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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. [ABSTRACT FROM AUTHOR]

Voluntary apnea showcases extreme human adaptability in trained individuals like professional free divers. We evaluated the psychological and physiological adaptation and the functional cerebral changes using electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) to 6.5 min of dry static apnea performed by a world champion free diver. Compared to resting state at baseline, breath holding was characterized by increased EEG power and functional connectivity in the alpha band, along with decreased delta band connectivity. fMRI connectivity was increased within the default mode network (DMN) and visual areas but decreased in pre- and postcentral cortices. While these changes occurred in regions overlapping with cerebral signatures of several meditation practices, they also display some unique features that suggest an altered somatosensory integration. As suggested by self-reports, these findings could reflect the ability of elite free divers to create a state of sensory dissociation when performing prolonged apnea. [ABSTRACT FROM AUTHOR]

The neural monitoring of visceral inputs might play a role in first-person perspective (i.e., the unified viewpoint of subjective experience). In healthy participants, how the brain responds to heartbeats, measured as the heartbeat-evoked response (HER), correlates with perceptual, bodily, and self-consciousness. Here we show that HERs in resting-state EEG data distinguishes between postcomatose male and female human patients (n = 68, split into training and validation samples) with the unresponsive wakefulness syndrome and in patients in a minimally conscious state with high accuracy (random forest classifier, 87% accuracy, 96% sensitivity, and 50% specificity in the validation sample). Random EEG segments not locked to heartbeats were useful to predict unconsciousness/consciousness, but HERs were more accurate, indicating that HERs provide specific information on consciousness. HERs also led to more accurate classification than heart rate variability. HER-based consciousness scores correlate with glucose metabolism in the default-mode network node located in the right superior temporal sulcus, as well as with the right ventral occipitotemporal cortex. These results were obtained when consciousness was inferred from brain glucose metabolism measured with positron emission topography. HERs reflected the consciousness diagnosis based on brain metabolism better than the consciousness diagnosis based on behavior (Coma Recovery Scale-Revised, 77% validation accuracy). HERs thus seem to capture a capacity for consciousness that does not necessarily translate into intentional overt behavior. These results confirm the role of HERs in consciousness, offer new leads for future bedside testing, and highlight the importance of defining consciousness and its neural mechanisms independently from behavior. [ABSTRACT FROM AUTHOR]

Objectives: Surrogate decision-making by family caregivers for patients with severe brain injury is influenced by the availability and understanding of relevant information and expectations for future rehabilitation. We aimed to compare the consistency of family caregivers' perceptions with clinical diagnoses and to inform their expectation of prognosis in the future. Methods: The Coma Recovery Scale-Revised was used to assess the diagnosis of inpatients with severe brain injury between February 2019 and February 2020. A main family caregiver was included per patient. The family caregiver's perception of the patient's consciousness and expectations of future recovery were collected through questionnaires and compared consistently with the clinical diagnosis. Results: The final sample included 101 main family caregivers of patients (57 UWS, unresponsive wakefulness syndrome, 37 MCS, minimally conscious state, 7 EMCS, emergence from MCS) with severe brain injury. Only 57 family caregivers correctly assessed the level of consciousness as indicated by the CRS-R, showing weak consistency (Kappa = 0.217, P = 0.002). Family caregivers' demographic characteristics and CRS-R diagnosis influenced the consistency between perception and clinical diagnosis. Family caregivers who provided hands-on care to patients showed higher levels of consistent perception (AOR = 12.24, 95% CI = 2.06-73.00, P = 0.006). Compared to UWS, the family caregivers of MCS patients were more likely to have a correct perception (OR = 7.68, 95% CI = 1.34–44.06). Family caregivers had positive expectations for patients' recovery in terms of both communication and returning to normal life. Conclusion: Nearly half of family caregivers have inadequate understanding of their relative's level of consciousness, and most of them report overly optimistic expectations that do not align with clinical diagnosis. Providing more medical information to family caregivers to support their surrogate decision-making process is essential. [ABSTRACT FROM AUTHOR]

