A history and review of quantitative electroencephalography in traumatic brain injury

Neurologic prognostication in coma and disorders of consciousness

Coma and disorders of consciousness (DoC) are clinical syndromes primarily resulting from severe acute brain injury, with uncertain recovery trajectories that often necessitate prolonged supportive care. This imposes significant socioeconomic burdens on patients, caregivers, and society. Predicting recovery in comatose patients is a critical aspect of neurocritical care, and while current prognostication heavily relies on clinical assessments, such as pupillary responses and motor movements, which are far from precise, contemporary prognostication has integrated more advanced technologies like neuroimaging and electroencephalogram (EEG). Nonetheless, neurologic prognostication remains fraught with uncertainty and significant inaccuracies and is impacted by several forms of prognostication biases, including self-fulfilling prophecy bias, affective forecasting, and clinician treatment biases, among others. However, neurologic prognostication in patients with disorders of consciousness impacts life-altering decisions including continuation of treatment interventions vs withdrawal of life-sustaining therapies (WLST), which have a direct influence on survival and recovery after severe acute brain injury. In recent years, advancements in neuro-monitoring technologies, artificial intelligence (AI), and machine learning (ML) have transformed the field of prognostication. These technologies have the potential to process vast amounts of clinical data and identify reliable prognostic markers, enhancing prediction accuracy in conditions such as cardiac arrest, intracerebral hemorrhage, and traumatic brain injury (TBI). For example, AI/ML modeling has led to the identification of new states of consciousness such as covert consciousness and cognitive motor dissociation, which may have important prognostic significance after severe brain injury. This chapter reviews the evolving landscape of neurologic prognostication in coma and DoC, highlights current pitfalls and biases, and summarizes the integration of clinical examination, neuroimaging, biomarkers, and neurophysiologic tools for prognostication in specific disease states. We will further discuss the future of neurologic prognostication, focusing on the integration of AI and ML techniques to deliver more individualized and accurate prognostication, ultimately improving patient outcomes and decision-making process in neurocritical care.

Association between EEG metrics and continuous cerebrovascular autoregulation assessment: a scoping review

OBJECTIVE

Cerebrovascular autoregulation is defined as the capacity of cerebral blood vessels to maintain stable cerebral blood flow despite changing blood pressure. It is assessed using the pressure reactivity index (the correlation coefficient between mean arterial blood pressure and intracranial pressure). The objective of this scoping review is to describe the existing evidence concerning the association of EEG and cerebrovascular autoregulation in order to identify key concepts and detect gaps in the current knowledge.

METHODS

Embase, MEDLINE, SCOPUS, and Web of Science were searched considering articles between their inception up to September 2023. Inclusion criteria were human (paediatric and adult) and animal studies describing correlations between continuous EEG and cerebrovascular autoregulation assessments.

RESULTS

Ten studies describing 481 human subjects (67% adult, 59% critically ill) were identified. Seven studies assessed qualitative (e.g. seizures, epileptiform potentials) and five evaluated quantitative (e.g. bispectral index, alpha-delta ratio) EEG metrics. Cerebrovascular autoregulation was evaluated based on intracranial pressure, transcranial Doppler, or near infrared spectroscopy. Specific combinations of cerebrovascular autoregulation and EEG metrics were evaluated by a maximum of two studies. Seizures, highly malignant patterns or burst suppression, alpha peak frequency, and bispectral index were associated with cerebrovascular autoregulation. The other metrics showed either no or inconsistent associations.

CONCLUSION

There is a paucity of studies evaluating the link between EEG and cerebrovascular autoregulation. The studies identified included a variety of EEG and cerebrovascular autoregulation acquisition methods, age groups, and diseases allowing for few overarching conclusions. However, the preliminary evidence for the presence of an association between EEG metrics and cerebrovascular autoregulation prompts further in-depth investigations.

A Deep Learning Approach to Estimate Multi-Level Mental Stress From EEG Using Serious Games

Stress is revealed by the inability of individuals to cope with their environment, which is frequently evidenced by a failure to achieve their full potential in tasks or goals. This study aims to assess the feasibility of estimating the level of stress that the user is perceiving related to a specific task through an electroencephalograpic (EEG) system. This system is integrated with a Serious Game consisting of a multi-level stress driving tool, and Deep Learning (DL) neural networks are used for classification. The game involves controlling a vehicle to dodge obstacles, with the number of obstacles increasing based on complexity. Assuming that there is a direct correlation between the difficulty level of the game and the stress level of the user, a recurrent neural network (RNN) with a structure based on gated recurrent units (GRU) was used to classify the different levels of stress. The results show that the RNN model is able to predict stress levels above current state-of-the-art with up to 94% accuracy in some cases, suggesting that the use of EEG systems in combination with Serious Games and DL represents a promising technique in the prediction and classification of mental stress levels.

