Neural oscillations track recovery of consciousness in acute traumatic brain injury patients

Toward an interventional science of recovery after coma

Recovery of consciousness after coma remains one of the most challenging areas for accurate diagnosis and effective therapeutic engagement in the clinical neurosciences. Recovery depends on preservation of neuronal integrity and evolving changes in network function that re-establish environmental responsiveness. It typically occurs in defined steps: it begins with eye opening and unresponsiveness in a vegetative state, then limited recovery of responsiveness characterizes the minimally conscious state, and this is followed by recovery of reliable communication. This review considers several points for novel interventions, for example, in persons with cognitive motor dissociation in whom a hidden cognitive reserve is revealed. Circuit mechanisms underlying restoration of behavioral responsiveness and communication are discussed. An emerging theme is the possibility to rescue latent capacities in partially damaged human networks across time. These opportunities should be exploited for therapeutic engagement to achieve individualized solutions for restoration of communication and environmental interaction across varying levels of recovery.

Common Data Elements for Disorders of Consciousness: Recommendations from the Electrophysiology Working Group

BACKGROUND

Electroencephalography (EEG) has long been recognized as an important tool in the investigation of disorders of consciousness (DoC). From inspection of the raw EEG to the implementation of quantitative EEG, and more recently in the use of perturbed EEG, it is paramount to providing accurate diagnostic and prognostic information in the care of patients with DoC. However, a nomenclature for variables that establishes a convention for naming, defining, and structuring data for clinical research variables currently is lacking. As such, the Neurocritical Care Society's Curing Coma Campaign convened nine working groups composed of experts in the field to construct common data elements (CDEs) to provide recommendations for DoC, with the main goal of facilitating data collection and standardization of reporting. This article summarizes the recommendations of the electrophysiology DoC working group.

METHODS

After assessing previously published pertinent CDEs, we developed new CDEs and categorized them into "disease core," "basic," "supplemental," and "exploratory." Key EEG design elements, defined as concepts that pertained to a methodological parameter relevant to the acquisition, processing, or analysis of data, were also included but were not classified as CDEs.

RESULTS

After identifying existing pertinent CDEs and developing novel CDEs for electrophysiology in DoC, variables were organized into a framework based on the two primary categories of resting state EEG and perturbed EEG. Using this categorical framework, two case report forms were generated by the working group.

CONCLUSIONS

Adherence to the recommendations outlined by the electrophysiology working group in the resting state EEG and perturbed EEG case report forms will facilitate data collection and sharing in DoC research on an international level. In turn, this will allow for more informed and reliable comparison of results across studies, facilitating further advancement in the realm of DoC research.

Evaluating the state of non-invasive imaging biomarkers for traumatic brain injury

Non-invasive imaging biomarkers are useful for prognostication in patients with traumatic brain injury (TBI) at high risk for morbidity with invasive procedures. The authors present findings from a scoping review discussing the pertinent biomarkers. Embase, Ovid-MEDLINE, and Scopus were queried for original research on imaging biomarkers for prognostication of TBI in adult patients. Two reviewers independently screened articles, extracted data, and evaluated risk of bias. Data was synthesized and confidence evaluated with the linked evidence according to the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) approach. Our search yielded 3104 unique citations, 44 of which were included in this review. Study populations varied in TBI severity, as defined by Glasgow Coma Scale (GCS), including: mild (n=9), mild and moderate (n=3), moderate and severe (n=7), severe (n=6), and all GCS scores (n=17). Diverse imaging modalities were used for prognostication, predominantly computed tomography (CT) only (n=11), magnetic resonance imaging (MRI) only (n=9), and diffusion tensor imaging (DTI) (N=9). The biomarkers included diffusion coefficient mapping, metabolic characteristics, optic nerve sheath diameter, T1-weighted signal changes, cortical cerebral blood flow, axial versus extra-axial lesions, T2-weighted gradient versus spin echo, translocator protein levels, and trauma imaging of brainstem areas. The majority (93%) of studies identified that the imaging biomarker of interest had a statistically significant prognostic value; however, these are based on a very low to low level of quality of evidence. No study directly compared the effects on specific TBI treatments on the temporal course of imaging biomarkers. The current literature is insufficient to make a strong recommendation about a preferred imaging biomarker for TBI, especially considering GRADE criteria revealing low quality of evidence. Rigorous prospective research of imaging biomarkers of TBI is warranted to improve the understanding of TBI severity.