Objective: To obtain a CRS-R index suitable for diagnosis of patients with disorders of consciousness (DOC) and compare it to other CRS-R based scores to evaluate its potential for clinics and research. Design: We evaluated the diagnostic accuracy of several CRS-R-based scores in 124 patients with DOC. ROC analysis of the CRS-R total score, the Rasch-based CRS-R score, CRS-R-MS and the CRS-R index evaluated the diagnostic accuracy for patients with the Unresponsive Wakefulness Syndrome (UWS) and Minimally Conscious State (MCS). Correlations were computed between the CRS-R-MS, CRS-R index, the Rasch-based score and the CRS-R total score. Results: Both the CRS-R-MS and CRS-R index ranged from 0 to 100, with a cut-off of 8.315 that perfectly distinguishes between patients with UWS and MCS. The CRS-R total score and Rasch-based score did not provide a cut-off score for patients with UWS and MCS. The proposed CRS-R index correlated with the CRS-R total score, Rasch-based score and the CRS-R-MS. Conclusion: The CRS-R index is reliable to diagnose patients with UWS and MCS and can be used in compliance with the CRS-R scoring guidelines. The obtained index offers the opportunity to improve the interpretation of clinical assessment and can be used in (longitudinal) research protocols. Abbreviations: CRS-R: Coma Recovery Scale-Revised; CRS-R-MS: Coma Recovery Scale-Revised Modified Score; DOC: Disorders of Consciousness; MCS: Minimally Conscious State; UWS: Unresponsive Wakefulness Syndrome; ROC: Receiver Operating Characteristic; AUC: Area Under the Curve; IRT: Item Response Theory. [ABSTRACT FROM AUTHOR]

Objective: The relationship between residual brain tissue in patients with disorders of consciousness (DOC) and the clinical condition is unclear. This observational study aimed to quantify gray (GM) and white matter (WM) atrophy in states of (altered) consciousness.Methods: Structural T1-weighted magnetic resonance images were processed for 102 severely brain-injured and 52 healthy subjects. Regional brain volume was quantified for 158 (sub)cortical regions using Freesurfer. The relationship between regional brain volume and clinical characteristics of patients with DOC and conscious brain-injured patients was assessed using a linear mixed-effects model. Classification of patients with unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) using regional volumetric information was performed and compared to classification using cerebral glucose uptake from fluorodeoxyglucose positron emission tomography. For validation, the T1-based classifier was tested on independent datasets.Results: Patients were characterized by smaller regional brain volumes than healthy subjects. Atrophy occurred faster in UWS compared to MCS (GM) and conscious (GM and WM) patients. Classification was successful (misclassification with leave-one-out cross-validation between 2% and 13%) and generalized to the independent data set with an area under the receiver operator curve of 79% (95% confidence interval [CI; 67-91.5]) for GM and 70% (95% CI [55.6-85.4]) for WM.Interpretation: Brain volumetry at the single-subject level reveals that regions in the default mode network and subcortical gray matter regions, as well as white matter regions involved in long range connectivity, are most important to distinguish levels of consciousness. Our findings suggest that changes of brain structure provide information in addition to the assessment of functional neuroimaging and thus should be evaluated as well. Ann Neurol 2018;83:842-853. [ABSTRACT FROM AUTHOR]

Background: Over the past 5 decades, advances in neuroimaging have yielded insights into the pathophysiologic mechanisms that cause disorders of consciousness (DoC) in patients with severe brain injuries. Structural, functional, metabolic, and perfusion imaging studies have revealed specific neuroanatomic regions, such as the brainstem tegmentum, thalamus, posterior cingulate cortex, medial prefrontal cortex, and occipital cortex, where lesions correlate with the current or future state of consciousness. Advanced imaging modalities, such as diffusion tensor imaging, resting-state functional magnetic resonance imaging (fMRI), and task-based fMRI, have been used to improve the accuracy of diagnosis and long-term prognosis, culminating in the endorsement of fMRI for the clinical evaluation of patients with DoC in the 2018 US (task-based fMRI) and 2020 European (task-based and resting-state fMRI) guidelines. As diverse neuroimaging techniques are increasingly used for patients with DoC in research and clinical settings, the need for a standardized approach to reporting results is clear. The success of future multicenter collaborations and international trials fundamentally depends on the implementation of a shared nomenclature and infrastructure. Methods: To address this need, the Neurocritical Care Society's Curing Coma Campaign convened an international panel of DoC neuroimaging experts to propose common data elements (CDEs) for data collection and reporting in this field. Results: We report the recommendations of this CDE development panel and disseminate CDEs to be used in neuroimaging studies of patients with DoC. Conclusions: These CDEs will support progress in the field of DoC neuroimaging and facilitate international collaboration. [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]

