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

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.

Electrophysiological Correlates of Dentate Nucleus Deep Brain Stimulation for Poststroke Motor Recovery

While ipsilesional cortical electroencephalography has been associated with poststroke recovery mechanisms and outcomes, the role of the cerebellum and its interaction with the ipsilesional cortex is still largely unknown. We have previously shown that poststroke motor control relies on increased corticocerebellar coherence (CCC) in the low beta band to maintain motor task accuracy and to compensate for decreased excitability of the ipsilesional cortex. We now extend our work to investigate corticocerebellar network changes associated with chronic stimulation of the dentato-thalamo-cortical pathway aimed at promoting poststroke motor rehabilitation. We investigated the excitability of the ipsilesional cortex, the dentate (DN), and their interaction as a function of treatment outcome measures. Relative to baseline, 10 human participants (two women) at the end of 4-8 months of DN deep brain stimulation (DBS) showed (1) significantly improved motor control indexed by computerized motor tasks; (2) significant increase in ipsilesional premotor cortex event-related desynchronization that correlated with improvements in motor function; and (3) significant decrease in CCC, including causal interactions between the DN and ipsilesional cortex, which also correlated with motor function improvements. Furthermore, we show that the functional state of the DN in the poststroke state and its connectivity with the ipsilesional cortex were predictive of motor outcomes associated with DN-DBS. The findings suggest that as participants recovered, the ipsilesional cortex became more involved in motor control, with less demand on the cerebellum to support task planning and execution. Our data provide unique mechanistic insights into the functional state of corticocerebellar-cortical network after stroke and its modulation by DN-DBS.

The impact of CSF-filled cavities on scalp EEG and its implications

Previous studies have found electroencephalogram (EEG) amplitude and scalp topography differences between neurotypical and neurological/neurosurgical groups, being interpreted at the cognitive level. However, these comparisons are invariably accompanied by anatomical changes. Critical to EEG are the so-called volume currents, which are affected by the spatial distribution of the different tissues in the head. We investigated the effect of cerebrospinal fluid (CSF)-filled cavities on simulated EEG scalp data. We simulated EEG scalp potentials for known sources using different volume conduction models: a reference model (i.e., unlesioned brain) and models with realistic CSF-filled cavities gradually increasing in size. We used this approach for a single source close or far from the CSF-lesion cavity, and for a scenario with a distributed configuration of sources (i.e., a "cognitive event-related potential effect"). The magnitude and topography errors between the reference and lesion models were quantified. For the single-source simulation close to the lesion, the CSF-filled lesion modulated signal amplitude with more than 17% magnitude error and topography with more than 9% topographical error. Negligible modulation was found for the single source far from the lesion. For the multisource simulations of the cognitive effect, the CSF-filled lesion modulated signal amplitude with more than 6% magnitude error and topography with more than 16% topography error in a nonmonotonic fashion. In conclusion, the impact of a CSF-filled cavity cannot be neglected for scalp-level EEG data. Especially when group-level comparisons are made, any scalp-level attenuated, aberrant, or absent effects are difficult to interpret without considering the confounding effect of CSF.

Biomarkers of Motor Outcomes After Stroke

Predicting motor outcomes after stroke based on clinical judgment alone is often inaccurate and can lead to inefficient and inequitable allocation of rehabilitation resources. Prediction tools are being developed so that clinicians can make evidence-based, accurate, and reproducible prognoses for individual patients. Biomarkers of corticospinal tract structure and function can improve prediction tool performance, particularly for patients with initially moderate to severe motor impairment. Being able to make accurate predictions for individual patients supports rehabilitation planning and communication with patients and families.

Quantitative EEG and prognosis for recovery in post-stroke patients: The effect of lesion laterality

OBJECTIVE

There is emerging confidence that quantitative EEG (qEEG) has the potential to inform clinical decision-making and guide individualized rehabilitation after stroke, but consensus on the best EEG biomarkers is needed for translation to clinical practice. This study investigates the spatial qEEG spectral and symmetry distribution in patients with a left/right hemispheric stroke, to evaluate their side-specific prognostic power in post-acute rehabilitation outcome.

METHODS

Resting-state 19-channel EEG recordings were collected with clinical information on admission to intensive inpatient rehabilitation (within 30 days post stroke), and six months post stroke. After preprocessing, spectral (Delta-to-Alpha Ratio, DAR) and symmetry (pairwise and hemispheric Brain Symmetry Index) features were extracted. Patients were divided into Affected Right and Left (AR/AL) groups, according to the location of their lesion. Within each group, DAR was compared between homologous electrode pairs and the pairwise difference between pairs was compared across pairs in the scalp. Then, the prognostic power of qEEG admission metrics was evaluated by performing correlations between admission metrics and discharge mBI values.

