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

A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. 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.

Correlation between Thalamocortical Tract and Default Mode Network with Consciousness Levels in Hypoxic-Ischemic Brain Injury Patients: A Comparative Study Using the Coma Recovery Scale-Revised.

BACKGROUND The thalamocortical tract (TCT) links nerve fibers between the thalamus and cerebral cortex, relaying motor/sensory information. The default mode network (DMN) comprises bilateral, symmetrical, isolated cortical regions of the lateral and medial parietal and temporal brain cortex. The Coma Recovery Scale-Revised (CRS-R) is a standardized neurobehavioral assessment of disorders of consciousness (DOC). In the present study, 31 patients with hypoxic-ischemic brain injury (HI-BI) were compared for changes in the TCT and DMN with consciousness levels assessed using the CRS-R. MATERIAL AND METHODS In this retrospective study, 31 consecutive patients with HI-BI (17 DOC,14 non-DOC) and 17 age- and sex-matched normal control subjects were recruited. Magnetic resonance imaging was used to diagnose HI-BI, and the CRS-R was used to evaluate consciousness levels at the time of diffusion tensor imaging (DTI). The fractional anisotropy (FA) values and tract volumes (TV) of the TCT and DMN were compared. RESULTS In patients with DOC, the FA values and TV of both the TCT and DMN were significantly lower compared to those of patients without DOC and the control subjects (p<0.05). When comparing the non-DOC and control groups, the TV of the TCT and DMN were significantly lower in the non-DOC group (p<0.05). Moreover, the CRS-R score had strong positive correlations with the TV of the TCT (r=0.501, p<0.05), FA of the DMN (r=0.532, p<0.05), and TV of the DMN (r=0.501, p<0.05) in the DOC group. CONCLUSIONS This study suggests that both the TCT and DMN exhibit strong correlations with consciousness levels in DOC patients with HI-BI.

Temporal and spatial variability of dynamic microstate brain network in disorders of consciousness.

BACKGROUND: Accurately diagnosing patients with the vegetative state (VS) and the minimally conscious state (MCS) reached a misdiagnosis of approximately 40%. METHODS: A method combined microstate and dynamic functional connectivity (dFC) to study the spatiotemporal variability of the brain in disorders of consciousness (DOC) patients was proposed. Resting-state EEG data were obtained from 16 patients with MCS and 16 patients with VS. Mutual information (MI) was used to assess the EEG connectivity in each microstate. MI-based features with statistical differences were selected as the total feature subset (TFS), then the TFS was utilized to feature selection and fed into the classifier, obtaining the optimal feature subsets (OFS) in each microstate. Subsequently, an OFS-based MI functional connectivity network (MIFCN) was constructed in the cortex. RESULTS: The group-average MI connectivity matrix focused on all channels revealed that all five microstates exhibited stronger information interaction in the MCS when comparing with the VS. While OFS-based MIFCN, which only focused on a few channels, revealed greater MI flow in VS patients than in MCS patients under microstates A, B, C, and E, except for microstate D. Additionally, the average classification accuracy of OFS in the five microstates was 96.2%. CONCLUSION: Constructing features based on microstates to distinguish between two categories of DOC patients had effectiveness.

Swallowing dysfunctions in patients with disorders of consciousness: Evidence from neuroimaging data, assessment, and management.

Following severe brain injuries, a subset of patients may remain in an altered state of consciousness; most of these patients require artificial feeding. Currently, a functional oral phase and the presence of exclusive oral feeding may constitute signs of consciousness. Additionally, the presence of pharyngo-laryngeal secretions, saliva aspiration, cough reflex and tracheostomy are related to the level of consciousness. However, the link between swallowing and consciousness is yet to be fully understood. The primary aim of this review is to establish a comprehensive overview of the relationship between an individual's conscious behaviour and swallowing (reflexive and voluntary). Previous studies of brain activation during volitional and non-volitional swallowing tasks in healthy subjects are also reviewed. We demonstrate that the areas activated by voluntary swallowing tasks (primary sensorimotor, cingulate, insula, premotor, supplementary motor, cerebellum, and operculum) are not specific to deglutitive function but are shared with other motor tasks and brain networks involved in consciousness. This review also outlines suitable assessment and treatment methods for dysphagic patients with disorders of consciousness. Finally, we propose that markers of swallowing could contribute to the development of novel diagnostic guidelines for patients with disorders of consciousness.

