Posts Tagged Motor recovery

[ARTICLE] Spasticity, Motor Recovery, and Neural Plasticity after Stroke – Full Text

Spasticity and weakness (spastic paresis) are the primary motor impairments after stroke and impose significant challenges for treatment and patient care. Spasticity emerges and disappears in the course of complete motor recovery. Spasticity and motor recovery are both related to neural plasticity after stroke. However, the relation between the two remains poorly understood among clinicians and researchers. Recovery of strength and motor function is mainly attributed to cortical plastic reorganization in the early recovery phase, while reticulospinal (RS) hyperexcitability as a result of maladaptive plasticity, is the most plausible mechanism for post-stroke spasticity. It is important to differentiate and understand that motor recovery and spasticity have different underlying mechanisms. Facilitation and modulation of neural plasticity through rehabilitative strategies, such as early interventions with repetitive goal-oriented intensive therapy, appropriate non-invasive brain stimulation, and pharmacological agents, are the key to promote motor recovery. Individualized rehabilitation protocols could be developed to utilize or avoid the maladaptive plasticity, such as RS hyperexcitability, in the course of motor recovery. Aggressive and appropriate spasticity management with botulinum toxin therapy is an example of how to create a transient plastic state of the neuromotor system that allows motor re-learning and recovery in chronic stages.


According to the CDC, approximately 800,000 people have a stroke every year in the United States. The continued care of seven million stroke survivors costs the nation approximately $38.6 billion annually. Spasticity and weakness (i.e., spastic paresis) are the primary motor impairments and impose significant challenges for patient care. Weakness is the primary contributor to impairment in chronic stroke (1). Spasticity is present in about 20–40% stroke survivors (2). Spasticity not only has downstream effects on the patient’s quality of life but also lays substantial burdens on the caregivers and society (2).

Clinically, poststroke spasticity is easily recognized as a phenomenon of velocity-dependent increase in tonic stretch reflexes (“muscle tone”) with exaggerated tendon jerks, resulting from hyperexcitability of the stretch reflex (3). Though underlying mechanisms of spasticity remain poorly understood, it is well accepted that there is hyperexcitability of the stretch reflex in spasticity (47). Accumulated evidence from animal (8) and human studies (918) supports supraspinal origins of stretch reflex hyperexcitability. In particular, reticulospinal (RS) hyperexcitability resulted from loss of balanced inhibitory, and excitatory descending RS projections after stroke is the most plausible mechanism for poststroke spasticity (19). On the other hand, animal studies have strongly supported the possible role of RS pathways in motor recovery (2036), while recent studies with stroke survivors have demonstrated that RS pathways may not always be beneficial (37, 38). The relation between spasticity and motor recovery and the role of plastic changes after stroke in this relation, particularly RS hyperexcitability, remain poorly understood among clinicians and researchers. Thus, management of spasticity and facilitation of motor recovery remain clinical challenges. This review is organized into the following sessions to understand this relation and its implication in clinical management.

• Poststroke spasticity and motor recovery are mediated by different mechanisms

• Motor recovery are mediated by cortical plastic reorganizations (spontaneous or via intervention)

• Reticulospinal hyperexcitability as a result of maladaptive plastic changes is the most plausible mechanism for spasticity

• Possible roles of RS hyperexcitability in motor recovery

• An example of spasticity reduction for facilitation of motor recovery

Continue —> Frontiers | Spasticity, Motor Recovery, and Neural Plasticity after Stroke | Stroke

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[ARTICLE] Task-Specific Motor Rehabilitation Therapy After Stroke Improves Performance in a Different Motor Task: Translational Evidence – Full Text


While the stroke survivor with a motor deficit strives for recovery of all aspects of daily life movements, neurorehabilitation training is often task specific and does not generalize to movements other than the ones trained. In rodent models of post-stroke recovery, this problem is poorly investigated as the training task is often the same as the one that measures motor function. The present study investigated whether motor training by pellet reaching translates into enhancement of different motor functions in rats after stroke. Adult rats were subjected to 60-min middle cerebral artery occlusion (MCAO). Five days after stroke, animals received either training consisting of 7 days of pellet reaching with the affected forelimb (n = 18) or no training (n = 18). Sensorimotor deficits were assessed using the sticky tape test and a composite neuroscore. Infarct volumes were measured by T2-weighted MRI on day 28. Both groups of rats showed similar lesion volume and forelimb impairment after stroke. Trained animals improved in the sticky tape test after day 7 post-stroke reaching peak performance on day 14. More reaching attempts during rehabilitation were associated with a better performance in the sticky tape removal time. Task-oriented motor training generalizes to other motor functions after experimental stroke. Training intensity correlates with recovery.


