Quantitative EEG indices of sub-acute ischaemic stroke correlate with clinical outcomes

Differentiating ischemic stroke patients from healthy subjects using a large-scale, retrospective EEG database and machine learning methods

OBJECTIVES

We set out to develop a machine learning model capable of distinguishing patients presenting with ischemic stroke from a healthy cohort of subjects. The model relies on a 3-min resting electroencephalogram (EEG) recording from which features can be computed.

MATERIALS AND METHODS

Using a large-scale, retrospective database of EEG recordings and matching clinical reports, we were able to construct a dataset of 1385 healthy subjects and 374 stroke patients. With subjects often producing more than one recording per session, the final dataset consisted of 2401 EEG recordings (63% healthy, 37% stroke).

RESULTS

Using a rich set of features encompassing both the spectral and temporal domains, our model yielded an AUC of 0.95, with a sensitivity and specificity of 93% and 86%, respectively. Allowing for multiple recordings per subject in the training set boosted sensitivity by 7%, attributable to a more balanced dataset.

CONCLUSIONS

Our work demonstrates strong potential for the use of EEG in conjunction with machine learning methods to distinguish stroke patients from healthy subjects. Our approach provides a solution that is not only timely (3-minutes recording time) but also highly precise and accurate (AUC: 0.95).

Predicting brain edema and outcomes after thrombectomy in stroke: Frontal delta/alpha ratio as an optimal quantitative EEG index

OBJECTIVE

We aimed to determine whether quantitative electroencephalography (QEEG) measures have predictive value for cerebral edema (CED) and clinical outcomes in acute ischemic stroke (AIS) patients with anterior circulation large vessel occlusion who underwent mechanical thrombectomy (MT).

METHODS

A total of 105 patients with AIS in the anterior circulation were enrolled in this prospective study. The occurrence and severity of CED were assessed through computed tomography conducted 24 h after MT. Clinical outcomes were evaluated based on early neurological deterioration (END) and 3-month functional status, as measured by the modified Rankin scale (mRS). Electroencephalography (EEG) recordings were performed 24 h after MT, and QEEG indices were calculated from the standard 16 electrodes and 2 frontal channels (F3-C3, F4-C4). The delta/alpha ratio (DAR), the (delta + theta) / (alpha + beta) ratio (DTABR), and relative delta power were averaged over all electrodes (global) and the F3-C3 and F4-C4 channels (frontal). The predictive effect and value of QEEG indices for CED and clinical outcomes were assessed using ordinal and logistic regression models, as well as receiver operating characteristic (ROC) curves.

RESULTS

Significantly, both global and frontal DAR were found to be associated with the severity of CED, END, and poor functional outcomes at 90 days, while global and frontal DTABR and relative delta power were not associated with outcomes. In ROC analysis, the best predictive effect was observed in frontal DAR, with an area under the curve of approximately 0.80. It exhibited approximately 75% sensitivity and 71% specificity for radiological and clinical outcomes when a threshold of 3.3 was used.

CONCLUSIONS

QEEG techniques may be considered an efficient bedside monitoring method for assessing treatment efficacy, identifying patients at higher risk of severe CED and END, and predicting long-term functional outcomes.

SIGNIFICANCE

QEEG can help identify patients at risk of severe neurological complications that can impact long-term functional recovery in AIS patients who underwent MT.

Alteration in Resting-State Brain Activity in Stroke Survivors After Repetitive Finger Stimulation

OBJECTIVE

This quasi-experimental study examined the effect of repetitive finger stimulation on brain activation in eight stroke and seven control subjects, measured by quantitative electroencephalogram.

METHODS

We applied 5 mins of 2-Hz repetitive bilateral index finger transcutaneous electrical nerve stimulation and compared differences pre- and post-transcutaneous electrical nerve stimulation using quantitative electroencephalogram metrics delta/alpha ratio and delta-theta/alpha-beta ratio.

RESULTS

Between-group differences before and after stimulation were significantly different in the delta/alpha ratio ( z = -2.88, P = 0.0040) and the delta-theta/alpha-beta ratio variables ( z = -3.90 with P < 0.0001). Significant decrease in the delta/alpha ratio and delta-theta/alpha-beta ratio variables after the transcutaneous electrical nerve stimulation was detected only in the stroke group (delta/alpha ratio diff = 3.87, P = 0.0211) (delta-theta/alpha-beta ratio diff = 1.19, P = 0.0074).

CONCLUSIONS

The decrease in quantitative electroencephalogram metrics in the stroke group may indicate improved brain activity after transcutaneous electrical nerve stimulation. This finding may pave the way for a future novel therapy based on transcutaneous electrical nerve stimulation and quantitative electroencephalogram measures to improve brain recovery after stroke.

Deciphering Post-Stroke Sleep Disorders: Unveiling Neurological Mechanisms in the Realm of Brain Science

Sleep disorders are the most widespread mental disorders after stroke and hurt survivors' functional prognosis, response to restoration, and quality of life. This review will address an overview of the progress of research on the biological mechanisms associated with stroke-complicating sleep disorders. Extensive research has investigated the negative impact of stroke on sleep. However, a bidirectional association between sleep disorders and stroke exists; while stroke elevates the risk of sleep disorders, these disorders also independently contribute as a risk factor for stroke. This review aims to elucidate the mechanisms of stroke-induced sleep disorders. Possible influences were examined, including functional changes in brain regions, cerebrovascular hemodynamics, neurological deficits, sleep ion regulation, neurotransmitters, and inflammation. The results provide valuable insights into the mechanisms of stroke complicating sleep disorders.

Monitoring the after-effects of ischemic stroke through EEG microstates

BACKGROUND AND PURPOSE

Stroke may cause extensive after-effects such as motor function impairments and disorder of consciousness (DoC). Detecting these after-effects of stroke and monitoring their changes are challenging jobs currently undertaken via traditional clinical examinations. These behavioural examinations often take a great deal of manpower and time, thus consuming significant resources. Computer-aided examinations of the electroencephalogram (EEG) microstates derived from bedside EEG monitoring may provide an alternative way to assist medical practitioners in a quick assessment of the after-effects of stroke.

METHODS

In this study, we designed a framework to extract microstate maps and calculate their statistical parameters to input to classifiers to identify DoC in ischemic stroke patients automatically. As the dataset is imbalanced with the minority of patients being DoC, an ensemble of support vector machines (EOSVM) is designed to solve the problem that classifiers always tend to be the majority classes in the classification on an imbalanced dataset.

RESULTS

The experimental results show EOSVM get better performance (with accuracy and F1-Score both higher than 89%), improving sensitivity the most, from lower than 60% (SVM and AdaBoost) to higher than 80%. This highlighted the usefulness of the EOSVM-aided DoC detection based on microstates parameters.

CONCLUSION

Therefore, the classifier EOSVM classification based on features of EEG microstates is helpful to medical practitioners in DoC detection with saved resources that would otherwise be consumed in traditional clinic checks.

Photothrombosis induced cortical stroke produces electrographic epileptic biomarkers in mice

Objective

Interictal epileptiform spikes, high-frequency ripple oscillations, and their co-occurrence (spike ripples) in human scalp or intracranial voltage recordings are well-established epileptic biomarkers. While clinically significant, the neural mechanisms generating these electrographic biomarkers remain unclear. To reduce this knowledge gap, we introduce a novel photothrombotic stroke model in mice that reproduces focal interictal electrographic biomarkers observed in human epilepsy.

Methods

We induced a stroke in the motor cortex of C57BL/6 mice unilaterally (N=7) using a photothrombotic procedure previously established in rats. We then implanted intracranial electrodes (2 ipsilateral and 2 contralateral) and obtained intermittent local field potential (LFP) recordings over several weeks in awake, behaving mice. We evaluated the LFP for focal slowing and epileptic biomarkers - spikes, ripples, and spike ripples - using both automated and semi-automated procedures.

Results

Delta power (1-4 Hz) was higher in the stroke hemisphere than the non-stroke hemisphere in all mice ( p <0.001). Automated detection procedures indicated that compared to the non-stroke hemisphere, the stroke hemisphere had an increased spike ripple ( p =0.006) and spike rates ( p =0.039), but no change in ripple rate ( p =0.98). Expert validation confirmed the observation of elevated spike ripple rates ( p =0.008) and a trend of elevated spike rate ( p =0.055) in the stroke hemisphere. Interestingly, the validated ripple rate in the stroke hemisphere was higher than the non-stroke hemisphere ( p =0.031), highlighting the difficulty of automatically detecting ripples. Finally, using optimal performance thresholds, automatically detected spike ripples classified the stroke hemisphere with the best accuracy (sensitivity 0.94, specificity 0.94).

Significance

Cortical photothrombosis-induced stroke in commonly used C57BL/6 mice produces electrographic biomarkers as observed in human epilepsy. This model represents a new translational cortical epilepsy model with a defined irritative zone, which can be broadly applied in transgenic mice for cell type specific analysis of the cellular and circuit mechanisms of pathologic interictal activity.

Key Points

Cortical photothrombosis in mice produces stroke with characteristic intermittent focal delta slowing.Cortical photothrombosis stroke in mice produces the epileptic biomarkers spikes, ripples, and spike ripples.All biomarkers share morphological features with the corresponding human correlate.Spike ripples better lateralize to the lesional cortex than spikes or ripples.This cortical model can be applied in transgenic mice for mechanistic studies.