Covert Tracking to Immersive Stimuli in Traumatic Brain Injury Subjects With Disorders of Consciousness.

Eye tracking assessments are clinician dependent and can contribute to misclassification of coma. We investigated responsiveness to videos with and without audio in traumatic brain injury (TBI) subjects using video eye-tracking (VET). We recruited 20 healthy volunteers and 10 unresponsive TBI subjects. Clinicians were surveyed whether the subject was tracking on their bedside assessment. The Coma Recovery Scale-Revised (CRS-R) was also performed. Eye movements in response to three different 30-second videos with and without sound were recorded using VET. The videos consisted of moving characters (a dancer, a person skateboarding, and Spiderman). Tracking on VET was defined as visual fixation on the character and gaze movement in the same direction of the character on two separate occasions. Subjects were classified as "covert tracking" (tracking using VET only), "overt tracking" (VET and clinical exam by clinicians), and "no tracking". A k-nearest-neighbors model was also used to identify tracking computationally. Thalamocortical connectivity and structural integrity were evaluated with EEG and MRI. The ability to obey commands was evaluated at 6- and 12-month follow-up. The average age was 29 (± 17) years old. Three subjects demonstrated "covert tracking" (CRS-R of 6, 8, 7), two "overt tracking" (CRS-R 22, 11), and five subjects "no tracking" (CRS-R 8, 6, 5, 6, 7). Among the 84 tested trials in all subjects, 11 trials (13%) met the criteria for "covert tracking". Using the k-nearest approach, 14 trials (17%) were classified as "covert tracking". Subjects with "tracking" had higher thalamocortical connectivity, and had fewer structures injured in the eye-tracking network than those without tracking. At follow-up, 2 out of 3 "covert" and all "overt" subjects recovered consciousness versus only 2 subjects in the "no tracking" group. Immersive stimuli may serve as important objective tools to differentiate subtle tracking using VET.

Multimodal Prediction of 3- and 12-Month Outcomes in ICU Patients with Acute Disorders of Consciousness.

BACKGROUND: In intensive care unit (ICU) patients with coma and other disorders of consciousness (DoC), outcome prediction is key to decision-making regarding prognostication, neurorehabilitation, and management of family expectations. Current prediction algorithms are largely based on chronic DoC, whereas multimodal data from acute DoC are scarce. Therefore, the Consciousness in Neurocritical Care Cohort Study Using Electroencephalography and Functional Magnetic Resonance Imaging (i.e. CONNECT-ME; ClinicalTrials.gov identifier: NCT02644265) investigates ICU patients with acute DoC due to traumatic and nontraumatic brain injuries, using electroencephalography (EEG) (resting-state and passive paradigms), functional magnetic resonance imaging (fMRI) (resting-state) and systematic clinical examinations. METHODS: We previously presented results for a subset of patients (n = 87) concerning prediction of consciousness levels in the ICU. Now we report 3- and 12-month outcomes in an extended cohort (n = 123). Favorable outcome was defined as a modified Rankin Scale score ≤ 3, a cerebral performance category score ≤ 2, and a Glasgow Outcome Scale Extended score ≥ 4. EEG features included visual grading, automated spectral categorization, and support vector machine consciousness classifier. fMRI features included functional connectivity measures from six resting-state networks. Random forest and support vector machine were applied to EEG and fMRI features to predict outcomes. Here, random forest results are presented as areas under the curve (AUC) of receiver operating characteristic curves or accuracy. Cox proportional regression with in-hospital death as a competing risk was used to assess independent clinical predictors of time to favorable outcome. RESULTS: Between April 2016 and July 2021, we enrolled 123 patients (mean age 51 years, 42% women). Of 82 (66%) ICU survivors, 3- and 12-month outcomes were available for 79 (96%) and 77 (94%), respectively. EEG features predicted both 3-month (AUC 0.79 [95% confidence interval (CI) 0.77-0.82]) and 12-month (AUC 0.74 [95% CI 0.71-0.77]) outcomes. fMRI features appeared to predict 3-month outcome (accuracy 0.69-0.78) both alone and when combined with some EEG features (accuracies 0.73-0.84) but not 12-month outcome (larger sample sizes needed). Independent clinical predictors of time to favorable outcome were younger age (hazard ratio [HR] 1.04 [95% CI 1.02-1.06]), traumatic brain injury (HR 1.94 [95% CI 1.04-3.61]), command-following abilities at admission (HR 2.70 [95% CI 1.40-5.23]), initial brain imaging without severe pathological findings (HR 2.42 [95% CI 1.12-5.22]), improving consciousness in the ICU (HR 5.76 [95% CI 2.41-15.51]), and favorable visual-graded EEG (HR 2.47 [95% CI 1.46-4.19]). CONCLUSIONS: Our results indicate that EEG and fMRI features and readily available clinical data predict short-term outcome of patients with acute DoC and that EEG also predicts 12-month outcome after ICU discharge.

