Comparison of Robotics, FES, and Motor Learning Methods for Treatment of Persistent Upper Extremity Dysfunction after Stroke: a Randomized Controlled Trial

 Good luck getting access to this since it is for chronic and your insurance will have long ago stopped paying for therapy.

Comparison of Robotics, FES, and Motor Learning Methods for Treatment of Persistent Upper Extremity Dysfunction after Stroke: a Randomized Controlled Trial

2014, Archives of Physical Medicine and Rehabilitation

 Abstract

Objective:
 

To compare response to upper limb treatment using robotics (ROB) + motor
learning (ML) vs. functional electrical stimulation (FES) + ML vs. ML alone, according to a
measure of complex functional everyday tasks for chronic, severely impaired stroke survivors.
 

Design:
 

single-blind, randomized trial.

Setting:

 

 Clinical research lab, Medical Center.

Participants:
 

39 enrolled subjects, >1 year post single stroke (attrition rate=10%; 35 completed
the study). No adverse effects.

Interventions:
 

 All groups received treatment 5 days/week, 5hrs/day (60 sessions), with unique
treatment as follows: ML alone (n=11), 5hrs/day partial and whole task practice of complex
functional tasks; ROB+ML (n=12), 3.5hrs/day ML and 1.5hrs/day shoulder/elbow robotics;
FES+ML (n=12), 3.5hrs/day ML and 1.5hrs/day FES wrist/hand coordination training.
 

Main Outcome Measures:

 

 Primary measure: Arm Motor Ability Test (AMAT), 13 complex
functional tasks; secondary measure: upper limb Fugl-Meyer coordination (FM).

Results:
 

 No significant difference found in treatment response across groups (AMAT (p≥.584)
and FM (p≥.590)). All three treatment groups demonstrated clinically and statistically significant
improvement in response to treatment (AMAT and FM, p≤.009). A group treatment paradigm of
1:3 (therapist:patient) ratio proved feasible for provision of the intensive treatment.

Conclusions:
 

Severely impaired stroke survivors with persistent (>1yr) upper extremity
dysfunction can make clinically and statistically significant gains in coordination and functional
task performance, in response to ROB+ML, FES+ML, and ML alone, in an intensive and long-
duration intervention, and no group difference was found. Additional study is warranted to
determine the effectiveness of these methods in the clinical setting

Multiple processes independently predict motor learning

Do you really think your doctor has the ability to translate this from healthy adults to neurologically compromised adults?

Multiple processes independently predict motor learning

• Christopher M. Perry,
• Tarkeshwar Singh,
• Kayla G. Springer,
• Adam T. Harrison,
• Alexander C. McLain &
• Troy M. Herter 

Journal of NeuroEngineering and Rehabilitation volume 17, Article number: 151 (2020) Cite this article

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Abstract

Background

Our ability to acquire, refine and adapt skilled limb movements is a hallmark of human motor learning that allows us to successfully perform many daily activities. The capacity to acquire, refine and adapt other features of motor performance, such as visual search, eye-hand coordination and visuomotor decisions, may also contribute to motor learning. However, the extent to which refinements of multiple behavioral features and their underlying neural processes independently contribute to motor learning remains unknown. In the current study, we used an ethological approach to test the hypothesis that practice-related refinements of multiple behavioral features would be independently predictive of motor learning.

Methods

Eighteen healthy, young adults used an upper-limb robot with eye-tracking to practice six trials of a continuous, visuomotor task once a week for six consecutive weeks. Participants used virtual paddles to hit away 200 “Targets” and avoid hitting 100 “Distractors” that continuously moved towards them from the back of the workspace. Motor learning was inferred from trial-by-trial acquisition and week-by-week retention of improvements on two measures of task performance related to motor execution and motor inhibition. Adaptations involving underlying neural processes were inferred from trial-by-trial acquisition and week-by-week retention of refinements on measures of skilled limb movement, visual search, eye-hand coordination and visuomotor decisions. We tested our hypothesis by quantifying the extent to which refinements on measures of multiple behavioral features (predictors) were independently predictive of improvements on our two measures of task performance (outcomes) after removing all shared variance between predictors.

Results

We found that refinements on measures of skilled limb movement, visual search and eye-hand coordination were independently predictive of improvements on our measure of task performance related to motor execution. In contrast, only refinements of eye-hand coordination were independently predictive of improvements on our measure of task performance related to motor inhibition.

Conclusion

Our results provide indirect evidence that refinements involving multiple, neural processes may independently contribute to motor learning, and distinct neural processes may underlie improvements in task performance related to motor execution and motor inhibition. This also suggests that refinements involving multiple, neural processes may contribute to motor recovery after stroke, and rehabilitation interventions should be designed to produce refinements of all behavioral features that may contribute to motor recovery.

Introduction

Humans learn to perform a broad repertoire of motor tasks that often require diverse and adaptable limb movements (i.e., skilled limb movements) to interact with our outside world. Many motor tasks, such as cooking, walking and driving, also employ diverse and adaptable patterns of eye movements (i.e., visual search) to actively gather visual information for planning and execution of skilled limb movements. Information gathered by visual search is also used to decide what skilled limb movements should be performed to achieve task goals (i.e., visuomotor decisions). Conversely, patterns of visual search are influenced by the available repertoire of skilled limb movements that can be used to achieve task goals. These interactions between skilled limb movements and visual search lead to coordinated patterns of eye and limb movements (e.g., eye-hand coordination). Overall, skilled limb movements, visual search, eye-hand coordination and visuomotor decisions may all contribute to learning and performance of motor tasks. However, we do not know the extent to which these behavioral features and their underlying neural processes are independently refined to produce improvements in task performance.

