Posts Tagged motor control

[ARTICLE] Upper Limb Motor Impairment Post Stroke – Full Text


Understanding upper limb impairment after stroke is essential to planning therapeutic efforts to restore function. However determining which upper limb impairment to treat and how is complex for two reasons: 1) the impairments are not static, i.e. as motor recovery proceeds, the type and nature of the impairments may change; therefore the treatment needs to evolve to target the impairment contributing to dysfunction at a given point in time. 2) multiple impairments may be present simultaneously, i.e., a patient may present with weakness of the arm and hand immediately after a stroke, which may not have resolved when spasticity sets in a few weeks or months later; hence there may be a layering of impairments over time making it difficult to decide what to treat first. The most useful way to understand how impairments contribute to upper limb dysfunction may be to examine them from the perspective of their functional consequences. There are three main functional consequences of impairments on upper limb function are: (1) learned nonuse, (2) learned bad-use, and (3) forgetting as determined by behavioral analysis of tasks. The impairments that contribute to each of these functional limitations are described.

The nature of upper limb motor impairment

According to the International Classification of Functioning, Disability and Health model (ICF) (Geyh, Cieza et al. 2004), impairments may be described as (1) impairments of body function such as a significant deviation or loss in neuromusculoskeletal and movement related function related to joint mobility, muscle power, muscle tone and/or involuntary movements, or (2) impairment of body structures such as a significant deviation in structure of the nervous system or structures related to movement, for example the arm and/or hand. A stroke may lead to both types of impairments. Upper limb impairments after stroke are the cause of functional limitations with regard to use of the affected upper limb after stroke, so a clear understanding of the underlying impairments is necessary to provide appropriate treatment. However understanding upper limb impairments in any given patient is complex for two reasons: 1) the impairments are not static, i.e. as motor recovery proceeds, the type and nature of the impairments may change; therefore the treatment needs to evolve to target the impairment contributing to dysfunction at a given point in time. 2) multiple impairments may be present simultaneously, i.e., a patient may present with weakness of the arm and hand immediately after a stroke, which may not have resolved when spasticity sets in a few weeks or months later; hence there may be a layering of impairments over time making it difficult to decide what to treat first. It is useful to review the progression of motor recovery as described by Twitchell (Twitchell 1951) and Brunnstrom (Brunnstom 1956) to understand how impairments may be layered over time (Figure 1).

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Sequential progression of motor recovery as described by Twitchell and Brunstrumm. Note that while recovery is proceeding from one stage to the next, residual impairment from preceding stages may still be present leading to the layering of impairment. Also note the underlying physiological processes that may account for progression from one stage to the next.

Understanding motor impairment from a functional perspective

The most useful way to understand how impairments contribute to upper limb dysfunction may be to examine them from the perspective of their functional consequences. There are three main functional consequences of stroke on the upper limb: (1) learned nonuse, (2) learned bad-use, and (3) forgetting as determined by behavioral analysis of a task such as reaching for a food pellet and bringing it to the mouth in animal models of stroke (Whishaw, Alaverdashvili et al. 2008). These are equally valid for human behavior. Each of the functional consequences and the underlying impairments are elaborated below.[…]


Continue —>  Upper Limb Motor Impairment Post Stroke


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[WEB SITE] Soft Robotic Glove

Soft Robotic Glove

A lightweight robotic glove to assist people suffering from loss of hand motor control to restore their ability to grasp objects independently

The majority of patients with partial or total loss of hand motor abilities, including those suffering from debilitating disorders like muscular dystrophy, amyotrophic lateral sclerosis (ALS), and incomplete spinal cord injury, experience greatly reduced quality of life due to their inability to perform many daily activities. Tasks often taken for granted by the able-bodied become frustrating and nearly impossible feats due to reduced gripping strength and motor control of the hand.