Multimodal approaches supporting the diagnosis, prognosis and investigation of neural correlates of disorders of consciousness: A systematic review.

The limits of the standard, behaviour-based clinical assessment of patients with disorders of consciousness (DoC) prompted the employment of functional neuroimaging, neurometabolic, neurophysiological and neurostimulation techniques, to detect brain-based covert markers of awareness. However, uni-modal approaches, consisting in employing just one of those techniques, are usually not sufficient to provide an exhaustive exploration of the neural underpinnings of residual awareness. This systematic review aimed at collecting the evidence from studies employing a multimodal approach, that is, combining more instruments to complement DoC diagnosis, prognosis and better investigating their neural correlates. Following the PRISMA guidelines, records from PubMed, EMBASE and Scopus were screened to select peer-review original articles in which a multi-modal approach was used for the assessment of adult patients with a diagnosis of DoC. Ninety-two observational studies and 32 case reports or case series met the inclusion criteria. Results highlighted a diagnostic and prognostic advantage of multi-modal approaches that involve electroencephalography-based (EEG-based) measurements together with neuroimaging or neurometabolic data or with neurostimulation. Multimodal assessment deepened the knowledge on the neural networks underlying consciousness, by showing correlations between the integrity of the default mode network and the different clinical diagnosis of DoC. However, except for studies using transcranial magnetic stimulation combined with electroencephalography, the integration of more than one technique in most of the cases occurs without an a priori-designed multi-modal diagnostic approach. Our review supports the feasibility and underlines the advantages of a multimodal approach for the diagnosis, prognosis and for the investigation of neural correlates of DoCs.

Neuroimaging in Disorders of Consciousness and Recovery.

There is a clinical need for more accurate diagnosis and prognostication in patients with disorders of consciousness (DoC). There are several neuroimaging modalities that enable detailed, quantitative assessment of structural and functional brain injury, with demonstrated diagnostic and prognostic value. Additionally, longitudinal neuroimaging studies have hinted at quantifiable structural and functional neuroimaging biomarkers of recovery, with potential implications for the management of DoC.

Deep brain stimulation in disorders of consciousness: 10 years of a single center experience.

Disorders of consciousness (DoC), namely unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS), represent severe conditions with significant consequences for patients and their families. Several studies have reported the regaining of consciousness in such patients using deep brain stimulation (DBS) of subcortical structures or brainstem nuclei. Our study aims to present the 10 years' experience of a single center using DBS as a therapy on a cohort of patients with DoC. Eighty Three consecutive patients were evaluated between 2011 and 2022; entry criteria consisted of neurophysiological and neurological evaluations and neuroimaging examinations. Out of 83, 36 patients were considered candidates for DBS implantation, and 32 patients were implanted: 27 patients had UWS, and five had MCS. The stimulation target was the centromedian-parafascicular complex in the left hemisphere in hypoxic brain lesion or the one better preserved in patients with traumatic brain injury. The level of consciousness was improved in seven patients. Three out of five MCS patients emerged to full awareness, with the ability to interact and communicate. Two of them can live largely independently. Four out of 27 UWS patients showed consciousness improvement with two patients emerging to full awareness, and the other two reaching MCS. In patients with DoC lasting longer than 12 months following traumatic brain injury or 6 months following anoxic-ischemic brain lesion, spontaneous recovery is rare. Thus, DBS of certain thalamic nuclei could be recommended as a treatment option for patients who meet neurological, neurophysiological and neuroimaging criteria, especially in earlier phases, before occurrence of irreversible musculoskeletal changes. Furthermore, we emphasize the importance of cooperation between centers worldwide in studies on the potentials of DBS in treating patients with DoC.

