Acute single channel EEG predictors of cognitive function after stroke

MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection

Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels of the brain which leads to brain tissue ischemia and hypoxia and ultimately results in cell necrosis. Without timely and effective treatment in the early time window, ischemic stroke can lead to long-term disability and even death. Therefore, rapid detection is crucial in patients with ischemic stroke. In this study, we developed a deep learning model based on fusion features extracted from electroencephalography (EEG) signals for the fast detection of ischemic stroke. Specifically, we recruited 20 ischemic stroke patients who underwent EEG examination during the acute phase of stroke and collected EEG signals from 19 adults with no history of stroke as a control group. Afterwards, we constructed correlation-weighted Phase Lag Index (cwPLI), a novel feature, to explore the synchronization information and functional connectivity between EEG channels. Moreover, the spatio-temporal information from functional connectivity and the nonlinear information from complexity were fused by combining the cwPLI matrix and Sample Entropy (SaEn) together to further improve the discriminative ability of the model. Finally, the novel MSE-VGG network was employed as a classifier to distinguish ischemic stroke from non-ischemic stroke data. Five-fold cross-validation experiments demonstrated that the proposed model possesses excellent performance, with accuracy, sensitivity, and specificity reaching 90.17%, 89.86%, and 90.44%, respectively. Experiments on time consumption verified that the proposed method is superior to other state-of-the-art examinations. This study contributes to the advancement of the rapid detection of ischemic stroke, shedding light on the untapped potential of EEG and demonstrating the efficacy of deep learning in ischemic stroke identification.

Resting-state EEG measures cognitive impairment in Parkinson's disease

Cognitive dysfunction is common in Parkinson's disease (PD). We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG. We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from National Institutes of Health (NIH) Toolbox using cross-validations, regression models, and randomization tests. Finally, we externally validated our approach on 32 PD participants. We observed cognition-related changes in EEG over multiple spectral rhythms. Utilizing only 8 best-performing electrodes, our proposed index strongly correlated with cognition (MoCA: rho = 0.68, p value < 0.001; NIH-Toolbox cognitive tests: rho ≥ 0.56, p value < 0.001) outperforming traditional spectral markers (rho = -0.30-0.37). The index showed a strong fit in regression models (R2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Notably, our approach was equally effective (rho = 0.68, p value < 0.001; MoCA) in out-of-sample testing. In summary, we introduced a computationally efficient data-driven approach for cross-domain cognition indexing using fewer than 10 EEG electrodes, potentially compatible with dynamic therapies like closed-loop neurostimulation. These results will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.

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 Hybrid Stacked CNN and Residual Feedback GMDH-LSTM Deep Learning Model for Stroke Prediction Applied on Mobile AI Smart Hospital Platform

Artificial intelligence (AI) techniques for intelligent mobile computing in healthcare has opened up new opportunities in healthcare systems. Combining AI techniques with the existing Internet of Medical Things (IoMT) will enhance the quality of care that patients receive at home remotely and the successful establishment of smart living environments. Building a real AI for mobile AI in an integrated smart hospital environment is a challenging problem due to the complexities of receiving IoT medical sensors data, data analysis, and deep learning algorithm complexity programming for mobile AI engine implementation AI-based cloud computing complexities, especially when we tackle real-time environments of AI technologies. In this paper, we propose a new mobile AI smart hospital platform architecture for stroke prediction and emergencies. In addition, this research is focused on developing and testing different modules of integrated AI software based on XAI architecture, this is for the mobile health app as an independent expert system or as connected with a simulated environment of an AI-cloud-based solution. The novelty is in the integrated architecture and results obtained in our previous works and this extended research on hybrid GMDH and LSTM deep learning models for the proposed artificial intelligence and IoMT engine for mobile health edge computing technology. Its main goal is to predict heart-stroke disease. Current research is still missing a mobile AI system for heart/brain stroke prediction during patient emergency cases. This research work implements AI algorithms for stroke prediction and diagnosis. The hybrid AI in connected health is based on a stacked CNN and group handling method (GMDH) predictive analytics model, enhanced with an LSTM deep learning module for biomedical signals prediction. The techniques developed depend on the dataset of electromyography (EMG) signals, which provides a significant source of information for the identification of normal and abnormal motions in a stroke scenario. The resulting artificial intelligence mHealth app is an innovation beyond the state of the art and the proposed techniques achieve high accuracy as stacked CNN reaches almost 98% for stroke diagnosis. The GMDH neural network proves to be a good technique for monitoring the EMG signal of the same patient case with an average accuracy of 98.60% to an average of 96.68% of the signal prediction. Moreover, extending the GMDH model and a hybrid LSTM with dense layers deep learning model has improved significantly the prediction results that reach an average of 99%.

Resting-state EEG measures cognitive impairment in Parkinson's disease

Background

Cognitive dysfunction is common in Parkinson's disease (PD) and is diagnosed by complex, time-consuming psychometric tests which are affected by language and education, subject to learning effects, and not suitable for continuous monitoring of cognition.

Objectives

We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG.

Methods

We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from the National Institutes of Health (NIH) Toolbox using cross-validation schemes, regression models, and randomization tests.

Results

We observed cognition-related changes in EEG activities over multiple spectral rhythms. Utilizing only 8 best-performing EEG electrodes, our proposed index strongly correlated with cognition (rho = 0.68, p value < 0.001 with MoCA; rho ≥ 0.56, p value < 0.001 with cognitive tests from the NIH Toolbox) outperforming traditional spectral markers (rho = -0.30 - 0.37). The index showed a strong fit in regression models (R2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants.

