Quantitative EEG Parameters for Prediction of Outcome in Severe Traumatic Brain Injury: Development Study

Detecting awareness after acute brain injury

Advances over the past two decades in functional neuroimaging have provided new diagnostic and prognostic tools for patients with severe brain injury. Some of the most pertinent developments in this area involve the assessment of residual brain function in patients in the intensive care unit during the acute phase of severe injury, when they are at their most vulnerable and prognosis is uncertain. Advanced neuroimaging techniques, such as functional MRI and EEG, have now been used to identify preserved cognitive processing, including covert conscious awareness, and to relate them to outcome in patients who are behaviourally unresponsive. Yet, technical and logistical challenges to clinical integration of these advanced neuroimaging techniques remain, such as the need for specialised expertise to acquire, analyse, and interpret data and to determine the appropriate timing for such assessments. Once these barriers are overcome, advanced functional neuroimaging technologies could improve diagnosis and prognosis for millions of patients worldwide.

Pitfalls and possibilities of using Root SedLine for continuous assessment of EEG waveform-based metrics in intensive care research

Objective.The Root SedLine device is used for continuous electroencephalography (cEEG)-based sedation monitoring in intensive care patients. The cEEG traces can be collected for further processing and calculation of relevant metrics not already provided. Depending on the device settings during acquisition, the acquired traces may be distorted by max/min value cropping or high digitization errors. We aimed to systematically assess the impact of these distortions on metrics used for clinical research in the field of neuromonitoring.Approach.A 16 h cEEG acquired using the Root SedLine device at the optimal screen settings was analyzed. Cropping and digitization error effects were simulated by consecutive reduction of the maximum cEEG amplitude by 2µV or by reducing the vertical resolution. Metrics were calculated within ICM+ using minute-by-minute data, including the total power, alpha delta ratio (ADR), and 95% spectral edge frequency. Data were analyzed by creating violin- or box-plots.Main Results.Cropping led to a continuous reduction in total and band power, leading to corresponding changes in variability thereof. The relative power and ADR were less affected. Changes in resolution led to relevant changes. While the total power and power of low frequencies were rather stable, the power of higher frequencies increased with reducing resolution.Significance.Care must be taken when acquiring and analyzing cEEG waveforms from Root SedLine for clinical research. To retrieve good quality metrics, the screen settings must be kept within the central vertical scale, while pre-processing techniques must be applied to exclude unacceptable periods.

Resting-State EEG Signature of Early Consciousness Recovery in Comatose Patients with Traumatic Brain Injury

BACKGROUND

Resting-state electroencephalography (rsEEG) is usually obtained to assess seizures in comatose patients with traumatic brain injury (TBI). We aim to investigate rsEEG measures and their prediction of early recovery of consciousness in patients with TBI.

METHODS

This is a retrospective study of comatose patients with TBI who were admitted to a trauma center (October 2013 to January 2022). Demographics, basic clinical data, imaging characteristics, and EEGs were collected. We calculated the following using 10-min rsEEGs: power spectral density, permutation entropy (complexity measure), weighted symbolic mutual information (wSMI, global information sharing measure), Kolmogorov complexity (Kolcom, complexity measure), and heart-evoked potentials (the averaged EEG signal relative to the corresponding QRS complex on electrocardiography). We evaluated the prediction of consciousness recovery before hospital discharge using clinical, imaging, and rsEEG data via a support vector machine.

RESULTS

We studied 113 of 134 (84%) patients with rsEEGs. A total of 73 (65%) patients recovered consciousness before discharge. Patients who recovered consciousness were younger (40 vs. 50 years, p = 0.01). Patients who recovered also had higher Kolcom (U = 1688, p = 0.01), increased beta power (U = 1,652 p = 0.003) with higher variability across channels (U = 1534, p = 0.034) and epochs (U = 1711, p = 0.004), lower delta power (U = 981, p = 0.04), and higher connectivity across time and channels as measured by wSMI in the theta band (U = 1636, p = 0.026; U = 1639, p = 0.024) than those who did not recover. The area under the receiver operating characteristic curve for rsEEG was higher than that for clinical data (using age, motor response, pupil reactivity) and higher than that for the Marshall computed tomography classification (0.69 vs. 0.66 vs. 0.56, respectively; p < 0.001).

CONCLUSIONS

We describe the rsEEG signature in recovery of consciousness prior to discharge in comatose patients with TBI. rsEEG measures performed modestly better than the clinical and imaging data in predicting recovery.

