Is Resting-State EEG Longitudinally Associated With Recovery of Clinical Neurological Impairments Early Poststroke? A Prospective Cohort Study

Restoring of Interhemispheric Symmetry in Patients With Stroke Following Bilateral or Unilateral Robot-Assisted Upper-Limb Rehabilitation: A Pilot Randomized Controlled Trial

Bilateral robotic rehabilitation has proven helpful in the recovery of upper limb motor function in patients with stroke, but its effects on the cortical reorganization mechanisms underlying recovery are still unclear. This pilot Randomized Controlled Trial (RCT) aimed to evaluate the effects on the interhemispheric balance of unilateral or bilateral robotic treatments in patients with subacute stroke, using Quantitative Electroencephalography (qEEG). 19 patients with ischemic stroke underwent a 30-session upper limb neurorehabilitation intervention using a bilateral upper limb exoskeleton. Each patient was randomly assigned to the bilateral (BG, n=10) or unilateral treatment group (UG, n=9). EEG evaluations were performed before (T0) and right after (T [Formula: see text] the first treatment session, after 30 treatment sessions (T1), and at 1-week follow-up (T2), in both eyes open and eyes closed conditions. From the acquired EEG data, the pairwise-derived Brain Symmetry Index (pdBSI) was computed. In addition, clinical evaluation was performed at T0 and T1 with validated clinical scales. After the treatment, a significant improvement in clinical and EEG evaluations was observed for both groups, but only the BG showed reduced pdBSI in delta and theta bands. In the cluster of sensorimotor channels, there was no significant difference between groups. The observed changes were not maintained at follow-up. No significant changes were observed in the pdBSI after a single rehabilitation session. Results suggest that balancing of interhemispheric symmetry comes along with a clinical improvement in the upper extremity and that the pdBSI can be used to investigate the mechanisms of neuronal plasticity involved in robotic rehabilitation after stroke.

Analysis of electroencephalography characteristics during walking in stroke patients under different conditions: a cross-sectional study

Aims/Background Backward walking is gaining traction in rehabilitation therapy, showing promise as an intervention for stroke patients with walking difficulties. However, the brain activity patterns (neurophysiological mechanisms) underlying backward walking in these patients remain unclear. This study investigated the neurophysiological mechanism in stroke patients within 1 year of their stroke. Methods Twenty-four subjects walked forward and backward for 5 min on an 8-m track while their electroencephalographic signals were collected. The power values of each frequency band were compared during forward and backward walking, and the delta to alpha power ratio (DAR) was calculated. Results The results showed a significant increase in α-band activity within the frontal cortex during backward walking (p < 0.05). This increase correlated positively with scores on the Fugl-Meyer lower extremity motor function assessment scale. Similarly, α-band activity showed significant enhancement within the right parietal cortex during backward walking (p < 0.05). There were no significant differences between forward and backward walking states in δ, θ, and β wavebands across the entire brain region (p > 0.05). Additionally, the DAR was significantly lower during backward walking than during forward walking (p < 0.05). Conclusion This study suggests that backward walking may more effectively activate neural activity in the prefrontal and right posterior parietal cortices. This finding supports the potential of backward walking to enhance motor execution and walking function in stroke patients, thereby supporting its application as a rehabilitation method.

Explainable Deep-Learning Prediction for Brain-Computer Interfaces Supported Lower Extremity Motor Gains Based on Multistate Fusion

Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction. The prediction accuracy of the multi-states fusion network was 82%, significantly improved compared with a single-state model. The neural network explanation result demonstrated the important region and frequency oscillation bands. Specifically, in those two states, power spectral density and functional connectivity were shown as the regions and bands related to motor recovery in frontal, central, and occipital. Moreover, the motor recovery relation in bands, the power spectrum density shows the bands at delta and alpha bands. The functional connectivity shows the delta, theta, and alpha bands in the EC state; delta, theta, and beta mid at the EO state are related to motor recovery. Multi-state fusion neural networks, which combine multiple states of EEG signals into a single network, can increase the accuracy of predicting motor recovery after BCI training, and reveal the underlying mechanisms of motor recovery in brain activity.

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

BACKGROUND

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

AIM

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

DESIGN

Prospective cohort study.

SETTING

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

POPULATION

Sub-acute and chronic stroke survivors.