RESULTS

Fifty-two patients with hemorrhagic or ischemic stroke (20 females, 38.5 %, median age 76 years [IQR = 22]) were included in the study. DAR was significantly higher in the affected hemisphere for both AR and AL groups, and, a higher frontal (to posterior) asymmetry was found independent of the side of the lesion. DAR was found to be a prognostic marker of 6-months modified Barthel Index (mBI) only for the AL group, while hemispheric asymmetry did not correlate with follow-up outcomes in either group.

DISCUSSION

While the presence of EEG abnormalities in the affected hemisphere of a stroke is well recognized, we have shown that the extent of DAR abnormalities seen correlates with disability at 6 months post stroke, but only for left hemispheric lesions. Routine prognostic evaluation, in addition to motor and functional scales, can add information concerning neuro-prognostication and reveal neurophysiological abnormalities to be assessed during rehabilitation.

Remapping and Reconnecting the Language Network after Stroke

Here, we review the literature on neurotypical individuals and individuals with post-stroke aphasia showing that right-hemisphere regions homologous to language network and other regions, like the right cerebellum, are activated in language tasks and support language even in healthy people. We propose that language recovery in post-stroke aphasia occurs largely by potentiating the right hemisphere network homologous to the language network and other networks that previously supported language to a lesser degree and by modulating connection strength between nodes of the right-hemisphere language network and undamaged nodes of the left-hemisphere language network. Based on this premise (supported by evidence we review), we propose that interventions should be aimed at potentiating the right-hemisphere language network through Hebbian learning or by augmenting connections between network nodes through neuroplasticity, such as non-invasive brain stimulation and perhaps modulation of neurotransmitters involved in neuroplasticity. We review aphasia treatment studies that have taken this approach. We conclude that further aphasia rehabilitation with this aim is justified.

Electroacupuncture alters brain network functional connectivity in subacute stroke: A randomised crossover trial

BACKGROUND

Electroacupuncture (EA) is a promising rehabilitation treatment for upper-limb motor recovery in stroke patients. However, the neurophysiological mechanisms underlying its clinical efficacy remain unclear. This study aimed to explore the immediate modulatory effects of EA on brain network functional connectivity and topological properties.

METHODS

The randomized, single-blinded, self-controlled two-period crossover trial was conducted among 52 patients with subacute subcortical stroke. These patients were randomly allocated to receive either EA as the initial intervention or sham electroacupuncture (SEA) as the initial intervention. After a washout period of 24 hours, participants underwent the alternate intervention (SEA or EA). Resting state electroencephalography signals were recorded synchronously throughout both phases of the intervention. The functional connectivity (FC) of the parietofrontal network and small-world (SW) property indices of the whole-brain network were compared across the entire course of the two interventions.

RESULTS

The results demonstrated that EA significantly altered ipsilesional parietofrontal network connectivity in the alpha and beta bands (alpha: F = 5.05, P = .011; beta: F = 3.295, P = .047), whereas no significant changes were observed in the SEA group. When comparing between groups, EA significantly downregulated ipsilesional parietofrontal network connectivity in both the alpha and beta bands during stimulation (alpha: t = -1.998, P = .049; beta: t = -2.342, P = .022). Significant differences were also observed in the main effects of time and the group × time interaction for the SW index (time: F = 5.516, P = .026; group × time: F = 6.892, P = .01). In terms of between-group comparisons, the EA group exhibited a significantly higher SW index than the SEA group at the post-stimulation stage (t = 2.379, P = .018).

CONCLUSION

These findings suggest that EA downregulates ipsilesional parietofrontal network connectivity and enhances SW properties, providing a potential neurophysiological mechanism for facilitating motor performance in stroke patients.

Biochemical, biomechanical and imaging biomarkers of ischemic stroke: Time for integrative thinking

Stroke is one of the leading causes of adult disability affecting millions of people worldwide. Post-stroke cognitive and motor impairments diminish quality of life and functional independence. There is an increased risk of having a second stroke and developing secondary conditions with long-term social and economic impacts. With increasing number of stroke incidents, shortage of medical professionals and limited budgets, health services are struggling to provide a care that can break the vicious cycle of stroke. Effective post-stroke recovery hinges on holistic, integrative and personalized care starting from improved diagnosis and treatment in clinics to continuous rehabilitation and support in the community. To improve stroke care pathways, there have been growing efforts in discovering biomarkers that can provide valuable insights into the neural, physiological and biomechanical consequences of stroke and how patients respond to new interventions. In this review paper, we aim to summarize recent biomarker discovery research focusing on three modalities (brain imaging, blood sampling and gait assessments), look at some established and forthcoming biomarkers, and discuss their usefulness and complementarity within the context of comprehensive stroke care. We also emphasize the importance of biomarker guided personalized interventions to enhance stroke treatment and post-stroke recovery.