Self-induced cognitive trance (SICT) is a voluntary non-ordinary state of consciousness characterized by a lucid yet narrowed awareness of the external surroundings. It involves a hyper-focused immersive experience of flow, expanded inner imagery, modified somatosensory processing, and an altered perception of self and time. SICT is gaining attention due to its potential clinical applications. Similar states of non-ordinary state of consciousness, such as meditation, hypnosis, and psychedelic experiences, have been reported to induce changes in the autonomic nervous system. However, the functioning of the autonomic nervous system during SICT remains poorly understood. In this study, we aimed to investigate the impact of SICT on the cardiac and respiratory signals of 25 participants proficient in SICT. To accomplish this, we measured various metrics of heart rate variability (HRV) and respiration rate variability (RRV) in three conditions: resting state, SICT, and a mental imagery task. Subsequently, we employed a machine learning framework utilizing a linear discriminant analysis classifier and a cross-validation scheme to identify the features that exhibited the best discrimination between these three conditions. The results revealed that during SICT, participants experienced an increased heart rate and a decreased level of high-frequency (HF) HRV compared to the control conditions. Additionally, specific increases in respiratory amplitude, phase ratio, and RRV were observed during SICT in comparison to the other conditions. These findings suggest that SICT is associated with a reduction in parasympathetic activity, indicative of a hyperarousal state of the autonomic nervous system during SICT. [ABSTRACT FROM AUTHOR]

Charles Bonnet syndrome (CBS) is a rare clinical condition characterized by complex visual hallucinations in people with loss of vision. So far, the neurobiological mechanisms underlying the hallucinations remain elusive. This case-report study aims at investigating electrical activity changes in a CBS patient during visual hallucinations, as compared to a resting-state period (without hallucinations). Prior to the EEG, the patient underwent neuropsychological, ophthalmologic, and neurological examinations. Spectral and connectivity, graph analyses and signal diversity were applied to high-density EEG data. Visual hallucinations (as compared to resting-state) were characterized by a significant reduction of power in the frontal areas, paralleled by an increase in the midline posterior regions in delta and theta bands and by an increase of alpha power in the occipital and midline posterior regions. We next observed a reduction of theta connectivity in the frontal and right posterior areas, which at a network level was complemented by a disruption of small-worldness (lower local and global efficiency) and by an increase of network modularity. Finally, we found a higher signal complexity especially when considering the frontal areas in the alpha band. The emergence of hallucinations may stem from these changes in the visual cortex and in core cortical regions encompassing both the default mode and the fronto-parietal attentional networks. [ABSTRACT FROM AUTHOR]

The evaluation of the level of consciousness in patients with disorders of consciousness (DOC) is primarily based on behavioural assessments. Patients with unresponsive wakefulness syndrome (UWS) do not show any sign of awareness of their environment, while minimally conscious state (MCS) patients show reproducible but fluctuating signs of awareness. Some patients, although with remaining cognitive abilities, are not able to exhibit overt voluntary responses at the bedside and may be misdiagnosed as UWS. Several studies investigated functional neuroimaging and neurophysiology as an additional tool to evaluate the level of consciousness and to detect covert command following in DOC. Most of these studies are based on auditory stimulation, neglecting patients suffering from decreased or absent hearing abilities. In the present study, we aim to assess the response to a P3-based paradigm in 40 patients with DOC and 12 healthy participants using auditory (AEP) and vibrotactile (VTP) stimulation. To this end, an EEG-based brain-computer interface was used at DOC patient's bedside. We compared the significance of the P3 performance (i.e., the interpretation of significance of the evoked P3 response) as obtained by 'direct processing' (i.e., theoretical-based significance threshold) and 'offline processing' (i.e., permutation-based single subject level threshold). We evaluated whether the P3 performances were dependent on clinical variables such as diagnosis (UWS and MCS), aetiology and time since injury. Last we tested the dependency of AEP and VTP performances at the single subject level. Direct processing tends to overestimate P3 performance. We did not find any difference in the presence of a P3 performance according to the level of consciousness (UWS vs. MCS) or the aetiology (traumatic vs. non-traumatic brain injury). The performance achieved at the AEP paradigm was independent from what was achieved at the VTP paradigm, indicating that some patients performed better on the AEP task while others performed better on the VTP task. Our results support the importance of using multimodal approaches in the assessment of DOC patients in order to optimise the evaluation of patient's abilities. [ABSTRACT FROM AUTHOR]

