Delta-alpha ratio correlates with level of recovery after neurorehabilitation in patients with acquired brain injury

Quantitative EEG Spectral Features Differentiate Genetic Epilepsies and Predict Neurologic Outcomes

EEG plays an integral part in the diagnosis and management of children with genetic epilepsies. Nevertheless, how quantitative EEG features differ between genetic epilepsies and neurological outcomes remains largely unknown. Here, we aimed to identify quantitative EEG biomarkers in children with epilepsy and a genetic diagnosis in STXBP1, SCN1A, or SYNGAP1, and to assess how quantitative EEG features associate with neurological outcomes in genetic epilepsies more broadly. We analyzed individuals with pathogenic variants in STXBP1 (95 EEGs, n=20), SCN1A (154 EEGs, n=68), and SYNGAP1 (46 EEGs, n=21) and a control cohort of individuals without epilepsy or known cerebral disease (847 EEGs, n=806). After removing artifacts and epochs with excess noise or altered state from EEGs, we extracted spectral features. We validated our preprocessing pipeline by comparing automatically-detected posterior dominant rhythm (PDR) to annotations from clinical EEG reports. Next, as a coarse measure of pathological slowing, we compared the alpha-delta bandpower ratio between controls and the different genetic epilepsies. We then trained random forest models to predict a diagnosis of STXBP1, SCN1A, and SYNGAP1. Finally, to understand how EEG features vary with neurological outcomes, we trained random forest models to predict seizure frequency and motor function. There was strong agreement between the automatically-calculated PDR and clinical EEG reports (R 2=0.75). Individuals with STXBP1-related epilepsy have a significantly lower alpha-delta ratio than controls (P<0.001) across all age groups. Additionally, individuals with a missense variant in STXBP1 have a significantly lower alpha-delta ratio than those with a protein-truncating variant in toddlers (P<0.001), children (P=0.02), and adults (P<0.001). Models accurately predicted a diagnosis of STXBP1 (AUC=0.91), SYNGAP1 (AUC=0.82), and SCN1A (AUC=0.86) against controls and from each other in a three-class model (accuracy=0.74). From these models, we isolated highly correlated biomarkers for these respective genetic disorders, including alpha-theta ratio in frontal, occipital, and parietal electrodes with STXBP1, SYNGAP1, and SCN1A, respectively. Models were unable to predict seizure frequency (AUC=0.53). Random forest models predicted motor scores significantly better than age-based null models (P<0.001), suggesting spectral features contain information pertinent to gross motor function. In summary, we demonstrate that STXBP1-, SYNGAP1-, and SCN1A-related epilepsies have distinct quantitative EEG signatures. Furthermore, EEG spectral features are predictive of some functional outcome measures in patients with genetic epilepsies. Large-scale retrospective quantitative analysis of clinical EEG has the potential to discover novel biomarkers and to quantify and track individuals' disease progression across development.

EEG connectivity and network analyses predict outcome in patients with disorders of consciousness - A systematic review and meta-analysis

Outcome prediction in prolonged disorders of consciousness (DOC) remains challenging. This can result in either inappropriate withdrawal of treatment or unnecessary prolongation of treatment. Electroencephalography (EEG) is a cheap, portable, and non-invasive device with various opportunities for complex signal analysis. Computational EEG measures, such as EEG connectivity and network metrics, might be ideal candidates for the investigation of DOC, but their capacity in prognostication is still undisclosed. We conducted a meta-analysis aiming to compare the prognostic power of the widely used clinical scale, Coma Recovery Scale-Revised - CRS-R and EEG connectivity and network metrics. We found that the prognostic power of the CRS-R scale was moderate (AUC: 0.67 (0.60-0.75)), but EEG connectivity and network metrics predicted outcome with significantly (p = 0.0071) higher accuracy (AUC:0.78 (0.70-0.86)). We also estimated the prognostic capacity of EEG spectral power, which was not significantly (p = 0.3943) inferior to that of the EEG connectivity and graph-theory measures (AUC:0.75 (0.70-0.80)). Multivariate automated outcome prediction tools seemed to outperform clinical and EEG markers.

QEEG indices in traumatic brain injury - insights from the CAPTAIN RTMS trial

This secondary analysis of the CAPTAIN-RTMS trial data focused on the significance of quantitative electroencephalography (qEEG) indices as indicators of recovery in patients with traumatic brain injury (TBI). By focusing on the delta alpha ratio (DAR), delta theta/alpha beta ratio (DTABR), and theta beta ratio (TBR), this study explored the shifts in brainwave activity as a response to an integrative treatment regimen of repetitive transcranial magnetic stimulation (rTMS) combined with the neurotrophic agent Cerebrolysin. Findings revealed significant increases in DAR and DTABR, suggesting changes in neurophysiological dynamics after treatment. However, variations in TBR were inconclusive in providing clear electrophysiological insights. These results indicate that further research is necessary to describe and understand the underlying mechanisms of brain recovery and to develop refined treatment frameworks for patients with TBI.

Quantitative EEG for the Monitoring of Walking Recovery in Chronic Stroke Patients Receiving Action Observation Training

The current study aimed to evaluate the effects of action observation on the walking ability and oscillatory brain activity of chronic stroke patients. Fourteen chronic stroke patients were allocated randomly to the action observation (AO) or sham observation (SO) groups. Both groups received 12 sessions of intervention. Each session composed of 12 min of observational training, which depicted exercises for the experimental group but nature pictures for the sham group and 40 min of occupational therapy, which was the same for the both groups. Walking ability was assessed by a motion analysis system and brain activity was monitored using quantitative electroencephalography (QEEG) before and after the intervention. Brain asymmetry at alpha frequency, the percentage of stance phase, and step length showed significant changes in the AO group. Only the change in global alpha power was significantly correlated with the change in velocity after the intervention in AO group. Despite more improvements in walking and brain activity of patients in the AO group, our study failed to show significant correlations between the brain activity changes and functional improvements after the intervention, which might be mainly due to the small sample size in our study. Trial registration: IRCT20181014041333N1.

Effects of anodal transcranial direct current stimulation over motor cortex on resting-state brain activity in the early subacute stroke phase: A power spectral density analysis

INTRODUCTION

Despite promising results, the effects of transcranial direct current stimulation (tDCS) in the early stages of stroke and its impact on brain activity have been poorly studied. Therefore, this study aimed to investigate the effect of tDCS applied over the ipsilesional motor cortex on resting-state brain activity in the early subacute phase of stroke.