Predictors of outcomes 3 to 12 months after traumatic brain injury: a systematic review and meta-analysis

The exact factors predicting outcomes following traumatic brain injury (TBI) remain elusive. In this systematic review and meta-analysis, we examined factors influencing outcomes in adult patients with TBI, from 3 months to 1 year after injury. A search of four electronic databases-PubMed, Scopus, Web of Science, and ScienceDirect-yielded 29 studies for review and 16 for meta-analysis, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. In patients with TBI of any severity, mean differences were observed in age (8.72 years; 95% confidence interval [CI], 4.77-12.66 years), lymphocyte count (-0.15 109/L; 95% CI, -0.18 to -0.11), glucose levels (1.20 mmol/L; 95% CI, 0.73-1.68), and haemoglobin levels (-0.91 g/dL; 95% CI, -1.49 to -0.33) between those with favourable and unfavourable outcomes. The prevalence rates of unfavourable outcomes were as follows: abnormal cisterns, 65.7%; intracranial pressure above 20 mmHg, 52.9%; midline shift of 5 mm or more, 63%; hypotension, 71%; hypoxia, 86.8%; blood transfusion, 70.3%; and mechanical ventilation, 90%. Several predictors were strongly associated with outcome. Specifically, age, lymphocyte count, glucose level, haemoglobin level, severity of TBI, pupillary reaction, and type of injury were identified as potential predictors of long-term outcomes.

Resting-State EEG Signature of Early Consciousness Recovery in Comatose Traumatic Brain Injury Patients

Background Resting-state electroencephalogram (rsEEG) is usually obtained to assess seizures in comatose patients with traumatic brain injury (TBI) patients. We aim to investigate rsEEG measures and their prediction of early recovery of consciousness in comatose TBI patients. Methods This is a retrospective study of comatose TBI patients who were admitted to a level-1 trauma center (10/2013-1/2022). Demographics, basic clinical data, imaging characteristics, and EEG data were collected. We calculated using 10-minute rsEEGs: power spectral density (PSD), permutation entropy (PE - complexity measure), weighted symbolic-mutual-information (wSMI - global information sharing measure), Kolmogorov complexity (Kolcom - complexity measure), and heart-evoked potentials (HEP - the averaged EEG signal relative to the corresponding QRS complex on electrocardiogram). We evaluated the prediction of consciousness recovery before hospital discharge using clinical, imaging, rsEEG data via Support Vector Machine with a linear kernel (SVM). Results We studied 113 (out of 134, 84%) patients with rsEEGs. A total of 73 (65%) patients recovered consciousness before discharge. Patients who recovered consciousness were younger (40 vs. 50, p .01). Patients who recovered consciousness had higher Kolcom (U = 1688, p = 0.01,), increased beta power (U = 1652 p = 0.003), with higher variability across channels ( U = 1534, p = 0.034), and epochs (U = 1711, p = 0.004), lower delta power (U = 981, p = 0.04) and showed higher connectivity across time and channels as measured by wSMI in the theta band (U = 1636, p = .026, U = 1639, p = 0.024) than those who didn't recover. The ROC-AUC improved from 0.66 (using age, motor response, pupils' reactivity, and CT Marshall classification) to 0.69 (p < 0.001) when adding rsEEG measures. Conclusion We describe the rsEEG EEG signature in recovery of consciousness prior to discharge in comatose TBI patients. Resting-state EEG measures improved prediction beyond the clinical and imaging data.

Electroencephalography as a Biomarker of Prognosis in Acute Brain Injury

Electroencephalography (EEG) is a noninvasive tool that allows the monitoring of cerebral brain function in critically ill patients, aiding with diagnosis, management, and prognostication. Specific EEG features have shown utility in the prediction of outcomes in critically ill patients with status epilepticus, acute brain injury (ischemic stroke, intracranial hemorrhage, subarachnoid hemorrhage, and traumatic brain injury), anoxic brain injury, and toxic-metabolic encephalopathy. Studies have also found an association between particular EEG patterns and long-term functional and cognitive outcomes as well as prediction of recovery of consciousness following acute brain injury. This review summarizes these findings and demonstrates the value of utilizing EEG findings in the determination of prognosis.

The effectiveness of high-tone therapy in the complex rehabilitation of servicemen with post-traumatic stress disorder complicated by traumatic brain injury

INTRODUCTION

As a result of local military conflicts that have become more frequent over the past decades, the number of military personnel subjected to combat stress has sharply increased. More than 50% of them suffer from combat posttraumatic stress disorder. The most common comorbidity in this category of patients is a traumatic brain injury. Due to the undesirability of the long-term use of pharmacological agents, for rehabilitation, preference should be given to physiotherapeutic procedures.

OBJECTS AND METHODS

We examined 50 patients with post-traumatic stress disorder in combination with a closed craniocerebral injury. Group 1-25 patients received standard complex treatment at the sanatoriumresort rehabilitation stage (diet therapy, climatotherapy, balneotherapy, exercise therapy, psychotherapy). Group 2-25 patients, in addition to the standard complex treatment, received a course of high-tone therapy.