The investigation of sleep in disorders of consciousness (DoC) has shown promising diagnostic and prognostic results. However, the methods employed in this field of research are diverse. This leads to confusion in the way forward for both scientific and clinical purposes. We review the literature that has investigated sleep in DoC patients and specifically outline the methodologies used next to the presented results. We highlight what knowledge we currently have and where increased efforts are needed before further clinical implementation. Specifically, the review shows that successful methods may employ a two-stage approach to sleep scoring, where one is the application of loosened standard criteria and the other a more general factor describing closeness of the electroencephalography to a healthy pattern, including a score that describes the extent to which sleep scoring criteria can be applied. This should be performed as part of a multimodal approach that also includes investigations of eye-opening/closure and that of circadian (24-hour) rhythmicity. Taken together, this puts the most promising methodologies in the field together for a comprehensive investigation. Large-scale approaches, incorporating multiple modalities and looking at individual variation, are now needed to advance our understanding of sleep in DoC and its role in diagnosis, treatment, and recovery. [ABSTRACT FROM AUTHOR]

A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. 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 defined 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 transitions by applying off-line in silico probing. Furthermore, this approach enabled us to evaluate the impact of local perturbations in terms of their global effects and sensitivity to stimulation, which is a model-based biomarker providing a deeper understanding of the mechanisms underlying DoC. Our results show that transitions were obtained in a synchronous protocol, in which the somatomotor network, thalamus, precuneus and insula were the most sensitive areas to perturbation. This motivates further work to continue understanding brain function and treatments of disorders of consciousness. Author summary: We studied disorders of consciousness (DoC) 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 simulate transitions 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 somatomotor network, thalamus, precuneus and insula. In the long-term, this could bring valuable insights for understanding DoC and open new avenues for future clinical therapies. [ABSTRACT FROM AUTHOR]

Objective: Brain‐injured patients who are unresponsive at the bedside (ie, vegetative state/unresponsive wakefulness syndrome – VS/UWS) may present brain activity similar to patients in minimally conscious state (MCS). This peculiar condition has been termed "non‐behavioural MCS" or "MCS*". In the present study we aimed to investigate the proportion and underlying brain characteristics of patients in MCS*. Methods: Brain 18F‐fluorodeoxyglucose Positron Emission Tomography (FDG‐PET) was acquired on 135 brain‐injured patients diagnosed in prolonged VS/UWS (n = 48) or MCS (n = 87). From an existing database, relative metabolic preservation in the fronto‐parietal network (measured with standardized uptake value) was visually inspected by three experts. Patients with hypometabolism of the fronto‐parietal network were labelled "VS/UWS", while its (partial) preservation either confirmed the behavioural diagnosis of "MCS" or, in absence of behavioural signs of consciousness, suggested a diagnosis of "MCS*". Clinical outcome at 1‐year follow‐up, functional connectivity, grey matter atrophy, and regional brain metabolic patterns were investigated in the three groups (VS/UWS, MCS* and MCS). Results: 67% of behavioural VS/UWS presented a partial preservation of brain metabolism (ie, MCS*). Compared to VS/UWS patients, MCS* patients demonstrated a better outcome, global functional connectivity and grey matter preservation more compatible with the diagnosis of MCS. MCS* patients presented lower brain metabolism mostly in the posterior brain regions compared to MCS patients. Interpretation: MCS* is a frequent phenomenon that is associated with better outcome and better brain preservation than the diagnosis of VS/UWS. Complementary exams should be provided to all unresponsive patients before taking medical decisions. ANN NEUROL 2021;90:89–100 [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Neuroimaging" published in the journal Neurocritical Care. The correction states that the article title was updated to include the phrase "Common Data Elements for Disorders of Consciousness," which was mistakenly omitted during production. The correction notice also mentions that Springer Nature, the publisher, remains neutral regarding jurisdictional claims and institutional affiliations. The authors of the article are listed as Brian L. Edlow, Varina L. Boerwinkle, Jitka Annen, Melanie Boly, Olivia Gosseries, Steven Laureys, Pratik Mukherjee, Louis Puybasset, Robert D. Stevens, Zachary D. Threlkeld, Virginia F. J. Newcombe, and Davinia Fernandez-Espejo. [Extracted from the article]

