Stroke Neurorehabilitation Research Articles

Research on the clinical application effectiveness of neurorehabilitation technology for stroke

Abstract. Stroke is one of the leading causes of death and disability in the world today, necessitating effective treatment options for patients. Brain-computer interface (BCI) technology, as one of the newly developed technologies, is gradually being incorporated into the rehabilitation treatment of stroke. At the same time, there are more evaluation indicators for the rehabilitation treatment of stroke using BCI, but related prognostic prediction methods still need further research. Therefore, the purpose of this study is to review the effects of the clinical application of BCI technology in neurorehabilitation for stroke. The results show that BCI technology has a positive clinical effect in the neurorehabilitation of stroke, improving patients quality of life and highlighting the importance of BCI technology for the neurorehabilitation of stroke. In the future, BCI technology will be further applied in the field of neurorehabilitation for stroke and combined with more technologies to achieve treatment for stroke and its various symptoms.

Mapping the Landscape of Biomechanics Research in Stroke Neurorehabilitation: A Bibliometric Perspective

Background/Objectives: The incorporation of biomechanics into stroke neurorehabilitation may serve to strengthen the effectiveness of rehabilitation strategies by increasing our understanding of human movement and recovery processes. The present bibliometric analysis of biomechanics research in stroke neurorehabilitation is conducted with the objectives of identifying influential studies, key trends, and emerging research areas that would inform future research and clinical practice. Methods: A comprehensive bibliometric analysis was performed using documents retrieved from the Scopus database on 6 August 2024. The analysis included performance metrics such as publication counts and citation analysis, as well as science mapping techniques, including co-authorship, bibliographic coupling, co-citation, and keyword co-occurrence analyses. Data visualization tools such as VOSviewer and Power BI were utilized to map the bibliometric networks and trends. Results: An overabundance of recent work has yielded substantial advancements in the application of brain–computer interfaces to electroencephalography and functional neuroimaging during stroke neurorehabilitation., which translate neural activity into control signals for external devices and provide critical insights into the biomechanics of motor recovery by enabling precise tracking and feedback of movement during rehabilitation. A sampling of the most impactful contributors and influential publications identified two leading countries of contribution: the United States and China. Three prominent research topic clusters were also noted: biomechanical evaluation and movement analysis, neurorehabilitation and robotics, and motor recovery and functional rehabilitation. Conclusions: The findings underscore the growing integration of advanced technologies such as robotics, neuroimaging, and virtual reality into neurorehabilitation practices. These innovations are poised to enhance the precision and effectiveness of therapeutic interventions. Future research should focus on the long-term impacts of these technologies and the development of accessible, cost-effective tools for clinical use. The integration of multidisciplinary approaches will be crucial in optimizing patient outcomes and improving the quality of life for stroke survivors.

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.

Noninvasive brain stimulation to improve motor outcomes after stroke.

This review highlights recent developments in noninvasive brain stimulation (NIBS) techniques and applications for improving motor outcomes after stroke. Two promising areas of development relate to deep brain neuromodulation and the use of single-pulse transcranial magnetic stimulation (TMS) within a prediction tool for predicting upper limb outcome for individual patients. Systematic reviews highlight the inconsistent effect sizes of interventional NIBS for motor outcome after stroke, as well as limited evidence supporting the interhemispheric competition model. To improve the therapeutic efficacy of NIBS, studies have leveraged metaplasticity and priming approaches. Transcranial temporal interference stimulation (tTIS) and low-intensity focused ultrasound stimulation (LIFUS) are emerging NIBS techniques with potential for modulating deeper brain structures, which may hold promise for stroke neurorehabilitation. Additionally, motor evoked potential (MEP) status obtained with single-pulse TMS is a prognostic biomarker that could be used to tailor NIBS for individual patients. Trials of interventional NIBS to improve stroke outcomes may be improved by applying NIBS in a more targeted manner. This could be achieved by taking advantage of NIBS techniques that can be targeted to deeper brain structures, using biomarkers of structural and functional reserve to stratify patients, and recruiting patients in more homogeneous time windows.

