Changes in mu and beta amplitude of the EEG during upper limb movement correlate with motor impairment and structural damage in subacute stroke

EEG Provides Insights Into Motor Control and Neuroplasticity During Stroke Recovery

In many branches of medicine, treatment is guided by measuring its effects on underlying physiology. In this regard, the efficacy of rehabilitation/recovery therapies could be enhanced if their administration was guided by measurements that directly capture treatment effects on neural function. Measures of brain function via EEG may be useful toward this goal and have advantages such as ease of bedside acquisition, safety, and low cost. This review synthetizes EEG studies during the subacute phase poststroke, when spontaneous recovery is maximal, and focuses on movement. Event-related measures reflect cortical activation and inhibition, while connectivity measures capture the function of cortical networks. Several EEG-based measures are related to motor outcomes poststroke and warrant further evaluation. Ultimately, they may be useful for clinical decision-making and clinical trial design in stroke neurorehabilitation.

Altered brain function during movement programming is linked with motor deficits after stroke: a high temporal resolution study

Introduction

Stroke leads to motor deficits, requiring rehabilitation therapy that targets mechanisms underlying movement generation. Cortical activity during the planning and execution of motor tasks can be studied using EEG, particularly via the Event Related Desynchronization (ERD). ERD is altered by stroke in a manner that varies with extent of motor deficits. Despite this consensus in the literature, defining precisely the temporality of these alterations during movement preparation and performance may be helpful to better understand motor system pathophysiology and might also inform development of novel therapies that benefit from temporal resolution.

Methods

Patients with chronic hemiparetic post-stroke (n = 27; age 59 ± 14 years) and age-matched healthy right-handed control subjects (n = 23; 59 ± 12 years) were included. They performed a shoulder rotation task following the onset of a stimulus. Cortical activity was recorded using a 256-electrode EEG cap. ERD was calculated in the beta frequency band (15-30 Hz) in ipsilesional sensorimotor cortex, contralateral to movement. The ERD was compared over time between stroke and control subjects using permutation tests. The correlation between upper extremity motor deficits (assessed by the Fugl-Meyer scale) and ERD over time was studied in stroke patients using Spearman and permutation tests.

Results

Patients with stroke showed on average less beta ERD amplitude than control subjects in the time window of -350 to 50 ms relative to movement onset (t(46) = 2.8, p = 0.007, Cohen's d = 0.31, 95% CI [0.22: 1.40]). Beta-ERD values correlated negatively with the Fugl-Meyer score during the time window -200 to 400 ms relative to movement onset (Spearman's r = -0.54, p = 0.003, 95% CI [-0.77 to -0.18]).

Discussion

Our results provide new insights into the precise temporal changes of ERD after hemiparetic stroke and the associations they have with motor deficits. After stroke, the average amplitude of cortical activity is reduced as compared to age-matched controls, and the extent of this decrease is correlated with the severity of motor deficits; both were true during motor programming and during motor performance. Understanding how stroke affects the temporal dynamics of cortical preparation and execution of movement paves the way for more precise restorative therapies. Studying the temporal dynamics of the EEG also strengthens the promising interest of ERD as a biomarker of post-stroke motor function.

From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke

Stroke is a severe medical condition which may lead to permanent disability conditions. The initial 8 weeks following a stroke are crucial for rehabilitation, as most recovery occurs during this period. Personalized approaches and predictive biomarkers are needed for tailored rehabilitation. In this context, EEG brain connectivity and Artificial Intelligence (AI) can play a crucial role in diagnosing and predicting stroke outcomes efficiently. In the present study, 127 patients with subacute ischemic lesions and 90 age- and gender-matched healthy controls were enrolled. EEG recordings were obtained from each participant within 15 days of stroke onset. Clinical evaluations were performed at baseline and at 40-days follow-up using the National Institutes of Health Stroke Scale (NIHSS). Functional connectivity analysis was conducted using Total Coherence (TotCoh) and Small Word (SW). Quadratic support vector machines (SVM) algorithms were implemented to classify healthy subjects compared to stroke patients (Healthy vs Stroke), determine the affected hemisphere (Left vs Right Hemisphere), and predict functional recovery (Functional Recovery Prediction). In the classification for Functional Recovery Prediction, an accuracy of 94.75%, sensitivity of 96.27% specificity of 92.33%, and AUC of 0.95 were achieved; for Healthy vs Stroke, an accuracy of 99.09%, sensitivity of 100%, specificity of 98.46%, and AUC of 0.99 were achieved. For Left vs Right Hemisphere classification, accuracy was 86.77%, sensitivity was 91.44%, specificity was 80.33%, and AUC was 0.87. These findings highlight the potential of utilizing functional connectivity measures based on EEG in combination with AI algorithms to improve patient outcomes by targeted rehabilitation interventions.

Human sensorimotor resting state beta events and aperiodic activity show good test-retest reliability

OBJECTIVE

Diseases affecting sensorimotor function impair physical independence. Reliable functional clinical biomarkers allowing early diagnosis or targeting treatment and rehabilitation could reduce this burden. Magnetoencephalography (MEG) non-invasively measures brain rhythms such as the somatomotor 'rolandic' rhythm which shows intermittent high-amplitude beta (14-30 Hz) 'events' that predict behavior across tasks and species and are altered by sensorimotor neurological diseases.

