Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients

Transcutaneous electrical acupoint stimulation induced sedative effects in healthy volunteers: A resting-state fMRI study

Background

Previous studies indicated the sedative effect of acupoint stimulation. However, its mechanism remains unclear. This study aimed to investigate the sedative effect of transcutaneous electrical acupoint stimulation (TEAS) and to explore the brain regions involved in this effect in healthy volunteers using functional magnetic resonance imaging (fMRI) techniques.

Methods

In this randomized trial, 26 healthy volunteers were randomly assigned to the TEAS group (receiving 30 min of acupoint stimulation at HT7/PC4) and the control group. fMRI was conducted before and after the intervention. The primary outcome was the BIS value during the intervention. Secondary outcomes included the amplitude of low-frequency fluctuation (ALFF) and region of interest (ROI)-based functional connectivity (FC) showed by fMRI.

Results

In healthy volunteers, compared with the control group, ALFF values in the TEAS-treated volunteers decreased in the left thalamus, right putamen, and midbrain, while they increased in the left orbitofrontal cortex. More FC existed between the thalamus and the insula, middle cingulate cortex, somatosensory cortex, amygdala, and putamen in subjects after TEAS treatment compared with subjects that received non-stimulation. In addition, ALFF values of the thalamus positively correlated with BIS in both groups.

Conclusion

Transcutaneous electrical acupoint stimulation could induce a sedative effect in healthy volunteers, and inhibition of the thalamus was among its possible mechanisms.

Clinical trial registration

www.ClinicalTrials.gov; identifier: NCT01896063.

Structural connectivity in recovery after coma: Connectome atlas approach

AIM

Pathological states of recovery after coma as a result of a severe brain injury are marked with changes in structural connectivity of the brain. This study aimed to identify a topological correlation between white matter integrity and the level of functional and cognitive impairment in patients recovering after coma.

METHODS

Structural connectomes were computed based on fractional anisotropy maps from 40 patients using a probabilistic human connectome atlas. We used a network based statistics approach to identify potential brain networks associated with a more favorable outcome, assessed with clinical neurobehavioral scores at the patient's discharge from the acute neurorehabilitation unit.

RESULTS

We identified a subnetwork whose strength of connectivity correlated with a more favorable outcome as measured with the Disability Rating Scale (network based statistics: t >3.5, P =.010). The subnetwork predominated in the left hemisphere and included the thalamic nuclei, putamen, precentral and postcentral gyri, and medial parietal regions. Spearman correlation between the mean fractional anisotropy value of the subnetwork and the score was ρ = -0.60 (P <.0001). A less extensive overlapping subnetwork correlated with the Coma Recovery Scale Revised score, consisting mostly of the left hemisphere connectivity between the thalamic nuclei and pre- and post-central gyri (network based statistics: t >3.5, P =.033; Spearman's ρ = 0.58, P <.0001).

CONCLUSION

The present findings suggest an important role of structural connectivity between the thalamus, putamen and somatomotor cortex in the recovery from coma as evaluated with neurobehavioral scores. These structures are part of the motor circuit involved in the generation and modulation of voluntary movement, as well as the forebrain mesocircuit supposedly underlying the maintenance of consciousness. As behavioural assessment of consciousness depends heavily on the signs of voluntary motor behaviour, further work will elucidate whether the identified subnetwork reflects the structural architecture underlying the recovery of consciousness or rather the ability to communicate its content.

Functional networks in prolonged disorders of consciousness

Prolonged disorders of consciousness (DoC) are characterized by extended disruptions of brain activities that sustain wakefulness and awareness and are caused by various etiologies. During the past decades, neuroimaging has been a practical method of investigation in basic and clinical research to identify how brain properties interact in different levels of consciousness. Resting-state functional connectivity within and between canonical cortical networks correlates with consciousness by a calculation of the associated temporal blood oxygen level-dependent (BOLD) signal process during functional MRI (fMRI) and reveals the brain function of patients with prolonged DoC. There are certain brain networks including the default mode, dorsal attention, executive control, salience, auditory, visual, and sensorimotor networks that have been reported to be altered in low-level states of consciousness under either pathological or physiological states. Analysis of brain network connections based on functional imaging contributes to more accurate judgments of consciousness level and prognosis at the brain level. In this review, neurobehavioral evaluation of prolonged DoC and the functional connectivity within brain networks based on resting-state fMRI were reviewed to provide reference values for clinical diagnosis and prognostic evaluation.

Repetitive transcranial magnetic stimulation over the posterior parietal cortex improves functional recovery in nonresponsive patients: A crossover, randomized, double-blind, sham-controlled study

Background

Recent studies have shown that patients with disorders of consciousness (DoC) can benefit from repetitive transcranial magnetic stimulation (rTMS) therapy. The posterior parietal cortex (PPC) is becoming increasingly important in neuroscience research and clinical treatment for DoC as it plays a crucial role in the formation of human consciousness. However, the effect of rTMS on the PPC in improving consciousness recovery remains to be studied.

Method

We conducted a crossover, randomized, double-blind, sham-controlled clinical study to assess the efficacy and safety of 10 Hz rTMS over the left PPC in unresponsive patients. Twenty patients with unresponsive wakefulness syndrome were recruited. The participants were randomly divided into two groups: one group received active rTMS treatment for 10 consecutive days (n = 10) and the other group received sham treatment for the same period (n = 10). After a 10-day washout period, the groups crossed over and received the opposite treatment. The rTMS protocol involved the delivery of 2000 pulses/day at a frequency of 10 Hz, targeting the left PPC (P3 electrode sites) at 90% of the resting motor threshold. The primary outcome measure was the JFK Coma Recovery Scele-Revised (CRS-R), and evaluations were conducted blindly. EEG power spectrum assessments were also conducted simultaneously before and after each stage of the intervention.

Result

rTMS-active treatment resulted in a significant improvement in the CRS-R total score (F = 8.443, p = 0.009) and the relative alpha power (F = 11.166, p = 0.004) compared to sham treatment. Furthermore, 8 out of 20 patients classified as rTMS responders showed improvement and evolved to a minimally conscious state (MCS) as a result of active rTMS. The relative alpha power also significantly improved in responders (F = 26.372, p = 0.002) but not in non-responders (F = 0.704, p = 0.421). No adverse effects related to rTMS were reported in the study.

Conclusions

This study suggests that 10 Hz rTMS over the left PPC can significantly improve functional recovery in unresponsive patients with DoC, with no reported side effects.

Clinical trial registration

www.ClinicalTrials.gov, identifier: NCT05187000.

Clinical Decision on Disorders of Consciousness After Acquired Brain Injury: Stepping Forward

Major advances have been made over the past few decades in identifying and managing disorders of consciousness (DOC) in patients with acquired brain injury (ABI), bringing the transformation from a conceptualized definition to a complex clinical scenario worthy of scientific exploration. Given the continuously-evolving framework of precision medicine that integrates valuable behavioral assessment tools, sophisticated neuroimaging, and electrophysiological techniques, a considerably higher diagnostic accuracy rate of DOC may now be reached. During the treatment of patients with DOC, a variety of intervention methods are available, including amantadine and transcranial direct current stimulation, which have both provided class II evidence, zolpidem, which is also of high quality, and non-invasive stimulation, which appears to be more encouraging than pharmacological therapy. However, heterogeneity is profoundly ingrained in study designs, and only rare schemes have been recommended by authoritative institutions. There is still a lack of an effective clinical protocol for managing patients with DOC following ABI. To advance future clinical studies on DOC, we present a comprehensive review of the progress in clinical identification and management as well as some challenges in the pathophysiology of DOC. We propose a preliminary clinical decision protocol, which could serve as an ideal reference tool for many medical institutions.