About 60% of stroke survivors suffer from motor disability 6 months after stroke [1, 2]. By training of motor skills, rehabilitation aims to maximize patients’ functional independence and quality of life. The physiological mechanisms of training interventions are incompletely understood, especially their generalization, i.e., how and how much improvement in the specific task trained generalizes to other movements. These mechanisms need to be explored in animal models to optimize and develop treatments.

In rodents, post-stroke motor rehabilitation by pellet-reaching training improves pellet-reaching success [3]. This is accompanied by reorganization in motor cortex regions controlling the affected limb [4], e.g., an increase in dendritic complexity [5, 6]. The issue of generalization of trained to other tasks has not been addressed in animal models of post-stroke recovery.

The present study investigated whether motor training by pellet reaching translates into improvement in other motor tasks in a rat stroke model. The transient middle cerebral artery occlusion (MCAO) was chosen for stroke induction, because the lesion is not confined to the motor cortex but has a variable spread towards adjacent cortical and subcortical areas, similar to human stroke.

Continue —> Task-Specific Motor Rehabilitation Therapy After Stroke Improves Performance in a Different Motor Task: Translational Evidence | SpringerLink

Fig. 1 Flow of the experiments. a Experimental schedule. b Photos illustrating rats during pellet-reaching training. c Representative MRI-T2 images from the rehabilitation and no rehabilitation group 28 days after MCAO

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[Abstract] Quantitative EEG for Predicting Upper-limb Motor Recovery in Chronic Stroke Robot-assisted Rehabilitation – IEEE Xplore Document


Stroke is a leading cause for adult disability, which in many cases causes motor deficits. Despite the developments in motor rehabilitation techniques, recovery of upper limb functions after stroke is limited and heterogeneous in terms of outcomes, and knowledge of important factors that may affect the outcome of the therapy is necessary to make a reasonable prediction for individual patients.
In this study, we assessed the relationship between quantitative electroencephalographic (QEEG) measures and the motor outcome in chronic stroke patients that underwent a robot-assisted rehabilitation program to evaluate the utility of QEEG indices to predict motor recovery. For this purpose, we acquired resting-state electroencephalographic signals from which the Power Ratio Index (PRI), Delta/Alpha Ratio (DAR), and Brain Symmetry Index (BSI) were calculated. The outcome of the motor rehabilitation was evaluated using upper-limb section of the Fugl-Meyer Assessment.
We found that PRI was significantly correlated with the motor recovery, suggesting that this index may provide useful information to predict the rehabilitation outcome.

Source: Quantitative EEG for Predicting Upper-limb Motor Recovery in Chronic Stroke Robot-assisted Rehabilitation – IEEE Xplore Document

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[ARTICLE] Anatomical Parameters of tDCS to Modulate the Motor System after Stroke: A Review – Full Text

Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation method to modulate the local field potential in neural tissue and consequently, cortical excitability. As tDCS is relatively portable, affordable, and accessible, the applications of tDCS to probe brain-behavior connections have rapidly increased in the last ten years. One of the most promising applications is the use of tDCS to modulate excitability in the motor cortex after stroke and promote motor recovery. However, the results of clinical studies implementing tDCS to modulate motor excitability have been highly variable, with some studies demonstrating that as many as 50% or more of patients fail to show a response to stimulation. Much effort has therefore been dedicated to understanding the sources of variability affecting tDCS efficacy. Possible suspects include the placement of the electrodes, task parameters during stimulation, dosing (current amplitude, duration of stimulation, frequency of stimulation), individual states (e.g., anxiety, motivation, attention), and more. In this review, we first briefly review potential sources of variability specific to stroke motor recovery following tDCS. We then examine how the anatomical variability in tDCS placement (e.g., neural target(s) and montages employed) may alter the neuromodulatory effects that tDCS exerts on the post-stroke motor system.