The soft robotic glove under development at the Wyss Institute could one day be an assistive device used for grasping objects, which could help patients suffering from muscular dystrophy, amyotrophic lateral sclerosis (ALS), incomplete spinal cord injury, or other hand impairments to regain some daily independence and control of their environment. Credit: Wyss Institute at Harvard University

Visit site for more —>  Soft Robotic Glove

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[Abstract] Post-stroke spasticity management including a chosen physiotherapeutic methods and improvements in motor control – review of the current scientific evidence.


Cerebrovascular diseases based on stroke etiology concern millions of people worldwide, and annual rates of disease are still increasing. In the era of an aging society and suffering from a number of risk factors, in particular those modifiable, strokes and muscles’ spastic paresis, subsequently resulting in damage of upper motor neuron structures will become a serious problem for the entire health care system. Effective management and physiotherapy treatment for post-stroke spasticity persisted, both in the acute and chronic, is still a significant medical problem in the interdisciplinary aspect. Care procedures for this type of patient becomes a kind of challenge for specialists in neurology, internal medicine, cardiology, dermatology or neurosurgery, but also for physiotherapists in their everyday clinical practice. The aim of this paper is to present the issues of cerebral stroke and resulting spastic hypertonia in terms of current pharmacological treatment and surgery, and primarily through the use of effective physiotherapy methods, the use of which was confirmed in the way of reliable scientific research in accordance with the principles of Evidence Based Medicine and Physiotherapy (EBMP).


via [Post-stroke spasticity management including a chosen physiotherapeutic methods and improvements in motor control – review of the current scientifi… – PubMed – NCBI

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[REVIEW] Biomechanics and neural control of movement, 20 years later: what have we learned and what has changed? – Full Text


We summarize content from the opening thematic session of the 20th anniversary meeting for Biomechanics and Neural Control of Movement (BANCOM). Scientific discoveries from the past 20 years of research are covered, highlighting the impacts of rapid technological, computational, and financial growth on motor control research. We discuss spinal-level communication mechanisms, relationships between muscle structure and function, and direct cortical movement representations that can be decoded in the control of neuroprostheses. In addition to summarizing the rich scientific ideas shared during the session, we reflect on research infrastructure and capacity that contributed to progress in the field, and outline unresolved issues and remaining open questions.


At the 20th anniversary meeting for Biomechanics and Neural Control of Movement (BANCOM), the opening thematic session was chaired by Dr. Fay Horak (Oregon Health & Science University). Presentations and discussions covered insights from 20 years of research in the field of motor control, delivered by Drs. Zev Rymer (Rehabilitation Institute of Chicago), Andy Biewener (Harvard University), Andy Schwartz (University of Pittsburgh), and Daofen Chen (National Institute of Neurological Disorders and Stroke). Presentation themes included the impact of technological advancements on motor control research, unresolved issues in muscle biology and neurophysiology, and changes in the scientific funding landscape. This brief review summarizes content presented by each speaker, along with discussions from the audience.

Considerable changes have occurred in the fields of biomechanics and motor control over the past 20 years, changes made possible by rapid technological advances in computing power and memory along with reduced physical size of biotechnology hardware. Because of these changes, research approaches have been reshaped and new questions have emerged. Previously, motor control research was constrained to laboratory-based assessments of individual neurons, muscles or joints, captured from low sample sizes. In the past, reliance on large, expensive, external recording devices, such as optical motion capture systems, understandably limited the feasibility of large-scale, multivariate research. Today, whole-body kinematic recordings using body-worn inertial measurement units, wireless electromyography (EMG), electroencephalography (EEG), and functional near infrared spectroscopy (fNIRS) systems, and electrode arrays for neural network recordings are increasingly commonplace. Alongside these technical leaps, sociocultural bounds have expanded research inclusion, as evidenced in the representation of speakers at the 2016 BANCOM meeting. In contrast to the 1996 meeting, which included three invited female speakers, 13 women were included as speakers in 2016. Such advancements will continue to shape our scientific landscape, driving innovation through new technologies and perspectives.[…]

Continue —>  Biomechanics and neural control of movement, 20 years later: what have we learned and what has changed? | Journal of NeuroEngineering and Rehabilitation | Full Text


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[WEB SITE] Robotic-Assisted Rehabilitation Therapy Designed to Aid Stroke Recovery

Pictured here is the experimental setup for the estimation of the 3 DOF human forearm and wrist impedance. (Photo courtesy of UNIST)

Pictured here is the experimental setup for the estimation of the 3 DOF human forearm and wrist impedance. (Photo courtesy of UNIST)

Scientists from Ulsan National Institute of Science and Technology (UNIST) have developed a new robotic tool to assess muscle overactivity and movement dysfunction in stroke survivors.

