Precision neurorehabilitation

 A complete oxymoron. There is NOTHING PRECISE ABOUT STROKE REHAB! NO objective damage diagnosis, NO EXACT REHAB PROTOCOLS! Really no recovery at all since 100% recovery is the goal of all stroke survivors and currently only 10% get there. Everything in stroke is a COMPLETE SHITSHOW and these people are trying to put lipstick on a pig!

Precision neurorehabilitation

Dr. Steven Kautz, Chair of the Department of Health Sciences and Research in the College of Health Professionals at MUSC

Provided

This week Bobbi Conner talks with Dr. Steven Kautz about precision neurorehabilitation and the $6.5 million grant from NIH for research in this specialty area. Dr. Kautz is the Chair of the Department of Health Sciences and Research in the College of Health Professionals at MUSC.

TRANSCRIPT:

Conner: I'm Bobbi Conner for South Carolina Public Radio with Health Focus here at the radio studio for the Medical University of South Carolina in Charleston. The National Institutes of Health recently awarded a $6.5 million grant to start a center focused on precision neurorehabilitation at MUSC to help people affected by strokes and other neurological conditions. Doctor Steven Kautz is here to talk about the details. Doctor Kautz is the principal investigator for this research grant, and he's the Chair of the Department of Health Sciences and Research in the College of Health Professions at MUSC. Doctor Kautz, first tell us about precision Neurorehabilitation. What is this really?

Dr. Kautz: Precision neurorehabilitation as we're developing it is based on identifying the neural circuits that are impaired by an injury and then targeting them with the neuromodulation technique in order to rehabilitate those circuits and restore function. Essentially, we are seeking to rewire the brain to help it recover.

Conner: What might be different about this precision neurorehabilitation approach to stroke compared to a current standard approach to providing rehab following a stroke.

Dr. Kautz: In a conventional rehabilitation, a person would work with a therapist, say if they're trying to improve their arm function, they would typically practice performing tasks with the therapist. In the approach that we are trying to develop, we want to give the therapist tools to understand which specific neural circuit was impaired, and then they're able to choose the best activity to help improve that function, so that they can get the most responsiveness to the rehabilitation.

Conner: And can you give us an example of what might be different for one person versus another, as far as the rehab that you're suggesting?

Dr. Kautz: Sure. So, depending on which neural circuit is injured, we may stimulate completely different parts of the brain, after the stroke. When one side is injured in one person, it might be most beneficial for them for us to stimulate the injured side of the brain. But in another person, we might need to stimulate the non-injured side of the brain to get more effective rehabilitation for them.

Conner: Tell us about the NIH research grant that you received recently to start a center focused on precision neurorehabilitation research.

Dr. Kautz: Yeah, so we're very excited about this. Our center is part of the National Rehabilitation Research Infrastructure Network, and NIH selected six centers to recognize the institutions that were national leaders in an area of rehabilitation, like we are here at MUSC in neuromodulation. And so, we're to do research and train researchers from around the country in this new precision neurorehabilitation approach, so that more people throughout the country can experience this type of an approach. And we'll be developing new tools and disseminating them throughout the country.

Conner: And you just received this NIH research grant this fall. What are the next steps to get things up and running?

Dr. Kautz: The grant is a five-year grant and it will run through 2030. We have started our research projects and getting those up and running, and we have started planning for workshops that we will put on for investigators throughout the country so that we can help grow this field of precision neurorehabilitation.

Conner: Doctor Kautz, thanks for talking with us about this research.

Dr. Kautz: You're welcome. Thank you for having me.

Conner: From the radio studio for the Medical University of South Carolina in Charleston, I'm Bobbi Conner for South Carolina Public Radio.

Health Focus transcripts are intended to accurately represent the original audio version of the program; however, some discrepancies or inaccuracies may exist. The audio format serves as the official record of Health Focus programming.

NSF DARE—Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

 Ask your doctor if anything here is going to get you closer to 100% recovery. If your doctor doesn't even know about this; YOU DON'T HAVE A FUNCTIONING STROKE DOCTOR! I don't know what you have but that person is not keeping up-to-date on stroke research!

NSF DARE—Transforming modeling in neurorehabilitation: Four threads for catalyzing progress

• Francisco J. Valero-Cuevas,
• James Finley,
• Amy Orsborn,
• Natalie Fung,
• Jennifer L. Hicks,
• He (Helen) Huang,
• David Reinkensmeyer,
• Nicolas Schweighofer,
• Douglas Weber &
• Katherine M. Steele 

Journal of NeuroEngineering and Rehabilitation volume 21, Article number: 46 (2024) Cite this article

• Metrics details

Abstract

We present an overview of the Conference on Transformative Opportunities for Modeling in Neurorehabilitation held in March 2023. It was supported by the Disability and Rehabilitation Engineering (DARE) program from the National Science Foundation’s Engineering Biology and Health Cluster. The conference brought together experts and trainees from around the world to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation to understand, optimize, and improve clinical translation of neurorehabilitation. We organized the conference around four key, relevant, and promising Focus Areas for modeling: Adaptation & Plasticity, Personalization, Human-Device Interactions, and Modeling ‘In-the-Wild’. We identified four common threads across the Focus Areas that, if addressed, can catalyze progress in the short, medium, and long terms. These were: (i) the need to capture and curate appropriate and useful data necessary to develop, validate, and deploy useful computational models (ii) the need to create multi-scale models that span the personalization spectrum from individuals to populations, and from cellular to behavioral levels (iii) the need for algorithms that extract as much information from available data, while requiring as little data as possible from each client (iv) the insistence on leveraging readily available sensors and data systems to push model-driven treatments from the lab, and into the clinic, home, workplace, and community. The conference archive can be found at (dare2023.usc.edu). These topics are also extended by three perspective papers prepared by trainees and junior faculty, clinician researchers, and federal funding agency representatives who attended the conference.