Ten-Year Change in Disorders of Consciousness: A Bibliometric Analysis

Objectives: Disorders of consciousness (DoC) is a dynamic and challenging discipline, presenting intriguing challenges to clinicians and neurorehabilitation specialists for the lack of reliable assessment methods and interventions. Understanding DoC keeps pace with scientific research is urgent to need. We quantitively analyzed publications on DoC over the recent 10 years via bibliometrics analysis, to summarize the intellectual structure, current research hotspots, and future research trends in the field of DoC. Methods: Literature was obtained from the Science Citation Index Expanded of Web of Science Core Collection (WoSCC). To illustrate the knowledge structure of DoC, CiteSpace 5.8.R3 was used to conduct a co-occurrence analysis of countries, institutions, and keywords, and a co-citation analysis of references and journals. Also, Gephi 0.9.2 contributed to the author and co-cited author analysis. We found the most influential journals, authors, and countries and the most talked about keywords in the last decade of research. Results: A total of 1919 publications were collected. Over the past 10 years, the total number of annual publications has continued to increase, with the largest circulation in 2018. We found most DoC research and close cooperation originated from developed countries, e.g., the USA, Canada, and Italy. Academics from Belgium appear to have a strong presence in the field of DoC. The most influential journals were also mainly distributed in the USA and some European countries. Conclusions: This bibliometric study sheds light on the knowledge architecture of DoC research over the past decade, reflecting current hotspots and emerging trends, and providing new insights for clinicians and academics interested in DoC. The hot issues in DoC were diagnosing and differentiating the level of consciousness, and detecting covert awareness in early severe brain-injured patients. New trends focus on exploring the recovery mechanism of DoC and neuromodulation techniques.

Dynamic Changes of Brain Activity in Different Responsive Groups of Patients with Prolonged Disorders of Consciousness

As medical technology continues to improve, many patients diagnosed with brain injury survive after treatments but are still in a coma. Further, multiple clinical studies have demonstrated recovery of consciousness after transcranial direct current stimulation. To identify possible neurophysiological mechanisms underlying disorders of consciousness (DOCs) improvement, we examined the changes in multiple resting-state EEG microstate parameters after high-definition transcranial direct current stimulation (HD-tDCS). Because the left dorsolateral prefrontal cortex is closely related to consciousness, it is often chosen as a stimulation target for tDCS treatment of DOCs. A total of 21 patients diagnosed with prolonged DOCs were included in this study, and EEG microstate analysis of resting state EEG datasets was performed on all patients before and after interventions. Each of them underwent 10 anodal tDCS sessions of the left dorsolateral prefrontal cortex over 5 consecutive working days. According to whether the clinical manifestations improved, DOCs patients were divided into the responsive (RE) group and the non-responsive (N-RE) group. The dynamic changes of resting state EEG microstate parameters were also analyzed. After multiple HD-tDCS interventions, the duration and coverage of class C microstates in the RE group were significantly increased. This study also found that the transition between microstates A and C increased, while the transition between microstates B and D decreased in the responsive group. However, these changes in EEG microstate parameters in the N-RE group have not been reported. Our findings suggest that EEG neural signatures have the potential to assess consciousness states and that improvement in the dynamics of brain activity was associated with the recovery of DOCs. This study extends our understanding of the neural mechanism of DOCs patients in consciousness recovery.

Relationship between the consciousness level and the structural neural connectivity of the medial prefrontal cortex in hypoxic-ischemic brain injury: a pilot study

This pilot study investigated the relationship between the consciousness level and the structural neural connectivity of the medial prefrontal cortex (mPFC SNC) in patients with hypoxic-ischemic brain injury (HI-BI), using diffusion tensor tractography (DTT). Twenty-three patients with HI-BI were recruited into the study based on predefined inclusion criteria. Their consciousness levels were evaluated using the Glasgow Coma Scale (GCS) and the Coma Recovery Scale-Revised (CRS-R). Using DTT, the mPFC SNC was reconstructed for each patient. The average of the fractional anisotropy (FA), apparent diffusion coefficient (ADC), and voxel number (VN) for the mPFC SNC in both hemispheres were determined. The GCS score showed moderate positive correlations with the FA value and VN of the mPFC SNC [(FA) r = 0.439; (VN) r = 0.466; P < 0.05], and a strong negative correlation with ADC value ( r = -0.531; P < 0.05). The CRS-R score had a strong positive and negative correlation with the FA and ADC values of the mPFC SNC, respectively, [(FA) r = 0.540; (ADC) r = -0.614; P < 0.05] and a moderate positive correlation with the VN of the mPFC SNC ( r = 0.488; P < 0.05). We found that the severity of the injury to the mPFC SNC was closely related to the consciousness level. Our results suggest that the mPFC SNC appears to be a neural correlate for the control of consciousness in patients with HI-BI. Based on these results, we believe that the mPFC could be a target area for noninvasive neurostimulation therapies for patients with impaired consciousness following HI-BI.

Intrinsic brain dynamics in the Default Mode Network predict involuntary fluctuations of visual awareness

Brain activity is intrinsically organised into spatiotemporal patterns, but it is still not clear whether these intrinsic patterns are functional or epiphenomenal. Using a simultaneous fMRI-EEG implementation of a well-known bistable visual task, we showed that the latent transient states in the intrinsic EEG oscillations can predict upcoming involuntarily perceptual transitions. The critical state predicting a dominant perceptual transition was characterised by the phase coupling between the precuneus (PCU), a key node of the Default Mode Network (DMN), and the primary visual cortex (V1). The interaction between the lifetime of this state and the PCU- > V1 Granger-causal effect is correlated with the perceptual fluctuation rate. Our study suggests that the brain's endogenous dynamics are phenomenologically relevant, as they can elicit a diversion between potential visual processing pathways, while external stimuli remain the same. In this sense, the intrinsic DMN dynamics pre-empt the content of consciousness.

The implication of a diversity of non-neuronal cells in disorders affecting brain networks

In the central nervous system (CNS) neurons are classically considered the functional unit of the brain. Analysis of the physical connections and co-activation of neurons, referred to as structural and functional connectivity, respectively, is a metric used to understand their interplay at a higher level. A myriad of glial cell types throughout the brain composed of microglia, astrocytes and oligodendrocytes are key players in the maintenance and regulation of neuronal network dynamics. Microglia are the central immune cells of the CNS, able to affect neuronal populations in number and connectivity, allowing for maturation and plasticity of the CNS. Microglia and astrocytes are part of the neurovascular unit, and together they are essential to protect and supply nutrients to the CNS. Oligodendrocytes are known for their canonical role in axonal myelination, but also contribute, with microglia and astrocytes, to CNS energy metabolism. Glial cells can achieve this variety of roles because of their heterogeneous populations comprised of different states. The neuroglial relationship can be compromised in various manners in case of pathologies affecting development and plasticity of the CNS, but also consciousness and mood. This review covers structural and functional connectivity alterations in schizophrenia, major depressive disorder, and disorder of consciousness, as well as their correlation with vascular connectivity. These networks are further explored at the cellular scale by integrating the role of glial cell diversity across the CNS to explain how these networks are affected in pathology.

