Posts Tagged motor impairment

[ARTICLE] Boosting robot-assisted rehabilitation of stroke hemiparesis by individualized selection of upper limb movements – a pilot study – Full Text



Intensive robot-assisted training of the upper limb after stroke can reduce motor impairment, even at the chronic stage. However, the effectiveness of practice for recovery depends on the selection of the practised movements. We hypothesized that rehabilitation can be optimized by selecting the movements to be practiced based on the trainee’s performance profile.


We present a novel principle (‘steepest gradients’) for performance-based selection of movements. The principle is based on mapping motor performance across a workspace and then selecting movements located at regions of the steepest transition between better and worse performance.

To assess the benefit of this principle we compared the effect of 15 sessions of robot-assisted reaching training on upper-limb motor impairment, between two groups of people who have moderate-to-severe chronic upper-limb hemiparesis due to stroke. The test group (N = 7) received steepest gradients-based training, iteratively selected according to the steepest gradients principle with weekly remapping, whereas the control group (N = 9) received a standard “centre-out” reaching training. Training intensity was identical.


Both groups showed improvement in Fugl-Meyer upper-extremity scores (the primary outcome measure). Moreover, the test group showed significantly greater improvement (twofold) compared to control. The score remained elevated, on average, for at least 4 weeks although the additional benefit of the steepest-gradients -based training diminished relative to control.


This study provides a proof of concept for the superior benefit of performance-based selection of practiced movements in reducing upper-limb motor impairment due to stroke. This added benefit was most evident in the short term, suggesting that performance-based steepest-gradients training may be effective in increasing the rate of initial phase of practice-based recovery; we discuss how long-term retention may also be improved.


Upper-limb (UL) motor impairment is a common outcome of stroke that can severely hamper basic daily living activities []. Training-based therapy can promote recovery with the outcome depending on the intensity and duration of the intervention []. Robot-assisted training allows intense practice without increasing the individual’s dependence on a therapist and can improve clinical scores of UL motor capacity []. However, the effects are usually small and provide limited improvement in motor function, especially in more severe hemiparesis []. Identifying training methods that can boost outcome is thus vital. Considering the extent of effort and sophistication invested in robot-assisted technology (e.g. []) perhaps it is time to focus on how to optimise its utility (in terms of training principles). Recent attempts have focussed on creating training scenarios which are more engaging or which simulate daily living activities. However, the evidence for the added benefit of this approach is mixed []. Another approach is to individualize the difficulty of the practised task (e.g. []). This is based on the idea that motor improvement depends on the ability to ‘make sense’ of information related to performance [], and postulates that matching the challenge (difficulty) level of the training task to the current ability of the trainee would optimise motor learning []. Individualizing task difficulty is commonly achieved by adjusting the parameters controlling task demands (e.g. movement speed or distance; or amount of assistance) across a pre-selected standard set of movements, to match the ability of the individual. Yet, so far there is little evidence for the added benefit of this approach for UL motor recovery. Hence, individually adjusting the task difficulty level might –by itself – not suffice for boosting UL rehabilitation outcome.

We hypothesised instead that appropriate selection of the practiced movements – in terms of the muscle coordination patterns – is a key for improving motor recovery. UL hemiparesis can affect various aspects of control. Thus, different motor impairments may benefit from different training movements. For example, training with movements involving mainly patterns of intact muscle coordination is unlikely to contribute much to improve other impaired movement patterns, regardless of the task difficulty level. Similarly, training that focuses only on movements that involve severely impaired muscle control may contribute little, even if the task can be performed by compensatory movements. Hence, to be optimally effective, individualized training may need to be expressed, not only by individually adjusting the level of difficulty of the task, but also in selecting tasks which are relevant for recovery. Little has been done to explore this possibility (for some attempts see []). Here we present a novel method for performance-based selection of the set of movement tasks for robot-assisted training. The method depends on the availability of a motor performance “map” that profiles performance across a workspace. Movements are selected within intermediate levels of performance, based on the variation of performance across the map. Specifically, we predicted that optimal reduction of UL hemiparesis would be achieved by training with movements located at points on the map of steep transition (steep gradient) from high to low performance (Fig. 1), thus promoting the cascade of generalisation of motor improvement. Improved performance of movements at these steep gradient locations on the performance map would steer improvement in neighbouring, but more impaired regions, and encourage recovery. Here, we present evidence supporting this hypothesis.

Fig. 1Illustrative sketch of the principle of selection of trained movements, based on the steepest gradients in a hypothetical motor performance profile (e.g. reaching aiming; vertical axis) measured across some particular task parameter (e.g. movement direction; horizontal axis); for simplicity, we show here a single dimension. The selected movements (grey horizontal bars) correspond to the regions with the steepest performance gradients, indicated by dashed ellipses. This movement selection principle can be applied where movement tasks can be defined by one or more continuous parameters, i.e. in a 1D, 2D, or higher dimensional map as long as the derivative of performance can be calculated. In this study we applied this principle on two measures of reaching performance (ability to move and ability to aim) each measured across two dimensions of the task (target location and movement direction)


Continue —> Boosting robot-assisted rehabilitation of stroke hemiparesis by individualized selection of upper limb movements – a pilot study | SpringerLink

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[WEB SITE] Recovering From A Left Side Stroke – Saebo

Have you ever heard someone describe themselves as “right-brained” or “left-brained”? This concept is based on the brain having two hemispheres that perform different, specialized functions. Creative types have a dominant right brain, while analytical people favor the left. It is necessary to understand the functions of both hemispheres when assessing consequences of neurological damage. This knowledge helps anticipate problems that might occur and customize strategies for recovery.