RESULTS

Complex rehabilitation of patients with the use of high-tone therapy contributes to a significant decrease in astheno-neurotic (p < 0.05) and asthenic depressive (p < 0.01) syndromes and has a psycho-relaxing effect on anxiety syndrome (p < 0.01). There was also a decrease in the severity of pyramidal symptoms and regression of the vestibulo-atactic syndrome (p < 0.05). The course application of hightone therapy was accompanied by a significant restoration of the elastotonic properties of the vascular wall and an improvement in cerebral perfusion (p < 0.05). Positive dynamics of electrophysiological indicators were noted: a decrease in the intensity of slow rhythms against the background of an increase in the frequency and intensity of the alpha rhythm in both hemispheres (p < 0.05), which indicates the harmonization of the bioelectrical activity of the brain.

Theta-Alpha Variability on Admission EEG Is Associated With Outcome in Pediatric Cerebral Malaria

PURPOSE

Pediatric cerebral malaria has high rates of mortality and neurologic morbidity. Although several biomarkers, including EEG, are associated with survival or morbidity, many are resource intensive or require skilled interpretation for clinical use. Automation of quantitative interpretation of EEG may be preferable in resource-limited settings, where trained interpreters are rare. As currently used quantitative EEG factors do not adequately describe the spectrum of variability seen in studies from children with cerebral malaria, the authors developed and validated a new quantitative EEG variable, theta-alpha variability (TAV).

METHODS

The authors developed TAV, a new quantitative variable, as a composite of multiple automated EEG outputs. EEG records from 194 children (6 months to 14 years old) with cerebral malaria were analyzed. Independent EEG interpreters performed standard quantitative and qualitative analyses, with the addition of the newly created variable. The associations of TAV with other quantitative EEG factors, a qualitative assessment of variability, and outcomes were assessed.

RESULTS

Theta-alpha variability was not highly correlated with alpha, theta, or delta power and was not associated with qualitative measures of variability. Children whose EEGs had higher values of TAV had a lower risk of death (odds ratio = 0.934, 95% confidence interval = 0.902-0.966) or neurologic sequelae (odds ratio = 0.960, 95% confidence interval = 0.932-0.990) compared with those with lower values. Receiver operating characteristic analysis in predicting death at a TAV threshold of 0.244 yielded a sensitivity of 74% and specificity of 70% for an area under the receiver operating characteristic curve of 0.755.

CONCLUSIONS

Theta-alpha variability is independently associated with outcome in pediatric cerebral malaria and can predict death with high sensitivity and specificity. Automated determination of this newly created EEG factor holds promise as a potential method to increase the clinical utility of EEG in resource-limited settings by allowing interventions to be targeted to those at higher risk of death or disability.

Nomogram for Early Prediction of Outcome in Coma Patients with Severe Traumatic Brain Injury Receiving Right Median Nerve Electrical Stimulation Treatment

BACKGROUND

Accurate outcome prediction can serve to approach, quantify and categorize severe traumatic brain injury (TBI) coma patients for right median electrical stimulation (RMNS) treatment, which can support rehabilitation plans. As a proof of concept for individual risk prediction, we created a novel nomogram model combining amplitude-integrated electroencephalography (AEEG) and clinically relevant parameters.

METHODS

This study retrospective collected and analyzed a total of 228 coma patients after severe TBI in two medical centers. According to the extended Glasgow Outcome Scale (GOSE), patients were divided into a good outcome (GOSE 3-8) or a poor outcome (GOSE 1-2) group. Their clinical and biochemical indicators, together with EEG features, were explored retrospectively. The risk factors connected to the outcome of coma patients receiving RMNS treatment were identified using Cox proportional hazards regression. The discriminative capability and calibration of the model to forecast outcome were assessed by C statistics, calibration plots, and Kaplan-Meier curves on a personalized nomogram forecasting model.

RESULTS

The study included 228 patients who received RMNS treatment for long-term coma after a severe TBI. The median age was 40 years, and 57.8% (132 of 228) of the patients were male. 67.0% (77 of 115) of coma patients in the high-risk group experienced a poor outcome after one year and the comparative data merely was 30.1% (34 of 113) in low-risk group patients. The following variables were integrated into the forecasting of outcome using the backward stepwise selection of Akaike information criterion: age, Glasgow Coma Scale (GCS) at admission, EEG reactivity (normal, absence, or the stimulus-induced rhythmic, periodic, or ictal discharges (SIRPIDs)), and AEEG background pattern (A mode, B mode, or C mode). The C statistics revealed that the nomograms' discriminative potential and calibration demonstrated good predictive ability (0.71).