METHODS

This is a pilot, randomized, double-blind, proof-of-concept study. The patients with stroke were randomly assigned into two groups: anodal tDCS (A-tDCS) or sham tDCS (S-tDCS). For A-tDCS, the anode was placed over the ipsilesional motor cortex, while the cathode was placed over the left or right supraorbital area (Fp2 for left stroke or Fp1 for right stroke). For the real stimulation, a constant current of 1.0 mA was delivered for 20 min and then ramped down linearly for 30 s, maintaining a resistance below 10 kΩ. For the sham stimulation, the stimulator was turned on, and the current intensity was gradually increased for 30 s, tapered off over 30 s, and maintained for 30 min without stimulation. Each stimulation was performed for three consecutive sessions with an interval of 1 h between them. The primary outcome was spectral electroencephalography (EEG) analysis based on the Power Spectral Density (PSD) determined by EEG records of areas F3, F4, C3, C4, P3, and P4. Brain Vision Analyzer software processed the signals, EEG power spectral density (PSD) was calculated before and after stimulation, and alpha, beta, delta, and theta power were analyzed. The secondary outcomes included hemodynamic variables based on the difference between baseline (D0) and post-intervention session (D1) values of systolic (SBP) and diastolic (DBP) blood pressure, heart rate (HR), respiratory rate (RR) and peripheral oxygen saturation (SPO2). Mann-Whitney test was used to compare position measurements of two independent samples; Fisher's exact test was used to compare two proportions; paired Wilcoxon signed-rank test was used to compare the median differences in the within-group comparison, and Spearman correlations matrix among spectral power analysis between EEG bands was performed to verify consistency of occurrence of oscillations. Statistical significance was set at P < 0.05.

RESULTS

An increase in PSD in the alpha frequency in the P4 region was observed after the intervention in the A-tDCS group, as compared to the placebo group (before = 6.13; after = 10.45; p < 0.05). In the beta frequency, an increase in PSD was observed in P4 (before = 4.40; after = 6.79; p < 0.05) and C4 (before = 4.43; after = 6.94; p < 0.05) after intervention in the A-tDCS group. There was a reduction in PSD at delta frequency in C3 (before = 293.8; after = 58.6; p < 0.05) after intervention in the A-tDCS group. In addition, it was observed a strong relationship between alpha and theta power in the A-tDCS group before and after intervention. However, the sham group showed correlations between more power bands (alpha and theta, alpha and delta, and delta and theta) after intervention. There was no difference in hemodynamic variables between the intra- (before and after stimulation) and inter-groups (mean difference).

CONCLUSION

Anodal tDCS over the ipsilesional motor cortex had significant effects on the brain electrical activity in the early subacute stroke phase, increasing alpha and beta wave activities in sensorimotor regions while reducing slow delta wave activity in motor regions. These findings highlight the potential of anodal tDCS as a therapeutic intervention in the early stroke phase.

Neurophysiological underpinnings of an intensive protocol for upper limb motor recovery in subacute and chronic stroke patients

BACKGROUND

Upper limb (UL) motor impairment following stroke is a leading cause of functional limitations in activities of daily living. Robot-assisted therapy supports rehabilitation, but how its efficacy and the underlying neural mechanisms depend on the time after stroke is yet to be assessed.

AIM

We investigated the response to an intensive protocol of robot-assisted rehabilitation in sub-acute and chronic stroke patients, by analyzing the underlying changes in clinical scores, electroencephalography (EEG) and end-effector kinematics. We aimed at identifying neural correlates of the participants' upper limb motor function recovery, following an intensive 2-week rehabilitation protocol.

DESIGN

Prospective cohort study.

SETTING

Inpatients and outpatients from the Neurorehabilitation Unit of Pisa University Hospital, Italy.

POPULATION

Sub-acute and chronic stroke survivors.

METHODS

Thirty-one stroke survivors (14 sub-acute, 17 chronic) with mild-to-moderate UL paresis were enrolled. All participants underwent ten rehabilitative sessions of task-oriented exercises with a planar end-effector robotic device. All patients were evaluated with the Fugl-Meyer Assessment Scale and the Wolf Motor Function Test, at recruitment (T0), end-of-treatment (T1), and one-month follow-up (T2). Along with clinical scales, kinematic parameters and quantitative EEG were collected for each patient. Kinematics metrics were related to velocity, acceleration and smoothness of the movement. Relative power in four frequency bands was extracted from the EEG signals. The evolution over time of kinematic and EEG features was analyzed, in correlation with motor recovery.

RESULTS

Both groups displayed significant gains in motility after treatment. Sub-acute patients displayed more pronounced clinical improvements, significant changes in kinematic parameters, and a larger increase in Beta-band in the motor area of the affected hemisphere. In both groups these improvements were associated to a decrease in the Delta-band of both hemispheres. Improvements were retained at T2.

CONCLUSIONS

The intensive two-week rehabilitation protocol was effective in both chronic and sub-acute patients, and improvements in the two groups shared similar dynamics. However, stronger cortical and behavioral changes were observed in sub-acute patients suggesting different reorganizational patterns.

CLINICAL REHABILITATION IMPACT

This study paves the way to personalized approaches to UL motor rehabilitation after stroke, as highlighted by different neurophysiological modifications following recovery in subacute and chronic stroke patients.

Brain microstate spatio-temporal dynamics as a candidate endotype of consciousness

Consciousness can be defined as a phenomenological experience continuously evolving. Current research showed how conscious mental activity can be subdivided into a series of atomic brain states converging to a discrete spatiotemporal pattern of global neuronal firing. Using the high temporal resolution of EEG recordings in patients with a severe Acquired Brain Injury (sABI) admitted to an Intensive Rehabilitation Unit (IRU), we detected a novel endotype of consciousness from the spatiotemporal brain dynamics identified via microstate analysis. Also, we investigated whether microstate features were associated with common neurophysiological alterations. Finally, the prognostic information comprised in such descriptors was analysed in a sub-cohort of patients with prolonged Disorder of Consciousness (pDoC). Occurrence of frontally-oriented microstates (C microstate), likelihood of maintaining such brain state or transitioning to the C topography and complexity were found to be indicators of consciousness presence and levels. Features of left-right asymmetric microstates and transitions toward them were found to be negatively correlated with antero-posterior brain reorganization and EEG symmetry. Substantial differences in microstates' sequence complexity and presence of C topography were found between groups of patients with alpha dominant background, cortical reactivity and antero-posterior gradient. Also, transitioning from left-right to antero-posterior microstates was found to be an independent predictor of consciousness recovery, stronger than consciousness levels at IRU's admission. In conclusions, global brain dynamics measured with scale-free estimators can be considered an indicator of consciousness presence and a candidate marker of short-term recovery in patients with a pDoC.