Although the brain is divided into two hemispheres, they work in tandem to absorb information and process details. For instance, visual signals are sent to the right hemisphere first, but the left hemisphere uses context from past experiences to comprehend what the right brain has “seen”.

A left-brain stroke comes with a particular set of symptoms, changes, and challenges. An understanding of these consequences may ease the frustrations of stroke survivors and their families during the recovery process.

What Does The Left Brain Control?

The left hemisphere is responsible for controlling the logic of information processing. Common functions of the left hemisphere include the following:

  • Language
  • Critical thinking and analysis
  • Judgment and reasoning
  • Decision-making
  • Mathematics and sequencing

In a way, the left hemisphere processes information in words and numbers, as opposed to images, as the right hemisphere does. This lends to the common belief that “right-brain thinkers” are often more “creative” types, while left-brain thinkers are more analytical and mathematical.

Possible Effects Of A Left-Brain Stroke

Motor Impairment

On a physical level, the left hemisphere controls the right side of the body, and vice versa. Most physical impairments and paralysis after a stroke stem from issues in the brain, not in the impaired limb itself. Right-sided limbs are likely to suffer complications after a left-brain stroke, possibly resulting in hemiplegia—the paralysis of one side of the body.

Those recovering from a stroke may experience paralysis in certain limbs, and/or less severe symptoms including motor function impairment, muscle weakness, and spasticity. A combination of impairments can make daily life more challenging, both physically and psychologically.


Since the left hemisphere bears most of the responsibility for receiving and deciphering language, a left-brain stroke can often impair both speech production and the interpretation of word-based information. These impairments are collectively known as aphasia, and the consequences for everyday life depend on the type of aphasia experienced.

There are two main types of aphasia:

  1. Receptive aphasia complicates the brain’s reception and interpretation of words from speech or text. An individual with receptive aphasia may experience a range of confusion, from missing a word here or there to needing things repeated several times before they are comprehended. Left-brain stroke survivors may respond best to simpler words and direct, one-on-one conversations. Excessive distractions or multiple people speaking at once may inhibit comprehension. A survivor may find it easier to read short sentences, while complicated sentences and large paragraphs may cause frustration.
  2. Expressive aphasia complicates the spoken or written expression of thoughts. The exact manifestation will vary from person to person. At times, an individual with expressive aphasia may leave words out of long sentences, use words they don’t intend to say, or even use incomprehensible sounds instead of words. Changes in pace and inflection are also indicative of this.

The effects of aphasia become particularly complex when stroke survivors try to express their needs, especially during the initial stages of recovery. Someone may intend to ask for water but end up asking for something else entirely because they cannot find the right words. Depending on where the neurological damage occurred, stroke survivors may experience a combination of both types of aphasia.

Intellectual Impairment

Since analytical tasks default to the left hemisphere, a left-brain stroke may impair the management of common household and daily activities. Paying bills, handling money, or taking care of other analytical tasks may become more difficult. The stroke survivor may become dependent on family or a caretaker to complete important organizational tasks.

Behavioral Changes

It is common for those with left-brain injuries to process information more slowly and therefore move with more caution. Rushing may cause confusion or even injury. The inability to move quickly may lead to frustration and even periods of anger or depression.

Visual Impairment

Vision issues are particularly common in the right eye after a left-brain stroke. Potential problems include drooping of the eyelid and impaired blood flow to the retina. The stroke survivor may experience hemianopia, or blindness in half of the visual field.

Agnosia—the inability to recognize and name items—may also occur for the same reasons that produce aphasia. An injury to brain regions that manage naming and recognition may prevent the survivor from identifying common items, adding a sensation of foreignness and confusion to daily life.

Recovering From A Left-Brain Stroke

Though changes after a left-brain stroke are often abrupt and severe, the brain has an incredible ability to adjust and even reconnect neurological pathways. This ability is called neuroplasticity and occurs before you’re even born. Throughout childhood and adulthood, new pathways form as new information is absorbed by the brain. After an injury, the brain’s neuroplasticity can be sparked to form new neurons and connections through the repetition of targeted rehabilitation exercises. It is only through this constant repetition that the brain rewires and brings to life the lost connections.

Exercises that focus on the right side of the body and reinforce analytical reasoning are the most effective methods to support the regrowth of neurological pathways in the left hemisphere. After all, the body and mind are forever learning. This is true even if portions of the brain are no longer fully functional. Neural functions can adjust and change, for the better, through the support of ongoing rehabilitation.

