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

Quantitative electroencephalography to assess post-stroke functional disability: A systematic review and meta-analysis

OBJECTIVE

Quantitative electroencephalography (QEEG) is a non-invasive, reliable and easily accessible modality to assess neuronal activity. QEEG in acute stroke may predict short and long-term functional outcomes. The role of individual indices has not been studied in a meta-analysis. We aim to assess individual QEEG-derived indices to predict post-stroke disability.

METHODS

We included studies (sample size ≥ 10) with stroke patients who underwent EEG and a follow-up outcome assessment was available either in the form of modified Rankin scale (mRS) or National Institute of Stroke scale (NIHSS) or Fugl-Meyer scale (FMA). QEEG indices analysed were delta-alpha ratio (DAR), delta-theta-alpha-beta ratio (DTABR), brain symmetry index (BSI) and pairwise derived brain symmetry (pdBSI).

RESULTS

Nine studies (8 had only ischemic stroke, and one had both ischemic and haemorrhagic stroke), including 482 participants were included for meta-analysis. Higher DAR was associated with worse mRS (n=300, Pearson's r 0.26, 95 % CI 0.21-0.31). Higher DTABR was associated with worse mRS (n=337, r=0.32, 95 % CI 0.26-0.39). Higher DAR was associated with higher NIHSS (n=161, r=0.42, 95 % CI0.24-0.6). Higher DTABR was associated with higher NIHSS (n=158, r=0.49, 95 % CI 0.31-0.67).

CONCLUSIONS

QEEG-derived indices DAR and DTABR have the potential to assess post-stroke disability. Adding QEEG to the clinical and imaging biomarkers in the acute phase may help in better prediction of post-stroke recovery.

REGISTRY

PROSPERO 2022 CRD42022292281.

EEG Spectral Power Changes in Patients With Dysexecutive Syndrome Following Cognitive Intervention

BACKGROUND

Acquired brain injury (ABI) leads to cognitive deficiencies, alteration of brain activity associated with an increase in slow-wave (delta and theta bands) power, and reduced fast-wave (alpha, beta, and gamma bands) power. To compensate for the cognitive deficits that impact autonomy and quality of life, patients in a chronic phase can benefit from cognitive intervention.

OBJECTIVE

This study explores the effects of cognitive intervention on brain activity, measured by electroencephalography (EEG), and on executive functioning, assessed by the Test of Attentional Performance (TAP) battery.

METHOD

We provided an ecological rehabilitation intervention, simulating real-life tasks adapted for patients with chronic cognitive disorders. A single-case experimental design (SCED) assessed patients' performance in terms of correct responses percentage (CRs) and reaction times (RTs), and EEG spectral powers before and 1 month after the intervention. The TAP tasks included working memory (WM), divided attention (DA), inhibition (GO), and flexibility (FL). EEG frequency powers were also measured during resting states.

RESULTS

One month after the intervention, significant improvements were observed in CRs and RTs for the FL task. Increases in all frequency band powers occurred during FL, WM, and DA tasks, except for alpha bands in DA. In the GO task, delta and gamma power also increased after the intervention. No significant changes were found during resting-state EEG. The results of this open study, without a control group, are preliminary.

CONCLUSION

The effects of the therapy are mostly reflected by changes in mental FL performance and altered EEG patterns during cognitive tasks, particularly in slow and fast-frequency bands. We argue that cognitive intervention could amplify the compensatory mechanisms following brain damage and/or ease restoration mechanisms in the fast-frequency activity bands. Further SCEDs or studies with control groups are needed to confirm these findings and the role of EEG biomarkers in rehabilitation.

The Relative Impact of Clinical and Investigational Factors to Predict the Outcome in Stroke Patients

OBJECTIVE

As stroke is still considered a significant cause of mortality and morbidity, it is crucial to find the factors affecting the outcome in these patients. We aimed to interpret the various clinical and investigational parameters and establish their association with the outcome in stroke patients.

MATERIALS AND METHODS

This is a retrospective, cross-sectional study, conducted in the Department of Neurology between June 2019 to November 2021. The study involved the review and analysis of medical records pertaining to 264 patients, admitted with the diagnosis of stroke. Various clinical, radiological, and electroencephalographic (EEG) patterns in stroke patients were analyzed and their association with outcome was established. The association between the studied variables was performed by the logistic regression (LR) and presented as odds ratio (OR) and 95% confidence interval (CI).

