Posts Tagged Motor recovery

[ARTICLE] Effects of Specific Virtual Reality-Based Therapy for the Rehabilitation of the Upper Limb Motor Function Post-Ictus: Randomized Controlled Trial – Full Text


This research analyzed the combined effect of conventional treatment and virtual reality exposure therapy on the motor function of the upper extremities in people with stroke. We designed a randomized controlled trial set in the rehabilitation and neurology departments of a hospital (Talavera de la Reina, Spain). The subjects included 43 participants, all randomized into experimental (conventional treatment + virtual reality exposure therapy) and control group (conventional treatment).; The main measures were Fugl-Meyer Assessment for upper extremity, Modified Ashworth Scale, and Stroke Impact Scale 3.0. The results included 23 patients in the experimental (62.6 ± 13.5 years) and 20 in the control group (63.6 ± 12.2 years) who completed the study. After the intervention, muscle tone diminished in both groups, more so in the experimental group (mean baseline/post-intervention: from 1.30 to 0.60; η2 = 0.237; p = 0.001). Difficulties in performing functional activities that implicate the upper limb also diminished. Regarding the global recovery from stroke, both groups improved scores, but the experimental group scored significantly higher than the controls (mean baseline/post-intervention: from 28.7 to 86.5; η2 = 0.633; p = 0.000). In conclusion, conventional rehabilitation combined with specific virtual reality seems to be more efficacious than conventional physiotherapy and occupational therapy alone in improving motor function of the upper extremities and the autonomy of survivors of stroke in activities of daily living.

1. Introduction

Stroke is one of the main causes of acquired disability in adulthood. The stroke epidemic is primarily driven by the aging of the world population, globalization and the urbanization of community settings [1,2]. The Stroke Alliance for Europe states that, every 20 s, a new case of stroke is detected in the adult population and predicts that the number of people affected will increase by 35% to 12 million people in 2040. As a result, it is estimated that the health and social costs for stroke diagnosis will increase to 75 million in 2030 (26% more than in 2017). In Spain, 550,941 people were diagnosed with stroke in 2017, generating a health expenditure of 1700 million euros and a total cost to the Spanish state of 3557 million euros [3].Around 80% of survivors present motor difficulties in the upper extremities, affecting the carrying out of activities of daily living (ADLs), the performance of roles in the community and the health-related quality of life (HRQoL) [4,5,6].Complications after stroke diagnosis can persist over time. Two-thirds of survivors are disabled 15 years later, two out of five are immersed in depressive states and more than a quarter develop cognitive impairment [7]. The costs derived from stroke diagnosis are high for survivors and their families, making their rehabilitation and survival processes a great challenge for health policymakers [8,9]. On average, an informal (non-professional) caregiver in Spain invests 2833 h per year in caring for the person affected by stroke and with limitations in ADLs [3].The general objective of neurological rehabilitation is to promote a rapid recovery from the multiple deficits after a stroke and the achievement of a lifestyle similar to the premorbid state [10,11]. Of all people diagnosed with stroke, only 30–40% regain certain skills in the upper limb after six months of intervention [12]. The upper limb remains non-functional for ADLs in up to 66% of survivors [13], constituting the most disabling of all residual disorders.In recent years, the use of neurorehabilitation approaches based on technology and virtual reality has increased, allowing the creation of effective rehabilitation environments and providing multimodal, controllable, and customizable stimulation [14], in which the recreation of virtual objects maximize visual feedback [15] and high intensity and high number of repetitions are key factors that influence neuroplasticity and functional improvement in patients [16]. Rehabilitation based on virtual reality offers the possibility of individualizing treatment needs, and at the same time, standardizing evaluation and training protocols [17,18]. In this sense, specific virtual reality technology for rehabilitation processes of people with neurological pathology allows working in a functional way and with specific intervention objectives, in addition to easily qualifying and documenting progress during the session [19]. Taking advantage of these characteristics, several researchers have used virtual reality exposure therapy (VRET) to recover motor function after stroke. In the treatment of the upper limb, studies indicate that this rehabilitation approach produces better motor and functional results than conventional therapy [20,21].The increasing clinical use of neurorehabilitation approaches based on technology and virtual reality leads to the assumption that spatial representations in virtual environments may vary slightly from the perceptions that the patient would experience in real spaces. In this sense, the team of Hruby et al. [22] insisted that spatial representations based in virtual reality systems should be realistic 1:1 replicas with regard to the individual characteristics of the subjects interacting with both virtual and real environments. This demand increases the validity of virtual reality techniques for therapeutic purposes, since interaction with a virtual space is safer and more profitable in the early phases of rehabilitation processes [23]. However, it is important for clinicians and researchers to consider that the interaction with a virtual environment continues to be different from the relationship that the subject maintains with the real environment [24] because people gradually build a mental representation of the geographic space that we work with or are immersed in. The locomotion techniques applied in the virtual model (software or hardware) can influence the cognitive representations of the person experiencing them [25].The present study aimed to analyze the combined effect of conventional treatment and VRET on motor function of the upper limb in people diagnosed with stroke in the acute phase and its evolution at three months in the Integrated Health Area of Talavera de la Reina.[…]


Figure 1. (a) Selection of analytical exergames; (b) selection of functional exergames; (c) adaptation of exergames at the beginning of a treatment session; (d) graphical representation of results or progress of the patient.