Stroke is a neurological deficit induced by the interruption of the blood flow to the brain due to either a vessel occlusion or less frequently an intracerebral hemorrhage (1). Both may induce direct damage of brain tissue at the site of the lesion, along with potential for additional damage in the surrounding tissue, and long-range dysfunction through the interruption of structural and functional pathways in the brain. This also leads to a deregulation of cortical excitability (24) and abnormal interhemispheric interactions. Stroke may thus induce many neurological deficits and could result in death. According to the World Stroke Organization, one out of six people will suffer from a stroke, making stroke a leading cause of adult long-term disability worldwide (57). Importantly, one of the main challenges after stroke is the loss of one’s functional motor abilities. Research suggests that only 12% of stroke survivors achieve complete motor recovery by 6 months after the stroke (8). In addition, older individuals are more vulnerable to stroke and thus the incidence of stroke is expected to continue rising over the next few decades (9, 10). Accordingly, there is a need to find new potential therapeutic tools to enhance post-stroke motor recovery. Rebalancing interhemispheric interactions and/or restoring excitability in the ipsilesional hemisphere is thought to be beneficial for post-stroke motor recovery (1117). Thus, techniques aimed at restoring functional brain activity are a promising way to enhance neural recovery after injury. Most of the literature on stroke recovery focuses on the recovery of upper limb motor function. Since the neural mechanisms involved in motor recovery of upper versus lower limbs may differ, in this review, we focus only on upper limb motor recovery after stroke.

Non-invasive brain stimulation (NIBS) techniques show strong therapeutic potential for post-stroke motor rehabilitation due to their ability to modulate cortical excitability (1821). In particular, transcranial direct current stimulation (tDCS) has emerged as a viable neurorehabilitation tool due to its limited side-effects (22, 23) and safety [e.g., no known risk of neural damage or induction of seizures, as can be found in other NIBS methods like repetitive transcranial magnetic stimulation (rTMS) (24, 25)]. In addition, tDCS stimulators are commercially available and relatively affordable, on the order of several hundred dollars, and application of tDCS is considered relatively simple. By delivering a low-intensity direct current (between 0.5 and 2 mA) to the scalp via two saline-soaked electrodes—an anode and a cathode—tDCS can modulate the transmembrane potential of neurons, modifying cortical excitability and inducing changes in neural plasticity (see Figure 1) (2630). In addition, recent work has attempted to enhance the spatial resolution of tDCS stimulation, using a new technique called high-definition tDCS (HD-tDCS) (3134). With this technique, brain regions are more focally targeted using arrays of smaller electrodes arranged on the scalp (Figure 2), using multiple anodes and cathodes (see section on Focal versus Broad Stimulation for a more detailed description). Recently, there has also been increased interest in combining tDCS with imaging methods, such as fMRI or EEG, in order to better understand the local and global effects of tDCS on neural plasticity throughout the brain (35). These methods have all contributed to the growth and interest of tDCS as a viable neuromodulatory method for stroke.

Figure 1. Conventional transcranial direct current stimulation (tDCS) setup. The conventional tDCS setup requires a small tDCS stimulator with a 9-V battery, two saline-soaked sponge electrodes and one rubber band to hold the electrodes in place on the head. While there are many options for convention tDCS, the unit shown here is the Chattanooga Iontophoresis device.

Continue —> Frontiers | Anatomical Parameters of tDCS to Modulate the Motor System after Stroke: A Review | Movement Disorders

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[ARTICLE] Can Neurological Biomarkers of Brain Impairment Be Used to Predict Poststroke Motor Recovery? – Full Text

Background. There is growing interest to establish recovery biomarkers, especially neurological biomarkers, in order to develop new therapies and prediction models for the promotion of stroke rehabilitation and recovery. However, there is no consensus among the neurorehabilitation community about which biomarker(s) have the highest predictive value for motor recovery. Objective. To review the evidence and determine which neurological biomarker(s) meet the high evidence quality criteria for use in predicting motor recovery. Methods. We searched databases for prognostic neuroimaging/neurophysiological studies. Methodological quality of each study was assessed using a previously employed comprehensive 15-item rating system. Furthermore, we used the GRADE approach and ranked the overall evidence quality for each category of neurologic biomarker. Results. Seventy-one articles met our inclusion criteria; 5 categories of neurologic biomarkers were identified: diffusion tensor imaging (DTI), transcranial magnetic stimulation (TMS), functional magnetic resonance imaging (fMRI), conventional structural MRI (sMRI), and a combination of these biomarkers. Most studies were conducted with individuals after ischemic stroke in the acute and/or subacute stage (~70%). Less than one-third of the studies (21/71) were assessed with satisfactory methodological quality (80% or more of total quality score). Conventional structural MRI and the combination biomarker categories ranked “high” in overall evidence quality. Conclusions. There were 3 prevalent methodological limitations: (a) lack of cross-validation, (b) lack of minimal clinically important difference (MCID) for motor outcomes, and (c) small sample size. More high-quality studies are needed to establish which neurological biomarkers are the best predictors of motor recovery after stroke. Finally, the quarter-century old methodological quality tool used here should be updated by inclusion of more contemporary methods and statistical approaches.