Near-death experiences (NDEs) are usually associated with positive affect, however, a small proportion are considered distressing. We aimed to look into the proportion of distressing NDEs in a sample of NDE narratives, categorise distressing narratives according to Greyson and Bush's classification (inverse, void or hellish), and compare distressing and "classical" NDEs. Participants wrote down their experience, completed the Memory Characteristics Questionnaire (assessing the phenomenology of memories) and the Greyson scale (characterising content of NDEs). The proportion of suicidal attempts, content and intensity of distressing and classical NDEs were compared using frequentist and Bayesian statistics. Distressing NDEs represent 14% of our sample (n = 123). We identified 8 inverse, 8 hellish and 1 void accounts. The proportion of suicide survivors is higher in distressing NDEs as compared to classical ones. Finally, memories of distressing NDEs appear as phenomenologically detailed as classical ones. Distressing NDEs deserve careful consideration to ensure their integration into experiencers' identity. [ABSTRACT FROM AUTHOR]

Hypnosis has been shown to be of clinical utility; however, its underlying neural mechanisms remain unclear. This study aims to investigate altered brain dynamics during the non-ordinary state of consciousness induced by hypnosis. We studied high-density EEG in 9 healthy participants during eyes-closed wakefulness and during hypnosis, induced by a muscle relaxation and eyes fixation procedure. Using hypotheses based on internal and external awareness brain networks, we assessed region-wise brain connectivity between six ROIs (right and left frontal, right and left parietal, upper and lower midline regions) at the scalp level and compared across conditions. Data-driven, graph-theory analyses were also carried out to characterize brain network topology in terms of brain network segregation and integration. During hypnosis, we observed (1) increased delta connectivity between left and right frontal, as well as between right frontal and parietal regions; (2) decreased connectivity for alpha (between right frontal and parietal and between upper and lower midline regions) and beta-2 bands (between upper midline and right frontal, frontal and parietal, also between upper and lower midline regions); and (3) increased network segregation (short-range connections) in delta and alpha bands, and increased integration (long-range connections) in beta-2 band. This higher network integration and segregation was measured bilaterally in frontal and right parietal electrodes, which were identified as central hub regions during hypnosis. This modified connectivity and increased network integration–segregation properties suggest a modification of the internal and external awareness brain networks that may reflect efficient cognitive-processing and lower incidences of mind-wandering during hypnosis. [ABSTRACT FROM AUTHOR]

The study of the brain's dynamical activity is opening a window to help the clinical assessment of patients with disorders of consciousness. For example, glucose uptake and the 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 data, fitted to a linear model of activity propagation. 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 severe reduction of 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 shed light on the mechanisms behind disorders of consciousness, triangulating network function with basic measures of brain integrity and behavior. [ABSTRACT FROM AUTHOR]

Background. Many patients with severe brain damage may survive and remain in a prolonged disorder of consciousness (PDoC), impacting the quality of life (QoL) and needs of their family caregivers. However, the current literature on the factors influencing these needs is contradictory. We aim to describe the needs, QoL, and emotional distress of caregivers of patients with PDoC. Methods. Questionnaires investigating the importance and satisfaction of six categories of needs (i.e., health information, emotional, instrumental, and professional supports, community support network, and involvement in care), QoL, and emotional distress were completed by the main caregivers of PDoC patients. Results. We analyzed 177 questionnaires. Seventy-nine percent of the needs were considered as important or very important, and 44% were partially met or unmet. The needs for health information and professional support were the most important, while the needs for involvement in care and for health information were the most satisfied. Mean QoL was low and emotional distress high. Variables such as care setting and time since brain injury affected the level of QoL and distress. Conclusion. The needs for health information and professional support should receive particular attention. Given their low QoL and high distress, adequate support structures should be provided to caregivers of PDoC patients. [ABSTRACT FROM AUTHOR]

The prospect of continued manned space missions warrants an in-depth understanding of how prolonged microgravity affects the human brain. Functional magnetic resonance imaging (fMRI) can pinpoint changes reflecting adaptive neuroplasticity across time. We acquired resting-state fMRI data of cosmonauts before, shortly after, and eight months after spaceflight as a follow-up to assess global connectivity changes over time. Our results show persisting connectivity decreases in posterior cingulate cortex and thalamus and persisting increases in the right angular gyrus. Connectivity in the bilateral insular cortex decreased after spaceflight, which reversed at follow-up. No significant connectivity changes across eight months were found in a matched control group. Overall, we show that altered gravitational environments influence functional connectivity longitudinally in multimodal brain hubs, reflecting adaptations to unfamiliar and conflicting sensory input in microgravity. These results provide insights into brain functional modifications occurring during spaceflight, and their further development when back on Earth. rs-fMRI is used to investigate human brain connectivity changes induced by prolonged microgravity in cosmonauts before and after spaceflight, with both persistent and reversible location specific changes in connectivity being observed. [ABSTRACT FROM AUTHOR]