Investigation of Machine Learning and Deep Learning Approaches for Detection of Mild Traumatic Brain Injury from Human Sleep Electroencephalogram

Traumatic Brain Injury (TBI) is a highly prevalent and serious public health concern. Most cases of TBI are mild in nature, yet some individuals may develop following-up persistent disability. The pathophysiologic causes for those with persistent postconcussive symptoms are most likely multifactorial and the underlying mechanism is not well understood, although it is clear that sleep disturbances feature prominently in those with persistent disability. The sleep electroencephalogram (EEG) provides a direct window into neuronal activity during an otherwise highly stereotyped behavioral state, and represents a promising quantitative measure for TBI diagnosis and prognosis. With the ever-evolving domain of machine learning, deep convolutional neural networks, and the development of better architectures, these approaches hold promise to solve some of the long entrenched challenges of personalized medicine for uses in recommendation systems and/or in health monitoring systems. In particular, advanced EEG analysis to identify putative EEG biomarkers of neurological disease could be highly relevant in the prognostication of mild TBI, an otherwise heterogeneous disorder with a wide range of affected phenotypes and disability levels. In this work, we investigate the use of various machine learning techniques and deep neural network architectures on a cohort of human subjects with sleep EEG recordings from overnight, in-lab, diagnostic polysomnography (PSG). An optimal scheme is explored for the classification of TBI versus non-TBI control subjects. The results were promising with an accuracy of ∼95% in random sampling arrangement and ∼70% in independent validation arrangement when appropriate parameters were used using a small number of subjects (10 mTBI subjects and 9 age- and sex-matched controls). We are thus confident that, with additional data and further studies, we would be able to build a generalized model to detect TBI accurately, not only via attended, in-lab PSG recordings, but also in practical scenarios such as EEG data obtained from simple wearables in daily life.

Utility of Quantitative EEG for Seizure Detection in Adults

SUMMARY

Traditional review of EEG for seizure detection requires time and the expertise of a trained neurophysiologist; therefore, it is time- and resource-intensive. Quantitative EEG (qEEG) encompasses a variety of methods to make EEG review more efficient and allows for nonexpert review. Literature supports that qEEG is commonly used by neurophysiologists and nonexperts in clinical practice. In this review, the different types of qEEG trends and spectrograms used for seizure detection in adults, from basic concepts to clinical applications, are discussed. The merits and drawbacks of the most common qEEG trends are detailed. The authors detail the retrospective literature on qEEG sensitivity, specificity, and false alarm rate as interpreted by experts and nonexperts alike. Finally, the authors discuss the future of qEEG as a useful screening tool and speculate on the trajectory of future investigations in the field.

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

Monitoring of quantitative EEG (QEEG) parameters in the intensive care unit (ICU) can aid in the treatment of traumatic brain injury (TBI) patients by complementing visual EEG review done by an expert. We performed an explorative study investigating the prognostic value of 59 QEEG parameters in predicting the outcome of patients with severe TBI. Continuous EEG recordings were done on 28 patients with severe TBI in the ICU of Turku University Hospital. We computed a set of QEEG parameters for each patient, and correlated these to patient outcome, measured by dichotomized Glasgow Outcome Scale (GOS) at a follow-up visit between 6 and 12 months, using area under receiver operating characteristic curve (AUC) as a nonlinear correlation measure. For 17 of the 59 QEEG parameters (28.8%), the AUC differed significantly from 0.5, most of these parameters measured EEG power or variability. The best QEEG parameters for outcome prediction were alpha power (AUC = 0.87, P < .01) and variability of the relative fast theta power (AUC = 0.84, P < .01). The results of this study indicate that QEEG parameters provide useful information for predicting outcome in severe TBI. Novel QEEG parameters with potential in outcome prediction were found, the prognostic value of these parameters should be confirmed in later studies. The results also provide further evidence of the usefulness of parameters studied in preexisting studies.