Functional MRI for Acute Covert Consciousness: Emerging Data and Implementation Case Series.

Although research studies have begun to demonstrate relationships between disorders of consciousness and brain network biomarkers, there are limited data on the practical aspects of obtaining such network biomarkers to potentially guide care. As the state of knowledge continues to evolve, guidelines from professional societies such as the American and European Academies of Neurology and many experts have advocated that the risk-benefit ratio for the assessment of network biomarkers has begun to favor their application toward potentially detecting covert consciousness. Given the lack of detailed operationalization guidance and the context of the ethical implications, herein we offer a roadmap based on local institutional experience with the implementation of functional MRI in the neonatal, pediatric, and adult intensive care units of our local government-supported health system. We provide a case-based demonstrative approach intended to review the current literature and to assist with the initiation of such services at other facilities.

Naturalistic auditory stimuli with fNIRS prefrontal cortex imaging: A potential paradigm for disorder of consciousness diagnostics (a study with healthy participants).

Disorder of consciousness (DOC) is a devastating condition due to brain damage. A patient in this condition is non-responsive, but nevertheless might be conscious at least at some level. Determining the conscious level of DOC patients is important for both medical and ethical reasons, but reliably achieving this has been a major challenge. Naturalistic stimuli in combination with neuroimaging have been proposed as a promising approach for DOC patient diagnosis. Capitalizing on and extending this proposal, the goal of the present study conducted with healthy participants was to develop a new paradigm with naturalistic auditory stimuli and functional near-infrared spectroscopy (fNIRS) - an approach that can be used at the bedside. Twenty-four healthy participants passively listened to 9 min of auditory story, scrambled auditory story, classical music, and scrambled classical music segments while their prefrontal cortex activity was recorded using fNIRS. We found much higher intersubject correlation (ISC) during story compared to scrambled story conditions both at the group level and in the majority of individual subjects, suggesting that fNIRS imaging of the prefrontal cortex might be a sensitive method to capture neural changes associated with narrative comprehension. In contrast, the ISC during the classical music segment did not differ reliably from scrambled classical music and was also much lower than the story condition. Our main result is that naturalistic auditory stories with fNIRS might be used in a clinical setup to identify high-level processing and potential consciousness in DOC patients.

Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.

Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges.

Whole-brain analyses indicate the impairment of posterior integration and thalamo-frontotemporal broadcasting in disorders of consciousness.

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 [18 F]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.

Electroencephalogram-Based Brain Connectivity Analysis in Prolonged Disorders of Consciousness.