They suggest, in a study published recently in IEEE Transactions on Neural Systems and Rehabilitation Engineering, that their robotic-assisted rehabilitation therapy may help improve the stroke patients’ mobility.

The study was led by Professor Sang Hoon Kang of Mechanical, Aerospace and Nuclear Engineering at UNIST in collaboration with Professor Pyung-Hun Chang of DGIST and Dr Kyungbin Park of Samsung Electronics Co Ltd, according to a media release from UNIST.

In their study, Kang and the others on the team developed a rehabilitation robotic system that quantitatively measures the 3 degrees-of-freedom (DOF) impedance of human forearm and wrist in minutes.

Using their impedance estimation device, which they call the distal internal model based impedance control (dIMBIC)-based method, the team was able to accurately characterize the 3 DOF forearm and wrist impedance, including inertia, damping, and stiffness, for the first time, the release continues.

“The dIMBIC-based method can be used to assist in the quantitative and objective evaluation of neurological disorders, like stroke,” Kang says, in the release. “Findings from this study will open a new chapter in robot-assisted rehabilitation in the workplace accident rehabilitation hospitals, as well as in nursing homes and assisted living facilities.”

The research team expects that, in the long run, the proposed 3 DOF impedance estimation may promote wrist and forearm motor control studies and complement the diagnosis of the alteration in wrist and forearm resistance post-stroke by providing objective impedance values including cross-coupled terms, the release concludes.

[Source(s): Ulsan National Institute of Science and Technology (UNIST), Science Daily]

Source: Robotic-Assisted Rehabilitation Therapy Designed to Aid Stroke Recovery – Rehab Managment

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[WEB SITE] Stroke rehabilitation gets personalised and interactive – CORDIS

Stroke rehabilitation gets personalised and interactive

The significant socioeconomic costs of stroke coupled with the rise in Europe’s ageing population highlights the need for effective but affordable stroke rehabilitation programmes. EU researchers made considerable headway in this regard through novel rehabilitation paradigms.
Stroke rehabilitation gets personalised and interactive
Computer-mediated rehabilitation tools require a high degree of motor control and are therefore inadequate for patients with significant impairment in motor control. Consequently, many stroke survivors are unable to benefit. The REHABNET (REHABNET: Neuroscience based interactive systems for motor rehabilitation) project came up with an innovative approach to address this critical need.

Researchers successfully developed a hybrid brain-computer interface (BCI)-virtual reality (VR) system that assesses user capability and dynamically adjusts its difficulty level. This motor imagery-based BCI system is tailored to meet the needs of patients using a VR environment for game training coupled with neurofeedback through multimodal sensing technologies.

The game training scenarios address both cognitive and motor abilities. The four rehabilitation scenarios include bimanual motor training, dual motor cognitive-motor training and a simulated city for training on daily living activities.

Pilot and longitudinal studies demonstrated the benefits of longitudinal VR training as compared to existing rehabilitation regimens. The self-report questionnaires also revealed a high user acceptance of the novel system.

Designed for at-home use, the REHABNET toolset is platform-independent and freely available globally as an app (Reh@Mote). Besides deeper insight on factors affecting stroke recovery, this could aid in further improvement of rehabilitation strategies. More importantly, these low-cost toolsets could also address the needs of patients with severe motor and cognitive deficits. Efforts are ongoing to facilitate future commercial exploitation through a technology transfer agreement.