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[ARTICLE] Neural Correlates of Motor Recovery after Robot-Assisted Training in Chronic Stroke: A Multimodal Neuroimaging Study – Full Text


Stroke is a leading cause of motor disability worldwide, and robot-assisted therapies have been increasingly applied to facilitate the recovery process. However, the underlying mechanism and induced neuroplasticity change remain partially understood, and few studies have investigated this from a multimodality neuroimaging perspective. The current study adopted BCI-guided robot hand therapy as the training intervention and combined multiple neuroimaging modalities to comprehensively understand the potential association between motor function alteration and various neural correlates. We adopted EEG-informed fMRI technique to understand the functional regions sensitive to training intervention. Additionally, correlation analysis among training effects, nonlinear property change quantified by fractal dimension (FD), and integrity of M1-M1 (M1: primary motor cortex) anatomical connection were performed. EEG-informed fMRI analysis indicated that for iM1 (iM1: ipsilesional M1) regressors, regions with significantly increased partial correlation were mainly located in contralesional parietal, prefrontal, and sensorimotor areas and regions with significantly decreased partial correlation were mainly observed in the ipsilesional supramarginal gyrus and superior temporal gyrus. Pearson’s correlations revealed that the interhemispheric asymmetry change significantly correlated with the training effect as well as the integrity of M1-M1 anatomical connection. In summary, our study suggested that multiple functional brain regions not limited to motor areas were involved during the recovery process from multimodality perspective. The correlation analyses suggested the essential role of interhemispheric interaction in motor rehabilitation. Besides, the underlying structural substrate of the bilateral M1-M1 connection might relate to the interhemispheric change. This study might give some insights in understanding the neuroplasticity induced by the integrated BCI-guided robot hand training intervention and further facilitate the design of therapies for chronic stroke patients.

1. Introduction

Stroke is the leading cause of death worldwide, and the survivors undergo various disabilities related to motor, sensory, and cognitive functions. Specifically, robot-assisted therapy is a kind of task-specific and high-intensity exercise in an active, functional, and highly repetitive manner [1]. It has been proven to be efficient to induce neuroplasticity modulation and promising long-term motor recovery [2]. A brain computer interface (BCI) can facilitate stroke rehabilitation by integrating the exoskeleton robots to develop the BCI-guided robot-assisted therapy, which is believed to engage various brain functional regions [3] in the recovery process.

Electroencephalography (EEG), which can capture subtle neurological changes, has been widely used in studying neural functions. EEG signals result from the mixture of propagating electric potential fluctuations, mainly reflecting the postsynaptic activity of large populations of cortical pyramidal cells [4]. Additionally, functional magnetic resonance imaging (fMRI) has become one of the most commonly used neuroimaging tools to assess the cortical alterations associated with learning, diseases, or rehabilitation [5]. Resting-state fMRI that measures the temporal correlation of the blood oxygen level-dependent (BOLD) signal between different regions at resting state has emerged as a powerful tool to map the functional organization of the brain [6]. fMRI measurements have an excellent spatial resolution in millimeters but low temporal resolution limited to few seconds. While EEG holds millisecond-level temporal resolution, allowing the adequate sampling of the rapidly changing electrical dynamics of neuronal populations [4]. Since EEG and fMRI exhibit highly complementary characteristics, their integration has been widely exploited [78]. Simultaneously recorded EEG and fMRI data make it possible to integrate these two neuroimaging modalities and have received substantial attention [9]. In our current study, we also adopted a concurrent EEG-fMRI technique to figure out related functional regions sensitive to the training effect. It should be noted that researchers have put numerous efforts to detecting these significant functional regions based on various neuroimaging techniques. For example, some studies have indicated the crucial role of supplementary motor area (SMA) in a motor system to execute various tasks including interlimb coordination [10] and many unimanual tasks involving movement sequencing as well as internal pacing [11]. Specifically, for stroke patients, the reduced partial correlation between SMA and M1 together with the interhemispheric correlation of both SMAs during visually paced hand movements has been found [12]. The reduced partial correlation between ipsilesional of SMA and M1 was also exhibited when stroke patients perform hand movements [13] and index finger-tapping task [14]. Meanwhile, it is noted that improved motor function of stroke patients might be highly correlated to a restitution of ipsilesional effective connectivity between SMA and M1 [15] and functional connectivity of the ipsilesional M1 with contralesional SMA [16]. Hence, in our study, we hypothesized SMA would also play an essential role in motor recovery with BCI-guided robot-hand training.