CONCLUSION

Our findings show that the nomogram model using AEEG parameters has the potential to predict outcomes in severe TBI coma patients receiving RMNS treatment. The model could classify patients into prognostic groups and worked well in internal validation.

Quantitative EEG During Critical Illness Correlates with Patterns of Long-Term Cognitive Impairment

OBJECTIVE

Many intensive care unit (ICU) survivors suffer disabling long-term cognitive impairment (LTCI) after critical illness. We compared EEG characteristics during critical illness with patients' 1-year neuropsychological outcomes.

METHODS

We performed a post hoc analysis of patients in the BRAIN-ICU study who had undergone EEG for clinical purposes during admission (n = 10). All survivors underwent formal cognitive assessments at 12-month follow-up. We evaluated EEGs by conventional visual inspection and computed 10 quantitative features. We explored associations between EEG and patterns of LTCI using Wilcoxon rank-sum tests and Spearman's rank correlations.

RESULTS

Of 521 Vanderbilt patients enrolled in the parent study, 24 had EEG recordings during admission. Ten survivors had EEG tracings available and completed follow-up cognitive testing. All but one inpatient EEG showed generalized background slowing. All patients demonstrated cognitive impairment in at least one domain at follow-up. The most common deficits occurred in delayed memory (DM-median index 62) and visuospatial/constructional (VC-median index 69) domains. Relative alpha power correlated with VC score (ρ = 0.78, P = .008). Peak interhemispheric coherence correlated negatively with DM (ρ = -0.81, P = .018).

CONCLUSIONS

Quantitative EEG features during critical illness correlated with domain-specific cognitive performance in our small cohort of ICU survivors. Further study in larger prospective cohorts is required to determine whether these relationships hold.

SIGNIFICANCE

EEG may serve as a prognostic biomarker predicting patterns of long-term cognitive impairment.

Role of Quantitative EEG and EEG Reactivity in Traumatic Brain Injury

OBJECTIVE

This study aimed to explore the effectiveness of quantitative electroencephalogram (EEG) and EEG reactivity (EEG-R) to predict the prognosis of patients with severe traumatic brain injury.

METHODS

This was a prospective observational study on severe traumatic brain injury. Quantitative EEG monitoring was performed for 8 to 12 hours within 14 days of onset. The EEG-R was tested during the monitoring period. We then followed patients for 3 months to determine their level of consciousness. The Glasgow Outcome Scale (GOS) score was used. The score 3, 4, 5 of GOS were defined good prognosis, and score 1 and 2 as poor prognosis. Univariate and multivariate analyses were employed to assess the association of predictors with poor prognosis.

RESULTS

A total of 56 patients were included in the study. Thirty-two patients (57.1%) awoke (good prognosis) in 3 months after the onset. Twenty-four patients (42.9%) did not awake (poor prognosis), including 11 cases deaths. Univariate analysis showed that Glasgow coma scale (GCS) score, the amplitude-integrated EEG (aEEG), the relative band power (RBP), the relative alpha variability (RAV), the spectral entropy (SE), and EEG-R reached significant difference between the poor-prognosis and good-prognosis groups. However, age, gender, and pupillary light reflex did not correlate significantly with poor prognosis. Furthermore, multivariate logistic regression analysis showed that only RAV and EEG-R were significant independent predictors of poor prognosis, and the prognostic model containing these 2 variables yielded a predictive performance with an area under the curve of 0.882.

CONCLUSIONS

Quantitative EEG and EEG-R may be used to assess the prognosis of patients with severe traumatic brain injury early. RAV and EEG-R were the good predictive indicators of poor prognosis.

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.

Quantitative EEG parameters can improve the predictive value of the non-traumatic neurological ICU patient prognosis through the machine learning method

Background

Better outcome prediction could assist in reliable classification of the illnesses in neurological intensive care unit (ICU) severity to support clinical decision-making. We developed a multifactorial model including quantitative electroencephalography (QEEG) parameters for outcome prediction of patients in neurological ICU.

Methods

We retrospectively analyzed neurological ICU patients from November 2018 to November 2021. We used 3-month mortality as the outcome. Prediction models were created using a linear discriminant analysis (LDA) based on QEEG parameters, APACHEII score, and clinically relevant features. Additionally, we compared our best models with APACHEII score and Glasgow Coma Scale (GCS). The DeLong test was carried out to compare the ROC curves in different models.

Results

A total of 110 patients were included and divided into a training set (n=80) and a validation set (n = 30). The best performing model had an AUC of 0.85 in the training set and an AUC of 0.82 in the validation set, which were better than that of GCS (training set 0.64, validation set 0.61). Models in which we selected only the 4 best QEEG parameters had an AUC of 0.77 in the training set and an AUC of 0.71 in the validation set, which were similar to that of APACHEII (training set 0.75, validation set 0.73). The models also identified the relative importance of each feature.