Given that many concepts in motor learning have unclear or ambiguous definitions, we will define several concepts based on how they are used in this study. “Motor tasks” refer to all tasks that require skilled limb movements to achieve their task goal. Accordingly, most activities of daily living (e.g., cooking, walking, driving) are considered motor tasks even if they engage perceptual, cognitive and motor functions. “Neural processes” refer to brain networks that manipulate perceptual, cognitive and motor information to perform motor tasks. “Motor learning” refers to acquisition and retention of practice-related improvements in task performance, where “task performance” refers to outcomes that are specific to achieving task goals and “improvements” necessitate increased achievement of task goals. We assume that motor learning results from neural adaptations that produce refinements of behavioral features of motor tasks (e.g., skilled limb movements, visual search, eye-hand coordination, visuomotor decisions), where “refinements” are practice-related changes that do not occur in a particular direction.

Traditional studies of motor learning have examined how skilled limb movements are refined during practice of motor tasks [1,2,3]. Studies of movement dynamics have found that muscle activations, joint torques and endpoint forces exhibit trial-by-trial refinements of coordination and efficiency [4,5,6]. Similarly, studies of movement kinematics have observed trial-by-trial refinements of speed, accuracy, smoothness and variability of skilled limb movements [7,8,9], and these refinements exhibit good day-by-day retention [10,11,12,13]. However, these studies were not designed to investigate if refinements of other behavioral features, such as visual search, eye-hand coordination and visuomotor decisions, contribute to motor learning.

Research on eye movements indicates that refinements of visual search may contribute to motor learning [14, 15]. Observational studies have found that experts at different visuomotor skills have better control of eye movements than novices [16,17,18,19,20]. Experimental studies have also demonstrated that interventions designed to improve control of eye movements and attention lead to improvements in visuomotor performance [21,22,23,24,25]. While none of these studies examined trial-by-trial or week-by-week refinements of eye movements, there is ample evidence that visual search is refined during practice of perceptual tasks [26,27,28,29,30]. However, these studies did not examine any relationships between refinements of visual search and improvements in task performance, nor did they investigate refinements of other behavioral features. Thus, we do not know if refinements of visual search independently contribute to motor learning.

Studies of spatiotemporal coupling between eye and hand movements have provided evidence that refinements of eye-hand coordination may contribute to motor learning. Patterns of eye-hand coordination vary with task demands [31, 32] and are refined during motor learning in a task-dependent manner [33,34,35,36]. However, it remains unclear if refinements of eye-hand coordination independently contribute to improvements in task performance, or if they result from refinements of skilled limb movements and visual search but do not actually contribute to motor learning.

It is widely accepted that sensory processes contribute to planning and execution of skilled limb movements [37]. In addition, information from sensory feedback provides reinforcement that is known to play an important role in motor learning [2]. Recent studies have also found that motor learning can induce changes in visual processing that are associated with refinements of skilled limb movement [38, 39]. This suggests that adaptations of visual and visuomotor processing contribute to motor learning. However, these studies were not designed to investigate the extent to which refinements of other behavioral features, such as visual search, eye-hand coordination and visuomotor decisions, may independently contribute to motor learning.

Despite evidence that refinements of multiple features might underlie motor learning, we do not know the extent to which they independently contribute to motor learning. Traditional experiments cannot easily address this problem because they are designed to isolate individual processes. In contrast, ethological approaches that study real-time, natural behavior can overcome this limitation by leveraging individual patterns of variability exhibited by several behavioral features [40]. However, this approach requires carefully controlling for any covariation between different features. For example, two or more processes may be associated with motor learning, but their individual patterns of variability might exhibit substantial covariance. This shared variance can cause regression analyses to produce incorrect estimates of the contributions made by each process. Accurate estimates of the individual contributions can only be obtained from the independent variance that remains after removing all shared variance.

The objective of the current study was to investigate the extent to which multiple neural processes might independently contribute to motor learning. Healthy young adults used an upper-limb robot with eye tracking to complete six weeks of practice of a novel, visuomotor task designed to mimic the richness of real-world visuomotor tasks. Motor learning was inferred from trial-by-trial acquisition and week-by-week retention of improvements on measures of task performance. Adaptations of multiple neural processes were inferred from trial-by-trial acquisition and week-by-week retention of refinements on measures of skilled limb movement, visual search, eye-hand coordination and visuomotor decisions. Our first hypothesis was that practicing our novel, visuomotor task would elicit trial-by-trial acquisition and week-by-week retention of improvements in task performance that are mirrored by concurrent refinements of skilled limb movements, visual search, eye-hand coordination and visuomotor decisions. Our second hypothesis was that refinements related to multiple neural processes would be independently predictive of improvements in task performance.

 

To compare the effectiveness of constraint induced movement therapy versus motor relearning programme to improve motor function of hemiplegic upper extremity after stroke

Except they don't bother to tell you that CIMT  is only possible to be used if your penumbra is still viable.  In my case, because nothing was done to stop the neuronal cascade of death in the first week, everything in the penumbra died. I have seen nothing that brings back functions that were controlled by what is now dead brain. 5.5 billion dead neurons is hard to recover from. Try it some time. 