METHODS

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

RESULTS

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

CONCLUSIONS

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

CLINICAL REHABILITATION IMPACT

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

Post-stroke upper limb recovery is correlated with dynamic resting-state network connectivity

Motor recovery is still limited for people with stroke especially those with greater functional impairments. In order to improve outcome, we need to understand more about the mechanisms underpinning recovery. Task-unbiased, blood flow-independent post-stroke neural activity can be acquired from resting brain electrophysiological recordings and offers substantial promise to investigate physiological mechanisms, but behaviourally relevant features of resting-state sensorimotor network dynamics have not yet been identified. Thirty-seven people with subcortical ischaemic stroke and unilateral hand paresis of any degree were longitudinally evaluated at 3 weeks (early subacute) and 12 weeks (late subacute) after stroke. Resting-state magnetoencephalography and clinical scores of motor function were recorded and compared with matched controls. Magnetoencephalography data were decomposed using a data-driven hidden Markov model into 10 time-varying resting-state networks. People with stroke showed statistically significantly improved Action Research Arm Test and Fugl-Meyer upper extremity scores between 3 weeks and 12 weeks after stroke (both P < 0.001). Hidden Markov model analysis revealed a primarily alpha-band ipsilesional resting-state sensorimotor network which had a significantly increased life-time (the average time elapsed between entering and exiting the network) and fractional occupancy (the occupied percentage among all networks) at 3 weeks after stroke when compared with controls. The life-time of the ipsilesional resting-state sensorimotor network positively correlated with concurrent motor scores in people with stroke who had not fully recovered. Specifically, this relationship was observed only in ipsilesional rather in contralesional sensorimotor network, default mode network or visual network. The ipsilesional sensorimotor network metrics were not significantly different from controls at 12 weeks after stroke. The increased recruitment of alpha-band ipsilesional resting-state sensorimotor network at subacute stroke served as functionally correlated biomarkers exclusively in people with stroke with not fully recovered hand paresis, plausibly reflecting functional motor recovery processes.

Quantitative measures of the resting EEG in stroke: a systematic review on clinical correlation and prognostic value

OBJECT

Quantitative electroencephalography (qEEG) has shown promising results as a predictor of clinical impairment in stroke. We systematically reviewed published papers that focus on qEEG metrics in the resting EEG of patients with mono-hemispheric stroke, to summarize current knowledge and pave the way for future research.

METHODS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched the literature for papers that fitted our inclusion criteria. Rayyan QCRR was used to allow deduplication and collaborative blinded paper review. Due to multiple outcomes and non-homogeneous literature, a scoping review approach was used to address the topic.

RESULTS

Or initial search (PubMed, Embase, Google scholar) yielded 3200 papers. After proper screening, we selected 71 papers that fitted our inclusion criteria and we developed a scoping review thar describes the current state of the art of qEEG in stroke. Notably, among selected papers 53 (74.3%) focused on spectral power; 11 (15.7%) focused on symmetry indexes, 17 (24.3%) on connectivity metrics, while 5 (7.1%) were about other metrics (e.g. detrended fluctuation analysis). Moreover, 42 (58.6%) studies were performed with standard 19 electrodes EEG caps and only a minority used high-definition EEG.

CONCLUSIONS

We systematically assessed major findings on qEEG and stroke, evidencing strengths and potential pitfalls of this promising branch of research.

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.

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.

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.

Reliability of electroencephalogram indicator and event related potential in subacute stroke

Cognitive impairment is observed in 12% to 56% of stroke patients, and screening for cognitive impairment is often complex and time-consuming, with results dependent on patient compliance. Therefore, there is a need for an objective method to assess cognitive impairment regardless of patient compliance. Objective evaluation methods include electroencephalogram (EEG) and event-related potential (ERP). This study was conducted to assess intra-tester reliability of resting EEG-based spectral features and auditory/visual P300 latency/amplitude in patients with subacute ischemic stroke. Twenty patients with subacute ischemic stroke were included in the study. The resting EEG and P300 wave using an auditory and visual oddball paradigm were measured at baseline and once again in 24 hours. The following electrode positions (10-20 system) were constantly recorded: F3 (Frontal), Fz, F4, C3 (Central), Cz, C4, P3 (Parietal), Pz, P4. DAR (delta/alpha ratio) and BSI (brain symmetry index) were determined using EEG data. F3 and F4, C3 and C4 and P3 and P4 were switched according to the stroke side and classified as affected hemisphere (AH) and unaffected hemisphere (UH) after the evaluation. In ERP, the amplitude and latency of P300 were obtained. In reliability analysis of EEG-based spectral characteristics, significant reliability was observed for DAR (ICC = 0.447), BSldir (ICC = 0.713) and BSIdirtheta (ICC = 0.724) (Table 4). DAR was showed a poor ICC level, and BSIdir and BSIdirtheta had a moderate ICC level. Visual P300 latency showed excellent intraclass correlation coefficient (ICC) in several montages (PUH = 0.972, Pz = 0.945). In 6 montages, auditory P300 latency was reliable, while in 9 montages, visual P300 latency was reliable. In 4 montages, auditory P300 amplitude was reliable, while visual P300 amplitude was reliable in 7. The visual P300 was more reliable than the auditory P300. The ICC values for P300 latency were greater than those for amplitude. Therefore, when ERP is performed on subacute stroke patients, visual has higher reliability than auditory.