An Overview of the Use of Neurofeedback Biofeedback for the Treatment of Symptoms of Traumatic Brain Injury in Military and Civilian Populations

Background: Neurofeedback, a type of biofeedback, is an operant conditioning treatment that has been studied for use in the treatment of traumatic brain injury (TBI) in both civilian and military populations. In this approach, users are able to see or hear representations of data related to their own physiologic responses to triggers, such as stress or distraction, in real time and, with practice, learn to alter these responses in order to reduce symptoms and/or improve performance. Objective: This article provides a brief overview of the use of biofeedback, focusing on neurofeedback, for symptoms related to TBI, with applications for both civilian and military populations, and describes a pilot study that is currently underway looking at the effects of a commercial neurofeedback device on patients with mild-to-moderate TBIs. Conclusions: Although more research, including blinded randomized controlled studies, is needed on the use of neurofeedback for TBI, the literature suggests that this approach shows promise for treating some symptoms of TBI with this modality. With further advances in technology, including at-home use of neurofeedback devices, preliminary data suggests that TBI survivors may benefit from improved motivation for treatment and some reduction of symptoms related to attention, mood, and mindfulness, with the addition of neurofeedback to treatment.

Making Waves in the Brain: What Are Oscillations, and Why Modulating Them Makes Sense for Brain Injury

Traumatic brain injury (TBI) can result in persistent cognitive, behavioral and emotional deficits. However, the vast majority of patients are not chronically hospitalized; rather they have to manage their disabilities once they are discharged to home. Promoting recovery to pre-injury level is important from a patient care as well as a societal perspective. Electrical neuromodulation is one approach that has shown promise in alleviating symptoms associated with neurological disorders such as in Parkinson’s disease (PD) and epilepsy. Consistent with this perspective, both animal and clinical studies have revealed that TBI alters physiological oscillatory rhythms. More recently several studies demonstrated that low frequency stimulation improves cognitive outcome in models of TBI. Specifically, stimulation of the septohippocampal circuit in the theta frequency entrained oscillations and improved spatial learning following TBI. In order to evaluate the potential of electrical deep brain stimulation for clinical translation we review the basic neurophysiology of oscillations, their role in cognition and how they are changed post-TBI. Furthermore, we highlight several factors for future pre-clinical and clinical studies to consider, with the hope that it will promote a hypothesis driven approach to subsequent experimental designs and ultimately successful translation to improve outcome in patients with TBI.

Coherence and Consciousness: Study of Fronto-Parietal Gamma Synchrony in Patients with Disorders of Consciousness

Evaluation of consciousness needs to be supported by the evidence of brain activation during external stimulation in patients with unresponsive wakefulness syndrome (UWS). Assessment of patients should include techniques that do not depend on overt motor responses and allow an objective investigation of the spontaneous patterns of brain activity. In particular, electroencephalography (EEG) coherence allows to easily measure functional relationships between pairs of neocortical regions and seems to be closely correlated with cognitive or behavioral measures. Here, we show the contribution of higher order associative cortices of patients with disorder of consciousness (N = 26) in response to simple sensory stimuli, such as visual, auditory and noxious stimulation. In all stimulus modalities an increase of short-range parietal and long-range fronto-parietal coherences in gamma frequencies were seen in the controls and minimally conscious patients. By contrast, UWS patients showed no significant modifications in the EEG patterns after stimulation. Our results suggest that UWS patients can not activate associative cortical networks, suggesting a lack of information integration. In fact, fronto-parietal circuits result to be connectively disrupted, conversely to patients that exhibit some form of consciousness. In the light of this, EEG coherence can be considered a powerful tool to quantify the involvement of cognitive processing giving information about the integrity of fronto-parietal network. This measure can represent a new neurophysiological marker of unconsciousness and help in determining an accurate diagnosis and rehabilitative intervention in each patient.