Quantification of EEG signal can be linked to the clinical features, such as the rehabilitation progress in chronic stroke. Nonetheless, due to the volume-conduction effect, the activities of scalp EEG signal are often assumed to come from multiple sources spatially dispersed in the brain cortex, which blurs the underlying neural mechanisms [17]. Therefore, EEG source imaging has emerged where the patient-specific anatomical properties could be taken into account using individual structural MRI images to disentangle useful neural information. However, few studies leveraged the indicators derived from EEG source space to investigate motor training effects for chronic stroke patients.

Although linear measurements have been widely recognized to reflect the brain characteristics, there is a growing tendency that different nonlinear measures have been proposed to depict the complexity of EEG signals and adopted to predict treatment response to repetitive transcranial magnetic stimulation in depression [18], evaluate the effect of stroke rehabilitation [19], and facilitate the classification system for hand recovery in stroke patients [20]. Fractal property that is quantified by the fractal dimension (FD) [21] is a nonlinear descriptor for brain signals, including EEG signals, which is closely associated with specialization and efficiency of brain functioning [22]. Investigation of such fractal nature as its correlation with the rehabilitation process for patients with neurological disorders, including stroke, is particularly important. The interhemispheric imbalance, especially the imbalance between homologous primary motor regions, always plays a crucial role in stroke motor rehabilitation [23]. Additionally, structural imaging, such as diffusion tensor imaging (DTI), has provided pivotal insights into the functional role of the underlying structural tracts in stroke-related changes [24]. Reductions in fractional anisotropy (FA), a DTI-derived measure of degree of anisotropy in white matter (WM), have been found in stroke individuals [25]. Specifically, the integrity of transcallosal motor fibers may play a role in monitoring the treatment response in chronic stroke [26].

The purpose of this study is to investigate the neural correlates of motor recovery after BCI-guided robot-assisted training in chronic stroke from a multimodality neuroimaging perspective. The EEG-informed fMRI analysis was utilized to locate the potential functional brain regions sensitive to the training effect. Furthermore, we hypothesized that the training effect should be related to the interhemispheric interaction change and such induced change was supposed to be based on the structural substrates connecting bilateral primary motor areas. Hence, the corresponding correlation analyses were performed to verify these hypotheses.

2. Materials and Methods

2.1. Subjects

Fourteen chronic stroke subjects (13 males,  years, right-handedness) with unilateral hemispheric impairment were recruited from local community. The inclusion criteria were as follows: (1) first-ever stroke, (2) more than 6 months since the stroke onset prior to the experiment, (3) a single unilateral brain lesion, (4) Hong Kong Montreal Cognitive Assessment (HK-MoCA) [2728]  to ensure sufficient cognitive function to understand instructions and perform tasks, (5) moderate to severe paretic hand dysfunctions (Fugl-Meyer Assessment score for upper ), and (6) no additional rehabilitation therapies applied to the patient. The exclusion criteria were as follows: (1) aphasia, neglect, apraxia, history of alcohol, drug abuse, or epilepsy; (2) severe hand spasticity; (3) hand deformity and wound; and (4) severe cognitive deficits. Motor functions of the paretic upper limbs for all stroke subjects were assessed with Fugl-Meyer Assessment for upper extremity (FMA) which is a reliable and widely used measurement [29] before and after the intervention, respectively. Table 1 summarizes the demographics and clinical properties of subjects.Table 1 Demographics and clinical properties of the participants.

2.2. Training Interventional Protocol

All subjects received a 20-session BCI-guided robot hand training therapy with an intensity of 3-5 sessions per week and completed the whole training process with 5-7 weeks. During each training session, the surface EEG signals of each subject were acquired using 16 electrodes (C1, C2, C3, C4, C5, C6, Cz, FC1, FC2, FC3, FC4, FCz, CP1, CP2, CP3, and CP4 according to international 10-20 system) at a sample rate of 256 Hz. The EEG signals were then amplified (g.LADYbird, g.USBamp, g.Tec Medical Engineering GmbH, Austria) and processed to generate the real-time topography of the brain electrical potential for surveillance. A paradigm with a fixed sequence showing instructions for motor imagery was played, during which the subjects were guided to imagine either grasping or releasing a cup following commands. EEG signal from C3 or C4 channel according to the subject’s lesion side was extracted to calculate the  suppression [30]. The EEG data were transformed into the frequency domain using Fourier transform, and the mean power in the  band (8-13 Hz) was derived. Then, the  suppression was calculated as follows:where  and  stand for the calculated  power during the motor imagery period and the resting-state, respectively. A trigger would be sent to an exoskeleton robot hand [31] (illustrated in Figure S1 B; a detailed description is provided in supplementary materials) to provide mechanical force and assist the paretic hand in grasping and opening if the  suppression exceeded 20% based on the previous study [32]. The success rate was defined as the percentage of correctly detected trials during motor imagery tasks at each session.

This study was approved by the Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee. All subjects gave written consent before the intervention and underwent the experiments in the Chinese University of Hong Kong rehabilitation labs. This study was registered at (NCT02323061).