Conclusions

Our approach is computationally efficient for real-time indexing of cognition across domains, implementable even in hardware with limited computing capabilities, making it potentially compatible with dynamic therapies such as closed-loop neurostimulation, and will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.

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.

An IoMT-Based Approach for Real-Time Monitoring Using Wearable Neuro-Sensors

The Internet of Things (IoT) has demonstrated over the past few decades to be a powerful tool for connecting various medical equipment with in-built sensors and healthcare professionals to deliver superior health services that also reach remote areas. In addition to reducing healthcare costs, increasing access to clinical services, and enhancing operational effectiveness in the healthcare industry, it has also enhanced patient health safety. Recent research has focused on using EEG to assist and comprehend brain changes in rehabilitation facilities. These technologies can spot fluctuations in EEG constraints during treatment, which could result in more effective therapy and better functional outcomes. As a result, we have tried to use an IoT-based system for real-time monitoring of the constraints. Another unknown patient who is suffering from acute ischemic stroke may experience stroke-in-evolution or an early worsening of neurological symptoms, which is frequently associated with poor clinical outcomes. Because of this, managing an acute stroke requires early detection of these indications. The present investigation work will act as a standard reference for academic researchers, medical professionals, and everyone else involved in the use of IoMT. This study aims to anticipate strokes sooner and prevent their consequences by early intervention using an Internet of Things (IoT)-based system. Also, this study proposes usage of wearable equipment that can monitor and analyze brain signals for improved treatment and the prevention of stroke-related complications.

Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal

State-of-the-art healthcare technologies are incorporating advanced Artificial Intelligence (AI) models, allowing for rapid and easy disease diagnosis. However, most AI models are considered "black boxes," because there is no explanation for the decisions made by these models. Users may find it challenging to comprehend and interpret the results. Explainable AI (XAI) can explain the machine learning (ML) outputs and contribution of features in disease prediction models. Electroencephalography (EEG) is a potential predictive tool for understanding cortical impairment caused by an ischemic stroke and can be utilized for acute stroke prediction, neurologic prognosis, and post-stroke treatment. This study aims to utilize ML models to classify the ischemic stroke group and the healthy control group for acute stroke prediction in active states. Moreover, XAI tools (Eli5 and LIME) were utilized to explain the behavior of the model and determine the significant features that contribute to stroke prediction models. In this work, we studied 48 patients admitted to a hospital with acute ischemic stroke and 75 healthy adults who had no history of identified other neurological illnesses. EEG was obtained within three months following the onset of ischemic stroke symptoms using frontal, central, temporal, and occipital cortical electrodes (Fz, C1, T7, Oz). EEG data were collected in an active state (walking, working, and reading tasks). In the results of the ML approach, the Adaptive Gradient Boosting models showed around 80% accuracy for the classification of the control group and the stroke group. Eli5 and LIME were utilized to explain the behavior of the stroke prediction model and interpret the model locally around the prediction. The Eli5 and LIME interpretable models emphasized the spectral delta and theta features as local contributors to stroke prediction. From the findings of this explainable AI research, it is expected that the stroke-prediction XAI model will help with post-stroke treatment and recovery, as well as help healthcare professionals, make their diagnostic decisions more explainable.

Pathological changes of brain oscillations following ischemic stroke

Brain oscillations recorded in the extracellular space are among the most important aspects of neurophysiology data reflecting the activity and function of neurons in a population or a network. The signal strength and patterns of brain oscillations can be powerful biomarkers used for disease detection and prediction of the recovery of function. Electrophysiological signals can also serve as an index for many cutting-edge technologies aiming to interface between the nervous system and neuroprosthetic devices and to monitor the efficacy of boosting neural activity. In this review, we provided an overview of the basic knowledge regarding local field potential, electro- or magneto- encephalography signals, and their biological relevance, followed by a summary of the findings reported in various clinical and experimental stroke studies. We reviewed evidence of stroke-induced changes in hippocampal oscillations and disruption of communication between brain networks as potential mechanisms underlying post-stroke cognitive dysfunction. We also discussed the promise of brain stimulation in promoting post stroke functional recovery via restoring neural activity and enhancing brain plasticity.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

SIGNIFICANCE

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

A Combination of Curcuma longa and Diazepam Attenuates Seizures and Subsequent Hippocampal Neurodegeneration

Epilepsy is one of the most common neurological disorders, which occurs due to the instability in the inhibitory and excitatory synaptic transmissions in the brain. However, many patients develop resistance to the available drugs, which results in cell degeneration caused due to inadequate control of the seizures. Curcumin, Curcuma longa, is known to be effective for the treatment of organic disorders and may prevent seizures, reduce oxidative stress, and decrease brain damage. Given this, the present study evaluated the antiepileptic effects of C. longa in comparison with both the diazepam and the combined application of these two substances, in terms of their effects on the brain activity and the potential histopathological changes in the hippocampus. This study used male Wistar rats (age: 10–12 weeks; weight: 260 ± 20 g), which were pretreated for 4 days with either saline, C. longa, diazepam, or C. longa + diazepam; and on the fifth day, pentylenetetrazol (PTZ) was administered to induce the seizure. In the C. longa group, a significant increase was observed in the latency of the onset of seizure-related behavior. Surprisingly, however, the combined treatment resulted in the best control of the seizure-related behavior, with the greatest latency of the onset of spasms and isolated clonic seizures. This group also obtained the best results in the electroencephalographic trace and seizure control, with a reduction in the frequency and amplitude of the spike-waves. In the saline group, PTZ significantly reduced the number of cells present in the CA1 and CA3 regions of the hippocampus, while the combined treatment obtained the best results in terms of the preservation of the neuron-like cells. These findings indicate that C. longa may contribute to the control of both seizures and the cell damage induced by PTZ, and that its association with diazepam may be a potentially effective option for the treatment of epilepsy in the future.