Quantitative measures of the resting EEG in stroke: a systematic review on clinical correlation and prognostic value

OBJECT

Quantitative electroencephalography (qEEG) has shown promising results as a predictor of clinical impairment in stroke. We systematically reviewed published papers that focus on qEEG metrics in the resting EEG of patients with mono-hemispheric stroke, to summarize current knowledge and pave the way for future research.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched the literature for papers that fitted our inclusion criteria. Rayyan QCRR was used to allow deduplication and collaborative blinded paper review. Due to multiple outcomes and non-homogeneous literature, a scoping review approach was used to address the topic.

RESULTS

Or initial search (PubMed, Embase, Google scholar) yielded 3200 papers. After proper screening, we selected 71 papers that fitted our inclusion criteria and we developed a scoping review thar describes the current state of the art of qEEG in stroke. Notably, among selected papers 53 (74.3%) focused on spectral power; 11 (15.7%) focused on symmetry indexes, 17 (24.3%) on connectivity metrics, while 5 (7.1%) were about other metrics (e.g. detrended fluctuation analysis). Moreover, 42 (58.6%) studies were performed with standard 19 electrodes EEG caps and only a minority used high-definition EEG.

CONCLUSIONS

We systematically assessed major findings on qEEG and stroke, evidencing strengths and potential pitfalls of this promising branch of research.

Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness

Introduction

Ischemic stroke patients commonly experience disorder of consciousness (DOC), leading to poorer discharge outcomes and higher mortality risks. Therefore, the identification of applicable electrophysiological biomarkers is crucial for the rapid diagnosis and evaluation of post-stroke disorder of consciousness (PS-DOC), while providing supportive evidence for cerebral neurology.

Methods

In our study, we conduct microstate analysis on resting-state electroencephalography (EEG) of 28 post-stroke patients with awake consciousness and 28 patients with PS-DOC, calculating the temporal features of microstates. Furthermore, we extract the Lempel-Ziv complexity of microstate sequences and the delta/alpha power ratio of EEG on spectral. Statistical analysis is performed to examine the distinctions in features between the two groups, followed by inputting the distinctive features into a support vector machine for the classification of PS-DOC.

Results

Both groups obtain four optimal topographies of EEG microstates, but notable distinctions are observed in microstate C. Within the PS-DOC group, there is a significant increase in the mean duration and coverage of microstates B and C, whereas microstate D displays a contrasting trend. Additionally, noteworthy variations are found in the delta/alpha ratio and Lempel-Ziv complexity between the two groups. The integration of the delta/alpha ratio with microstates' temporal and Lempel-Ziv complexity features demonstrates the highest performance in the classifier (Accuracy = 91.07%).

Discussion

Our results suggest that EEG microstates can provide insights into the abnormal brain network dynamics in DOC patients post-stroke. Integrating the temporal and Lempel-Ziv complexity microstate features with spectral features offers a deeper understanding of the neuro mechanisms underlying brain damage in patients with DOC, holding promise as effective electrophysiological biomarkers for diagnosing PS-DOC.

Neurophysiological biomarkers of motor improvement from Constraint-Induced Movement Therapy and Robot-Assisted Therapy in participants with stroke

Background

The mechanism of stroke recovery is related to the reorganization of cerebral activity that can be enhanced by rehabilitation therapy. Two well established treatments are Robot-Assisted Therapy (RT) and Constraint-Induced Movement Therapy (CIMT), however, it is unknown whether there is a difference in the neuroplastic changes induced by these therapies, and if the modifications are related to motor improvement. Therefore, this study aims to identify neurophysiological biomarkers related to motor improvement of participants with chronic stroke that received RT or CIMT, and to test whether there is a difference in neuronal changes induced by these two therapies.

Methods

This study included participants with chronic stroke that took part in a pilot experiment to compare CIMT vs. RT. Neurophysiological evaluations were performed with electroencephalography (EEG) and transcranial magnetic stimulation (TMS), pre and post rehabilitation therapy. Motor function was measured by the Wolf Motor Function Test (WMFT) and Fugl-Meyer Assessment Upper Limb (FMA-UL).

Results

Twenty-seven participants with chronic stroke completed the present study [mean age of 58.8 years (SD ± 13.6), mean time since stroke of 18.2 months (SD ± 9.6)]. We found that changes in motor threshold (MT) and motor evoked potential (MEP) in the lesioned hemisphere have a positive and negative correlation with WMFT improvement, respectively. The absolute change in alpha peak in the unlesioned hemisphere and the absolute change of the alpha ratio (unlesioned/lesioned hemisphere) is negatively correlated with WMFT improvement. The decrease of EEG power ratio (increase in the lesioned hemisphere and decrease in the unlesioned hemisphere) for high alpha bandwidths is correlated with better improvement in WMFT. The variable "type of treatment (RT or CIMT)" was not significant in the models.

Conclusion

Our results suggest that distinct treatments (RT and CIMT) have similar neuroplastic mechanisms of recovery. Moreover, motor improvements in participants with chronic stroke are related to decreases of cortical excitability in the lesioned hemisphere measured with TMS. Furthermore, the balance of both EEG power and EEG alpha peak frequency in the lesioned hemisphere is related to motor improvement.

Preclinical examination of early-onset thalamic-cortical seizures after hemispheric stroke

OBJECTIVE

Ischemic stroke is one of the main causes of death and disability worldwide and currently has limited treatment options. Electroencephalography (EEG) signals are significantly affected in stroke patients during the acute stage. In this study, we preclinically characterized the brain electrical rhythms and seizure activity during the hyperacute and late acute phases in a hemispheric stroke model with no reperfusion.

METHODS

EEG signals and seizures were studied in a model of hemispheric infarction induced by permanent occlusion of the middle cerebral artery (pMCAO), which mimics the clinical condition of stroke patients with permanent ischemia. Electrical brain activity was also examined using a photothrombotic (PT) stroke model. In the PT model, we induced a similar (PT group-1) or smaller (PT group-2) cortical lesion than in the pMCAO model. For all models, we used a nonconsanguineous mouse strain that mimics human diversity and genetic variation.

RESULTS

The pMCAO hemispheric stroke model exhibited thalamic-origin nonconvulsive seizures during the hyperacute stage that propagated to the thalamus and cortex. The seizures were also accompanied by progressive slowing of the EEG signal during the acute phase, with elevated delta/theta, delta/alpha, and delta/beta ratios. Cortical seizures were also confirmed in the PT stroke model of similar lesions as in the pMCAO model, but not in the PT model of smaller injuries.

SIGNIFICANCE

In the clinically relevant pMCAO model, poststroke seizures and EEG abnormalities were inferred from recordings of the contralateral hemisphere (noninfarcted hemisphere), emphasizing the reciprocity of interhemispheric connections and that injuries affecting one hemisphere had consequences for the other. Our results recapitulate many of the EEG signal hallmarks seen in stroke patients, thereby validating this specific mouse model for the examination of the mechanistic aspects of brain function and for the exploration of the reversion or suppression of EEG abnormalities in response to neuroprotective and anti-epileptic therapies.

The GPER1 agonist G1 reduces brain injury and improves the qEEG and behavioral outcome of experimental ischemic stroke

Stroke is one of the principal cerebrovascular diseases in human populations and contributes to a majority of the functional impairments in the elderly. Recent discoveries have led to the inclusion of electroencephalography (EEG) in the complementary prognostic evaluation of patients. The present study describes the EEG, behavioral, and histological changes that occur following cerebral ischemia associated with treatment by G1, a potent and selective G protein-coupled estrogen receptor 1 (GPER1) agonist in a rat model. Treatment with G1 attenuated the neurological deficits induced by ischemic stroke from the second day onward, and reduced areas of infarction. Treatment with G1 also improved the total brainwave power, as well as the theta and alpha wave activity, specifically, and restored the delta band power to levels similar to those observed in the controls. Treatment with G1 also attenuated the peaks of harmful activity observed in the EEG indices. These improvements in brainwave activity indicate that GPER1 plays a fundamental role in the mediation of cerebral injury and in the behavioral outcome of ischemic brain injuries, which points to treatment with G1 as a potential pharmacological strategy for the therapy of stroke.

A Narrative Review on Multi-Domain Instrumental Approaches to Evaluate Neuromotor Function in Rehabilitation

In clinical scenarios, the use of biomedical sensors, devices and multi-parameter assessments is fundamental to provide a comprehensive portrait of patients' state, in order to adapt and personalize rehabilitation interventions and support clinical decision-making. However, there is a huge gap between the potential of the multidomain techniques available and the limited practical use that is made in the clinical scenario. This paper reviews the current state-of-the-art and provides insights into future directions of multi-domain instrumental approaches in the clinical assessment of patients involved in neuromotor rehabilitation. We also summarize the main achievements and challenges of using multi-domain approaches in the assessment of rehabilitation for various neurological disorders affecting motor functions. Our results showed that multi-domain approaches combine information and measurements from different tools and biological signals, such as kinematics, electromyography (EMG), electroencephalography (EEG), near-infrared spectroscopy (NIRS), and clinical scales, to provide a comprehensive and objective evaluation of patients' state and recovery. This multi-domain approach permits the progress of research in clinical and rehabilitative practice and the understanding of the pathophysiological changes occurring during and after rehabilitation. We discuss the potential benefits and limitations of multi-domain approaches for clinical decision-making, personalized therapy, and prognosis. We conclude by highlighting the need for more standardized methods, validation studies, and the integration of multi-domain approaches in clinical practice and research.

