The Nature of EEG Reactivity to Light, Sound, and Pain Stimulation in Neurosurgical Comatose Patients Evaluated by a Quantitative Method

Methods of Evaluating EEG Reactivity in Adult Intensive Care Units: A Review

PURPOSE

EEG reactivity (EEG-R) has become widely used in intensive care units for diagnosing and prognosticating patients with disorders of consciousness. Despite efforts toward standardization, including the establishment of terminology for critical care EEG in 2012, the processes of testing and interpreting EEG-R remain inconsistent.

METHODS

A review was conducted on PubMed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Inclusion criteria consisted of articles published between January 2012, and November 2022, testing EEG-R on adult intensive care unit patients. Exclusion criteria included articles focused on highly specialized stimulation equipment or animal, basic science, or small case report studies. The Quality In Prognostic Studies tool was used to assess risk of bias.

RESULTS

One hundred and five articles were identified, with 26 variables collected for each. EEG-R testing varied greatly, including the number of stimuli (range: 1-8; 26 total described), stimulus length (range: 2-30 seconds), length between stimuli (range: 10 seconds-5 minutes), frequency of stimulus application (range: 1-9), frequency of EEG-R testing (range: 1-3 times daily), EEG electrodes (range: 4-64), personnel testing EEG-R (range: neurophysiologists to nonexperts), and sedation protocols (range: discontinuing all sedation to no attempt). EEG-R interpretation widely varied, including EEG-R definitions and grading scales, personnel interpreting EEG-R (range: EEG specialists to nonneurologists), use of quantitative methods, EEG filters, and time to detect EEG-R poststimulation (range: 1-30 seconds).

CONCLUSIONS

This study demonstrates the persistent heterogeneity of testing and interpreting EEG-R over the past decade, and contributing components were identified. Further many institutional efforts must be made toward standardization, focusing on the reproducibility and unification of these methods, and detailed documentation in the published literature.

EEG reactivity in neurologic prognostication in post-cardiac arrest patients: A narrative review

Electroencephalographic reactivity (EEG-R) is a promising early predictor of arousal in comatose patients after cardiac arrest. Despite recent guidelines advocating for the integration of EEG-R into the multimodal prognostication model, EEG-R testing methods remain heterogeneous across studies. While efforts towards standardization have been made to reduce interrater variability by the development of quantitative approaches and machine learning models, future validation studies are needed to increase clinical applicability. Furthermore, the specific neurophysiological mechanisms and neuroanatomical correlates underlying EEG-R are not fully understood. In this narrative review, we explore the value and possible mechanisms of EEG-R, focusing on post-cardiac arrest comatose patients. We aim to discuss the current standard of knowledge and future directions, as well as elucidate possible implications for patient care and research.

Cortical responses to auditory stimulation predict the prognosis of patients with disorders of consciousness

OBJECTIVE

This study aimed to assess the prognosis of patients with disorders of consciousness (DoC) using auditory stimulation with electroencephalogram (EEG) recordings.

METHODS

We enrolled 72 patients with DoC in the study, which involved subjecting patients to auditory stimulation while EEG responses were recorded. Coma Recovery Scale-Revised (CRS-R) scores and Glasgow Outcome Scale (GOS) were determined for each patient and followed up for three months. A frequency spectrum analysis was performed on the EEG recordings. Finally, the power spectral density (PSD) index was used to predict the prognosis of patients with DoC based on a support vector machine (SVM) model.

RESULTS

Power spectral analyses revealed that the cortical response to auditory stimulation showed a decreasing trend with decreasing consciousness levels. Auditory stimulation-induced changes in absolute PSD at the delta and theta bands were positively correlated with the CRS-R and GOS scores. Furthermore, these cortical responses to auditory stimulation had a good ability to discriminate between good and poor prognoses of patients with DoC.

CONCLUSIONS

Auditory stimulation-induced changes in the PSD were highly predictive of DoC outcomes.

SIGNIFICANCE

Our findings showed that cortical responses to auditory stimulation may be an important electrophysiological indicator of prognosis in patients with DoC.

Common patterns of EEG reactivity in post-anoxic coma identified by quantitative analyses

OBJECTIVE

Description of typical kinds of EEG reactivity (EEG-R) in post-anoxic coma using a quantitative method.

METHODS

Study of 101 out-of-hospital cardiac arrest patients, 71 with good outcome (cerebral performance category scale ≤ 2). EEG was recorded 12-24 hours after cardiac arrest and four noxious, one auditory, and one visual stimulation were applied for 30 seconds each. Individual reference intervals for the power in the delta, theta, alpha, and beta bands were calculated based on six 2-second resting epochs just prior to stimulations. EEG-R in consecutive 2-second epochs after stimulation was expressed in Z-scores.

RESULTS

EEG-R occurred roughly equally frequent as an increase or as a decrease in EEG activity. Sternal rub and sound stimulation were most provocative with the most pronounced changes as an increase in delta activity 4.5-8.5 seconds after stimulation and a decrease in theta activity 0.5-4.5 seconds after stimulation. These parameters predicted good outcome with an AUC of 0.852 (95 % CI: 0.771-0.932).

