUC) of ~0.77 that was only marginally affected when using alternative EEG configurations (different numbers and positions of sensors, numbers of epochs, average AUC = 0.750 ± 0.014). In a second step, we observed that classifiers based on multiple as well as single EEG features generalize to recordings obtained from different patient cohorts, EEG protocols and different centres. However, the multivariate model always performed best with a predictive AUC of 0.73 for generalization from Paris 1 to Paris 2 datasets, and an AUC of 0.78 from Paris to Liège datasets. Using simulations, we subsequently demonstrate that multivariate pattern classification has a decisive performance advantage over univariate classification as the stability of EEG features decreases, as different EEG configurations are used for feature-extraction or as noise is added. Moreover, we show that the generalization performance from Paris to Liège remains stable even if up to 20% of the diagnostic labels are randomly flipped. Finally, consistent with recent literature, analysis of the learned decision rules of our classifier suggested that markers related to dynamic fluctuations in theta and alpha frequency bands carried independent information and were most influential. Our findings demonstrate that EEG markers of consciousness can be reliably, economically and automatically identified with machine learning in various clinical and acquisition contexts. [ABSTRACT FROM AUTHOR]

We assess cerebral integrity with cortical and subcortical FDG-PET and cortical electroencephalography (EEG) within the mesocircuit model framework in patients with disorders of consciousness (DoCs). The mesocircuit hypothesis proposes that subcortical activation facilitates cortical function. We find that the metabolic balance of subcortical mesocircuit areas is informative for diagnosis and is associated with four EEG-based power spectral density patterns, cortical metabolism, and α power in healthy controls and patients with a DoC. Last, regional electrometabolic coupling at the cortical level can be identified in the θ and α ranges, showing positive and negative relations with glucose uptake, respectively. This relation is inverted in patients with a DoC, potentially related to altered orchestration of neural activity, and may underlie suboptimal excitability states in patients with a DoC. By understanding the neurobiological basis of the pathophysiology underlying DoCs, we foresee translational value for diagnosis and treatment of patients with a DoC. [Display omitted] • Biologically founded EEG spectral regimes are related to cortical glucose uptake • Subcortical metabolism correlates with cortical α power, glucose uptake, and consciousness • Cortical α is negatively and θ is positively related to cortical glucose uptake in controls • Cortical electrometabolic coupling is inverted in patients with a severe brain injury Patients with disorders of consciousness are characterized by reduced cerebral function as measured by glucose uptake and electrical activity. Annen et al. characterize the role of subcortical areas in sustaining cortical function and quantify electrometabolic coupling in healthy conscious and reduced conscious states after coma. [ABSTRACT FROM AUTHOR]

Recent advances in functional neuroimaging have demonstrated novel potential for informing diagnosis and prognosis in the unresponsive wakeful syndrome and minimally conscious states. However, these technologies come with considerable expense and difficulty, limiting the possibility of wider clinical application in patients. Here, we show that high density electroencephalography, collected from 104 patients measured at rest, can provide valuable information about brain connectivity that correlates with behaviour and functional neuroimaging. Using graph theory, we visualize and quantify spectral connectivity estimated from electroencephalography as a dense brain network. Our findings demonstrate that key quantitative metrics of these networks correlate with the continuum of behavioural recovery in patients, ranging from those diagnosed as unresponsive, through those who have emerged from minimally conscious, to the fully conscious locked-in syndrome. In particular, a network metric indexing the presence of densely interconnected central hubs of connectivity discriminated behavioural consciousness with accuracy comparable to that achieved by expert assessment with positron emission tomography. We also show that this metric correlates strongly with brain metabolism. Further, with classification analysis, we predict the behavioural diagnosis, brain metabolism and 1-year clinical outcome of individual patients. Finally, we demonstrate that assessments of brain networks show robust connectivity in patients diagnosed as unresponsive by clinical consensus, but later rediagnosed as minimally conscious with the Coma Recovery Scale-Revised. Classification analysis of their brain network identified each of these misdiagnosed patients as minimally conscious, corroborating their behavioural diagnoses. If deployed at the bedside in the clinical context, such network measurements could complement systematic behavioural assessment and help reduce the high misdiagnosis rate reported in these patients. These metrics could also identify patients in whom further assessment is warranted using neuroimaging or conventional clinical evaluation. Finally, by providing objective characterization of states of consciousness, repeated assessments of network metrics could help track individual patients longitudinally, and also assess their neural responses to therapeutic and pharmacological interventions. [ABSTRACT FROM AUTHOR]