Qualitative electroencephalogram and its predictors in the diagnosis of stroke

Introduction

Stroke is a typical medical emergency that carries significant disability and morbidity. The diagnosis of stroke relies predominantly on the use of neuroimaging. Accurate diagnosis is pertinent for management decisions of thrombolysis and/or thrombectomy. Early identification of stroke using electroencephalogram (EEG) in the clinical assessment of stroke has been underutilized. This study was conducted to determine the relevance of EEG and its predictors with the clinical and stroke features.

Methods

A cross-sectional study was carried out where routine EEG assessment was performed in 206 consecutive acute stroke patients without seizures. The demographic data and clinical stroke assessment were collated using the National Institutes of Health Stroke Scale (NIHSS) score with neuroimaging. Associations between EEG abnormalities and clinical features, stroke characteristics, and NIHSS scores were evaluated.

Results

The mean age of the study population was 64.32 ± 12 years old, with 57.28% consisting of men. The median NIHSS score on admission was 6 (IQR 3-13). EEG was abnormal in more than half of the patients (106, 51.5%), which consisted of focal slowing (58, 28.2%) followed by generalized slowing (39, 18.9%) and epileptiform changes (9, 4.4%). NIHSS score was significantly associated with focal slowing (13 vs. 5, p < 0.05). Type of stroke and imaging characteristics were significantly associated with EEG abnormalities (p < 0.05). For every increment in NIHSS score, there are 1.08 times likely for focal slowing (OR 1.089; 95% CI 1.033, 1.147, p = 0.002). Anterior circulation stroke has 3.6 times more likely to have abnormal EEG (OR 3.628; 95% CI 1.615, 8.150, p = 0.002) and 4.55 times higher to exhibit focal slowing (OR 4.554; 95% CI 1.922, 10.789, p = 0.01).

Conclusion

The type of stroke and imaging characteristics are associated with EEG abnormalities. Predictors of focal EEG slowing are NIHSS score and anterior circulation stroke. The study emphasized that EEG is a simple yet feasible investigational tool, and further plans for advancing stroke evaluation should consider the inclusion of this functional modality.

Assessing stroke rehabilitation degree based on quantitative EEG index and nonlinear parameters

The assessment of motor function is critical to the rehabilitation of stroke patients. However, commonly used evaluation methods are based on behavior scoring, which lacks neurological indicators that directly reflect the motor function of the brain. The objective of this study was to investigate whether resting-state EEG indicators could improve stroke rehabilitation evaluation. We recruited 68 participants and recorded their resting-state EEG data. According to Brunnstrom stage, the participants were divided into three groups: severe, moderate, and mild. Ten quantitative electroencephalographic (QEEG) and five non-linear parameters of resting-state EEG were calculated for further analysis. Statistical tests were performed, and the genetic algorithm-support vector machine was used to select the best feature combination for classification. We found the QEEG parameters show significant differences in Delta, Alpha1, Alpha2, DAR, and DTABR (P < 0.05) among the three groups. Regarding nonlinear parameters, ApEn, SampEn, Lz, and C0 showed significant differences (P < 0.05). The optimal feature classification combination accuracy rate reached 85.3%. Our research shows that resting-state EEG indicators could be used for stroke rehabilitation evaluation.

Evaluation of movement functional rehabilitation after stroke: A study via graph theory and corticomuscular coupling as potential biomarker

Changes in the functional connections between the cerebral cortex and muscles can evaluate motor function in stroke rehabilitation. To quantify changes in functional connections between the cerebral cortex and muscles, we combined corticomuscular coupling and graph theory to propose dynamic time warped (DTW) distances for electroencephalogram (EEG) and electromyography (EMG) signals as well as two new symmetry metrics. EEG and EMG data from 18 stroke patients and 16 healthy individuals, as well as Brunnstrom scores from stroke patients, were recorded in this paper. First, calculate DTW-EEG, DTW-EMG, BNDSI and CMCSI. Then, the random forest algorithm was used to calculate the feature importance of these biological indicators. Finally, based on the results of feature importance, different features were combined and validated for classification. The results showed that the feature importance was from high to low as CMCSI/BNDSI/DTW-EEG/DTW-EMG, while the feature combination with the highest accuracy was CMCSI+BNDSI+DTW-EEG. Compared to previous studies, combining the CMCSI+BNDSI+DTW-EEG features of EEG and EMG achieved better results in the prediction of motor function rehabilitation at different levels of stroke. Our work implies that the establishment of a symmetry index based on graph theory and cortical muscle coupling has great potential in predicting stroke recovery and promises to have an impact on clinical research applications.

Motor imagery brain–computer interface rehabilitation system enhances upper limb performance and improves brain activity in stroke patients: A clinical study

This study compared the efficacy of Motor Imagery brain-computer interface (MI-BCI) combined with physiotherapy and physiotherapy alone in ischemic stroke before and after rehabilitation training. We wanted to explore whether the rehabilitation effect of MI-BCI is affected by the severity of the patient’s condition and whether MI-BCI was effective for all patients. Forty hospitalized patients with ischemic stroke with motor deficits participated in this study. The patients were divided into MI and control groups. Functional assessments were performed before and after rehabilitation training. The Fugl-Meyer Assessment (FMA) was used as the primary outcome measure, and its shoulder and elbow scores and wrist scores served as secondary outcome measures. The motor assessment scale (MAS) was used to assess motor function recovery. We used non-contrast CT (NCCT) to investigate the influence of different types of middle cerebral artery high-density signs on the prognosis of ischemic stroke. Brain topographic maps can directly reflect the neural activity of the brain, so we used them to detect changes in brain function and brain topological power response after stroke. Compared the MI group and control group after rehabilitation training, better functional outcome was observed after MI-BCI rehabilitation, including a significantly higher probability of achieving a relevant increase in the Total FMA scores (MI = 16.70 ± 12.79, control = 5.34 ± 10.48), FMA shoulder and elbow scores (MI = 12.56 ± 6.37, control = 2.45 ± 7.91), FMA wrist scores (MI = 11.01 ± 3.48, control = 3.36 ± 5.79), the MAS scores (MI = 3.62 ± 2.48, control = 1.85 ± 2.89), the NCCT (MI = 21.94 ± 2.37, control = 17.86 ± 3.55). The findings demonstrate that MI-BCI rehabilitation training could more effectively improve motor function after upper limb motor dysfunction after stroke compared with routine rehabilitation training, which verifies the feasibility of active induction of neural rehabilitation. The severity of the patient’s condition may affect the rehabilitation effect of the MI-BCI system.

Epileptiform activity in the acute phase of stroke predicts the outcomes in patients without seizures

Background and purpose

The abnormalities in EEG of stroke-patients increase the risk of epilepsy but their significancy for poststroke outcome is unclear. This presented study was aimed at determining the prevalence and nature of changes in EEG recordings from the stroke hemisphere and from the contralateral hemisphere. Another objective was to determine the significance of abnormalities in EEG in the first days of stroke for the post-stroke functional status on the acute and chronic phase of disease.

Methods

In all qualified stroke-patients, EEG was performed during the first 3 days of hospitalization and at discharge. The correlation between EEG abnormalities both in the stroke hemisphere and in the collateral hemisphere with the neurological and functional state in various time points was performed.

Results

One hundred thirty-one patients were enrolled to this study. Fifty-eight patients (44.27%) had abnormal EEG. The sporadic discharges and generalized rhythmic delta activity were the most common abnormalities in the EEG. The neurological status on the first day and the absence of changes in the EEG in the hemisphere without stroke were the independent factors for good neurological state (0–2 mRS) at discharge. The age-based analysis model (OR 0.981 CI 95% 0.959–1.001, p = 0.047), neurological status on day 1 (OR 0.884 CI 95% 0.82–0.942, p &lt; 0.0001) and EEG recording above the healthy hemisphere (OR 0.607 CI 95% 0.37–0.917, p = 0.028) had the highest prognostic value in terms of achieving good status 90 days after stroke.

Conclusions

Abnormalities in EEG without clinical manifestation are present in 40% of patients with acute stroke. Changes in EEG in acute stroke are associated with a poor neurological status in the first days and poor functional status in the chronic period of stroke.

Neuromonitoring in Children with Cerebrovascular Disorders

BACKGROUND

Cerebrovascular disorders are an important cause of morbidity and mortality in children. The acute care of a child with an ischemic or hemorrhagic stroke or cerebral sinus venous thrombosis focuses on stabilizing the patient, determining the cause of the insult, and preventing secondary injury. Here, we review the use of both invasive and noninvasive neuromonitoring modalities in the care of pediatric patients with arterial ischemic stroke, nontraumatic intracranial hemorrhage, and cerebral sinus venous thrombosis.

METHODS

Narrative review of the literature on neuromonitoring in children with cerebrovascular disorders.

RESULTS

Neuroimaging, near-infrared spectroscopy, transcranial Doppler ultrasonography, continuous and quantitative electroencephalography, invasive intracranial pressure monitoring, and multimodal neuromonitoring may augment the acute care of children with cerebrovascular disorders. Neuromonitoring can play an essential role in the early identification of evolving injury in the aftermath of arterial ischemic stroke, intracranial hemorrhage, or sinus venous thrombosis, including recurrent infarction or infarct expansion, new or recurrent hemorrhage, vasospasm and delayed cerebral ischemia, status epilepticus, and intracranial hypertension, among others, and this, is turn, can facilitate real-time adjustments to treatment plans.