Altered functional connectivity and regional brain activity in a triple-network model in minimally conscious state and vegetative-state/unresponsive wakefulness syndrome patients: A resting-state functional magnetic resonance imaging study

The purpose of this study was to investigate changes in functional connectivity and regional brain activity between and within the default mode network (DMN), salience network (SN), and executive control network (ECN) among individuals with disorders of consciousness (DOC) in the conditions of minimally conscious state (MCS) and vegetative-state/unresponsive wakefulness syndrome (VS/UWS). Twenty-five VS/UWS patients, 14 MCS patients, and 30 healthy individuals as normal control, completed resting-state fMRI scans. ROI-wise functional connectivity and fractional amplitude of low-frequency fluctuation (fALFF) were implemented to examine group differences. All ROI-wise and fALFF analyses masks were identified from the triple-network model. ROI-wise analyses indicated significantly decreased functional connectivity between posterior cingulate cortex (DMN)-left anterior insula (SN), right anterior insula (SN)-left dorsolateral prefrontal cortex (ECN), and right anterior insula (SN)-right amygdala (SN) in VS/UWS patients compared to MCS patients. Moreover, fALFF were observed reduced in the triple-network across all DOC patients, and as the clinical manifestations of DOC deteriorated from MCS to VS/UWS, fALFF in dorsal DMN, anterior/posterior SN, and left ECN became significantly reduced. Moreover, a positive correlation between fALFF of the left ECN and Coma Recovery Scale-Revised (CRS-R) total scores was found across all DOC patients. These findings contribute to a better understanding of the underlying neural mechanism of functional connectivity and regional brain activity in DOC patients, and this triple-network model provides new connectivity pattern changes that may be integrated in future diagnostic tools based on the neural signatures of conscious states.

Paradoxical markers of conscious levels: Effects of propofol on patients in disorders of consciousness

Human consciousness is widely understood to be underpinned by rich and diverse functional networks, whose breakdown results in unconsciousness. Candidate neural correlates of anesthetic-induced unconsciousness include: (1) disrupted frontoparietal functional connectivity; (2) disrupted brain network hubs; and (3) reduced spatiotemporal complexity. However, emerging counterexamples have revealed that these markers may appear outside of the state they are associated with, challenging both their inclusion as markers of conscious level, and the theories of consciousness that rely on their evidence. In this study, we present a case series of three individuals in disorders of consciousness (DOC) who exhibit paradoxical brain responses to exposure to anesthesia. High-density electroencephalographic data were recorded from three patients with unresponsive wakefulness syndrome (UWS) while they underwent a protocol of propofol anesthesia with a targeted effect site concentration of 2 μg/ml. Network hubs and directionality of functional connectivity in the alpha frequency band (8–13 Hz), were estimated using the weighted phase lag index (wPLI) and directed phase lag index (dPLI). The spatiotemporal signal complexity was estimated using three types of Lempel-Ziv complexity (LZC). Our results illustrate that exposure to propofol anesthesia can paradoxically result in: (1) increased frontoparietal feedback-dominant connectivity; (2) posterior network hubs; and (3) increased spatiotemporal complexity. The case examples presented in this paper challenge the role of functional connectivity and spatiotemporal complexity in theories of consciousness and for the clinical evaluation of levels of human consciousness.

Prefrontal cortex as a key node in arousal circuitry

The role of the prefrontal cortex (PFC) in the mechanism of consciousness is a matter of active debate. Most theoretical and empirical investigations have focused on whether the PFC is critical for the content of consciousness (i.e., the qualitative aspects of conscious experience). However, there is emerging evidence that, in addition to its well-established roles in cognition, the PFC is a key regulator of the level of consciousness (i.e., the global state of arousal). In this opinion article we review recent data supporting the hypothesis that the medial PFC is a critical node in arousal-promoting networks.

Investigating the mechanism and prognosis of patients with disorders of consciousness on the basis of brain networks between the thalamus and whole-brain

Purpose

This study aimed to investigate the changes in the functional connectivity between the bilateral thalamus and the whole-brain in patients with severe traumatic brain injury (sTBI) patients suffering from disorders of consciousness (DOC) and to explore their potential prognostic representation capacity.

Methods

The sTBI patients suffering from DOC and healthy controls underwent functional magnetic resonance imaging. We defined patients with the Extended Glasgow Outcome Score (GOS-E) ≥ 3 as the wake group and GOS-E = 2 as the coma group. The differences in functional connectivity between sTBI and healthy controls and between wake and coma groups were compared. Based on the brain regions with altered functional connectivity between wake and coma groups, they were divided into 26 regions of interest. Based on the Z-values of regions of interest, the receiver operating characteristic analysis was conducted to classify the prognosis of patients.

Results

A total of 28 patients and 15 healthy controls were finally included. Patients who had DOC indicated a significant disruption of functional connectivity between the bilateral thalamus and the whole-brain (FDR corrected, P &lt; 0.0007). The functional connectivity strength (bilateral thalamus to whole-brain) was significantly different between coma patients who went on to wake and those who were eventually non-awake at 6 months after sTBI (Alphasim corrected, P &lt; 0.05). Furthermore, the 26 regions of interest had a similar or even better prognostic distinction ability than the admission Glasgow coma score.

Conclusion

The thalamus-based system of consciousness of sTBI patients suffering from DOC is disrupted. There are differences in the thalamus-to-whole-brain network between wake and coma groups and these differences have potential prognostic characterization capability.

Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study

Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary centre cohort, diagnostic phase IIb study 'Consciousness in neurocritical care cohort study using EEG and fMRI' (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks was assessed. Next, we used EEG and fMRI data at study enrolment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel) to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS) at time of study enrolment and at ICU discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC patients (mean age, 50.0 ± 18 years, 43% female), 51 (59%) were ≤UWS and 36 (41%) were ≥ MCS at study enrolment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrolment and ICU discharge, with maximum AUCs of 0.79 (95% CI 0.77-0.80) and 0.71 (95% CI 0.77-0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrolment and ICU discharge with maximum AUCs of 0.78 (95% CI 0.71-0.86) and 0.83 (95% CI 0.75-0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.

Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach

Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients’ etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC.

Default mode network components and its relationship with anomalous self-experiences in schizophrenia: A rs-fMRI exploratory study

Anomalous self-experiences (ASEs) in schizophrenia have been under research for the last 20 years. However, no neuroimage studies have provided insight of the possible biological underpinning of ASEs. In this novel approach, the connectivity within the default mode network, calculated through a ROI-based analysis of functional magnetic resonance imaging data, was correlated to the ASEs scores assessed by the Inventory of Psychotic-Like Anomalous Self-Experiences (IPASE) in a sample of 22 schizophrenia patients. The Pearson's correlation coefficients between IPASE scores and intrahemispheric connectivity of the parahippocampal gyrus with the isthmus cingulate cortex in both hemispheres, and right parahippocampal gyrus with the right rostral anterior cingulate cortex were positive and significant suggesting a relation between hyperactive functional connectivity and anomalous self-experiences intensity. Prior literature reported these areas to have a role in self-processing and consciousness as well as being anatomically connected. Further research with larger sample size and comparison with controls are needed to confirm the relationship of this connectivity with anomalous self-experiences.

Altered Intestinal Microbiomes and Lipid Metabolism in Patients With Prolonged Disorders of Consciousness

The intestinal microbiota regulate the brain function of the host through the production of a myriad of metabolites and are associated with various neurological diseases. Understanding the intestinal microbiome of patients with prolonged disorders of consciousness (DoC) is important for the evaluation and treatment of the disease. To investigate the differences in the intestinal microbiome and short-chain fatty acids (SCFAs) among patients in a vegetative state (VS), a minimally conscious state (MCS), and emerged from MCS (EMCS), as well as the influence of antibiotics on these patients, 16S ribosomal RNA (16S rRNA) sequencing and targeted lipidomics were performed on fecal samples from patients; in addition, analysis of the electroencephalogram (EEG) signals was performed to evaluate the brain function of these patients. The results showed that the intestinal microbiome of the three groups differed greatly, and some microbial communities showed a reduced production of SCFAs in VS patients compared to the other two groups. Moreover, reduced microbial communities and five major SCFAs, along with attenuated brain functional connectivity, were observed in MCS patients who were treated with antibiotics compared to those who did not receive antibiotic treatment, but not in the other pairwise comparisons. Finally, three genus-level microbiota—Faecailbacterium, Enterococcus, and Methanobrevibacter—were considered as potential biomarkers to distinguish MCS from VS patients, with high accuracy both in the discovery and validation cohorts. Together, our findings improved the understanding of patients with prolonged DoC from the intestinal microbiome perspective and provided a new reference for the exploration of therapeutic targets.