All content provided on this blog is for informational purposes only and is not intended to be a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition. If you think you may have a medical emergency, call your doctor or 911 immediately. Reliance on any information provided by the Saebo website is solely at your own risk.

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[Abstract] Motor Impairment–Related Alterations in Biceps and Triceps Brachii Fascicle Lengths in Chronic Hemiparetic Stroke

Poststroke deficits in upper extremity function occur during activities of daily living due to motor impairments of the paretic arm, including weakness and abnormal synergies, both of which result in altered use of the paretic arm. Over time, chronic disuse and a resultant flexed elbow posture may result in secondary changes in the musculoskeletal system that may limit use of the arm and impact functional mobility. This study utilized extended field-of-view ultrasound to measure fascicle lengths of the biceps (long head) and triceps (distal portion of the lateral head) brachii in order to investigate secondary alterations in muscles of the paretic elbow. Data were collected from both arms in 11 individuals with chronic hemiparetic stroke, with moderate to severe impairment as classified by the Fugl-Meyer assessment score. Across all participants, significantly shorter fascicles were observed in both biceps and triceps brachii (P < .0005) in the paretic limb under passive conditions. The shortening in paretic fascicle length relative to the nonparetic arm measured under passive conditions remained observable during active muscle contraction for the biceps but not for the triceps brachii. Finally, average fascicle length differences between arms were significantly correlated to impairment level, with more severely impaired participants showing greater shortening of paretic biceps fascicle length relative to changes seen in the triceps across all elbow positions (r = −0.82, P = .002). Characterization of this secondary adaptation is necessary to facilitate development of interventions designed to reduce or prevent the shortening from occurring in the acute stages of recovery poststroke.


via Motor Impairment–Related Alterations in Biceps and Triceps Brachii Fascicle Lengths in Chronic Hemiparetic Stroke – Christa M. Nelson, Wendy M. Murray, Julius P. A. Dewald, 2018

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[ARTICLE] Technological Approaches for Neurorehabilitation: From Robotic Devices to Brain Stimulation and Beyond – Full Text

Neurological diseases causing motor/cognitive impairments are among the most common causes of adult-onset disability. More than one billion of people are affected worldwide, and this number is expected to increase in upcoming years, because of the rapidly aging population. The frequent lack of complete recovery makes it desirable to develop novel neurorehabilitative treatments, suited to the patients, and better targeting the specific disability. To date, rehabilitation therapy can be aided by the technological support of robotic-based therapy, non-invasive brain stimulation, and neural interfaces. In this perspective, we will review the above methods by referring to the most recent advances in each field. Then, we propose and discuss current and future approaches based on the combination of the above. As pointed out in the recent literature, by combining traditional rehabilitation techniques with neuromodulation, biofeedback recordings and/or novel robotic and wearable assistive devices, several studies have proven it is possible to sensibly improve the amount of recovery with respect to traditional treatments. We will then discuss the possible applied research directions to maximize the outcome of a neurorehabilitation therapy, which should include the personalization of the therapy based on patient and clinician needs and preferences.


According to the World Health Organization (WHO), neurological disorders and injuries account for the 6.3% of the global burden of disease (GBD) (12). With more than 6% of DALY (disability-adjusted life years) in the world, neurological disorders represent one of the most widespread clinical condition. Among neurological disorders, more than half of the burden in DALYs is constituted by cerebral-vascular disease (55%), such as stroke. Stroke, together with spinal cord injury (SCI), accounts for 52% of the adult-onset disability and, over a billion people (i.e., about a 15% of the population worldwide) suffer from some form of disability (3). These numbers are likely to increase in the coming years due to the aging of the population (4), since disorders affecting people aged 60 years and older contribute to 23% of the total GBD (5).

Standard physical rehabilitation favors the functional recovery after stroke, as compared to no treatment (6). However, the functional recovery is not always satisfactory as only 20% of patients fully resume their social life and job activities (7). Hence, the need of more effective and patient-tailored rehabilitative approaches to maximize the functional outcome of neurological injuries as well as patients’ quality of life (8). Modern technological methodologies represent one of the most recent advances in neurorehabilitation, and an increasing body of evidence supports their role in the recovery from brain and/or medullary insults. This manuscript provides a perspective on how technologies and methodologies could be combined in order to maximize the outcome of neurorehabilitation.

Current Systems and Therapeutic Approaches for Neurorehabilitation

The great progress made in interdisciplinary fields, such as neural engineering (910), has allowed to investigate many neural mechanisms, by detecting and processing the neural signals at high spatio-temporal resolution, and by interfacing the nervous system with external devices, thus restoring neurological functions lost due to disease/injury. The progress continues in parallel to technological advancements. The last two decades there has seen a large proliferation of technological approaches for human rehabilitation, such as robots, wearable systems, brain stimulation, and virtual environments. In the next sections, we will focus on: robotic therapy, non-invasive brain stimulation (NIBS), and neural interfaces.