METHODS

We assessed test-retest stability, a prerequisite for biomarkers, of spontaneous sensorimotor aperiodic (1/f) signal and beta events in 50 healthy human controls across two MEG sessions using the intraclass correlation coefficient (ICC). Beta events were determined using an amplitude-thresholding approach on a narrow-band filtered amplitude envelope obtained using Morlet wavelet decomposition.

RESULTS

Resting sensorimotor characteristics showed good to excellent test-retest stability. Aperiodic component (ICC 0.77-0.88) and beta event amplitude (ICC 0.74-0.82) were very stable, whereas beta event duration was more variable (ICC 0.55-0.7). 2-3 minute recordings were sufficient to obtain stable results. Analysis automatization was successful in 86%.

CONCLUSIONS

Sensorimotor beta phenotype is a stable feature of an individual's resting brain activity even for short recordings easily measured in patients.

SIGNIFICANCE

Spontaneous sensorimotor beta phenotype has potential as a clinical biomarker of sensorimotor system integrity.

Time synchronization between parietal-frontocentral connectivity with MRCP and gait in post-stroke bipedal tasks

BACKGROUND

In post-stroke rehabilitation, functional connectivity (FC), motor-related cortical potential (MRCP), and gait activities are common measures related to recovery outcomes. However, the interrelationship between FC, MRCP, gait activities, and bipedal distinguishability have yet to be investigated.

METHODS

Ten participants were equipped with EEG devices and inertial measurement units (IMUs) while performing lower limb motor preparation (MP) and motor execution (ME) tasks. MRCP, FCs, and bipedal distinguishability were extracted from the EEG signals, while the change in knee degree during the ME phase was calculated from the gait data. FCs were analyzed with pairwise Pearson's correlation, and the brain-wide FC was fed into support vector machine (SVM) for bipedal classification.

RESULTS

Parietal-frontocentral connectivity (PFCC) dysconnection and MRCP desynchronization were related to the MP and ME phases, respectively. Hemiplegic limb movement exhibited higher PFCC strength than nonhemiplegic limb movement. Bipedal classification had a short-lived peak of 75.1% in the pre-movement phase. These results contribute to a better understanding of the neurophysiological functions during motor tasks, with respect to localized MRCP and nonlocalized FC activities. The difference in PFCCs between both limbs could be a marker to understand the motor function of the brain of post-stroke patients.

CONCLUSIONS

In this study, we discovered that PFCCs are temporally dependent on lower limb gait movement and MRCP. The PFCCs are also related to the lower limb motor performance of post-stroke patients. The detection of motor intentions allows the development of bipedal brain-controlled exoskeletons for lower limb active rehabilitation.

Upper limb motor assessment for stroke with force, muscle activation and interhemispheric balance indices based on sEMG and fNIRS

Introduction

Upper limb rehabilitation assessment plays a pivotal role in the recovery process of stroke patients. The current clinical assessment tools often rely on subjective judgments of healthcare professionals. Some existing research studies have utilized physiological signals for quantitative assessments. However, most studies used single index to assess the motor functions of upper limb. The fusion of surface electromyography (sEMG) and functional near-infrared spectroscopy (fNIRS) presents an innovative approach, offering simultaneous insights into the central and peripheral nervous systems.

Methods

We concurrently collected sEMG signals and brain hemodynamic signals during bilateral elbow flexion in 15 stroke patients with subacute and chronic stages and 15 healthy control subjects. The sEMG signals were analyzed to obtain muscle synergy based indexes including synergy stability index (SSI), closeness of individual vector (CV) and closeness of time profile (CT). The fNIRS signals were calculated to extract laterality index (LI).

Results

The primary findings were that CV, SSI and LI in posterior motor cortex (PMC) and primary motor cortex (M1) on the affected hemisphere of stroke patients were significantly lower than those in the control group (p < 0.05). Moreover, CV, SSI and LI in PMC were also significantly different between affected and unaffected upper limb movements (p < 0.05). Furthermore, a linear regression model was used to predict the value of the Fugl-Meyer score of upper limb (FMul) (R2 = 0.860, p < 0.001).

Discussion

This study established a linear regression model using force, CV, and LI features to predict FMul scale values, which suggests that the combination of force, sEMG and fNIRS hold promise as a novel method for assessing stroke rehabilitation.