Introduction

Why do we create computational models? Simply put, to help us move from costly and inefficient trial-and-error empiricism towards mechanistic, hypothesis-driven, and evidence-based systematic processes to develop clinical treatments and products [1, 2]. Along these lines, computational modeling has had profound impacts on our scientific understanding. For example, computational models of sensorimotor control and plasticity underlie the design and application of neuromodulatory approaches to enhance motor function during development, aging, and following neurological injury or disease [3,4,5,6,7,8,9,10,11]. Additionally, computational musculoskeletal models have been used to inform treatment decisions in orthopedics and sports medicine. Computational models describing the trajectories of development, disability, and recovery have the potential to help prioritize and focus treatment. Moreover, models of musculoskeletal dynamics and neural control are regularly used by researchers to design and implement control strategies of assistive technologies [12,13,14,15,16,17,18]. The impacts of computational modeling are only set to increase in the coming decades: the emergence of multimodal remote sensors, machine learning, and multiscale datasets (from genomics to behavior) will enable a future in which personalized neurorehabilitation that adapts throughout the course of treatment becomes the norm. In fact, the interest in these areas continues to accelerate (Fig. 1).

As interest and potential impact in these areas accelerates, we convened the NSF DARE Conference: Transformative Opportunities for Modeling in Neurorehabilitation that brought together experts and trainees to discuss critical questions, challenges, and opportunities at the intersection of computational modeling and neurorehabilitation. We identified four Focus Areas prior to the conference through discussions amongst the PI team, Advisory Board, and federal funding representatives. These areas were identified as areas of high growth and potential impact for computational modeling in rehabilitation research (Table 1). They represent—in our opinion—pressing challenges and timely opportunities within neurorehabilitation where advances in science, computational methods, and implementation can converge for actionable change. These areas also exemplify the potential for innovation and impact when merging multiple modeling methods (e.g., machine learning and physics-based models) with technology (e.g., exoskeletons or wearable sensors) to support scientific understanding, target neurorehabilitation outcomes, and improve quality of life. These areas are highly synergistic with NSF’s Disability and Rehabilitation Engineering Program and NIH’s 2021 Research Plan on Rehabilitation.

In this paper we summarize and comment on the perspective and insights from the expert community assembled to help establish a foundation by which computational modeling can create the scientific directions, theories, and actionable platforms to improve the efficacy and personalization of neurorehabilitation. This is based upon discussions with participants during the meeting in general, and among the co-authors during the writing of this paper. Additional concluding remarks are included in the three other companion papers that reflect key take-away points from other conference constituents (e.g., trainees, federal funding representatives, and clinician scientists). Mirroring the structure of the meeting, we now visit and comment on each focus area. It is important to note that the main conclusions are summaries drawn by the authors. As such, they represent the viewpoints of the authors, and not a consensus process of conference attendees. The companion papers further clarify this distinction where each set of authors provides their point of view and provide further conclusions and key take-aways from other constituent groups who participated in the conference (e.g., trainees, clinician scientists, and federal funding representatives). Those additional three papers in this same issue are titled, respectively:

• NSF DARE—Transforming Modeling in Neurorehabilitation: A Patient-in-the-Loop Framework

• NSF DARE—Transforming Modeling in Neurorehabilitation: Clinical Insights for Personalized Rehabilitation

• and

• NSF DARE—Transforming Modeling in Neurorehabilitation: Perspectives and Opportunities from US Funding Agencies

Fig. 1

The field of computational neurorehabilitation has grown tremendously over the preceding decades. Usage of the terms “computational” and “neurorehabilitation” in articles indexed by Pubmed. Data generated by Pubmed by Year (https://esperr.github.io/pubmed-by-year/)

Full size image

Table 1 Focus areas for the DARE 2023 conference

Full size table

Modeling adaptation and plasticity

Brief background

Adaptation and plasticity are central to our nervous system’s ability to acquire new abilities, adapt them to changing situations, and recover function after injury. The role of synaptic plasticity in sensorimotor learning and adaptation is the subject of much work described in several reviews [19,20,21,22]. Here, our main interest is the role of plasticity in the recovery of function after injury for neurorehabilitation. For example, after stroke, neurons re-wire connections both immediately surrounding the injury and across distant brain areas [23,24,25,26,27,28], and these changes in connectivity are correlated with improvements in function with and without rehabilitation [27, 28]. Critically, adaptation and plasticity are not guaranteed to be fast, well-guided, or beneficial (i.e., adaptive plasticity). Plasticity can also be maladaptive [29, 30], a neurorehabilitation analog to focal dystonias [31, 32].

A key goal of neurorehabilitation therapies is to promote plasticity mechanisms that improve function while also mitigating maladaptive changes to neural circuits. Many therapeutic approaches have been proposed and attempted to achieve this goal—with varying degrees of success—ranging from targeted behavioral training to implantable devices that stimulate neural circuits.

Computational modeling of adaptation and plasticity can provide paths to maximize the impact of neurorehabilitation therapies [3, 33]. The plasticity and adaptation that occurs after injury span many levels of the nervous system, from cellular processes (e.g., changes in ion channel expression) [34], to network interactions (e.g., changes in synaptic connections) [27], to behavioral changes (e.g., compensatory strategies) [35]. As a result, a wide range of modeling methods have been used to describe plasticity at different levels [36]. In lieu of an exhaustive survey of existing models, we highlight some useful categories of model types. For a particular phenomenon, such as synaptic plasticity, models may focus on different levels of abstraction. For example, phenomenological models like Hebb’s rule describe input-output relationships between the rate/timing of neural activity and connection changes, describing the computational principle without directly modeling the biological implementation [37]. Biophysical models of spike-timing dependent plasticity, in contrast, describe the physiological changes within neurons that give rise to synaptic changes [38]. Data-driven models (i.e., machine learning) have also been employed for modeling a variety of plasticity phenomena (e.g., [39]).