Subject-Specific Session-to-Session Transfer Learning Strategies for Increasing Brain-Computer Interface Performance during Upper Extremity Neurorehabilitation in Stroke

Subject-Specific Session-to-Session Transfer Learning Strategies for Increasing Brain-Computer Interface Performance during Upper Extremity Neurorehabilitation in Stroke

Post-acute Neurorehabilitation for Stroke as a Community Effort (P8-11.020)

Post-acute Neurorehabilitation for Stroke as a Community Effort (P8-11.020)

Safety of ipsilesional anodal transcranial direct current stimulation in acute photothrombotic stroke: implications for early neurorehabilitation

Early rehabilitation in the acute phase of stroke, that bears unique neuroplastic properties, is the current standard to reduce disability. Anodal transcranial direct current stimulation can augment neurorehabilitation in chronic stroke. Studies in the acute phase are sparse and held back by inconclusive preclinical data pointing towards potential negative interaction of the excitability increasing tDCS modality with stroke-induced glutamate toxicity. In this present study, we aimed to evaluate structural and behavioral safety of anodal tDCS applied in the acute phase of stroke. Photothrombotic stroke including the right primary motor cortex was induced in rats. 24 h after stroke anodal tDCS was applied for 20 min ipsilesionally at one of four different current densities in freely moving animals. Effects on the infarct volume and on stroke induced neuroinflammation were assessed. Behavioral consequences were monitored. Infarct volume and the modified Neurological Severity Score were not affected by anodal tDCS. Pasta handling, a more sensitive task for sensorimotor deficits, and microglia reactivity indicated potentially harmful effects at the highest tDCS current density tested (47.8 A/m2), which is more than 60 times higher than intensities commonly used in humans. Compared to published safety limits of anodal tDCS in healthy rats, recent stroke does not increase the sensitivity of the brain to anodal tDCS, as assessed by lesion size and neuroinflammatory response. Behavioral deficits only occurred at the highest intensity, which was associated with increased neuroinflammation. When safety limits of commonly used clinical tDCS are met, augmentation of early neurorehabilitation after stroke by anodal tDCS appears to be feasible.

Integrating Movement Quality in Stroke Neurorehabilitation Could Enhance Arm Recovery.

Integrating Movement Quality in Stroke Neurorehabilitation Could Enhance Arm Recovery.

Impact of virtual reality-based therapy on post-stroke depression: A systematic review and meta-analysis of randomized controlled trials.

Post-stroke depression is the most common neuropsychiatric consequence and reduces rehabilitation effectiveness. However, the efficacy of virtual reality (VR) on mental health treatment for patients after a stroke is uncertain. The aim of this study was to evaluate the efficacy of VR as a co-adjuvant form of treatment to reduce depression in stroke patients admitted to neurorehabilitation units. We systematically searched medical databases including PubMed, CINAHL, PsycINFO, Embase, Cochrane Library, Web of Science, and ClinicalTrials.gov from inception to November 16, 2023. Clinical trials comparing the use of VR as an adjuvant form of treatment in stroke patients' rehabilitation with the usual treatment were included. Pooled standardized mean differences were calculated using a random-effects model. Subgroup analyses were performed according to type of stroke, VR characteristics, and the scale used to measure depression. Meta-regression analysis was performed for intervention duration and to determine the mean age of the participants. Eight studies and 388 stroke patients were included. The VR interventions were associated with a lower risk of depression in patients (ES = -0.69; 95% CI [-1.05, -0.33]; I2 = 57.6%; p ≤ .02). The estimates were not affected by the type of stroke, the type of VR used, the blinding process, the type of scale used to detect depression, the duration of the intervention (weeks and minutes), and the total number of sessions. Meta-regression shows that younger samples (p = .00; 95% CI [0.01, 0.08) and longer interventions (p = < .05; 95% CI [-0.00, -0.00) lead to a greater reduction in depression. This review provides an important basis for treating depression in patients after a stroke. Professionals working in stroke neurorehabilitation units should consider VR as a form of co-adjuvant treatment for depression in patients. CRD42022303968.