Conclusion

Multifactorial machine learning models using QEEG parameters, clinical data, and APACHEII score have a better potential to predict 3-month mortality in non-traumatic patients in neurological ICU.

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.

Lower cortical volume is associated with poor sleep quality after traumatic brain injury

Traumatic brain injury (TBI) is known to be associated with poor sleep. In this report, we aimed to identify associations between differences in cortical volume and sleep quality post-TBI. MRI anatomical scans from 88 cases with TBI were analyzed in this report. Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Voxel Based Morphometry (VBM), was used to obtain statistical maps of the association between PSQI and cortical volume in gray matter and white matter voxels. Higher PSQI total scores (poor sleep quality) were strongly associated with smaller gray matter volume in the cerebellum. White matter volume was not associated with total PSQI. The sleep disturbance subcomponent showed a significant association with gray and white matter volumes in the cerebellum. Although not significant, cortical areas such as the cingulate and medial frontal regions were associated with sleep quality. The cerebellum with higher contribution to motor and autonomic systems was associated strongly with poor sleep quality. Additionally, regions that play critical roles in inhibitory brain function and suppress mind wandering (i.e., default mode network including medial frontal and cingulate regions) were associated (although to a lesser extent) with sleep. Our findings suggest that poor sleep quality following TBI is significantly associated with lower cerebellar volume, with trending relationships in regions associated with inhibitory function.

[Long-term EEG monitoring in patients with acute traumatic brain injury]

OBJECTIVE

To investigate the informativeness of long-term scalp EEG monitoring in patients with acute traumatic brain injury (TBI).

MATERIAL AND METHODS

The informativity of long-term EEG monitoring (LTM) was performed in 60 patients with acute severe TBI. Odd ratios (OR) of unfavorable outcome and non-convulsive status epilepticus (NCSE) among clinical, neurophysiological and radiological features were calculated.

RESULTS

EEG features of the unfavorable outcome are: slowing of the dominant background rhythm below q range (OR 3.5, CI 1.2-10.7), absence of frontal-occipital gradient (OR 10.2, CI 1.89-10.12), absence of reactivity (OR 8.75, CI 2.14-35.7), absence of variability (OR 6.25, CI 1.72-22.6) and absence of NREM sleep, stage 2 (OR 5.8, CI 1.79-18.91). Clinical features associated with the unfavorable outcome are: a decrease in GCS score (OR 1.25, CI 1.07-1.47), TBI severity (OR 2.46, CI 1.16-5.18), axial dislocation (OR 4.45, CI 1.08-18.29). ORs for NCSE are significant for the following EEG features: presence of rhythmic and periodic patterns (RPP) (OR 11.92, CI 1.37-103.39), stimulus induced RPP (OR 23.14, CI 2.56-209.34), "plus" modifier (OR 4.11, CI 1.13-14.91) and electrographic evolution (OR 13.05, CI 3.59-47.39). Background rhythm slowing below q range reduces NCSE probability (OR 3.33, CI 1.09-10).

CONCLUSION

Long-term EEG monitoring is an informative tool for prognosis of outcome and diagnosis of NCSE in patients with severe TBI. The risk of NCSE increases with Marshall score but NCSE is not associated with poor outcome that requires an individual selection of intensive care.

Toward a global and reproducible science for brain imaging in neurotrauma: the ENIGMA adult moderate/severe traumatic brain injury working group

The global burden of mortality and morbidity caused by traumatic brain injury (TBI) is significant, and the heterogeneity of TBI patients and the relatively small sample sizes of most current neuroimaging studies is a major challenge for scientific advances and clinical translation. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Adult moderate/severe TBI (AMS-TBI) working group aims to be a driving force for new discoveries in AMS-TBI by providing researchers world-wide with an effective framework and platform for large-scale cross-border collaboration and data sharing. Based on the principles of transparency, rigor, reproducibility and collaboration, we will facilitate the development and dissemination of multiscale and big data analysis pipelines for harmonized analyses in AMS-TBI using structural and functional neuroimaging in combination with non-imaging biomarkers, genetics, as well as clinical and behavioral measures. Ultimately, we will offer investigators an unprecedented opportunity to test important hypotheses about recovery and morbidity in AMS-TBI by taking advantage of our robust methods for large-scale neuroimaging data analysis. In this consensus statement we outline the working group's short-term, intermediate, and long-term goals.