Background: Prolonged disorders of consciousness (pDOC) are common in neurology and place a heavy burden on families and society. This study is aimed at investigating the characteristics of brain connectivity in patients with pDOC based on quantitative EEG (qEEG) and extending a new direction for the evaluation of pDOC. Methods: Participants were divided into a control group (CG) and a DOC group by the presence or absence of pDOC. Participants underwent magnetic resonance imaging (MRI) T1 three-dimensional magnetization with a prepared rapid acquisition gradient echo (3D-T1-MPRAGE) sequence, and video EEG data were collected. After calculating the power spectrum by EEG data analysis tool, DTABR ((δ + θ)/(α + ß) ratio), Pearson's correlation coefficient (Pearson r), Granger's causality, and phase transfer entropy (PTE), we performed statistical analysis between two groups. Finally, receiver operating characteristic (ROC) curves of connectivity metrics were made. Results: The proportion of power in frontal, central, parietal, and temporal regions in the DOC group was lower than that in the CG. The percentage of delta power in the DOC group was significantly higher than that in the CG, the DTABR in the DOC group was higher than that in the CG, and the value was inverted. The Pearson r of the DOC group was higher than that of CG. The Pearson r of the delta band (Z = -6.71, P < 0.01), theta band (Z = -15.06, P < 0.01), and alpha band (Z = -28.45, P < 0.01) were statistically significant. Granger causality showed that the intensity of directed connections between the two hemispheres in the DOC group at the same threshold was significantly reduced (Z = -82.43, P < 0.01). The PTE of each frequency band in the DOC group was lower than that in the CG. The PTE of the delta band (Z = -42.68, P < 0.01), theta band (Z = -56.79, P < 0.01), the alpha band (Z = -35.11, P < 0.01), and beta band (Z = -63.74, P < 0.01) had statistical significance. Conclusion: Brain connectivity analysis based on EEG has the advantages of being noninvasive, convenient, and bedside. The Pearson r of DTABR, delta, theta, and alpha bands, Granger's causality, and PTE of the delta, theta, alpha, and beta bands can be used as biological markers to distinguish between pDOC and healthy people, especially when behavior evaluation is difficult or ambiguous; it can supplement clinical diagnosis.

The use of epidural spinal cord stimulation in patients with chronic disorders of consciousness - neuroimaging and clinical results.

OBJECTIVE: Chronic disorders of consciousness are considered as a serious unresolved problem complicated by significant medical, social, and economic burden. Therefore, improving the conditions and facilitating the rehabilitation management of these patients is of particular interest. In recent years, interesting results of the use of spinal cord stimulation in patients with chronic disorders of consciousness appeared in the world literature, which makes the use of this technique promising in this category of patients. PATIENTS AND METHODS: We analyzed the results of high cervical spinal cord stimulation, both, in tonic and "Burst" modes, in 21 patients with chronic disorders of consciousness and severe spasticity managed in the last two years in FRCC ICMR. In 9 of them pre- and post-stimulation fMRI before and right after the stimulation was also performed for brain functional connectivity assessment. RESULTS: Improvement of the consciousness level was observed in 38.1% (n = 8) and a decrease in spasticity was obtained in 52.4% (n=11) of the patients. The difference in CRS-R score before and after spinal cord stimulation was statistically significant (p=0.028). The fMRI results revealed an increase in functional connectivity of the right anterior insula with several areas that are part of the Dorsal Attention, Visual and Default Mode networks after spinal cord stimulation. CONCLUSIONS: Epidural spinal stimulation at the upper cervical spine level demonstrated its effectiveness in patients with chronic diseases of consciousness of various etiology. Evaluation of the effect of specific stimulation modes requires further controlled study in larger group of patients.

Disorder of consciousness: Structural integrity of brain networks for the clinical assessment.

AIM: When studying brain networks in patients with Disorders of Consciousness (DoC), it is important to evaluate the structural integrity of networks in addition to their functional activity. Here, we investigated whether structural MRI, together with clinical variables, can be useful for diagnostic purposes and whether a quantitative analysis is feasible in a group of chronic DoC patients. METHODS: We studied 109 chronic patients with DoC and emerged from DoC with structural MRI: 65 in vegetative state/unresponsive wakefulness state (VS/UWS), 34 in minimally conscious state (MCS), and 10 with severe disability. MRI data were analyzed through qualitative and quantitative approaches. RESULTS: The qualitative MRI analysis outperformed the quantitative one, which resulted to be hardly feasible in chronic DoC patients. The results of the qualitative approach showed that the structural integrity of HighOrder networks, altogether, had better diagnostic accuracy than LowOrder networks, particularly when the model included clinical variables (AUC = 0.83). Diagnostic differences between VS/UWS and MCS were stronger in anoxic etiology than vascular and traumatic etiology. MRI data of all LowOrder and HighOrder networks correlated with the clinical score. The integrity of the left hemisphere was associated with a better clinical status. CONCLUSIONS: Structural integrity of brain networks is sensitive to clinical severity. When patients are chronic, the qualitative analysis of MRI data is indicated.

The temporal dynamics of Large-Scale brain network changes in disorders of consciousness: A Microstate-Based study.