Brain symmetry index predicts 3-month mortality in patients with acute large hemispheric infarction

Quantitative electroencephalography data are helpful to predict outcomes of cerebral infarction patients. The study was performed to evaluate the value of brain symmetry index by quantitative electroencephalography in predicting 3-month mortality of large hemispheric infarction. We studied a prospective, consecutive series of patients with large supratentorial cerebral infarction confirmed within 3 days from the onset in 2 intensive care units from August 2017 to February 2020. The electroencephalography was recorded once admission. The brain symmetry index (BSI) which is divided into BSIfast and BSIslow were calculated for each electrodes pair. The outcome was mortality at 3 months after the onset. A total of 38 patients were included. The subjects were divided into the mortality group (15 patients) and survival group (23 patients). Of the BSIfast and BSIslow at each electrodes pair, higher BSIfastC3-C4, higher BSIslowC3-C4, and higher BSIslowO1-O2 were noticed in the mortality group than that in the survival group at 3 months (P = .001; P = .010; P = .009). Multivariable analysis indicated that BSIfastC3-C4 was an independent predictor of 3-month mortality (odds ratio = 1.059, 95%CI 1.003, 1.119, P = .039). BSIfastC3-C4 could significant predict 3-month mortality (area under curve = 0.805, P = .005). And when we combined BSIfastC3-C4, Glasgow Coma Scale and infarct volume together to predict the 3-month mortality, the predicted value increased (area under curve = 0.840, P = .002). BSIfastC3-C4 could independently predict the 3-month mortality of large hemispheric infarction. The combination marker which includes Glasgow Coma Scale, infarct volume, and BSIfastC3-C4 has a better diagnostic value. Further clinical trials with a large sample size are still needed.

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.

Resting-state electroencephalography changes in poststroke patients with visuospatial neglect

Background

This study aimed to explore the electrophysiological characteristics of resting-state electroencephalography (rsEEG) in patients with visuospatial neglect (VSN) after stroke.

Methods

A total of 44 first-event sub-acute strokes after right hemisphere damage (26 with VSN and 18 without VSN) were included. Besides, 18 age-matched healthy participants were used as healthy controls. The resting-state electroencephalography (EEG) of 64 electrodes was recorded to obtain the power of the spectral density of different frequency bands. The global delta/alpha ratio (DAR), DAR over the affected hemispheres (DAR_AH), DAR over the unaffected hemispheres (DAR_UH), and the pairwise-derived brain symmetry index (pdBSI; global and four bands) were compared between groups and receiver operating characteristic (ROC) curve analysis was conducted. The Barthel index (BI), Fugl-Meyer motor function assessment (FMA), and Berg balance scale (BBS) were used to assess the functional state of patients. Visuospatial neglect was assessed using a battery of standardized tests.

Results

We found that patients with VSN performed poorly compared with those without VSN. Analysis of rsEEG revealed increased delta and theta power and decreased alpha and beta power in stroke patients with VSN. Compared to healthy controls and poststroke non-VSN patients, patients with VSN showed a higher DAR (P < 0.001), which was significantly positively correlated with the BBS (DAR: r = –0.522, P = 0.006; DAR_AH: r = –0.521, P = 0.006; DAR_UH: r = –0.494, P = 0.01). The line bisection task was positively correlated with DAR (r = 0.458, P = 0.019) and DAR_AH (r = 0.483, P = 0.012), while the star cancellation task was only positively correlated with DAR_AH (r = 0.428, P = 0.029). DAR_AH had the best discriminating value between VSN and non-VSN, with an area under the curve (AUC) of 0.865. Patients with VSN showed decreased alpha power in the parietal and occipital areas of the right hemisphere. A higher parieto-occipital pdBSI_alpha was associated with a worse line bisection task (r = 0.442, P = 0.024).