The prognostic value of resting-state EEG in acute post-traumatic unresponsive states

Accurate early prognostication is vital for appropriate long-term care decisions after traumatic brain injury. While measures of resting-state EEG oscillations and their network properties, derived from graph theory, have been shown to provide clinically useful information regarding diagnosis and recovery in patients with chronic disorders of consciousness, little is known about the value of these network measures when calculated from a standard clinical low-density EEG in the acute phase post-injury. To investigate this link, we first validated a set of measures of oscillatory network features between high-density and low-density resting-state EEG in healthy individuals, thus ensuring accurate estimation of underlying cortical function in clinical recordings from patients. Next, we investigated the relationship between these features and the clinical picture and outcome of a group of 18 patients in acute post-traumatic unresponsive states who were not following commands 2 days+ after sedation hold. While the complexity of the alpha network, as indexed by the standard deviation of the participation coefficients, was significantly related to the patients' clinical picture at the time of EEG, no network features were significantly related to outcome at 3 or 6 months post-injury. Rather, mean relative alpha power across all electrodes improved the accuracy of outcome prediction at 3 months relative to clinical features alone. These results highlight the link between the alpha rhythm and clinical signs of consciousness and suggest the potential for simple measures of resting-state EEG band power to provide a coarse snapshot of brain health for stratification of patients for rehabilitation, therapy and assessments of both covert and overt cognition.

Standardized visual EEG features predict outcome in patients with acute consciousness impairment of various etiologies

BACKGROUND

Early prognostication in patients with acute consciousness impairment is a challenging but essential task. Current prognostic guidelines vary with the underlying etiology. In particular, electroencephalography (EEG) is the most important paraclinical examination tool in patients with hypoxic ischemic encephalopathy (HIE), whereas it is not routinely used for outcome prediction in patients with traumatic brain injury (TBI).

METHOD

Data from 364 critically ill patients with acute consciousness impairment (GCS ≤ 11 or FOUR ≤ 12) of various etiologies and without recent signs of seizures from a prospective randomized trial were retrospectively analyzed. Random forest classifiers were trained using 8 visual EEG features-first alone, then in combination with clinical features-to predict survival at 6 months or favorable functional outcome (defined as cerebral performance category 1-2).

RESULTS

The area under the ROC curve was 0.812 for predicting survival and 0.790 for predicting favorable outcome using EEG features. Adding clinical features did not improve the overall performance of the classifier (for survival: AUC = 0.806, p = 0.926; for favorable outcome: AUC = 0.777, p = 0.844). Survival could be predicted in all etiology groups: the AUC was 0.958 for patients with HIE, 0.955 for patients with TBI and other neurosurgical diagnoses, 0.697 for patients with metabolic, inflammatory or infectious causes for consciousness impairment and 0.695 for patients with stroke. Training the classifier separately on subgroups of patients with a given etiology (and thus using less training data) leads to poorer classification performance.

CONCLUSIONS

While prognostication was best for patients with HIE and TBI, our study demonstrates that similar EEG criteria can be used in patients with various causes of consciousness impairment, and that the size of the training set is more important than homogeneity of ACI etiology.

Resting-State Electroencephalography for Prognosis in Disorders of Consciousness Following Traumatic Brain Injury

Although the majority of patients recover consciousness after a traumatic brain injury (TBI), a minority develop a prolonged disorder of consciousness, which may never fully resolve. For these patients, accurate prognostication is essential to treatment decisions and long-term care planning. In this review, we evaluate the use of resting-state electroencephalography (EEG) as a prognostic measure in disorders of consciousness following TBI. We highlight that routine clinical EEG recordings have prognostic utility in the short to medium term. In particular, measures of alpha power and variability are indicative of relatively better functional outcomes within the first year post-TBI. This is hypothesized to reflect intact thalamocortical loops, and thus the potential for recovery of consciousness even in the apparent absence of current consciousness. However, there is a lack of research into the use of resting-state EEG for predicting longer-term recovery following TBI. We conclude that, given the potential for patients to demonstrate improvements in consciousness and functional capacity even years after TBI, a research focus on EEG-augmented prognostication in very long-term disorders of consciousness is now required.

Classification of Non-Severe Traumatic Brain Injury from Resting-State EEG Signal Using LSTM Network with ECOC-SVM

Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact due to an accident or fall and is one of the most frequently submitted insurance claims. However, it is often always misused when individuals attempt an insurance fraud claim by providing false medical conditions. Therefore, there is a need for an instant brain condition classification system. This study presents a novel classification architecture that can classify non-severe TBI patients and healthy subjects employing resting-state electroencephalogram (EEG) as the input, solving the immobility issue of the computed tomography (CT) scan and magnetic resonance imaging (MRI). The proposed architecture makes use of long short term memory (LSTM) and error-correcting output coding support vector machine (ECOC-SVM) to perform multiclass classification. The pre-processed EEG time series are supplied to the network by each time step, where important information from the previous time step will be remembered by the LSTM cell. Activations from the LSTM cell is used to train an ECOC-SVM. The temporal advantages of the EEG were amplified and able to achieve a classification accuracy of 100%. The proposed method was compared to existing works in the literature, and it is shown that the proposed method is superior in terms of classification accuracy, sensitivity, specificity, and precision.