Robotic Devices

Robots for neurorehabilitation are designed to support the administration of physical exercises to the upper or lower extremities, with the purpose of promoting neuro-motor recovery. This technology has a relatively long history, dating back to the early 1990s (11). Robot devices for rehabilitation differ widely in terms of mechanical design, number of degrees of freedom, and control architectures. As regards the mechanical design, robots may have either a single point of interaction (i.e., end effector) with the user body (endpoint robots or manipulanda) or multiple points of interaction (exoskeletons and wearable robots) (12).

Endpoint robots for the upper extremity, include Inmotion2 (IMT, USA) (13), KINARM End-Point (BKIN, Canada), and Braccio di Ferro (14) (Figure 1A1, left). Only some of these devices have been tested in randomized clinical trials (15), confirming an improvement of upper limb motor function after stroke (16). However, convincing evidence in favor of significant changes in activities of daily living (ADL) indicators is lacking (17), possibly because performance in ADL is highly affected by hand functionality. A good example of lower limb endpoint robot is represented by gait trainer GT1 (Reha-Stim, Germany). Its efficacy was tested by Picelli et al. (18), who demonstrated an improvement in multiple clinical measures in subjects with Parkinson’s disease following robotic-assisted rehabilitation when compared to physical rehabilitation alone (18). Endpoint robots are also available for postural rehabilitation. For instance, Hunova (Movendo Technology, Italy, launched in 2017) is equipped with a seat and a platform that induce multidirectional movements to improve postural stability (Figure 1A1, right).


Figure 1. Neurorehabilitation therapies. (A1) Endpoint robots: on the left the “Braccio di Ferro” manipulandum, on the right the postural robot Hunova. Braccio di ferro (14) is a planar manipulandum with 2-DOF, developed at the University of Genoa (Italy). It is equipped with direct-drive brushless motors and is specially designed to minimize endpoint inertia. It uses the H3DAPI programming environment, which allows to share exercise protocol with other devices. Written informed consent was obtained from the subject depicted in the panel. Movendo Technology’s Hunova is a robotic device that permits full-body rehabilitation. It has two 2-DOF actuated and sensorized platforms located under the seat and on the floor level that allow it to rehabilitate several body districts, including lower limb (thanks to the floor-level platform), the core, and the back, using the platform located underneath the seat. Different patient categories (orthopedic, neurological, and geriatric) can be treated, and interact with the machine through a GUI based on serious games. (A2) Wearable device: the recent exoskeleton Twin. Twin is a fully modular device developed at IIT and co-funded by INAIL (the Italian National Institute for Insurance against Accidents at Work). The device can be easily assembled/disassembled by the patient/therapist. It provides total assistance to patients in the 5–95th percentile range with a weight up to 110 kg. Its modularity is implemented by eight quick release connectors, each located at both mechanical ends of each motor, that allow mechanical and electrical connection with the rest of the structure. It can implement three different walking patterns that can be fully customized according to the patient’s needs viaa GUI on mobile device, thus enabling personalization of the therapy. Steps can be triggered via an IMU-based machine state controller. (B1) Repetitive transcranial magnetic stimulation (rTMS) representation. rTMS refers to the application of magnetic pulses in a repetitive mode. Conventional rTMS applied at low frequency (0.2–1 Hz) results in plastic inhibition of cortical excitability, whereas when it is applied at high frequency (≥5Hz), it leads to excitation (19). rTMS can also be applied in a “patterned mode.” Theta burst stimulation involves applying bursts of high frequency magnetic stimulation (three pulses at 50 Hz) repeated at intervals of 200 ms (20). Intermittent TBS increases cortical excitability for a period of 20–30 min, whereas continuous TBS leads to a suppression of cortical activity for approximately the same amount of time (20). (B2) Transcranial current stimulation (tCS) representation. tCS uses ultra-low intensity current, to manipulate the membrane potential of neurons and modulate spontaneous firing rates, but is insufficient on its own to discharge resting neurons or axons (21). tCS is an umbrella term for a number of brain modulating paradigms, such as transcranial direct current stimulation (22), transcranial alternating current stimulation (23), and transcranial random noise stimulation (24). (C) A typical BCI system. Five stages are represented: brain-signal acquisition, preprocessing, feature extraction/selection, classification, and application interface. In the first stage, brain-signal acquisition, suitable signals are acquired using an appropriate modality. Since the acquired signals are normally weak and contain noise (physiological and instrumental) and artifacts, preprocessing is needed, which is the second stage. In the third stage, some useful data or so-called “features” are extracted. These features, in the fourth stage, are classified using a suitable classifier. Finally, in the fifth stage, the classified signals are transmitted to a computer or other external devices for generating the desired control commands to the devices. In neurofeedback applications, the application interface is a real-time display of brain activity, which enables self-regulation of brain functions (25).

Continue —> Frontiers | Technological Approaches for Neurorehabilitation: From Robotic Devices to Brain Stimulation and Beyond | Neurology

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[ARTICLE] Using Corticomuscular Coherence to Reflect Function Recovery of Paretic Upper Limb after Stroke: A Case Study – Full Text

Purpose: Motor deficits after stroke are supposed to arise from the reduced neural drive from the brain to muscles. This study aimed to demonstrate the feasibility of reflecting the motor function improvement after stroke with the measurement of corticomuscular coherence (CMC) in an individual subject.