CONCLUSIONS

Our understanding of pediatric cerebrovascular disorders has increased dramatically over the past several years, in part due to advances in the neuromonitoring modalities that allow us to better understand these conditions. We are now poised, as a field, to take advantage of advances in neuromonitoring capabilities to determine how best to manage and treat acute cerebrovascular disorders in children.

Literature review of stroke assessment for upper-extremity physical function via EEG, EMG, kinematic, and kinetic measurements and their reliability

BACKGROUND

Significant clinician training is required to mitigate the subjective nature and achieve useful reliability between measurement occasions and therapists. Previous research supports that robotic instruments can improve quantitative biomechanical assessments of the upper limb, offering reliable and more sensitive measures. Furthermore, combining kinematic and kinetic measurements with electrophysiological measurements offers new insights to unlock targeted impairment-specific therapy. This review presents common methods for analyzing biomechanical and neuromuscular data by describing their validity and reporting their reliability measures.

METHODS

This paper reviews literature (2000-2021) on sensor-based measures and metrics for upper-limb biomechanical and electrophysiological (neurological) assessment, which have been shown to correlate with clinical test outcomes for motor assessment. The search terms targeted robotic and passive devices developed for movement therapy. Journal and conference papers on stroke assessment metrics were selected using PRISMA guidelines. Intra-class correlation values of some of the metrics are recorded, along with model, type of agreement, and confidence intervals, when reported.

RESULTS

A total of 60 articles are identified. The sensor-based metrics assess various aspects of movement performance, such as smoothness, spasticity, efficiency, planning, efficacy, accuracy, coordination, range of motion, and strength. Additional metrics assess abnormal activation patterns of cortical activity and interconnections between brain regions and muscle groups; aiming to characterize differences between the population who had a stroke and the healthy population.

CONCLUSION

Range of motion, mean speed, mean distance, normal path length, spectral arc length, number of peaks, and task time metrics have all demonstrated good to excellent reliability, as well as provide a finer resolution compared to discrete clinical assessment tests. EEG power features for multiple frequency bands of interest, specifically the bands relating to slow and fast frequencies comparing affected and non-affected hemispheres, demonstrate good to excellent reliability for populations at various stages of stroke recovery. Further investigation is needed to evaluate the metrics missing reliability information. In the few studies combining biomechanical measures with neuroelectric signals, the multi-domain approaches demonstrated agreement with clinical assessments and provide further information during the relearning phase. Combining the reliable sensor-based metrics in the clinical assessment process will provide a more objective approach, relying less on therapist expertise. This paper suggests future work on analyzing the reliability of metrics to prevent biasedness and selecting the appropriate analysis.

The role of neurophysiological tools in the evaluation of ischemic stroke evolution: a narrative review

Ischemic stroke is characterized by a complex cascade of events starting from vessel occlusion. The term "penumbra" denotes the area of severely hypo-perfused brain tissue surrounding the ischemic core that can be potentially recovered if blood flow is reestablished. From the neurophysiological perspective, there are local alterations-reflecting the loss of function of the core and the penumbra-and widespread changes in neural networks functioning, since structural and functional connectivity is disrupted. These dynamic changes are closely related to blood flow in the affected area. However, the pathological process of stroke does not end after the acute phase, but it determines a long-term cascade of events, including changes of cortical excitability, that are quite precocious and might precede clinical evolution. Neurophysiological tools-such as Transcranial Magnetic Stimulation (TMS) or Electroencephalography (EEG)-have enough time resolution to efficiently reflect the pathological changes occurring after stroke. Even if they do not have a role in acute stroke management, EEG and TMS might be helpful for monitoring ischemia evolution-also in the sub-acute and chronic stages. The present review aims to describe the changes occurring in the infarcted area after stroke from the neurophysiological perspective, starting from the acute to the chronic phase.

Quantitative EEG as a Biomarker in Evaluating Post-Stroke Depression

Introduction: Post-stroke depression (PSD) has complex pathophysiology determined by various biological and psychological factors. Although it is a long-term complication of stroke, PSD is often underdiagnosed. Given the diagnostic role of quantitative electroencephalography (qEEG) in depression, it was investigated whether a possible marker of PSD could be identified by observing the evolution of the (Delta + Theta)/(Alpha + Beta) Ratio (DTABR), respectively the Delta/Alpha Ratio (DAR) values in post-stroke depressed patients (evaluated through the HADS-D subscale). Methods: The current paper analyzed the data of 57 patients initially selected from a randomized control trial (RCT) that assessed the role of N-Pep 12 in stroke rehabilitation. EEG recordings from the original trial database were analyzed using signal processing techniques, respecting the conditions (eyes open, eyes closed), and several cognitive tasks. Results: We observed two significant associations between the DTABR values and the HADS-D scores of post-stroke depressed patients for each of the two visits (V1 and V2) of the N-Pep 12 trial. We recorded the relationships in the Global (V1 = 30 to 120 days after stroke) and Frontal Extended (V2 = 90 days after stroke) regions during cognitive tasks that trained attention and working memory. For the second visit, the association between the analyzed variables was negative. Conclusions: As both our relationships were described during the cognitive condition, we can state that the neural networks involved in processing attention and working memory might go through a reorganization process one to four months after the stroke onset. After a period longer than six months, the process could localize itself at the level of frontal regions, highlighting a possible divergence between the local frontal dynamics and the subjective well-being of stroke survivors. QEEG parameters linked to stroke progression evolution (like DAR or DTABR) can facilitate the identification of the most common neuropsychiatric complication in stroke survivors.

Electroencephalogram in the intensive care unit: a focused look at acute brain injury

Over the past decades, electroencephalography (EEG) has become a widely applied and highly sophisticated brain monitoring tool in a variety of intensive care unit (ICU) settings. The most common indication for EEG monitoring currently is the management of refractory status epilepticus. In addition, a number of studies have associated frequent seizures, including nonconvulsive status epilepticus (NCSE), with worsening secondary brain injury and with worse outcomes. With the widespread utilization of EEG (spot and continuous EEG), rhythmic and periodic patterns that do not fulfill strict seizure criteria have been identified, epidemiologically quantified, and linked to pathophysiological events across a wide spectrum of critical and acute illnesses, including acute brain injury. Increasingly, EEG is not just qualitatively described, but also quantitatively analyzed together with other modalities to generate innovative measurements with possible clinical relevance. In this review, we discuss the current knowledge and emerging applications of EEG in the ICU, including seizure detection, ischemia monitoring, detection of cortical spreading depolarizations, assessment of consciousness and prognostication. We also review some technical aspects and challenges of using EEG in the ICU including the logistics of setting up ICU EEG monitoring in resource-limited settings.

Application of quantitative EEG in acute ischemic stroke patients who underwent thrombectomy: A comparison with CT perfusion

OBJECTIVE

This study aimed to evaluate the predictive value of quantitative electroencephalography (QEEG) in the outcome of patients with acute ischemic stroke (AIS) who underwent mechanical thrombectomy (MT) and to assess the correlation between clinical outcome and QEEG and CT perfusion (CTP) data.

METHODS

Twenty-nine MT patients were included in this prospective study. Continuous electroencephalography (EEG) monitoring was performed, in which delta power, the δ/α ratio (DAR), and the (θ + δ)/(α + β) ratio (DTABR) were calculated. The clinical scores at different points were recorded. Based on the modified Ranking scale, the patients were divided into good and poor outcome groups. Several CTP parameters were recorded before MT. The correlation between QEEG, CTP parameters, and clinical scores was analyzed using the Spearman correlation analysis. The predictive value of QEEG indices and CTP parameters for the 3-month outcome was compared using the receiver operating characteristic (ROC) curve.

RESULTS

Delta power except for 7 days after MT, DAR, DATBR, and several CTP parameters were all significantly associated with the clinical scores. Although some CTP parameters were associated with the clinical scores, they were less powerful than QEEG in predicting a good or poor outcome at 3 months. Among the different explored EEG indicators, the predictive value of delta 24 h after MT was the highest.

CONCLUSIONS

QEEG indices may have a certain predictive value for the outcome of AIS patients who underwent MT.

SIGNIFICANCE

QEEG may become a new prognostic tool in AIS patients who underwent MT, facilitating the planning and management of related rehabilitation plans.

The focused quantitative EEG bio-marker in studying childhood atrophic encephalopathy

Although it is a normal involution process in advanced age, brain atrophy—also termed atrophic encephalopathy—can also occur prematurely in childhood as a consequential effect of brain tissues injury through trauma or central nervous system infection, though in both normal and premature occurrences this condition always presents with loss of volume relative to the skull. A common tool for the functional study of brain activities is an electroencephalogram, but analyses of this have reportedly identified mismatches between qualitative and quantitative forms, particularly in the use of Delta-alpha ratio (DAR) indices, meaning that the values may be case dependent. The current study thus examines the value of Focused Occipital Beta-Alpha Ratio (FOBAR) as a modified biomarker for evaluating brain functional changes resulting from brain atrophy. This cross-sectional design study involves 260 patients under 18 years of age. Specifically, 207 patients with brain atrophy are compared with 53 control subjects with CT scan-proven normal brain volume. All the children underwent digital electroencephalography with brain mapping. Results show that alpha posterior dominant rhythm was present in 88 atrophic children and 44 controls. Beta as posterior dominant rhythm was present in an overwhelming 91.5% of atrophic subjects, with 0.009 p-values. The focused occipital Beta-alpha ratio correlated significantly with brain volume loss presented in diagonal brain fraction. The FOBAR and DAR values of the QEEG showed no significant correlation. This work concludes that QEEG cerebral dysfunctional studies may be etiologically and case dependent from the nature of the brain injury. Also, the focused Beta-alpha ratio of the QEEG is a prospective and potential biomarker of consideration in studying childhood atrophic encephalopathy.