Brain Metabolic Connectivity Patterns in Patients with Prolonged Disorder of Consciousness after Hypoxic-Ischemic Injury: A Preliminary Study

Understanding the patterns of brain glucose metabolism and connectivity in hypoxic-ischemic encephalopathy (HIE) patients with prolonged disorders of consciousness (DOC) may be of positive significance to the accurate assessment of consciousness and the optimization of neuromodulation strategy. We retrospectively analyzed the brain glucose metabolism pattern and its correlation with clinical Coma Recovery Scale-Revised (CRS-R) score in six HIE patients with prolonged DOC who had undergone 18F-deoxyglucose brain positron emission tomography scanning (FDG-PET). We also compared the differences in global metabolic connectivity patterns and the characteristics of several brain networks between HIE patients and healthy controls (HC). The metabolism of multiple brain regions decreased significantly in HIE patients, and the degree of local metabolic preservation was correlated with CRS-R score. The internal metabolic connectivity of occipital lobe and limbic system in HIE patients decreased, and their metabolic connectivity with frontal lobe, parietal lobe and temporal lobe also decreased. The metabolic connectivity patterns of default mode network, dorsal attention network, salience network, executive control network and subcortex network of HIE also changed compared with HC. The present study suggested that pattern of cerebral glucose metabolism and network connectivity of HIE patients with prolonged DOC were significantly different from those of healthy people.

Neuroprognostication: a conceptual framework

Neuroprognostication, or the prediction of recovery from disorders of consciousness caused by severe brain injury, is as critical as it is complex. With profound implications for mortality and quality of life, neuroprognostication draws upon an intricate set of biomedical, probabilistic, psychosocial and ethical factors. However, the clinical approach to neuroprognostication is often unsystematic, and consequently, variable among clinicians and prone to error. Here, we offer a stepwise conceptual framework for reasoning through neuroprognostic determinations - including an evaluation of neurological function, estimation of a recovery trajectory, definition of goals of care and consideration of patient values - culminating in a clinically actionable formula for weighing the risks and benefits of life-sustaining treatment. Although the complexity of neuroprognostication might never be fully reducible to arithmetic, this systematic approach provides structure and guidance to supplement clinical judgement and direct future investigation.

tDCS-EEG for Predicting Outcome in Patients With Unresponsive Wakefulness Syndrome

Objectives

We aimed to assess the role of transcranial direct current stimulation (tDCS) combined with electroencephalogram (EEG) for predicting prognosis in UWS cases.

Methods

This was a historical control study that enrolled 85 patients with UWS. The subjects were assigned to the control (without tDCS) and tDCS groups. Conventional treatments were implemented in both the control and tDCS groups, along with 40 multi-target tDCS sessions only in the tDCS group. Coma Recovery Scale-Revised (CRS-R) was applied at admission. The non-linear EEG index was evaluated after treatment. The modified Glasgow Outcome Scale (mGOS) was applied 12 months after disease onset.

Results

The mGOS improvement rate in the tDCS group (37.1%) was higher than the control value (22.0%). Linear regression analysis revealed that the local and remote cortical networks under unaffected pain stimulation conditions and the remote cortical network under affected pain stimulation conditions were the main relevant factors for mGOS improvement. Furthermore, the difference in prefrontal-parietal cortical network was used to examine the sensitivity of prognostic assessment in UWS patients. The results showed that prognostic sensitivity could be increased from 54.5% (control group) to 84.6% (tDCS group).

Conclusions

This study proposes a tDCS-EEG protocol for predicting the prognosis of UWS. With multi-target tDCS combined with EEG, the sensitivity of prognostic assessment in patients with UWS was improved. The recovery might be related to improved prefrontal-parietal cortical networks of the unaffected hemisphere.

Evaluating denoising strategies in resting-state functional magnetic resonance in traumatic brain injury (EpiBioS4Rx)

Resting-state functional MRI is increasingly used in the clinical setting and is now included in some diagnostic guidelines for severe brain injury patients. However, to ensure high-quality data, one should mitigate fMRI-related noise typical of this population. Therefore, we aimed to evaluate the ability of different preprocessing strategies to mitigate noise-related signal (i.e., in-scanner movement and physiological noise) in functional connectivity (FC) of traumatic brain injury (TBI) patients. We applied nine commonly used denoising strategies, combined into 17 pipelines, to 88 TBI patients from the Epilepsy Bioinformatics Study for Anti-epileptogenic Therapy clinical trial. Pipelines were evaluated by three quality control (QC) metrics across three exclusion regimes based on the participant's head movement profile. While no pipeline eliminated noise effects on FC, some pipelines exhibited relatively high effectiveness depending on the exclusion regime. Once high-motion participants were excluded, the choice of denoising pipeline becomes secondary - although this strategy leads to substantial data loss. Pipelines combining spike regression with physiological regressors were the best performers, whereas pipelines that used automated data-driven methods performed comparatively worse. In this study, we report the first large-scale evaluation of denoising pipelines aimed at reducing noise-related FC in a clinical population known to be highly susceptible to in-scanner motion and significant anatomical abnormalities. If resting-state functional magnetic resonance is to be a successful clinical technique, it is crucial that procedures mitigating the effect of noise be systematically evaluated in the most challenging populations, such as TBI datasets.

EEG Power spectra and subcortical pathology in chronic disorders of consciousness

BACKGROUND

Despite a growing understanding of disorders of consciousness following severe brain injury, the association between long-term impairment of consciousness, spontaneous brain oscillations, and underlying subcortical damage, and the ability of such information to aid patient diagnosis, remains incomplete.

METHODS

Cross-sectional observational sample of 116 patients with a disorder of consciousness secondary to brain injury, collected prospectively at a tertiary center between 2011 and 2013. Multimodal analyses relating clinical measures of impairment, electroencephalographic measures of spontaneous brain activity, and magnetic resonance imaging data of subcortical atrophy were conducted in 2018.

RESULTS

In the final analyzed sample of 61 patients, systematic associations were found between electroencephalographic power spectra and subcortical damage. Specifically, the ratio of beta-to-delta relative power was negatively associated with greater atrophy in regions of the bilateral thalamus and globus pallidus (both left > right) previously shown to be preferentially atrophied in chronic disorders of consciousness. Power spectrum total density was also negatively associated with widespread atrophy in regions of the left globus pallidus, right caudate, and in the brainstem. Furthermore, we showed that the combination of demographics, encephalographic, and imaging data in an analytic framework can be employed to aid behavioral diagnosis.

CONCLUSIONS

These results ground, for the first time, electroencephalographic presentation detected with routine clinical techniques in the underlying brain pathology of disorders of consciousness and demonstrate how multimodal combination of clinical, electroencephalographic, and imaging data can be employed in potentially mitigating the high rates of misdiagnosis typical of this patient cohort.