The self-organising global dynamics underlying brain states emerge from complex recursive nonlinear interactions between interconnected brain regions. Until now, most efforts of capturing the causal mechanistic generating principles have supposed underlying stationarity, being unable to describe the non-stationarity of brain dynamics, i.e. time-dependent changes. Here, we present a novel framework able to characterise brain states with high specificity, precisely by modelling the time-dependent dynamics. Through describing a topological structure associated to the brain state at each moment in time (its attractor or 'information structure'), we are able to classify different brain states by using the statistics across time of these structures hitherto hidden in the neuroimaging dynamics. Proving the strong potential of this framework, we were able to classify resting-state BOLD fMRI signals from two classes of post-comatose patients (minimally conscious state and unresponsive wakefulness syndrome) compared with healthy controls with very high precision. Author summary: Brain states emerge through continuously evolving dynamics of brain networks. The usual way of modelling these dynamics is by using stationary systems: there is one structure (attractor) which is responsible of the brain dynamics. We adopt a different approach by characterising the brain activity through a landscape of informational structures (IS) changing in time. We use a model transformation procedure to produce these structures and look at several properties related to how the different brain networks interact not in the observed resting-state fMRI signal but in the information structure underlying it. These properties provide measures strongly related with relevant characteristics of conscious activity, such as metastability, information integration or synchronisation. The distribution of IS measures is studied for healthy controls (HC) and two groups of post-comatose patients with disorders of consciousness (DOC): minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS). Based on IS measures, machine learners classifiers identify the state of consciousness with an outstanding discrimination (precision of 95.6% por HC/DOC and 86.6% for MCS/UWS). [ABSTRACT FROM AUTHOR]

Significant advances have been made by identifying the levels of synchrony of the underlying dynamics of a given brain state. This research has demonstrated that non-conscious dynamics tend to be more synchronous than in conscious states, which are more asynchronous. Here we go beyond this dichotomy to demonstrate that different brain states are underpinned by dissociable spatiotemporal dynamics. We investigated human neuroimaging data from different brain states (resting state, meditation, deep sleep and disorders of consciousness after coma). The model-free approach was based on Kuramoto's turbulence framework using coupled oscillators. This was extended by a measure of the information cascade across spatial scales. Complementarily, the model-based approach used exhaustive in silico perturbations of whole-brain models fitted to these measures. This allowed studying of the information encoding capabilities in given brain states. Overall, this framework demonstrates that elements from turbulence theory provide excellent tools for describing and differentiating between brain states. A unifying turbulent dynamics framework using both model-free and modelbased measures of whole-brain information provides insights into brain states. [ABSTRACT FROM AUTHOR]

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. • Study on three different non-ordinary states of consciousness (NSCs). • Rajyoga meditation (RM), hypnosis, and auto-induced cognitive trance (AICT). • The first study to utilize high-order measures in three different NSCs. • Synergy increases during RM and decreases during hypnosis and AICT. • Redundancy decreases during RM in delta and beta bands. [ABSTRACT FROM AUTHOR]

Keywords: consciousness; islands of awareness; cortical complexity EN consciousness islands of awareness cortical complexity 545 546 2 07/25/20 20200801 NES 200801 Bayne et al. (2020) [1] envision the fascinating possibility that disconnected hemispheres (through hemispherotomy), ex cranio brains, and cerebral organoids may be islands of awareness (IOAs). Perhaps Bayne et al.[1] do not refer to subjective experience when speaking about consciousness in IOAs. Even if we accept the possibility of reducing consciousness to a certain measure of cortical complexity, we are still faced with the problem of defining a set of values consistent with consciousness. [Extracted from the article]

Low-level states of consciousness are characterized by disruptions of brain activity that sustain arousal and awareness. Yet, how structural, dynamical, local and network brain properties interplay in the different levels of consciousness is unknown. Here, we study fMRI brain dynamics from patients that suffered brain injuries leading to a disorder of consciousness and from healthy subjects undergoing propofol-induced sedation. We show that pathological and pharmacological low-level states of consciousness display less recurrent, less connected and more segregated synchronization patterns than conscious state. We use whole-brain models built upon healthy and injured structural connectivity to interpret these dynamical effects. We found that low-level states of consciousness were associated with reduced network interactions, together with more homogeneous and more structurally constrained local dynamics. Notably, these changes lead the structural hub regions to lose their stability during low-level states of consciousness, thus attenuating the differences between hubs and non-hubs brain dynamics. López-González et al study the fMRI brain dynamics and their underlying mechanism from patients that suffered brain injuries leading to a disorder of consciousness as well as from healthy subjects undergoing propofol-induced sedation. They show that pathological and pharmacological low-level states of consciousness display disrupted synchronization patterns, higher constraint to the anatomy and a loss of heterogeneity and stability in the structural hubs compared to conscious states. [ABSTRACT FROM AUTHOR]