 To compare the effectiveness of constraint induced movement therapy versus motor relearning programme to improve motor function of hemiplegic upper extremity after stroke

ana Batool
1
, Nabila Soomro
2
, Fareeha Amjad
3
, Rabia Fauz
4

ABSTRACT

Objective: To compare the effectiveness of constraint induced movement therapy versus motor relearning programme to improve motor function of hemiplegic upper extremity after stroke.
 Method:

A sample of 42 patients was recruited from the Physiotherapy Department of IPM&R and Neurology OPD of Civil Hospital Karachi through non probability purposive sampling technique. Twenty one patients were placed to each experimental and control groups. Experimental group was treated with Constraint Induced Movement Therapy (CIMT) and control group was treated with motor relearning programme (MRP) for three consecutive weeks. Pre and post treatment measurements were determined by upper arm section of Motor Assessment Scale (MAS) and Self Care item of Functional Independence Measure (FIM) Scale.
Results:
 Intra group analysis showed statistically significant results (p-value<0.05) However, advanced hand activities item of MAS in MRP group showed insignicant result (p-value=0.059). Self-care items of FIM Scale also showed signicant result (p-value<0.05) except dressing upper body item (p-value<0.059) in CIMT group and grooming and dressing upper body
items (p-value=0.059 & 0.063) in MRP group showed insignificant p-values.
Conclusion:
CIMT group showed more significant improvement in motor function and self-care
performance of hemiplegic upper extremity as compared to MRP group in patients with sub-acute stroke assessed by the MAS and FIM scales. Thus CIMT is proved to be more statistically
 significant and clinically effective
intervention in comparison to motor relearning programme among the patients aged between 35-60 years. Further studies are needed to evaluate CIMT effects in acute and chronic post stroke population.
KEY WORDS:
 Constraint movement therapy, Motor learning programme, Physiotherapy, Rehabilitation, Stroke.
doi: http://dx.doi.org/10.12669/pjms.315.7910
How to cite this:
Batool S, Soomro N, Amjad F, Fauz R. To compare the effectiveness of constraint induced movement therapy versus motor relearning  programme to improve motor function of hemiplegic upper extremity after stroke. Pak J Med Sci 2015;31(5):1167-1171.

Transcranial Direct Current Stimulation Enhances Motor Skill Learning but Not Generalization in Chronic Stroke

I'm not sure what the weasel words used in this mean, so ask your doctor for clarification.
http://journals.sagepub.com/doi/abs/10.1177/1545968318769164

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Manuela Hamoudi¹

Manuela Hamoudi

1University Hospital Freiburg, Freiburg, Germany
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, Heidi M. Schambra, MD²³

Heidi M. Schambra

2New York University, NY, USA3National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
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, Brita Fritsch, MD¹

Brita Fritsch

1University Hospital Freiburg, Freiburg, Germany
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, , ,

Annika Schoechlin-Marx, MD¹

Annika Schoechlin-Marx

1University Hospital Freiburg, Freiburg, Germany
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, Cornelius Weiller, MD¹

Cornelius Weiller

1University Hospital Freiburg, Freiburg, Germany
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, Leonardo G. Cohen, MD³

Leonardo G. Cohen

3National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
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, Janine Reis, MD¹³

Janine Reis

1University Hospital Freiburg, Freiburg, Germany3National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
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, Manuela Hamoudi¹

Manuela Hamoudi

1University Hospital Freiburg, Freiburg, Germany
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, Heidi M. Schambra, MD²³

Heidi M. Schambra

2New York University, NY, USA3National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
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, Brita Fritsch, MD¹

Brita Fritsch

1University Hospital Freiburg, Freiburg, Germany
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, Annika Schoechlin-Marx, MD¹

Annika Schoechlin-Marx

1University Hospital Freiburg, Freiburg, Germany
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, Cornelius Weiller, MD¹

Cornelius Weiller

1University Hospital Freiburg, Freiburg, Germany
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, Leonardo G. Cohen, MD³

Leonardo G. Cohen

3National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
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, Janine Reis, MD¹³

Janine Reis

1University Hospital Freiburg, Freiburg, Germany3National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
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...

First Published April 22, 2018 Research Article

https://doi.org/10.1177/1545968318769164

Article information

Article Information

Volume: 32 issue: 4-5, page(s): 295-308

Article first published online: April 22, 2018; Issue published: April 1, 2018

https://doi.org/10.1177/1545968318769164

Manuela Hamoudi¹, Heidi M. Schambra, MD^{2, 3}, Brita Fritsch, MD¹, Annika Schoechlin-Marx, MD¹, Cornelius Weiller, MD¹, Leonardo G. Cohen, MD³, Janine Reis, MD^{1, 3}

¹University Hospital Freiburg, Freiburg, Germany

²New York University, NY, USA

³National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA

Corresponding Author: Janine Reis, MD, Department of Neurology, Albert-Ludwigs-University Freiburg, Breisacher Str 64, 79106 Freiburg, Germany. Email: janine.reis@uniklinik-freiburg.de

• Abstract

Abstract

Background. Motor training alone or combined with transcranial direct current stimulation (tDCS) positioned over the motor cortex (M1) improves motor function in chronic stroke. Currently, understanding of how tDCS influences the process of motor skill learning after stroke is lacking. Objective. To assess the effects of tDCS on the stages of motor skill learning and on generalization to untrained motor function.  
Methods. In this randomized, sham-controlled, blinded study of 56 mildly impaired chronic stroke patients, tDCS (anode over the ipsilesional M1 and cathode on the contralesional forehead) was applied during 5 days of training on an unfamiliar, challenging fine motor skill task (sequential visual isometric pinch force task). We assessed online and offline learning during the training period and retention over the following 4 months. We additionally assessed the generalization to untrained tasks. Results. With training alone (sham tDCS group), patients acquired a novel motor skill. This skill improved online, remained stable during the offline periods and was largely retained at follow-up. When tDCS was added to training (real tDCS group), motor skill significantly increased relative to sham, mostly in the online stage. Long-term retention was not affected by tDCS. Training effects generalized to untrained tasks, but those performance gains were not enhanced further by tDCS. Conclusions. Training of an unfamiliar skill task represents a strategy to improve fine motor function in chronic stroke. tDCS augments motor skill learning, but its additive effect is restricted to the trained skill.