Applications of Advanced MRI to Disorders of Consciousness

Disorder of consciousness (DoC) after severe brain injury presents numerous challenges to clinicians, as the diagnosis, prognosis, and management are often uncertain. Magnetic resonance imaging (MRI) has long been used to evaluate brain structure in patients with DoC. More recently, advances in MRI technology have permitted more detailed investigations of the brain's structural integrity (via diffusion MRI) and function (via functional MRI). A growing literature has begun to show that these advanced forms of MRI may improve our understanding of DoC pathophysiology, facilitate the identification of patient consciousness, and improve the accuracy of clinical prognostication. Here we review the emerging evidence for the application of advanced MRI for patients with DoC.

Brain Activity Characteristics of Patients With Disorders of Consciousness in the EEG Resting State Paradigm: A Review

The assessment of the level of consciousness in disorders of consciousness (DoC) is still one of the most challenging problems in contemporary medicine. Nevertheless, based on the multitude of studies conducted over the last 20 years on resting states based on electroencephalography (EEG) in DoC, it is possible to outline the brain activity profiles related to both patients without preserved consciousness and minimally conscious ones. In the case of patients without preserved consciousness, the dominance of low, mostly delta, frequency, and the marginalization of the higher frequencies were observed, both in terms of the global power of brain activity and in functional connectivity patterns. In turn, the minimally conscious patients revealed the opposite brain activity pattern—the characteristics of higher frequency bands were preserved both in global power and in functional long-distance connections. In this short review, we summarize the state of the art of EEG-based research in the resting state paradigm, in the context of providing potential support to the traditional clinical assessment of the level of consciousness.

Abnormal default mode network could be a potential prognostic marker in patients with disorders of consciousness

OBJECTIVES

The study aimed to investigate disorders of consciousness (DOC) mechanisms of patients with severe traumatic brain injury (sTBI) related to default mode network (DMN) and to introduce a machine learning model that predicts the prognosis of these patients for 6 months.

METHODS

The sTBI patients suffering from DOC and healthy controls underwent functional magnetic resonance imaging. We defined patients with Extended Glasgow Outcome Score ≥ 5 as good outcome group, otherwise they were poor outcome group. The differences of DMN between sTBI and healthy controls and between good and poor outcome groups were compared. Based on the brain regions with altered functional connectivity between good and poor outcome groups, they were divided into 8 regions of interests according to side. The Z values of the regions of interests were extracted by Rest 1.8. Based on Z values, the Subspace K-Nearest Neighbor (Subspace KNN) was conducted to classify prognosis of sTBI patients suffering from DOC.

RESULTS

A total of 84 DMNs derived from patients and 45 DMNs from healthy controls were finally analyzed. The connectivity of the DMN was significantly decreased in sTBI patients suffering from DOC (Alphasim corrected, P < 0.05). In addition, compared with the poor outcome group (DMN samples = 60), the brain regions of DMN with decreased functional connectivity in the good outcome group (DMN samples = 24) the following bilateral areas: brodman Area 11, anterior cingulate and paracingulate gyri, brodman Area 25, olfactory cortex (Alphasim corrected, P < 0.05). The ability of Subspace KNN machine learning to distinguish the prognosis of patients (area under curve) was 0.97.

CONCLUSIONS

The interruption of DMN may be one of the reasons for DOC in patients with sTBI. Furthermore, based on early DMN (1-4 weeks), Subspace KNN machine learning has the potential value to distinguish the prognosis (6 months after brain trauma) of sTBI patients suffering from DOC.

Identifying the neural network for neuromodulation in epilepsy through connectomics and graphs

Deep brain stimulation is a treatment option for patients with drug-resistant epilepsy. The precise mechanism of neuromodulation in epilepsy is unknown, and biomarkers are needed for optimizing treatment. The aim of this study was to describe the neural network associated with deep brain stimulation targets for epilepsy and to explore its potential application as a novel biomarker for neuromodulation. Using seed-to-voxel functional connectivity maps, weighted by seizure outcomes, brain areas associated with stimulation were identified in normative resting state functional scans of 1000 individuals. To pinpoint specific regions in the normative epilepsy deep brain stimulation network, we examined overlapping areas of functional connectivity between the anterior thalamic nucleus, centromedian thalamic nucleus, hippocampus and less studied epilepsy deep brain stimulation targets. Graph network analysis was used to describe the relationship between regions in the identified network. Furthermore, we examined the associations of the epilepsy deep brain stimulation network with disease pathophysiology, canonical resting state networks and findings from a systematic review of resting state functional MRI studies in epilepsy deep brain stimulation patients. Cortical nodes identified in the normative epilepsy deep brain stimulation network were in the anterior and posterior cingulate, medial frontal and sensorimotor cortices, frontal operculum and bilateral insulae. Subcortical nodes of the network were in the basal ganglia, mesencephalon, basal forebrain and cerebellum. Anterior thalamic nucleus was identified as a central hub in the network with the highest betweenness and closeness values, while centromedian thalamic nucleus and hippocampus showed average centrality values. The caudate nucleus and mammillothalamic tract also displayed high centrality values. The anterior cingulate cortex was identified as an important cortical hub associated with the effect of deep brain stimulation in epilepsy. The neural network of deep brain stimulation targets shared hubs with known epileptic networks and brain regions involved in seizure propagation and generalization. Two cortical clusters identified in the epilepsy deep brain stimulation network included regions corresponding to resting state networks, mainly the default mode and salience networks. Our results were concordant with findings from a systematic review of resting state functional MRI studies in patients with deep brain stimulation for epilepsy. Our findings suggest that the various epilepsy deep brain stimulation targets share a common cortico-subcortical network, which might in part underpin the antiseizure effects of stimulation. Interindividual differences in this network functional connectivity could potentially be used as biomarkers in selection of patients, stimulation parameters and neuromodulation targets.

Automated Classification of Resting-State fMRI ICA Components Using a Deep Siamese Network

Manual classification of functional resting state networks (RSNs) derived from Independent Component Analysis (ICA) decomposition can be labor intensive and requires expertise, particularly in large multi-subject analyses. Hence, a fully automatic algorithm that can reliably classify these RSNs is desirable. In this paper, we present a deep learning approach based on a Siamese Network to learn a discriminative feature representation for single-subject ICA component classification. Advantages of this supervised framework are that it requires relatively few training data examples and it does not require the number of ICA components to be specified. In addition, our approach permits one-shot learning, which allows generalization to new classes not seen in the training set with only one example of each new class. The proposed method is shown to out-perform traditional convolutional neural network (CNN) and template matching methods in identifying eleven subject-specific RSNs, achieving 100% accuracy on a holdout data set and over 99% accuracy on an outside data set. We also demonstrate that the method is robust to scan-rescan variation. Finally, we show that the functional connectivity of default mode and salience networks identified by the proposed technique is altered in a group analysis of mild traumatic brain injury (TBI), severe TBI, and healthy subjects.

Flowchart for Implementing Advanced Imaging and Electrophysiology in Patients With Disorders of Consciousness: To fMRI or Not to fMRI?

The American Academy of Neurology and the European Academy of Neurology have recognized, for the first time, the value of advanced neuroimaging and electrophysiology techniques (AIEs) in the context of diagnosing patients with a disorder of consciousness (DOC). This recognition is part of an important agenda of promoting evidence-based competency in the management of patients with DOC. Nonetheless, considering that these techniques (and the required knowledge) are seldom available outside of advanced medical centers, it is important to provide physicians with a framework for balancing risks and benefits and deciding, on a single patient basis, whether AIEs are suitable. This issue is all the more urgent considering that family members are increasingly aware of the use of AIEs in patients with DOC, pressure for these assessments is likely to increase in the context of ethical and clinical imperatives to meet standards of care, and pathways for reimbursement for such assessments in DOC are yet to be established. The new guidelines, however, provide no guiding principle for physicians to decide when such assessments are appropriate, a limitation that impedes their wide adoption. We address this important gap by proposing an easy to use algorithmic flowchart that is based on the new guidelines and can be used to determine the appropriateness of AIEs for any given patient with DOC and ensure that evidence-based best practices are being followed. We also provide a brief context for understanding the main categories of AIEs available to clinicians, their advantages, and their limitations.