Cerebral electrometabolic coupling in disordered and normal states of consciousness

We assess cerebral integrity with cortical and subcortical FDG-PET and cortical electroencephalography (EEG) within the mesocircuit model framework in patients with disorders of consciousness (DoCs). The mesocircuit hypothesis proposes that subcortical activation facilitates cortical function. We find that the metabolic balance of subcortical mesocircuit areas is informative for diagnosis and is associated with four EEG-based power spectral density patterns, cortical metabolism, and α power in healthy controls and patients with a DoC. Last, regional electrometabolic coupling at the cortical level can be identified in the θ and α ranges, showing positive and negative relations with glucose uptake, respectively. This relation is inverted in patients with a DoC, potentially related to altered orchestration of neural activity, and may underlie suboptimal excitability states in patients with a DoC. By understanding the neurobiological basis of the pathophysiology underlying DoCs, we foresee translational value for diagnosis and treatment of patients with a DoC.

Mesocircuit mechanisms in the diagnosis and treatment of disorders of consciousness

The 'mesocircuit hypothesis' proposes mechanisms underlying the recovery of consciousness following severe brain injuries. The model builds up from a single premise that multifocal brain injuries resulting in coma and subsequent disorders of consciousness produce widespread neuronal death and dysfunction. Considering the general properties of cortical, thalamic, and striatal neurons, a lawful and specific circuit-level mechanism is constructed based on these known anatomical and physiological specializations of neuronal subtypes. The mesocircuit model generates many testable predictions at the mesocircuit, local circuit, and cellular level across multiple cerebral structures to correlate diagnostic measurements and interpret therapeutic interventions. The anterior forebrain mesocircuit is integrally related to the frontal-parietal network, another network demonstrated to show strong correlation with levels of recovery in disorders of consciousness. A further extension known as the "ABCD" model has been used to examine interaction of these models in recovery of consciousness using electrophysiological data types. Many studies have examined predictions of the mesocircuit model; here we first present the model and review the accumulated evidence for several predictions of model across multiple stages of recovery function in human subjects. Recent studies linking the mesocircuit model, the ABCD model, and interactions with the frontoparietal network are reviewed. Finally, theoretical implications of the mesocircuit model at the neuronal level are considered to interpret recent studies of deep brain stimulation in the central lateral thalamus in patients recovering from coma and in new experimental models in the context of emerging understanding of neuronal and local circuit mechanisms underlying conscious brain states.

Dexmedetomidine-mediated sleep phase modulation ameliorates motor and cognitive performance in a chronic blast-injured mouse model

Multiple studies have shown that blast injury is followed by sleep disruption linked to functional sequelae. It is well established that improving sleep ameliorates such functional deficits. However, little is known about longitudinal brain activity changes after blast injury. In addition, the effects of directly modulating the sleep/wake cycle on learning task performance after blast injury remain unclear. We hypothesized that modulation of the sleep phase cycle in our injured mice would improve post-injury task performance. Here, we have demonstrated that excessive sleep electroencephalographic (EEG) patterns are accompanied by prominent motor and cognitive impairment during acute stage after secondary blast injury (SBI) in a mouse model. Over time we observed a transition to more moderate and prolonged sleep/wake cycle disturbances, including changes in theta and alpha power. However, persistent disruptions of the non-rapid eye movement (NREM) spindle amplitude and intra-spindle frequency were associated with lasting motor and cognitive deficits. We, therefore, modulated the sleep phase of injured mice using subcutaneous (SC) dexmedetomidine (Dex), a common, clinically used sedative. Dex acutely improved intra-spindle frequency, theta and alpha power, and motor task execution in chronically injured mice. Moreover, dexmedetomidine ameliorated cognitive deficits a week after injection. Our results suggest that SC Dex might potentially improve impaired motor and cognitive behavior during daily tasks in patients that are chronically impaired by blast-induced injuries.

Is frontoparietal electroencephalogram activity related to the level of functional disability in patients emerging from a minimally conscious state? A preliminary study

Objective

When regaining consciousness, patients who emerge from a minimally conscious state (EMCS) present with different levels of functional disability, which pose great challenges for treatment. This study investigated the frontoparietal activity in EMCS patients and its effects on functional disability.