Dissociation of vegetative and minimally conscious patients based on brain operational architectonics: factor of etiology

Discrimination between patients in vegetative (VS) and minimally conscious state (MCS) is currently based upon the behavioral gold standard. Behavioral assessment remains equivocal and difficult to interpret as evidence for the presence or absence of consciousness, resulting in possible clinical misdiagnosis in such patients. Application of an operational architectonics (OA) strategy to electroencephalogram (EEG) analysis reveals that absence of consciousness in patients in VS is paralleled by significant impairment in overall EEG operational architecture compared to patients in MCS: neuronal assemblies become smaller, their life span shortened, and they became highly unstable and functionally disconnected (desynchronized). However, in a previous study, patients with different brain damage etiologies were intermixed. Therefore, the goal of the present study was to investigate whether the application of OA methodology to EEG could reliably dissociate patients in VS and MCS independent of brain damage etiology. We conclude that the observed EEG OA structure impairment in patients in VS and partial preservation in patients in MCS is a marker of consciousness/unconsciousness rather than physiological damage. Results of this study may have neuroscientific, clinical, and ethical implications.

Psychosis Following Traumatic Brain Injury

Psychosis following traumatic brain injury (PFTBI) has received modest empirical investigation, and is subsequently poorly understood, identified and treated. The current article reports on consistencies in PFTBI phenomenology according to the existing peer-reviewed literature. The potential for psychotic symptoms post TBI, aetiological propositions, prevalence, significance of onset latency and injury severity, clinical and cognitive neuropsychological presentation and injury localisation/neuroimaging data are reviewed. Substantial methodological limitations associated with the majority of publications informing this work are also discussed. Despite controversies in the literature, psychosis following TBI appears to be three times more prevalent than psychotic disorders in the general population, and comparable in presentation to other idiopathic psychotic spectrum disorders, including schizophrenia.

Prognostic Value of Resting-State Electroencephalography Structure in Disentangling Vegetative and Minimally Conscious States

Background. Patients in a vegetative state pose problems in diagnosis, prognosis, and treatment. Currently, no prognostic markers predict the chance of recovery, which has serious consequences, especially in end-of-life decision making. Objective. We aimed to assess an objective measurement of prognosis using advanced electroencephalography (EEG). Methods. EEG data (19 channels) were collected in 14 patients who were diagnosed to be persistently vegetative based on repeated clinical evaluations at 3 months following brain damage. EEG structure parameters (amplitude, duration, and variability within quasi-stationary segments, as well as the spatial synchrony between such segments and the strength of this synchrony) were used to predict recovery of consciousness 3 months later. Results. The number and strength of cortical functional connections between EEG segments were higher in patients who recovered consciousness ( P < .05 to P < .001) compared with those who did not recover. Linear regression analysis confirms that EEG structure parameters are capable of predicting ( P = .0025) recovery of consciousness 6 months postinjury, whereas the same analysis failed to significantly predict patient outcome based on aspects of their clinical history alone ( P = .629) or conventional EEG spectrum power ( P = .473). Conclusions. The result of this preliminary study demonstrates that structural strategy of EEG analysis is better suited for providing prognosis of consciousness recovery than existing methods of clinical assessment and of conventional EEG. Our results may be a starting point for developing reliable prognosticators in patients who are in a vegetative state, with the potential to improve their day-to-day management, quality of life, and access to early interventions.

Scalp EEG-based discrimination of cognitive deficits after traumatic brain injury using event-related Tsallis entropy analysis

Traumatic brain injury (TBI) is the leading cause of death and disability in children and adolescents in the U.S. This is a pilot study, which explores the discrimination of chronic TBI from normal controls using scalp EEG during a memory task. Tsallis entropies are computed for responses during an old-new memory recognition task. A support vector machine model is constructed to discriminate between normal and moderate/severe TBI individuals using Tsallis entropies as features. Numerical analyses of 30 records (15 normal and 15 TBI) show a maximum discrimination accuracy of 93% (p-value = 7.8557E-5) using four features. These results suggest the potential of scalp EEG as an efficacious method for noninvasive diagnosis of TBI.

Is a diagnosis of "mild traumatic brain injury" a category mistake?

BACKGROUND

Efforts to produce definitions and diagnostic standards for mild traumatic brain injury (TBI) have a long and complex history. The diagnosis of TBI must be considered in the larger context of neuropsychiatric diagnosis. A major reconceptualization of diagnosis is now underway in which the classical syndrome conceptualization is being discarded. We address the question, what are the implications of this revision of thinking in the specific context of TBI?

METHODS

A recent literature on logical structures for neuropsychiatric disorders was reviewed. The symptom pattern of TBI was identified, and a literature survey determined the frequency of these symptom patterns in other disorders and in healthy control populations.