Resting-State fMRI in Chronic Patients with Disorders of Consciousness: The Role of Lower-Order Networks for Clinical Assessment

Resting-state fMRI (rs-fMRI) is a widely used technique to investigate the residual brain functions of patients with Disorders of Consciousness (DoC). Nonetheless, it is unclear how the networks that are more associated with primary functions, such as the sensory–motor, medial/lateral visual and auditory networks, contribute to clinical assessment. In this study, we examined the rs-fMRI lower-order networks alongside their structural MRI data to clarify the corresponding association with clinical assessment. We studied 109 chronic patients with DoC and emerged from DoC with structural MRI and rs-fMRI: 65 in vegetative state/unresponsive wakefulness state (VS/UWS), 34 in minimally conscious state (MCS) and 10 with severe disability. rs-fMRI data were analyzed with independent component analyses and seed-based analyses, in relation to structural MRI and clinical data. The results showed that VS/UWS had fewer networks than MCS patients and the rs-fMRI activity in each network was decreased. Visual networks were correlated to the clinical status, and in cases where no clinical response occurred, rs-fMRI indicated distinctive networks conveying information in a similar way to other techniques. The information provided by single networks was limited, whereas the four networks together yielded better classification results, particularly when the model included rs-fMRI and structural MRI data (AUC = 0.80). Both quantitative and qualitative rs-fMRI analyses yielded converging results; vascular etiology might confound the results, and disease duration generally reduced the number of networks observed. The lower-order rs-fMRI networks could be used clinically to support and corroborate visual function assessments in DoC.

Quantifying arousal and awareness in altered states of consciousness using interpretable deep learning

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

Thalamo-Prefrontal Connectivity Correlates With Early Command-Following After Severe Traumatic Brain Injury

Recovery of consciousness after traumatic brain injury (TBI) is heterogeneous and difficult to predict. Structures such as the thalamus and prefrontal cortex are thought to be important in facilitating consciousness. We sought to investigate whether the integrity of thalamo-prefrontal circuits, assessed via diffusion tensor imaging (DTI), was associated with the return of goal-directed behavior after severe TBI. We classified a cohort of severe TBI patients (N = 25, 20 males) into Early and Late/Never outcome groups based on their ability to follow commands within 30 days post-injury. We assessed connectivity between whole thalamus, and mediodorsal thalamus (MD), to prefrontal cortex (PFC) subregions including dorsolateral PFC (dlPFC), medial PFC (mPFC), anterior cingulate (ACC), and orbitofrontal (OFC) cortices. We found that the integrity of thalamic projections to PFC subregions (L OFC, L and R ACC, and R mPFC) was significantly associated with Early command-following. This association persisted when the analysis was restricted to prefrontal-mediodorsal (MD) thalamus connectivity. In contrast, dlPFC connectivity to thalamus was not significantly associated with command-following. Using the integrity of thalamo-prefrontal connections, we created a linear regression model that demonstrated 72% accuracy in predicting command-following after a leave-one-out analysis. Together, these data support a role for thalamo-prefrontal connectivity in the return of goal-directed behavior following TBI.

Disrupted Control Architecture of Brain Network in Disorder of Consciousness

The human brain controls various cognitive functions via the functional coordination of multiple brain regions in an efficient and robust way. However, the relationship between consciousness state and the control mode of brain networks is poorly explored. Using multi-channel EEG, the present study aimed to characterize the abnormal control architecture of functional brain networks in the patients with disorders of consciousness (DOC). Resting state EEG data were collected from 40 DOC patients with different consciousness levels and 24 healthy subjects. Functional brain networks were constructed in five different EEG frequency bands and the broadband in the source level. Subsequently, a control architecture framework based on the minimum dominating set was applied to investigate the of control mode of functional brain networks for the subjects with different conscious states. Results showed that regardless of the consciousness levels, the functional networks of human brain operate in a distributed and overlapping control architecture different from that of random networks. Compared to the healthy controls, the patients have a higher control cost manifested by more minimum dominating nodes and increased degree of distributed control, especially in the alpha band. The ability to withstand network attack for the control architecture is positive correlated with the consciousness levels. The distributed of control increased correlation levels with Coma Recovery Scale-Revised score and improved separation between unresponsive wakefulness syndrome and minimal consciousness state. These findings may benefit our understanding of consciousness and provide potential biomarkers for the assessment of consciousness levels.

Regional Homogeneity Alterations in Patients with Impaired Consciousness. An Observational Resting-State fMRI Study

PURPOSE

It is always challenging to correctly differentiate between minimally conscious state (MCS) and vegetative state/unresponsive wakefulness syndrome (VS/UWS) among disorders of consciousness (DOC) patients. However, the underlying neural mechanisms of awareness identification remain incompletely understood.

METHODS

Using regional homogeneity (ReHo) analysis, we evaluated how regional connectivity of brain regions is disrupted in MCS and VS/UWS patients. Resting-state functional magnetic resonance imaging was conducted in 14 MCS patients, 25 VS/UWS patients, and 30 age-matched healthy individuals.

RESULTS

We found that MCS and VS/UWS patients demonstrated DOC-dependent reduced ReHo within widespread brain regions including posterior cingulate cortices (PCC), medial prefrontal cortices (mPFC), and bilateral fronto-parieto-temporal cortices and showed increased ReHo in limbic structures. Moreover, a positive correlation between Coma Recovery Scale-Revised (CRS-R) total scores and reduced ReHo in the left precuneus was observed in VS/UWS patients, despite the linear trend was not found in MCS patients. In addition, ReHo were also observed reduced in three mainly intrinsic connectivity networks (ICNs), including default mode network (DMN), executive control network (ECN), and salience network (SN). Notably, as the clinical symptoms of consciousness disorders worsen from MCS to VS/UWS, ReHo in dorsal DMN, left ECN, and posterior SN became significantly reduced.

CONCLUSION

These findings make a further understanding of the underlying neural mechanism of regional connectivity among DOC patients and provide additional neuroimaging-based biomarkers for the clinical diagnosis of MCS and VS/UWS patients.

Altered Effective Connectivity Measured by Resting-State Functional Magnetic Resonance Imaging in Posterior Parietal-Frontal-Striatum Circuit in Patients With Disorder of Consciousness

Objective

Disorder of consciousness (DoC) resulting from severe brain injury is characterized by cortical and subcortical dysconnectivity. However, research on seed-based effective connectivity (EC) of DoC might be questioned as to the heterogeneity of prior assumptions.

Methods

Functional MRI data of 16 DoC patients and 16 demographically matched healthy individuals were analyzed. Revised coma recovery scale (CRS-R) scores of patients were acquired. Seed-based d mapping permutation of subject images (SDM-PSI) of meta-analysis was performed to quantitatively synthesize results from neuroimaging studies that evaluated resting-state functional activity in DoC patients. Spectral dynamic causal modeling (spDCM) was used to assess how EC altered between brain regions in DoC patients compared to healthy individuals.

Results

We found increased effective connectivity in left striatum and decreased effective connectivity in bilateral precuneus (preCUN)/posterior cingulate cortex (PCC), bilateral midcingulate cortex and left middle frontal gyrus in DoC compared with the healthy controls. The resulting pattern of interaction in DoC indicated disrupted connection and disturbance of posterior parietal-frontal-striatum, and reduced self-inhibition of preCUN/PCC. The strength of self-inhibition of preCUN/PCC was negatively correlated with the total score of CRS-R.