A Combined Virtual Electrode-Based ESA and CNN Method for MI-EEG Signal Feature Extraction and Classification

A Brain-Computer Interface (BCI) is a medium for communication between the human brain and computers, which does not rely on other human neural tissues, but only decodes Electroencephalography (EEG) signals and converts them into commands to control external devices. Motor Imagery (MI) is an important BCI paradigm that generates a spontaneous EEG signal without external stimulation by imagining limb movements to strengthen the brain's compensatory function, and it has a promising future in the field of computer-aided diagnosis and rehabilitation technology for brain diseases. However, there are a series of technical difficulties in the research of motor imagery-based brain-computer interface (MI-BCI) systems, such as: large individual differences in subjects and poor performance of the cross-subject classification model; a low signal-to-noise ratio of EEG signals and poor classification accuracy; and the poor online performance of the MI-BCI system. To address the above problems, this paper proposed a combined virtual electrode-based EEG Source Analysis (ESA) and Convolutional Neural Network (CNN) method for MI-EEG signal feature extraction and classification. The outcomes reveal that the online MI-BCI system developed based on this method can improve the decoding ability of multi-task MI-EEG after training, it can learn generalized features from multiple subjects in cross-subject experiments and has some adaptability to the individual differences of new subjects, and it can decode the EEG intent online and realize the brain control function of the intelligent cart, which provides a new idea for the research of an online MI-BCI system.

Altered Functional Networks of Alpha and Low-Beta Bands During Upper Limb Movement and Association with Motor Impairment in Chronic Stroke

Background: Impaired movement after stroke is closely associated with altered brain functions, and thus the investigation on neural substrates of patients with stroke can pave a way for not only understanding the underlying mechanisms of neuropathological traits, but also providing an innovative solution for stroke rehabilitation. The objective of this study was to precisely investigate altered brain functions in terms of power spectral and brain network analyses. Methods: Altered brain function was investigated by using electroencephalography (EEG) measured while 34 patients with chronic stroke performed movement tasks with the affected and unaffected hands. The relationships between functional brain network indices and Fugl-Meyer Assessment (FMA) scores were also investigated. Results: A stronger low-beta event-related desynchronization was found in the contralesional hemisphere for both affected and unaffected movement tasks compared with that of the ipsilesional hemisphere. More efficient whole-brain networks (increased strength and clustering coefficient, and prolonged path length) in the low-beta frequency band were revealed when moving the unaffected hand compared with when moving the affected hand. In addition, the brain network indices of the contralesional hemisphere indicated higher efficiency and cost-effectiveness than those of the ipsilesional hemisphere in both the alpha and low-beta frequency bands. Moreover, the alpha network indices (strength, clustering coefficient, path length, and small-worldness) were significantly correlated with the FMA scores. Conclusions: Efficient functional brain network indices are associated with better motor outcomes in patients with stroke and could be useful biomarkers to monitor stroke recovery during rehabilitation.

Motor-Related Mu/Beta Rhythm in Older Adults: A Comprehensive Review

Mu rhythm, also known as the mu wave, occurs on sensorimotor cortex activity at rest, and the frequency range is defined as 8-13Hz, the same frequency as the alpha band. Mu rhythm is a cortical oscillation that can be recorded from the scalp over the primary sensorimotor cortex by electroencephalogram (EEG) and magnetoencephalography (MEG). The subjects of previous mu/beta rhythm studies ranged widely from infants to young and older adults. Furthermore, these subjects were not only healthy people but also patients with various neurological and psychiatric diseases. However, very few studies have referred to the effect of mu/beta rhythm with aging, and there was no literature review about this theme. It is important to review the details of the characteristics of mu/beta rhythm activity in older adults compared with young adults, focusing on age-related mu rhythm changes. By comprehensive review, we found that, compared with young adults, older adults showed mu/beta activity change in four characteristics during voluntary movement, increased event-related desynchronization (ERD), earlier beginning and later end, symmetric pattern of ERD and increased recruitment of cortical areas, and substantially reduced beta event-related desynchronization (ERS). It was also found that mu/beta rhythm patterns of action observation were changing with aging. Future work is needed in order to investigate not only the localization but also the network of mu/beta rhythm in older adults.

Biomarkers for prognostic functional recovery poststroke: A narrative review

Background and objective: Prediction of poststroke recovery can be expressed by prognostic biomarkers that are related to the pathophysiology of stroke at the cellular and molecular level as well as to the brain structural and functional reserve after stroke at the systems neuroscience level. This study aimed to review potential biomarkers that can predict poststroke functional recovery. Methods: A narrative review was conducted to qualitatively summarize the current evidence on biomarkers used to predict poststroke functional recovery. Results: Neurophysiological measurements and neuroimaging of the brain and a wide diversity of molecules had been used as prognostic biomarkers to predict stroke recovery. Neurophysiological studies using resting-state electroencephalography (EEG) revealed an interhemispheric asymmetry, driven by an increase in low-frequency oscillation and a decrease in high-frequency oscillation in the ipsilesional hemisphere relative to the contralesional side, which was indicative of individual recovery potential. The magnitude of somatosensory evoked potentials and event-related desynchronization elicited by movement in task-related EEG was positively associated with the quantity of recovery. Besides, transcranial magnetic stimulation (TMS) studies revealed the potential values of using motor-evoked potentials (MEP) and TMS-evoked EEG potentials from the ipsilesional motor cortex as prognostic biomarkers. Brain structures measured using magnetic resonance imaging (MRI) have been implicated in stroke outcome prediction. Specifically, the damage to the corticospinal tract (CST) and anatomical motor connections disrupted by stroke lesion predicted motor recovery. In addition, a wide variety of molecular, genetic, and epigenetic biomarkers, including hemostasis, inflammation, tissue remodeling, apoptosis, oxidative stress, infection, metabolism, brain-derived, neuroendocrine, and cardiac biomarkers, etc., were associated with poor functional outcomes after stroke. However, challenges such as mixed evidence and analytical concerns such as specificity and sensitivity have to be addressed before including molecular biomarkers in routine clinical practice. Conclusion: Potential biomarkers with prognostic values for the prediction of functional recovery after stroke have been identified; however, a multimodal approach of biomarkers for prognostic prediction has rarely been studied in the literature. Future studies may incorporate a combination of multiple biomarkers from big data and develop algorithms using data mining methods to predict the recovery potential of patients after stroke in a more precise way.