Effects of transcranial magnetic stimulation on cortical structures during motor imagination performance in the brain-computer interface

The aftermath of a stroke can frequently result in impaired motor functions causing problems performing habitual limb movements. These movement disorders stem from damage to the cerebral cortex and disruptions to neuronal connections in the central pyramidal pathways [1]. Restoring motor skills following a stroke is a time-consuming and challenging process, requiring resources from both external medical sources and the patient. Despite these challenges, it is still feasible for patients to regain control over their limb movements. The common approach for stroke neurorehabilitation consists of therapeutic physical activities and kinesiotherapy. These methods rely on afferent information during motor tasks to repair connections between intact brain areas [2]. Through exercising the impacted limbs, synaptic rearrangement happens in the cortex, awakening dormant neurons and increasing cortical areas adjacent to inactive regions. Although these techniques can partially restore movement, a significant number of stroke patients still face impairments. It is crucial to acknowledge the limitations of the current interventions and explore new approaches for better outcomes. Traditional rehabilitation methods often do not fully restore movement control, prompting researchers to explore alternative approaches. Motor-imagery-based brain-computer interfaces (BCIs) have gained attention recently [3, 4]. They allow for integrating various feedback mechanisms, which can be combined with upper and lower limb exoskeletons. The level of control a subject has over a BCI system directly affects the recovery process [5]. Introducing transcranial magnetic stimulation (TMS) into motor-imagery BCIs shows promise for developing a unified and highly efficient approach to post-stroke rehabilitation. Twenty-nine healthy adult participants (21 females) with a mean age of 20.93±2.14 years were recruited for this experiment. All participants were right-handed and had no prior experience with brain-computer interfaces. Ethical approval was obtained from the local ethics committee of Lobachevsky State University of Nizhny Novgorod (ethical approval No. 2, dated 03/19/2021), and written informed consent was obtained from all participants. Subjects were randomly assigned to receive either a sham intervention (n=15) or active TMS (n=14). The experiment displayed tasks on a 24-inch LCD screen placed 2 meters away. Participants sat in a reclining chair with hands on adjustable armrests for EEG signal recording. Motor-imagery-based BCI training took place over two days with four daily tasks: motor performance, quasi-motor, and two motor imaginations of the dominant hand. EEG activity was recorded prior to and after completing the tasks. Each task comprised of 20 trials, each lasting 10 seconds. TMS was administered in between two motor imagination tasks, and a rest period of 2 minutes followed the stimulation. The researcher objectively monitored electromyography (EMG) in real-time during motor tasks. Certified NVX 52 amplifiers with 32 Cl/Ag electrodes positioned according to the international 10-10 system recorded electroencephalography (EEG) signals. The EEG was digitized at a sampling rate of 1000 Hz and filtered with a 50 Hz Notch filter. Disposable electrodes were used to record EMG data from musculus flexor digitorum superficialis on the right hand. EMG signals were digitized at 1000 Hz and filtered using a 50 Hz Notch filter. TMS was applied to the dorsolateral prefrontal cortex (dlPFC) using a figure-8 coil connected to a Neuro MS/D magnetic stimulator. Sham stimulation was performed with the same parameters, except the coil was tilted 90 degrees to simulate the sound of real stimulation. A nonparametric permutation test was conducted to assess the statistically significant dissimilarities between rest periods and periods subsequent to conducting motor imagery after TMS. Significant clusters of the highest event-related desynchronization (ERD) were observed. The analysis demonstrated a significant negative cluster in the theta rhythm (0–6 Hz) that was distinct from the magnetic stimulation site. Similar significant ERD clusters were identified during the first and second series of imaginary movements. The study examined alterations in motor cortex function following targeted rTMS. The results demonstrated that TMS using particular parameters resulted in the pre-activation of brain regions comparable to those stimulated during motor imagery. The implementation of this stimulation protocol increased activity in cortical regions related to motor imagery. The outcomes propose the probable effectiveness of TMS in elevating motor cortex activation during the rehabilitation process.