The Prognostic Utility of Electroencephalography in Stroke Recovery: A Systematic Review and Meta-Analysis

BACKGROUND

Improved ability to predict patient recovery would guide post-stroke care by helping clinicians personalize treatment and maximize outcomes. Electroencephalography (EEG) provides a direct measure of the functional neuroelectric activity in the brain that forms the basis for neuroplasticity and recovery, and thus may increase prognostic ability.

OBJECTIVE

To examine evidence for the prognostic utility of EEG in stroke recovery via systematic review/meta-analysis.

METHODS

Peer-reviewed journal articles that examined the relationship between EEG and subsequent clinical outcome(s) in stroke were searched using electronic databases. Two independent researchers extracted data for synthesis. Linear meta-regressions were performed across subsets of papers with common outcome measures to quantify the association between EEG and outcome.

RESULTS

75 papers were included. Association between EEG and clinical outcomes was seen not only early post-stroke, but more than 6 months post-stroke. The most studied prognostic potential of EEG was in predicting independence and stroke severity in the standard acute stroke care setting. The meta-analysis showed that EEG was associated with subsequent clinical outcomes measured by the Modified Rankin Scale, National Institutes of Health Stroke Scale, and Fugl-Meyer Upper Extremity Assessment (r = .72, .70, and .53 from 8, 13, and 12 papers, respectively). EEG improved prognostic abilities beyond prediction afforded by standard clinical assessments. However, the EEG variables examined were highly variable across studies and did not converge.

CONCLUSIONS

EEG shows potential to predict post-stroke recovery outcomes. However, evidence is largely explorative, primarily due to the lack of a definitive set of EEG measures to be used for prognosis.

EEG spectral exponent as a synthetic index for the longitudinal assessment of stroke recovery

•

The Spectral Exponent (SE) indexes power-law features of the resting EEG in stroke patients.

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SE is consistently steeper in the affected hemisphere of patients after middle cerebral artery stroke.

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SE is linked to clinical status and seems to be a good predictor of clinical outcome.

Objective

Quantitative Electroencephalography (qEEG) can capture changes in brain activity following stroke. qEEG metrics traditionally focus on oscillatory activity, however recent findings highlight the importance of aperiodic (power-law) structure in characterizing pathological brain states. We assessed neurophysiological alterations and recovery after mono-hemispheric stroke by means of the Spectral Exponent (SE), a metric that reflects EEG slowing and quantifies the power-law decay of the EEG Power Spectral Density (PSD).

Methods

Eighteen patients (n = 18) with mild to moderate mono-hemispheric Middle Cerebral Artery (MCA) ischaemic stroke were retrospectively enrolled for this study. Patients underwent EEG recording in the sub-acute phase (T0) and after 2 months of physical rehabilitation (T1). Sixteen healthy controls (HC; n = 16) matched by age and sex were enrolled as a normative group. SE values and narrow-band PSD were estimated for each recording. We compared SE and band-power between patients and HC, and between the affected (AH) and unaffected hemisphere (UH) at T0 and T1 in patients.

Results

At T0, stroke patients showed significantly more negative SE values than HC (p = 0.003), reflecting broad-band EEG slowing. Most important, in patients SE over the AH was consistently more negative compared to the UH and showed a renormalization at T1. This SE renormalization significantly correlated with National Institute of Health Stroke Scale (NIHSS) improvement (R = 0.63, p = 0.005).

Conclusions

SE is a reliable readout of the neurophysiological and clinical alterations occurring after an ischaemic cortical lesion.

Significance

SE promise to be a robust method to monitor and predict patients’ functional outcome.

Surface electroencephalography (EEG) during the acute phase of stroke to assist with diagnosis and prediction of prognosis: a scoping review

BACKGROUND

Stroke is a common medical emergency responsible for significant mortality and disability. Early identification improves outcomes by promoting access to time-critical treatments such as thrombectomy for large vessel occlusion (LVO), whilst accurate prognosis could inform many acute management decisions. Surface electroencephalography (EEG) shows promise for stroke identification and outcome prediction, but evaluations have varied in technology, setting, population and purpose. This scoping review aimed to summarise published literature addressing the following questions: 1. Can EEG during acute clinical assessment identify: a) Stroke versus non-stroke mimic conditions. b) Ischaemic versus haemorrhagic stroke. c) Ischaemic stroke due to LVO. 2. Can these states be identified if EEG is applied < 6 h since onset. 3. Does EEG during acute assessment predict clinical recovery following confirmed stroke.

METHODS

We performed a systematic search of five bibliographic databases ending 19/10/2020. Two reviewers assessed eligibility of articles describing diagnostic and/or prognostic EEG application < 72 h since suspected or confirmed stroke.

RESULTS

From 5892 abstracts, 210 full text articles were screened and 39 retained. Studies were small and heterogeneous. Amongst 21 reports of diagnostic data, consistent associations were reported between stroke, greater delta power, reduced alpha/beta power, corresponding ratios and greater brain asymmetry. When reported, the area under the curve (AUC) was at least good (0.81-1.00). Only one study combined clinical and EEG data (AUC 0.88). There was little data found describing whether EEG could identify ischaemic versus haemorrhagic stroke. Radiological changes suggestive of LVO were also associated with increased slow and decreased fast waves. The only study with angiographic proof of LVO reported AUC 0.86 for detection < 24 h since onset. Amongst 26 reports of prognostic data, increased slow and reduced fast wave EEG changes were associated with future dependency, neurological impairment, mortality and poor cognition, but there was little evidence that EEG enhanced outcome prediction relative to clinical and/or radiological variables. Only one study focussed solely on patients < 6 h since onset for predicting neurological prognosis post-thrombolysis, with more favourable outcomes associated with greater hemispheric symmetry and a greater ratio of fast to slow waves.

CONCLUSIONS

Although studies report important associations with EEG biomarkers, further technological development and adequately powered real-world studies are required before recommendations can be made regarding application during acute stroke assessment.

Electroencephalogram (EEG) With or Without Transcranial Magnetic Stimulation (TMS) as Biomarkers for Post-stroke Recovery: A Narrative Review

Stroke is one of the leading causes of death and disability. Despite the high prevalence of stroke, characterizing the acute neural recovery patterns that follow stroke and predicting long-term recovery remains challenging. Objective methods to quantify and characterize neural injury are still lacking. Since neuroimaging methods have a poor temporal resolution, EEG has been used as a method for characterizing post-stroke recovery mechanisms for various deficits including motor, language, and cognition as well as predicting treatment response to experimental therapies. In addition, transcranial magnetic stimulation (TMS), a form of non-invasive brain stimulation, has been used in conjunction with EEG (TMS-EEG) to evaluate neurophysiology for a variety of indications. TMS-EEG has significant potential for exploring brain connectivity using focal TMS-evoked potentials and oscillations, which may allow for the system-specific delineation of recovery patterns after stroke. In this review, we summarize the use of EEG alone or in combination with TMS in post-stroke motor, language, cognition, and functional/global recovery. Overall, stroke leads to a reduction in higher frequency activity (≥8 Hz) and intra-hemispheric connectivity in the lesioned hemisphere, which creates an activity imbalance between non-lesioned and lesioned hemispheres. Compensatory activity in the non-lesioned hemisphere leads mostly to unfavorable outcomes and further aggravated interhemispheric imbalance. Balanced interhemispheric activity with increased intrahemispheric coherence in the lesioned networks correlates with improved post-stroke recovery. TMS-EEG studies reveal the clinical importance of cortical reactivity and functional connectivity within the sensorimotor cortex for motor recovery after stroke. Although post-stroke motor studies support the prognostic value of TMS-EEG, more studies are needed to determine its utility as a biomarker for recovery across domains including language, cognition, and hemispatial neglect. As a complement to MRI-based technologies, EEG-based technologies are accessible and valuable non-invasive clinical tools in stroke neurology.

Motor Impairment in Stroke Patients is Associated with Network Properties During Consecutive Motor Imagery

OBJECTIVE

Our study aimed to predict the Fugl-Meyer assessment (FMA) upper limb using network properties during motor imagery using electroencephalography (EEG) signals.

METHODS

The subjects performed a finger tapping imagery task according to consecutive cues. We measured the weighted phase lag index (wPLI) as functional connectivity and directed transfer function (DTF) as causal connectivity in healthy controls and stroke patients. The network properties based on the wPLI and DTF were calculated. We predicted the FMA upper limb using partial least squares regression.

RESULTS

A higher DTF in the mu band was observed in stroke patients than in healthy controls. Notably, the difference in local properties at node F3 was negatively correlated with motor impairment in stroke patients. Finally, using significant network properties based on the wPLI and DTF, we predicted motor impairments using the FMA upper limb with a root-mean-square error of 1.68 (R² = 0.97). This outperformed the state-of-the-art predictors.

CONCLUSION

These findings demonstrate that network properties based on functional and causal connectivity were highly associated with motor function in stroke patients.