IRIS: A Modular Platform for Continuous Monitoring and Caretaker Notification in the Intensive Care Unit

OBJECTIVE

New approaches are needed to interpret large amounts of physiologic data continuously recorded in the ICU. We developed and prospectively validated a versatile platform (IRIS) for real-time ICU physiologic monitoring, clinical decision making, and caretaker notification.

METHODS

IRIS was implemented in the neurointensive care unit to stream multimodal time series data, including EEG, intracranial pressure (ICP), and brain tissue oxygenation (PbtO2), from ICU monitors to an analysis server. IRIS was applied for 364 patients undergoing continuous EEG, 26 patients undergoing burst suppression monitoring, and four patients undergoing intracranial pressure and brain tissue oxygen monitoring. Custom algorithms were used to identify periods of elevated ICP, compute burst suppression ratios (BSRs), and detect faulty or disconnected EEG electrodes. Hospital staff were notified of clinically relevant events using our secure API to route alerts through a password-protected smartphone application.

RESULTS

Sustained increases in ICP and concordant decreases in PbtO2 were reliably detected using user-defined thresholds and alert throttling. BSR trends computed by the platform correlated highly with manual neurologist markings (r2 0.633-0.781; p < 0.0001). The platform identified EEG electrodes with poor signal quality with 95% positive predictive value, and reduced latency of technician response by 93%.

CONCLUSION

This study validates a flexible real-time platform for monitoring and interpreting ICU data and notifying caretakers of actionable results, with potential to reduce the manual burden of continuous monitoring services on care providers.

SIGNIFICANCE

This work represents an important step toward facilitating translational medical data analytics to improve patient care and reduce health care costs.

Continuous Electroencephalography Monitoring in Critically Ill Infants and Children

Continuous video electroencephalography (CEEG) monitoring of critically ill infants and children has expanded rapidly in recent years. Indications for CEEG include evaluation of patients with altered mental status, characterization of paroxysmal events, and detection of electrographic seizures, including monitoring of patients with limited neurological examination or conditions that put them at high risk for electrographic seizures (e.g., cardiac arrest or extracorporeal membrane oxygenation cannulation). Depending on the inclusion criteria and clinical characteristics of the population studied, the percentage of pediatric patients with electrographic seizures varies from 7% to 46% and with electrographic status epilepticus from 1% to 23%. There is also evidence that epileptiform and background CEEG patterns may provide important information about prognosis in certain clinical populations. Quantitative EEG techniques are emerging as a tool to enhance the value of CEEG to provide real-time bedside data for management and prognosis. Continued research is needed to understand the clinical value of seizure detection and identification of other CEEG patterns on the outcomes of critically ill infants and children.

Hamid AI, Abdullah MZ, Azman A, Abdullah JM. Detection of Moderate Traumatic Brain Injury from Resting-State Eye-Closed Electroencephalography

Traumatic brain injury (TBI) is one of the injuries that can bring serious consequences if medical attention has been delayed. Commonly, analysis of computed tomography (CT) or magnetic resonance imaging (MRI) is required to determine the severity of a moderate TBI patient. However, due to the rising number of TBI patients these days, employing the CT scan or MRI scan to every potential patient is not only expensive, but also time consuming. Therefore, in this paper, we investigate the possibility of using electroencephalography (EEG) with computational intelligence as an alternative approach to detect the severity of moderate TBI patients. EEG procedure is much cheaper than CT or MRI. Although EEG does not have high spatial resolutions as compared with CT and MRI, it has high temporal resolutions. The analysis and prediction of moderate TBI from EEG using conventional computational intelligence approaches are tedious as they normally involve complex preprocessing, feature extraction, or feature selection of the signal. Thus, we propose an approach that uses convolutional neural network (CNN) to automatically classify healthy subjects and moderate TBI patients. The input to this computational intelligence system is the resting-state eye-closed EEG, without undergoing preprocessing and feature selection. The EEG dataset used includes 15 healthy volunteers and 15 moderate TBI patients, which is acquired at the Hospital Universiti Sains Malaysia, Kelantan, Malaysia. The performance of the proposed method has been compared with four other existing methods. With the average classification accuracy of 72.46%, the proposed method outperforms the other four methods. This result indicates that the proposed method has the potential to be used as a preliminary screening for moderate TBI, for selection of the patients for further diagnosis and treatment planning.