A variety of model types, spanning different levels of the nervous system, have been used to describe how the nervous system will respond to an intervention to inform rehabilitation therapies [3, 40]. Mechanistic models of how behavior evolves as we adapt to altered dynamics like a split-belt treadmill, for example, informed training interventions to improve gait post-stroke [41]. Phenomenological Hebbian plasticity models informed stimulation protocols to increase the functional connections among regions in the brain [11, 42, 43], and from the brain to muscles or spinal circuits [44,45,46,47]. Models describing nervous system changes over time are also valuable for predicting outcomes and to guide clinical decision-making. Many examples of these models rely on data-driven discovery from large datasets. For example, the increasing prevalence of neural imaging technologies in clinical practice have led to large datasets to develop algorithms that predict functional recovery after stroke [48]. Machine learning approaches have also been used to assess whether devices like non-invasive brain-computer interfaces will be effective [49, 50] and optimal parameters for therapies like deep brain stimulation [51].

These examples highlight the diversity of plasticity models and applications in neurorehabilitation. As with any other computational modeling effort [2], decisions must be made about the level of abstraction and detail. The challenge of these decisions is acutely clear in the realm of adaptation and plasticity, where mechanisms span spatial scales from synapses to behavior, and timescales from milliseconds to months [20]. Many existing models used for neurorehabilitation focus on a single scale (e.g., describing behavioral changes). Models that bridge neurological mechanisms of plasticity to behavior will likely be needed to improve the precision of neurorehabilitation therapies. Such models will require cross-disciplinary collaboration to develop and validate. Similarly, most existing models focus on describing a particular time-point, such as functional recovery after a certain time with a particular, static therapy. The many time-scales of adaptation and plasticity present challenges for modeling overall trajectories, including the impact of interventions and changes in treatment over time [52].

Extending models of plasticity to span spatial and temporal scales could open new ways to harness the power of computational methods in neurorehabilitation. The dynamic nature of the nervous system creates a variety of challenges for building therapies. Assuring an assistive device provides meaningful functionality for extended periods of time requires characterizing plasticity that may occur in response to the device and developing devices that can adapt accordingly [53]. Similar considerations are needed for therapies where protocols may need to adapt over time as abilities change [54]—a form of meta-adaptation that mirrors meta-plasticity (i.e., ‘plasticity of plasticity’ [36]). Achieving the goal of smart, personalized, and adaptive neurorehabilitation therapies will require models that can capture the dynamics of plasticity processes as well as human-device interactions that will influence those dynamics. This will require new approaches to bridge across models that predict how the nervous system will change in response to a given intervention and those to describe how interventions inpact the trajectory of changes in the nervous system and changes in behavior over time.

Commentary

The DARE workshop highlighted many fundamental challenges and opportunities in modeling adaptation and plasticity for rehabilitation applications. Multiple presentations (see Appendix for speaker summaries) speak to the promise of using computational models to disentangle diverse learning mechanisms used by the nervous system (e.g., Roth, Mariscal). Mechanistic models such as those used by Roth shed light on the neurophysiological underpinnings of disorders. Their findings, for instance, suggest that Parkinson’s disease can lead to deficits in a single learning mechanism while leaving others intact. Similarly, data-driven methods to identify components of learning used by Mariscal allowed them to characterize how learning generalized to new contexts more precisely than past studies. A critical next step missing from most workshop submissions is using these model-derived insights to directly guide clinical therapies. Future work towards these efforts will have to contend with challenges closely related to those faced in personalization efforts (see below). For example, do models and their parameters need to be estimated on populations of people or individuals? Models may also require updating over time as learning proceeds, closely mirroring challenges faced in human-device interactions.

Multiple presentations (e.g., Liew, Orsborn, Hight, Schwock) aimed to characterize plasticity that occurs as the result of therapies and interventions or injuries. Schwock’s work highlights the potential benefits of computational models to quantify changes between regions of the nervous system when they are embedded within a large network (see also [55]). This work highlights the challenge of identifying the most useful measures of nervous system plasticity, since nearly all metrics will be approximations. Data-driven studies probing how physiological measurements relate to clinical outcomes will likely be critical to identify the most useful experimental and computational measures of plasticity for neurorehabilitation. Collaborations between researchers developing novel assays of plasticity and those using large clinical datasets to predict clinical outcomes, such as discussed by Liew, will be invaluable for future research and translation. Though such collaborations will likely involve navigating the challenges of measurement feasibility, such challenges highlight the potential promise of extending neurorehabilitation ‘in-the-wild’ (see below) and research into quantifying plasticity metrics.

Designing interventions that induce plasticity is central to any rehabilitation effort. Data-driven predictive models, such as those developed by Liew, provide methods for predicting how someone may respond to an intervention dose. However, these models have largely been used to predict a single endpoint, which may miss dynamic interactions between plasticity and an intervention, as highlighted by other presentations (e.g., Orsborn, Hight). Research with brain-computer interfaces and cochlear implants demonstrate that even interventions that intend to replace a function (rather than rehabilitate) induce plasticity. This plasticity may be influenced by how the device is designed (e.g., Orsborn’s investigations into co-adaptation with brain computer interfaces), and could be further manipulated by purposeful device interventions (e.g., vagus nerve stimulation presented by Hight). User-device interactions to shape plasticity open a huge opportunity to shape plasticity for rehabilitation. Capitalizing on this opportunity, however, will require improving models of how devices induce plasticity. Translating methods to shape plasticity with devices into meaningful clinical therapies will also require methods to predict functional outcomes.