Method: A stroke patient was recruited to participate in an experiment before and after the function recovery of his paretic upper limb, respectively. An elbow flexion task with a constant muscle contraction level was involved in the experiment. Electromyography and electroencephalography signals were recorded simultaneously to estimate the CMC. The non-parameter statistical analysis was used to test the significance of CMC differences between the first and second times of experiments.

Result: The strongest corticomuscular coupling emerged at the motor cortex contralateral to the contracting muscles for both the affected and unaffected limbs. The strength of the corticomuscular coupling between activities from the paretic limb muscles and the contralateral motor cortex for the second time of experiment increased significantly compared with that for the first time. However, the CMC of the unaffected limb had no significant changes between two times of experiments.

Conclusion: The results demonstrated that the increased corticomuscular coupling strength resulted from the motor function restoration of the paretic limb. The measure of CMC can reflect the recovery of motor function after stroke by quantifying interactions between activities from the motor cortex and controlled muscles.


Stroke is one of the major diseases that cause long-term motor deficits of adults (1). However, our poor understanding of the mechanisms underlying motor impairments after stroke limits greatly the development of effective intervention and evaluation methods. In general, motor impairments after stroke are deemed to arise from changes in both neural and muscle properties. Poststroke changes in the neural system have been studied from different points of view such as the decreased excitability of the affected cortex (23) and the increased inhibitory effect from the unaffected hemisphere on the affected hemisphere (4). Spasm and flaccid paresis of muscles are believed to result from the loss of control input from the brain at different phases after stroke. Even though stroke survivors have been demonstrated to have significant descending information flow in the affected side during the chronic period (5), there is evidence that poststroke impairments reflect the reduced central neural drive to muscles. Mima et al. and Fang et al. found that the functional coupling between cortical commands and consequent muscle activities of stroke subjects were weaker than that of healthy controls (67). The conduction time from the central cortical rhythm to peripheral oscillations in the affected side was significantly prolonged compared with that of the unaffected side after stroke (8).

It is believed that stroke interrupts the motor-related neural network and then reduces the neural drive to the muscles. The coherent activities between the motor cortex and the muscles are believed to reflect the synchronized discharge of corticospinal cells (9). It can be estimated by analyzing the frequency domain coherence (10) between electromyography (EMG) and electroencephalography (EEG) signals termed as corticomuscular coherence (CMC). Although previous studies have demonstrated that the CMC strength of poststroke subjects was weaker than that of healthy controls, it is still not clear whether the corticomuscular coupling will enhance along with the motor function recovery to directly reflect the motor function state of paretic limbs after stroke. In the current study, a poststroke patient was recruited to participate in two times of experiments involving an elbow flexion task. The time interval between two times of experiments was determined to guarantee that the patient had obtained an obvious motor function recovery of the affected upper extremity. CMC from two times of experiments was estimated and compared to verify whether motor function recovery can be reflected by the change of corticomuscular coupling strength.


Experiment and Subject

An elbow flexion task was designed for the stroke patient because only poor rehabilitation outcomes can be generally obtained for hand. The force applied by the elbow flexion was monitored by a strain gage and fed back to the patient visually to help him finish the task with moderate and constant muscle contractions (11), because coherence analyses (1213) have demonstrated that the coupling is most pronounced in the beta-band range during steady muscle contractions and the beta-band CMC is assumed to be associated with strategies for controlling submaximal muscle forces (121415). The designed motion task and the visual feedback information on screen are illustrated in Figures 1A,B, respectively. A trial was initiated when a circle and a target ring showed on screen and was over when they disappeared. Each trial lasted 11 s and there was a 2-s long interval between adjacent trials. Each run contained 20 trials and each side of upper limbs performed two runs, respectively. The subject practiced before data recording until the target force could be reached within the first 2 s of each trial.

Figure 1. The motion task of elbow flexion (A) and the visual feedback information on screen (B). When the biceps brachii contracts, the wrist will press the strain gage and the force level can be detected. The circle can be shifted vertically by applying force to the strain gage and the position of the ring is fixed. The subject was requested to move the circle into the ring as soon as possible when a trial started and maintain the force until the end of a trial when the circle and the ring both disappeared. The force needed to shift the circle into the ring was 3 N.

Continue —> Frontiers | Using Corticomuscular Coherence to Reflect Function Recovery of Paretic Upper Limb after Stroke: A Case Study | Neurology

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[ARTICLE] The spasticity in the motor and functional disability in adults with post-stroke hemiparetic


Introduction: Spasticity acts as a limiting factor in motor and functional recovery after Stroke, impairing the performance of daily living activities.

Objective: To analyze the influence of spasticity on main muscle groups and to associate it with motor impairment and functional level of chronic hemiparetic patients after stroke.