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.

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.

Alfa band power increases in posterior brain regions in attention deficit hyperactivity disorder after digital cognitive stimulation treatment

The changes triggered by pharmacological treatments in resting-state alpha-band (8–14 Hz) oscillations have been widely studied in attention deficit hyperactivity disorder. However, to date, there has been no evidence regarding the possible changes in cognitive stimulation treatments on these oscillations. This paper sets out to verify whether cognitive stimulation treatments based on progressive increases in cognitive load can be effective in triggering changes in alpha-band power in attention deficit hyperactivity disorder. With this objective, we compared a cognitive stimulation treatment (n = 13) to placebo treatment (n = 13) for 12 weeks (36 sessions of 15 min) in child patients (8–11 years old) with attention deficit hyperactivity disorder. Two magnetoencephalographic recordings were acquired for all the participants. In order to extract the areas with changes in alpha power between both magnetoencephalographic recordings, the differences in the power ratio (pre/post-condition) were calculated using an Analysis of Covariance test adjusted for the age variable. The results show an increase in the post-treatment power ratio in the experimental group versus the placebo group (P &lt; 0.01) in posterior regions and the default mode network. In addition, these alpha changes were related to measures of attention, working memory and cognitive flexibility. The results seem to indicate that cognitive stimulation treatment based on progressive increases in cognitive load triggers alpha-band power changes in child attention deficit hyperactivity disorder patients in the direction of their peers without this disorder.

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.

SSA with CWT and k-Means for Eye-Blink Artifact Removal from Single-Channel EEG Signals

Recently, the use of portable electroencephalogram (EEG) devices to record brain signals in both health care monitoring and in other applications, such as fatigue detection in drivers, has been increased due to its low cost and ease of use. However, the measured EEG signals always mix with the electrooculogram (EOG), which are results due to eyelid blinking or eye movements. The eye-blinking/movement is an uncontrollable activity that results in a high-amplitude slow-time varying component that is mixed in the measured EEG signal. The presence of these artifacts misled our understanding of the underlying brain state. As the portable EEG devices comprise few EEG channels or sometimes a single EEG channel, classical artifact removal techniques such as blind source separation methods cannot be used to remove these artifacts from a single-channel EEG signal. Hence, there is a demand for the development of new single-channel-based artifact removal techniques. Singular spectrum analysis (SSA) has been widely used as a single-channel-based eye-blink artifact removal technique. However, while removing the artifact, the low-frequency components from the non-artifact region of the EEG signal are also removed by SSA. To preserve these low-frequency components, in this paper, we have proposed a new methodology by integrating the SSA with continuous wavelet transform (CWT) and the k-means clustering algorithm that removes the eye-blink artifact from the single-channel EEG signals without altering the low frequencies of the EEG signal. The proposed method is evaluated on both synthetic and real EEG signals. The results also show the superiority of the proposed method over the existing methods.

An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors

In recent years, neurological diseases have become a standout amongst all the other diseases and are the most important reasons for mortality and morbidity all over the world. The current study's aim is to conduct a pilot study for testing the prototype of the designed glove-wearable technology that could detect and analyze the heart rate and EEG for better management and avoiding stroke consequences. The qualitative, clinical experimental method of assessment was explored by incorporating use of an IoT-based real-time assessing medical glove that was designed using heart rate-based and EEG-based sensors. We conducted structured interviews with 90 patients, and the results of the interviews were analyzed by using the Barthel index and were grouped accordingly. Overall, the proportion of patients who followed proper daily heart rate recording behavior went from 46.9% in the first month of the trial to 78.2% after 3-10 months of the interventions. Meanwhile, the percentage of individuals having an irregular heart rate fell from 19.5% in the first month of the trial to 9.1% after 3-10 months of intervention research. In T5, we found that delta relative power decreased by 12.1% and 5.8% compared with baseline at 3 and at 6 months and an average increase was 24.3 ± 0.08. Beta-1 remained relatively steady, while theta relative power grew by 7% and alpha relative power increased by 31%. The T1 hemisphere had greater mean values of delta and theta relative power than the T5 hemisphere. For alpha (p < 0.05) and beta relative power, the opposite pattern was seen. The distinction was statistically significant for delta (p < 0.001), alpha (p < 0.01), and beta-1 (p < 0.05) among T1 and T5 patient groups. In conclusion, our single center-based study found that such IoT-based real-time medical monitoring devices significantly reduce the complexity of real-time monitoring and data acquisition processes for a healthcare provider and thus provide better healthcare management. The emergence of significant risks and controlling mechanisms can be improved by boosting the awareness. Furthermore, it identifies the high-risk factors besides facilitating the prevention of strokes. The EEG-based brain-computer interface has a promising future in upcoming years to avert DALY.

Continuous Quantitative Electroencephalogram (EEG) Monitoring for Early Detection of Brain Herniation in Large Hemispheric Infarction (LHI): A Case Report

BACKGROUND

Computer-assisted electroencephalography (EEG) systems may improve the likelihood of detecting abnormal EEGs in adult patients with severe disease.