Conclusion

This impaired EC in DoC may underlie disruption in the posterior parietal-frontal-striatum circuit, particularly damage to the cortico-striatal connection and possible loss of preCUN/PCC function as the main regulatory hub.

Diagnostic Developments in Differentiating Unresponsive Wakefulness Syndrome and the Minimally Conscious State

When treating patients with a disorder of consciousness (DOC), it is essential to obtain an accurate diagnosis as soon as possible to generate individualized treatment programs. However, accurately diagnosing patients with DOCs is challenging and prone to errors when differentiating patients in a Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS) from those in a Minimally Conscious State (MCS). Upwards of ~40% of patients with a DOC can be misdiagnosed when specifically designed behavioral scales are not employed or improperly administered. To improve diagnostic accuracy for these patients, several important neuroimaging and electrophysiological technologies have been proposed. These include Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), and Transcranial Magnetic Stimulation (TMS). Here, we review the different ways in which these techniques can improve diagnostic differentiation between VS/UWS and MCS patients. We do so by referring to studies that were conducted within the last 10 years, which were extracted from the PubMed database. In total, 55 studies met our criteria (clinical diagnoses of VS/UWS from MCS as made by PET, fMRI, EEG and TMS- EEG tools) and were included in this review. By summarizing the promising results achieved in understanding and diagnosing these conditions, we aim to emphasize the need for more such tools to be incorporated in standard clinical practice, as well as the importance of data sharing to incentivize the community to meet these goals.

Mapping the Unconscious Brain: Insights From Advanced Neuroimaging

SUMMARY

Recent advances in neuroimaging have been a preeminent factor in the scientific effort to unravel mechanisms of conscious awareness and the pathophysiology of disorders of consciousness. In the first part of this review, we selectively discuss operational models of consciousness, the biophysical signal that is measured using different imaging modalities, and knowledge on disorders of consciousness that has been gleaned with each neuroimaging modality. Techniques considered include diffusion-weighted imaging, diffusion tensor imaging, different types of nuclear medicine imaging, functional MRI, magnetoencephalography, and the combined transcranial magnetic stimulation-electroencephalography approach. In the second part of this article, we provide an overview of how advanced neuroimaging can be leveraged to support neurological prognostication, the use of machine learning to process high-dimensional imaging data, potential applications in clinical practice, and future directions.

Nasal Air Puff Promotes Default Mode Network Activity in Mechanically Ventilated Comatose Patients: A Noninvasive Brain Stimulation Approach

OBJECTIVES

Coma state and loss of consciousness are associated with impaired brain activity, particularly gamma oscillations, that integrate functional connectivity in neural networks, including the default mode network (DMN). Mechanical ventilation (MV) in comatose patients can aggravate brain activity, which has decreased in coma, presumably because of diminished nasal airflow. Nasal airflow, known to drive functional neural oscillations, synchronizing distant brain networks activity, is eliminated by tracheal intubation and MV. Hence, we proposed that rhythmic nasal air puffing in mechanically ventilated comatose patients may promote brain activity and improve network connectivity.

MATERIALS AND METHODS

We recorded electroencephalography (EEG) from 15 comatose patients (seven women) admitted to the intensive care unit because of opium poisoning and assessed the activity, complexity, and connectivity of the DMN before and during the nasal air-puff stimulation. Nasal cavity air puffing was done through a nasal cannula controlled by an electrical valve (open duration of 630 ms) with a frequency of 0.2 Hz (ie, 12 puff/min).

RESULTS

Our analyses demonstrated that nasal air puffing enhanced the power of gamma oscillations (30-100 Hz) in the DMN. In addition, we found that the coherence and synchrony between DMN regions were increased during nasal air puffing. Recurrence quantification and fractal dimension analyses revealed that EEG global complexity and irregularity, typically seen in wakefulness and conscious state, increased during rhythmic nasal air puffing.

CONCLUSIONS

Rhythmic nasal air puffing, as a noninvasive brain stimulation method, opens a new window to modifying the brain connectivity integration in comatose patients. This approach may potentially influence comatose patients' outcomes by increasing brain reactivity and network connectivity.

A method to assess the default EEG macrostate and its reactivity to stimulation

OBJECTIVE

The default mode network (DMN) is deactivated by stimulation. We aimed to assess the DMN reactivity impairment by routine EEG recordings in stroke patients with impaired consciousness.

METHODS

Binocular light flashes were delivered at 1 Hz in 1-minute epochs, following a 1-minute baseline (PRE). The EEG was decomposed in a series of binary oscillatory macrostates by topographic spectral clustering. The most deactivated macrostate was labeled the default EEG macrostate (DEM). Its reactivity (DER) was quantified as the decrease in DEM occurrence probability during stimulation. A normalized DER index (DERI) was calculated as DER/PRE. The measures were compared between 14 healthy controls and 32 comatose patients under EEG monitoring following an acute stroke.

RESULTS

The DEM was mapped to the posterior DMN hubs. In the patients, these DEM source dipoles were 3-4 times less frequent and were associated with an increased theta activity. Even in a reduced 6-channel montage, a DER below 6.26% corresponding to a DERI below 0.25 could discriminate the patients with sensitivity and specificity well above 80%.

CONCLUSION

The method detected the DMN impairment in post-stroke coma patients.

SIGNIFICANCE

The DEM and its reactivity to stimulation could be useful to monitor the DMN function at bedside.

Residual Cognitive Capacities in Patients With Cognitive Motor Dissociation, and Their Implications for Well-Being

Patients with severe disorders of consciousness are thought to be unaware of themselves or their environment. However, research suggests that a minority of patients diagnosed as having a disorder of consciousness remain aware. These patients, designated as having “cognitive motor dissociation” (CMD), can demonstrate awareness by imagining specific tasks, which generates brain activity detectable via functional neuroimaging. The discovery of consciousness in these patients raises difficult questions about their well-being, and it has been argued that it would be better for these patients if they were allowed to die. Conversely, I argue that CMD patients may have a much higher level of well-being than is generally acknowledged. It is far from clear that their lives are not worth living, because there are still significant gaps in our understanding of how these patients experience the world. I attempt to fill these gaps, by analyzing the neuroscientific research that has taken place with these patients to date. Having generated as comprehensive a picture as possible of the capacities of CMD patients, I examine this picture through the lens of traditional philosophical theories of well-being. I conclude that the presumption that CMD patients do not have lives worth living is not adequately supported.

A Precuneal Causal Loop Mediates External and Internal Information Integration in the Human Brain

Human brains interpret external stimuli based on internal representations. One untested hypothesis is that the default-mode network (DMN), widely considered responsible for internally oriented cognition, can decode external information. Here, we posit that the unique structural and functional fingerprint of the precuneus (PCu) supports a prominent role for the posterior part of the DMN in this process. By analyzing the imaging data of 100 participants performing two attention-demanding tasks, we found that the PCu is functionally divided into dorsal and ventral subdivisions. We then conducted a comprehensive examination of their connectivity profiles and found that at rest, both the ventral PCu (vPCu) and dorsal PCu (dPCu) are mainly connected with the DMN but also are differentially connected with internally oriented networks (IoN) and externally oriented networks (EoN). During tasks, the double associations between the v/dPCu and the IoN/EoN are correlated with task performance and can switch depending on cognitive demand. Furthermore, dynamic causal modeling (DCM) revealed that the strength and direction of the effective connectivity (EC) between v/dPCu is modulated by task difficulty in a manner potentially dictated by the balance of internal versus external cognitive demands. Our study provides evidence that the posterior medial part of the DMN may drive interactions between large-scale networks, potentially allowing access to stored representations for moment-to-moment interpretation of an ever-changing environment.SIGNIFICANCE STATEMENT The default-mode network (DMN) is widely known for its association with internalized thinking processes, e.g., spontaneous thoughts, which is the most interesting but least understood component in human consciousness. The precuneus (PCu), a posteromedial DMN hub, is thought to play a role in this, but a mechanistic explanation has not yet been established. In this study we found that the associations between ventral PCu (vPCu)/dorsal PCu (dPCu) subdivisions and internally oriented network (IoN)/externally oriented network (EoN) are flexibly modulated by cognitive demand and correlate with task performance. We further propose that the recurrent causal connectivity between the ventral and dorsal PCu supports conscious processing by constantly interpreting external information based on an internal model, meanwhile updating the internal model with the incoming information.