CONCLUSIONS

Quantitative EEG-R is a feasible method for identification of common types of reactivity, for evaluation of stimulation methods, and for prognostication.

SIGNIFICANCE

This method provides an objective measure of EEG-R revealing knowledge about the nature of EEG-R and its use as a diagnostic tool.

Stimulation-related modifications of evolving functional brain networks in unresponsive wakefulness

Recent advances in neurophysiological brain network analysis have demonstrated novel potential for diagnosis and prognosis of disorders of consciousness. While most progress has been achieved on the population-sample level, time-economic and easy-to-apply personalized solutions are missing. This prospective controlled study combined EEG recordings, basal stimulation, and daily behavioral assessment as applied routinely during complex early rehabilitation treatment. We investigated global characteristics of EEG-derived evolving functional brain networks during the repeated (3–6 weeks apart) evaluation of brain dynamics at rest as well as during and after multisensory stimulation in ten patients who were diagnosed with an unresponsive wakefulness syndrome (UWS). The age-corrected average clustering coefficient C* allowed to discriminate between individual patients at first (three patients) and second assessment (all patients). Clinically, only two patients changed from UWS to minimally conscious state. Of note, most patients presented with significant changes of C* due to stimulations, along with treatment, and with an increasing temporal distance to injury. These changes tended towards the levels of nine healthy controls. Our approach allowed to monitor both, short-term effects of individual therapy sessions and possibly long-term recovery. Future studies will need to assess its full potential for disease monitoring and control of individualized treatment decisions.

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

Objectives

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

Methods

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

Results

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

Conclusions

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

Automatic continuous EEG signal analysis for diagnosis of delirium in patients with sepsis

OBJECTIVE

In critical care, continuous EEG (cEEG) monitoring is useful for delirium diagnosis. Although visual cEEG analysis is most commonly used, automatic cEEG analysis has shown promising results in small samples. Here we aimed to compare visual versus automatic cEEG analysis for delirium diagnosis in septic patients.

METHODS

We obtained cEEG recordings from 102 septic patients who were scored for delirium six times daily. A total of 1252 cEEG blocks were visually analyzed, of which 805 blocks were also automatically analyzed.

RESULTS

Automatic cEEG analyses revealed that delirium was associated with 1) high mean global field power (p < 0.005), mainly driven by delta activity; 2) low average coherence across all electrode pairs and all frequencies (p < 0.01); 3) lack of intrahemispheric (fronto-temporal and temporo-occipital regions) and interhemispheric coherence (p < 0.05); and 4) lack of cEEG reactivity (p < 0.005). Classification accuracy was assessed by receiver operating characteristic (ROC) curve analysis, revealing a slightly higher area under the curve for visual analysis (0.88) than automatic analysis (0.74) (p < 0.05).

CONCLUSIONS

Automatic cEEG analysis is a useful supplement to visual analysis, and provides additional cEEG diagnostic classifiers.

SIGNIFICANCE

Automatic cEEG analysis provides useful information in septic patients.

Outcome Prediction in Unresponsive Wakefulness Syndrome and Minimally Conscious State by Non-linear Dynamic Analysis of the EEG

Objectives: This study aimed to investigate the role of non-linear dynamic analysis (NDA) of the electroencephalogram (EEG) in predicting patient outcome in unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS).

Methods: This was a prospective longitudinal cohort study. A total of 98 and 64 UWS and MCS cases, respectively, were assessed. During admission, EEGs were acquired under eyes-closed and pain stimulation conditions. EEG nonlinear indices, including approximate entropy (ApEn) and cross-ApEn, were calculated. The modified Glasgow Outcome Scale (mGOS) was employed to assess functional prognosis 1 year following brain injury.

Results: The mGOS scores were improved in 25 (26%) patients with UWS and 42 (66%) with MCS. Under the painful stimulation condition, both non-linear indices were lower in patients with UWS than in those with MCS. The frontal region, periphery of the primary sensory area (S1), and forebrain structure might be the key points modulating disorders of consciousness. The affected local cortical networks connected to S1 and unaffected distant cortical networks connecting S1 to the prefrontal area played important roles in mGOS score improvement.

Conclusions: NDA provides an objective assessment of cortical excitability and interconnections of residual cortical functional islands. The impaired interconnection of the residual cortical functional island meant a poorer prognosis. The activation in the affected periphery of the S1 and the increase in the interconnection of affected local cortical areas around the S1 and unaffected S1 to the prefrontal and temporal areas meant a relatively favorable prognosis.

Rhythmic air-puff into nasal cavity modulates activity across multiple brain areas: A non-invasive brain stimulation method to reduce ventilator-induced memory impairment

Mechanical ventilation (MV) can result in long-term brain impairments that are resistant to treatment. The mechanisms underlying MV-induced brain function impairment remain unclear. Since nasal airflow modulates brain activity, here we evaluated whether reinstating airflow during MV could influence the memory performance of rats after recovery. Rats were allocated into two study groups: one group received rhythmic air-puff into the nasal cavity during MV and a control group that underwent ventilation without air-puff. During MV, air-puffs induced time-locked event potentials in OB, mPFC and vHPC and significantly increased the oscillatory activity at the air-puff frequency. Furthermore, in mPFC and vHPC, (but not in OB), delta and theta oscillations were more prominent during air-puff application. After recovery, working memory performance was significantly higher in the air-puff group compared to control. Our study thus suggests a promising non-invasive brain stimulation approach to alleviate the neurological complications of prolonged mechanical ventilation.