Consciousness transiently fades away during deep sleep, more stably under anesthesia, and sometimes permanently due to brain injury. The development of an index to quantify the level of consciousness across these different states is regarded as a key problem both in basic and clinical neuroscience. We argue that this problem is ill-defined since such an index would not exhaust all the relevant information about a given state of consciousness. While the level of consciousness can be taken to describe the actual brain state, a complete characterization should also include its potential behavior against external perturbations. We developed and analyzed whole-brain computational models to show that the stability of conscious states provides information complementary to their similarity to conscious wakefulness. Our work leads to a novel methodological framework to sort out different brain states by their stability and reversibility, and illustrates its usefulness to dissociate between physiological (sleep), pathological (brain-injured patients), and pharmacologically-induced (anesthesia) loss of consciousness. Author summary: How can different states of reduced consciousness be characterized and classified? This question carries great significance both for basic and clinical neuroscience, since loss of consciousness is transient when induced by anesthesia or deep sleep, but can be permanent in certain brain-injured patients. We demonstrated that perturbational analyses applied to semi-empirical whole-brain models can disentangle conscious states of different stability, providing information that complements data-driven metrics, and opening the way for new computational tools for the diagnosis and prognosis of disorders of consciousness. [ABSTRACT FROM AUTHOR]

Charles Bonnet syndrome (CBS) is a rare condition characterized by visual impairment associated with complex visual hallucinations in elderly people. Although studies suggested that visual hallucinations may be caused by brain damage in the visual system in CBS patients, alterations in specific brain regions in the occipital cortex have not been studied. Functional connectivity during resting-state functional magnetic resonance imaging (rs-fMRI; without hallucinations) in CBS patients, has never been explored. We aimed to investigate brain structural and functional changes in a patient with CBS, as compared with late blind (LB) and normally sighted subjects. We employed voxel-based morphometry and cortical thickness analyses to investigate alterations in grey matter characteristics, and rs-fMRI to study changes in functional brain connectivity. Decreased grey matter volume was observed in the middle occipital gyrus and in the cuneus in the CBS patient, and in the middle occipital gyrus and in the lingual gyrus within LB subjects, compared to their respective control groups. Reductions in cortical thickness in associative and multimodal cortices were observed in the CBS patient when comparing with LB subjects. The precuneus exhibited increased functional connectivity with the secondary visual cortex in the CBS patient compared to the controls. In contrast, LB patients showed decreased functional connectivity compared to sighted controls between the DMN and the temporo-occipital fusiform gyrus, a region known to support hallucinations. Our findings suggest a reorganization of the functional connectivity between regions involved in self-awareness and in visual and salience processing in CBS that may contribute to the appearance of visual hallucinations. [ABSTRACT FROM AUTHOR]

Different behavioural signs of consciousness can distinguish patients with an unresponsive wakefulness syndrome from patients in minimally conscious state (MCS). The Coma Recovery Scale-Revised (CRS-R) is the most sensitive scale to differentiate the different altered states of consciousness and eleven items detect the MCS. The aim of this study is to document the prevalence of these items. We analysed behavioural assessments of 282 patients diagnosed in MCS based on the CRS-R. Results showed that some items are particularly frequent among patients in MCS, namely fixation, visual pursuit, and reproducible movement to command, which were observed in more than 50% of patients. These responses were also the most probably observed items when the patients only showed one sign of consciousness. On the other hand, some items were rarely or never observed alone, e.g., object localisation (reaching), object manipulation, intelligible verbalisation, and object recognition. The results also showed that limiting the CRS-R assessment to the five most frequently observed items (i.e., fixation, visual pursuit, reproducible movement to command, automatic motor response and localisation to noxious stimulation) detected 99% of the patients in MCS. If clinicians have only limited time to assess patients with disorders of consciousness, we suggest to evaluate at least these five items of the CRS-R. [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]