RESULTS

The frequency of symptom endorsement in populations without a history of TBI can be equal to endorsement frequencies in populations with a history of mild TBI. In some studies, the frequency of symptom endorsement in healthy controls having no history of head injury actually exceeded the endorsement rates in a comparison group with a history mild TBI.

CONCLUSION

The heterogeneity of this clinical population and their clinical presentations, the absence of a unitary etiology of postinjury deficits, and the complex idiosyncratic time course of the appearance of these deficits argue against the valid implementation of the classical model of diagnosis. In addition, the accepted criteria of diagnostic utility are not satisfied. TBI is not a disease; it is an event. More precisely, TBI is an event or a sequence of events that can, in some instances, lead to a diagnosable neurological or psychiatric disorder.

Bench-to-bedside and bedside back to the bench; coordinating clinical and experimental traumatic brain injury studies

Traumatic brain injury (TBI) is one of the leading cause of death and long-term disability in virtually every country. Advances in neurointensive care have resulted in steadily decreasing morbidity, but the number of individuals with severe long-term disability have not changed significantly and the number of moderate disability has shown steady increase over the last 3 decades. Despite years of intensive preclinical research – and millions spent – there are virtually no drugs specifically developed to mitigate the consequences of TBI. Here we discuss some of the existing gaps between clinical and experimental TBI studies that may have contributed to the current status. We do this hoping that clinical, basic, and translational scientists will design and coordinate studies in order to achieve maximum benefits for TBI patients. In conclusion, we suggest to: (1) Develop consensus-based guidelines for experimental TBI research, similar to “best practices” in the clinic; (2) Generate a consensus-based template for clinical data collection and deposition as well as for experimental TBI data collection and deposition; (3) Use a systems biology approach and create a database for integrating existing data from basic and clinical research.

Reductions in qEEG slowing over 1 year and after treatment with Cerebrolysin in patients with moderate-severe traumatic brain injury

Changes in quantitative EEG (qEEG) recordings over a 1-year period and the effects of Cerebrolysin (Cere) on qEEG slowing and cognitive performance were investigated in postacute moderate-severe traumatic brain injury (TBI) patients. Time-related changes in qEEG activity frequency bands (increases of alpha and beta, and reductions of theta and delta relative power) and in qEEG slowing (reduction of EEG power ratio) were statistically significant in patients with a disease progress of less than 2 years at baseline, but not in those patients having a longer disease progress time. Slowing of qEEG activity was also found to be significantly reduced in TBI patients after 1 month of treatment with Cere and 3 months later. Therefore, Cere seems to accelerate the time-related reduction of qEEG slowing occurring in untreated patients. The decrease of qEEG slowing induced by Cere correlated with the improvement of attention and working memory. Results of this exploratory study suggest that Cere might improve the functional recovery after brain injury and encourage the conduction of further controlled clinical trials.

Impact of Continuous Low Level Heatwrap Therapy in Acute Low Back Pain Patients: Subjective and Objective Measurements

OBJECTIVES

Muscular pain is usually associated with increased muscle tension resulting in a vicious tension-pain-cycle, leading to increased alertness and stress. However, this has not been broadly evaluated using objective methods, for example, looking at neurophysiologic changes. The focus of this study was, therefore, to combine objective [spontaneous electroencephalogram (EEG) as a surrogate of alertness and stress] with subjective parameters (self-assessed pain affected variables) to investigate the effect of continuous low-level heat therapy in low back pain (LBP)-patients.

METHODS

This investigation was a randomized, active controlled, parallel-designed study. Thirty patients were randomly assigned to one of 2 groups: the control group, in which patients were provided with oral analgesics (nonsteroidal anti-inflammatory drug) and instructed to use it if needed, and the treatment group, in which patients in addition to oral analgesics as rescue medication were provided with a heatwrap therapy. The objective parameters were assessed by measuring the power of frequency bands in the spontaneous EEG. The subjective parameters (sleep pattern, well-being, pain intensity, etc.) were assessed by a Pain, Sleep, and Stress Questionnaire.

RESULTS

In the EEG-recordings, the heatwrap therapy group showed decreased power in Beta-1 and Beta-2 frequency bands compared with the control group, indicating a reduction in arousal. Also, in comparison to the control group, the heatwrap therapy group reported significantly reduced LBP, everyday situations being less stressful, a better night's sleep, and a decreased number of daytime naps.