Methods: Twenty-seven chronic hemiparetic patients of both sexes were selected at the Physical Therapy and Occupational Therapy Service of the Unicamp Clinics Hospital. Assessments were carried out in two sessions, in the first one the motor impairment (Fugl-Meyer Assessment – FM) and functional impairment (Barthel Index – BI) were evaluated, and in the second, the degree of spasticity of the main muscle groups (Modified Ashworth Scale – MAS).

Results: A negative correlation was detected between upper limb spasticity and motor and functional impairment. No muscle group evaluated in the lower limbs showed correlation between muscle tone and the level of impairment of the lower extremity on FM and the functional level measured by BI.

Conclusion: Spasticity has been shown to be a negative influence factor in the level of motor and functional impairment of the upper limbs of chronic hemiparetic patients after stroke.

Full Text PDF

via The spasticity in the motor and functional disability in adults with post-stroke hemiparetic | de Oliveira Cacho | Fisioterapia em Movimento

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[Abstract] An adaptive self-organizing fuzzy logic controller in a serious game for motor impairment rehabilitation


Rehabiliation robotics combined with video game technology provides a means of assisting in the rehabilitation of patients with neuromuscular disorders by performing various facilitation movements. The current work presents ReHabGame, a serious game using a fusion of implemented technologies that can be easily used by patients and therapists to assess and enhance sensorimotor performance and also increase the activities in the daily lives of patients. The game allows a player to control avatar movements through a Kinect Xbox, Myo armband and rudder foot pedal, and involves a series of reach-grasp-collect tasks whose difficulty levels are learnt by a fuzzy interface. The orientation, angular velocity, head and spine tilts and other data generated by the player are monitored and saved, whilst the task completion is calculated by solving an inverse kinematics algorithm which orientates the upper limb joints of the avatar. The different values in upper body quantities of movement provide fuzzy input from which crisp output is determined and used to generate an appropriate subsequent rehabilitation game level. The system can thus provide personalised, autonomously-learnt rehabilitation programmes for patients with neuromuscular disorders with superior predictions to guide the development of improved clinical protocols compared to traditional theraputic activities.

Source: An adaptive self-organizing fuzzy logic controller in a serious game for motor impairment rehabilitation – IEEE Xplore Document

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[ARTICLE] Upper Extremity Motor Impairments and Microstructural Changes in Bulbospinal Pathways in Chronic Hemiparetic Stroke – Full Text

Following hemiparetic stroke, precise, individuated control of single joints is often replaced by highly stereotyped patterns of multi-joint movement, or abnormal limb synergies, which can negatively impact functional use of the paretic arm. One hypothesis for the expression of these synergies is an increased dependence on bulbospinal pathways such as the rubrospinal (RubST) tract and especially the reticulospinal (RetST) tracts, which co-activate multiple muscles of the shoulder, elbow, wrist, and fingers. Despite indirect evidence supporting this hypothesis in humans poststroke, it still remains unclear whether it is correct. Therefore, we used high-resolution diffusion tensor imaging (DTI) to quantify white matter microstructure in relation to severity of arm synergy and hand-related motor impairments. DTI was performed on 19 moderately to severely impaired chronic stroke individuals and 15 healthy, age-matched controls. In stroke individuals, compared to controls, there was significantly decreased fractional anisotropy (FA) and significantly increased axial and radial diffusivity in bilateral corona radiata and body of the corpus callosum. Furthermore, poststroke, the contralesional (CL) RetST FA correlated significantly with both upper extremity (UE) synergy severity (r = −0.606, p = 0.003) and hand impairment (r = −0.609, p = 0.003). FA in the ipsilesional RubST significantly correlated with hand impairment severity (r = −0.590, p = 0.004). For the first time, we separately evaluate RetST and RubST microstructure in chronic stroke individuals with UE motor impairment. We demonstrate that individuals with the greatest UE synergy severity and hand impairments poststroke have the highest FA in the CL RetST a pattern consistent with increased myelination and suggestive of neuroplastic reorganization. Since the RetST pathway microstructure, in particular, is sensitive to abnormal joint coupling and hand-related motor impairment in chronic stroke, it could help test the effects of specific, and novel, anti-synergy neurorehabilitation interventions for recovery from hemiparesis.


Approximately 85% of stroke survivors experience significant motor impairment in the contralesional (CL) arm (1), which can include a loss of independent joint control (2, 3), weakness (4), and spasticity (5). After stroke, precise, individuated control of single joints is often replaced by highly stereotyped patterns of multi-joint movement caused by abnormal muscle co-activation patterns (6). The most prevalent of these patterns is the flexion synergy, which is characterized by an abnormal coupling of shoulder abduction and elbow, wrist, and finger flexion (7, 8). This impairment has a negative impact on reaching ability (9) and hand function (3, 10), both critical components of functional use of the arm during activities of daily living. Despite the debilitating nature of this motor impairment, the underlying neuropathophysiology is not fully understood.