CASE PRESENTATION

We implemented long-range EEG monitoring in a patient with large hemispheric infarction (LHI) and explored its real-time changes in reflecting the patient's brain function. The bands of Alpha, Beta, Delta, Theta, DAR (Delta/Alpha), DTABR (Delta+Theta/Alpha+Beta), and brain symmetry index (BSI) were calculated as a ratio of total power. The test results showed that this patient presents a progressive worsening trend and developed brain herniation. The sigh at the electrophysiological level of brain herniation could be seen 6 h in advance based on the quantitative EEG (QEEG) parameters test. We calculated QEEG at both C3 and C4, electrode locations simultaneously, and the results showed that the trend of QEEG at both electrodes was consistent with the global, affected, and unaffected side.

CONCLUSIONS

QEEG parameters can reflect the trend of LHI patients in real-time and may predict the occurrence of LHI brain herniation. For LHI patients, monitoring with fewer EEG electrodes can be tried to predict the changes in conditions.

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.

Local field potentials identify features of cortico-hippocampal communication impacted by stroke and environmental enrichment therapy

Objective. Cognitive and memory impairments are common sequelae after stroke, yet how middle cerebral artery (MCA) stroke chronically affects the neural activity of the hippocampus, a brain region critical for memory but remote from the stroke epicenter, is poorly understood. Environmental enrichment (EE) improves cognition following stroke; however, the electrophysiology that underlies this behavioral intervention is still elusive.Approach.We recorded extracellular local field potentials simultaneously from sensorimotor cortex and hippocampus in rats during urethane anesthesia following MCA occlusion and subsequent EE treatment.Main results.We found that MCA stroke significantly impacted the electrophysiology in the hippocampus, in particular it disrupted characteristics of sharp-wave associated ripples (SPW-Rs) altered brain state, and disrupted phase amplitude coupling (PAC) within the hippocampus and between the cortex and hippocampus. Importantly, we show that EE mitigates stroke-induced changes to SPW-R characteristics but does not restore hippocampal brain state or PAC.Significance.These results begin to uncover the complex interaction between cognitive deficit following stroke and EE treatment, providing a testbed to assess different strategies for therapeutics following stroke.

N-Pep-12 supplementation after ischemic stroke positively impacts frequency domain QEEG

BACKGROUND

N-Pep-12 is a dietary supplement with neuroprotective and pro-cognitive effects, as shown in experimental models and clinical studies on patients after ischemic stroke. We tested the hypothesis that N-Pep-12 influences quantitative electroencephalography (QEEG) parameters in patients with subacute to chronic supratentorial ischemic lesions.

METHODS

We performed secondary data analysis on an exploratory clinical trial (ISRCTN10702895), assessing the efficacy and safety of 90 days of once-daily treatment with 90 mg N-Pep-12 on neurocognitive function and neurorecovery outcome in patients with post-stroke cognitive impairment against a control group. All participants performed two 32-channel QEEG in resting and active states at baseline (30-120 days after stroke) and 90 days later. Power spectral density on the alpha, beta, theta, delta frequency bands, delta/alpha power ratio (DAR), and (delta+theta)/(alpha+beta) ratio (DTABR) were computed and compared across study groups using means comparison and descriptive methods. Secondarily, associations between QEEG parameters and available neuropsychological tests were explored.

RESULTS

Our analysis showed a statistically significant main effect of EEG segments (p<0.001) in alpha, beta, delta, theta, DA, and DTAB power spectral density. An interaction effect between EEG segments and time was noticed in the alpha power. There was a significant difference in theta spectral power between patients with N-Pep-12 supplementation versus placebo at 0.05 alpha level (p=0.023), independent of time points.

CONCLUSION

A 90-day, 90 mg daily administration of N-Pep-12 had significant impact on some QEEG indicators in patients after supratentorial ischemic stroke, confirming possible enhancement of post-stroke neurorecovery. Further research is needed to consolidate our findings.

Cognitive Workload in an Auditory Digit Span Task When Memory Span Is in the Neighborhood of Seven Items

Abstract. Task performance of digit span has been widely used in the research on human short-term memory. The present study was conducted to show that the dynamic change of underlying mental effort can be further estimated by measuring the strength of theta oscillations at a forehead site on the scalp. Fourteen healthy adults ( Mage = 26.1 years) performed a passive listening (PL) task and an auditory digit span (DS) task, and electroencephalography (EEG) data were recorded simultaneously during the two tasks. Considering that the digit span paradigm has often been conducted in a non-laboratory location, the EEG data were collected with a wireless single-channel headset system. The headset system was validated in this study by replicating the EEG (an enhancement of frontal theta power) as well as event-related potential (N200 and P300) responses to the deviant tone stimuli in the PL task. The outcomes of the DS task showed that the memory span of the participants was at least eight items. Moreover, frontal theta power in response to a list of six to eight digits increased significantly. This pattern of results supports a hypothesis that additional mental effort is required for short-term retention of verbal items when the number of stimulus items exceeds the newly proposed limit of short-term memory capacity. Some strengths and limitations of the current EEG headset system are also discussed.

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.