RESULTS

The study sample consisted of 264 patients. Males comprised 165 (62.5%) with the mean participant age of 57.17 ± 18.7 3 years (range: 18-94). Patients younger than 50 years had a better likelihood of a good outcome in comparison to patients older than 50. The admission location was the most significant factor in predicting the outcome ( P = 0.00) in favor of inpatient department and outpatient department (OPD), in contrast to patients admitted directly to intensive care unit (ICU). Normal EEG was associated with good outcome ( P = 0.04; OR, 3.3; CI, 1.01-10.88) even after adjustment of the confounders, whereas patients having marked EEG slowing had a poor outcome ( P = 0.05; OR, 2.4; CI, 0.65-8.79). Among the clinical parameters, hemiparesis ( P = 0.03), trauma ( P = 0.01), generalized tonic-clonic seizures (GTC) ( P = 0.00), and National Institutes of Health Stroke Scale of more than 4 were more likely associated with a poor outcome as well as the presence of intracranial hemorrhage (ICH) or infarction in the cortical and cortical/subcortical locations were associated with poor outcomes. After adjustment of confounders, the factors found to have prognostic significance in favor of good outcomes were inpatients or OPD referrals and normal EEG while direct admission to ICU, marked slowing on EEG, and presence of ICH were found to be associated with poor outcome.

CONCLUSION

Certain patterns are predictive of good or worse outcomes in stroke patients. Early identification of these factors can lead to early intervention, which in turn might help in a better outcome. The results of the study, therefore, have some prognostic significance.

Safety, feasibility, and acceptability of a novel device to monitor ischaemic stroke patients

This study assessed the safety, feasibility, and acceptability of a novel device to monitor ischaemic stroke patients. The device captured electroencephalography (EEG) and electrocardiography (ECG) data to compute an ECG-based metric, termed the Electrocardiography Brain Perfusion index (EBPi), which may function as a proxy for cerebral blood flow (CBF). Seventeen ischaemic stroke patients wore the device for nine hours and reported feedback at 1, 3, 6 and 9 h regarding user experience, comfort, and satisfaction (acceptability). Safety was assessed as the number of adverse events reported. Feasibility was assessed as the percentage of uninterrupted EEG/ECG data recorded (data capture efficiency). No adverse events were reported, only minor incidences of discomfort. Overall device comfort (mean ± 1 standard deviation (SD) (range)) (92.5% ± 10.3% (57.0-100%)) and data capture efficiency (mean ± 1 SD (range)) (95.8% ± 6.8% (54.8-100%)) were very high with relatively low variance. The device didn't restrict participants from receiving clinical care and rarely (n = 6) restricted participants from undertaking routine tasks. This study provides a promising evidence base for the deployment of the device in a clinical setting. If clinically validated, EBPi may be able to detect CBF changes to monitor early neurological deterioration and treatment outcomes, thus filling an important gap in current monitoring options.TRIAL REGISTRATION: The study was prospectively registered with the Australian New Zealand Clinical Trials Registry (ACTRN12622000112763).

A scoping review on the use of consumer-grade EEG devices for research

BACKGROUND

Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience.

PURPOSE

The aim of this study was to chart peer-review articles that used consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG.

METHODS

We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following online databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author's country.

RESULTS

We identified 916 studies that used data recorded with consumer-grade EEG: 531 were reported in journal articles and 385 in conference papers. Emotiv devices were used most, followed by the NeuroSky MindWave, OpenBCI, interaXon Muse, and MyndPlay Mindband. The most common usage was for brain-computer interfaces, followed by experimental research, signal processing, validation, and clinical purposes.

CONCLUSIONS

Consumer-grade EEG is a useful tool for neuroscientific research and will likely continue to be used well into the future. Our study provides a comprehensive review of their application, as well as future directions for researchers who plan to use these devices.

The predictive value of EEG reactivity by electrical stimulation and quantitative analysis in critically ill patients with large hemispheric infarction

PURPOSE

The intensive care of critically ill patients with large hemispheric infarction improves the survival rate. However, established prognostic markers for neurological outcome show variable accuracy. We aimed to assess the value of electrical stimulation and quantitative analysis of EEG reactivity for early prognostication in this critically ill population.

MATERIALS AND METHODS

We prospectively enrolled consecutive patients between January 2018 and December 2021. EEG reactivity was randomly performed by pain or electrical stimulation via visual and quantitative analysis. Neurological outcome within 6-month was dichotomized as good (modified Rankin Scale, mRS 0-3) or poor (mRS 4-6).