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.

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.

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.

Oral Treatment with the Extract of Euterpe oleracea Mart. Improves Motor Dysfunction and Reduces Brain Injury in Rats Subjected to Ischemic Stroke

Ischemic stroke is one of the principal causes of morbidity and mortality around the world. The pathophysiological mechanisms that lead to the formation of the stroke lesions range from the bioenergetic failure of the cells and the intense production of reactive oxygen species to neuroinflammation. The fruit of the açaí palm, Euterpe oleracea Mart. (EO), is consumed by traditional populations in the Brazilian Amazon region, and it is known to have antioxidant and anti-inflammatory properties. We evaluated whether the clarified extract of EO was capable of reducing the area of lesion and promoting neuronal survival following ischemic stroke in rats. Animals submitted to ischemic stroke and treated with EO extract presented a significant improvement in their neurological deficit from the ninth day onward. We also observed a reduction in the extent of the cerebral injury and the preservation of the neurons of the cortical layers. Taken together, our findings indicate that treatment with EO extract in the acute phase following a stroke can trigger signaling pathways that culminate in neuronal survival and promote the partial recovery of neurological scores. However, further detailed studies of the intracellular signaling pathways are needed to better understand the mechanisms involved.

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.

Pathological Slow-Wave Activity and Impaired Working Memory Binding in Post-Traumatic Amnesia

Associative binding is key to normal memory function and is transiently disrupted during periods of post-traumatic amnesia (PTA) following traumatic brain injury (TBI). Electrophysiological abnormalities, including low-frequency activity, are common following TBI. Here, we investigate associative memory binding during PTA and test the hypothesis that misbinding is caused by pathological slowing of brain activity disrupting cortical communication. Thirty acute moderate to severe TBI patients (25 males; 5 females) and 26 healthy controls (20 males; 6 females) were tested with a precision working memory paradigm requiring the association of object and location information. Electrophysiological effects of TBI were assessed using resting-state EEG in a subsample of 17 patients and 21 controls. PTA patients showed abnormalities in working memory function and made significantly more misbinding errors than patients who were not in PTA and controls. The distribution of localization responses was abnormally biased by the locations of nontarget items for patients in PTA, suggesting a specific impairment of object and location binding. Slow-wave activity was increased following TBI. Increases in the δ-α ratio indicative of an increase in low-frequency power specifically correlated with binding impairment in working memory. Connectivity changes in TBI did not correlate with binding impairment. Working memory and electrophysiological abnormalities normalized at 6 month follow-up. These results show that patients in PTA show high rates of misbinding that are associated with a pathological shift toward lower-frequency oscillations.SIGNIFICANCE STATEMENT How do we remember what was where? The mechanism by which information (e.g., object and location) is integrated in working memory is a central question for cognitive neuroscience. Following significant head injury, many patients will experience a period of post-traumatic amnesia (PTA) during which this associative binding is disrupted. This may be because of electrophysiological changes in the brain. Using a precision working memory test and resting-state EEG, we show that PTA patients demonstrate impaired binding ability, and this is associated with a shift toward slower-frequency activity on EEG. Abnormal EEG connectivity was observed but was not specific to PTA or binding ability. These findings contribute to both our mechanistic understanding of working memory binding and PTA pathophysiology.

Dexmedetomidine-mediated sleep phase modulation ameliorates motor and cognitive performance in a chronic blast-injured mouse model

Multiple studies have shown that blast injury is followed by sleep disruption linked to functional sequelae. It is well established that improving sleep ameliorates such functional deficits. However, little is known about longitudinal brain activity changes after blast injury. In addition, the effects of directly modulating the sleep/wake cycle on learning task performance after blast injury remain unclear. We hypothesized that modulation of the sleep phase cycle in our injured mice would improve post-injury task performance. Here, we have demonstrated that excessive sleep electroencephalographic (EEG) patterns are accompanied by prominent motor and cognitive impairment during acute stage after secondary blast injury (SBI) in a mouse model. Over time we observed a transition to more moderate and prolonged sleep/wake cycle disturbances, including changes in theta and alpha power. However, persistent disruptions of the non-rapid eye movement (NREM) spindle amplitude and intra-spindle frequency were associated with lasting motor and cognitive deficits. We, therefore, modulated the sleep phase of injured mice using subcutaneous (SC) dexmedetomidine (Dex), a common, clinically used sedative. Dex acutely improved intra-spindle frequency, theta and alpha power, and motor task execution in chronically injured mice. Moreover, dexmedetomidine ameliorated cognitive deficits a week after injection. Our results suggest that SC Dex might potentially improve impaired motor and cognitive behavior during daily tasks in patients that are chronically impaired by blast-induced injuries.

Changes in EEG Activity Following Live Z-Score Training Predict Changes in Persistent Post-concussive Symptoms: An Exploratory Analysis

A specific variant of neurofeedback therapy (NFT), Live Z-Score Training (LZT), can be configured to not target specific EEG frequencies, networks, or regions of the brain, thereby permitting implicit and flexible modulation of EEG activity. In this exploratory analysis, the relationship between post-LZT changes in EEG activity and self-reported symptom reduction is evaluated in a sample of patients with persistent post-concussive symptoms (PPCS). Penalized regressions were used to identify EEG metrics associated with changes in physical, cognitive, and affective symptoms; the predictive capacity of EEG variables selected by the penalized regressions were subsequently validated using linear regression models. Post-treatment changes in theta/alpha ratio predicted reduction in pain intensity and cognitive symptoms and changes in beta-related power metrics predicted improvements in affective symptoms. No EEG changes were associated with changes in a majority of physical symptoms. These data highlight the potential for NFT to target specific EEG patterns to provide greater treatment precision for PPCS patients. This exploratory analysis is intended to promote the refinement of NFT treatment protocols to improve outcomes for patients with PPCS.

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.