Increased Relative Delta Bandpower and Delta Indices Revealed by Continuous qEEG Monitoring in a Rat Model of Ischemia-Reperfusion

The present study describes the electroencephalographic changes that occur during cerebral ischemia and reperfusion in animals submitted to transient focal cerebral ischemia by middle cerebral artery occlusion (MCAO) for 30 min. For this, male Wistar rats were divided into two groups (n = 6 animals/group): (1) sham (control) group, and (2) ischemic/reperfusion group. The quantitative electroencephalography (qEEG) was recorded during the ischemic and immediate reperfusion (acute) phases, and then once a day for 7 days after the MCAO (subacute phase). The acute phase was characterized by a marked increase in the relative delta wave band power (p < 0.001), with a smaller, but significant increase in the relative alpha wave bandpower in the ischemic stroke phase, in comparison with the control group (p = 0.0054). In the immediate reperfusion phase, however, there was an increase in the theta, alpha, and beta waves bandpower (p < 0.001), but no alteration in the delta waves (p = 0.9984), in comparison with the control group. We also observed high values in the delta/theta ratio (DTR), the delta/alpha ratio (DAR), and the (delta+theta)/(alpha+beta) ratio (DTABR) indices during the ischemia (p < 0.05), with a major reduction in the reperfusion phase. In the subacute phase, the activity of all the waves was lower than that of the control group (p < 0.05), although the DTR, DAR, and DTABR indices remained relatively high. In conclusion, early and accurate identification of decreased delta wave bandpower, DTR, DAR, and DTABR indices, and an increase in the activity of other waves in the immediate reperfusion phase may represent an important advance for the recognition of the effectiveness of reperfusion therapy.

Multi-Center Validation Study of Automated Classification of Pathological Slowing in Adult Scalp Electroencephalograms Via Frequency Features

Pathological slowing in the electroencephalogram (EEG) is widely investigated for the diagnosis of neurological disorders. Currently, the gold standard for slowing detection is the visual inspection of the EEG by experts, which is time-consuming and subjective. To address those issues, we propose three automated approaches to detect slowing in EEG: Threshold-based Detection System (TDS), Shallow Learning-based Detection System (SLDS), and Deep Learning-based Detection System (DLDS). These systems are evaluated on channel-, segment-, and EEG-level. The three systems perform prediction via detecting slowing at individual channels, and those detections are arranged in histograms for detection of slowing at the segment- and EEG-level. We evaluate the systems through Leave-One-Subject-Out (LOSO) cross-validation (CV) and Leave-One-Institution-Out (LOIO) CV on four datasets from the US, Singapore, and India. The DLDS achieved the best overall results: LOIO CV mean balanced accuracy (BAC) of 71.9%, 75.5%, and 82.0% at channel-, segment- and EEG-level, and LOSO CV mean BAC of 73.6%, 77.2%, and 81.8% at channel-, segment-, and EEG-level. The channel- and segment-level performance is comparable to the intra-rater agreement (IRA) of an expert of 72.4% and 82%. The DLDS can process a 30 min EEG in 4 s and can be deployed to assist clinicians in interpreting EEGs.

Quantification of stroke lesion volume using epidural EEG in a cerebral ischaemic rat model

Precise monitoring of the brain after a stroke is essential for clinical decision making. Due to the non-invasive nature and high temporal resolution of electroencephalography (EEG), it is widely used to evaluate real-time cortical activity. In this study, we investigated the stroke-related EEG biomarkers and developed a predictive model for quantifying the structural brain damage in a focal cerebral ischaemic rat model. We enrolled 31 male Sprague-Dawley rats and randomly assigned them to mild stroke, moderate stroke, severe stroke, and control groups. We induced photothrombotic stroke targeting the right auditory cortex. We then acquired EEG signal responses to sound stimuli (frequency linearly increasing from 8 to 12 kHz with 750 ms duration). Power spectral analysis revealed a significant correlation of the relative powers of alpha, theta, delta, delta/alpha ratio, and (delta + theta)/(alpha + beta) ratio with the stroke lesion volume. The auditory evoked potential analysis revealed a significant association of amplitude and latency with stroke lesion volume. Finally, we developed a multiple regression model combining EEG predictors for quantifying the ischaemic lesion (R2 = 0.938, p value < 0.001). These findings demonstrate the potential application of EEG as a valid modality for monitoring the brain after a stroke.

Electroencephalography-Derived Prognosis of Functional Recovery in Acute Stroke Through Machine Learning Approaches

Stroke, if not lethal, is a primary cause of disability. Early assessment of markers of recovery can allow personalized interventions; however, it is difficult to deliver indexes in the acute phase able to predict recovery. In this perspective, evaluation of electrical brain activity may provide useful information. A machine learning approach was explored here to predict post-stroke recovery relying on multi-channel electroencephalographic (EEG) recordings of few minutes performed at rest. A data-driven model, based on partial least square (PLS) regression, was trained on 19-channel EEG recordings performed within 10 days after mono-hemispheric stroke in 101 patients. The band-wise (delta: 1-4[Formula: see text]Hz, theta: 4-7[Formula: see text]Hz, alpha: 8-14[Formula: see text]Hz and beta: 15-30[Formula: see text]Hz) EEG effective powers were used as features to predict the recovery at 6 months (based on clinical status evaluated through the NIH Stroke Scale, NIHSS) in an optimized and cross-validated framework. In order to exploit the multimodal contribution to prognosis, the EEG-based prediction of recovery was combined with NIHSS scores in the acute phase and both were fed to a nonlinear support vector regressor (SVR). The prediction performance of EEG was at least as good as that of the acute clinical status scores. A posteriori evaluation of the features exploited by the analysis highlighted a lower delta and higher alpha activity in patients showing a positive outcome, independently of the affected hemisphere. The multimodal approach showed better prediction capabilities compared to the acute NIHSS scores alone ([Formula: see text] versus [Formula: see text], AUC = 0.80 versus AUC = 0.70, [Formula: see text]). The multimodal and multivariate model can be used in acute phase to infer recovery relying on standard EEG recordings of few minutes performed at rest together with clinical assessment, to be exploited for early and personalized therapies. The easiness of performing EEG may allow such an approach to become a standard-of-care and, thanks to the increasing number of labeled samples, further improving the model predictive power.