Relation Between EEG Measures and Upper Limb Motor Recovery in Stroke Patients: A Scoping Review

Current clinical practice does not leverage electroencephalography (EEG) measurements in stroke patients, despite its potential to contribute to post-stroke recovery predictions. We review the literature on the effectiveness of various quantitative and qualitative EEG-based measures after stroke as a tool to predict upper limb motor outcome, in relation to stroke timeframe and applied experimental tasks. Moreover, we aim to provide guidance on the use of EEG in the assessment of upper limb motor recovery after stroke, suggesting a high potential for some metrics in the appropriate context. We identified relevant papers (N = 16) from databases ScienceDirect, Web of Science and MEDLINE, and assessed their methodological quality with the Joanna Briggs Institute (JBI) Critical Appraisal. We applied the Preferred Reporting Systems for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) Framework. Identified works used EEG to identify properties including event-related activation, spectral power in physiologically relevant bands, symmetry in brain dynamics, functional connectivity, cortico-muscular coherence and rhythmic coordination. EEG was acquired in resting state or in relation to behavioural conditions. Motor outcome was mainly evaluated with the Upper Limb Fugl-Meyer Assessment. Despite great variability in the literature, data suggests that the most promising EEG quantifiers for predicting post-stroke motor outcome are event-related measures. Measures of spectral power in physiologically relevant bands and measures of brain symmetry also show promise. We suggest that EEG measures may improve our understanding of stroke brain dynamics during recovery, and contribute to establishing a functional prognosis and choosing the rehabilitation approach.

Prediction of balance function for stroke based on EEG and fNIRS features during ankle dorsiflexion

Balance rehabilitation is exceedingly crucial during stroke rehabilitation and is highly related to the stroke patients’ secondary injuries (caused by falling). Stroke patients focus on walking ability rehabilitation during the early stage. Ankle dorsiflexion can activate the brain areas of stroke patients, similar to walking. The combination of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) was a new method, providing more beneficial information. We extracted the event-related desynchronization (ERD), oxygenated hemoglobin (HBO), and Phase Synchronization Index (PSI) features during ankle dorsiflexion from EEG and fNIRS. Moreover, we established a linear regression model to predict Berg Balance Scale (BBS) values and used an eightfold cross validation to test the model. The results showed that ERD, HBO, PSI, and age were critical biomarkers in predicting BBS. ERD and HBO during ankle dorsiflexion and age were promising biomarkers for stroke motor recovery.

A Semi-Supervised Progressive Learning Algorithm for Brain-Computer Interface

Brain-computer interface (BCI) usually suffers from the problem of low recognition accuracy and large calibration time, especially when identifying motor imagery tasks for subjects with indistinct features and classifying fine grained motion control tasks by electroencephalogram (EEG)-electromyogram (EMG) fusion analysis. To fill the research gap, this paper presents an end-to-end semi-supervised learning framework for EEG classification and EEG-EMG fusion analysis. Benefiting from the proposed metric learning based label estimation strategy, sampling criterion and progressive learning scheme, the proposed framework efficiently extracts distinctive feature embedding from the unlabeled EEG samples and achieves a 5.40% improvement on BCI Competition IV Dataset IIa with 80% unlabeled samples and an average 3.35% improvement on two public BCI datasets. By employing synchronous EMG features as pseudo labels for the unlabeled EEG samples, the proposed framework further extracts deep level features of the synergistic complementarity between the EEG signals and EMG features based on the deep encoders, which improves the performance of hybrid BCI (with a 5.53% improvement for the Upper Limb Motion Dataset and an average 4.34% improvement on two hybrid datasets). Moreover, the ablation experiments show that the proposed framework can substantially improve the performance of the deep encoders (with an average 5.53% improvement). The proposed framework not only largely improves the performance of deep networks in the BCI system, but also significantly reduces the calibration time for EEG-EMG fusion analysis, which shows great potential for building an efficient and high-performance hybrid BCI for the motor rehabilitation process.