Implementation of motor learning principles in physical therapy practice: Survey of physical therapists’ perceptions and reported implementation

These surveys should never be necessary, the therapists should just have to look for the protocols that have the best efficacy for the damage diagnosis. Without those steps: diagnosis, protocols, efficacy; your therapists are completely flying blind.
https://www.tandfonline.com/doi/abs/10.1080/09593985.2018.1456585

Michal Kafri PhD, PT

Received 20 May 2017, Accepted 27 Oct 2017, Published online: 28 Mar 2018

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• https://doi.org/10.1080/09593985.2018.1456585

 

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ABSTRACT

Introduction: The field of motor learning (ML) plays a pivotal role in physical therapy (PT), and its implementation has been shown to improve intervention outcomes. The objective of this study was to assess physical therapists’ ML-related self-efficacy, self-reported implementation, and environmental workplace factors. An additional aim was to report the psychometric properties of a questionnaire that was developed to assess the above-mentioned constructs.
Methods: An observational, cross-sectional survey was completed by 289 physical therapists (average age: 38.7 (9.7), with 11.3 (9.7) years of experience and 74% female). Construct validity, internal consistency, and test–retest reliability were tested. The main outcome measures were the scores of the three scales of the questionnaire, referring to self-efficacy in ML, implementation of ML principles, and workplace environment features.
Results: The questionnaire had sound psychometric qualities. Respondents perceived ML as an integral part of PT. ML-related self-efficacy and implementation of ML principles were moderate (2.95/5 (0.7) and 3.04/5(0.8), respectively). PT practice had a significant effect on ML-related self-efficacy (p = 0.035) and implementation (p = 0.0031). Respondents who had undergone ML training in their graduate program reported higher ML-related self-efficacy (p = 0.007). Respondents who had postgraduate training in ML reported significantly more extensive implementation (p = 0.024). Lack of knowledge and lack of time were perceived as the major barriers to implementation. Conclusions: Level of self-efficacy might be insufficient to support the systematic implementation of ML principles in practice. Addressing impeding individual- and organizational-level factors might facilitate ML self-efficacy and implementation. Postgraduate education facilitates ML implementation.

Study provides new insight into cerebellum's role in motor learning process

Your doctor should be able to use this to create a protocol for relearning your movements. But I bet s/he does absolutely nothing with this because they are waiting for SOMEONE ELSE TO SOLVE THE PROBLEM.
http://www.news-medical.net/news/20170330/Study-provides-new-insight-into-cerebellums-role-in-motor-learning-process.aspx

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March 30, 2017 at 9:35 PM

The human brain's cerebellum controls the body's ability to tightly and accurately coordinate and time movements as fine as picking up a pin and as muscular as running a foot race. Now, Johns Hopkins researchers have added to evidence that this structure also helps transfer so-called motor learning from one part of the body to another.
One implication of the research, the Johns Hopkins investigators say, is that practicing a newly learned task involving the hands can also improve a person's ability to do the same task with the foot, and vice versa.
"Our study gives us new insight into the cerebellum's role in the learning process, information that maybe someday we can use to enhance the learning transfer between limbs so that we can rehabilitate patients who have lost function in hands, feet, arms or legs," says Pablo Celnik, M.D., director of physical medicine and rehabilitation at the Johns Hopkins University School of Medicine.
The study, described in The Journal of Neuroscience on March 1, was primarily designed to demonstrate the value of a brain stimulation technique called cerebellar inhibition that can be used to investigate how connections in the brain change as people learn new motor skills.
For the study, investigators recruited 32 healthy subjects with an average age of 23.9. The subjects were asked to learn to play a computer-based game in which they needed to move a cursor from a starting point to a target. However, the researchers adjusted the movement of the cursor so that it moved at a 30-degree angle from the position of the mouse, forcing the subjects to adapt their movements to reach the target with the cursor.
Each subject learned the new task either with the hand or the foot. During this process, the researchers used magnetic stimulation to measure activity in two areas of the brain, the motor cortex and the cerebellum. The electrical brain activity between these areas was used to calculate the degree of connectivity between them.
In one part of the experiment, Celnik's team tested to see if learning a new task incited change in the connection between the motor cortex and the cerebellum. Twenty subjects trained at the task with their right hand. After measuring the subjects' baseline performance, the researchers switched to the angle-adjusted mouse, and the subjects completed 144 more trials. The measurements from these subjects showed that the connectivity between the cerebellum and the motor cortex changed not just for the areas of the motor cortex that controlled the right hand, but also in the areas known to control the right foot.