Differentiating Responders and Non-responders to rTMS Treatment for Disorder of Consciousness Using EEG After-Effects

Background: It is controversial whether repetitive transcranial magnetic stimulation (rTMS) has potential benefits in improving the awareness of patients with disorder of consciousness (DOC). We hypothesized that rTMS could improve consciousness only in DOC patients who have measurable brain responses to rTMS.

Objective: In this study, we aimed to investigate the EEG after-effects induced by rTMS in DOC patients and attempted to propose a prediction algorithm to discriminate between DOC patients who would respond to rTMS treatment from those who would not.

Methods: Twenty-five DOC patients were enrolled in this study. Over 4 weeks, each patient received 20 sessions of 20 Hz rTMS that was applied over the left dorsolateral prefrontal cortex (DLPFC). For each patient, resting-state EEG was recorded before and immediately after one session of rTMS to assess the neurophysiologic modification induced by rTMS. The coma recovery scale revised (CRS-R) was used to define responders with improved consciousness.

Results: Of the 25 DOC patients, 10 patients regained improved consciousness and were classified as responders. The responders were characterized by more preserved alpha power and a significant reduction of delta power induced by rTMS. The analysis of receiver operating characteristic (ROC) curves showed that the algorithm calculated from the relative alpha power and the relative delta power had a high accuracy in identifying DOC patients who were responders.

Conclusions: DOC patients who had more preserved alpha power and a significant reduction in the delta band that was induced by rTMS are likely to regain improved consciousness, which provides a tool to identify DOC patients who may benefit in terms of therapeutic consciousness.

Predicting stroke severity with a 3-min recording from the Muse portable EEG system for rapid diagnosis of stroke

In this study, we demonstrated the use of low-cost portable electroencephalography (EEG) as a method for prehospital stroke diagnosis. We used a portable EEG system to record data from 25 participants, 16 had acute ischemic stroke events, and compared the results to age-matched controls that included stroke mimics. Delta/alpha ratio (DAR), (delta + theta)/(alpha + beta) ratio (DBATR) and pairwise-derived Brain Symmetry Index (pdBSI) were investigated, as well as head movement using the on-board accelerometer and gyroscope. We then used machine learning to distinguish between different subgroups. DAR and DBATR increased in ischemic stroke patients with increasing stroke severity (p = 0.0021, partial η² = 0.293; p = 0.01, partial η² = 0.234). Also, pdBSI decreased in low frequencies and increased in high frequencies in patients who had a stroke (p = 0.036, partial η² = 0.177). Using classification trees, we were able to distinguish moderate to severe stroke patients and from minor stroke and controls, with a 63% sensitivity, 86% specificity and accuracy of 76%. There are significant differences in DAR, DBATR, and pdBSI between patients with ischemic stroke when compared to controls, and these effects scale with severity. We have shown the utility of a low-cost portable EEG system to aid in patient triage and diagnosis as an early detection tool.

Experimental cortical stroke induces aberrant increase of sharp-wave-associated ripples in the hippocampus and disrupts cortico-hippocampal communication

The functional consequences of ischemic stroke in the remote brain regions are not well characterized. The current study sought to determine changes in hippocampal oscillatory activity that may underlie the cognitive impairment observed following distal middle cerebral artery occlusion (dMCAO) without causing hippocampal structural damage. Local field potentials were recorded from the dorsal hippocampus and cortex in urethane-anesthetized rats with multichannel silicon probes during dMCAO and reperfusion, or mild ischemia induced by bilateral common carotid artery occlusion (CCAO). Bilateral change of brain state was evidenced by reduced theta/delta amplitude ratio and shortened high theta duration following acute dMCAO but not CCAO. An aberrant increase in the occurrence of sharp-wave-associated ripples (150-250 Hz), crucial for memory consolidation, was only detected after dMCAO reperfusion, coinciding with an increased occurrence of high-frequency discharges (250-450 Hz). dMCAO also significantly affected the modulation of gamma amplitude in the cortex coupled to hippocampal theta phase, although both hippocampal theta and gamma power were temporarily decreased during dMCAO. Our results suggest that MCAO may disrupt the balance between excitatory and inhibitory circuits in the hippocampus and alter the function of cortico-hippocampal network, providing a novel insight in how cortical stroke affects function in remote brain regions.

Resting-state EEG topographies: Reliable and sensitive signatures of unilateral spatial neglect

Theoretical advances in the neurosciences are leading to the development of an increasing number of proposed interventions for the enhancement of functional recovery after brain damage. Integration of these novel approaches in clinical practice depends on the availability of reliable, simple, and sensitive biomarkers of impairment level and extent of recovery, to enable an informed clinical-decision process. However, the neuropsychological tests currently in use do not tap into the complex neural re-organization process that occurs after brain insult and its modulation by treatment. Here we show that topographical analysis of resting-state electroencephalography (rsEEG) patterns using singular value decomposition (SVD) could be used to capture these processes. In two groups of subacute stroke patients, we show reliable detection of deviant neurophysiological patterns over repeated measurement sessions on separate days. These patterns generalized across patients groups. Additionally, they maintained a significant association with ipsilesional attention bias, discriminating patients with spatial neglect of different severity levels. The sensitivity and reliability of these rsEEG topographical analyses support their use as a tool for monitoring natural and treatment-induced recovery in the rehabilitation process.