EEG signal varies with different outcomes in comatose patients: A quantitative method of electroencephalography reactivity

BACKGROUND

Electroencephalographic reactivity (EEG-R) is a major predictor of outcome in comatose patients; however, the inter-rater reliability is limited due to the lack of homogeneous stimuli and quantitative interpretation.

NEW METHODS

EEG-R testing was employed in comatose patients by quantifiable electrical stimulation. Reactivity at different frequency bands was computed as the difference between pre- and post-stimulations in power spectra and connectivity function (including magnitude squared coherence and transfer entropy). The clinical outcomes were dichotomized as good and poor according to the recovery of consciousness. Signal discrimination of EEG-R was compared between the two groups.

RESULTS

A total of 18 patients (43%) regained consciousness at a 3-month follow-up. In the patients who regained consciousness, the EEG power increased significantly (P < 0.05) at the Alpha and Beta frequency bands after stimulation as compared to those with no behavioral awakening. Also, connectivity enhancement (including linear and nonlinear analysis) in the Beta and Gamma bands and connectivity decrease (nonlinear transfer entropy analysis) in the Delta band after stimulus were observed in the good outcome group.

COMPARISON WITH EXISTING METHOD(S)

In this study, the combined use of quantifiable stimulation and quantitative analysis shed new light on differentiating brain responses in comatose patients with good and poor outcomes as well as exploring the nature of EEG changes concerning the recovery of consciousness.

CONCLUSIONS

The combination of quantifiable electrical stimulation and quantitative analysis with spectral power and connectivity for the EEG-R may be a promising method to predict the outcome of comatose patients.

Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury

OBJECTIVE

Electroencephalogram (EEG) reactivity is a robust predictor of neurological recovery after cardiac arrest, however interrater-agreement among electroencephalographers is limited. We sought to evaluate the performance of machine learning methods using EEG reactivity data to predict good long-term outcomes in hypoxic-ischemic brain injury.

METHODS

We retrospectively reviewed clinical and EEG data of comatose cardiac arrest subjects. Electroencephalogram reactivity was tested within 72 h from cardiac arrest using sound and pain stimuli. A Quantitative EEG (QEEG) reactivity method evaluated changes in QEEG features (EEG spectra, entropy, and frequency features) during the 10 s before and after each stimulation. Good outcome was defined as Cerebral Performance Category of 1-2 at six months. Performance of a random forest classifier was compared against a penalized general linear model (GLM) and expert electroencephalographer review.

RESULTS

Fifty subjects were included and sixteen (32%) had good outcome. Both QEEG reactivity methods had comparable performance to expert EEG reactivity assessment for good outcome prediction (mean AUC 0.8 for random forest vs. 0.69 for GLM vs. 0.69 for expert review, respectively; p non-significant).

CONCLUSIONS

Machine-learning models utilizing quantitative EEG reactivity data can predict long-term outcome after cardiac arrest.

SIGNIFICANCE

A quantitative approach to EEG reactivity assessment may support prognostication in cardiac arrest.

EEG Reactivity Evaluation Practices for Adult and Pediatric Hypoxic-Ischemic Coma Prognostication in North America

PURPOSE

The aim of this study was to assess the variability in EEG reactivity evaluation practices during cardiac arrest prognostication.

METHODS

A survey of institutional representatives from North American academic hospitals participating in the Critical Care EEG Monitoring Research Consortium was conducted to assess practice patterns involving EEG reactivity evaluation. This 10-question multiple-choice survey evaluated metrics related to technical, interpretation, personnel, and procedural aspects of bedside EEG reactivity testing and interpretation specific to cardiac arrest prognostication. One response per hospital was obtained.

RESULTS

Responses were received from 25 hospitals, including 7 pediatric hospitals. A standardized EEG reactivity protocol was available in 44% of centers. Sixty percent of respondents believed that reactivity interpretation was subjective. Reactivity bedside testing always (100%) started during hypothermia and was performed daily during monitoring in the majority (71%) of hospitals. Stimulation was performed primarily by neurodiagnostic technologists (76%). The mean number of activation procedures modalities tested was 4.5 (SD 2.1). The most commonly used activation procedures were auditory (83.3%), nail bed pressure (63%), and light tactile stimuli (63%). Changes in EEG amplitude alone were not considered consistent with EEG reactivity in 21% of centers.

CONCLUSIONS

There is substantial variability in EEG reactivity evaluation practices during cardiac arrest prognostication among North American academic hospitals. Efforts are needed to standardize protocols and nomenclature according with national guidelines and promote best practices in EEG reactivity evaluation.

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.