Aerobic Exercise Improves Cortical Inhibitory Function After Stroke: A Preliminary Investigation

BACKGROUND AND PURPOSE

Aerobic exercise can elicit positive effects on neuroplasticity and cognitive executive function but is poorly understood after stroke. We tested the effect of 4 weeks of aerobic exercise training on inhibitory and facilitatory elements of cognitive executive function and electroencephalography markers of cortical inhibition and facilitation. We investigated relationships between stimulus-evoked cortical responses, blood lactate levels during training, and aerobic fitness postintervention.

METHODS

Twelve individuals with chronic (>6 months) stroke completed an aerobic exercise intervention (40 minutes, 3×/wk). Electroencephalography and motor response times were assessed during congruent (response facilitation) and incongruent (response inhibition) stimuli of a Flanker task. Aerobic fitness capacity was assessed as o2peak during a treadmill test pre- and postintervention. Blood lactate was assessed acutely (<1 minute) after exercise each week. Cortical inhibition (N2) and facilitation (frontal P3) were quantified as peak amplitudes and latencies of stimulus-evoked electroencephalographic activity over the frontal cortical region.

RESULTS

Following exercise training, the response inhibition speed increased while response facilitation remained unchanged. A relationship between earlier cortical N2 response and faster response inhibition emerged postintervention. Individuals who produced higher lactate during exercise training achieved faster response inhibition and tended to show earlier cortical N2 responses postintervention. There were no associations between o2peak and metrics of behavioral or neurophysiologic function.

DISCUSSION AND CONCLUSIONS

These preliminary findings provide novel evidence for selective benefits of aerobic exercise on inhibitory control during the initial 4-week period after initiation of exercise training and implicate a potential therapeutic effect of lactate on poststroke inhibitory control.

Changes in EEG Activity and Cognition Related to Physical Activity in Older Adults: A Systematic Review

Aging is generally associated with a decline in important cognitive functions that can be observed in EEG. Physical activity in older adults should be considered one of the main strategies to promote health and prevent disease in the elderly. The present study aimed to systematically review studies of EEG activity and cognitive function changes associated with physical activity in older adults. Records from PubMed, Scopus, and EBSCO databases were searched and, following the PRISMA guidelines, nine studies were included in the present systematic review. A risk of bias assessment was performed using the National Institute of Health Quality Assessment Tool for Case-control Studies instrument. The studies analyzed used two main strategies to determine the effects of physical activity on cognition and EEG: (1) multiscale entropy and power frequencies; and (2) event-related potentials. In terms of EEG activity, it can be concluded that exercise-induced neuroplasticity underlies improvements in cognitive function in healthy older adults.

Integrating EEG and Machine Learning to Analyze Brain Changes during the Rehabilitation of Broca's Aphasia

The fusion of electroencephalography (EEG) with machine learning is transforming rehabilitation. Our study introduces a neural network model proficient in distinguishing pre- and post-rehabilitation states in patients with Broca's aphasia, based on brain connectivity metrics derived from EEG recordings during verbal and spatial working memory tasks. The Granger causality (GC), phase-locking value (PLV), weighted phase-lag index (wPLI), mutual information (MI), and complex Pearson correlation coefficient (CPCC) across the delta, theta, and low- and high-gamma bands were used (excluding GC, which spanned the entire frequency spectrum). Across eight participants, employing leave-one-out validation for each, we evaluated the intersubject prediction accuracy across all connectivity methods and frequency bands. GC, MI theta, and PLV low-gamma emerged as the top performers, achieving 89.4%, 85.8%, and 82.7% accuracy in classifying verbal working memory task data. Intriguingly, measures designed to eliminate volume conduction exhibited the poorest performance in predicting rehabilitation-induced brain changes. This observation, coupled with variations in model performance across frequency bands, implies that different connectivity measures capture distinct brain processes involved in rehabilitation. The results of this paper contribute to current knowledge by presenting a clear strategy of utilizing limited data to achieve valid and meaningful results of machine learning on post-stroke rehabilitation EEG data, and they show that the differences in classification accuracy likely reflect distinct brain processes underlying rehabilitation after stroke.