The researchers then explored whether this change in activity resulted in an actual transfer of skills from the hand to the foot. Ten patients completed 48 training trials with the angle-adjusted cursor using their right foot, followed by similar test trials for the right hand.
"Without first training the right hand, the subjects' ability to complete the task improved from the baseline measurements, showing that the learning transferred from the foot," Celnik says.
In a third part of the experiment, the researchers investigated whether the brain changes were exclusive to learning a new task. Instead of having the subjects train using the adjusted mouse, they instructed subjects to perform a task they already knew, like lifting a finger. Researchers measured the connectivity between the motor cortex and the cerebellum and found that unlike learning a new task, the activity between the motor cortex and the cerebellum did not change when executing a familiar task.
"This shows us there is something special about learning something new that changes how areas of the brain interact that does not happen when we do a movement we already knew how to do," says Danny Spampinato, a biomedical engineering graduate student at the Johns Hopkins University School of Medicine.
In the future, the researchers say they hope to use the same cerebellar measurements to get a better understanding of this brain area's role in executing everyday tasks useful to those undergoing rehabilitation after injury or stroke, for example.

Source:

http://www.hopkinsmedicine.org/news/media/releases/getting_a_leg_up_hand_task_training_transfers_motor_knowledge_to_feet

Moving Forward by Stimulating the Brain: Transcranial Direct Current Stimulation in Post-Stroke Hemiparesis

I don't give a shit about the mechanisms of tDCS, motor learning and neuroplasticity. It is very fucking simple, write up repeatable stroke protocols. Survivors want to know exactly how to recover, they don't care about the big words you are using to impress your colleagues.
http://journal.frontiersin.org/article/10.3389/fnhum.2016.00394/full?

Heather T. Peters^1,2*, Dylan J. Edwards^3,4, Susan Wortman-Jutt⁵ and Stephen J. Page^1,2

• ¹Division of Occupational Therapy, The Ohio State University, Columbus, OH, USA
• ²Better Rehabilitation and Assessment for Improved Neuro-recovery (B.R.A.I.N.) Laboratory, Ohio State University, Columbus, OH, USA
• ³Laboratory for Non-Invasive Brain Stimulation and Human Motor Control, The Burke Medical Research Institute, White Plains, NY, USA
• ⁴Department of Neurology, Weill Cornell Medical College, White Plains, NY, USA
• ⁵Burke Rehabilitation Hospital, White Plains, NY, USA

Stroke remains a leading cause of disability worldwide, with a majority of survivors experiencing long term decrements in motor function that severely undermine quality of life. While many treatment approaches and adjunctive strategies exist to remediate motor impairment, many are only efficacious or feasible for survivors with active hand and wrist function, a population who constitute only a minority of stroke survivors. Transcranial direct current stimulation (tDCS), a type of non-invasive brain stimulation, has been increasingly utilized to increase motor function following stroke as it is able to be used with stroke survivors of varying impairment levels, is portable, is relatively inexpensive and has few side effects and contraindications. Accordingly, in recent years the number of studies investigating its efficacy when utilized as an adjunct to motor rehabilitation regimens has drastically increased. While many of these trials have reported positive and promising efficacy, methodologies vary greatly between studies, including differences in stimulation parameters, outcome measures and the nature of physical practice. As such, an urgent need remains, centering on the need to investigate these methodological differences and synthesize the most current evidence surrounding the application of tDCS for post-stroke motor rehabilitation. Accordingly, the purpose of this paper is to provide a detailed overview of the most recent tDCS literature (published 2014-2015), while highlighting these variations in methodological approach, as well to elucidate the mechanisms associated with tDCS and post-stroke motor re-learning and neuroplasticity.

Many stroke survivors exhibit hemiparesis, which undermines independence and quality of life (Mayo et al., 2002). Several rehabilitative approaches targeting hemiparetic limbs have been developed (Page et al., 2013a,b) with most incorporating task-specific physical practice of the paretic limb. These approaches attempt to exploit surviving brain tissue and, specifically, to modulate synaptic networks and strengthen connections subserving these networks. While such regimens demonstrate promise, these brain processes can be difficult to modulate with a high precision and consistency when physical practice is the singular method applied.

The therapeutic application of electrical currents to the brain has been documented for centuries (Nitsche et al., 2008), with its use becoming more clinically-plausible through the introduction of the electrical battery in the eighteenth century. In subsequent decades, brain stimulation was successfully applied for a variety of psychological disorders (Kubera et al., 2015; Xie et al., 2015), as well as pain (Ma et al., 2015). More recently, transcranial direct current stimulation (tDCS)—non-invasive brain stimulation administering a constant, low current—has been used to facilitate neurophysiological (Nitsche and Paulus, 2000) and motor changes in post-stroke hemiparesis (Butler et al., 2013). tDCS offers the additional advantage of being portable, relatively inexpensive, and straightforward in administration. As a treatment for targeting post-stroke motor impairments, tDCS can enhance or suppress brain excitability with great focality and over prolonged periods, which constitutes decided benefits over physical therapy alone.

The continued prevalence and impact of stroke-induced hemiparesis suggests a need to evaluate the efficacy of tDCS as an adjuvant to physical therapy for post-stroke motor rehabilitation. In recent years, the number of studies utilizing tDCS alongside a variety of physical rehabilitation regimens has drastically increased. While results from these trials are promising, methodologies vary greatly in terms of the nature of physical practice, stimulation montage and type of outcome measures, among other important factors. There is an urgent need to highlight these methodological differences and provide a snapshot of the most current evidence surrounding the application of tDCS for post-stroke motor rehabilitation. This paper provides an overview of recent tDCS literature (published 2014-2015), with an emphasis on methodological approach, as well a description of the mechanisms associated with tDCS and post-stroke motor re-learning and neuroplasticity.