Reconstructing Synergy-Based Hand Grasp Kinematics from Electroencephalographic Signals

Brain-machine interfaces (BMIs) have become increasingly popular in restoring the lost motor function in individuals with disabilities. Several research studies suggest that the CNS may employ synergies or movement primitives to reduce the complexity of control rather than controlling each DoF independently, and the synergies can be used as an optimal control mechanism by the CNS in simplifying and achieving complex movements. Our group has previously demonstrated neural decoding of synergy-based hand movements and used synergies effectively in driving hand exoskeletons. In this study, ten healthy right-handed participants were asked to perform six types of hand grasps representative of the activities of daily living while their neural activities were recorded using electroencephalography (EEG). From half of the participants, hand kinematic synergies were derived, and a neural decoder was developed, based on the correlation between hand synergies and corresponding cortical activity, using multivariate linear regression. Using the synergies and the neural decoder derived from the first half of the participants and only cortical activities from the remaining half of the participants, their hand kinematics were reconstructed with an average accuracy above 70%. Potential applications of synergy-based BMIs for controlling assistive devices in individuals with upper limb motor deficits, implications of the results in individuals with stroke and the limitations of the study were discussed.

Evaluation of Machine Learning Algorithms for Classification of EEG Signals

In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen’s Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices.

Relation Between Sensorimotor Rhythm During Motor Attempt/Imagery and Upper-Limb Motor Impairment in Stroke

Motor attempt (MA)/motor imagery (MI)-based brain-computer interface (BCI) is a newly developing rehabilitation technology for motor impairment. This study aims to explore the relationship between electroencephalography sensorimotor rhythm and motor impairment to provide reference for a BCI design. Twenty-eight stroke survivors with varying levels of motor dysfunction and spasticity status in the subacute or chronic stage were enrolled in the study to perform MA and MI tasks. Event-related desynchronization (ERD)/event-related synchronization (ERS) during and immediately after motor tasks were calculated. The Fugl-Meyer assessment scale (FMA) and the modified Ashworth scale (MAS) were applied to characterize upper-limb motor dysfunction and spasticity. There was a positive correlation between FMA total scores and ERS in the contralesional hemisphere in the MI task (P < .05) and negative correlations between FMA total scores and ERD in both hemispheres in the MA task (P < .05). Negative correlations were found between MAS scores of wrist flexors and ERD in the ipsilesional hemisphere (P < .05) in the MA task. It suggests that motor dysfunction may be more correlated to ERS in the MI task and to ERD in the MA task while spasticity may be more correlated to ERD in the MA task.

Bilaterally Reduced Rolandic Beta Band Activity in Minor Stroke Patients

Stroke patients with hemiparesis display decreased beta band (13-25 Hz) rolandic activity, correlating to impaired motor function. However, clinically, patients without significant weakness, with small lesions far from sensorimotor cortex, exhibit bilateral decreased motor dexterity and slowed reaction times. We investigate whether these minor stroke patients also display abnormal beta band activity. Magnetoencephalographic (MEG) data were collected from nine minor stroke patients (NIHSS < 4) without significant hemiparesis, at ~1 and ~6 months postinfarct, and eight age-similar controls. Rolandic relative beta power during matching tasks and resting state, and Beta Event Related (De)Synchronization (ERD/ERS) during button press responses were analyzed. Regardless of lesion location, patients had significantly reduced relative beta power and ERS compared to controls. Abnormalities persisted over visits, and were present in both ipsi- and contra-lesional hemispheres, consistent with bilateral impairments in motor dexterity and speed. Minor stroke patients without severe weakness display reduced rolandic beta band activity in both hemispheres, which may be linked to bilaterally impaired dexterity and processing speed, implicating global connectivity dysfunction affecting sensorimotor cortex independent of lesion location. Findings not only illustrate global network disruption after minor stroke, but suggest rolandic beta band activity may be a potential biomarker and treatment target, even for minor stroke patients with small lesions far from sensorimotor areas.

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.

The Neurophysiological Impact of Subacute Stroke: Changes in Cortical Oscillations Evoked by Bimanual Finger Movement

Introduction. To design more effective interventions, such as neurostimulation, for stroke rehabilitation, there is a need to understand early physiological changes that take place that may be relevant for clinical monitoring. We aimed to study changes in neurophysiology following recent ischemic stroke, both at rest and with motor planning and execution. Materials and Methods. We included 10 poststroke patients, between 7 and 10 days after stroke, and 20 age-matched controls to assess changes in cortical motor output via transcranial magnetic stimulation and in dynamics of oscillations, as recorded using electroencephalography (EEG). Results. We found significant differences in cortical oscillatory patterns comparing stroke patients with healthy participants, particularly in the beta rhythm during motor planning ( $p=0.011$ ) and execution ( $p=0.004$ ) of a complex movement with fingers from both hands simultaneously. Discussion. The stroke lesion induced a decrease in event-related desynchronization in patients, in comparison to controls, providing evidence for decreased disinhibition. Conclusions. After a stroke lesion, the dynamics of cortical oscillations is changed, with an increasing neural beta synchronization in the course of motor preparation and performance of complex bimanual finger tasks. The observed patterns may provide a potential functional measure that could be used to monitor and design interventional approaches in subacute stages.