Prediction of patient survival following postanoxic coma using EEG data and clinical features

Electroencephalography (EEG) is an effective and non-invasive technique commonly used to monitor brain activity and assist in outcome prediction for comatose patients post cardiac arrest. EEG data may demonstrate patterns associated with poor neurological outcome for patients with hypoxic injury. Thus, both quantitative EEG (qEEG) and clinical data contain prognostic information for patient outcome. In this study we use machine learning (ML) techniques, random forest (RF) and support vector machine (SVM) to classify patient outcome post cardiac arrest using qEEG and clinical feature sets, individually and combined. Our ML experiments show RF and SVM perform better using the joint feature set. In addition, we extend our work by implementing a convolutional neural network (CNN) based on time-frequency images derived from EEG to compare with our qEEG ML models. The results demonstrate significant performance improvement in outcome prediction using non-feature based CNN compared to our feature based ML models. Implementation of ML and DL methods in clinical practice have the potential to improve reliability of traditional qualitative assessments for postanoxic coma 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.

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.

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.

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.

Spectral Resting-State EEG (rsEEG) in Chronic Aphasia Is Reliable, Sensitive, and Correlates With Functional Behavior

We investigated spectral resting-state EEG in persons with chronic stroke-induced aphasia to determine its reliability, sensitivity, and relationship to functional behaviors. Resting-state EEG has not yet been characterized in this population and was selected given the demonstrated potential of resting-state investigations using other neuroimaging techniques to guide clinical decision-making. Controls and persons with chronic stroke-induced aphasia completed two EEG recording sessions, separated by approximately 1 month, as well as behavioral assessments of language, sensorimotor, and cognitive domains. Power in the classic frequency bands (delta, theta, alpha, and beta) was examined via spectral analysis of resting-state EEG data. Results suggest that power in the theta, alpha, and beta bands is reliable for use as a repeated measure. Significantly greater theta and lower beta power was observed in persons with aphasia (PWAs) than controls. Finally, in PWAs theta power negatively correlated with performance on a discourse informativeness measure, while alpha and beta power positively correlated with performance on the same measure. This indicates that spectral rsEEG slowing observed in PWAs in the chronic stage is pathological and suggests a possible avenue for directly altering brain activation to improve behavioral function. Taken together, these results suggest that spectral resting-state EEG holds promise for sensitive measurement of functioning and change in persons with chronic aphasia. Future studies investigating the utility of these measures as biomarkers of frank or latent aphasic deficits and treatment response in chronic stroke-induced aphasia are warranted.

Monitoring of patients with brainstem hemorrhage: A simultaneous study of quantitative electroencephalography and transcranial Doppler

OBJECTIVE

To explore whether quantitative electroencephalography (QEEG) and transcranial Doppler (TCD) can be used to evaluate patients with acute severe brainstem hemorrhage (ASBH).

METHODS

We prospectively enrolled patients with ASBH and assessed their mortality at the 90-day follow-up. The patients' demographic data, serological data, and clinical factors were recorded. Quantitative brain function monitoring was performed using a TCD-QEEG recording system attached to the patient's bedside.

RESULTS

Thirty-one patients (55.3 ± 10.6 years; 17 men) were studied. Mortality at 90 days was at 61.3%. There was no significant difference in TCD-related parameters between the survival group and the death group (p > 0.05). Among the QEEG-related indexes, only the (delta + theta)/(alpha + beta) ratio (DTABR) (odds ratio 11.555, 95%confidence interval 1.413-94.503, p = 0.022) was an independent predictor of clinical outcome; the area under the ROC curve of DTABR was 0.921, cut-off point was 3.88, sensitivity was 79%, and specificity was 100%.

CONCLUSIONS

In patients with ASBH, QEEG can effectively inform the clinical prognosis regarding 90-day mortality, while TCD cannot.

SIGNIFICANCE

QEEG shows promise for informing the mortality prognosis of patients with ASBH.

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.

Normobaric Oxygen May Ameliorate Cerebral Venous Outflow Disturbance-Related Neurological Symptoms

Cerebral venous outflow disturbance (CVOD) has begun to garner the attention of researches owing to a series of clinical symptoms that impose a significant impact on people's quality of life. Herein, we aimed to investigate whether normobaric oxygen (NBO) can ameliorate CVOD-induced neurological symptoms. This was one part of the prospective trial registered in ClinicalTrials.gov (NCT03373292). A total of 37 CVOD patients were divided into the NBO group (5–8 L/min of oxygen inhalation, 1 h per time, 3 times daily, n = 19) and the control group (without oxygen inhalation, n = 18) randomly. The assessments were performed at admission, 1-week hospitalization, and 6-month follow-up. Quantitative electroencephalogram (qEEG) data were recorded prior to and post 1 h of NBO in some patients. R software was used for data analysis. No NBO-related adverse events were observed during the whole NBO intervention process. The 1-week Patient Global Impression of Change (PGIC) scale showed that the symptom improvement occurred in nine patients in the NBO group (47.4%) while none in the control group (p = 0.001). NBO could improve headache evaluated with visual analog scale (pre-NBO vs. post-NBO: 4.70 ± 2.16 vs. 2.90 ± 2.03, p = 0.024) and Headache Impact Test-6 (53.40 ± 12.15 vs. 50.30 ± 13.04, p = 0.041). As for 6-month PGIC follow-up, eight out of 14 cases (57.1%) in the NBO group reported improvement, while only one out of 12 patients in the control group replied mild improvement (p = 0.014). The qEEG revealed that NBO reduced the ratio of theta to alpha power (0.65 ± 0.38 vs. 0.56 ± 0.35, p = 0.030) over the fronto-central electrodes. To sum up, NBO may be a safe and effective approach to attenuate CVOD-related symptoms (especially for headache) by brain functional improvement resulting from increasing oxygen supply to the brain tissues.

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.

The Feasibility of Longitudinal Upper Extremity Motor Function Assessment Using EEG

Motor function assessment is crucial in quantifying motor recovery following stroke. In the rehabilitation field, motor function is usually assessed using questionnaire-based assessments, which are not completely objective and require prior training for the examiners. Some research groups have reported that electroencephalography (EEG) data have the potential to be a good indicator of motor function. However, those motor function scores based on EEG data were not evaluated in a longitudinal paradigm. The ability of the motor function scores from EEG data to track the motor function changes in long-term clinical applications is still unclear. In order to investigate the feasibility of using EEG to score motor function in a longitudinal paradigm, a convolutional neural network (CNN) EEG model and a residual neural network (ResNet) EEG model were previously generated to translate EEG data into motor function scores. To validate applications in monitoring rehabilitation following stroke, the pre-established models were evaluated using an initial small sample of individuals in an active 14-week rehabilitation program. Longitudinal performances of CNN and ResNet were evaluated through comparison with standard Fugl-Meyer Assessment (FMA) scores of upper extremity collected in the assessment sessions. The results showed good accuracy and robustness with both proposed networks (average difference: 1.22 points for CNN, 1.03 points for ResNet), providing preliminary evidence for the proposed method in objective evaluation of motor function of upper extremity in long-term clinical applications.