DISCUSSION

In addition to classic psychophysical assessment of pain-related parameters and sleep quality, performance in daily life, we were able to obtain objective measures (EEG) that suggest an acute therapeutic relaxation on the basis of the central nervous system effects accompanying the reported significant pain relief. We believe that this was due to a reduced nociceptive information load in LBP-patients after the use of the heatwrap therapy.

A meta-analysis of quantitative EEG power associated with attention-deficit hyperactivity disorder

A meta-analysis was performed on quantitative EEG (QEEG) studies that evaluated attention-deficit hyperactivity disorder (ADHD) using the criteria of the DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th edition). The nine eligible studies (N = 1498) observed QEEG traits of a theta power increase and a beta power decrease, summarized in the theta/beta ratio with a pooled effect size of 3.08 (95% confidence interval, 2.90, 3.26) for ADHD versus controls (normal children, adolescents, and adults). By statistical extrapolation, an effect size of 3.08 predicts a sensitivity and specificity of 94%, which is similar to previous results 86% to 90% sensitivity and 94% to 98% specificity. It is important to note that the controlled group studies were often with retrospectively set limits, and that in practice the sensitivity and specificity results would likely be more modest. The literature search also uncovered 32 pre-DSM-IV studies of ADHD and EEG power, and 29 of the 32 studies demonstrated results consistent with the meta-analysis. The meta-analytic results are also supported by the observation that the theta/beta ratio trait follows age-related changes in ADHD symptom presentation (Pearson correlation coefficient, 0.996, P = 0.004). In conclusion, this meta-analysis supports that a theta/beta ratio increase is a commonly observed trait in ADHD relative to normal controls. Because it is known that the theta/beta ratio trait may arise with other conditions, a prospective study covering differential diagnosis would be required to determine generalizability to clinical applications. Standardization of the QEEG technique is also needed, specifically with control of mental state, drowsiness, and medication.

Using EEG to monitor anesthesia drug effects during surgery

The use of processed electroencephalography (EEG) using a simple frontal lead system has been made available for assessing the impact of anesthetic medications during surgery. This review discusses the basic principles behind these devices. The foundations of anesthesia monitoring rest on the observations of Guedel with ether that the depth of anesthesia relates to the cortical, brainstem and spinal effects of the anesthetic agents. Anesthesiologists strive to have a patient who is immobile, is unconscious, is hemodynamically stable and who has no intraoperative awareness or recall. These anesthetic management principles apply today, despite the absence of ether from the available anesthetic medications. The use of the EEG as a supplement to the usual monitoring techniques rests on the observation that anesthetic medications all alter the synaptic function which produces the EEG. Frontal EEG can be viewed as a surrogate for the drug effects on the entire central nervous system (CNS). Using mathematical processing techniques, commercial EEG devices create an index usually between 0 and 100 to characterize this drug effect. Critical aspects of memory formation occur in the frontal lobes making EEG monitoring in this area a possible method to assess risk of recall. Integration of processed EEG monitoring into anesthetic management is evolving and its ability to characterize all of the anesthetic effects on the CNS (in particular awareness and recall) and improve decision making is under study.

Correlation Of Bispectral Index With Glasgow Coma Score In Mild And Moderate Head Injuries

BACKGROUND

Bispectral Index (BIS) derived from electroencephalogram (EEG) is primarily developed to monitor the depth of unconsciousness. Recent evidence suggests that BIS may also help in the detection of cerebral ischemia and prognostication of outcome of traumatic brain injury (TBI). The present study was designed to investigate the correlation between Glasgow Coma Score (GCS) and BIS in mild and moderate head injury.

METHODS

In 29 patients with mild (GCS 13-15) and moderate (GCS 9-12) head injuries who underwent craniotomy, GCS and BIS were measured before surgery, after surgery and once a day for the first 10 days.

RESULTS

A significant correlation was found between GCS and BIS in the data sets from all the patients (r = 0.67; p < 0.001). Mean BIS values increased with increasing GCS scores. However, the scatter of BIS values for any GCS score was high limiting the value of BIS in predicting GCS. Mean BIS values were significantly different between mild and moderate head injuries [65.7 +/- 16.1 vs. 85.7 +/- 6.1, p = 0.006].