Recovering arm function in chronic stroke patients using combined anodal HD-tDCS and virtual reality therapy (ReArm): a study protocol for a randomized controlled trial

BACKGROUND

After a stroke, 80% of the chronic patients have difficulties to use their paretic upper limb (UL) in activities of daily life (ADL) even after rehabilitation. Virtual reality therapy (VRT) and anodal transcranial direct current stimulation (tDCS) are two innovative methods that have shown independently to positively impact functional recovery of the paretic UL when combined with conventional therapy. The objective of the project will be to evaluate the impact of adding anodal high-definition (HD)-tDCS during an intensive 3-week UL VRT and conventional therapy program on paretic UL function in chronic stroke.

METHODS

The ReArm project is a quadruple-blinded, randomized, sham-controlled, bi-centre, two-arm parallel, and interventional study design. Fifty-eight chronic (> 3 months) stroke patients will be recruited from the Montpellier and Nimes University Hospitals. Patients will follow a standard 3-week in-patient rehabilitation program, which includes 13 days of VRT (Armeo Spring, 1 × 30 min session/day) and conventional therapy (3 × 30 min sessions/day). Twenty-nine patients will receive real stimulation (4x1 anodal HD-tDCS montage, 2 mA, 20 min) to the ipsilesional primary motor cortex during the VRT session and the other 29 patients will receive active sham stimulation (2 mA, 30 s). All outcome measures will be assessed at baseline, at the end of rehabilitation and again 3 months later. The primary outcome measure will be the wolf motor function test. Secondary outcomes will include measures of UL function (Box and Block Test), impairment (Fugl Meyer Upper Extremity), compensation (Proximal Arm Non-Use), ADL (Actimetry, Barthel Index). Other/exploratory outcomes will include pain, fatigue, effort and performance, kinematics, and motor cortical region activation during functional motor tasks.

DISCUSSION

This will be the first trial to determine the impact of adding HD-tDCS during UL VRT and conventional therapy in chronic stroke patients. We hypothesize that improvements in UL function will be greater and longer-lasting with real stimulation than in those receiving sham.

TRIAL REGISTRATION

The ReArm project was approved by The French Research Ethics Committee, (Comité de Protection des Personnes-CPP SUD-EST II, N°ID-RCB: 2019-A00506-51, http://www.cppsudest2.fr/ ). The ReArm project was registered on ClinicalTrials.gov ( NCT04291573 , 2nd March 2020.

Corticospinal Tract Microstructure Correlates With Beta Oscillatory Activity in the Primary Motor Cortex After Stroke

BACKGROUND AND PURPOSE

Cortical beta oscillations are reported to serve as robust measures of the integrity of the human motor system. Their alterations after stroke, such as reduced movement-related beta desynchronization in the primary motor cortex, have been repeatedly related to the level of impairment. However, there is only little data whether such measures of brain function might directly relate to structural brain changes after stroke.

METHODS

This multimodal study investigated 18 well-recovered patients with stroke (mean age 65 years, 12 males) by means of task-related EEG and diffusion-weighted structural MRI 3 months after stroke. Beta power at rest and movement-related beta desynchronization was assessed in 3 key motor areas of the ipsilesional hemisphere that are the primary motor cortex (M1), the ventral premotor area and the supplementary motor area. Template trajectories of corticospinal tracts (CST) originating from M1, premotor cortex, and supplementary motor area were used to quantify the microstructural state of CST subcomponents. Linear mixed-effects analyses were used to relate tract-related mean fractional anisotropy to EEG measures.

RESULTS

In the present cohort, we detected statistically significant reductions in ipsilesional CST fractional anisotropy but no alterations in EEG measures when compared with healthy controls. However, in patients with stroke, there was a significant association between both beta power at rest (P=0.002) and movement-related beta desynchronization (P=0.003) in M1 and fractional anisotropy of the CST specifically originating from M1. Similar structure-function relationships were neither evident for ventral premotor area and supplementary motor area, particularly with respect to their CST subcomponents originating from premotor cortex and supplementary motor area, in patients with stroke nor in controls.

CONCLUSIONS

These data suggest there might be a link connecting microstructure of the CST originating from M1 pyramidal neurons and beta oscillatory activity, measures which have already been related to motor impairment in patients with stroke by previous reports.

Development of a Low-Cost EEG-Controlled Hand Exoskeleton 3D Printed on Textiles

Stroke survivors can be affected by motor deficits in the hand. Robotic equipment associated with brain–machine interfaces (BMI) may aid the motor rehabilitation of these patients. BMIs involving orthotic control by motor imagery practices have been successful in restoring stroke patients' movements. However, there is still little acceptance of the robotic devices available, either by patients and clinicians, mainly because of the high costs involved. Motivated by this context, this work aims to design and construct the Hand Exoskeleton for Rehabilitation Objectives (HERO) to recover extension and flexion movements of the fingers. A three-dimensional (3D) printing technique in association with textiles was used to produce a lightweight and wearable device. 3D-printed actuators have also been designed to reduce equipment costs. The actuator transforms the torque of DC motors into linear force transmitted by Bowden cables to move the fingers passively. The exoskeleton was controlled by neuroelectric signal—electroencephalography (EEG). Concept tests were performed to evaluate control performance. A healthy volunteer was submitted to a training session with the exoskeleton, according to the Graz-BCI protocol. Ergonomy was evaluated with a two-dimensional (2D) tracking software and correlation analysis. HERO can be compared to ordinary clothing. The weight over the hand was around 102 g. The participant was able to control the exoskeleton with a classification accuracy of 91.5%. HERO project resulted in a lightweight, simple, portable, ergonomic, and low-cost device. Its use is not restricted to a clinical setting. Thus, users will be able to execute motor training with the HERO at hospitals, rehabilitation clinics, and at home, increasing the rehabilitation intervention time. This may support motor rehabilitation and improve stroke survivors life quality.