There is growing interest in establishing stroke recovery biomarkers. Researchers define stroke recovery biomarkers as surrogate indicators of disease state that can have predictive value for recovery or treatment response.1 Specifically, previous studies have suggested that better understanding of neurological biomarkers, derived from brain imaging and neurophysiological assessments, is likely to move stroke rehabilitation research forward.1,2

Recovery biomarkers acquired during the acute and subacute phases (acute—within 1 week after onset; subacute—between 1 week and 3 months after onset) may be vital to set attainable neurorehabilitation goals and to choose proper therapeutic approaches based on the recovery capacity. Furthermore, motor recovery prediction using neurological biomarkers in the chronic phase (more than 3 months after onset) can be useful to determine whether an individual will benefit from specific therapeutic interventions applied after the normal period of rehabilitation has ended. Hence, use of recovery biomarkers is likely to improve customization of physical interventions for individual stroke survivors regarding their capacity for recovery, and to facilitate development of new neurorehabilitation approaches.

There have been fundamental changes in recovery biomarkers from simple clinical behavioral biomarkers to brain imaging and neurophysiological biomarkers. In particular, a number of recent studies have shown that neurologic biomarkers (ie, neuroimaging and/or neurophysiological measures of brain) are more predictive of motor recovery than clinical behavioral biomarkers.35

Although there is some evidence that neurological biomarkers are more valuable as predictors of motor recovery than clinical behavioral biomarkers, there are significant gaps between the published evidence and clinical usage. First, there is no consensus on which specific neurological biomarkers would be best for prediction models.4,6,7 Viable neurological biomarker of motor recovery have evolved from lesion size and location, prevalent in the early 1990s8 to more contemporary complex brain network analysis variables.9 Despite this evolution, there is a paucity of high-level evidence for determining the most critical neurological biomarkers of motor recovery. A number of literature reviews and systematic reviews of studies published since the 1990s aimed to identify the most appropriate biomarkers of motor recovery or functional independence.8,1012 Among these reviews, only one by Schiemanck and colleagues8 assessed the evidence quality of neurologic biomarkers, while many focused on clinical measures (ie, clinical motor and/or functional measures).11 Their review was limited to only 13 studies that employed structural magnetic resonance imaging (sMRI) measures of lesion volume as neurologic biomarkers. Besides lesion volume derived from structural MRI, there are other viable neurological biomarkers of brain impairment. Therefore, this systematic review includes a broad set of relevant biomarkers for consideration as critical predictors for inclusion in motor recovery prediction models.

Furthermore, there is some evidence to suggest that multivariate prediction models that use neurological biomarkers in addition to clinical outcome measures are more accurate than those that use clinical outcome measures alone.2,13 However there is still no consensus about whether incorporating behavioral and neurological predictors in a multimodal prediction model is superior (ie, more accurate) to a univariate model that includes either behavioral or neurological predictors alone.

Taken together, this systematic review has 2 aims. The first is to conduct a critical and systematic comparison of selected studies to determine which neurological biomarker(s) is likely to have sufficient high-level evidence in order to render the most accurate prediction of motor recovery after stroke. The second aim is to identify whether adding clinical measures along with neurological biomarkers in the model improves the accuracy of the model compared to the models that use neurological biomarkers alone.

Continue —> Can Neurological Biomarkers of Brain Impairment Be Used to Predict Poststroke Motor Recovery? A Systematic Review – Aug 08, 2016


Figure 1. Evidence search strategy diagram.

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[ARTICLE] Using Biophysical Models to Understand the Effect of tDCS on Neurorehabilitation: Searching for Optimal Covariates to Enhance Poststroke Recovery – Full Text