CONCLUSION

In patients with mild and moderate head injury, significant correlation exists between GCS and BIS. But the high degree of scatter of BIS values for any given GCS score limits its use as a monitor of depth of coma in TBI. Further studies are required to understand the relation between BIS algorithm and cerebral electrical activity following TBI to define the role of BIS as an electrophysiological correlate of consciousness in TBI.

Impact of bispectral index monitoring on sedation and outcomes in critically ill adults: a case series

In situations in which clinical assessment of sedation level is compromised, such as deep sedation/analgesia with and without neuromuscular blockade (NMB), electroencephalogram-based monitoring may potentially assist in achieving balance between inadequate and excessive levels of sedation. To validate the bispectral index (BIS) for use in clinical practice, correlation and possible differences in outcome using clinical assessment versus clinical assessment augmented by electroencephalogram-based monitoring were determined. BIS monitoring was decisive in ICU care in 9 of 15 patients in this series. The most significant potential benefit was obtained in the subset of patients receiving NMB.

Refractory increased intracranial pressure in severe traumatic brain injury: barbiturate coma and bispectral index monitoring

Patients with severe traumatic brain injury resulting in increased intracranial pressure refractory to first-tier interventions challenge the critical care team. After exhausting these initial interventions, critical care practitioners may utilize barbiturate-induced coma in an attempt to reduce the intracranial pressure. Titrating appropriate levels of barbiturate is imperative. Underdosing the drug may fail to control the intracranial pressure, whereas overdosing may lead to untoward effects such as hypotension and cardiac compromise. Monitoring for a therapeutic level of barbiturate coma includes targeting drug levels and using continuous electroencephalogram monitoring, considered the gold standard. New technology, the Bispectral Index monitor, utilizes electroencephalogram principles to monitor the level of sedation and hypnosis in the critical care environment. This technology is now being considered for targeting appropriate levels of barbiturate coma.

The usefulness of quantitative EEG (QEEG) and neurotherapy in the assessment and treatment of post-concussion syndrome

Mild traumatic brain injury (TBI) is associated with damage to frontal, temporal and parietal lobes. Post-concussion syndrome has been used to describe a range of residual symptoms that persist 12 months or more after the injury, often despite a lack of evidence of brain abnormalities on MRI and CT scans. The core deficits of post-concussion syndrome are similar to those of ADHD and mood disorders, and sufferers often report memory, socialization problems and frequent headaches. While cognitive rehabilitation and psychological support are widely used, neither has been shown to be effective in redressing the core deficits of post-concussion syndrome. On the other hand, quantitative EEG has been shown to be highly sensitive (96%) in identifying post-concussion syndrome, and neurotherapy has been shown in a number of studies to be effective in significantly improving or redressing the symptoms of post-concussion syndrome, as well as improving similar symptoms in non-TBI patients.

Neuroprotection assessment by topographic electroencephalographic analysis: effects of a sodium channel blocker to reduce polymorphic delta activity following ischaemic brain injury in rats

The spatiotemporal electroencephalogram (EEG) pathology associated with brain injury was studied using high-resolution, 10-electrode cortical EEG mapping in a rat model of middle cerebral artery occlusion (MCAo). Using this model we evaluated the ability of the novel sodium channel blocker and neuroprotective agent RS100642 to resolve injury-induced EEG abnormalities as a measure of neurophysiological recovery from brain injury. The middle cerebral artery (MCA) was occluded for 1 h during which a dramatic loss of EEG power was measured over the injured cortex with near complete recovery upon reperfusion of blood to the MCA region in all rats. The resultant progression of the MCAo/reperfusion injury (6-72 h) included the appearance of diffuse polymorphic delta activity (PDA), as visually indicated by the presence of high-amplitude slow-waves recorded from both brain hemispheres. PDA was associated with large increases in EEG power, particularly evident in outer 'peri-infarct' regions of the ipsilateral parietal cortex as visualized using topographic EEG mapping. Post-injury treatment with RS100642 (1.0 mg/kg, i.v.) significantly reduced the PDA activity and attenuated the increase in EEG power throughout the course of the injury. These effects were associated with a reduction in brain infarct volume and improved neurological function. These methods of EEG analysis may be helpful tools to evaluate the physiological recovery of the brain from injury in humans following treatment with an experimental neuroprotective compound.