Feasibility of a Wearable Soft Robot Triggered by Brain-Computer Interface for Stroke Upper Extremity Rehabilitation

Abstract Conventional robot designs have been applied as feedback stages in brain-computer interfaces (BCI) for stroke upper limb neurorehabilitation, showing promising results. Soft -robotic devices can be simpler and less expensive to manufacture, and provide ergonomic advantages over traditional designs, allowing to increase the efficacy and availableness of BCI systems for stroke neurorehabilitation. However, patients' degrees of control, neurophysiological activity, and system's usability with a BCI, using a soft robotic device as feedback, have not been assessed in stroke. For these reasons, a BCI system with a soft robotic feedback device was assessed in stroke patients. Fifty trials were acquired to setup the system, and another fifty trials were performed for evaluating patients' BCI control and cortical activity during movement intention (MI) and robotic feedback. User experience with the BCI was also assessed. Classification accuracy was in the range of 71.3% to 97.5%. Significant decrease in alpha power was observed during both motor intention and robotic feedback, but significant decrease in beta power was only observed during motor intention. BCI performance was high and in the range of reported BCI stroke interventions that used traditional robotics as feedback. Power decrease observed predominantly in alpha during soft robotic feedback was likely due to the eliciting of motor-related mechanisms. Quantification of user experience with the BCI implied that the system complexity is adequate for stroke patients. Therefore, a BCI system aimed at stroke neurorehabilitation can incorporate a soft robotic design as feedback and has potential for upper extremity interventions.

Advances in Stroke Neurorehabilitation.

Stroke is one of the leading causes of disability worldwide despite recent advances in hyperacute interventions to lessen the initial impact of stroke. Stroke recovery therapies are crucial in reducing the long-term disability burden after stroke. Stroke recovery treatment options have rapidly expanded within the last decade, and we are in the dawn of an exciting era of multimodal therapeutic approaches to improve post-stroke recovery. In this narrative review, we highlighted various promising advances in treatment and technologies targeting stroke rehabilitation, including activity-based therapies, non-invasive and minimally invasive brain stimulation techniques, robotics-assisted therapies, brain-computer interfaces, pharmacological treatments, and cognitive therapies. These new therapies are targeted to enhance neural plasticity as well as provide an adequate dose of rehabilitation and improve adherence and participation. Novel activity-based therapies and telerehabilitation are promising tools to improve accessibility and provide adequate dosing. Multidisciplinary treatment models are crucial for post-stroke neurorehabilitation, and further adjuvant treatments with brain stimulation techniques and pharmacological agents should be considered to maximize the recovery. Among many challenges in the field, the heterogeneity of patients included in the study and the mixed methodologies and results across small-scale studies are the cardinal ones. Biomarker-driven individualized approaches will move the field forward, and so will large-scale clinical trials with a well-targeted patient population.

A comprehensive guide to BCI-based stroke neurorehabilitation interventions

Brain-Computer Interfaces (BCIs) offer the potential to facilitate neurorehabilitation in stroke patients by decoding user intentions from the central nervous system, thereby enabling control over external devices. Despite their promise, the diverse range of intervention parameters and technical challenges in clinical settings have hindered the accumulation of substantial evidence supporting the efficacy and effectiveness of BCIs in stroke rehabilitation. This article introduces a practical guide designed to navigate through these challenges in conducting BCI interventions for stroke rehabilitation. Applicable regardless of infrastructure and study design limitations, this guide acts as a comprehensive reference for executing BCI-based stroke interventions. Furthermore, it encapsulates insights gleaned from administering hundreds of BCI rehabilitation sessions to stroke patients.•Presents a comprehensive methodology for implementing BCI-based upper extremity therapy in stroke patients.•Provides detailed guidance on the number of sessions, trials, as well as the necessary hardware and software for effective intervention.

Alterations in the preferred direction of individual arm muscle activation after stroke.