Stroke is a leading cause of worldwide disability, and up to 75% of survivors suffer from some degree of arm paresis. Recently, rehabilitation of stroke patients has focused on recovering motor skills by taking advantage of use-dependent neuroplasticity, where high-repetition of goal-oriented movement is at times combined with non-invasive brain stimulation, such as transcranial direct current stimulation (tDCS). Merging the two approaches is thought to provide outlasting clinical gains, by enhancing synaptic plasticity and motor relearning in the motor cortex primary area. However, this general approach has shown mixed results across the stroke population. In particular, stroke location has been found to correlate with the likelihood of success, which suggests that different patients might require different protocols. Understanding how motor rehabilitation and stimulation interact with ongoing neural dynamics is crucial to optimize rehabilitation strategies, but it requires theoretical and computational models to consider the multiple levels at which this complex phenomenon operate. In this work, we argue that biophysical models of cortical dynamics are uniquely suited to address this problem. Specifically, biophysical models can predict treatment efficacy by introducing explicit variables and dynamics for damaged connections, changes in neural excitability, neurotransmitters, neuromodulators, plasticity mechanisms and repetitive movement, which together can represent brain state, effect of incoming stimulus and movement-induced activity. In this work, we hypothesize that effects of tDCS depend on ongoing neural activity, and that tDCS effects on plasticity may be also related to enhancing inhibitory processes. We propose a model design for each step of this complex system, and highlight strengths and limitations of the different modeling choices within our approach. Our theoretical framework proposes a change in paradigm, where biophysical models can contribute to the future design of novel protocols, in which combined tDCS and motor rehabilitation strategies are tailored to the ongoing dynamics that they interact with, by considering the known biophysical factors recruited by such protocols and their interaction.

Source: Frontiers | Using Biophysical Models to Understand the Effect of tDCS on Neurorehabilitation: Searching for Optimal Covariates to Enhance Poststroke Recovery | Stroke

Figure 1. Diagram of top-down and bottom-up stroke neurorehabilitation strategies. Sensory–motor training and brain stimulation contribute to rehabilitation protocols that exploit neural plasticity. The bottom-up approach includes sensory–motor training, which can be aided by robots, electrical stimulation of the periphery, and constrains. The top-down approaches include methods to stimulate the brain non-invasively.

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[Abstract] Strength of ~20-Hz Rebound and Motor Recovery After Stroke.

Background. Stroke is a major cause of disability worldwide, and effective rehabilitation is crucial to regain skills for independent living. Recently, novel therapeutic approaches manipulating the excitatory-inhibitory balance of the motor cortex have been introduced to boost recovery after stroke. However, stroke-induced neurophysiological changes of the motor cortex may vary despite of similar clinical symptoms. Therefore, better understanding of excitability changes after stroke is essential when developing and targeting novel therapeutic approaches.

Objective and Methods. We identified recovery-related alterations in motor cortex excitability after stroke using magnetoencephalography. Dynamics (suppression and rebound) of the ~20-Hz motor cortex rhythm were monitored during passive movement of the index finger in 23 stroke patients with upper limb paresis at acute phase, 1 month, and 1 year after stroke.

Results. After stroke, the strength of the ~20-Hz rebound to stimulation of both impaired and healthy hand was decreased with respect to the controls in the affected (AH) and unaffected (UH) hemispheres, and increased during recovery. Importantly, the rebound strength was lower than that of the controls in the AH and UH also to healthy-hand stimulation despite of intact afferent input. In the AH, the rebound strength to impaired-hand stimulation correlated with hand motor recovery.

Conclusions. Motor cortex excitability is increased bilaterally after stroke and decreases concomitantly with recovery. Motor cortex excitability changes are related to both alterations in local excitatory-inhibitory circuits and changes in afferent input. Fluent sensorimotor integration, which is closely coupled with excitability changes, seems to be a key factor for motor recovery.

Source: Strength of ~20-Hz Rebound and Motor Recovery After Stroke – Feb 04, 2017

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[Stroke Rehabilitation Clinician Handbook] 4. Motor Rehabilitation – 4A. Lower Extremity and Mobility – Full Text PDF

4.1 Motor Recovery of the Lower Extremity Post Stroke

Factors that Predict Motor Recovery

Motor deficits post-stroke are the most obvious impairment (Langhorne et al. 2012) and have a disabling impact on valued activities and independence. Motor deficits are defined as “a loss or limitation of function in muscle control or movement or a limitation of movement” (Langhorne et al. 2012; Wade 1992). Given its importance, a large proportion of stroke rehabilitation efforts are directed towards the recovery of movement disorders. Langhorne et al. (2012) notes that motor recovery after stroke is complex with many treatments designed to promote recovery of motor impairment and function.

The two most important factors which predict motor recovery are:

  1. Stroke Severity: The most important predictive factor which reduces the capacity for brain reorganization.
  2. Age: Younger patients demonstrate greater neurological and functional recovery and hence have a better prognosis compared to older stroke patients (Adunsky et al. 1992; Hindfelt & Nilsson 1977; Marini et al. 2001; Nedeltchev et al. 2005).