EEG Biomarkers Related With the Functional State of Stroke Patients

Introduction

Recent studies explored promising new quantitative methods to analyze electroencephalography (EEG) signals. This paper analyzes the correlation of two EEG parameters, Brain Symmetry Index (BSI) and Laterality Coefficient (LC), with established functional scales for the stroke assessment.

Methods

Thirty-two healthy subjects and thirty-six stroke patients with upper extremity hemiparesis were recruited for this study. The stroke patients where subdivided in three groups according to the stroke location: Cortical, Subcortical, and Cortical + Subcortical. The participants performed assessment visits to record the EEG in the resting state and perform functional tests using rehabilitation scales. Then, stroke patients performed 25 sessions using a motor-imagery based Brain Computer Interface system (BCI). BSI was calculated with the EEG data in resting state and LC was calculated with the Event-Related Synchronization maps.

Results

The results of this study demonstrated significant differences in the BSI between the healthy group and Subcortical group (P = 0.001), and also between the healthy and Cortical+Subcortical group (P = 0.019). No significant differences were found between the healthy group and the Cortical group (P = 0.505). Furthermore, the BSI analysis in the healthy group based on gender showed statistical differences (P = 0.027). In the stroke group, the correlation between the BSI and the functional state of the upper extremity assessed by Fugl-Meyer Assessment (FMA) was also significant, ρ = −0.430 and P = 0.046. The correlation between the BSI and the FMA-Lower extremity was not significant (ρ = −0.063, P = 0.852). Similarly, the LC calculated in the alpha band has significative correlation with FMA of upper extremity (ρ = −0.623 and P < 0.001) and FMA of lower extremity (ρ = −0.509 and P = 0.026). Other important significant correlations between LC and functional scales were observed. In addition, the patients showed an improvement in the FMA-upper extremity after the BCI therapy (ΔFMA = 1 median [IQR: 0–8], P = 0.002).

Conclusion

The quantitative EEG tools used here may help support our understanding of stroke and how the brain changes during rehabilitation therapy. These tools can help identify changes in EEG biomarkers and parameters during therapy that might lead to improved therapy methods and functional prognoses.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Mapping critical hubs of receptive and expressive language using MEG: A comparison against fMRI

The complexity of the widespread language network makes it challenging for accurate localization and lateralization. Using large-scale connectivity and graph-theoretical analyses of task-based magnetoencephalography (MEG), we aimed to provide robust representations of receptive and expressive language processes, comparable with spatial profiles of corresponding functional magnetic resonance imaging (fMRI). We examined MEG and fMRI data from 12 healthy young adults (age 20-37 years) completing covert auditory word-recognition task (WRT) and covert auditory verb-generation task (VGT). For MEG language mapping, broadband (3-30 Hz) beamformer sources were estimated, voxel-level connectivity was quantified using phase locking value, and highly connected hubs were characterized using eigenvector centrality graph measure. fMRI data were analyzed using a classic general linear model approach. A laterality index (LI) was computed for 20 language-specific frontotemporal regions for both MEG and fMRI. MEG network analysis showed bilateral and symmetrically distributed hubs within the left and right superior temporal gyrus (STG) during WRT and predominant hubs in left inferior prefrontal gyrus (IFG) during VGT. MEG and fMRI localization maps showed high correlation values within frontotemporal regions during WRT and VGT (r = 0.63, 0.74, q < 0.05, respectively). Despite good concordance in localization, notable discordances were observed in lateralization between MEG and fMRI. During WRT, MEG favored a left-hemispheric dominance of left STG (LI = 0.25 ± 0.22) whereas fMRI supported a bilateral representation of STG (LI = 0.08 ± 0.2). Laterality of MEG and fMRI during VGT consistently showed a strong asymmetry in left IFG regions (MEG-LI = 0.45 ± 0.35 and fMRI-LI = 0.46 ± 0.13). Our results demonstrate the utility of a large-scale connectivity and graph theoretical analyses for robust identification of language-specific regions. MEG hubs are in great agreement with the literature in revealing with canonical and extra-canonical language sites, thus providing additional support for the underlying topological organization of receptive and expressive language cortices. Discordances in lateralization may emphasize the need for multimodal integration of MEG and fMRI to obtain an excellent predictive value in a heterogeneous healthy population and patients with neurosurgical conditions.

Prognostic and Monitory EEG-Biomarkers for BCI Upper-Limb Stroke Rehabilitation

With the availability of multiple rehabilitative interventions, identifying the one that elicits the best motor outcome based on the unique neuro-clinical profile of the stroke survivor is a challenging task. Predicting the potential of recovery using biomarkers specific to an intervention hence becomes important. To address this, we investigate intervention-specific prognostic and monitory biomarkers of motor function improvements using quantitative electroencephalography (QEEG) features in 19 chronic stroke patients following two different upper extremity rehabilitative interventions viz. Brain-computer interface (BCI) and transcranial direct current stimulation coupled BCI (tDCS-BCI). Brain symmetry index was found to be the best prognostic QEEG for clinical gains following BCI intervention ( r = -0.80 , p = 0.02 ), whereas power ratio index (PRI) was observed to be the best predictor for tDCS-BCI ( r = -0.96 , p = 0.004 ) intervention. Importantly, statistically significant between-intervention differences observed in the predictive capabilities of these features suggest that intervention-specific biomarkers can be identified. This approach can be further pursued to distinctly predict the expected response of a patient to available interventions. The intervention with the highest predicted gains may then be recommended to the patient, thereby enabling a personalized rehabilitation regime.

Comparison of complex regional pain syndrome and fibromyalgia: Differences in beta and gamma bands on quantitative electroencephalography

Complex regional pain syndrome (CRPS) and fibromyalgia (FM) share many features. Both can cause severe pain and are considered to have a mechanism of action, including dysfunction of the sympathetic nervous system. However, they have clinical differences in pain range and degree. The present study aimed to find neurophysiologic differences between CRPS and FM using quantitative electroencephalography (QEEG). Thirty-eight patients with CRPS and 33 patients with FM were included in the analysis. Resting-state QEEG data were grouped into frontal, central, and posterior regions to analyze for regional differences. General linear models were utilized to test for group differences in absolute and relative powers. As a result, the CRPS group relative to FM group showed lower total absolute powers in the beta band (F = 5.159, P < .05), high beta (F = 14.120, P < .05), and gamma band (F = 15.034, P < .05). There were no significant differences between 2 groups in the delta, theta, and alpha bands. The present findings show that the CRPS and FM groups differ mainly in the high frequency, which may reflect their distinct pathophysiology and symptomatology. Our study suggests that the QEEG differences can be clinically useful in assessing brain function in patients with CRPS and FM.