A Neurophysiological Pattern as a Precursor of Work-Related Musculoskeletal Disorders Using EEG Combined with EMG

We aimed to determine the neurophysiological pattern that is associated with the development of musculoskeletal pain that is induced by biomechanical constraints. Twelve (12) young healthy volunteers (two females) performed two experimental realistic manual tasks for 30 min each: (1) with the high risk of musculoskeletal pain development and (2) with low risk for pain development. During the tasks, synchronized electroencephalographic (EEG) and electromyography (EMG) signals data were collected, as well as pain scores. Subsequently, two main variables were computed from neurophysiological signals: (1) cortical inhibition as Task-Related Power Increase (TRPI) in beta EEG frequency band (β.TRPI) and (2) muscle variability as Coefficient of Variation (CoV) from EMG signals. A strong effect size was observed for pain measurement under the high risk condition during the last 5 min of the task execution; with muscle fatigue, because the CoV has decreased below 18%. An increase in cortical inhibition (β.TRPI >50%) was observed after the 5th min of the task in both experimental conditions. These results suggest the following neurophysiological pattern—β.TRPI ≥ 50% and CoV ≤ 18%—as a possible indicator to monitor the development of musculoskeletal pain in the shoulder in the context of repeated and prolonged exposure to manual tasks.

In Vivo Neuroelectrophysiological Monitoring of Atomically Precise Au25 Clusters at an Ultrahigh Injected Dose

Atomically precise Au₂₅(SG)₁₈ clusters have shown great promise in near-infrared II cerebrovascular imaging, X-ray imaging, and cancer radiotherapy due to their high atomic number, unique molecular-like electronic structure, and renal clearable properties. Therefore, it is important to study the in vivo toxicity of Au₂₅ clusters. Unfortunately, previous toxicological investigations focused on low injected doses (<100 mg kg^-1) and routine research methods, such as blood chemistry and biochemistry, which cannot reflect neurotoxicity or tiny changes in neural activity. In this work, in vivo neuroelectrophysiology of Au₂₅ clusters at ultrahigh injected doses (200, 300, and 500 mg kg^-1) was investigated. Local field potential showed that the Au₂₅-treated mice showed a spike in delta rhythm and moved to lower frequency over time. The power spectrum showed a 38.3% reduction in the peak value at 10 h post-injection of Au₂₅ clusters compared with 3 h post-injection, which gradually became close to the normal level, indicating no permanent damage to the nervous system. Moreover, no significant structural changes were found in both neurons and glial cells at the histological level. These results of in vivo neuroelectrophysiology will encourage scientists to make more exciting discoveries on nervous system diseases by employing Au₂₅ clusters even at ultrahigh injected doses.

EEG Changes in Time and Time-Frequency Domain During Movement Preparation and Execution in Stroke Patients

This study investigated electroencephalogram (EEG) changes during movement preparation and execution in stroke patients. EEG-based event-related potential (ERP) technology was used to measure brain activity changes. Seventeen stroke patients participated in this study and completed ERP tests that were designed to measure EEG changes during unilateral upper limb movements in preparation and execution stages, with Instruction Response Movement (IRM) and Cued Instruction Response Movement (CIRM) paradigms. EEG data were analyzed using motor potential (MP) in the time domain and the mu-rhythm and beta frequency band response mean value (R-means) in the time-frequency domain. In IRM, the MP amplitude at Cz was higher during hemiplegic arm movement than during unaffected arm movement. MP latency was shorter at Cz and the contralesional motor cortex during hemiplegic arm movement in CIRM compared to IRM. No significant differences were found in R-means among locations, between movement sides in both ERP tests. This study presents the brain activity changes in the time and time-frequency domains in stroke patients during movement preparation and execution and supports the contralesional compensation and adjacent-region compensation mechanism of post-stroke brain reconstruction. These findings may contribute to future rehabilitation research about neuroplasticity and technology development such as the brain-computer interface.

Longitudinal Electroencephalography Analysis in Subacute Stroke Patients During Intervention of Brain-Computer Interface With Exoskeleton Feedback

Background

Brain-computer interface (BCI) has been regarded as a newly developing intervention in promoting motor recovery in stroke survivors. Several studies have been performed in chronic stroke to explore its clinical and subclinical efficacy. However, evidence in subacute stroke was poor, and the longitudinal sensorimotor rhythm changes in subacute stroke after BCI with exoskeleton feedback were still unclear.

Materials and Methods

Fourteen stroke patients in subacute stage were recruited and randomly allocated to BCI group (n = 7) and the control group (n = 7). Brain-computer interface training with exoskeleton feedback was applied in the BCI group three times a week for 4 weeks. The Fugl-Meyer Assessment of Upper Extremity (FMA-UE) scale was used to assess motor function improvement. Brain-computer interface performance was calculated across the 12-time interventions. Sensorimotor rhythm changes were explored by event-related desynchronization (ERD) changes and topographies.