RESULTS

Ninety-four patients were admitted, and 56 were included in the final analysis. EEG reactivity using electrical stimulation was superior to pain stimulation for good outcome prediction (visual analysis: AUC 0.825 vs. 0.763, P = 0.143; quantitative analysis: AUC 0.931 vs. 0.844, P = 0.058). The AUC of EEG reactivity by pain stimulation with visual analysis was 0.763, which increased to 0.931 by electrical stimulation with quantitative analysis (P = 0.006). When using quantitative analysis, the AUC of EEG reactivity increased (pain stimulation 0.763 vs. 0.844, P = 0.118; electrical stimulation 0.825 vs. 0.931, P = 0.041).

CONCLUSION

EEG reactivity by electrical stimulation and quantitative analysis seems a promising prognostic factor in these critical patients.

Method for analyzing sequential services using EEG: Micro-meso analysis of emotional changes in real flight service

Capturing customers' emotional changes in sequential service should be realized using physiological measurements to assess customer delight. Questionnaire-based customer surveys may miss significant and dissipating emotional responses. This study developed a micro‑meso analysis method of capturing emotional changes for sequential service using electroencephalograph (EEG) measurement, dealing with both service encounters (micro-level) and servicescape (meso‑level) over a couple of hours. Customers' emotion states were defined based on emotional arousal and valence. Emotional responses caused by human interactions were evaluated, and periods of high positive affect throughout the customer journey were visualized. Experiments in actual flight services demonstrated successful emotion estimation across flight phases using a single-channel EEG measurement over two hours. Analysis results on the measurement data revealed emotional peaks outside service encounters that are not captured in customers' individual self-reports. The results also statistically revealed that two individual services (asking about a refill and conversations started by flight attendants) evoked high positive affect. Temporal dynamic analyses around high positive affect suggested patterns of interplay between joy and surprise, which are key components of customer delight. Compared with questionnaire-based evaluation, the proposed method contributes significantly to empirical studies on sequential services in marketing and design by enabling the extraction of "high positive affect," which needs to be identified for customer delight. This study supplements existing research on the interactions among physiology (EEG), behavior (emotional changes), and customer service research.

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

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

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.

Action Observation Therapy for Arm Recovery after Stroke: A Preliminary Investigation on a Novel Protocol with EEG Monitoring

This preliminary study introduces a novel action observation therapy (AOT) protocol associated with electroencephalographic (EEG) monitoring to be used in the future as a rehabilitation strategy for the upper limb in patients with subacute stroke. To provide initial evidence on the usefulness of this method, we compared the outcome of 11 patients who received daily AOT for three weeks with that of patients who undertook two other approaches recently investigated by our group, namely intensive conventional therapy (ICT), and robot-assisted therapy combined with functional electrical stimulation (RAT-FES). The three rehabilitative interventions showed similar arm motor recovery as indexed by Fugl-Meyer's assessment of the upper extremity (FMA_UE) and box and block test (BBT). The improvement in the FMA_UE was yet more favourable in patients with mild/moderate motor impairments who received AOT, in contrast with patients carrying similar disabilities who received the other two treatments. This suggests that AOT might be more effective in this subgroup of patients, perhaps because the integrity of their mirror neurons system (MNS) was more preserved, as indexed by EEG recording from central electrodes during action observation. In conclusion, AOT may reveal an effective rehabilitative tool in patients with subacute stroke; the EEG evaluation of MNS integrity may help to select patients who could maximally benefit from this intervention.

Application of Network Analysis to Uncover Variables Contributing to Functional Recovery after Stroke

To estimate network structures to discover the interrelationships among variables and distinguish the difference between networks. Three hundred and forty-eight stroke patients were enrolled in this retrospective study. A network analysis was used to investigate the association between those variables. A Network Comparison Test was performed to compare the correlation of variables between networks. Three hundred and twenty-five connections were identified, and 22 of these differed significantly between the high- and low-Functional Independence Measurement (FIM) groups. In the high-FIM network structure, brain-derived neurotrophic factor (BDNF) and length of stay (LOS) had associations with other nodes. However, there was no association with BDNF and LOS in the low-FIM network. In addition, the use of amantadine was associated with shorter LOS and lower FIM motor subscores in the high-FIM network, but there was no such connection in the low-FIM network. Centrality indices revealed that amantadine use had high centrality with others in the high-FIM network but not the low-FIM network. Coronary artery disease (CAD) had high centrality in the low-FIM network structure but not the high-FIM network. Network analysis revealed a new correlation of variables associated with stroke recovery. This approach might be a promising method to facilitate the discovery of novel factors important for stroke recovery.

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.

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.