Related information

Source: European Commission : CORDIS : Projects and Results : Stroke rehabilitation gets personalised and interactive

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[ARTICLE] Kinematics in the brain: unmasking motor control strategies? – Full Text



In rhythmical movement performance, our brain has to sustain movement while correcting for biological noise-induced variability. Here, we explored the functional anatomy of brain networks during voluntary rhythmical elbow flexion/extension using kinematic movement regressors in fMRI analysis to verify the interest of method to address motor control in a neurological population. We found the expected systematic activation of the primary sensorimotor network that is suggested to generate the rhythmical movement. By adding the kinematic regressors to the model, we demonstrated the potential involvement of cerebellar–frontal circuits as a function of the irregularity of the variability of the movement and the primary sensory cortex in relation to the trajectory length during task execution. We suggested that different functional brain networks were related to two different aspects of rhythmical performance: rhythmicity and error control. Concerning the latter, the partitioning between more automatic control involving cerebellar–frontal circuits versus less automatic control involving the sensory cortex seemed thereby crucial for optimal performance. Our results highlight the potential of using co-registered fine-grained kinematics and fMRI measures to interpret functional MRI activations and to potentially unmask the organisation of neural correlates during motor control.


During rhythmical movement, sensory and motor systems need to interact closely to sustain the rhythm and to meet task requirements. Understanding how our system controls such a basic, all day movement is a prerequisite to improve motor (re)learning models to ameliorate rehabilitation in case of neurological movement disorders, like stroke. Mathematically, the simplest way to model rhythmicity is by means of a continuous oscillator (e.g. Haken et al. 1985). Biological noise interfering with planning and execution makes human movements unavoidably variable, which asks for correction processes (Franklin and Wolpert 2011). One of the principles governing human motor control states that optimised control is characterised by a maximum efficiency, e.g. minimal costs (Guigon et al. 2007). Minimal cost is dependent on the varying interaction between different system characteristics, including anatomical constraints, force generating capacities, and biological noise inducing the intra and interpersonal variability that is inherent to our system’s output (van Galen and Hueygevoort 2000).

Current knowledge about the neural correlates of rhythmical upper limb movement is based on standard finger and wrist movement paradigms that compare different movement conditions within people (high frequency versus low frequency, Kelso et al. 1998; rhythmic versus discrete movements, Schaal et al. 2004). Using this paradigm, simple unilateral rhythmical movements have been shown to elicit contralateral activations of the primary sensorimotor cortex (S1 + M1) and of the supplementary motor area (SMA), complemented by an ipsilateral activation of the anterior cerebellum (Allison et al. 2000; Ball et al. 1999; Schaal et al. 2004). Bilateral movements are associated with a symmetric facilitation of neural activity in the sensorimotor network, with additional frontal activations to ensure coordination between limbs. It is mediated by increased intrahemispheric connectivity and enhanced transcallosal coupling of SMA and M1 (Grefkes et al. 2008; Jäncke et al. 2000).

The activation pattern is comparable between dominant and non-dominant sided movements in extension and intensity when people move at their preferred frequency (Lutz et al. 2005; Jäncke et al. 2000; Koeneke et al. 2004). However, when movement frequency is imposed, activations during non-dominant sided movements increase in intensity compared to those of the dominant side (Lutz et al. 2005). Second, activation increases and expands for both uni and bilateral movements when movement frequency is increased above the preferred frequency (e.g. Kelso et al. 1998; Rao et al. 1996). Together, this demonstrates that moving at a non-preferred frequency is marked by an increase in costs. Therefore, imposing a fixed frequency may lead to different task-induced cost levels between participants and thus lead to biased results when comparing rhythmical motion and its neural correlates between people.

Over the time course of the movement, fine-grained kinematic variables capture the outcome of the interaction between the planned movement and the noise-dependent variability (Newell and Corcos 1993). Here, we explored whether kinematics may additionally provide information on the underlying control system, when the kinematic outcome is linked directly to brain activity. We simultaneously recorded brain activation (fMRI) and movement kinematics during a sensorimotor task that consisted of a self-paced continuous flexion/extension of the elbow. We focused on uni as well as bilateral movements, as many daily living tasks involve bilateral coordination. The task is evaluated as a simple well-known movement that does not require complex motor learning.