2.3. Data Acquisition

MRI scans were acquired for all the 14 subjects before and after the training sessions. A 3T Philips MR scanner (Achieva TX, Philips Medical System, Best, Netherlands) with an 8-channel head coil was used to acquire high-resolution T1-weighted anatomical images ( ms, flip , 308 slices, voxel  ) using a T1-TFE sequence (ultrafast spoiled gradient echo pulse sequence), and BOLD fMRI images (, , 37 slices/volume, voxel  ) using an EPI sequence (gradient-echo echo-planar-imaging sequence). Besides, diffusion-weighted images were acquired using a diffusion-weighted single-shot spin-echo echo-planar pulse (DWISE) sequence (,  from 32 directions, 60 slices/volume, voxel  ). When acquiring resting-state fMRI data, subjects were presented with a white cross in a black background and instructed to rest while focusing on the fixation cross. The resting-state fMRI acquisition lasted for 8 minutes.

The EEG data were acquired simultaneously with the fMRI using Neuroscan system (SynAmps2, Neuroscan Inc., Herndon, USA). A 64-channel MR-compatible EEG cap according to a standard 10-20 system was utilized, combined with 2 extra electrocardiogram (ECG) electrodes attached at the left lower and near-midline upper chest and 1 electrooculogram (EOG) electrode placed below the right eye. All recording impedances were kept below 5 k. The reference channel was located at the point between Cz and CPz; an AFz electrode was treated as the ground. Signals were filtered between 0.1 and 256 Hz using an analog filter and sampled at 1000 Hz for off-line processing. The whole scheme of experimental protocol is shown in Figure S1 A.

2.4. Data Analysis

In our study, the analysis was mainly performed from multimodality perspective including fMRI, EEG, and DTI neuroimaging data, and the whole analysis pipeline is summarized in Figure 1. The left column included the preprocessing of DTI data, M1-M1 fiber tractography, and calculation of FA value of M1-M1 tract (please refer to section 2.7 in supplementary materials). The middle column included the analysis of EEG data including preprocessing, distributed source estimation, time series extraction from cM1 and iM1 seeds, and the calculation of indices characterizing nonlinear properties (please refer to sections 2.22.3, and 2.6 in supplementary materials). The right column mainly included the preprocessing of fMRI data, iM1 EEG regressor construction, and integrated EEG-informed fMRI analysis (please refer to sections 2.1 and 2.4 in supplementary materials). The detailed description of each step is provided in the supplementary materials.

Figure 1 Illustration of analysis pipeline. The whole analysis included processing of fMRI, EEG, and DTI data; EEG-informed fMRI analysis; correlation analysis between training effect and nonlinear property change characterized by FD; and correlation analysis between interhemispheric asymmetry change and integrity of M1-M1 anatomical connection quantified by FA.


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[Abstract] Self-rehabilitation for post-stroke motor recovery and activity – Poster

Background: Due to rises in stroke incidence, a lack of resources to implement effective rehabilitation and a significant proportion of patients with remaining impairments after treatment, there is an increased demand for effective and prolonged rehabilitation. Development of self-rehabilitation programs provides an opportunity to meet these increasing demands.

Objective: The primary aim of this meta-analysis was to determine the motor outcome effectiveness of self-rehabilitation in comparison to conventional rehabilitation among patients with stroke. The secondary aim was to assess the influence of trial location (continent), technology, time since stroke (acute/subacute vs chronic), dose (total training duration > vs ≤ 15 hours) and intervention design (self-rehabilitation in addition/substitution to conventional therapy) on effectiveness.

Methods: Studies were selected if participants were adults with stroke; the intervention consisted of a self-rehabilitation program defined as a tailored program where for most of the time, the patient performed rehabilitation exercises independently; the control group received conventional therapy; outcomes included motor function and activity; and the study was a randomized controlled trial with a PEDro score ≥ 5.

Results: Thirty-five trials were selected (2225 participants) and accumulated motor outcome data analysed. Trials had a median PEDro Score of 7 [6-8]. Self-rehabilitation programs were shown to be as effective as conventional therapy. Trial location, use of technology, stroke stage and intervention design did not appear to have a significant influence on outcomes.

Conclusion: This meta-analysis showed low to moderate certainty of evidence that self-rehabilitation and conventional therapy efficacy was equally valuable for post-stroke motor function and activity.