Beyond these examples of scientific challenges, we also noticed important challenges and opportunities to create the scientific community needed to tackle these challenges. All talks focused on plasticity, but we were particularly struck by the topic diversity. For instance, the presentations spanned upper limb movements (Roth, Orsborn), locomotion (Mariscal), clinical sensorimotor function assessments (Liew), and hearing/speech (Hight). There was also a diverse range of methods used to quantify plasticity, from behavior (Roth, Mariscal, Liew), clinical neuroimaging (Liew), and high-resolution electrophysiology (Orsborn, Schwock). This breadth fostered rich discussions across sub-fields that do not regularly interact. Integrating the knowledge gained from this diversity of methods and applications and refining models of plasticity and adaptation for rehabilitation will require bridges across these communities and translating terminology between fields.

More at link.

How are we addressing big questions in neurorehabilitation?

You're not. You're doing nothing to get stroke solved to 100% recovery. You'll want that when you are the 1 in 4 per WHO that has a stroke!

As far as I can tell nobody in stroke has a functioning brain!  Prove me wrong!

 

 How are we addressing big questions in neurorehabilitation?

UCL Centre for Neurorehabilitation

CNR Research Networking Event

Thursday 19 October 2023, 17:30-19:00

Basement Lecture Theatre, 33 Queen Square, WC1N 3BG

CNR is excited to announce our next Research Networking Event on 19th October 2023. We want to create a forum in which researchers and clinicians talk to each other about questions they have on aspects of neurorehabilitation, and possible techniques to explore those questions. Research in neurorehabilitation often takes a long time to carry out, and even longer to publish, and this event will give us the chance to learn what research is under way and what is being achieved. It offers an opportunity to give input and feedback and may inspire more clinicians to get involved in research. Please join us after the event for wine, soft drinks, crisps and networking!

We are inviting abstracts with a view to delivering a short talk (7 minutes, maximum 3 slides) showcasing your research, particularly highlighting methods and how you may use them to answer research questions in neurorehabilitation. We will then give each speaker a station and invite colleagues to join them for further discussions. If you would like to submit an abstract, please contact cnr@ucl.ac.uk before 10th October for more information.


If you would like to attend this Research Networking Event, please register below for a free ticket.

Dates and times

Women With Cerebral Infarction Feature Worse Clinical Profiles at Admission but Comparable Success to Men During Long-Term Inpatient Neurorehabilitation

 So women have the same failure rate of only 10% full recovery? THAT IS A COMPLETE DISASTER!

Women With Cerebral Infarction Feature Worse Clinical Profiles at Admission but Comparable Success to Men During Long-Term Inpatient Neurorehabilitation

 

This article was originally published here

Front Aging Neurosci. 2021 Nov 18;13:663215. doi: 10.3389/fnagi.2021.663215. eCollection 2021.

ABSTRACT

Objective: 

Little is known about possible sex and gender differences in post-stroke neurorehabilitation outcomes. We aimed to analyze if functional performance, prevalence and impact of comorbidities at admission, and success of inpatient stroke-neurorehabilitation differ between men and women. 

Methods: 

Retrospective cohort analysis of 1,437 men and 907 women with prior cerebral infarction treated at a neurorehabilitation clinic between 2012 and 2017; multiple linear regression was used to examine the influence of sex/gender as well as multiple confounders on health and functional outcomes. The main outcome measures were Barthel index (BI) at admission and its change during 4 weeks inpatient neurorehabilitation.  

Results: 

Men had been diagnosed with osteoporosis less frequently than women but more often with type 2 diabetes mellitus, coronary artery or chronic kidney disease (p ≤ 0.01). Although twice as many women presented with pre-stroke depression compared to men, the risk of post-stroke depression detected during rehabilitation was comparable. Men were more likely to have less than 30 days between diagnosis and neurorehabilitation start than women (p < 0.03). At admission, women exhibited less autonomy, a lower BI, a higher pain score and worse 2-min walk test (2’WT) compared to men (p < 0.001). Among males osteoporosis and peripheral artery disease independently predicted BI at admission, in women it was pre-stroke depression, dementia, and arterial fibrillation. During neurorehabilitation, both sexes improved regarding BI, pain and walk tests (p < 0.001). Despite comparable rehabilitation effectiveness, women still had worse functional outcomes than males at discharge. Time after stroke to start of neurorehabilitation and length of the stay but, most strongly, the simple 2’WT at admission, and in women, pain intensity independently predicted post-stroke functional status and recovery. 

Conclusion: 

Women presented with worse functional status at admission to neurorehabilitation. Although men and women showed similar rehabilitation effectiveness(You don't tell us full recovery statistics, so what the fuck are you doing working in stroke?), women still displayed worse clinical outcome measures and higher levels of pain at discharge. Early access and gender-sensitive, personalized post-stroke care with more focus on different comorbidities and psychosocial factors like pain levels and management, could further improve neurorehabilitation outcomes.

PMID:34867261 | PMC:PMC8637730 | DOI:10.3389/fnagi.2021.663215

Modulation of brain plasticity in stroke: a novel model for neurorehabilitation

 Useless. A lot of words to say nothing specific.