Study shows motor cortices encode error signals that drive adaptation in reaching

I'm sure your doctor won't update your stroke reaching protocol based on this science, probably because your doctor doesn't read and apply research to their practice. You are going to have to interpret this and update your own protocol. You are completely on your own.
http://www.news-medical.net/news/20160722/Study-shows-motor-cortices-encode-error-signals-that-drive-adaptation-in-reaching.aspx

Adaptation in reaching -- gradual improvement of motor control in response to a perturbation -- is a central issue in motor neuroscience.However, even the cortical origin of errors that drive adaptation has remained elusive. In a new paper published in Neuron, Inoue, Uchimura and Kitazawa have shown that error signals encoded by motor cortical neurons drive adaptation in reaching.

• The premotor and primary motor cortices encoded visual error in reaching.
• Stimulation to the motor cortices induced trial-by-trial increases in reach errors.
• The error increased opposite to the preferred direction of errors at each location.
• The after-effect of stimulation subsided gradually as in ordinary adaptation.

The neural mechanisms of motor learning and adaptation constitute a central issue in both basic and clinical neuroscience. However, it is surprising that very little is known about the neural mechanisms underlying the motor learning and adaptation of voluntary arm movements. For example, the origin of cortical error signals that drive adaptation in reaching remains an unanswered question. A major theory in motor learning (feedback error learning) proposed by Kawato and Gomi (1992) hypothesized that error signals are provided by premotor circuits, including the motor cortical circuits. However, neuroimaging studies to date have not indicated whether motor cortices encode error signals. Preceding human imaging studies unanimously implicated parietal regions, such as areas 2, 5 and 7, in representing reaching errors.

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In the current study, Inoue and colleagues were successful for the first time in inducing trial-by-trial "adaptation" in voluntary arm movements by artificial electrical stimulation of the premotor cortex (PM) or the primary motor cortex (M1). When the stimulation was terminated, the error (after-effect) did not decrease at once but recovered with practice, as observed after typical adaptation. The direction of the increase in the error was opposite to the "preferred" error direction of the neuron recorded in the stimulation site. The results clearly show that the motor cortices submit error signals that drive adaptation in voluntary arm movements, as predicted by the feedback error learning scheme.
The novel technique to artificially "improve" a motor skill by a small amount of stimulation would be applicable to performance enhancement in athletes as well as for restoring motor control in neurological patients.

Source:

Osaka University

Sleep and Motor Learning: Implications for Physical Rehabilitation After Stroke

But I still don't see a sleep protocol coming out of this. If not this research was a waste.
http://journal.frontiersin.org/article/10.3389/fneur.2015.00241/full?

Christel Gudberg^1,2* and Heidi Johansen-Berg¹

• ¹Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
• ²Sleep and Circadian Neuroscience Institute (SCNi), Nuffield Department of Clinical Neurosciences, University of Oxford, Sir William Dunn School of Pathology, Oxford, UK

Sleep is essential for healthy brain function and plasticity underlying learning and memory. In the context of physical impairment such as following a stroke, sleep may be particularly important for supporting critical recovery of motor function through similar processes of reorganization in the brain. Despite a link between stroke and poor sleep, current approaches to rehabilitative care often neglect the importance of sleep in clinical assessment and treatment. This review assimilates current evidence on the role of sleep in motor learning, with a focus on the implications for physical rehabilitation after stroke. We further outline practical considerations for integrating sleep assessment as a vital part of clinical care.

Introduction

The adult brain is highly adaptable, even after injury it often exhibits an impressive capacity for reorganization. Activity in the brain during sleep is thought to be critically involved in supporting these processes of plasticity. Briefly, sleep can be thought of as a state of consciousness, or alternations in consciousness, which oscillates between states of reduced awareness of external real-world stimuli to a complete loss of consciousness (1). While the precise mechanisms have yet to be clearly defined, sleep has been associated with many important functions, including those of the immune and memory systems (2–5). In memory, sleep is consistently attributed a particularly prominent role in supporting time-sensitive processes associated with the consolidation of memories. Consolidation here refers to dynamic processes in the brain that occur after initial (“on-line”) memory encoding takes place, such as when we practice a new skill. Subsequent (“off-line”) mechanisms of consolidation serve to further process these new memory traces, for instance, to enable the integration of knowledge and long-term memory storage.

One reason memory consolidation may be particularly important in a clinical context is because of how it applies to neurological rehabilitation, such as motor recovery after lesion to the brain. Here, the primary aim of physical rehabilitation is to facilitate recovery of functional motor capacity after initial impairment. Another way to look at physical rehabilitation, therefore, is as a form of motor learning, or relearning, which in turn may tap into some of the same processes of memory formation and consolidation as other forms of procedural memory (6, 7). Consequently, experimental insights into processes in the brain that support motor memory are likely to have more wide-ranging application that may benefit understanding and development of useful strategies for improving long-term rehabilitative outcomes in the clinic. The primary objective of this review is to provide an assimilation of current evidence on the role of sleep in motor learning and to identify specific factors of learning and consolidation that may have important implications for rehabilitation. For the purposes of this review, we will focus primarily on sleep-dependent motor memory with relevance to physical rehabilitation after stroke, although many of the discussion points included here will likely apply more broadly to other types of memory and rehabilitation. Meanwhile, what is some of the evidence linking sleep, in particular, to motor memory?