SIGNIFICANCE

Our network properties can help calculate the predictor of motor impairments in stroke rehabilitation and provide insight into the neural correlates related to motor function based on EEG after reorganization induced by stroke.

Quantitative EEG Correlates with NIHSS and MoCA for Assessing the Initial Stroke Severity in Acute Ischemic Stroke Patients

BACKGROUND: National Institutes of Health Stroke Scale (NIHSS) and Montreal Cognitive Assessment (MoCA) measure stroke severity by assessing the functional and cognitive outcome, respectively. However, they cannot be used to measure subtle evolution in clinical symptoms during the early phase. Quantitative EEG (qEEG) can detect any subtle changes in CBF and brain metabolism thus may also benefit for assessing the severity. AIM: This study aims to identify the correlation between qEEG with NIHSS and MoCA for assessing the initial stroke severity in acute ischemic stroke patients. METHODS: This was a cross-sectional study. We recruited 30 patients with first-ever acute ischemic stroke hospitalized in Dr. Sardjito General Hospital, Yogyakarta, Indonesia. We measured the NIHSS, MoCA score, and qEEG parameter during the acute phase of stroke. Correlation and regression analysis was completed to investigate the relationship between qEEG parameter with NIHSS and MoCA. RESULTS: Four acute qEEG parameter demonstrated moderate-to-high correlations with NIHSS and MoCA. DTABR had positive correlation with NIHSS (r = 0.379, p = 0.04). Meanwhile, delta-absolute power, DTABR, and DAR were negatively correlated with MoCA score (r = −0.654, p = 0.01; r = −0.397, p = 0.03; and r = −0.371, p = 0.04, respectively). After adjusted with the confounding variables, delta-absolute power was independently associated with MoCA score, but not with NIHSS (B = −2.887, 95% CI (−4.304–−1.470), p < 0.001). CONCLUSIONS: Several qEEG parameters had significant correlations with NIHSS and MoCA in acute ischemic stroke patients. The use of qEEG in acute clinical setting may provide a reliable and efficient prediction of initial stroke severity. Further cohort study with larger sample size and wide range of stroke severity is still needed.

Chiropractic Spinal Adjustment Increases the Cortical Drive to the Lower Limb Muscle in Chronic Stroke Patients

This study aimed to investigate the effects of a single session of chiropractic spinal adjustment on the cortical drive to the lower limb in chronic stroke patients. In a single-blinded, randomized controlled parallel design study, 29 individuals with chronic stroke and motor weakness in a lower limb were randomly divided to receive either chiropractic spinal adjustment or a passive movement control intervention. Before and immediately after the intervention, transcranial magnetic stimulation (TMS)-induced motor evoked potentials (MEPs) were recorded from the tibialis anterior (TA) muscle of the lower limb with the greatest degree of motor weakness. Differences in the averaged peak-peak MEP amplitude following interventions were calculated using a linear regression model. Chiropractic spinal adjustment elicited significantly larger MEP amplitude (pre = 0.24 ± 0.17 mV, post = 0.39 ± 0.23 mV, absolute difference = +0.15 mV, relative difference = +92%, p &lt; 0.001) compared to the control intervention (pre = 0.15 ± 0.09 mV, post = 0.16 ± 0.09 mV). The results indicate that chiropractic spinal adjustment increases the corticomotor excitability of ankle dorsiflexor muscles in people with chronic stroke. Further research is required to investigate whether chiropractic spinal adjustment increases dorsiflexor muscle strength and walking function in people with stroke.

Comparing Neuroplasticity Changes Between High and Low Frequency Gait Training in Subacute Stroke: Protocol for a Randomized, Single-Blinded, Controlled Study

Background

Walking recovery post stroke can be slow and incomplete. Determining effective stroke rehabilitation frequency requires the assessment of neuroplasticity changes. Neurobiological signals from electroencephalogram (EEG) can measure neuroplasticity through incremental changes of these signals after rehabilitation. However, changes seen with a different frequency of rehabilitation require further investigation. It is hypothesized that the association between the incremental changes from EEG signals and the improved functional outcome measure scores are greater in higher rehabilitation frequency, implying enhanced neuroplasticity changes.

Objective

The purpose of this study is to identify the changes in the neurobiological signals from EEG, to associate these with functional outcome measures scores, and to compare their associations in different therapy frequency for gait rehabilitation among subacute stroke individuals.

Methods

A randomized, single-blinded, controlled study among patients with subacute stroke will be conducted with two groups: an intervention group (IG) and a control group (CG). Each participant in the IG and CG will receive therapy sessions three times a week (high frequency) and once a week (low frequency), respectively, for a total of 12 consecutive weeks. Each session will last for an hour with strengthening, balance, and gait training. The main variables to be assessed are the 6-Minute Walk Test (6MWT), Motor Assessment Scale (MAS), Berg Balance Scale (BBS), Modified Barthel Index (MBI), and quantitative EEG indices in the form of delta to alpha ratio (DAR) and delta-plus-theta to alpha-plus-beta ratio (DTABR). These will be measured at preintervention (R0) and postintervention (R1). Key analyses are to determine the changes in the 6MWT, MAS, BBS, MBI, DAR, and DTABR at R0 and R1 for the CG and IG. The changes in the DAR and DTABR will be analyzed for association with the changes in the 6MWT, MAS, BBS, and MBI to measure neuroplasticity changes for both the CG and IG.

Results

We have recruited 18 participants so far. We expect to publish our results in early 2023.

Conclusions

These associations are expected to be positive in both groups, with a higher correlation in the IG compared to the CG, reflecting enhanced neuroplasticity changes and objective evaluation on the dose-response relationship.

International Registered Report Identifier (IRRID)

DERR1-10.2196/27935

EEG as a marker of brain plasticity in clinical applications

Neural networks are dynamic, and the brain has the capacity to reorganize itself. This capacity is named neuroplasticity and is fundamental for many processes ranging from learning and adaptation to new environments to the response to brain injuries. Measures of brain plasticity involve several techniques, including neuroimaging and neurophysiology. Electroencephalography, often used together with other techniques, is a common tool for prognostic and diagnostic purposes, and cortical reorganization is reflected by EEG measurements. Changes of power bands in different cortical areas occur with fatigue and in response to training stimuli leading to learning processes. Sleep has a fundamental role in brain plasticity, restoring EEG bands alterations and promoting consolidation of learning. Exercise and physical inactivity have been extensively studied as both strongly impact brain plasticity. Indeed, EEG studies showed the importance of the physical activity to promote learning and the effects of inactivity or microgravity on cortical reorganization to cope with absent or altered sensorimotor stimuli. Finally, this chapter will describe some of the EEG changes as markers of neural plasticity in neurologic conditions, focusing on cerebrovascular and neurodegenerative diseases. In conclusion, neuroplasticity is the fundamental mechanism necessary to ensure adaptation to new stimuli and situations, as part of the dynamicity of life.

Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

BACKGROUND

With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.

METHODS

QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.

RESULTS

A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.

CONCLUSION

The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.

Multimodal Monitoring in Large Hemispheric Infarction: Quantitative Electroencephalography Combined With Transcranial Doppler for Prognosis Prediction

Background: We aimed to explore whether transcranial Doppler (TCD) combined with quantitative electroencephalography (QEEG) can improve prognosis evaluation in patients with a large hemispheric infarction (LHI) and to establish an accurate prognosis prediction model.

Methods: We prospectively assessed 90-day mortality in patients with LHI. Brain function was monitored using TCD-QEEG at the bedside of the patient.

Results: Of the 59 (55.3 ± 10.6 years; 17 men) enrolled patients, 37 (67.3%) patients died within 90 days. The Cox regression analyses revealed that the Glasgow Coma Scale (GCS) score ≤ 8 [hazard ratio (HR), 3.228; 95% CI, 1.335–7.801; p = 0.009], TCD-terminal internal carotid artery as the offending vessel (HR, 3.830; 95% CI, 1.301–11.271; p = 0.015), and QEEG-a (delta + theta)/(alpha + beta) ratio ≥ 3 (HR, 3.647; 95% CI, 1.170–11.373; p = 0.026) independently predicted survival duration. Combining these three factors yielded an area under the receiver operating characteristic curve of 0.905 and had better predictive accuracy than those of individual variables (p < 0.05).

Conclusion: TCD and QEEG complement the GCS score to create a reliable multimodal method for monitoring prognosis in patients with LHI.

Integrated Gait Triggered Mixed Reality and Neurophysiological Monitoring as a Framework for Next-Generation Ambulatory Stroke Rehabilitation

Brain stroke affects millions of people in the world every year, with 50 to 60 percent of stroke survivors suffering from functional disabilities, for which early and sustained post-stroke rehabilitation is highly recommended. However, approximately one third of stroke patients do not receive early in hospital rehabilitation programs due to insufficient medical facilities or lack of motivation. Gait triggered mixed reality (GTMR) is a cognitive-motor dual task with multisensory feedback tailored for lower-limb post-stroke rehabilitation, which we propose as a potential method for addressing these rehabilitation challenges. Simultaneous gait and EEG data from nine stroke patients was recorded and analyzed to assess the applicability of GTMR to different stroke patients, determine any impacts of GTMR on patients, and better understand brain dynamics as stroke patients perform different rehabilitation tasks. Walking cadence improved significantly for stroke patients and lower-limb movement induced alpha band power suppression during GTMR tasks. The brain dynamics and gait performance across different severities of stroke motor deficits was also assessed; the intensity of walking induced event related desynchronization (ERD) was found to be related to motor deficits, as classified by Brunnstrom stage. In particular, stronger lower-limb movement induced ERD during GTMR rehabilitation tasks was found for patients with moderate motor deficits (Brunnstrom stage IV). This investigation demonstrates the efficacy of the GTMR paradigm for enhancing lower-limb rehabilitation, explores the neural activities of cognitive-motor tasks in different stages of stroke, and highlights the potential for joining enhanced rehabilitation and real-time neural monitoring for superior stroke rehabilitation.