Changes in walking ability and gait pattern often persist long-term and include increased tone, gait asymmetry, changes in muscle activation and reduced functional abilities (Wooley 2001; Robbins et al. 2006; Pizzi et al. 2007, Pereira et al. 2012). Ambulation post stroke is often less efficient and associated with increased energy expenditure (Pereira et al. 2012). Hemiplegic individuals have been reported to utilize 50-67% more metabolic energy that normal individuals when walking at the same velocity (Wooley et al. 2001).

For mobility outcome, trunk balance is an additional predictor of recovery (Veerbeek et al. 2011). Nonambulant patients who regained sitting balance and some voluntary movement of the hip, knee and/or ankle within the first 72 hours post stroke predicted 98% chance of regaining independent gait within 6 months. In contrast, those who were unable to sit independently for 30 seconds and could not contract the paretic lower limb within the first 72 hours post stroke had a 27% probability of achieving independent gait.

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[ARTICLE] Parietal operculum and motor cortex activities predict motor recovery in moderate to severe stroke – Full Text


While motor recovery following mild stroke has been extensively studied with neuroimaging, mechanisms of recovery after moderate to severe strokes of the types that are often the focus for novel restorative therapies remain obscure. We used fMRI to: 1) characterize reorganization occurring after moderate to severe subacute stroke, 2) identify brain regions associated with motor recovery and 3) to test whether brain activity associated with passive movement measured in the subacute period could predict motor outcome six months later.

Because many patients with large strokes involving sensorimotor regions cannot engage in voluntary movement, we used passive flexion-extension of the paretic wrist to compare 21 patients with subacute ischemic stroke to 24 healthy controls one month after stroke. Clinical motor outcome was assessed with Fugl-Meyer motor scores (motor-FMS) six months later. Multiple regression, with predictors including baseline (one-month) motor-FMS and sensorimotor network regional activity (ROI) measures, was used to determine optimal variable selection for motor outcome prediction. Sensorimotor network ROIs were derived from a meta-analysis of arm voluntary movement tasks. Bootstrapping with 1000 replications was used for internal model validation.

During passive movement, both control and patient groups exhibited activity increases in multiple bilateral sensorimotor network regions, including the primary motor (MI), premotor and supplementary motor areas (SMA), cerebellar cortex, putamen, thalamus, insula, Brodmann area (BA) 44 and parietal operculum (OP1-OP4). Compared to controls, patients showed: 1) lower task-related activity in ipsilesional MI, SMA and contralesional cerebellum (lobules V-VI) and 2) higher activity in contralesional MI, superior temporal gyrus and OP1-OP4. Using multiple regression, we found that the combination of baseline motor-FMS, activity in ipsilesional MI (BA4a), putamen and ipsilesional OP1 predicted motor outcome measured 6 months later (adjusted-R2 = 0.85; bootstrap p < 0.001). Baseline motor-FMS alone predicted only 54% of the variance. When baseline motor-FMS was removed, the combination of increased activity in ipsilesional MI-BA4a, ipsilesional thalamus, contralesional mid-cingulum, contralesional OP4 and decreased activity in ipsilesional OP1, predicted better motor outcome (djusted-R2 = 0.96; bootstrap p < 0.001).

In subacute stroke, fMRI brain activity related to passive movement measured in a sensorimotor network defined by activity during voluntary movement predicted motor recovery better than baseline motor-FMS alone. Furthermore, fMRI sensorimotor network activity measures considered alone allowed excellent clinical recovery prediction and may provide reliable biomarkers for assessing new therapies in clinical trial contexts. Our findings suggest that neural reorganization related to motor recovery from moderate to severe stroke results from balanced changes in ipsilesional MI (BA4a) and a set of phylogenetically more archaic sensorimotor regions in the ventral sensorimotor trend. OP1 and OP4 processes may complement the ipsilesional dorsal motor cortex in achieving compensatory sensorimotor recovery.

Fig. 2

Fig. 2. Four axial slices representative showing stroke lesion extent in 21 patients (FLAIR images).