Conclusion

rsEEG may be a useful tool for screening for stroke patients with visuospatial neglect, and DAR and parieto-occipital pdBSI_alpha may be useful biomarkers for visuospatial neglect after stroke.

Robustness of individualized inferences from longitudinal resting state EEG dynamics

Tracking how individual human brains change over extended timescales is crucial to clinical scenarios ranging from stroke recovery to healthy aging. The use of resting state (RS) activity for tracking is a promising possibility. However, it is unresolved how a person's RS activity over time can be decoded to distinguish neurophysiological changes from confounding cognitive variability. Here, we develop a method to screen RS activity changes for these confounding effects by formulating it as a problem of change classification. We demonstrate a novel solution to change classification by linking individual-specific change to inter-individual differences. Individual RS-EEG was acquired over five consecutive days including task states devised to simulate the effects of inter-day cognitive variation. As inter-individual differences are shaped by neurophysiological differences, the inter-individual differences in RS activity on one day were analyzed (using machine learning) to identify distinctive configurations in each individual's RS activity. Using this configuration as a decision-rule, an individual could be re-identified from 2-second samples of the instantaneous oscillatory power spectrum acquired on a different day both from RS and confounded-RS with a limited loss in accuracy. Importantly, the low loss in accuracy in cross-day vs same-day classification was achieved with classifiers that combined information from multiple frequency bands at channels across the scalp (with a concentration at characteristic fronto-central and occipital zones). Taken together, these findings support the technical feasibility of screening RS activity for confounding effects and the suitability of longitudinal RS for robust individualized inferences about neurophysiological change in health and disease.

Quantifying Quality of Reaching Movements Longitudinally Post-Stroke: A Systematic Review

Background

Disambiguation of behavioral restitution from compensation is important to better understand recovery of upper limb motor control post-stroke and subsequently design better interventions. Measuring quality of movement (QoM) during standardized performance assays and functional tasks using kinematic and kinetic metrics potentially allows for this disambiguation.

Objectives

To identify longitudinal studies that used kinematic and/or kinetic metrics to investigate post-stroke recovery of reaching and assess whether these studies distinguish behavioral restitution from compensation.

Methods

A systematic literature search was conducted using the databases PubMed, Embase, Scopus, and Wiley/Cochrane Library up to July 1st, 2020. Studies were identified if they performed longitudinal kinematic and/or kinetic measurements during reaching, starting within the first 6 months post-stroke.

Results

Thirty-two longitudinal studies were identified, which reported a total of forty-six different kinematic metrics. Although the majority investigated improvements in kinetics or kinematics to quantify recovery of QoM, none of these studies explicitly addressed the distinction between behavioral restitution and compensation. One study obtained kinematic metrics for both performance assays and a functional task.

Conclusions

Despite the growing number of kinematic and kinetic studies on post-stroke recovery, longitudinal studies that explicitly seek to delineate between behavioral restitution and compensation are still lacking in the literature. To rectify this situation, future studies should measure kinematics and/or kinetics during performance assays to isolate restitution and during a standardized functional task to determine the contributions of restitution and compensation.

The Cortical Response Evoked by Robotic Wrist Perturbations Reflects Level of Proprioceptive Impairment After Stroke

Background: Proprioception is important for regaining motor function in the paretic upper extremity after stroke. However, clinical assessments of proprioception are subjective and require verbal responses from the patient to applied proprioceptive stimuli. Cortical responses evoked by robotic wrist perturbations and measured by electroencephalography (EEG) may be an objective method to support current clinical assessments of proprioception.

Objective: To establish whether evoked cortical responses reflect proprioceptive deficits as assessed by clinical scales and whether they predict upper extremity motor function at 26 weeks after stroke.