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[Abstract] Neurophysiological changes accompanying reduction in upper limb motor impairments in response to exercise-based virtual rehabilitation after stroke: systematic review


Virtual reality-augmented therapist-delivered exercise-based training has promise for enhancing upper limb motor recovery after stroke. However, the neurophysiological mechanisms are unclear.
To find if neurophysiological changes are correlated with or accompany a reduction in motor impairment in response to virtual reality-aided exercise-based training
Data sources Databases searched from inception to August 2020: MEDLINE, AMED, EMBASE, PUBMED, COCHRANE, CINHAL, PROQUEST and OPEN GREY.
Eligibility criteria Studies that investigated virtual reality-augmented exercise-based training for the upper limb in adults with stroke, and, measured motor impairment and neurophysiological outcomes. Studies that combined VR with another technology were excluded.
Data extraction and synthesis
Using pre-prepared proformas, three reviewers independently: identified eligible
studies, assessed potential risk-of-bias, and extracted data. A critical narrative
synthesis was conducted. A meta-analysis was not possible because of heterogeneity in participants, interventions and outcome measures.
Of 1,387 records identified, four studies were eligible and included in the review.
Overall, included studies were assessed as having high potential risk-of-bias. The VR equipment, and control interventions varied between studies. Two studies measured motor impairment with the Fugl-Meyer Assessment but there was no commonality in the use of neurophysiological measures. One study found improvement in neurophysiological measures only. The other three studies found a reduction in motor impairment and changes in neurophysiological outcomes, but did not calculate correlation coefficients.
There is insufficient evidence to identify the neurophysiological changes that are
correlated with, or accompany, reduction in upper limb motor impairment in response to virtual reality-augmented exercise-based training after stroke.


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[Abstract] A New Definition of Poststroke Spasticity and the Interference of Spasticity With Motor Recovery From Acute to Chronic Stages


The relationship of poststroke spasticity and motor recovery can be confusing. “True” motor recovery refers to return of motor behaviors to prestroke state with the same end-effectors and temporo-spatial pattern. This requires neural recovery and repair, and presumably occurs mainly in the acute and subacute stages. However, according to the International Classification of Functioning, Disability and Health, motor recovery after stroke is also defined as “improvement in performance of functional tasks,” i.e., functional recovery, which is mainly mediated by compensatory mechanisms. Therefore, stroke survivors can execute motor tasks in spite of disordered motor control and the presence of spasticity. Spasticity interferes with execution of normal motor behaviors (“true” motor recovery), throughout the evolution of stroke from acute to chronic stages. Spasticity reduction does not affect functional recovery in the acute and subacute stages; however, appropriate management of spasticity could lead to improvement of motor function, that is, functional recovery, during the chronic stage of stroke. We assert that spasticity results from upregulation of medial cortico-reticulo-spinal pathways that are disinhibited due to damage of the motor cortex or corticobulbar pathways. Spasticity emerges as a manifestation of maladaptive plasticity in the early stages of recovery and can persist into the chronic stage. It coexists and shares similar pathophysiological processes with related motor impairments, such as abnormal force control, muscle coactivation and motor synergies, and diffuse interlimb muscle activation. Accordingly, we propose a new definition of spasticity to better account for its pathophysiology and the complex nuances of different definitions of motor recovery.


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[Abstract] Robotic assistive and rehabilitation devices leading to motor recovery in upper limb: a systematic review



Stroke, spinal cord injury and other neuromuscular disorders lead to impairments in the human body. Upper limb impairments, especially hand impairments affect activities of daily living (ADL) and reduce the quality of life. The purpose of this review is to compare and evaluate the available robotic rehabilitation and assistive devices that can lead to motor recovery or maintain the current motor functional level.


A systematic review was conducted of the literature published in the years from 2016–2021, to focus on the most recent rehabilitation and assistive devices available in the market or research environments.


A total of 230 studies published between 2016 and 2021 were identified from various databases. 107 were excluded with various reasons. Twenty-eight studies were taken into detailed review, to determine the efficacy of robotic devices in improving upper limb impairments or maintaining the current level from getting worse.


It was concluded that with a good strategy and treatment plan; appropriate and regular use of these robotic rehabilitation and assistive devices do lead to improvements in current conditions of most of the subjects and prolonged use may lead to motor recovery.

  • Implications for Rehabilitation
  • Stroke, accidents, spinal cord injuries and other neuromuscular disorders lead to impairments. Upper limb impairments have a tremendous adverse affect on ADL and reduces quality of life drastically.
  • Advancement in technology has led to the designing of many robotic assistive and rehabilitation devices to assist in motor recovery or aid in ADL.
  • This review analyses different available devices for rehabilitation and assistance and points out that use of these devices in time does help in motor recovery. Most of the studies reviewed showed improvements for the user.
  • Future devices should be more portable and easier to use from home,


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[ARTICLE] Head-Mounted Display-Based Therapies for Adults Post-Stroke: A Systematic Review and Meta-Analysis – Full Text


Immersive virtual reality techniques have been applied to the rehabilitation of patients after stroke, but evidence of its clinical effectiveness is scarce. The present review aims to find studies that evaluate the effects of immersive virtual reality (VR) therapies intended for motor function rehabilitation compared to conventional rehabilitation in people after stroke and make recommendations for future studies. Data from different databases were searched from inception until October 2020. Studies that investigated the effects of immersive VR interventions on post-stroke adult subjects via a head-mounted display (HMD) were included. These studies included a control group that received conventional therapy or another non-immersive VR intervention. The studies reported statistical data for the groups involved in at least the posttest as well as relevant outcomes measuring functional or motor recovery of either lower or upper limbs. Most of the studies found significant improvements in some outcomes after the intervention in favor of the virtual rehabilitation group. Although evidence is limited, immersive VR therapies constitute an interesting tool to improve motor learning when used in conjunction with traditional rehabilitation therapies, providing a non-pharmacological therapeutic pathway for people after stroke.