Predicting outcome in patients with moderate to severe traumatic brain injury using electroencephalography

BACKGROUND

Better outcome prediction could assist in reliable quantification and classification of traumatic brain injury (TBI) severity to support clinical decision-making. We developed a multifactorial model combining quantitative electroencephalography (qEEG) measurements and clinically relevant parameters as proof of concept for outcome prediction of patients with moderate to severe TBI.

METHODS

Continuous EEG measurements were performed during the first 7 days of ICU admission. Patient outcome at 12 months was dichotomized based on the Extended Glasgow Outcome Score (GOSE) as poor (GOSE 1-2) or good (GOSE 3-8). Twenty-three qEEG features were extracted. Prediction models were created using a Random Forest classifier based on qEEG features, age, and mean arterial blood pressure (MAP) at 24, 48, 72, and 96 h after TBI and combinations of two time intervals. After optimization of the models, we added parameters from the International Mission for Prognosis And Clinical Trial Design (IMPACT) predictor, existing of clinical, CT, and laboratory parameters at admission. Furthermore, we compared our best models to the online IMPACT predictor.

RESULTS

Fifty-seven patients with moderate to severe TBI were included and divided into a training set (n = 38) and a validation set (n = 19). Our best model included eight qEEG parameters and MAP at 72 and 96 h after TBI, age, and nine other IMPACT parameters. This model had high predictive ability for poor outcome on both the training set using leave-one-out (area under the receiver operating characteristic curve (AUC) = 0.94, specificity 100%, sensitivity 75%) and validation set (AUC = 0.81, specificity 75%, sensitivity 100%). The IMPACT predictor independently predicted both groups with an AUC of 0.74 (specificity 81%, sensitivity 65%) and 0.84 (sensitivity 88%, specificity 73%), respectively.

CONCLUSIONS

Our study shows the potential of multifactorial Random Forest models using qEEG parameters to predict outcome in patients with moderate to severe TBI.

Transcranial direct current stimulation (tDCS) effects on traumatic brain injury (TBI) recovery: A systematic review

Traumatic brain injury (TBI) is a major cause of chronic disability. Less than a quarter of moderate and severe TBI patients improved in their cognition within 5 years. Non-invasive brain stimulation, including transcranial direct current stimulation (tDCS), may help neurorehabilitation by boosting adaptive neuroplasticity and reducing pathological sequelae following TBI.

Value and mechanisms of EEG reactivity in the prognosis of patients with impaired consciousness: a systematic review

Background

Electroencephalography (EEG) is a well-established tool for assessing brain function that is available at the bedside in the intensive care unit (ICU). This review aims to discuss the relevance of electroencephalographic reactivity (EEG-R) in patients with impaired consciousness and to describe the neurophysiological mechanisms involved.

Methods

We conducted a systematic search of the term “EEG reactivity and coma” using the PubMed database. The search encompassed articles published from inception to March 2018 and produced 202 articles, of which 42 were deemed relevant, assessing the importance of EEG-R in relationship to outcomes in patients with impaired consciousness, and were therefore included in this review.

Results

Although definitions, characteristics and methods used to assess EEG-R are heterogeneous, several studies underline that a lack of EEG-R is associated with mortality and unfavorable outcome in patients with impaired consciousness. However, preserved EEG-R is linked to better odds of survival. Exploring EEG-R to nociceptive, auditory, and visual stimuli enables a noninvasive trimodal functional assessment of peripheral and central sensory ascending pathways that project to the brainstem, the thalamus and the cerebral cortex. A lack of EEG-R in patients with impaired consciousness may result from altered modulation of thalamocortical loop activity by afferent sensory input due to neural impairment. Assessing EEG-R is a valuable tool for the diagnosis and outcome prediction of severe brain dysfunction in critically ill patients.

Conclusions

This review emphasizes that whatever the etiology, patients with impaired consciousness featuring a reactive electroencephalogram are more likely to have a favorable outcome, whereas those with a nonreactive electroencephalogram are prone to having an unfavorable outcome. EEG-R is therefore a valuable prognostic parameter and warrants a rigorous assessment. However, current assessment methods are heterogeneous, and no consensus exists. Standardization of stimulation and interpretation methods is needed.

Unexpected good outcome in severe cerebral fat embolism syndrome

In this case study, we report the longitudinal and multimodal follow-up of a catastrophic initial presentation of cerebral fat embolism syndrome. We show that despite the initial severity, the cognitive outcome was ultimately very good but with a highly nonlinear time-course and prolonged loss of consciousness (more than 2 months). Repeated clinical assessments and brain-imaging techniques (electroencephalography, event-related potential, ¹⁸-Fluoro-Deoxy-Glucose-PET and magnetic resonance imaging) allowed us to monitor and anticipate this dynamic, providing relevant information to guide decision making in front of withdrawal of life-sustaining therapy discussions. This case illustrates the value of multimodal functional imaging in devastating brain injuries.