Results

After 1 month BCI intervention, both the BCI group (p = 0.032) and the control group (p = 0.048) improved in FMA-UE scores. The BCI group (12.77%) showed larger percentage of improvement than the control group (7.14%), and more patients obtained good motor recovery in the BCI group (57.1%) than did the control group (28.6%). Patients with good recovery showed relatively higher online BCI performance, which were greater than 70%. And they showed a continuous improvement in offline BCI performance and obtained a highest value in the last six sessions of interventions during BCI training. However, patients with poor recovery reached a platform in the first six sessions of interventions and did not improve any more or even showed a decrease. In sensorimotor rhythm, patients with good recovery showed an enhanced ERD along with time change. Topographies showed that the ipsilesional hemisphere presented stronger activations after BCI intervention.

Conclusion

Brain-computer interface training with exoskeleton feedback was feasible in subacute stroke patients. Brain-computer interface performance can be an index to evaluate the efficacy of BCI intervention. Patients who presented increasingly stronger or continuously strong activations (ERD) may obtain better motor recovery.

Neurophysiological signatures of hand motor response to dual-transcranial direct current stimulation in subacute stroke: a TMS and MEG study

Background

Dual transcranial direct current stimulation (tDCS) to the bilateral primary motor cortices (M1s) has potential benefits in chronic stroke, but its effects in subacute stroke, when behavioural effects might be expected to be greater, have been relatively unexplored. Here, we examined the neurophysiological effects and the factors influencing responsiveness of dual-tDCS in subacute stroke survivors.

Methods

We conducted a randomized sham-controlled crossover study in 18 survivors with first-ever, unilateral subcortical ischaemic stroke 2–4 weeks after stroke onset and 14 matched healthy controls. Participants had real dual-tDCS (with an ipsilesional [right for controls] M1 anode and a contralesional M1 [left for controls] cathode; 2 mA for 20mins) and sham dual-tDCS on separate days, with concurrent paretic [left for controls] hand exercise. Using transcranial magnetic stimulation (TMS) and magnetoencephalography (MEG), we recorded motor evoked potentials (MEPs), the ipsilateral silent period (iSP), short-interval intracortical inhibition, and finger movement-related cortical oscillations before and immediately after tDCS.

Results

Stroke survivors had decreased excitability in ipsilesional M1 with a relatively excessive transcallosal inhibition from the contralesional to ipsilesional hemisphere at baseline compared with controls, as quantified by decreased MEPs and increased iSP duration. Dual-tDCS led to increased MEPs and decreased iSP duration in ipsilesional M1. The magnitude of the tDCS-induced MEP increase in stroke survivors was predicted by baseline contralesional-to-ipsilesional transcallosal inhibition (iSP) ratio. Baseline post-movement synchronization in α-band activity in ipsilesional M1 was decreased after stroke compared with controls, and its tDCS-induced increase correlated with upper limb score in stroke survivors. No significant adverse effects were observed during or after dual-tDCS.

Conclusions

Task-concurrent dual-tDCS in subacute stroke can safely and effectively modulate bilateral M1 excitability and inter-hemispheric imbalance and also movement-related α-activity.

β-Oscillations Reflect Recovery of the Paretic Upper Limb in Subacute Stroke

Background. Recovery of upper limb function post-stroke can be partly predicted by initial motor function, but the mechanisms underpinning these improvements have yet to be determined. Here, we sought to identify neural correlates of post-stroke recovery using longitudinal magnetoencephalography (MEG) assessments in subacute stroke survivors. Methods. First-ever, subcortical ischemic stroke survivors with unilateral mild to moderate hand paresis were evaluated at 3, 5, and 12 weeks after stroke using a finger-lifting task in the MEG. Cortical activity patterns in the β-band (16-30 Hz) were compared with matched healthy controls. Results. All stroke survivors (n=22; 17 males) had improvements in action research arm test (ARAT) and Fugl-Meyer upper extremity (FM-UE) scores between 3 and 12 weeks. At 3 weeks post-stroke the peak amplitudes of the movement-related ipsilesional β-band event-related desynchronization (β-ERD) and synchronization (β-ERS) in primary motor cortex (M1) were significantly lower than the healthy controls (p<0.001) and were correlated with both the FM-UE and ARAT scores (r=0.51-0.69, p<0.017). The decreased β-ERS peak amplitudes were observed both in paretic and non-paretic hand movement particularly at 3 weeks post-stroke, suggesting a generalized disinhibition status. The peak amplitudes of ipsilesional β-ERS at week 3 post-stroke correlated with the FM-UE score at 12 weeks (r=0.54, p=0.03) but no longer significant when controlling for the FM-UE score at 3 weeks post-stroke.Conclusions. Although early β-band activity does not independently predict outcome at 3 months after stroke, it mirrors functional changes, giving a potential insight into the mechanisms underpinning recovery of motor function in subacute stroke.