EEG Amplitude Modulation Analysis across Mental Tasks: Towards Improved Active BCIs

Brain-computer interface (BCI) technology has emerged as an influential communication tool with extensive applications across numerous fields, including entertainment, marketing, mental state monitoring, and particularly medical neurorehabilitation. Despite its immense potential, the reliability of BCI systems is challenged by the intricacies of data collection, environmental factors, and noisy interferences, making the interpretation of high-dimensional electroencephalogram (EEG) data a pressing issue. While the current trends in research have leant towards improving classification using deep learning-based models, our study proposes the use of new features based on EEG amplitude modulation (AM) dynamics. Experiments on an active BCI dataset comprised seven mental tasks to show the importance of the proposed features, as well as their complementarity to conventional power spectral features. Through combining the seven mental tasks, 21 binary classification tests were explored. In 17 of these 21 tests, the addition of the proposed features significantly improved classifier performance relative to using power spectral density (PSD) features only. Specifically, the average kappa score for these classifications increased from 0.57 to 0.62 using the combined feature set. An examination of the top-selected features showed the predominance of the AM-based measures, comprising over 77% of the top-ranked features. We conclude this paper with an in-depth analysis of these top-ranked features and discuss their potential for use in neurophysiology.

Associating Functional Neural Connectivity and Specific Aspects of Sensorimotor Control in Chronic Stroke

Hand sensorimotor deficits often result from stroke, limiting the ability to perform daily living activities. Sensorimotor deficits are heterogeneous among stroke survivors. Previous work suggests a cause of hand deficits is altered neural connectivity. However, the relationships between neural connectivity and specific aspects of sensorimotor control have seldom been explored. Understanding these relationships is important for developing personalized rehabilitation strategies to improve individual patients' specific sensorimotor deficits and, thus, rehabilitation outcomes. Here, we investigated the hypothesis that specific aspects of sensorimotor control will be associated with distinct neural connectivity in chronic stroke survivors. Twelve chronic stroke survivors performed a paretic hand grip-and-relax task while EEG was collected. Four aspects of hand sensorimotor grip control were extracted, including reaction time, relaxation time, force magnitude control, and force direction control. EEG source connectivity in the bilateral sensorimotor regions was calculated in α and β frequency bands during grip preparation and execution. Each of the four hand grip measures was significantly associated with a distinct connectivity measure. These results support further investigations into functional neural connectivity signatures that explain various aspects of sensorimotor control, to assist the development of personalized rehabilitation that targets the specific brain networks responsible for the individuals' distinct sensorimotor deficits.

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

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

Yijinjing Qigong intervention shows strong evidence on clinical effectiveness and electroencephalography signal features for early poststroke depression: A randomized, controlled trial

Objective

Although Traditional Chinese Yijinjing Qigong Exercise (YJJQE) as mind-body intervention is popularly used among adults to ameliorate depressive symptoms in China, no randomized controlled trials (RCTs) are available to evaluate the effects of YJJQE in patients with poststroke depression (PSD). This study aims to explore the clinical efficacy and the neurological and psychiatric mechanism in brain network functional connectivity underlying electroencephalography (EEG).

Materials and methods

A total of 60 patients, diagnosed with mild PSD, were randomly (1:1) assigned to YJJQE group (n = 30) and control group of routine segmental rehabilitation training group (n = 30) for a 60-min exercise session once a day for 3 weeks. All outcome measures were collected at baseline and 3-weeks ending intervention. The primary outcome was the 24-item Hamilton Depression Scale (HAMD-24) score, evaluation at more time points for 1 month of follow-up. The secondary outcomes were EEG data in four frequency domains (δ, θ, α, and β), global efficiency (GE), local efficiency (LE), GE/LE curve [areas under the curve (AUC)], Phase Lag Index (PLI), (HAMD-24) Score and EEG correlation analysis.

Results

All patients showed no significant differences in baseline data. After 3 weeks and 1 month of follow-up, the YJJQE group demonstrated significant decreasing changes compared to the control group on the HAMD-24 scores (p < 0.001). Furthermore, the YJJQE group also showed a significant reduction in θ wave, and an increase in both GE and LE. Compared to the control group, the YJJQE Qigong group showed significantly greater functional connectivity in the δ, θ, and β frequency bands in the brain network of the degree of phase synchronization (p < 0.001). HAMD-24 Score and EEG correlation analysis negative correlation in the Qigong group θ wave (p < 0.001).

Conclusion

Our findings demonstrated that YJJQE is estimated to effectively alleviate the depressed mood of patients with PSD by promoting the efficiency in information transmission of network functional connectivity and its integration ability in different brain regions. Therefore, the YJJQE would be useful as a non-pharmacological treatment to prevent PSD.

Clinical trial registration

[http://www.chictr.org.cn/showproj.aspx?proj=55789], identifier [ChiCTR2000035588].