Stroke survivors have challenges appropriately coordinating the multiple muscles, resulting in a deficit in motor control. Therefore, comprehending the mechanism underlying abnormal intermuscular coordination becomes crucial in developing effective rehabilitation strategies. Quantitative analyses have been employed at pairwise or multi-dimensional levels to understand the underlying mechanism of abnormal intermuscular coordination and its relationship to motor impairment. However, how alterations in individual muscle activation contribute to abnormal intermuscular coordination, motor impairment, and motor performance remains unclear. Thus, we investigated the alterations in the preferred direction of individual muscles after stroke and their relationship with stroke-induced changes in intermuscular coordination, clinical motor impairment, and qualities of motor performance during isometric force generation in the upper extremity. Twenty-four stroke survivors and six age-matched controls were recruited and performed isometric force target matches while recording electromyographic signals from eight upper limb muscles. We determined the preferred activation direction of each muscle, evaluated abnormal intermuscular coordination through a muscle synergy analysis, assessed motor impairment using upper extremity Fugl-Meyer Assessment scores, and examined motor performance characteristics defined by force trajectory features. The post-stroke alterations in the preferred direction of the brachioradialis, anterior, middle, and posterior deltoid were correlated with the motor impairment level and attributed to the changes in muscle synergy characteristics. Only alterations in the preferred direction of the brachioradialis and posterior deltoid activation in forward-backward and upward-downward axes were associated with the qualities of isometric force generation, respectively. These findings imply that alterations in the preferred direction of individual muscle activation contribute to various aspects of motor deficit following stroke. This insight may serve as a foundation for the development of innovative stroke neurorehabilitation approaches that take into account specific attributes of individual muscle activation, including their preferred activation direction.

Alterations in learning-related cortical activation and functional connectivity by high-definition transcranial direct current stimulation after stroke: an fNIRS study.

Motor learning is a key component of stroke neurorehabilitation. High-definition transcranial direct current stimulation (HD-tDCS) was recently developed as a tDCS technique that increases the accuracy of current delivery to the brain using arrays of small electrodes. The purpose of this study was to investigate whether HD-tDCS alters learning-related cortical activation and functional connectivity in stroke patients using functional near-infrared spectroscopy (fNIRS). Using a sham-controlled crossover study design, 16 chronic stroke patients were randomly assigned to one of two intervention conditions. Both groups performed the sequential finger tapping task (SFTT) on five consecutive days, either with (a) real HD-tDCS or (b) with sham HD-tDCS. HD-tDCS (1 mA for 20 min, 4 × 1) was administered to C3 or C4 (according to lesion side). fNIRS signals were measured during the SFTT with the affected hand before (baseline) and after each intervention using fNIRS measurement system. Cortical activation and functional connectivity of NIRS signals were analyzed using a statistical parametric mapping open-source software package (NIRS-SPM), OptoNet II®. In the real HD-tDCS condition, oxyHb concentration increased significantly in the ipsilesional primary motor cortex (M1). Connectivity between the ipsilesional M1 and the premotor cortex (PM) was noticeably strengthened after real HD-tDCS compared with baseline. Motor performance also significantly improved, as shown in response time during the SFTT. In the sham HD-tDCS condition, functional connectivity between contralesional M1 and sensory cortex was enhanced compared with baseline. There was tendency toward improvement in SFTT response time, but without significance. The results of this study indicated that HD-tDCS could modulate learning-related cortical activity and functional connectivity within motor networks to enhance motor learning performance. HD-tDCS can be used as an additional tool for enhancing motor learning during hand rehabilitation for chronic stroke patients.