Based on the described theoretical model of motor control, we hypothesised that rhythmic voluntary flexion of the elbow is modulated by neural networks involved in (1) the sustained execution of the basic oscillatory rhythmical component and (2) correction processes in reaction to the variability resulting from biological noise. Sustaining the movement in rhythmical motion has been shown to involve the primary sensorimotor network, whereas discrete movements solicit additional higher cortical planning areas (Schaal et al. 2004). First, we expected to confirm the role of the sensorimotor network by performing a standard general linear-model analysis. Second, because task costs were as much equalised over participants as possible, we expected that correlating natural variation in movement execution with variation in BOLD-activation might unmask different brain regions involved in the secondary correction processes that could be (partly) separated from the primary sensorimotor network. […]

Continue —> Kinematics in the brain: unmasking motor control strategies? | SpringerLink

Fig. 2 Functional basis network: the main effect of task (flexion/extension of the elbow), FWE corrected, p < 0.05 at voxel level and the condition-specific activations p < 0.001, FWE corrected at cluster level, 22 degrees of freedom. R right sided, L left sided, B bilateral, U unilateral movement, RH right hemisphere, LH left hemisphere

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[Abstract] Motor compensation and its effects on neural reorganization after stroke


Stroke instigates a dynamic process of repair and remodelling of remaining neural circuits, and this process is shaped by behavioural experiences. The onset of motor disability simultaneously creates a powerful incentive to develop new, compensatory ways of performing daily activities. Compensatory movement strategies that are developed in response to motor impairments can be a dominant force in shaping post-stroke neural remodelling responses and can have mixed effects on functional outcome. The possibility of selectively harnessing the effects of compensatory behaviour on neural reorganization is still an insufficiently explored route for optimizing functional outcome after stroke.

Source: Motor compensation and its effects on neural reorganization after stroke : Nature Reviews Neuroscience : Nature Research

Figure 1: The motor cortex and its descending projection pathways are often affected by strokes that result in upper-extremity impairments.

The motor cortex and its descending projection pathways are often affected by strokes that result in upper-extremity impairments.

a | Simplified illustrations of motor cortical regions of a human (left), and of motor cortical regions of a naive rat, derived using intracortical microstimulation (right), are shown. The colours show the cortical territories that are…

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[ARTICLE] Hemispheric asymmetry in myelin after stroke is related to motor impairment and function – Full Text

Fig. 1


The relationships between impairment, function, arm use and underlying brain structure following stroke remain unclear. Although diffusion weighted imaging is useful in broadly assessing white matter structure, it has limited utility in identifying specific underlying neurobiological components, such as myelin. The purpose of the present study was to explore relationships between myelination and impairment, function and activity in individuals with chronic stroke. Assessments of paretic upper-extremity impairment and function were administered, and 72-hour accelerometer based activity monitoring was conducted on 19 individuals with chronic stroke. Participants completed a magnetic resonance imaging protocol that included a high resolution T1 anatomical scan and a multi-component T2 relaxation imaging scan to quantify myelin water fraction (MWF). MWF was automatically parcellated from pre- and post-central subcortical regions of interest and quantified as an asymmetry ratio (contralesional/ipsilesional). Cluster analysis was used to group more and less impaired individuals based on Fugl-Meyer upper extremity scores. A significantly higher precentral MWF asymmetry ratio was found in the more impaired group compared to the less impaired group (p < 0.001). There were no relationships between MWF asymmetry ratio and upper-limb use. Stepwise multiple linear regression identified precentral MWF asymmetry as the only variable to significantly predict impairment and motor function in the upper extremity (UE). These results suggest that asymmetric myelination in a motor specific brain area is a significant predictor of upper-extremity impairment and function in individuals with chronic stroke. As such, myelination may be utilized as a more specific marker of the neurobiological changes that predict long term impairment and recovery from stroke.