Methods: Thirty-one patients with stroke were included. In week 1, 3, 5, 12, and 26 after stroke, the upper extremity sections of the Erasmus modified Nottingham Sensory Assessment (EmNSA-UE) and the Fugl-Meyer Motor Assessment (FM-UE) and the EEG responses (64 channels) to robotic wrist perturbations were measured. The extent to which proprioceptive input was conveyed to the affected hemisphere was estimated by the signal-to-noise ratio (SNR) of the evoked response. The relationships between SNR and EmNSA-UE as well as SNR and time after stroke were investigated using linear regression. Receiver-operating-characteristic curves were used to compare the predictive values of SNR and EmNSA-UE for predicting whether patients regained some selective motor control (FM-UE > 22) or whether they could only move their paretic upper extremity within basic limb synergies (FM-UE ≤ 22) at 26 weeks after stroke.

Results: Patients (N = 7) with impaired proprioception (EmNSA-UE proprioception score < 8) had significantly smaller SNR than patients with unimpaired proprioception (N = 24) [EmNSA-UE proprioception score = 8, t(29) = 2.36, p = 0.03]. No significant effect of time after stroke on SNR was observed. Furthermore, there was no significant difference in the predictive value between EmNSA-UE and SNR for predicting motor function at 26 weeks after stroke.

Conclusion: The SNR of the evoked cortical response does not significantly change as a function of time after stroke and differs between patients with clinically assessed impaired and unimpaired proprioception, suggesting that SNR reflects persistent damage to proprioceptive pathways. A similar predictive value with respect to EmNSA-UE suggests that SNR may be used as an objective predictor next to clinical sensory assessments for predicting motor function at 26 weeks after stroke.

Correlation Between Poststroke Balance Function and Brain Symmetry Index in Sitting and Standing Postures

Balance problems are the main sequelae of stroke, which increases the risk of falling. The assessment of balance ability can guide doctors to formulate rehabilitation plans, thereby reducing the risk of falls. Studies have reported the role of resting-state EEG during sitting in the motor assessment of the upper extremity and prognosis of stroke patients. However, the above research in the sitting posture lacks specificity in evaluating the balance ability of the lower limbs. Herein, this article investigated whether EEG was different in sitting and standing positions with different difficulty levels and validated the feasibility of EEG in assessing body balance ability. The resting-state EEG signals were collected from 11 stroke patients. The pairwise-derived brain symmetry index (pdBSI) was used to identify the differences in EEG-quantified interhemispheric cortical power asymmetry observable in healthy versus cortical and subcortical stroke patients by calculating the absolute value of the difference in power at each pair of electrodes. Subsequently, we computed the pdBSI over different frequency bands. Balance function was assessed using the BBS (Berg Balance Scale). Stroke survivors showed higher pdBSI (1-25 Hz) values in standing posture compared to sitting (p <0.05) and the pdBSI was significantly negatively correlated with BBS (r = -0.671, p =0.034). Additionally, the pdBSI within beta band was also significantly negatively correlated with BBS (r = -0.711, p=0.017). In conclusion, stroke brain asymmetry in standing posture was significantly more severe and the pdBSIs in 1-25Hz and beta hand were related to balance function. BBS and NIHSS was significantly negatively correlated (r = -0.701, p = 0.024), and NIHSS was significantly correlated with age (r = 0.822, p = 0.004). The present study suggests that stroke can seriously affect the body's balance ability. Compared with the sitting posture, the asymmetry of cortical energy in the standing posture can better assess the patient's balance ability.

Inter trial coherence of low-frequency oscillations in the course of stroke recovery

OBJECTIVE

The aim was to find a sensitive method to highlight the remodeling of the brain's bioelectric activity in post-stroke repair.

METHODS

Fifteen mild upper limb paretic stroke patients and age-matched healthy controls were included. Repeated trials of finger tapping around the 10th and 100th days after stroke onset were recorded with a 128-channel EEG. Power spectra and Inter Trial Coherence (ITC) calculations were synchronized to tappings. ITC was correlated with motor performance.

RESULTS

ITC, in low frequency bands, designates the motor related bioelectric activity in channel space in both healthy subjects and patients. Ten days after stroke onset, delta-theta ITC was severely reduced compared to baseline, while three months later ITC reorganized partially over the ipsilesional central-parietal areas reflecting the improvement of motor networks. Decreased ITC in the central-parietal area remained significant compared to controls. Delta band ITC over the dorsolateral-prefrontal cortex correlates with the performance on Nine Hole Peg Test. At post-recovery, non-paretic hand tappings show significantly decreased delta-theta ITC over the supplementary motor area, which reflects network remodeling.

CONCLUSIONS

Inter Trial Coherence is a useful measure of brain reorganization during stroke recovery.