NeuroSuitUp: System Architecture and Validation of a Motor Rehabilitation Wearable Robotics and Serious Game Platform

This article presents the system architecture and validation of the NeuroSuitUp body-machine interface (BMI). The platform consists of wearable robotics jacket and gloves in combination with a serious game application for self-paced neurorehabilitation in spinal cord injury and chronic stroke. The wearable robotics implement a sensor layer, to approximate kinematic chain segment orientation, and an actuation layer. Sensors consist of commercial magnetic, angular rate and gravity (MARG), surface electromyography (sEMG), and flex sensors, while actuation is achieved through electrical muscle stimulation (EMS) and pneumatic actuators. On-board electronics connect to a Robot Operating System environment-based parser/controller and to a Unity-based live avatar representation game. BMI subsystems validation was performed using exercises through a Stereoscopic camera Computer Vision approach for the jacket and through multiple grip activities for the glove. Ten healthy subjects participated in system validation trials, performing three arm and three hand exercises (each 10 motor task trials) and completing user experience questionnaires. Acceptable correlation was observed in 23/30 arm exercises performed with the jacket. No significant differences in glove sensor data during actuation state were observed. No difficulty to use, discomfort, or negative robotics perception were reported. Subsequent design improvements will implement additional absolute orientation sensors, MARG/EMG based biofeedback to the game, improved immersion through Augmented Reality and improvements towards system robustness.

A Review of Online Classification Performance in Motor Imagery-Based Brain–Computer Interfaces for Stroke Neurorehabilitation

Motor imagery (MI)-based brain–computer interfaces (BCI) have shown increased potential for the rehabilitation of stroke patients; nonetheless, their implementation in clinical practice has been restricted due to their low accuracy performance. To date, although a lot of research has been carried out in benchmarking and highlighting the most valuable classification algorithms in BCI configurations, most of them use offline data and are not from real BCI performance during the closed-loop (or online) sessions. Since rehabilitation training relies on the availability of an accurate feedback system, we surveyed articles of current and past EEG-based BCI frameworks who report the online classification of the movement of two upper limbs in both healthy volunteers and stroke patients. We found that the recently developed deep-learning methods do not outperform the traditional machine-learning algorithms. In addition, patients and healthy subjects exhibit similar classification accuracy in current BCI configurations. Lastly, in terms of neurofeedback modality, functional electrical stimulation (FES) yielded the best performance compared to non-FES systems.

Targeting CNS Neural Mechanisms of Gait in Stroke Neurorehabilitation.

The central nervous system (CNS) control of human gait is complex, including descending cortical control, affective ascending neural pathways, interhemispheric communication, whole brain networks of functional connectivity, and neural interactions between the brain and spinal cord. Many important studies were conducted in the past, which administered gait training using externally targeted methods such as treadmill, weight support, over-ground gait coordination training, functional electrical stimulation, bracing, and walking aids. Though the phenomenon of CNS activity-dependent plasticity has served as a basis for more recently developed gait training methods, neurorehabilitation gait training has yet to be precisely focused and quantified according to the CNS source of gait control. Therefore, we offer the following hypotheses to the field: Hypothesis 1. Gait neurorehabilitation after stroke will move forward in important ways if research studies include brain structural and functional characteristics as measures of response to treatment. Hypothesis 2. Individuals with persistent gait dyscoordination after stroke will achieve greater recovery in response to interventions that incorporate the current and emerging knowledge of CNS function by directly engaging CNS plasticity and pairing it with peripherally directed, plasticity-based motor learning interventions. These hypotheses are justified by the increase in the study of neural control of motor function, with emerging research beginning to elucidate neural factors that drive recovery. Some are developing new measures of brain function. A number of groups have developed and are sharing sophisticated, curated databases containing brain images and brain signal data, as well as other types of measures and signal processing methods for data analysis. It will be to the great advantage of stroke survivors if the results of the current state-of-the-art and emerging neural function research can be applied to the development of new gait training interventions.

Systematic Review of Existing Stroke Guidelines: Case for a Change.