1. Introduction

In the past decades, there has been an sharp increase in the application of virtual reality (VR) treatments to the rehabilitation of a range of disorders resulting from lesions of the nervous system [1,2]. The area of rehabilitation of patients with stroke is the most productive in terms of technology-based interventions in both upper and lower extremities [3]. VR therapies have been successfully used after stroke [4,5] since they apply concepts that are relevant to stroke rehabilitation, such as high repetition, high intensity, and task-oriented training [6,7].

Virtual reality can provide an engaging and motivational experience, allowing the user to practice motor movements while manipulating an interface device [5]. The virtual environment (VE) can be easily changeable, allowing the design of individualized therapies that are adapted to patient needs. VR can provide functional, rich stimuli (cues) and motivating context (feedback), encouraging the more active participation of the subject [8]. Furthermore, different studies have reported improvements in motor abilities, as well as a great level of participant motivation after including virtual reality in stroke rehabilitation [9,10,11,12,13,14,15]. In stroke rehabilitation, providing an intervention that is motivating and engaging is crucial to patient involvement and participation. Semi-immersive and non-immersive VR systems have been widely used for stroke patient rehabilitation.[…]


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[ARTICLE] Graded motor imagery training as a home exercise program for upper limb motor function in patients with chronic stroke – Full Text

A randomized controlled trial



Although several types of occupational therapy for motor recovery of the upper limb in patients with chronic stroke have been investigated, most treatments are performed in a hospital or clinic setting. We investigated the effect of graded motor imagery (GMI) training, as a home exercise program, on upper limb motor recovery and activities of daily living (ADL) in patients with stroke.


This prospective randomized controlled trial recruited 42 subjects with chronic stroke. The intervention group received instruction regarding the GMI program and performed it at home over 8 weeks (30 minutes a day). The primary outcome measure was the change in motor function between baseline and 8 weeks, assessed the Manual Function Test (MFT) and Fugl-Meyer Assessment (FMA). The secondary outcome measure was the change in ADL, assessed with the Modified Barthel Index (MBI).


Of the 42 subjects, 37 completed the 8-week program (17 in the GMI group and 20 controls). All subjects showed significant improvements in the MFT, FMA, and MBI over time (P < .05). However, the improvements in the total scores for the MFT, FMA, and MBI did not differ between the GMI and control groups. The MFT arm motion score for the GMI group was significantly better than that of the controls (P < .05).


The GMI program may be useful for improving upper extremity function as an adjunct to conventional rehabilitation for patients with chronic stroke.

1 Introduction

Following stroke, rehabilitation programs are needed to reduce disability. Most of the functional recovery is achieved within 3 months after onset.[1–5] During the chronic stage, functional recovery is slow and then reaches a plateau.[3,5] Thus, in-patient rehabilitation usually continues in the acute and subacute phases of stroke. Chronic stroke patients may undergo outpatient and community-based rehabilitation. However, there are few community-based rehabilitation programs. Graded motor imagery (GMI) was introduced to promote motor recovery in patients with complex regional pain syndrome.[6,7] A non-randomized trial demonstrated some effect of GMI on motor function in patients with chronic stroke.[8]

We hypothesized that GMI might improve motor function in patients with chronic stroke, and investigated the effects of home-based GMI on upper limb motor function in the patients over the 3-month period after stroke onset

2 Methods

2.1 Participants

This was a prospective randomized controlled trial. All subjects had suffered supratentorial strokes and met the following criteria:

  • 1. first-ever unilateral stroke and over 3 months since onset[9,10];
  • 2. Mini-Mental State Examination score ≥24; and
  • 3. Fugl-Meyer Assessment (FMA) score <60 for the upper extremity.[11,12]

The exclusion criteria were

  • 1. musculoskeletal disorders hindering routine activities of daily living (ADL) and
  • 2. other neurological disorders affecting ADL or mood.

We screened 47 right-handed subjects with first-ever stroke; 42 subjects were enrolled and randomly assigned to the control (n = 21) or GMI (experimental) (n = 21) group (randomized block design). All subjects were tested 3 times: at baseline (0 weeks), and at 4, and 8 weeks (Fig. 1).

Figure 1: Flow diagram of the study.

The study was approved by the Ethics Committee of the Catholic University of Korea (VC18EESI0226). Informed consent was obtained from all subjects according to the Declaration of Helsinki.