Modulation of brain plasticity in stroke: a novel model for neurorehabilitation

 Giovanni Di Pino, Giovanni Pellegrino, Giovanni Assenza, Fioravante Capone, Florinda Ferreri, Domenico Formica, Federico Ranieri, Mario Tombini, Ulf Ziemann, John C. Rothwell and Vincenzo Di Lazzaro

Abstract

 | Noninvasive brain stimulation (NIBS) techniques can be used to monitor and modulate the excitability of intracortical neuronal circuits. Long periods of cortical stimulation can produce lasting effects on brain function, paving the way for therapeutic applications of NIBS in chronic neurological disease. The potential of NIBS in stroke rehabilitation has been of particular interest, because stroke is the main cause of permanent disability in industrial nations, and treatment outcomes often fail to meet the expectations of patients. Despite promising reports from many clinical trials on NIBS for stroke recovery, the number of studies reporting a null effect remains a concern. One possible explanation is that the interhemispheric competition model—which posits that suppressing the excitability of the hemisphere not affected by stroke will enhance recovery by reducing interhemispheric inhibition of the stroke hemisphere, and forms the rationale for many studies—is oversimplified or even incorrect. Here, we critically review the proposed mechanisms of synaptic and functional reorganization after stroke, and suggest a bimodal balance–recovery model that links interhemispheric balancing and functional recovery to the structural reserve spared by the lesion. The proposed model could enable NIBS to be tailored to the needs of individual patients.

 
Nat. Rev. Neurol.
 advance online publication 9 September 2014; doi:10.1038/nrneurol.2014.162

Introduction

Stroke is among the principal causes of death worldwide, and although most stroke survivors achieve at least some spontaneous recovery after the ictus, it remains the main cause of permanent disability in Europe and the USA. ^1–3

The only existing treatment for stroke is tissue plasminogen activator, which can salvage tissue at risk in the penumbra of a brain infarct and reduce future disability if administered within a few hours after stroke onset.⁴No treatments specifically designed to restore function through repair of damaged tissue have been developed to date. Current best practice in stroke management is to reduce initial impact, take precautions to avoid the further burden of complications, and maximize functional ability through extensive physiotherapy.Although early rehabilitative interventions can improve arm function above what would be achieved by natural recovery alone,
5
 the majority of patients are still unable to use the affected hand and/or arm for simple activities of daily living 6 months after the stroke.⁶Robot-assisted therapy has been proposed as an excellent low-cost way to increase the amount of therapy that an individual patient receives, because a robot can deliver a higher amount of exercise in the same time than can a human physiotherapist,⁷but in controlled trials robot-assisted therapy has shown disappointingly little additional benefit over conventional regimes of intensive physiotherapy.^8,9The lack of effective neurorepair after stroke and the limitations of functional recovery attained by physio-therapy have led researchers to consider alternative approaches that improve the scope for recovery by enhancing the natural plasticity of the sensorimotor system. If recovery depends on ‘relearning’ new patterns of activity to maximize the use of remaining undamaged brain, interventions that increase plasticity, and thereby the ability to learn, should improve recovery. Two approaches are of great current interest: pharmaco logical interventions and noninvasive brain stimulation (NIBS). In this Review, we focus on NIBS. Although NIBS has been used to improve gait, neglect symptoms and lan-guage after stroke, we will concentrate here on its appli-cation to recovery of arm and hand function, because most studies have assessed the efficacy of NIBS on upper limb function.More than 350 articles have been published on trans-cranial stimulation in stroke since 2012. These include more than 50 small clinical trials that evaluated the potential for NIBS to enhance recovery of hand and arm function. Recent meta-analyses disagree on the effective-ness of these techniques: two Cochrane reviews do not support the use of repetitive transcranial magnetic stimu-lation (rTMS)¹⁰or transcranial direct current stimu lation (tDCS)¹¹in stroke rehabilitation, whereas two other systematic reviews conclude that NIBS can have a moder-ate benefit in terms of stroke recovery, with few major adverse effects.^12,13Here, we discuss the factors that have contributed to the slow progress of accepting NIBS as a part of routine clinical practice, and suggest ways to develop the use of NIBS in stroke rehabilitation.

What factors influence clinicians’ use of technology in neurorehabilitation? A multisite qualitative study

The way to get around this problem is to have EXACT PROTOCOLS WITH EFFICACY RATINGS. Thus survivors will drive what rehab is provided. We have to get clinicians out of the selection of rehab.  As proven by the 10% full recovery rate of rehab.

What factors influence clinicians’ use of technology in neurorehabilitation? A multisite qualitative study

Kelly J Bower, PhD, Michele Verdonck, PhD, Anita Hamilton, PhD, Gavin Williams, PhD, Dawn Tan, ClinDocPT, Ross A Clark, PhD

Physical Therapy, pzab031, https://doi.org/10.1093/ptj/pzab031

Published:

30 January 2021

Article history

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Abstract

Objective

Technology is being increasingly used for physical assessment and interventions in health care settings. However, clinical adoption is relatively slow, and the factors affecting use remain underexplored. This study aimed to investigate factors influencing technology use by clinicians working in neurorehabilitation.

Methods

In this qualitative study, 9 physical therapists and 9 occupational therapists (n = 18) were recruited from urban and regional locations in Australia and in Singapore. Three 60-minute focus groups were conducted via video conferencing. Each group comprised 3 physical therapists and 3 occupational therapists working across different neurorehabilitation settings. Participants were asked to discuss which technologies they used in their workplace for physical assessment and treatment, and barriers, motivators and future desires for technology use. Transcripts were analysed independently using an inductive approach to generate codes and themes.

Results

Our results comprised 3 themes and 7 categories. These were encompassed by a single overarching theme, namely ‘Technology use is influenced by the benefits and challenges of the technology itself, users and organisational context’. Themes showed that technology should promote effective interventions, was preferred if easy to use, and should be dependable. Furthermore, clinical reasoning is important, and users have varying levels of receptivity and confidence in technology use. Also, organisational resources are required, along with supportive cultures and processes to facilitate technology use.