Movement-Related EEG Oscillations of Contralesional Hemisphere Discloses Compensation Mechanisms of Severely Affected Motor Chronic Stroke Patients

Conventional rehabilitation strategies for stroke survivors become difficult when voluntary movements are severely disturbed. Combining passive limb mobilization, robotic devices and EEG-based brain-computer interfaces (BCI) systems might improve treatment and clinical follow-up of these patients, but detailed knowledge of neurophysiological mechanisms involved in functional recovery, which might help for tailoring stroke treatment strategies, is lacking. Movement-related EEG changes (EEG event-related desynchronization (ERD) in [Formula: see text] and [Formula: see text] bands, an indicator of motor cortex activation traditionally used for BCI systems), were evaluated in a group of 23 paralyzed chronic stroke patients in two unilateral motor tasks alternating paretic and healthy hands ((i) passive movement, using a hand exoskeleton, and (ii) voluntary movement), and compared to nine healthy subjects. In tasks using unaffected hand, we observed an increase of contralesional hemisphere activation for stroke patients group. Unexpectedly, when using paralyzed hand, motor cortex activation was reduced or absent in severely affected group of patients, while patients with moderate motor deficit showed an activation greater than control group. Cortical activation was reduced or absent in damaged hemisphere of all the patients in both tasks. Significant differences related to severity of motor deficit were found in the time course of [Formula: see text] bands power ratio in EEG of contralesional hemisphere while moving affected hand. These findings suggest the presence of different compensation mechanisms in contralesional hemisphere of stroke patients related to the grade of motor disability, that might turn quantitative EEG during a movement task, obtained from a BCI system controlling a robotic device included in a rehabilitation task, into a valuable tool for monitoring clinical progression, evaluating recovery, and tailoring treatment of stroke patients.

Intrahemispheric EEG: A New Perspective for Quantitative EEG Assessment in Poststroke Individuals

The ratio between slower and faster frequencies of brain activity may change after stroke. However, few studies have used quantitative electroencephalography (qEEG) index of ratios between slower and faster frequencies such as the delta/alpha ratio (DAR) and the power ratio index (PRI; delta + theta/alpha + beta) for investigating the difference between the affected and unaffected hemisphere poststroke. Here, we proposed a new perspective for analyzing DAR and PRI within each hemisphere and investigated the motor impairment-related interhemispheric frequency oscillations. Forty-seven poststroke subjects and twelve healthy controls were included in the study. Severity of upper limb motor impairment was classified according to the Fugl-Meyer assessment in mild/moderate (n = 25) and severe (n = 22). The qEEG indexes (PRI and DAR) were computed for each hemisphere (intrahemispheric index) and for both hemispheres (cerebral index). Considering the cerebral index (DAR and PRI), our results showed a slowing in brain activity in poststroke patients when compared to healthy controls. Only the intrahemispheric PRI index was able to find significant interhemispheric differences of frequency oscillations. Despite being unable to detect interhemispheric differences, the DAR index seems to be more sensitive to detect motor impairment-related frequency oscillations. The intrahemispheric PRI index may provide insights into therapeutic approaches for interhemispheric asymmetry after stroke.

Quantitative EEG provides early prediction of poor outcome in acute ischemic stroke after endovascular treatment: a preliminary study

Background and Purpose: Quantitative electroencephalogram (QEEG) parameters have been previously utilized in prognosis following acute ischemic stroke (AIS). However, the use and interpretation of QEEG parameters remain scarce following endovascular treatment (EVT) of AIS.Methods: AIS patients were prospectively enrolled following EVT, and 24-hour EEG monitoring was conducted. Global delta/alpha ratio (DAR), (delta + theta)/(alpha + beta) ratio (DTABR), and relative band power were analyzed. Primary outcome was a poor outcome (modified Rankin Scale ≥4 at 90-day follow-up). Multivariate logistic regression and diagnostic analyses were performed.Results: Poor outcome was seen in 35.5% (11/31) of enrolled patients. Multivariable logistic regression identified that higher DAR (OR 1.10, 95% CI 1.02-1.18, p = 0.02) and higher DTABR (OR 1.13, 95% CI 1.01-1.27, p = 0.02) were associated with poor outcome. DAR ≥14.3 demonstrated high sensitivity (90.9%), specificity (90.0%) and accuracy (90.3%) for poor outcome.Conclusions: Early evidence of elevated DAR and DTABR on quantitative EEG was associated with poor outcome at 90 days following EVT for AIS.

Inter trial coherence of low-frequency oscillations in the course of stroke recovery

OBJECTIVE

The aim was to find a sensitive method to highlight the remodeling of the brain's bioelectric activity in post-stroke repair.

METHODS

Fifteen mild upper limb paretic stroke patients and age-matched healthy controls were included. Repeated trials of finger tapping around the 10th and 100th days after stroke onset were recorded with a 128-channel EEG. Power spectra and Inter Trial Coherence (ITC) calculations were synchronized to tappings. ITC was correlated with motor performance.

RESULTS

ITC, in low frequency bands, designates the motor related bioelectric activity in channel space in both healthy subjects and patients. Ten days after stroke onset, delta-theta ITC was severely reduced compared to baseline, while three months later ITC reorganized partially over the ipsilesional central-parietal areas reflecting the improvement of motor networks. Decreased ITC in the central-parietal area remained significant compared to controls. Delta band ITC over the dorsolateral-prefrontal cortex correlates with the performance on Nine Hole Peg Test. At post-recovery, non-paretic hand tappings show significantly decreased delta-theta ITC over the supplementary motor area, which reflects network remodeling.

CONCLUSIONS

Inter Trial Coherence is a useful measure of brain reorganization during stroke recovery.

SIGNIFICANCE

Delta- theta ITC is a sensitive indicator of impaired motor execution.

Simultaneously stimulating both brain hemispheres by rTMS in patients with unilateral brain lesions decreases interhemispheric asymmetry

BACKGROUND

Interhemispheric asymmetry caused by brain lesions is an adverse factor in the recovery of patients with neurological deficits. Repetitive transcranial magnetic stimulation (rTMS) has been shown to modulate cortical oscillation and proposed as an approach to rebalance the symmetry, which has not been documented well.

OBJECTIVE

In this study, we investigated the influence of repetitive transcranial magnetic stimulation (rTMS) on EEG power in patients with unilateral brain lesions by simultaneously stimulating both brain hemispheres and to elucidate asymmetrical changes in rTMS-induced neurophysiological activity.

METHODS

Fourteen patients with unilateral brain lesions were treated with one active and one sham session of 10 Hz rTMS over the vertex (Cz position). Resting-state EEGs were recorded before and immediately after rTMS. The brain symmetry index (BSI), calculated from a fast Fourier transform, was employed to quantify the power asymmetry in both hemispheres and paired channels over the entire range and five frequency bands (delta, theta, alpha, beta and gamma bands).

RESULTS

Comparison between active and sham sessions demonstrated rTMS-induced EEG after-effects. rTMS in the active session significantly reduced the BSI in patients with unilateral brain lesions over the entire frequency range (t = 2.767, P = 0.016). Among the five frequency bands, rTMS only induced a noticeable decrease in the BSI in the delta band (t = 2.254, P = 0.042). Furthermore, analysis of different brain regions showed that significant changes in the BSI of the alpha band were only demonstrated in the posterior parietal lobe. In addition, EEG topographic mapping showed a decreased power of delta oscillations in the ipsilesional hemisphere, whereas distinct cortical oscillations were observed in the alpha band around the parietal-occipital lobe in the contralesional hemisphere.

CONCLUSIONS

When both brain hemispheres were simultaneously activated, rTMS decreased interhemispheric asymmetry primarily via reducing the delta band in the lesioned hemisphere.

Normobaric oxygen may correct chronic cerebral ischemia-mediated EEG anomalies

AIMS

To explore the safety and efficacy of normobaric oxygen (NBO) on correcting chronic cerebral ischemia (CCI) and related EEG anomalies.

METHODS

This prospective randomized trial (NCT03745092) enrolled 50 cases of CCI patients, which were divided into NBO (8 L/min of oxygen supplement) group and control group (room air) randomly, and also enrolled 21 healthy volunteers. Two times of 30-min EEG recordings with the interval of 45min of NBO or room air were analyzed quantitatively.

RESULTS

The CCI-mediated EEG presented with two patterns of electrical activities: high-power oscillations (high-power EEG, n = 26) and paroxysmal slow activities under the normal-power background (normal-power EEG, n = 24). The fronto-central absolute power (AP) of the beta, alpha, theta, and delta in the high-power EEG was higher than that in healthy EEG (p < 0.05). The fronto-central theta/alpha, delta/alpha and (delta + theta)/(alpha + beta) ratios in the normal-power EEG were higher than those in healthy EEG (p < 0.05). The high-power EEG in NBO group had higher fronto-central AP reduction rates than those in control group (p < 0.05). NBO remarkably reduced the fronto-central theta/alpha, delta/alpha, and (delta + theta)/(alpha + beta) ratios in the normal-power EEG (p < 0.05).