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Neural substrates underlying motor skill learning in chronic hemiparetic stroke patients

Lots of big words used but nothing on how this could be used in protocols to help survivors. Whomever sponsors stroke research should require an 8th grade reading level and protocols to help survivors.
http://journal.frontiersin.org/article/10.3389/fnhum.2015.00320/full?utm_source=newsletter&utm_medium=email&utm_campaign=Neuroscience-w33-2015

• U Dinant-Godinne UCL Namur, Université Catholique de Louvain, Yvoir, Belgium
• ³Imagilys, Brussels, Belgium
• ⁴Faculty of Electronics and Information Technology, Institute of Radioelectronics, Warsaw University of Technology, Warsaw, Poland
• ⁵Neurology Department, Site Saint-Joseph, CHC, Liège, Belgium
• ⁶Neurology Department, Clinique Saint-Pierre, Ottignies, Belgium
• ⁷Service de Neurologie, Unité Neuro-Vasculaire, Cliniques Universitaires Saint Luc UCL, Université Catholique de Louvain, Brussels, Belgium
• ⁸Scientific Support Unit, CHU Dinant-Godinne UCL Namur, Université Catholique de Louvain, Yvoir, Belgium
• ⁹Louvain Bionics, Université Catholique de Louvain, Louvain-la-Neuve, Belgium

Motor skill learning is critical in post-stroke motor recovery, but little is known about its underlying neural substrates. Recently, using a new visuomotor skill learning paradigm involving a speed/accuracy trade-off in healthy individuals we identified three subpopulations based on their behavioral trajectories: fitters (in whom improvement in speed or accuracy coincided with deterioration in the other parameter), shifters (in whom speed and/or accuracy improved without degradation of the other parameter), and non-learners. We aimed to identify the neural substrates underlying the first stages of motor skill learning in chronic hemiparetic stroke patients and to determine whether specific neural substrates were recruited in shifters versus fitters. During functional magnetic resonance imaging (fMRI), 23 patients learned the visuomotor skill with their paretic upper limb. In the whole-group analysis, correlation between activation and motor skill learning was restricted to the dorsal prefrontal cortex of the damaged hemisphere (DLPFC_damh: r = −0.82) and the dorsal premotor cortex (PMd_damh: r = 0.70); the correlations was much lesser (−0.16 < r > 0.25) in the other regions of interest. In a subgroup analysis, significant activation was restricted to bilateral posterior parietal cortices of the fitters and did not correlate with motor skill learning. Conversely, in shifters significant activation occurred in the primary sensorimotor cortex_damh and supplementary motor area_damh and in bilateral PMd where activation changes correlated significantly with motor skill learning (r = 0.91). Finally, resting-state activity acquired before learning showed a higher functional connectivity in the salience network of shifters compared with fitters (qFDR < 0.05). These data suggest a neuroplastic compensatory reorganization of brain activity underlying the first stages of motor skill learning with the paretic upper limb in chronic hemiparetic stroke patients, with a key role of bilateral PMd.

Introduction

Stroke is a devastating disorder that causes life-long upper limb hemiparesis in 30–70% of survivors (Lai et al., 2002; Kwakkel et al., 2003). The biochemical mechanisms triggered by acute stroke (e.g., edema resolution, inflammation, up- and down-regulation of neurotransmitters) play a prominent role in early recovery (Kreisel et al., 2006; Carey and Seitz, 2007). Beyond these biochemical cascades, recovery of motor function also relies on neuroplastic reconfiguration of the cortical motor network and its descending projections, which support transfer of impaired functions toward undamaged areas of the brain (Feydy et al., 2002; Johansen-Berg et al., 2002; Lotze et al., 2006; Lindenberg et al., 2010; Schulz et al., 2012). Although this neuroplastic reorganization may reflect a simple re-routing of information flow through pre-existing, undamaged pathways, stroke patients must learn how to recruit these neuronal resources. To some extent, recovering from hemiparesis might be conceptualized as a particular form of motor skill learning, in other words, learning to use the reconfigured motor network to optimize planning, execution and movement control of the paretic upper limb. Indeed, the idea that motor skill learning plays a central role in post-stroke motor recovery is becoming a major focus in neurorehabilitation (Matthews et al., 2004; Krakauer, 2006; Dipietro et al., 2012; Kitago and Krakauer, 2013).

The neural substrates of motor skill learning are relatively well elucidated in healthy individuals. Functional magnetic resonance imaging (fMRI) studies demonstrated that motor skill learning relies on a network encompassing the primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PM), dorsolateral prefrontal cortex (DLPFC), cerebellum and basal ganglia (Ghilardi et al., 2000; Halsband and Lange, 2006; Debas et al., 2010; Hardwick et al., 2013). Recently, the definition of motor skill learning has been refined to a training-induced acquisition and improvement of motor performance (i.e., skills), persisting over time and characterized by a shift of the speed/accuracy trade-off (SAT), automatisation and reduction of performance variability (Reis et al., 2009; Dayan and Cohen, 2011; Krakauer and Mazzoni, 2011). Using a visuomotor skill learning paradigm involving a SAT, we demonstrated that different that different behavioral trajectories can be observed in healthy individuals during the first stages of motor skill learning (Lefebvre et al., 2012). The first one was characterized either by improvement in both speed and accuracy or by improvement of one parameter without a concomitant worsening of the other one; this resulted in a shift of the SAT, which suggests a rapid and successful motor skill learning. The second behavioral trajectory was characterized by opposite changes in speed and accuracy over time, resulting in less efficient motor skill learning. E.g., when speed improved, accuracy worsened, resulting in slight improvement in the SAT. Finally, the third behavioral trajectory was characterized by a deterioration of both speed and accuracy or a lack of any improvement. According to their behavioral trajectory, the subjects were refereed as shifters, fitters and non-learners respectively. These different behavioral trajectories were observed despite identical instructions and experimental conditions, and they were associated with specific brain activation patterns. Specifically, in the efficient shifters, activation was found in the M1, cerebellum and the SMA where the activation changes correlated with performance improvement, suggesting that the SMA plays a key role in early motor skill learning involving a SAT. In the less efficient fitters, there was only a non-significant correlation in the cerebellum (Lefebvre et al., 2012).