Materials and methods

In this preliminary study, 12 EMCS patients and 12 healthy controls were recruited. We recorded a resting-state scalp electroencephalogram (EEG) for at least 5 min for each participant. Each patient was assessed using the disability rating scale (DRS) to determine the level of functional disability. We analyzed the EEG power spectral density and sensor-level functional connectivity in relation to the patient’s functional disability.

Results

In the frontoparietal region, EMCS patients demonstrated lower relative beta power (P < 0.01) and higher weighted phase lag index (wPLI) values in the theta (P < 0.01) and gamma (P < 0.01) bands than healthy controls. The frontoparietal theta wPLI values of EMCS patients were positively correlated with the DRS scores (r_s = 0.629, P = 0.029). At the whole-brain level, EMCS patients only had higher wPLI values in the theta band (P < 0.01) than healthy controls. The whole-brain theta wPLI values of EMCS patients were also positively correlated with the DRS scores (r_s = 0.650, P = 0.022). No significant difference in the power and connectivity between the frontoparietal region and the whole brain in EMCS patients was observed.

Conclusion

EMCS patients still experience neural dysfunction, especially in the frontoparietal region. However, the theta connectivity in the frontoparietal region did not increase specifically. At the level of the whole brain, the same shift could also be seen. Theta functional connectivity in the whole brain may underlie different levels of functional disability.

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.

Hyperbaric oxygen therapy promotes consciousness, cognitive function, and prognosis recovery in patients following traumatic brain injury through various pathways

Objective

The aim of this study was to investigate the clinical curative effect of hyperbaric oxygen (HBO) treatment and its mechanism in improving dysfunction following traumatic brain injury (TBI).

Methods

Patients were enrolled into control and HBO groups. Glasgow coma scale (GCS) and coma recovery scale-revised (CRS-R) scores were used to measure consciousness; the Rancho Los Amigos scale-revised (RLAS-R) score was used to assess cognitive impairment; the Stockholm computed tomography (CT) score, quantitative electroencephalography (QEEG), and biomarkers, including neuron-specific enolase (NSE), S100 calcium-binding protein beta (S100β), glial fibrillary acidic protein (GFAP), brain-derived neurotrophic factor (BDNF), nerve growth factor (NGF), and vascular endothelial growth factor (VEGF), were used to assess TBI severity. The patients were followed up 6 months after discharge and assessed with the Glasgow outcome scale-extended (GOSE), functional independence measure (FIM), and the disability rating scale (DRS).

Results

The CRS-R scores were higher in the HBO group than the control group at 10 days after treatment. The RLAS-R scores were higher in the HBO group than the control group at 10 and 20 days after treatment. The Stockholm CT scores were significantly lower in the HBO group than the control group at 10 days after treatment. HBO depressed the (δ + θ)/(α + β) ratio (DTABR) of EEG, with lower δ band relative power and higher α band relative power than those in the control group. At 20 days after treatment, the expression of NSE, S100β, and GFAP in the HBO group was lower than that in controls, whereas the expression of BDNF, NGF, and VEGF in the HBO group was higher than that in controls. Six months after discharge, the HBO group had lower DRS scores and higher FIM and GOSE scores than the control group significantly.

Conclusions

HBO may be an effective treatment for patients with TBI to improve consciousness, cognitive function and prognosis through decreasing TBI-induced hematoma volumes, promoting the recovery of EEG rhythm, and modulating the expression of serum NSE, S100β, GFAP, BDNF, NGF, and VEGF.

Conventional and Investigational Approaches Leveraging Clinical EEG for Prognosis in Acute Disorders of Consciousness

Prediction of recovery of consciousness after severe brain injury is difficult and limited by a lack of reliable, standardized biomarkers. Multiple approaches for analysis of clinical electroencephalography (EEG) that shed light on prognosis in acute severe brain injury have emerged in recent years. These approaches fall into two major categories: conventional characterization of EEG background and quantitative measurement of resting state or stimulus-induced EEG activity. Additionally, a small number of studies have associated the presence of electrophysiologic sleep features with prognosis in the acute phase of severe brain injury. In this review, we focus on approaches for the analysis of clinical EEG that have prognostic significance and that could be readily implemented with minimal additional equipment in clinical settings, such as intensive care and intensive rehabilitation units, for patients with acute disorders of consciousness.