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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]

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]

Despite many calls, functional brain magnetic resonance imaging (fMRI) studies are relatively rare in the domain of entrepreneurship research. This methodological brief presents the brain-imaging method of resting-state fMRI (rs-fMRI) and illustrates its application in neuroentrepreneurship for the first time. In contrast to the traditional task-based fMRI approach, rs-fMRI observes the brain in the absence of cognitive tasks or presentation of stimuli, which offers benefits for improving our understanding of the entrepreneurial mind. Here, we describe the method and provide methodological motivations for performing brain resting-state functional neuroimaging studies on entrepreneurs. In addition, we illustrate the use of seed-based correlation analysis, one of the most common analytical approaches for analyzing rs-fMRI data. In this illustration, we show that habitual entrepreneurs have increased functional connectivity between the insula (a region associated with cognitive flexibility) and the anterior prefrontal cortex (a key region for explorative choice) as compared to managers. This increased connectivity could help promote flexible behavior. Thus in brief, we provide an exemplar of a novel way to expand our understanding of the brain in the domain of entrepreneurship. We discuss possible directions for future research and challenges to be addressed to facilitate the inclusion of re-fMRI studies into neuroentrepreneurship. [ABSTRACT FROM AUTHOR]

Background: Disconnected consciousness describes a state in which subjective experience (i.e., consciousness) becomes isolated from the external world. It appears frequently during sleep or sedation, when subjective experiences remain vivid but are unaffected by external stimuli. Traditional methods of differentiating connected and disconnected consciousness, such as relying on behavioral responsiveness or on post-anesthesia reports, have demonstrated limited accuracy: unresponsiveness has been shown to not necessarily equate to unconsciousness and amnesic effects of anesthesia and sleep can impair explicit recollection of events occurred during sleep/sedation. Due to these methodological challenges, our understanding of the neural mechanisms underlying sensory disconnection remains limited. Methods: To overcome these methodological challenges, we employ a distinctive strategy by combining a serial awakening paradigm with auditory stimulation during mild propofol sedation. While under sedation, participants are systematically exposed to auditory stimuli and questioned about their subjective experience (to assess consciousness) and their awareness of the sounds (to evaluate connectedness/disconnectedness from the environment). The data collected through interviews are used to categorize participants into connected and disconnected consciousness states. This method circumvents the requirement for responsiveness in assessing consciousness and mitigates amnesic effects of anesthesia as participants are questioned while still under sedation. Functional MRI data are concurrently collected to investigate cerebral activity patterns during connected and disconnected states, to elucidate sensory disconnection neural gating mechanisms. We examine whether this gating mechanism resides at the thalamic level or results from disruptions in information propagation to higher cortices. Furthermore, we explore the potential role of slow-wave activity (SWA) in inducing disconnected consciousness by quantifying high-frequency BOLD oscillations, a known correlate of slow-wave activity. Discussion: This study represents a notable advancement in the investigation of sensory disconnection. The serial awakening paradigm effectively mitigates amnesic effects by collecting reports immediately after regaining responsiveness, while still under sedation. Ultimately, this research holds the potential to understand how sensory gating is achieved at the neural level. These biomarkers might be relevant for the development of sensitive anesthesia monitoring to avoid intraoperative connected consciousness and for the assessment of patients suffering from pathologically reduced consciousness. [ABSTRACT FROM AUTHOR]

This article provides an overview of disorders of consciousness (DoCs) such as coma, unresponsive wakefulness syndrome (UWS), and minimally conscious state (MCS). It discusses the challenges in diagnosing and treating DoC patients and the impact on their families. The article also mentions resources and organizations that offer support and information for patients and their families. The authors emphasize the importance of further research and collaboration to enhance diagnosis, communication, treatment, and care for DoC patients. [Extracted from the article]

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]

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 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]

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]

Humans undergo extreme physiological changes when subjected to long periods of weightlessness, and as we continue to become a space-faring species, it is imperative that we fully understand the physiological changes that occur in the human body, including the brain. In this study, we present findings of brain structural changes associated with long-duration spaceflight based on diffusion magnetic resonance imaging (dMRI) data. Twelve cosmonauts who spent an average of six months aboard the International Space Station (ISS) were scanned in an MRI scanner pre-flight, ten days after flight, and at a follow-up time point seven months after flight. We performed differential tractography, a technique that confines white matter fiber tracking to voxels showing microstructural changes. We found significant microstructural changes in several large white matter tracts, such as the corpus callosum, arcuate fasciculus, corticospinal, corticostriatal, and cerebellar tracts. This is the first paper to use fiber tractography to investigate which specific tracts exhibit structural changes after long-duration spaceflight and may direct future research to investigate brain functional and behavioral changes associated with these white matter pathways. [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]

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]

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]

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]

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]

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]

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]

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]

Patients with prolonged disorders of consciousness (DoC) after severe brain injury may present residual behavioral and cognitive functions. Yet the bedside assessment of these functions is compromised by patients' multiple impairments. Standardized behavioral scales such as the Coma Recovery Scale-Revised (CRS-R) have been developed to diagnose DoC, but there is also a need for neuropsychological measurement in these patients. The Cognitive Assessment by Visual Election (CAVE) was therefore recently created. In this study, we describe five patients in minimally conscious state (MCS) or emerging from the MCS (EMCS). Their cognitive profiles, derived from the CRS-R and CAVE, are presented alongside their neuroimaging results using structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). Scores on the CAVE decreased along with the CRS-R total score, establishing a consistent behavioral/cognitive profile for each patient. Out of these five cases, the one with highest CRS-R and CAVE performance had the least extended cerebral hypometabolism. All patients showed structural and functional brain impairments that were consistent with their behavioral/cognitive profile as based on previous literature. For instance, the presence of visual and motor residual functions was respectively associated with a relative preservation of occipital and motor cortex/cerebellum metabolism. Moreover, residual language comprehension skills were found in the presence of preserved temporal and angular cortex metabolism. Some patients also presented structural impairment of hippocampus, suggesting the presence of memory impairments. Our results suggest that brain-behavior relationships might be observed even in severely brain-injured patients and they highlight the importance of developing new tools to assess residual cognition and language in MCS and EMCS patients. Indeed, a better characterization of their cognitive profile will be helpful in preparation of rehabilitation programs and daily routines. [ABSTRACT FROM AUTHOR]