In stroke patients, functional brain imaging has been used extensively to explore the reorganization of the network controlling the paretic arm or hand. Grossly, early after stroke, this reorganized network is characterized by compensatory recruitment of the undamaged hemisphere, especially the motor and premotor areas (Feydy et al., 2002; Tombari et al., 2004; Jaillard et al., 2005; Ward and Frackowiak, 2006) and/or widespread activation in the damaged hemisphere with extensive activation of the somatosensory and premotor areas (Tombari et al., 2004; Ward et al., 2006). Over time, motor recovery is associated with a shift of activation back toward the damaged hemisphere (Pineiro et al., 2001; Jaillard et al., 2005; Favre et al., 2014; Grefkes and Ward, 2014) and a progressive recruitment of the cerebellum ipsilateral to the paretic hand (Small et al., 2002). In addition, changes in brain connectivity have been associated with motor function recovery after stroke (Schaechter et al., 2009; Jiang et al., 2013; Grefkes and Ward, 2014). Early after stroke, both anatomical and functional connectivity (FC) decrease within the damaged hemisphere; over time, motor function recovery is associated with gradual recovery of connectivity (Pannek et al., 2009; Westlake et al., 2012; Golestani et al., 2013). Thus, the more similar the reorganized motor network becomes to that of healthy individuals, the better the recovery. Nevertheless, the undamaged hemisphere may still play a vicarious role in recovered motor control of the paretic hand (Johansen-Berg et al., 2002; Werhahn et al., 2003; Tombari et al., 2004; Lotze et al., 2006; Bestmann et al., 2010; Grefkes and Ward, 2014). It has also been suggested that the resting-state FC correlates with the motor recovery potential (Park et al., 2011, 2014; Yin et al., 2012; Golestani et al., 2013; Ovadia-Caro et al., 2013; Dacosta-Aguayo et al., 2014).

Since functional reorganization occurs in the network supporting motor recovery of the paretic upper limb after stroke, it seems logical that similar neuroplasticity should occur in the network underlying motor skill learning. However, despite extensive fMRI studies of the functional neuroanatomy of motor skill learning in healthy individuals (Ghilardi et al., 2000; Halsband and Lange, 2006; Debas et al., 2010; Lefebvre et al., 2012; Hardwick et al., 2013), very few studies have assessed stroke patients. Using a region of interest (ROI) approach, one study with 10 chronic stroke patients performing visuomotor tracking with the paretic hand showed a bilaterally reorganized pattern with a predominance in the undamaged hemisphere during the pre-training fMRI session (Carey et al., 2002). After training, activation was partially transferred back toward the damaged hemisphere, suggesting functional reorganization (Carey et al., 2002). Another study using ROI showed decreased task-related fMRI activation in the contralesional M1 of nine chronic stroke patients after 3 days of training on a serial targeting task (Boyd et al., 2010). A recent fMRI study highlighted the differences in brain activation patterns between nine healthy individuals and nine chronic stroke patients during training over several days on an implicit sequential visuomotor tracking task (Meehan et al., 2011). Compared with healthy individuals, motor skill learning and retention in stroke patients relied on a reorganized network involving compensatory activations, especially in prefrontal attentional areas such as the DLPFC. Finally, during baseline performance of a sequential grip-force tracking task, 10 chronic stroke patients showed reduced fMRI activation in the damaged hemisphere compared with healthy controls (Bosnell et al., 2011). After repeated training, fMRI activation decreased in healthy controls but was maintained or increased in stroke patients.

These four studies involved relatively small cohorts of mostly high-functioning patients, typically with sub-cortical strokes, and they did not characterize motor skill learning through SAT. Instead, they compared fMRI activation related to motor performance pre- and post-training, and two used an ROI approach (Carey et al., 2002; Boyd et al., 2010). Since motor learning plays a key role in motor function recovery, better knowledge of the neurophysiology of motor skill learning after stroke should lead to the refinement of recovery models and translate into the development of specific neurorehabilitation methods based on the principles of motor learning.

Motor skill learning can be divided in two stages: a fast on-line learning process leading to large performance improvement over a single training session (i.e., early stages of motor skill learning as described is the present study), and a slower process involving smaller performance gains obtained through repeated training sessions (Dayan and Cohen, 2011).

This study aimed to specifically explore the early stages of motor skill learning with the paretic hand in chronic hemiparetic stroke patients, using an innovative motor skill learning paradigm with a SAT. This is a first step to understand the “recovery process” in stroke patients, whether residual motor learning aptitudes are present and which brain areas are (neuroplastically?) involved. A better knowledge about motor (skill) learning in stroke patients could help to refine neurorehabilitation protocols, in which motor learning is often imbedded as an implicit assumption but poorly recognized. The purposes of this study were: (i) to use random effect (RFX) analyses of whole-brain fMRI activation to identify the neural substrates underlying the first stages of motor skill learning involving a SAT in a larger cohort of chronic stroke patients using their paretic upper limb, (ii) to determine whether shifter and fitter stroke patients recruit specific neural substrates, and (iii) to determine whether resting-state FC acquired before training would predict the behavioral trajectory (shifter/fitter) during the first stage of motor skill learning and/or correlate with the amount of motor skill learning.

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