Conclusions

The themes identified multiple and interlinking factors influencing clinicians’ use of technology in neurorehabilitation settings. Clinicians often consider context-specific benefits and challenges when deciding whether to use technology. While our study found that clinicians generally perceived technology as having a beneficial role in improving health outcomes, there were several challenges raised. Therefore, the characteristics of the technology itself, individual users, and organisational context should be considered.

Impact

These findings will guide successful technology implementation and future developments.

 

Robotics for Rehabilitation

This one line in here really chaps my ass. That new president of that great stroke association needs to read these people the riot act. THE ONLY GOAL IN STROKE IS 100% RECOVERY

. 

The goal of post strokes neurorehabilitation programs remains in reducing of the grade of permanent disability.

Robotics for Rehabilitation

  Adriana Vilaça A72355; Cláudia Oliveira A73605; Mariana Pires A71536

 ABSTRACT

 The objectives for this project are to discuss and analyze the importance of the use of robotics in rehabilitation, two applications and potential future.
Keywords: Rehabilitation, Robotics, Lokomat, Mit-Manus
II.

INTRODUCTION
 The Lower limb or Upper limb disabilities, that we will discuss on this paper, usually result from neurological injuries, such as strokes and spinal cord injuries.1

Therefore, in literature stroke it’s seen as leading cause of disability, despite continued advances in prevention and treatment techniques. The stroke generate lesion to the nervous system that disrupts the normal motor commands for limb movement. Developed medical treatment to reduce the complications has contributed to decreased mortality, but 90% of the survivors have significant neurological deficits. The goal of post strokes neurorehabilitation programs remains in reducing of the grade of permanent disability. As in the development of any other field, some ideas and inventions have worked and been expanded upon, therefore a number of laboratories using robotic devices, have indicated modest success in reducing impairment and increasing motor power in the exercised limb of patients with stroke when compared with control individuals.6
 During this research the focus stands with two robots used in rehabilitation, as MIT-Manus and LOKOMAT. Some of the designs of this robotics have been created to enhance the performance of the human body while others were created for medical treatments to improve daily life of people who have been affected by a degenerative disease or traumatic injury.7
 Traditionally a robotic system embraces five main components: a mechanical structure with degrees of freedom;  joint-controlling actuators, either electric, pneumatic or hydraulic for loads in the range of thousands of Newtons; designated ambient, the space within reach of the robotic devices; sequences of tasks to be executed as detailed by the system computer in suitable language and a computer generating the signals that control the robot joints consistent, actual and previous operative conditions and environment.3
 In particularly, the electromechanical systems result from the evolution of robotics. This system has highly flexibility mechanic structures. Therefore, is arranged on them actuators; the source of energy; sensors providing information on the machine functional status and interaction with environment; a computer single chips processing the signals transmitted by the sensors and instructing the motor controllers; a machine interface that collect information/instructions from users and providing online feedback.3

The patient’s subjective control of voluntary movements, somatosensory inputs, proprioceptive static and dynamic information, pertinent visual information, motivation, perception of achievement and reward, are some of the characteristics, that is essential for the development of the robots, which differ with a variety of sensory, motor and cognitive inputs.
 

Application of Virtual Reality in Neuro-Rehabilitation: An Overview

We don't need an overview, we need EXACT DEFINED STROKE PROTOCOLS. When the hell will you get there?

Maybe after you are the 1 in 4 per WHO that has a stroke? Will that finally get you to do your job properly?