2.2 GMI intervention

GMI training involves implicit motor imagery, explicit motor imagery, and mirror therapy (Fig. 2).[6,8] Regarding the implicit motor imagery, a discrimination task (left vs right) was conducted using an Android smartphone and Orientate software (Reflex Pain Management Ltd.). Twenty five photographs of the right or left hand were presented at random, and the participant pressed the screen as soon as possible to indicate which hand was shown. Regarding the explicit motor imagery, 25 photographs were again randomly displayed on the smartphone screen. The subjects were asked to imagine that they were working without any movement on the injured side.[13] Regarding the mirror therapy, a 24 × 24 × 35 cm3 mirror (Folding Mirror Therapy Box; Reflex Pain Management Ltd.) was placed directly in front of the subject,[14] who confirmed that the hand on the affected side was reflected in the mirror. They were then instructed to move the hand on the unaffected side (grasp/release the hand and supinate/pronate the forearm), and to try to emulate that movement with the hand on the affected side. The control group exercised the upper limb for the same amount of time. All 3 tasks were repeated 3 times, with rest periods provided between sessions.

Figure 2: The application of graded motor imagery (GMI) training. A; implicit motor imagery, B; explicit motor imagery, C; mirror therapy.



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[WEB PAGE] A dual-therapy approach to boost motor recovery after a stroke

by Ecole Polytechnique Federale de Lausanne

**A dual-therapy approach to boost motor recovery after a stroke
Credit: Ecole Polytechnique Federale de Lausanne

Paralysis of an arm and/or leg is one of the most common effects of a stroke. But thanks to research carried out by scientists at the Defitech Foundation Chair in Brain-Machine Interface and collaborators, stroke victims may soon be able to recover greater use of their paralyzed limbs. The scientists’ pioneering approach brings together two known types of therapies—a brain-computer interface (BCI) and functional electrical stimulation (FES) – and has been published in Nature Communications.

“The key is to stimulate the nerves of the paralyzed arm precisely when the stroke-affected part of the brain activates to move the limb, even if the patient can’t actually carry out the movement. That helps reestablish the link between the two nerve pathways where the signal comes in and goes out,” says José del R. Millán, who holds the Defitech Chair at EPFL.

Twenty-seven patients aged 36 to 76 took part in the clinical trial. All had a similar lesion that resulted in moderate to severe arm paralysis following a stroke occurring at least ten months earlier. Half of the patients were treated with the scientists‘ dual-therapy approach and reported clinically significant improvements. The other half were treated only with FES and served as a control group.

For the first group, the scientists used a BCI system to link the patients’ brains to computers using electrodes. That let the scientists pinpoint exactly where the electrical activity occurred in the brain tissue when the patients tried to reach out their hands. Every time that the electrical activity was identified, the system immediately stimulated the arm muscle controlling the corresponding wrist and finger movements. The patients in the second group also had their arm muscles stimulated, but at random times. This control group enabled the scientists to determine how much of the additional motor-function improvement could be attributed to the BCI system.

Reactivated tissue

The scientists noted a significant improvement in arm mobility among patients in the first group after just ten one-hour sessions. When the full round of treatment was completed, some of the first-group patients’ scores on the Fugl-Meyer Assessment—a test used to evaluate motor recovery among patients with post-stroke hemiplegia—were over twice as high as those of the second group.

“Patients who received the BCI treatment showed more activity in the neural tissue surrounding the affected area. Due to their plasticity, they could help make up for the functioning of the damaged tissue,” says Millán.

Electroencephalographies (EEGs) of the patients clearly showed an increase in the number of connections among the motor cortex regions of their damaged brain hemisphere, which corresponded with the increased ease in carrying out the associated movements. What’s more, the enhanced motor function didn’t seem to diminish with time. Evaluated again 6-12 months later, the patients hadn’t lost any of their recovered mobility.

Explore further Electrically stimulating the brain may restore movement after stroke

More information: A. Biasiucci et al, Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke, Nature Communications (2018). DOI: 10.1038/s41467-018-04673-z

Journal information: Nature Communications

Provided by Ecole Polytechnique Federale de Lausanne

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[ARTICLE] Using Transcranial Direct Current Stimulation to Augment the Effect of Motor Imagery-Assisted Brain-Computer Interface Training in Chronic Stroke Patients—Cortical Reorganization Considerations – Full Text


Introduction: Transcranial direct current stimulation (tDCS) has been shown to modulate cortical plasticity, enhance motor learning and post-stroke upper extremity motor recovery. It has also been demonstrated to facilitate activation of brain-computer interface (BCI) in stroke patients. We had previously demonstrated that BCI-assisted motor imagery (MI-BCI) can improve upper extremity impairment in chronic stroke participants. This study was carried out to investigate the effects of priming with tDCS prior to MI-BCI training in chronic stroke patients with moderate to severe upper extremity paresis and to investigate the cortical activity changes associated with training.