Continue —> Parietal operculum and motor cortex activities predict motor recovery in moderate to severe stroke

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[ARTICLE] Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation – Full Text

Stroke patients with severe motor deficits of the upper extremity may practice rehabilitation exercises with the assistance of a multi-joint exoskeleton. Although this technology enables intensive task-oriented training, it may also lead to slacking when the assistance is too supportive. Preserving the engagement of the patients while providing “assistance-as-needed” during the exercises, therefore remains an ongoing challenge. We applied a commercially available seven degree-of-freedom arm exoskeleton to provide passive gravity compensation during task-oriented training in a virtual environment. During this 4-week pilot study, five severely affected chronic stroke patients performed reach-to-grasp exercises resembling activities of daily living. The subjects received virtual reality feedback from their three-dimensional movements. The level of difficulty for the exercise was adjusted by a performance-dependent real-time adaptation algorithm. The goal of this algorithm was the automated improvement of the range of motion. In the course of 20 training and feedback sessions, this unsupervised adaptive training concept led to a progressive increase of the virtual training space (p < 0.001) in accordance with the subjects’ abilities. This learning curve was paralleled by a concurrent improvement of real world kinematic parameters, i.e., range of motion (p = 0.008), accuracy of movement (p = 0.01), and movement velocity (p < 0.001). Notably, these kinematic gains were paralleled by motor improvements such as increased elbow movement (p = 0.001), grip force (p < 0.001), and upper extremity Fugl-Meyer-Assessment score from 14.3 ± 5 to 16.9 ± 6.1 (p = 0.026). Combining gravity-compensating assistance with adaptive closed-loop feedback in virtual reality provides customized rehabilitation environments for severely affected stroke patients. This approach may facilitate motor learning by progressively challenging the subject in accordance with the individual capacity for functional restoration. It might be necessary to apply concurrent restorative interventions to translate these improvements into relevant functional gains of severely motor impaired patients in activities of daily living.


Despite their participation in standard rehabilitation programs (Jørgensen et al., 1999; Dobkin, 2005), restoration of arm and hand function for activities of daily living is not achieved in the majority of stroke patients. In the first weeks and months after stroke, a positive relationship between the dose of therapy and clinically meaningful improvements has been demonstrated (Lohse et al., 2014; Pollock et al., 2014). In stroke patients with long-standing (>6 months) upper limb paresis, however, treatment effects were small, with no evidence of a dose-response effect of task-specific training on the functional capacity (Lang et al., 2016). This has implications for the use of assistive technologies such as robot-assisted training during stroke rehabilitation. These devices are usually applied to further increase and standardize the amount of therapy. They have the potential to improve arm/hand function and muscle strength, albeit currently available clinical trials provide on the whole only low-quality evidence (Mehrholz et al., 2015). It has, notably, been suggested that technology-assisted improvements during stroke rehabilitation might at least partially be due to unspecific influences such as increased enthusiasm for novel interventions on the part of both patients and therapists (Kwakkel and Meskers, 2014). In particular, a comparison between robot-assisted training and dose-matched conventional physiotherapy in controlled trials revealed no additional, clinically relevant benefits (Lo et al., 2010; Klamroth-Marganska et al., 2014). This might be related to saturation effects. Alternatively, the active robotic assistance might be too supportive when providing “assistance-as-needed” during the exercises (Chase, 2014). More targeted assistance might therefore be necessary during these rehabilitation exercises to maintain engagement without compromising the patients’ motivation; i.e., by providing only as much support as necessary and as little as possible (Grimm and Gharabaghi, 2016). In this context, passive gravity compensation with a multi-joint arm exoskeleton may be a viable alternative to active robotic assistance (Housman et al., 2009; Grimm et al., 2016a). In severely affected patients, performance-dependent, neuromuscular electrical stimulation of individual upper limb muscles integrated in the exoskeleton may increase the range of motion even further (Grimm and Gharabaghi, 2016; Grimm et al., 2016b). These approaches focus on the improvement of motor control, which is defined as the ability to make accurate and precise goal-directed movements without reducing movement speed (Reis et al., 2009; Shmuelof et al., 2012), or using compensatory movements (Kitago et al., 2013, 2015). Functional gains in hemiparetic patients, however, are often achieved by movements that aim to compensate the diminished range of motion of the affected limb (Cirstea and Levin, 2000; Grimm et al., 2016a). Although these compensatory strategies might be efficient in short-term task accomplishment, they may lead to long-term complications such as pain and joint-contracture (Cirstea and Levin, 2007; Grimm et al., 2016a). In this context, providing detailed information about how the movement is carried out, i.e., the quality of the movement, is more likely to recover natural movement patterns and avoid compensatory movements, than to provide information about movement outcome only (Cirstea et al., 2006; Cirstea and Levin, 2007; Grimm et al., 2016a). This feedback, however, needs to be provided implicitly, since explicit information has been shown to disrupt motor learning in stroke patients (Boyd and Winstein, 2004, 2006; Cirstea and Levin, 2007). Information on movement quality has therefore been incorporated as implicit closed-loop feedback in the virtual environment of an exoskeleton-based rehabilitation device (Grimm et al., 2016a). Specifically, the continuous visual feedback of the whole arm kinematics allowed the patients to adjust their movement quality online during each task; an approach closely resembling natural motor learning (Grimm et al., 2016a).