Application of Virtual Reality in Neuro-Rehabilitation: An Overview

 Lucia F Lucca
1
, Antonio Candelieri
1,2
 and Loris Pignolo
1

1
S. Anna Institute and RAN - Research in Advanced Neuro-rehabilitation, Crotone,
2
Laboratory of Decision Engineering for Health Care Delivery, University of Cosenza, Italy
1. Introduction
Virtual reality (VR) collectively refers to the realistic, albeit artificial environments that are simulated by computer and are experienced by end-users via human-machine interfaces involving multiple sensory channels. In this respect, comparable technical solutions are applicable across different domains such as cyberspace, virtual environments, teleoperation, telerobotics, augmented reality,
 and synthetic environments.
This makes application possible in a variety of conditions such as (1) design, engineering, manufacturing, and marketing; (2) medicine and healthcare; (3) online monitoring of children and the elderly at home and accident prevention; (4) hazardous operations in extreme or hostile surroundings; and (5) training in military and industrial machine operation, medical teaching and surgery planning/training. An implement of VR with live direct or indirect view of a physical real-world environment whose elements are purportedly enhanced (augmented) by virtual computer-generated imagery to meet the viewer needs, Augmented Reality is extensively used in open surgery, virtual endoscopy, radiosurgery, neuropsychological assessment and medical rehabilitation. Application in psychotherapy ranked 3rd among 38 psychotherapy interventions predicted to increase in use in the next future (Gorini & Riva, 2008a; Gorini & Riva, 2008b). Application in rehabilitation is increasing and expanding; innovative technical solutions in motor and sensory-cognitive rehabilitation result in substantial developments from the available procedures and in prototypes for clinical testing. The clinical results appear promising.
2. Rationale for VR-mediated neuro-rehabilitation
The rationale for application mainly rests on the available evidence that a functional re-arrangement of the injured motor cortex can be induced with the mediation of the mirror neurons system (Eng et al, 2007; Holden, 2005; Rose et al, 2005) or through the subject’s motor imagery and learning (Gaggioli et al, 2006). Intensive training (repetition) facilitating re-arrangement of cortical function and motivation reinforced by feedback information about the ongoing improvement are necessary for motor learning to be possible after brain damage. These conditions are easily made available in VR-mediated neuro-rehabilitation paradigms. Motor impairment and recovery can be measured in real time (e.g. at the end of each trial or a series of trials) to give the user the knowledge-of-performance (about his/her movement patterns) and knowledge-of-results (about the outcome predictable at each time point during rehabilitation) that reinforce motivation and the training procedure itself. VR allows online or offline feedback, that has been extensively investigated with a general agreement that it improves learning (Bilodeau & Bilodeau, 1962; Gentile, 1972; Khan & Franks, 2000; Newell & Carlton, 1987; Winstein, 1991; Young & Schmidt, 1992; Woldag & Hummelsheim, 2002). The expectation is, that VR-mediated rehabilitation should improve the approach efficacy and the outcome by making tasks easier, less demanding and less tedious/distractive, and more informative for the subject. Interactive VR environments are flexible and customizable for different therapeutic purposes; individual treatments can be personalized in order to facilitate movement retraining, to force the user to focus on the task key elements, and to facilitate transfer of motor patters learned in VR environments to the real world.
3. Studies
3.1 VR in the upper limb motor rehabilitation
VR was first applied in the rehabilitation of the paretic upper-limb after stroke in a setting designed to promote motor (re)learning for different movements (hand, elbow and shoulder) and functional tasks or goals (Holden et al, 1999). The approach implemented a learning-by-imitation paradigm through three components: a motion tracking device to record the trajectories to be performed in the VR environment, a desktop computer display and a VR editing software specifically developed to create suitable 3D-simulated tasks at varying level of complexity. Once the scenario had been defined, the programmed motor learning tasks to be performed within the virtual environment were stored into the motion tracking device. Patients were then requested to reproduce the trajectories set by the
virtual teacher or to devise appropriate trajectories in the absence of it, while the upper limb movements were monitored by the virtual teacher, displayed in real time and recorded. The approach also assessed the degree of matching between the virtual teacher  and the patient’s trajectories and provided trainer and trainee with a measure of each trial efficency. In a pilot study (Holden et al, 1999), two chronic patients with massive stroke were trained on a reach-and-grasp task involving shoulder flexion, elbow extension and forearm supination at six increasing levels of complexity. Efficacy was assessed through a 3D kinematic reach test performed in the real world before and after VR-supported rehabilitation; the Fugl-Meyer Test of Motor Recovery for Stroke test (Fugl-Meyer et al, 1975) and the motor task section of the Structured Assessment of Independent Living Skills (SAILS) test of UE function (Mahurin et al, 1991) were used for clinical evaluation. The patients were able to export the abilities learned in VR to the real world and to similar but untrained activities, but hand orientation proved difficult to learn. In successive studies (Holden et al, 2001; Holden et al, 2002), information about the specific (as measured in real tasks designed to evaluate generalization in space, gravitoinertial force, combined spatial/gravitoinertial force, and in tasks requiring novel recombination of trained movement elements, and control tasks with untrained elements) and non-specific (as measured by variations in motor recovery tests after VR supported training activity) motor generalization was used to measure in detail the ability to transfer to the real world what learned in VR. Patients improved in three standard clinical tests of function, even if practicing in two movements only during VR training. It was suggested that VR mediated rehabilitation is an effective and efficient approach to (re)train a set of basic tasks with upgrading to a wide variety of skilled movements (Holden, 2005). Piron et al. (2005) replicated these results in a study on 50 patients with impaired upper limb motion after stroke. The VR supported rehabilitation system included a virtual environment (a PC workstation with a wall screen), a motion tracking device and the dedicated software for editing 3D-scenarios in a learning-by-imitation rehabilitation process with a
virtual teacher. Therapists set the virtual scenarios characteristics and the motor training complexity to match each patient’s motor impairment and rehabilitation protocol, set the starting position, target location and orientation, designed simple/complex tasks, added or removed non-pertinent virtual objects (distractive elements) to increase/reduce the task level of difficulty, recorded trajectory of the desired movement to be (re)learned by the subject, with the virtual teacher visible or hidden as advisable. The degree of motor impairment or recovery and the attained levels of autonomy in daily living activities were measured by the Fugel-Meyer (FM) UE score and the Functional Independence Measure scale (FIM) before and after the therapy, and by means of kinematic measures such as the movement morphology and mean duration and speed. Improvement was observed in the FM UE and FIM mean scores (with 15% and 6% increases, respectively) and in the movement mean duration (18%) and speed (23%), with better regularity of trajectories. Improvements do not appear to have been influenced by age, time since stroke or site of brain damage, as already noted in previous studies (Jeffery & Good, 1995; Johnston et al, 1992; Bagg et al, 2002; Tangeman et al, 1990; Dam et al, 1993). Instead, the severity of impairment was crucial for the outcome and a severe initial impairment was more difficult to rehabilitate. Comparison between the degrees of recovery attained after standard or VR-reinforced learning in two randomly assigned post-stroke patient groups (Turolla et al, 2007) showed significantly increased FM UE scores in all 30 patients, but improvement was greater after VR-supported therapy. The different outcome was ascribed, at least in part, to the feedback information about knowledge-of-results and knowledge-of-performance provided by the system (Todorov et al, 1997; Schmidt & Young, 1991; Winstein et al, 1996) and to the reinforced learning provided by the VR-based rehabilitation approach (Barto, 1994; Doya, 2000; Fagg & Arbib, 1992; Rummelhart et al, 1986).