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.

Frail phenotype is associated with distinct quantitative electroencephalographic findings among end-stage renal disease patients: an observational study

Background

Frailty is prevalent among patients with end-stage renal disease (ESRD) and is associated with an increased risk of cognitive impairment. However, apart from its influence on cognition, it is currently unknown whether frailty affects subtler cerebral function in patients with ESRD.

Methods

Patients with ESRD were prospectively enrolled, with clinical features and laboratory data recorded. The severity of frailty among these patients with ESRD was ascertained using the previously validated simple FRAIL scale, and was categorized as none-to-mild and moderate-to-severe frailty. All participants underwent quantitative electroencephalography (EEG), with band powers documented following the generation of the delta to alpha ratio (DAR) and delta/theta to alpha/beta ratio (DTABR). EEG results were then compared between groups of different levels of frailty.

Results

In this cohort, (mean age: 68.9 ± 10.4 years, 37% male, 3.4 ± 3 years of dialysis), 20, 60, 40, 17, and 6% patients exhibited positivity in the fatigue, resistance, ambulation, illness, and loss-of-body-weight domains, respectively, with 45.7% being none to mildly frail and 54.3% being moderately to severely frail. Those with mild frailty had a significantly higher delta power compared to those with more severe frailty, involving all topographic sites. Patients with ESRD and severe frailty had significantly lower global, left frontal, left temporo-occipital, and right temporo-occipital DAR and DTABR, except in the right frontal area, and tended to have central accentuation of alpha, beta, and theta power, and more homogeneous DTABR and DAR distribution compared to the findings in those with mild frailty.

Conclusions

Frailty in patients with ESRD can have subtler neurophysiological influences, presenting as altered EEG findings, which warrant our attention.

Acute single channel EEG predictors of cognitive function after stroke

BACKGROUND

Early and accurate identification of factors that predict post-stroke cognitive outcome is important to set realistic targets for rehabilitation and to guide patients and their families accordingly. However, behavioral measures of cognition are difficult to obtain in the acute phase of recovery due to clinical factors (e.g. fatigue) and functional barriers (e.g. language deficits). The aim of the current study was to test whether single channel wireless EEG data obtained acutely following stroke could predict longer-term cognitive function.

METHODS

Resting state Relative Power (RP) of delta, theta, alpha, beta, delta/alpha ratio (DAR), and delta/theta ratio (DTR) were obtained from a single electrode over FP1 in 24 participants within 72 hours of a first-ever stroke. The Montreal Cognitive Assessment (MoCA) was administered at 90-days post-stroke. Correlation and regression analyses were completed to identify relationships between 90-day cognitive function and electrophysiological data, neurological status, and demographic characteristics at admission.

RESULTS

Four acute qEEG indices demonstrated moderate to high correlations with 90-day MoCA scores: DTR (r = -0.57, p = 0.01), RP theta (r = 0.50, p = 0.01), RP delta (r = -0.47, p = 0.02), and DAR (r = -0.45, p = 0.03). Acute DTR (b = -0.36, p < 0.05) and stroke severity on admission (b = -0.63, p < 0.01) were the best linear combination of predictors of MoCA scores 90-days post-stroke, accounting for 75% of variance.

CONCLUSIONS

Data generated by a single pre-frontal electrode support the prognostic value of acute DAR, and identify DTR as a potential marker of post-stroke cognitive outcome. Use of single channel recording in an acute clinical setting may provide an efficient and valid predictor of cognitive function after stroke.

Delta Power Is Higher and More Symmetrical in Ischemic Stroke Patients with Cortical Involvement

A brain injury resulting from unilateral stroke critically alters brain functionality and the complex balance within the cortical activity. Such modifications may critically depend on lesion location and cortical involvement. Indeed, recent findings pointed out the necessity of applying a stratification based on lesion location when investigating inter-hemispheric balance in stroke. Here, we tested whether cortical involvement could imply differences in band-specific activity and brain symmetry in post stroke patients with cortico-subcortical and subcortical strokes. We explored brain activity related to lesion location through EEG power analysis and quantitative Electroencephalography (qEEG) measures. Thirty stroke patients in the subacute phase and 10 neurologically intact age-matched right-handed subjects were enrolled. Stroke patients were equally subdivided in two groups based on lesion location: cortico-subcortical (CS, mean age ± SD: 72.21 ± 10.97 years; time since stroke ± SD: 31.14 ± 11.73 days) and subcortical (S, mean age ± SD: 68.92 ± 10.001 years; time since stroke ± SD: 26.93 ± 13.08 days) group. We assessed patients’ neurological status by means of National Institutes of Health Stroke Scale (NIHSS). High density EEG at rest was recorded and power spectral analysis in Delta (1–4 Hz) and Alpha (8–14 Hz) bands was performed. qEEG metrics as pairwise derived Brain Symmetry Index (pdBSI) and Delta/Alpha Ratio (DAR) were computed and correlated with NIHSS score. S showed a lower Delta power in the Unaffected Hemisphere (UH) compared to Affected Hemisphere (AH; z = −1.98, p < 0.05) and a higher Alpha power compared to CS (z = −2.18, p < 0.05). pdBSI was negatively correlated with NIHSS (R = −0.59, p < 0.05). CS showed a higher value and symmetrical distribution of Delta band activity (z = −2.37, p < 0.05), confirmed also by a higher DAR value compared to S (z = −2.48, p < 0.05). Patients with cortico-subcortical and subcortical lesions show different brain symmetry in the subacute phase. Interestingly, in subcortical stroke patient brain activity is related with the clinical function. qEEG measures can be explicative of brain activity related to lesion location and they could allow precise definition of diagnostic-therapeutic algorithms in stroke patients.

Continuous EEG Monitoring for Early Detection of Delayed Cerebral Ischemia in Subarachnoid Hemorrhage: A Pilot Study

INTRODUCTION

Early identification of delayed cerebral ischemia (DCI) in patients with aneurysmal subarachnoid hemorrhage (aSAH) is a major challenge. The aim of this study was to investigate whether quantitative EEG (qEEG) features can detect DCI prior to clinical or radiographic findings.