Quantitative and Qualitative EEG as a Prediction Tool for Outcome and Complications in Acute Stroke Patients

Currently, the relevance of EEG measurements in acute stroke patients is considered low in clinical practice. However, recent studies on the predictive value of EEG measurements after stroke for various outcomes may increase the role of EEG in patients with stroke. We aimed to review the current literature on the utility of EEG measurements after stroke as a tool to predict outcome and complications, focusing on studies in which the EEG measurement was performed in the acute phase after the event and in which long-term outcome measures were reported. In our literature review, we identified 4 different outcome measures (functional outcome, mortality, development of post-stroke cognitive decline, and development of post-stroke epilepsy) where studies on the utility of acute EEG measurements exist. There is a large body of evidence for the prediction of functional outcome, in which a multitude of associated quantitative and qualitative EEG parameters are described. In contrast, only few studies focus on mortality as outcome parameter. We found studies of high methodical quality on the prediction of post-stroke cognitive decline, though the number of patients in these studies often was small. The role of EEG as a prediction tool for seizures and epilepsy after stroke could increase after a recently published study, especially if its result can be incorporated into already existing post-stroke epilepsy prediction tools. In summary, EEG is useful for the prediction of functional outcome, mortality, development of post-stroke cognitive decline and epilepsy, even though there is a discrepancy between the large amount of studies on EEG in acute stroke patients and its underuse in clinical practice.

Predicting functional outcomes after stroke: an observational study of acute single-channel EEG

Background: Early and objective prediction of functional outcome after stroke is an important issue in rehabilitation. Electroencephalography (EEG) has long been utilized to describe and monitor brain function following neuro-trauma, and technological advances have improved usability in the acute setting. However, skepticism persists whether EEG can provide the same prognostic value as neurological examination.Objective: The current cohort study examined the relationship between acute single-channel EEG and functional outcomes after stroke.Methods: Resting-state EEG recorded at a single left pre-frontal EEG channel (FP1) was obtained from 16 adults within 72 h of first stroke. At 30 and 90 days, measures of disability (modified Rankin Scale; mRS) and involvement in daily activities (modified Barthel Index; mBI) were obtained. Acute EEG measures were correlated with functional outcomes and compared to an early neurological examination of stroke severity using the National Institute of Health Stroke Scale (NIHSS). Classification of good outcomes (mRS ≤1 or mBI ≥95) was also examined using Receiver Operator Curve analyses.Results: One-third to one-half of participants experienced incomplete post-stroke recovery, depending on the time point and measure. Functional outcomes correlated with acute theta values (r_s 0.45-0.60), with the strength of associations equivalent to previously reported values obtained from conventional multi-channel systems. Acute theta values ≥0.25 were associated with good outcomes, with positive (67-83%) and negative predictive values (70-90%) comparable to those obtained using the NIHSS.Conclusions: Acute, single-channel EEG can provide unique, non-overlapping clinical information, which may facilitate objective prediction of functional outcome after stroke.

Cortical stroke affects activity and stability of theta/delta states in remote hippocampal regions

Cognitive impairment is a common outcome of ischemic stroke. Our previous work has shown that an experimental stroke in the cortex reduces activity in remote hippocampal layers in rats. This study seeks to uncover the underlying functional connections between these areas by analyzing changes to oscillatory activity, signal power, and communication. We induced an ischemic stroke in the left somatosensory cortex of rats and used linear micro-electrode arrays to simultaneously record from cortex and hippocampus under urethane anesthesia at two weeks and one month after stroke. We found significant increase in signal power, as well as an increase in the number of brain state changes in response to stroke. Our results suggest that the cortex modulates the activity and stability of hippocampal oscillations, which is disrupted following cortical stroke that can lead to cognitive impairment.

Elements virtual rehabilitation improves motor, cognitive, and functional outcomes in adult stroke: evidence from a randomized controlled pilot study

BACKGROUND

Virtual reality technologies show potential as effective rehabilitation tools following neuro-trauma. In particular, the Elements system, involving customized surface computing and tangible interfaces, produces strong treatment effects for upper-limb and cognitive function following traumatic brain injury. The present study evaluated the efficacy of Elements as a virtual rehabilitation approach for stroke survivors.

METHODS

Twenty-one adults (42-94 years old) with sub-acute stroke were randomized to four weeks of Elements virtual rehabilitation (three weekly 30-40 min sessions) combined with treatment as usual (conventional occupational and physiotherapy) or to treatment as usual alone. Upper-limb skill (Box and Blocks Test), cognition (Montreal Cognitive Assessment and selected CogState subtests), and everyday participation (Neurobehavioral Functioning Inventory) were examined before and after inpatient training, and one-month later.

RESULTS

Effect sizes for the experimental group (d = 1.05-2.51) were larger compared with controls (d = 0.11-0.86), with Elements training showing statistically greater improvements in motor function of the most affected hand (p = 0.008), and general intellectual status and executive function (p ≤ 0.001). Proportional recovery was two- to three-fold greater than control participants, with superior transfer to everyday motor, cognitive, and communication behaviors. All gains were maintained at follow-up.

CONCLUSION

A course of Elements virtual rehabilitation using goal-directed and exploratory upper-limb movement tasks facilitates both motor and cognitive recovery after stroke. The magnitude of training effects, maintenance of gains at follow-up, and generalization to daily activities provide compelling preliminary evidence of the power of virtual rehabilitation when applied in a targeted and principled manner.