SIGNIFICANCE

Delta- theta ITC is a sensitive indicator of impaired motor execution.

EEG Fractal Analysis Reflects Brain Impairment after Stroke

Stroke is the commonest cause of disability. Novel treatments require an improved understanding of the underlying mechanisms of recovery. Fractal approaches have demonstrated that a single metric can describe the complexity of seemingly random fluctuations of physiological signals. We hypothesize that fractal algorithms applied to electroencephalographic (EEG) signals may track brain impairment after stroke. Sixteen stroke survivors were studied in the hyperacute (<48 h) and in the acute phase (∼1 week after stroke), and 35 stroke survivors during the early subacute phase (from 8 days to 32 days and after ∼2 months after stroke): We compared resting-state EEG fractal changes using fractal measures (i.e., Higuchi Index, Tortuosity) with 11 healthy controls. Both Higuchi index and Tortuosity values were significantly lower after a stroke throughout the acute and early subacute stage compared to healthy subjects, reflecting a brain activity which is significantly less complex. These indices may be promising metrics to track behavioral changes in the very early stage after stroke. Our findings might contribute to the neurorehabilitation quest in identifying reliable biomarkers for a better tailoring of rehabilitation pathways.

Computerised patient-specific prediction of the recovery profile of upper limb capacity within stroke services: the next step

INTRODUCTION

Predicting upper limb capacity recovery is important to set treatment goals, select therapies and plan discharge. We introduce a prediction model of the patient-specific profile of upper limb capacity recovery up to 6 months poststroke by incorporating all serially assessed clinical information from patients.

METHODS

Model input was recovery profile of 450 patients with a first-ever ischaemic hemispheric stroke measured using the Action Research Arm Test (ARAT). Subjects received at least three assessment sessions, starting within the first week until 6 months poststroke. We developed mixed-effects models that are able to deal with one or multiple measurements per subject, measured at non-fixed time points. The prediction accuracy of the different models was established by a fivefold cross-validation procedure.

RESULTS

A model with only ARAT time course, finger extension and shoulder abduction performed as good as models with more covariates. For the final model, cross-validation prediction errors at 6 months poststroke decreased as the number of measurements per subject increased, from a median error of 8.4 points on the ARAT (Q1-Q3:1.7-28.1) when one measurement early poststroke was used, to 2.3 (Q1-Q3:1-7.2) for seven measurements. An online version of the recovery model was developed that can be linked to data acquisition environments.

CONCLUSION

Our innovative dynamic model can predict real-time, patient-specific upper limb capacity recovery profiles up to 6 months poststroke. The model can use all available serially assessed data in a flexible way, creating a prediction at any desired moment poststroke, stand-alone or linked with an electronic health record system.

Are early measured resting-state EEG parameters predictive for upper limb motor impairment six months poststroke?

OBJECTIVES

Investigate whether resting-state EEG parameters recorded early poststroke can predict upper extremity motor impairment reflected by the Fugl-Meyer motor score (FM-UE) after six months, and whether they have prognostic value in addition to FM-UE at baseline.

METHODS

Quantitative EEG parameters delta/alpha ratio (DAR), brain symmetry index (BSI) and directional BSI (BSIdir) were derived from 62-channel resting-state EEG recordings in 39 adults within three weeks after a first-ever ischemic hemispheric stroke. FM-UE scores were acquired within three weeks (FM-UE_baseline) and at 26 weeks poststroke (FM-UE_w26). Linear regression analyses were performed using a forward selection procedure to predict FM-UE_w26.

RESULTS

BSI calculated over the theta band (BSI_theta) (β = -0.40; p = 0.013) was the strongest EEG-based predictor regarding FM-UE_w26. BSI_theta (β = -0.27; p = 0.006) remained a significant predictor when added to a regression model including FM-UE_baseline, increasing explained variance from 61.5% to 68.1%.

CONCLUSION

Higher BSI_theta values, reflecting more power asymmetry over the hemispheres, predict more upper limb motor impairment six months after stroke. Moreover, BSI_theta shows additive prognostic value regarding FM-UE_w26 next to FM-UE_baseline scores, and thereby contains unique information regarding upper extremity motor recovery.

SIGNIFICANCE

To our knowledge, we are the first to show that resting-state EEG parameters can serve as prognostic biomarkers of stroke recovery, in addition to FM-UE_baseline scores.