BACKGROUND AND OBJECTIVES: The resting-state brain is composed of several discrete networks, which remain stable for 10-100 ms. These functional microstates are considered the building blocks of spontaneous consciousness. Electroencephalography (EEG) microstate analysis may provide insight into the altered brain dynamics underlying consciousness recovery in patients with disorders of consciousness (DOC). We aimed to analyze microstates in the resting-state EEG source space in patients with DOC, the relationship between state-specific features and consciousness levels, and the corresponding patterns of microstates and functional networks. METHODS: We obtained resting-state EEG data from 84 patients with DOC (27 in a minimally conscious state [MCS] and 57 in a vegetative state [VS] or with unresponsive wakefulness syndrome). We conducted a microstate analysis of the resting-state (EEG) source space and developed a state-transition analysis protocol for patients with DOC. RESULTS: We identified seven microstates with distinct spatial distributions of cortical activation. Multivariate pattern analyses revealed that different functional connectivity patterns were associated with source-level microstates. There were significant differences in the microstate properties, including spatial activation patterns, temporal dynamics, state shifts, and connectivity construction, between the MCS and VS groups. DISCUSSION: Our findings suggest that consciousness depends on complex dynamics within the brain and may originate from the anterior cortex.

Cerebral Glucose Metabolism in Patients with Chronic Disorders of Consciousness.

OBJECTIVE: To measure regional cerebral metabolic rate of glucose (CMRGlu) in patients with chronic disorders of consciousness (DOCs) using 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). METHODS: This retrospective cohort study examined 50 patients (mean age: 40.9 ± 20.1 years) with traumatic brain injury (TBI)-induced chronic DOCs [minimally conscious state (MCS)+, n = 20; MCS-, n = 15 and vegetative state (VS), n = 15]. We measured FDG-PET-based CMRGlu values in 12 regions of both brain hemispheres and compared those among MCS+, MCS - and VS patients. RESULTS: In both hemispheres, the regional CMRGlu reduced with consciousness deterioration in 11 of 12 regions (91.7%). In seven right hemisphere regions, CMRGlu values were markedly higher in MCS+ patients than in MCS- patients. Furthermore, CMRGlu was suggestively higher in the left occipital region in MCS- patients than in VS patients. CONCLUSION: Functional preservation in the left occipital region in patients with chronic DOCs might reflect an awareness of external environments, whereas extensive functional preservation in the right cerebral hemisphere might reflect communication motivation.

Detecting residual brain networks in disorders of consciousness: A resting-state fNIRS study.

Functional near infrared spectroscopy (fNIRS) is an emerging non-invasive technique that allows bedside measurement of blood oxygenation level-dependent hemodynamic signals. We aimed to examine the efficacy of resting-state fNIRS in detecting the residual functional networks in patients with disorders of consciousness (DOC). We performed resting-state fNIRS in 23 DOC patients of whom 12 were in minimally conscious state (MCS) and 11 were in unresponsive wakefulness state (UWS). Ten regions of interest (ROIs) in the prefrontal cortex (PFC) were selected: both sides of Brodmann area (BA) 9, BA10, BA44, BA45, and BA46. Graph-theoretical analysis and seed-based correlation analyses were used to investigate the network topology and the strength of pairwise connections between ROIs and channels. MCS and UWS exhibited varying degrees of the loss of topological architecture, and the regional nodal properties of BA10 were significantly different between them (Nodal degree, PLeft BA10 = 0.01, PRight BA10 < 0.01; nodal efficiency, PLeft BA10 = 0.03, PRight BA10 < 0.01). Compared to healthy controls, UWS had impaired functions in both short- and long-distance connectivity, however, MCS had significantly impaired functions only in long-distance connectivity. The functional connectivity of right BA10 (AUC = 0.88) and the connections between left BA46 and right BA10 (AUC = 0.86) had excellent performance in differentiating MCS and UWS. MCS and UWS have different patterns of topological architecture and short- and long-distance connectivity in PFC. Intraconnections within BA10 and interhemispheric connections between BA10 and 46 are excellent resting-state fNIRS classifiers for distinguishing between MCS and UWS.