One hypothesis for why the flexion synergy emerges is that following a reduction of corticofugal input from the lesioned hemisphere, there is an increased dependence on CL motor cortex and bulbospinal pathways, such as reticulospinal (RetST) and rubrospinal (RubST) tracts. Therefore, in the present study, we quantify microstructural properties in white matter of both the brain and the brainstem, focusing primarily on corticoreticulospinal and corticorubrospinal systems. We evaluate whether these microstructural properties increase in integrity in relation to arm synergy and hand impairment severity, which could be indicative of increased use.

Although the RetST was previously believed to be predominantly involved in gross movements, such as locomotion (11, 12) and posture (13, 14), recent work in primates suggests the RetST also influences the motor neurons that control forearm and intrinsic hand muscles (15). In the non-human primate, stimulation of the RetST produces ipsilateral wrist flexor, elbow flexor, and shoulder abductor activation (16), mirroring the flexion synergy pattern observed in humans poststroke. Furthermore, stimulating the RetST after a corticospinal tract (CST) lesion elicits increased excitatory post-synaptic potentials in motoneurons innervating the forearm flexor and intrinsic hand muscles (17). This evidence makes the contralesional corticoreticulospinal system a compelling candidate for underlying abnormal joint coupling in humans with hemiparetic stroke.

In the non-human primate, the RubST also contributes to reaching and grasping movements (18) and has been shown to be important in recovery of hand function after CST damage (19, 20). One study showed that increased white matter integrity in bilateral red nucleus (RN) correlated with worse clinical outcomes in humans with chronic stroke (21); however, the RubST has been reported as relatively insignificant in humans (22, 23). The evidence for whether the RetST and the RubST contribute to abnormal joint coupling and hand impairment in humans poststroke still remains indirect and inconclusive.

We used high-resolution diffusion tensor imaging (DTI) (24) tract-based spatial statistics (TBSS) (25) to perform a voxel-wise comparison of white matter microstructure between stroke and control individuals. We analyzed fractional anisotropy (FA), a measurement typically associated with tract integrity, as well as axial diffusivity (AD) and radial diffusivity (RD), which represent diffusion parallel and perpendicular to the principle direction of diffusion, respectively. Because previous studies have reported altered diffusion properties in lesioned tissue (2628), we excluded potential lesion-compromised voxels from our TBSS analysis to assess changes in normal-appearing white matter. We used the TBSS-derived white matter skeleton to investigate whether microstructural tissue properties within specific regions of the brainstem (CST, RetST, RubST) and subcortical white matter within CL motor areas [primary motor area (M1), premotor area (PM), supplementary motor area (SMA), body of the corpus callosum] are sensitive to upper extremity (UE) motor impairment in chronic stroke individuals.

We evaluated UE motor impairment using the Fugl-Meyer Assessment (FMA), a stroke-specific, performance-based motor impairment index, which measures impairments, such as loss of independent joint function, stretch reflex hyper-excitability, and altered sensation (29). It is one of the most widely used clinical scales of motor impairment poststroke (30). While previous studies have looked at diffusion MRI metrics in relation to the entire FMA score (31, 32), we used only the UE measurements of arm synergies and hand function to determine whether microstructural properties in specific white matter regions of interest (ROIs) were correlated.

In the present study, we hypothesized that microstructural integrity in specific regions of the extrapyramidal brainstem would be increased in chronic stroke in a manner sensitive to synergy and hand-related impairment severity. We demonstrate a significant decrease in FA in bilateral corona radiata and body of the corpus callosum in chronic stroke when compared to controls; however, within stroke subjects, specific brainstem regions show the highest FA in individuals with the most synergy-driven arm and hand impairment. More precisely, we describe the relation between CL RetST integrity and both expression of synergy and hand impairment and between ipsilesional (IL) RubST integrity and hand impairment in chronic hemiparetic stroke individuals.[…]

Continue —> Frontiers | Upper Extremity Motor Impairments and Microstructural Changes in Bulbospinal Pathways in Chronic Hemiparetic Stroke | Neurology

Figure 1. Region of interest masks in Montreal Neurological Institute’s space. (A) Primary motor area (red), supplementary motor area (green), premotor area (blue), (B) body of the corpus callosum (light blue), (C) horizontal midbrain cross-section showing cerebral peduncle (CP) portion of the corticospinal tract (yellow) and red nucleus (RN) (red), (D) horizontal pontine cross-section showing reticular formation (RF) (green), and (E) sagittal brainstem showing RF including reticulospinal (green) and RN including rubrospinal tracts (red).