CONCLUSIONS

NBO rapidly ameliorates CCI-mediated EEG anomalies, including attenuation of the abnormal high-power oscillations and the paroxysmal slow activities associated with CCI.

Quantitative Evaluation of Task-Induced Neurological Outcome after Stroke

Electroencephalography (EEG) can access ischemic stroke-derived cortical impairment and is believed to be a prospective predictive method for acute stroke prognostics, neurological outcome, and post-stroke rehabilitation management. This study aims to quantify EEG features to understand task-induced neurological declines due to stroke and evaluate the biomarkers to distinguish the ischemic stroke group and the healthy adult group. We investigated forty-eight stroke patients (average age 72.2 years, 62% male) admitted to the rehabilitation center and seventy-five healthy adults (average age 77 years, 31% male) with no history of known neurological diseases. EEG was recorded through frontal, central, temporal, and occipital cortical electrodes (Fz, C1, C2, T7, T8, Oz) using wireless EEG devices and a newly developed data acquisition platform within three months after the appearance of symptoms of ischemic stroke (clinically confirmed). Continuous EEG data were recorded during the consecutive resting, motor (walking and working activities), and cognitive reading tasks. The statistical results showed that alpha, theta, and delta activities are biomarkers classifying the stroke patients and the healthy adults in the motor and cognitive states. DAR and DTR of the stroke group differed significantly from those of the healthy control group during the resting, motor, and cognitive tasks. Using the machine-learning approach, the C5.0 model showed 78% accuracy for the resting state, 89% accuracy in the functional motor walking condition, 84% accuracy in the working condition, and 85% accuracy in the cognitive reading state for classification the stroke group and the control group. This study is expected to be helpful for post-stroke treatment and post-stroke recovery.

Connectivity Measures Differentiate Cortical and Subcortical Sub-Acute Ischemic Stroke Patients

Brain lesions caused by cerebral ischemia lead to network disturbances in both hemispheres, causing a subsequent reorganization of functional connectivity both locally and remotely with respect to the injury. Quantitative electroencephalography (qEEG) methods have long been used for exploring brain electrical activity and functional connectivity modifications after stroke. However, results obtained so far are not univocal. Here, we used basic and advanced EEG methods to characterize how brain activity and functional connectivity change after stroke. Thirty-three unilateral post stroke patients in the sub-acute phase and ten neurologically intact age-matched right-handed subjects were enrolled. Patients were subdivided into two groups based on lesion location: cortico-subcortical (CS, n = 18) and subcortical (S, n = 15), respectively. Stroke patients were evaluated in the period ranging from 45 days since the acute event (T0) up to 3 months after stroke (T1) with both neurophysiological (resting state EEG) and clinical assessment (Barthel Index, BI) measures, while healthy subjects were evaluated once. Brain power at T0 was similar between the two groups of patients in all frequency bands considered (δ, θ, α, and β). However, evolution of θ-band power over time was different, with a normalization only in the CS group. Instead, average connectivity and specific network measures (Integration, Segregation, and Small-worldness) in the β-band at T0 were significantly different between the two groups. The connectivity and network measures at T0 also appear to have a predictive role in functional recovery (BI T1-T0), again group-dependent. The results obtained in this study showed that connectivity measures and correlations between EEG features and recovery depend on lesion location. These data, if confirmed in further studies, on the one hand could explain the heterogeneity of results so far observed in previous studies, on the other hand they could be used by researchers as biomarkers predicting spontaneous recovery, to select homogenous groups of patients for the inclusion in clinical trials.

Transcranial Doppler Combined With Quantitative Electroencephalography Brain Function Monitoring for Estimating the Prognosis of Patients With Posterior Circulation Cerebral Infarction

Posterior circulation cerebral infarction (PCCI) can lead to deceased infratentorial cerebral blood flow (CBF) and metabolism. Neural activity is closely related to regional cerebral blood flow both spatially and temporally. Transcranial Doppler (TCD) combined with quantitative electroencephalography (QEEG) is a technique that evaluates neurovascular coupling and involves synergy between the metabolic and vascular systems. This study aimed to monitor brain function using TCD-QEEG and estimate the efficacy of TCD-QEEG for predicting the prognosis of patients with PCCI. We used a TCD-QEEG recording system to perform quantitative brain function monitoring; we recorded the related clinical variables simultaneously. The data were analyzed using a Cox proportional hazards regression model. Receiver-operating characteristic (ROC) curve analysis was used to evaluate the cut-off for the diastolic flow velocity (VD) and (delta + theta)/(alpha + beta) ratio (DTABR). The area under the ROC curve (AUROC) was calculated to assess the predictive validity of the study variables. Forty patients (aged 63.7 ± 9.9 years; 30 men) were assessed. Mortality at 90 days was 40%. The TCD indicators of VD [hazard ratio (HR) 0.168, confidence interval (CI) 0.047-0.597, p = 0.006] and QEEG indicators of DTABR (HR 12.527, CI 1.637-95.846, p = 0.015) were the independent predictors of the clinical outcomes. The AUROC after combination of VD and DTABR was 0.896 and showed better predictive accuracy than the Glasgow Coma Scale score (0.75), VD (0.76), and DTABR (0.781; all p < 0.05). TCD-QEEG provides a good understanding of the coupling mechanisms in the brain and can improve our ability to predict the prognosis of patients with PCCI.

Pitfalls of Commonly Used Ischemic and Dementia Models Due to Early Seizure by Carotid Ligation

While the bilateral common carotid artery (CCA) ligation model is widely used in cerebrovascular disease and dementia studies, it can frequently cause seizures. We examined the validity of seizure as an experimental model of ischemia. Eight-week-old male Wistar and Sprague-Dawley (SD) rats were implanted with electrocorticography (ECoG) electrodes and bilateral CCA ligation was performed and compared to the sham groups. ECoG monitoring was used to confirm the seizure discharge and count the number of spikes in the interictal phase 2 h after ligation, followed by power spectral analysis. Magnetic resonance imaging (MRI) was performed 6 h after bilateral CCA ligation to assess fractional anisotropy (FA), apparent diffusion coefficient (ADC), and cerebral blood flow (CBF) values. Magnetic resonance spectroscopy (MRS) was also performed and the ischemic parameters and electrophysiological changes were compared. The Wistar rat group had significantly higher mortality, frequency of seizures, incidence of non-convulsive seizures, and number of spikes in the interictal period compared to those in the SD rat group. Power spectral analysis showed increased power in the delta band in both Wistar and SD rat groups. MRI, after CCA ligation, showed significantly lower ADC values, lower glutamine and glutamate levels, and higher lactate values in Wistar rats, although there was no difference in FA values. Metabolic and electrophysiological changes after CCA ligation differed according to the rat strain. Wistar rats were prone to increased lactate and decreased glutamine and glutamate levels and the development of status epilepticus. Seizures can affect the results of ischemic experiments.

EEG Fractal Analysis Reflects Brain Impairment after Stroke

Stroke is the commonest cause of disability. Novel treatments require an improved understanding of the underlying mechanisms of recovery. Fractal approaches have demonstrated that a single metric can describe the complexity of seemingly random fluctuations of physiological signals. We hypothesize that fractal algorithms applied to electroencephalographic (EEG) signals may track brain impairment after stroke. Sixteen stroke survivors were studied in the hyperacute (<48 h) and in the acute phase (∼1 week after stroke), and 35 stroke survivors during the early subacute phase (from 8 days to 32 days and after ∼2 months after stroke): We compared resting-state EEG fractal changes using fractal measures (i.e., Higuchi Index, Tortuosity) with 11 healthy controls. Both Higuchi index and Tortuosity values were significantly lower after a stroke throughout the acute and early subacute stage compared to healthy subjects, reflecting a brain activity which is significantly less complex. These indices may be promising metrics to track behavioral changes in the very early stage after stroke. Our findings might contribute to the neurorehabilitation quest in identifying reliable biomarkers for a better tailoring of rehabilitation pathways.

Detection of Large Vessel Occlusion Stroke in the Prehospital Setting: Electroencephalography as a Potential Triage Instrument

A reliable and fast instrument for prehospital detection of large vessel occlusion (LVO) stroke would be a game-changer in stroke care, because it would enable direct transportation of LVO stroke patients to the nearest comprehensive stroke center for endovascular treatment. This strategy would substantially improve treatment times and thus clinical outcomes of patients. Here, we outline our view on the requirements of an effective prehospital LVO detection method, namely: high diagnostic accuracy; fast application and interpretation; user-friendliness; compactness; and low costs. We argue that existing methods for prehospital LVO detection, including clinical scales, mobile stroke units and transcranial Doppler, do not fulfill all criteria, hindering broad implementation of these methods. Instead, electroencephalography may be suitable for prehospital LVO detection since in-hospital studies have shown that quantification of hypoxia-induced changes in the electroencephalography signal have good diagnostic accuracy for LVO stroke. Although performing electroencephalography measurements in the prehospital setting comes with challenges, solutions for fast and simple application of this method are available. Currently, the feasibility and diagnostic accuracy of electroencephalography in the prehospital setting are being investigated in clinical trials.