METHODS

A prospective cohort study was performed in aSAH patients in whom continuous EEG (cEEG) was recorded. We studied 12 qEEG features. We compared the time point at which qEEG changed with the time point that clinical deterioration occurred or new ischemia was noted on CT scan.

RESULTS

Twenty aSAH patients were included of whom 11 developed DCI. The alpha/delta ratio (ADR) was the most promising feature that showed a significant difference in change over time in the DCI group (median -62% with IQR -87 to -39%) compared to the control group (median +27% with IQR -32 to +104%, p = 0.013). Based on the ROC curve, a threshold was chosen for a combined measure of ADR and alpha variability (AUC: 91.7, 95% CI 74.2-100). The median time that elapsed between change of qEEG and clinical DCI diagnosis was seven hours (IQR -11-25). Delay between qEEG and CT scan changes was 44 h (median, IQR 14-117).

CONCLUSION

In this study, ADR and alpha variability could detect DCI development before ischemic changes on CT scan was apparent and before clinical deterioration was noted. Implementation of cEEG in aSAH patients can probably improve early detection of DCI.

EEG slow waves in traumatic brain injury: Convergent findings in mouse and man

OBJECTIVE

Evidence from previous studies suggests that greater sleep pressure, in the form of EEG-based slow waves, accumulates in specific brain regions that are more active during prior waking experience. We sought to quantify the number and coherence of EEG slow waves in subjects with mild traumatic brain injury (mTBI).

METHODS

We developed a method to automatically detect individual slow waves in each EEG channel, and validated this method using simulated EEG data. We then used this method to quantify EEG-based slow waves during sleep and wake states in both mouse and human subjects with mTBI. A modified coherence index that accounts for information from multiple channels was calculated as a measure of slow wave synchrony.

RESULTS

Brain-injured mice showed significantly higher theta:alpha amplitude ratios and significantly more slow waves during spontaneous wakefulness and during prolonged sleep deprivation, compared to sham-injured control mice. Human subjects with mTBI showed significantly higher theta:beta amplitude ratios and significantly more EEG slow waves while awake compared to age-matched control subjects. We then quantified the global coherence index of slow waves across several EEG channels in human subjects. Individuals with mTBI showed significantly less EEG global coherence compared to control subjects while awake, but not during sleep. EEG global coherence was significantly correlated with severity of post-concussive symptoms (as assessed by the Neurobehavioral Symptom Inventory scale).

CONCLUSION AND IMPLICATIONS

Taken together, our data from both mouse and human studies suggest that EEG slow wave quantity and the global coherence index of slow waves may represent a sensitive marker for the diagnosis and prognosis of mTBI and post-concussive symptoms.

Sleep/Wake Modulation of Polysomnographic Patterns has Prognostic Value in Pediatric Unresponsive Wakefulness Syndrome

STUDY OBJECTIVE

Sleep patterns of pediatric patients in unresponsive wakefulness syndrome (UWS) have been poorly investigated, and the prognostic potential of polysomnography (PSG) in these subjects is still uncertain. The goal of the study was to identify quantitative PSG indices to be applied as possible prognostic markers in pediatric UWS.

METHODS

We performed PSG in 27 children and adolescents with UWS due to acquired brain damage in the subacute phase. Patients underwent neurological examination and clinical assessment with standardized scales. Outcome was assessed after 36 mo. PSG tracks were scored for sleep stages and digitally filtered. The spectral difference between sleep and wake was computed, as the percent difference at specific spectral frequencies. We computed (1) the ratio between percent power in the delta and alpha frequency bands, (2) the ratio between alpha and theta frequency bands, and (3) the power ratio index, during wake and sleep, as proposed in previous literature. The predictive role of several clinical and PSG measures was tested by logistic regression.

RESULTS

Correlation was found between the differential measures of electroencephalographic activity during sleep and wake in several frequency bands and the clinical scales (Glasgow Outcome Score, Level of Cognitive Functioning Assessment Scale, and Disability Rating Scale) at follow-up; the Sleep Patterns for Pediatric Unresponsive Wakefulness Syndrome (SPPUWS) scores correlated with the differential measures, and allowed outcome prediction with 96.3% of accuracy.

CONCLUSIONS

The differential measure of electroencephalographic activity during sleep and wake in the beta band and, more incisively, SPPUWS can help in determining the capability to recover from pediatric UWS well before the confirmation provided by suitable clinical scales.

Low-Complexity Seizure Prediction From iEEG/sEEG Using Spectral Power and Ratios of Spectral Power

Prediction of seizures is a difficult problem as the EEG patterns are not wide-sense stationary and change from seizure to seizure, electrode to electrode, and from patient to patient. This paper presents a novel patient-specific algorithm for prediction of seizures in epileptic patients from either one or two single-channel or bipolar channel intra-cranial or scalp electroencephalogram (EEG) recordings with low hardware complexity. Spectral power features are extracted and their ratios are computed. For each channel, a total of 44 features including 8 absolute spectral powers, 8 relative spectral powers and 28 spectral power ratios are extracted every two seconds using a 4-second window with a 50% overlap. These features are then ranked and selected in a patient-specific manner using a two-step feature selection. Selected features are further processed by a second-order Kalman filter and then input to a linear support vector machine (SVM) classifier. The algorithm is tested on the intra-cranial EEG (iEEG) from the Freiburg database and scalp EEG (sEEG) from the MIT Physionet database. The Freiburg database contains 80 seizures among 18 patients in 427 hours of recordings. The MIT EEG database contains 78 seizures from 17 children in 647 hours of recordings. It is shown that the proposed algorithm can achieve a sensitivity of 100% and an average false positive rate (FPR) of 0.0324 per hour for the iEEG (Freiburg) database and a sensitivity of 98.68% and an average FPR of 0.0465 per hour for the sEEG (MIT) database. These results are obtained with leave-one-out cross-validation where the seizure being tested is always left out from the training set. The proposed algorithm also has a low complexity as the spectral powers can be computed using FFT. The area and power consumption of the proposed linear SVM are 2 to 3 orders of magnitude less than a radial basis function kernel SVM (RBF-SVM) classifier. Furthermore, the total energy consumption of a system using linear SVM is reduced by 8% to 23% compared to system using RBF-SVM.

Eye-Movement Training Results in Changes in qEEG and NIH Stroke Scale in Subjects Suffering from Acute Middle Cerebral Artery Ischemic Stroke: A Randomized Control Trial

Context

Eye-movement training (EMT) can induce altered brain activation and change the functionality of saccades with changes of the brain in general.