Cellular-resolution monitoring of ischemic stroke pathologies in the rat cortex

Stroke is a leading cause of disability in the Western world. Current post-stroke rehabilitation treatments are only effective in approximately half of the patients. Therefore, there is a pressing clinical need for developing new rehabilitation approaches for enhancing the recovery process, which requires the use of appropriate animal models. Here, we demonstrate the use of nonlinear microscopy of calcium sensors in the rat brain to study the effects of ischemic stroke injury on cortical activity patterns. We longitudinally recorded from thousands of neurons labeled with a genetically-encoded calcium indicator before and after an ischemic stroke injury in the primary motor cortex. We show that this injury has an effect on the activity patterns of neurons not only in the motor and somatosensory cortices, but also in the more distant visual cortex, and that these changes include modified firing rates and kinetics of neuronal activity patterns in response to a sensory stimulus. Changes in neuronal population activity provided animal-specific, circuit-level information on the post-stroke cortical reorganization process, which may be essential for evaluating the efficacy of new approaches for enhancing the recovery process.

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.

Development of Frontal EEG Differences Between Eyes-Closed and Eyes-Open Resting Conditions in Children: Data From a Single-Channel Dry-Sensor Portable Device

Changes in EEG when moving from an eyes-closed to an eyes-open resting condition result from bottom-up sensory processing and have been referred to as activation. In children, activation is characterized by a global reduction in alpha, frontally present reductions for delta and theta, and a frontal increase for beta. The present study aimed to replicate frontal EEG activation effects using single-channel, dry-sensor EEG, and to extend current understanding by examining developmental change in children. Frontal EEG was recorded using a single-channel, dry-sensor EEG device while 182 children aged 7 to 12 years completed eyes-closed resting (EC), eyes-open resting (EO), and focus (FO) tasks. Results indicated that frontal delta, theta, and alpha power were reduced, and frontal beta power was increased, in the EO compared with the EC condition. Exploratory analysis of a form of top-down activation showed that frontal beta power was increased in the FO compared with to the EO condition, with no differences for other bands. The activation effects were robust at the individual level. The bottom-up activation effects reduced with age for frontal delta and theta, increased for frontal alpha, with no developmental change for top-down or bottom-up frontal beta activation. These findings contribute further to validation of the single-channel, dry-sensor, frontal EEG and provide support for use in a range of medical, therapeutic, and clinical domains.

Recovery from stroke: current concepts and future perspectives

Stroke is a leading cause of acquired, permanent disability worldwide. Although the treatment of acute stroke has been improved considerably, the majority of patients to date are left disabled with a considerable impact on functional independence and quality of life. As the absolute number of stroke survivors is likely to further increase due to the demographic changes in our aging societies, new strategies are needed in order to improve neurorehabilitation. The most critical driver of functional recovery post-stroke is neural reorganization. For developing novel, neurobiologically informed strategies to promote recovery of function, an improved understanding of the mechanisms enabling plasticity and recovery is mandatory. This review provides a comprehensive survey of recent developments in the field of stroke recovery using neuroimaging and non-invasive brain stimulation. We discuss current concepts of how the brain reorganizes its functional architecture to overcome stroke-induced deficits, and also present evidence for maladaptive effects interfering with recovery. We demonstrate that the combination of neuroimaging and neurostimulation techniques allows a better understanding of how brain plasticity can be modulated to promote the reorganization of neural networks. Finally, neurotechnology-based treatment strategies allowing patient-tailored interventions to achieve enhanced treatment responses are discussed. The review also highlights important limitations of current models, and finally closes with possible solutions and future directions.

Nurse Manager Core Competencies: A Proposal in the Spanish Health System

Nurses who are capable of developing their competencies appropriately in the field of management are considered fundamental to the sustainability and improvement of health outcomes. These core competencies are the critical competencies to be developed in specific areas. There are different core competencies for nurse managers, but none in the Spanish health system. The objective of this research is to identify the core competencies needed for nurse managers in the Spanish health system. The research was carried out using the Delphi method to reach a consensus on the core competencies and a Principal Component Analysis (PCA) to determine construct validity, reducing the dimensionality of a dataset by finding the causes of variability in the set and organizing them by importance. A panel of 50 experts in management and healthcare engaged in a four-round Delphi study with Likert scored surveys. We identified eight core competencies from an initial list of 51: decision making, relationship management, communication skills, listening, Leadership, conflict management, ethical principles, collaboration and team management skills. PCA indicated the structural validity of the core competencies by saturation into three components (α Cronbach >0.613): communication, leadership and decision making. The research shows that eight competencies must be developed by the nursing managers in the Spanish health system. Nurse managers can use these core competencies as criteria to develop and plan their professional career. These core competencies can serve as a guideline for the design of nurse managers’ development programs in Spain.