1. Introduction

Improved medical management of stroke has resulted in decreasing mortality rates (Grefkes and Ward, 2014). As a result, the number of individuals living with long-term disability as a result of stroke is rising (Krueger et al., 2015). Due to the heterogeneity of clinical presentation following stroke, it is imperative to identify biomarkers that may predict long-term impairment and function in order to appropriately individualize clinical rehabilitation goals and objectives (Bernhardt et al., 2016). With advances in diagnostic and prognostic tools, it is necessary to isolate modalities that can predict long-term outcomes for individuals with stroke, and to understand the underlying neurobiology that contributes to the predictive value of those measures.

Neuroimaging can be utilized to aid in the identification of biomarkers that may predict recovery status in individuals with stroke. White matter imaging is often used as a predictor of stroke recovery (Feng et al., 2015 and Stinear et al., 2012). Diffusion tensor imaging (DTI) can be performed within 10 days post stroke to quantify initial post stroke structural degeneration (Werring, 2000). Such indices have been found to strongly predict upper-extremity motor function at both 3- and 6-months post stroke (Puig et al., 2010 and Stinear et al., 2012). The combination of acute corticomotor function, derived from DTI and motor evoked potentials, using transcranial magnetic stimulation, has also been demonstrated to strongly predict recovery from upper-extremity impairment after stroke (Byblow et al., 2015). Although these modalities are predictive of long-term upper-extremity impairment, the underlying neurobiological bases driving the relationship between white matter microstructure and motor capacity remains unclear. Although relationships between white matter integrity, quantified with DTI, and motor impairment have been established after stroke, it is important to note that DTI measures are not a specific marker for myelination (Arshad et al., 2016). While DTI can grossly identify water movement, it is unable to differentiate between individual white matter substrates, which may produce the observed signal. Multiple structural features can be individually or collectively responsible for the observed changes in DTI measures, including: 1) axonal membrane status, 2) myelin sheath thickness, 3) number of intracellular neurofilaments and microtubules, and 4) axonal packing density (Alexander et al., 2007 and Beaulieu, 2002). To understand the neurobiological components contributing to the change in motor outcome observed there is a need to adopt neuroimaging techniques that can quantify these structural features.

Myelin formation has been identified as a specific target for therapeutic intervention following stroke, as recovery of axonal fibres is not complete without adequate myelination (Mifsud et al., 2014). Oligodendrocytes are responsible for initiating a cascade of events that result in the formation of myelin. Acute cerebral ischemia, such as that caused by a stroke, causes a rapid breakdown of oligodendrocytes and demyelination (Tekkök and Goldberg, 2001), which greatly limits overall axonal integrity in the lesioned area (Saab and Nave, 2016). Although animal work has underlined the importance of active myelination on motor recovery after stroke (Chida et al., 2011 and McKenzie et al., 2014), it is unclear how these findings transfer to humans.

Until recently, technical limitations prevented the imaging of myelin in vivo. Myelin water fraction (MWF) can be derived in humans non-invasively in vivo from multi-component T2-relaxation imaging (Alonso-Ortiz et al., 2014 and Prasloski et al., 2012b). Formalin-fixed human brains yield T2 distributions similar to those found in vivo, and histopathological studies show strong correlations between MWF and staining for myelin (Laule et al., 2004 and Moore et al., 2000). With the development of non-invasive imaging techniques, myelin can be quantified in the human brain (Prasloski et al., 2012b), both cross-sectionally and longitudinally (Lakhani et al., 2016) Work form the Human Connectome Project and others have identified that the primary motor and sensory regions are among the most densely myelinated and most easily delineated in the human brain, allowing for more reliable automatic identification and parcellation of myelinated regions (Glasser et al., 2016, Glasser and Van Essen, 2011 and Nieuwenhuys and Broere, 2016). In addition, myelination of corticospinal projections from these regions may vary based on the length of the tract and the size the axon. As such, quantification of corticospinal tract (CST) myelin using in vivo neuroimaging has not been validated to date (Glasser and Van Essen, 2011). Previous work from our group did not reveal a relationship between ipsi- and contralesional CST MWF, measured from the posterior limb of the internal capsule, and motor function or impairment (Borich et al., 2013). In order to limit variability arising from CST tract heterogeneity between individuals with stroke, the current study focused on the most well defined, myelinated regions of interest, located in precentral and postcentral areas.