Positive effects of cerebrolysin on electroencephalogram slowing, cognition and clinical outcome in patients with postacute traumatic brain injury: an exploratory study

The potential effects of Cerebrolysin (EBEWE Pharma, Unterach, Austria), a peptide preparation with neurotrophic activity, on brain bioelectrical activity, cognitive performance and clinical outcome in postacute traumatic brain injury (TBI) patients, were investigated in an exploratory study. A decrease in slow electroencephalogram (EEG) activity and an increase in fast frequencies were observed after the administration of Cerebrolysin. This EEG-activating effect was not influenced by TBI time course or severity, nor by the chronic treatment with nootropic compounds. Cognitive performance, evaluated with the Syndrome Kurztest test, improved in TBI patients after Cerebrolysin treatment, independent of disease severity, time course or disability. A significant improvement in the patients' clinical outcome, only evident during the first year after brain trauma, was also found following Cerebrolysin infusions. No relevant changes in biological parameters nor drug-related adverse events were observed. These promising preliminary results suggest that Cerebrolysin might be a useful treatment to improve the recovery of patients with traumatic brain damage, and encourage the conduction of confirmatory clinical trials.

Continuous nervous system monitoring, EEG, the bispectral index, and neuromuscular transmission

In critically ill patients, the central nervous system remains vulnerable to multiple insults including ischemia, hemorrhagic events, and encephalopathy. The peripheral nervous system is vulnerable in the setting of neuro-muscular blockade (NMB), related drug-drug interactions, and drug-clinical state interactions. Optimal assessment of the nervous system is done by means of the clinical neurological examination. In this manner, orientation, arousal, and responsiveness to stimulation provide feedback on focal and global stability of the central nervous system. Where clinical evaluation is compromised, such as with deep sedation and NMB, risk of undetected seizure activity, and/or progression of neurological injury increases dramatically. A patient receiving NMB risks breakthrough awareness and pain. Long-term complications of NMB including prolonged weakness or paralysis as well as post-traumatic stress dramatically increase morbidity and length of stay. Technologies such as electroencephalogram (EEG) and bispectral index (BIS trade mark ) monitoring are effective for assessing cerebral function as well as level of sedation or arousal, respectively, in patients with a compromised neurological assessment. Neuromuscular transmission (NMT) monitoring by means of peripheral nerve stimulation and assessment of the evoked response may be utilized, within the context of clinical assessment, to determine level of chemical paralysis and minimize dosing of NMB agents. This article explores utilization and differentiates technologies such as EEG, BIS, and NMT monitoring. Monitoring parameters are illustrated using a case study approach.

[Traumatic brain injuries in adults: from coma to wakefulness. Neurophysiological data]

OBJECTIVE

To analyse relevant literature and to express an expert point of view concerning the interest of electroencephalography and evoked potentials recordings in the evaluation of severe head trauma in adults in the context of a consensus conference.

MATERIAL AND METHODS

Scientific databases have been checked on the Internet using key-words. The summaries of 340 papers have checked out. Consequently 94 papers have been thoroughly analysed. Fifty-nine of them are cited in the text of this paper.

RESULTS

Electroencephalography (EEG) and evoked potentials (Eps) evaluate the functional status of the brain. They augment the clinical examination. They are non invasive and easy to perform at patient's bedside. The EEG evaluate globally the functional status of the brain but it is very sensitive to sedative and anaesthetic drugs. It can disclose subclinical or electroclinical epileptic seizures. When reactivity to sensory stimulations can be elicited, this can be considered a prognostic indicator for a good outcome. Evoked potentials are less influenced by sedative drugs. There are several types of evoked potentials, each one with a different localizing value. Brainstem auditory evoked potentials (or short-latency Eps) evaluate the auditory nerve and brainstem. When normal they have no specificity. When abnormal they are an indicator of a poor or bad outcome. Somatosensory and auditory middle-latency Eps evaluate the primary cortex. In coma due to traumatic brain injury the presence of primary cortex components is an indicator of a good outcome and its absence is an indicator of a poor outcome at least when there is no focal brain lesion as to have the primary cortex component to be absent. Event-related potentials evaluate associative brain areas. When they are present in a comatose patient they favor the idea that some cognitive processes are active and they have a high positive predictive value for a return to consciousness. The electrophysiological evaluation can help to identify atypical situations and pathologies close to coma, disclose nonconvulsive seizures and localize certain complications or dysfunctions in atypical cases.