Introduction Given that recent research has shown that functional connectivity is not a static phenomenon, we aim to investigate the dynamic properties of the default mode network's (DMN) connectivity in patients with disorders of consciousness. Methods Resting-state fMRI volumes of a convenience sample of 17 patients in unresponsive wakefulness syndrome (UWS) and controls were reduced to a spatiotemporal point process by selecting critical time points in the posterior cingulate cortex (PCC). Spatial clustering was performed on the extracted PCC time frames to obtain 8 different co-activation patterns (CAPs). We investigated spatial connectivity patterns positively and negatively correlated with PCC using both CAPs and standard stationary method. We calculated CAPs occurrences and the total number of frames. Results Compared to controls, patients showed (i) decreased within-network positive correlations and between-network negative correlations, (ii) emergence of 'pathological' within-network negative correlations and between-network positive correlations (better defined with CAPs), and (iii) 'pathological' increases in within-network positive correlations and between-network negative correlations (only detectable using CAPs). Patients showed decreased occurrence of DMN-like CAPs (1-2) compared to controls. No between-group differences were observed in the total number of frames Conclusion CAPs reveal at a more fine-grained level the multifaceted spatial connectivity reconfiguration following the DMN disruption in UWS patients, which is more complex than previously thought and suggests alternative anatomical substrates for consciousness. BOLD fluctuations do not seem to differ between patients and controls, suggesting that BOLD response represents an intrinsic feature of the signal, and therefore that spatial configuration is more important for consciousness than BOLD activation itself. Hum Brain Mapp 39:89-103, 2018. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Examining task-free functional connectivity (FC) in the human brain offers insights on how spontaneous integration and segregation of information relate to human cognition, and how this organization may be altered in different conditions, and neurological disorders. This is particularly relevant for patients in disorders of consciousness (DOC) following severe acquired brain damage and coma, one of the most devastating conditions in modern medical care. We present a novel data-driven methodology, connICA , which implements Independent Component Analysis (ICA) for the extraction of robust independent FC patterns (FC-traits) from a set of individual functional connectomes, without imposing any a priori data stratification into groups. We here apply connICA to investigate associations between network traits derived from task-free FC and cognitive/clinical features that define levels of consciousness. Three main independent FC-traits were identified and linked to consciousness-related clinical features. The first one represents the functional configuration of a “resting” human brain, and it is associated to a sedative (sevoflurane), the overall effect of the pathology and the level of arousal. The second FC-trait reflects the disconnection of the visual and sensory-motor connectivity patterns. It also relates to the time since the insult and to the ability of communicating with the external environment. The third FC-trait isolates the connectivity pattern encompassing the fronto-parietal and the default-mode network areas as well as the interaction between left and right hemispheres, which are also associated to the awareness of the self and its surroundings. Each FC-trait represents a distinct functional process with a role in the degradation of conscious states of functional brain networks, shedding further light on the functional sub-circuits that get disrupted in severe brain-damage. [ABSTRACT FROM AUTHOR]

Background. Transcranial direct current stimulation (tDCS) over the left dorsolateral prefrontal cortex (lDLPFC) was reported to promote the recovery of signs of consciousness in some patients in a minimally conscious state (MCS), but its electrophysiological effects on brain activity remain poorly understood. Objective. We aimed to assess behavioral (using the Coma Recovery Scale—Revised; CRS-R) and neurophysiological effects (using high density electroencephalography; hdEEG) of lDLPFC-tDCS in patients with prolonged disorders of consciousness (DOC). Methods. In a double-blind, sham-controlled, crossover design, one active and one sham tDCS (2 mA, 20 min) were delivered in a randomized order. Directly before and after tDCS, 10 min of hdEEG were recorded and the CRS-R was administered. Results. Thirteen patients with severe brain injury were enrolled in the study. We found higher relative power at the group level after the active tDCS session in the alpha band in central regions and in the theta band over the frontal and posterior regions (uncorrected results). Higher weighted symbolic mutual information (wSMI) connectivity was found between left and right parietal regions, and higher fronto-parietal weighted phase lag index (wPLI) connectivity was found, both in the alpha band (uncorrected results). At the group level, no significant treatment effect was observed. Three patients showed behavioral improvement after the active session and one patient improved after the sham. Conclusion. We provide preliminary indications that neurophysiological changes can be observed after a single session of tDCS in patients with prolonged DOC, although they are not necessarily paralleled with significant behavioral improvements. [ABSTRACT FROM AUTHOR]