What it is like to be a bit: an integrated information decomposition account of emergent mental phenomena

A central question in neuroscience concerns the relationship between consciousness and its physical substrate. Here, we argue that a richer characterization of consciousness can be obtained by viewing it as constituted of distinct information-theoretic elements. In other words, we propose a shift from quantification of consciousness-viewed as integrated information-to its decomposition. Through this approach, termed Integrated Information Decomposition (ΦID), we lay out a formal argument that whether the consciousness of a given system is an emergent phenomenon depends on its information-theoretic composition-providing a principled answer to the long-standing dispute on the relationship between consciousness and emergence. Furthermore, we show that two organisms may attain the same amount of integrated information, yet differ in their information-theoretic composition. Building on ΦID's revised understanding of integrated information, termed ΦR, we also introduce the notion of ΦR-ing ratio to quantify how efficiently an entity uses information for conscious processing. A combination of ΦR and ΦR-ing ratio may provide an important way to compare the neural basis of different aspects of consciousness. Decomposition of consciousness enables us to identify qualitatively different 'modes of consciousness', establishing a common space for mapping the phenomenology of different conscious states. We outline both theoretical and empirical avenues to carry out such mapping between phenomenology and information-theoretic modes, starting from a central feature of everyday consciousness: selfhood. Overall, ΦID yields rich new ways to explore the relationship between information, consciousness, and its emergence from neural dynamics.

Contribution of animal models toward understanding resting state functional connectivity

Functional connectivity, which reflects the spatial and temporal organization of intrinsic activity throughout the brain, is one of the most studied measures in human neuroimaging research. The noninvasive acquisition of resting state functional magnetic resonance imaging (rs-fMRI) allows the characterization of features designated as functional networks, functional connectivity gradients, and time-varying activity patterns that provide insight into the intrinsic functional organization of the brain and potential alterations related to brain dysfunction. Functional connectivity, hence, captures dimensions of the brain's activity that have enormous potential for both clinical and preclinical research. However, the mechanisms underlying functional connectivity have yet to be fully characterized, hindering interpretation of rs-fMRI studies. As in other branches of neuroscience, the identification of the neurophysiological processes that contribute to functional connectivity largely depends on research conducted on laboratory animals, which provide a platform where specific, multi-dimensional investigations that involve invasive measurements can be carried out. These highly controlled experiments facilitate the interpretation of the temporal correlations observed across the brain. Indeed, information obtained from animal experimentation to date is the basis for our current understanding of the underlying basis for functional brain connectivity. This review presents a compendium of some of the most critical advances in the field based on the efforts made by the animal neuroimaging community.

Dynamic Patterns of Global Brain Communication Differentiate Conscious From Unconscious Patients After Severe Brain Injury

The neurophysiology of the subjective sensation of being conscious is elusive; therefore, it remains controversial how consciousness can be recognized in patients who are not responsive but seemingly awake. During general anesthesia, a model for the transition between consciousness and unconsciousness, specific covariance matrices between the activity of brain regions that we call patterns of global brain communication reliably disappear when people lose consciousness. This functional magnetic imaging study investigates how patterns of global brain communication relate to consciousness and unconsciousness in a heterogeneous sample during general anesthesia and after brain injury. First, we describe specific patterns of global brain communication during wakefulness that disappear during propofol (n = 11) and sevoflurane (n = 14) general anesthesia. Second, we search for these patterns in a cohort of unresponsive wakeful patients (n = 18) and unmatched healthy controls (n = 20) in order to evaluate their potential use in clinical practice. We found that patterns of global brain communication characterized by high covariance in sensory and motor areas or low overall covariance and their dynamic change were strictly associated with intact consciousness in this cohort. In addition, we show that the occurrence of these two patterns is significantly related to activity within the frontoparietal network of the brain, a network known to play a crucial role in conscious perception. We propose that this approach potentially recognizes consciousness in the clinical routine setting.

A Systematic Review of Sleep in Patients with Disorders of Consciousness: From Diagnosis to Prognosis

With the development of intensive care technology, the number of patients who survive acute severe brain injury has increased significantly. At present, it is difficult to diagnose the patients with disorders of consciousness (DOCs) because motor responses in these patients may be very limited and inconsistent. Electrophysiological criteria, such as event-related potentials or motor imagery, have also been studied to establish a diagnosis and prognosis based on command-following or active paradigms. However, the use of such task-based techniques in DOC patients is methodologically complex and requires careful analysis and interpretation. The present paper focuses on the analysis of sleep patterns for the evaluation of DOC and its relationships with diagnosis and prognosis outcomes. We discuss the concepts of sleep patterns in patients suffering from DOC, identification of this challenging population, and the prognostic value of sleep. The available literature on individuals in an unresponsive wakefulness syndrome (UWS) or minimally conscious state (MCS) following traumatic or nontraumatic severe brain injury is reviewed. We can distinguish patients with different levels of consciousness by studying sleep patients with DOC. Most MCS patients have sleep and wake alternations, sleep spindles and rapid eye movement (REM) sleep, while UWS patients have few EEG changes. A large number of sleep spindles and organized sleep-wake patterns predict better clinical outcomes. It is expected that this review will promote our understanding of sleep EEG in DOC.

What have we really learned from functional connectivity in clinical populations?

Functional connectivity (FC), or the statistical interdependence of blood-oxygen dependent level (BOLD) signals between brain regions using fMRI, has emerged as a widely used tool for probing functional abnormalities in clinical populations due to the promise of the approach across conceptual, technical, and practical levels. With an already vast and steadily accumulating neuroimaging literature on neurodevelopmental, psychiatric, and neurological diseases and disorders in which FC is a primary measure, we aim here to provide a high-level synthesis of major concepts that have arisen from FC findings in a manner that cuts across different clinical conditions and sheds light on overarching principles. We highlight that FC has allowed us to discover the ubiquity of intrinsic functional networks across virtually all brains and clarify typical patterns of neurodevelopment over the lifespan. This understanding of typical FC maturation with age has provided important benchmarks against which to evaluate divergent maturation in early life and degeneration in late life. This in turn has led to the important insight that many clinical conditions are associated with complex, distributed, network-level changes in the brain, as opposed to solely focal abnormalities. We further emphasize the important role that FC studies have played in supporting a dimensional approach to studying transdiagnostic clinical symptoms and in enhancing the multimodal characterization and prediction of the trajectory of symptom progression across conditions. We highlight the unprecedented opportunity offered by FC to probe functional abnormalities in clinical conditions where brain function could not be easily studied otherwise, such as in disorders of consciousness. Lastly, we suggest high priority areas for future research and acknowledge critical barriers associated with the use of FC methods, particularly those related to artifact removal, data denoising and feasibility in clinical contexts.