Objective

To determine if EMT would result in changes in quantitative electroencephalogram (qEEG) and NIH Stroke Scale (NIHSS) in patients suffering from acute middle cerebral artery (MCA) infarction. Our hypothesis is that there would be positive changes in qEEG and NIHSS after EMT in patients suffering from acute MCA ischemic stroke.

Design

Double-blind randomized controlled trial.

Setting and participants

Thirty-four subjects with acute MCA ischemic stroke treated at university affiliated hospital intensive care unit.

Interventions

Subjects were randomized into a “control” group treated only with aspirin (125 mg/day) and a “treatment” group treated with aspirin (125 mg/day) and a subject-specific EMT.

Main outcome measures

Delta–alpha ratio, power ratio index, and the brain symmetry index calculated by qEEG and NIHSS.

Results

There was strong statistical and substantive significant improvement in all outcome measures for the group of stroke patients undergoing EMT. Such improvement was not observed for the “control” group, and there were no adverse effects.

Conclusion

The addition of EMT to a MCA ischemic stroke treatment paradigm has demonstrated statistically significant changes in outcome measures and is a low cost, safe, and effective complement to standard treatment.

A Review of the Effectiveness of Neuroimaging Modalities for the Detection of Traumatic Brain Injury

The incidence of traumatic brain injury (TBI) in the United States was 3.5 million cases in 2009, according to the Centers for Disease Control and Prevention. It is a contributing factor in 30.5% of injury-related deaths among civilians. Additionally, since 2000, more than 260,000 service members were diagnosed with TBI, with the vast majority classified as mild or concussive (76%). The objective assessment of TBI via imaging is a critical research gap, both in the military and civilian communities. In 2011, the Department of Defense (DoD) prepared a congressional report summarizing the effectiveness of seven neuroimaging modalities (computed tomography [CT], magnetic resonance imaging [MRI], transcranial Doppler [TCD], positron emission tomography, single photon emission computed tomography, electrophysiologic techniques [magnetoencephalography and electroencephalography], and functional near-infrared spectroscopy) to assess the spectrum of TBI from concussion to coma. For this report, neuroimaging experts identified the most relevant peer-reviewed publications and assessed the quality of the literature for each of these imaging technique in the clinical and research settings. Although CT, MRI, and TCD were determined to be the most useful modalities in the clinical setting, no single imaging modality proved sufficient for all patients due to the heterogeneity of TBI. All imaging modalities reviewed demonstrated the potential to emerge as part of future clinical care. This paper describes and updates the results of the DoD report and also expands on the use of angiography in patients with TBI.

Defining abnormal slow EEG activity in acute ischaemic stroke: Delta/alpha ratio as an optimal QEEG index

OBJECTIVE

Quantitative electroencephalographic (QEEG) indices sensitive to abnormal slow (relative to faster) activity power seem uniquely informative for clinical management of ischaemic stroke (IS), including around acute reperfusion therapies. However these have not been compared between IS and control samples. The primary objective was to identify the QEEG slowing index and threshold value which can most accurately discriminate between IS patients and controls.

METHODS

The samples comprised 28 controls (mean age: 70.4; range: 56-84) and 18 patients (mean age: 69.3; range: 51-86). Seven indices were analysed: relative bandpower (delta, theta, alpha, beta), delta/alpha power ratio (DAR), (delta+theta)/(alpha+beta) ratio (DTABR) and QSLOWING. The accuracies of each index for classifying participants (IS or control) were analysed using receiver operating characteristic (ROC) techniques.

RESULTS

All indices differed significantly between the samples (p<.001). DAR alone exhibited optimal classifier accuracy, with a threshold of 3.7 demonstrating 100% sensitivity and 100% specificity for discriminating between radiologically-confirmed, acute IS or control. DTABR and relative delta were the next most accurate classifiers.

CONCLUSIONS

DAR of 3.7 demonstrated maximal accuracy for classifying all 46 participants as acute IS or control.

SIGNIFICANCE

DAR assessment may inform clinical management of IS and perhaps other neurocritical patients.

Is There a Relation between EEG-Slow Waves and Memory Dysfunction in Epilepsy? A Critical Appraisal

Is there a relationship between peri-ictal slow waves, loss of consciousness, memory, and slow-wave sleep, in patients with different forms of epilepsy? We hypothesize that mechanisms, which result in peri-ictal slow-wave activity as detected by the electroencephalogram, could negatively affect memory processes. Slow waves (≤4 Hz) can be found in seizures with impairment of consciousness and also occur in focal seizures without impairment of consciousness but with inhibited access to memory functions. Peri-ictal slow waves are regarded as dysfunctional and are probably caused by mechanisms, which are essential to disturb the consolidation of memory entries in these patients. This is in strong contrast to physiological slow-wave activity during deep sleep, which is thought to group memory-consolidating fast oscillatory activity. In patients with epilepsy, slow waves may not only correlate with the peri-ictal clouding of consciousness, but could be the epiphenomenon of mechanisms, which interfere with normal brain function in a wider range. These mechanisms may have transient impacts on memory, such as temporary inhibition of memory systems, altered patterns of hippocampal-neocortical interactions during slow-wave sleep, or disturbed cross-frequency coupling of slow and fast oscillations. In addition, repeated tonic-clonic seizures over the years in uncontrolled chronic epilepsy may cause a progressive cognitive decline. This hypothesis can only be assessed in long-term prospective studies. These studies could disentangle the reversible short-term impacts of seizures, and the impacts of chronic uncontrolled seizures. Chronic uncontrolled seizures lead to irreversible memory impairment. By contrast, short-term impacts do not necessarily lead to a progressive cognitive decline but result in significantly impaired peri-ictal memory performance.

Treating Postconcussion Syndrome with LORETA Z-Score Neurofeedback and Heart Rate Variability Biofeedback: Neuroanatomical/Neurophysiological Rationale, Methods, and Case Examples

Media attention has highlighted the critical problem of concussion injuries in sport and the challenge of treating and rehabilitating individuals with traumatic brain injury. The authors present a framework for the treatment of traumatic brain injury, using low-resolution electromagnetic tomography Z-score based neurofeedback and heart rate–variability biofeedback. The article advocates a comprehensive assessment process including the use of a 19-channel quantitative electroencephalogram, a heart rate variability baseline, and symptom severity questionnaires for attention deficit/hyperactivity disorder, depression, and anxiety. The initial medical assessment, neuropsychological assessment, and evoked potential studies also have potential for a more precise assessment of deficits in brain activation patterns, which assists the targeting of neurofeedback training.