Along these lines, virtual reality and interactive video gaming have emerged as treatment approaches in stroke rehabilitation (Laver et al., 2015). They have been used as an adjunct to conventional care (to increase overall therapy time) or compared with the same dose of conventional therapy. These studies have demonstrated benefits in improving upper limb function and activities of daily living, albeit currently available clinical trials tend to provide only low-quality evidence (Laver et al., 2015). Most of these studies were conducted with mildly to moderately affected patients. In the remaining patient group with moderate to severe upper limp impairment, the intervention effects were more heterogeneous and affected by the impairment level, with either no or only modest additional gains in comparison to dose-matched conventional treatments (Housman et al., 2009; Byl et al., 2013; Subramanian et al., 2013).

With respect to the restoration of arm and hand function in severely affected stroke patients in particular, there is still a lack of evidence for additional benefits from technology-assisted interventions for activities of daily living. The only means of providing such evidence is by sufficiently powered, randomized and adequately controlled trials (RCT).

However, such high-quality RCT studies require considerable resources. Pilot data acquired earlier in the course of feasibility studies may provide the rationale and justification for later large-scale RCT. Such studies therefore need to demonstrate significant improvements, with functional relevance for the participating patients. Then again, costly RCT can be avoided when innovative interventions prove to be feasible but not effective with regard to the treatment goal, i.e., that they do not result in functionally relevant upper extremity improvements in severely affected stroke patients.

One recent pilot study, for example, applied brain signals to control an active robotic exoskeleton within the framework of a brain-robot interface (BRI) for stroke rehabilitation. This device provided patient control over the training device via motor imagery-related oscillations of the ipsilesional cortex (Brauchle et al., 2015). The study illustrated that a BRI may successfully link three-dimensional robotic training to the participant’s effort. Furthermore, the BRI allowed the severely impaired stroke patients to perform task-oriented activities with a physiologically controlled multi-joint exoskeleton. However, this approach did not result in significant upper limb improvements with functional relevance for the participating patients. This training approach was potentially too challenging and may even have frustrated the patients (Fels et al., 2015). The patients’ cognitive resources for coping with the mental load of performing such a neurofeedback task must therefore be taken into consideration (Bauer and Gharabaghi, 2015a; Naros and Gharabaghi, 2015). Mathematical modeling on the basis of Bayesian simulation indicates that this might be achieved when the task difficulty is adapted in the course of the training (Bauer and Gharabaghi, 2015b). Such an adaptation strategy has the potential to facilitate reinforcement learning (Naros et al., 2016b) by progressively challenging the patient (Naros and Gharabaghi, 2015). Recent studies explored automated adaptation of training difficulty in stroke rehabilitation of less severely affected patients (Metzger et al., 2014; Wittmann et al., 2015). More specifically, both robot-assisted rehabilitation of proprioceptive hand function (Metzger et al., 2014) and inertial sensor-based virtual reality feedback of the arm (Wittmann et al., 2015) benefit from assessment-driven adjustments of exercise difficulty. Furthermore, a direct comparison between adaptive BRI training and non-adaptive training (Naros et al., 2016b) or sham adaptation (Bauer et al., 2016a) in healthy patients revealed the impact of reinforcement-based adaptation for the improvement of performance. Moreover, the exercise difficulty has been shown to influence the learning incentive during the training; more specifically, the optimal difficulty level could be determined empirically while disentangling the relative contribution of neurofeedback specificity and sensitivity (Bauer et al., 2016b).

In the present 4-week pilot study, we combined these approaches and customized them for the requirements of patients with severe upper extremity impairment by applying a multi-joint exoskeleton for task-oriented arm and hand training in an adaptive virtual environment. Notably, due to the severity of their impairment, these patients were not able to practice the reach-to-grasp movements without the exoskeleton. The set-up was, however, limited to pure antigravity support, i.e., it provided passive rather than active assistance. Furthermore, it tested the feasibility of closed-loop online adaptation of exercise difficulty and aimed at automated progression of task challenge.

Continue —> Frontiers | Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation | Neuroprosthetics

Figure 1. Training set-up with the exoskeleton (upper row) and the provided visual feedback in virtual reality (lower row).

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