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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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[ARTICLE] Motor Learning in Stroke – Full Text

Background and Objective: Stroke rehabilitation assumes motor learning contributes to motor recovery, yet motor learning in stroke has received little systematic investigation. Here we aimed to illustrate that despite matching levels of performance on a task, a trained patient should not be considered equal to an untrained patient with less impairment. Methods: We examined motor learning in healthy control participants and groups of stroke survivors with mild-to-moderate or moderate-to-severe motor impairment. Participants performed a series of isometric contractions of the elbow flexors to navigate an on-screen cursor to different targets, and trained to perform this task over a 4-day period. The speed-accuracy trade-off function (SAF) was assessed for each group, controlling for differences in self-selected movement speeds between individuals. Results: The initial SAF for each group was proportional to their impairment. All groups were able to improve their performance through skill acquisition. Interestingly, training led the moderate-to-severe group to match the untrained (baseline) performance of the mild-to-moderate group, while the trained mild-to-moderate group matched the untrained (baseline) performance of the controls. Critically, this did not make the two groups equivalent; they differed in their capacity to improve beyond this matched performance level. Specifically, the trained groups had reached a plateau, while the untrained groups had not. Conclusions: Despite matching levels of performance on a task, a trained patient is not equal to an untrained patient with less impairment. This has important implications for decisions both on the focus of rehabilitation efforts for chronic stroke, as well as for returning to work and other activities.

Stroke is a leading cause of adult disability, leaving 30% to 66% of patients with lasting motor impairment.1,2 It has long been proposed that motor recovery following stroke is a form of relearning3,4 and that there is considerable overlap between the brain regions involved in both processes.57 However, while acquiring skill at a task may allow a patient to perform at the same level as an individual with lesser impairment, this does not necessarily make them equal. For example, well-recovered stroke patients can match the performance of healthy controls on a motor task, but differences exist in the neural networks that underlie performance for each group.8 Furthermore, matched performance does not necessarily imply that both groups have the same ability to continue improving given the opportunity for practice. These differences can complicate judgments regarding patients’ capacity to return to work and other activities,9 and which rehabilitation activities they should focus on. In this article, we propose that acquiring skill through motor training raises a similar issue—a patient who has trained on a task may “appear better,” masking categorical differences in his or her abilities. Consider two hypothetical patients—Patient A, who has mild motor impairment, and Patient B, who is more severely impaired. Patient A performs better in a movement task than Patient B. Patient B then trains at the task, reaching the same performance level as Patient A. If Patient B is now equal to Patient A, he or she should have a similar capacity for further improvement with training. If this is not the case (eg, if Patient B has reached a performance plateau beyond which further training has a limited effect), then a categorical difference remains between these patients despite their matching task performance.

In comparison to healthy individuals, stroke patients select slower voluntary movement speeds when performing movement tasks.10 As speed and accuracy are inherently linked,11 a confound arises when comparing the accuracy of movements performed at different speeds. This limitation makes it difficult to interpret previous results, such as cases where patients improve their accuracy yet decrease their speed.12 In such cases, it is impossible to determine whether a patient improved his or her ability to perform the task (through skill acquisition) or whether he or she simply changed the aspect of performance on which they focused (eg, sacrificed speed for accuracy while remaining at the same overall level of ability). The only way to disambiguate these alternatives is to first derive the speed-accuracy trade-off function (SAF13) for a given task; participants are required to complete the task in a fixed time, allowing accuracy to be measured without the confounding effects of differences in speed. Once derived, skill represents a shift in the SAF.1315

Here we introduce a serial voluntary isometric elbow force task, a modified version of the serial voluntary isometric pinch task (SVIPT). This task is based on an established laboratory-based model of motor learning in which participants learn to control a cursor by producing isometric forces.1319 In the task used in the present study, participants controlled a cursor by exerting forces with their elbow flexor muscles, allowing comparisons of performance across participants with greater ranges of impairment than would be possible with the standard (hand controlled) SVIPT paradigm. To control for differences in movement speeds across groups, performance was assessed by comparing the speed-accuracy trade-off pre and post training, using measures of task-level performance (ie, binary success/failure to complete all specified aspects of the task)1318 and trial-level measures of endpoint error and variability.20 We predicted that the severity of a participant’s motor impairment would limit his or her ability to perform the task and that training may allow him or her to achieve a similar level of performance as an individual with lesser impairment. However, we hypothesized that despite their matching performance, there would be a categorical difference between these individuals; the previously untrained participant with lesser impairment would be able to make large, rapid improvements through training, while the trained participant would not.

Figure 1. Experimental setup and procedure. (A) Participants sat with their (affected) arm supported by a robotic exoskeleton. A force transducer measured contractions of their elbow flexors. (B) On screen display. Contracting the elbow flexors moved the cursor (white circle) to the right, while relaxing moved the cursor to the home position (grey square). A “go” indicator (used in training trials) indicated to participants that they could begin a trial when ready (illustrated here as a green circle). Each trial involved navigating the cursor through the sequence Home-1–Home-2–Home-3–Home-4–Home-5. Target positions and sequence order remained fixed throughout the study. (C) Procedure. Participants first completed a pretraining skill assessment, performing the task at trial durations set by an auditory metronome (indicated by tempos presented in beats per minute—see main text for further detail). One “run” of the task involved completing 10 trials at each tempo in a pseudorandom order. This procedure was repeated to generate 2 runs of data (ie, a total of 20 trials for each tempo). Participants later trained to perform the task over consecutive days, aiming to complete the sequence as quickly and as accurately as possible. Finally, on a separate day, participants completed a posttraining skill assessment.

Continue —> Motor Learning in Stroke – Oct 27, 2016

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