Methods We systematically searched for guideline recommendation on the day-to-day use of peripheral inflammatory markers such as NLR published in the English language between January 1, 2005, and October 2020. Any other evidence of system biology-based approach or recommendation was explored within the selected guidelines for this scoping review. Only the latest guideline per writing group was selected. Each guideline was analyzed independently by 2 to 4 authors to determine clinical scenarios explained/given, scientific evidence used, and recommendations presented in the context of system biology. Results The scoping review found 2,911 titles at the beginning of the search. Final review included with 15 guidelines. Stroke-related organizations wrote sixty-five percent of the guidelines while national ministries wrote a fewer number of guidelines. We were primarily interested in recommendations for acute management in AIS published in the English language. Fifteen eligible guidelines were identified from 15 different countries/regions. None of the guidelines recommended the routine use of peripheral markers of inflammation, such as NLR, among their acute assessment and management recommendations. None of the existing guidelines explored the system biology approach to one of the most complex diseases affecting the human brain, stroke. Conclusions This systematic review has identified a significant evidence-practice gap in all existing national stroke guidelines published in English medium as of October 2020. These guidelines included the only current “living stroke guidelines,” stroke guidelines from Australia with a real opportunity to modernize the living stroke guidelines with systems biology approach, and provide 2020 vision towards better stroke care globally. Investigation of complex disease such as stroke is best served through a systems biology approach. One of the easiest places to start is simple blood tests such as total white cell count and NLR. Systems biology approach point us towards simple tools such immune-inflammatory index (SII) and serial systemic immune inflammatory indices (SSIIi) which should pave the way for the stroke physician community address the challenges in systems biology approach in stroke care. These challenges include translating bench research to the bedside, managing big data (continuous pulse, blood pressure, sleep, oxygen saturation, progressive changes in NLR, SII, SSIIi, etc.). Working with an interdisciplinary team also provides a distinct advantage. Recent adoption of historic WHO-IGAP calls for immediate action. The 2022 World Brain Day campaign on Brain Health for All is the perfect opportunity to raise awareness and start the process.

Brain-Computer Interface-Robot Training Enhances Upper Extremity Performance and Changes the Cortical Activation in Stroke Patients: A Functional Near-Infrared Spectroscopy Study.

IntroductionWe evaluated the efficacy of brain–computer interface (BCI) training to explore the hypothesized beneficial effects of physiotherapy alone in chronic stroke patients with moderate or severe paresis. We also focused on the neuroplastic changes in the primary motor cortex (M1) after BCI training.MethodsIn this study, 18 hospitalized chronic stroke patients with moderate or severe motor deficits participated. Patients were operated on for 20 sessions and followed up after 1 month. Functional assessments were performed at five points, namely, pre1-, pre2-, mid-, post-training, and 1-month follow-up. Wolf Motor Function Test (WMFT) was used as the primary outcome measure, while Fugl-Meyer Assessment (FMA), its wrist and hand (FMA-WH) sub-score and its shoulder and elbow (FMA-SE) sub-score served as secondary outcome measures. Neuroplastic changes were measured by functional near-infrared spectroscopy (fNIRS) at baseline and after 20 sessions of BCI training. Pearson correlation analysis was used to evaluate functional connectivity (FC) across time points.ResultsCompared to the baseline, better functional outcome was observed after BCI training and 1-month follow-up, including a significantly higher probability of achieving a clinically relevant increase in the WMFT full score (ΔWMFT score = 12.39 points, F = 30.28, and P < 0.001), WMFT completion time (ΔWMFT time = 248.39 s, F = 16.83, and P < 0.001), and FMA full score (ΔFMA-UE = 12.72 points, F = 106.07, and P < 0.001), FMA-WH sub-score (ΔFMA-WH = 5.6 points, F = 35.53, and P < 0.001), and FMA-SE sub-score (ΔFMA-SE = 8.06 points, F = 22.38, and P < 0.001). Compared to the baseline, after BCI training the FC between the ipsilateral M1 and the contralateral M1 was increased (P < 0.05), which was the same as the FC between the ipsilateral M1 and the ipsilateral frontal lobe, and the FC between the contralateral M1 and the contralateral frontal lobe was also increased (P < 0.05).ConclusionThe findings demonstrate that BCI-based rehabilitation could be an effective intervention for the motor performance of patients after stroke with moderate or severe upper limb paresis and represents a potential strategy in stroke neurorehabilitation. Our results suggest that FC between ipsilesional M1 and frontal cortex might be enhanced after BCI training.Clinical Trial Registrationwww.chictr.org.cn, identifier: ChiCTR2100046301.