TRIAL REGISTRATION

this pilot study was not registered.

Acute EEG Patterns Associated With Transient Ischemic Attack

BACKGROUND

Transient ischemic attack (TIA) is characterized by stroke-like neurologic signs and symptoms in the absence of demonstrable structural neuropathology. There is no test for TIA, with classification often reliant on subjective, retrospective report. Functional brain measures such as the electroencephalogram (EEG) may be helpful in objectively detecting and describing the pathophysiology of TIA, but this has not been adequately examined.

METHODS

EEG was obtained from a single electrode over the left frontal lobe during 3-minute resting-state and auditory oddball conditions administered to consecutive patients within 72 hours of admission to the acute stroke ward of a tertiary hospital. Separately, patients were classified by their treating team as having suffered either an ischemic stroke (n = 10) or a TIA (n = 10). Relative power of delta, theta, alpha, and beta EEG frequency bands were extracted for comparison between the 2 clinical groups and an existing normative sample of 10 healthy, age-, gender-, and education-matched older adults.

RESULTS

Analysis of variance with post hoc testing identified pronounced delta activity in stroke patients, while alpha and beta power were elevated in TIA patients. Both patient groups exhibited attenuated theta activity compared with healthy controls. Receiver operating characteristic curve analysis identified thresholds for each EEG frequency capable of distinguishing the 3 participant groups.

CONCLUSIONS

TIA, ischemic stroke, and healthy aging are each associated with distinct electrophysiological profiles. These preliminary findings suggest that acute EEG may be helpful in elucidating the pathophysiology and reversibility of TIA symptoms, and further exploration of the value of this unique functional brain data is encouraged.

Longitudinal quantitative electroencephalographic study in mono-hemispheric stroke patients

The identification of individual factors modulating clinical recovery after a stroke is fundamental to personalize the therapeutic intervention to enhance the final clinical outcome. In this framework, electrophysiological factors are promising since are more directly related to neuroplasticity, which supports recovery in stroke patients, than neurovascular factors. In this retrospective observational study, we investigated brain neuronal activity assessed via spectral features and Higuchi’s fractal dimension (HFD) of electroencephalographic signals in acute phase (2–10 days from symptom onset, T0) and sub-acute phase (2.5 months, T1) in 24 patients affected by unilateral middle cerebral artery stroke. Longitudinal assessment of the clinical deficits was performed using the National Institutes of Health Stroke Scale (NIHSS), together with the effective recovery calculated as the ratio between difference of NIHSS at T0 and T1 over the NIHSS value at T0. We observed that delta and alpha band electroencephalographic signal power changed between the two phases in both the hemispheres ipsilateral (ILH) and contralateral (CHL) to the lesion. Moreover, at T0, bilateral higher delta band power correlated with worse clinical conditions (Spearman’s r_s = 0.460, P = 0.027 for ILH and r_s = 0.508, P = 0.013 for CLH), whereas at T1 this occurred only for delta power in ILH (r_s = 0.411, P = 0.046) and not for CHL. Inter-hemispheric difference (ILH vs. CLH) of alpha power in patients was lower at T0 than at T1 (P = 0.020). HFD at T0 was lower than at T1 (P = 0.005), and at both phases, ILH HFD was lower than CLH HFD (P = 0.020). These data suggest that inter-hemispheric low band asymmetry and fractal dimension changes from the acute to the sub-acute phase are sensitive to neuroplasticity processes which subtend clinical recovery. The study protocol was approved by the Bioethical Committee of Ospedale San Giovanni Calibita Fatebenefretelli (No. 40/2011) on July 14, 2011.

What do randomized controlled trials say about virtual rehabilitation in stroke? A systematic literature review and meta-analysis of upper-limb and cognitive outcomes

BACKGROUND

Virtual-reality based rehabilitation (VR) shows potential as an engaging and effective way to improve upper-limb function and cognitive abilities following a stroke. However, an updated synthesis of the literature is needed to capture growth in recent research and address gaps in our understanding of factors that may optimize training parameters and treatment effects.

METHODS

Published randomized controlled trials comparing VR to conventional therapy were retrieved from seven electronic databases. Treatment effects (Hedge's g) were estimated using a random effects model, with motor and functional outcomes between different protocols compared at the Body Structure/Function, Activity, and Participation levels of the International Classification of Functioning.

RESULTS

Thirty-three studies were identified, including 971 participants (492 VR participants). VR produced small to medium overall effects (g = 0.46; 95% CI: 0.33-0.59, p < 0.01), above and beyond conventional therapies. Small to medium effects were observed on Body Structure/Function (g = 0.41; 95% CI: 0.28-0.55; p < 0.01) and Activity outcomes (g = 0.47; 95% CI: 0.34-0.60, p < 0.01), while Participation outcomes failed to reach significance (g = 0.38; 95% CI: -0.29-1.04, p = 0.27). Superior benefits for Body Structure/Function (g = 0.56) and Activity outcomes (g = 0.62) were observed when examining outcomes only from purpose-designed VR systems. Preliminary results (k = 4) suggested small to medium effects for cognitive outcomes (g = 0.41; 95% CI: 0.28-0.55; p < 0.01). Moderator analysis found no advantage for higher doses of VR, massed practice training schedules, or greater time since injury.

CONCLUSION

VR can effect significant gains on Body Structure/Function and Activity level outcomes, including improvements in cognitive function, for individuals who have sustained a stroke. The evidence supports the use of VR as an adjunct for stroke rehabilitation, with effectiveness evident for a variety of platforms, training parameters, and stages of recovery.