Methods: This is a double-blinded randomized clinical trial. Participants were randomized to receive 10 sessions of 20-min 1 mA tDCS or sham-tDCS before MI-BCI, with the anode applied to the ipsilesional, and the cathode to the contralesional primary motor cortex (M1). Upper extremity sub-scale of the Fugl-Meyer Assessment (UE-FM) and corticospinal excitability measured by transcranial magnetic stimulation (TMS) were assessed before, after and 4 weeks after intervention.

Results: Ten participants received real tDCS and nine received sham tDCS. UE-FM improved significantly in both groups after intervention. Of those with unrecordable motor evoked potential (MEP-) to the ipsilesional M1, significant improvement in UE-FM was found in the real-tDCS group, but not in the sham group. Resting motor threshold (RMT) of ipsilesional M1 decreased significantly after intervention in the real-tDCS group. Short intra-cortical inhibition (SICI) in the contralesional M1 was reduced significantly following intervention in the sham group. Correlation was found between baseline UE-FM score and changes in the contralesional SICI for all, as well as between changes in UE-FM and changes in contralesional RMT in the MEP- group.

Conclusion: MI-BCI improved the motor function of the stroke-affected arm in chronic stroke patients with moderate to severe impairment. tDCS did not confer overall additional benefit although there was a trend toward greater benefit. Cortical activity changes in the contralesional M1 associated with functional improvement suggests a possible role for the contralesional M1 in stroke recovery in more severely affected patients. This has important implications in designing neuromodulatory interventions for future studies and tailoring treatment.


Post-stroke recovery of upper extremity (UE) function remains a challenge. Less than 15% of stroke survivors with severe impairment experience complete motor recovery (12). Intensive and repetitive practice is effective for motor recovery (3), but is labor-intensive and costly. More effective rehabilitation strategies that will deliver better functional outcomes without increasing cost of care are needed.

Motor imagery (MI), or mental practice is a mental rehearsal process of a specific movement without physical performance to enhance post-stroke upper extremity motor recovery (410). It has been demonstrated to be a safe, self-paced method to improve motor performance in athletes (6) and is effective in augmenting the effects of motor practice in stroke patients (79).

MI shares similar neural substrates with motor execution (1112). Functional neural changes induced by MI is similar to that of short-term motor learning (5) with corresponding changes in corticospinal excitability and reorganization of motor representation have been demonstrated with MI (413).

Robot-assisted training is typically applied to deliver intensive, task-specific training in rehabilitation of motor function, but has also been used to provide appropriate sensorimotor integration through guidance of movement along a trajectory (1418). The coupling of MI and robot-assisted arm movement through brain computer interface (MI-BCI) has been postulated to enhance sensorimotor integration by bridging the motor intent and providing appropriate somatosensory feedback through passive manipulation of the paretic arm, thereby guiding activity-dependent cortical plasticity through feedback on brain activity (19). Our previous studies of MI-BCI in chronic stroke demonstrated better improvement in motor function with fewer repetitions in the same time of training (2021). Others have found similar benefit using BCI-driven orthoses for rehabilitation of severe UE paresis (22).

Transcranial direct current stimulation (tDCS) is a non-invasive method of modulating corticospinal excitability by changing the firing threshold of neuronal membrane and modifying spontaneous activity according to the direction of current, such that cathodal tDCS decreases cortical excitability while anodal tDCS increases it (2325). Good functional recovery has frequently been associated with a rebalancing of interhemispheric inhibition (1726). Based on this, cathodal tDCS is applied to the contralesional primary motor cortex (M1) and anodal tDCS to the ipsilesional M1 to enhance corticospinal excitability. This is the paradigm most frequently studied to enhance motor recovery after stroke (2731), and has thus far yielded mixed results (32).

Additionally, tDCS has also been explored as a priming tool to improve the accuracy of BCI, both in healthy subjects (3334) and in stroke patients with mixed results (3536). We had previously reported the preliminary results of the first ever study to investigate the effect of a course of training with BCI-assisted motor imagery (MI-BCI) with tDCS priming (simultaneous anodal stimulation to the ipsilesional M1 and cathodal stimulation to the contralesional M1) prior to each session, compared to MI-BCI with sham tDCS, on recovery of chronic stroke patients with moderate to severe impairment (37). This population was chosen as they have the most difficulty engaging in active motor task training. The stimulation protocol was selected based on the intent to rebalance transcallosal inhibition, as suggested by previous studies (283038). Clinical improvement was observed post-training, with online BCI accuracies being significantly better in the tDCS group, compared to the sham group.

The neurobiological principles that govern post-stroke recovery of motor function are incompletely understood. While task-specific training, and MI as an extension, is applied based on principles of activity-dependent cortical plasticity, and non-invasive brain stimulation is applied based on rebalancing of interhemispheric inhibitions, a more detailed understanding of the cortical reorganization associated with the combination of therapeutic modalities, and indeed of the recovery process itself, is required in order to tailor therapeutic approaches. TMS may be used to probe these changes in cortical excitability. Here we report the changes in cortical activity associated with this training protocol, which will inform the design of future studies.[…]


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