Recent work has demonstrated that oligodendrocyte precursor cell proliferation and myelin structure are associated with motor learning in rodent models (Gibson et al., 2014 and Xiao et al., 2016). In particular, this work emphasized the possibility that functional motor activity may influence myelination of redundant neural pathways and improve conduction velocity via more efficient neural synchrony (Fields, 2015). The current study will extend previous lines of inquiry by exploring the relationship between real-world activity in the upper-extremity to myelination in humans. The ability to use the stroke-affected upper-limb in ‘everyday tasks’ is cited as a primary goal for individuals living with stroke (Barker and Brauer, 2005 and Barker et al., 2007). Monitoring upper-extremity usage after stroke using accelerometers is a low-cost, non-invasive way to measure functional activity and to quantify overall real-world activity (Hayward et al., 2015). Use of the stroke affected upper-limb correlates with long-term motor impairment as greater activity generally results in reduced impairment (Gebruers et al., 2014, Lang et al., 2007 and Shim et al., 2014). Identifying relationships between accelerometer based measures of activity and myelination will inform future investigations about the potential specificity of myelin as predictive biomarker for understanding what people can do, via measurement of impairment and function, versus what people actually do in the real-world.

Given the important relationships between white matter, activity and post-stroke impairment as well as the recent advances in imagining techniques, it is imperative to consider the contribution of myelination to post-stroke impairment, function and activity in humans. In order to identify potential differences in myelination based on the level of impairment after stroke, the current study identified ‘more impaired (M)’ and ‘less impaired (L)’ groups of participants. Therefore, the primary objective of the current investigation is to understand whether MWF in sensorimotor regions of interest is a biomarker of long term impairment, function or arm use in a population of individuals living with chronic stroke. Furthermore, we sought to identify if there were differences in MWF in sensorimotor regions of interest between individuals classified as ‘more impaired’ versus those who were ‘less impaired’.

Continue —> Hemispheric asymmetry in myelin after stroke is related to motor impairment and function

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[Abstract] A Meta-Analysis and Systematic Literature Review of Virtual Reality Rehabilitation Programs


  • Virtual reality rehabilitation (VRR) programs are growing in popularity
  • VRR programs are more effective than traditional rehabilitation programs
  • Excitement, physical fidelity, and cognitive fidelity may cause VRR program success
  • More research is needed to better understand VRR programs


A recent advancement in the study of physical rehabilitation is the application of virtual reality rehabilitation (VRR) programs, in which patients perform practice behaviors while interacting with the computer-simulation of an environment that imitates a physical presence in real or imagined worlds. Despite enthusiasm, much remains unknown about VRR programs. Particularly, two important research questions have been left unanswered: Are VRR programs effective? And, if so, why are VRR programs effective? A meta-analysis is performed in the current article to determine the efficacy of VRR programs, in general, as well as their ability to develop four specific rehabilitation outcomes: motor control, balance, gait, and strength. A systematic literature review is also performed to determine the mechanisms that may cause VRR program success or failure. The results demonstrate that VRR programs are more effective than traditional rehabilitation programs for physical outcome development. Further, three mechanisms have been proposed to cause these improved outcomes: excitement, physical fidelity, and cognitive fidelity; however, empirical research has yet to show that these mechanisms actually prompt better rehabilitation outcomes. The implications of these results and possible avenues for future research and practice are discussed.

Source: A Meta-Analysis and Systematic Literature Review of Virtual Reality Rehabilitation Programs

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