3.2 VR in the lower limb motor rehabilitation
Several VR applications were designed to recover efficient walking in patients with lower limb motor impairment after stroke (Deutsch et al, 2002 and Deutsch et al, 2004). Fung, et al. (Fung, et al 2004, 2006), performed studies on gait training by using a treadmill mounted on a 6-degree-of-freedom motion platform with a motion-coupled VR environment. The system provided the unique feature of simulated turning within the environment; also provided auditory and visual cues as positive/negative feedback. Subjects were required to wear 3D stereo glasses to visualize the virtual environment. Test results from this project demonstrated improved gait speed with training. More recently, Mirelman et al. studied the effects on impaired gait kinetics of robot-assisted rehabilitation with or without VR support (Mirelman et al, 2007). Subjects in the two subgroups were trained with the same exercises; requested movements were inversion and eversion, dorsiflexion and plantar flexion, or combinations of these. A motion capture system was used to measure movements in association with a force-feedback system permitting navigation within a virtual environment displayed on a computer screen. Gait was estimated at baseline, one week after the training session, and three months after end of the therapy. Feedback information was provided directly by the system to the patients treated in the VR setting and by the therapist to those undergoing rehabilitation withouth VR support. Both groups improved, but patients treated with VR support did better, with increased ankle strenght at the end of treatment and at follow-up. Patients undergoing robotic- assisted rehabilitation without VR support reported fatigue earlier. Park and colleagues (Park et al, 2007) developed a VR system for motor rehabilitation with a PC camera and two markers of movement in a very simple virtual scenario with a crossing-stepping stone task. The success rate in 9 hemiplegic patients with stiff-knee gait after stroke was computed as the ratio of successful trials to the total gait cycles, with a ~30% improvement after treatment.
3.3 VR and telemedicine: the upper limb tele-rehabilitation
VR settings are usable in the transfer of available occupational treatments to a platform for rehabilitation at home, with remote control by therapist. Broeren and coworkers have emphasized the reduced labor, logistics and costs of the state-of art web-based video/audio systems for telemedicine and tested their protocol in a case study, with VR associated to the haptic force feedback necessary for VR object manipulation. The hand fine dexterity and grip improved after treatment (Broeren et al, 2002). More recently, Trotti and colleagues proposed VR-supported training as an integration of the conventional rehabilitation protocols (Trotti et al, 2009). They used kinematics indexes (such as movement execution time and precision) and validated clinical scales, such as Nine-Hole Peg Test (NHPT) (Mathiowetz, 1985), Frenchay Arm Test (FAT) (Heller, 1987), Medical Research Council (MRC) (Florence, 1992), Motricity Index (MI) (Bohannon, 1999), and the Motor Evaluation Scale for Upper extremity in Stroke Patients (MESUPES) (Van de Winckel, 2006) to measure the upper limb impairment in a patient with stroke before and after therapy with VR-supported upper limb rehabilitation. Kinematic analysis and most clinical scales (MRC of fingers, MESUPES and NHPT time, but not MI and FAT) showed a decrease in movement execution time and increase in precision, with improved muscle strenght and movement control (Trotti et al, 2009). VR-mediated telerehabilitation was further investigated (Piron et al, 2009) by comparing two groups (18 subjects each) of patients with stroke treated for four weeks by a VR-assisted rehabilitation program operated through Internet or by conventional therapy. Motor impairment was assessed one month before, at the beginning and end of therapy, and one month later by means of the Fugl-Meyer Upper Extremity (FM EU), Abilhand (Penta et al, 2001) and Ashworth (Bohannon & Smith, 1987) scales. The setting included a virtual teacher showing the correct trajectories as set by the therapist in association with the patient’s actual movement. The knowledge-of-performance was provided via videoconference. No differences were observed when comparing the assessments one month before and at beginning of therapy, but both groups improved after therapy and the improvement was evident also one month after the end of therapy. The FM UE showed better recovery for patients treated through VR-based telerehabilitation.
4. Systems and applications
4.1 Systems and applications for VR-supported upper limb rehabilitation
A VR system purported to measure the impairment in speed, strength, fractionation and range of fingers movements was designed to be distributed over three sites connected via Internet (for rehabilitation, data storage and data access, respectively) (Boian et al, 2002). At the rehabilitation site, the system featured a workstation and two sensing (cyber and haptic) gloves; the data storage site organized the information acquired during the VR-supported rehabilitation; open access to data was through Internet. An algorithm was implemented to increase or decrease according to the achieved performance the difficulty of the target task. The system was tested in a pilot study on 4 patients with stroke. A screen provided the patients with knowledge-of-results and performance (feedback) through a transparent hand representing the target and numerical scores about the trial execution. Trained patients achieved various degrees of improvement, with a good retention in gains and a positive evaluation of the system both by patients and therapists. A virtual tabletop environment for the upper limb rehabilitation after traumatic brain injury was developed (Wilson et al, 2007) to measure the residual function and kinematic markers like speed, precision, distance, accuracy of targeting. The system was innovative because flexible, automated, and relatively inexpensive, with components specifically designed to be user-friendly: LCD panels easy to carry and reducing the set-up time were favored; the virtual environment was displayed on the LCD panel placed horizontally on a tabletop surface, and users could interact with the system by moving sensing-objects over it; knowledge of results was provided to the patient via another LCD panel. Distractive elements appeared on the LCD to increase or decrease the task difficulty. Low-cost implements, such as commercial game controllers and marker tracking were used. Wilson and coworkers suggested that psychometric measures should be preferred in the future and predicted broad application in assessing movement impairment after stroke and ischemic or traumatic brain damage or in movement disorders (e.g. Parkinson or Huntington’s diseases). Therapy WREX (T-WREX) was designed by Reinkensmeyer and Housman (2007) for the hand and arm rehabilitation after stroke to make rehabilitation possible also in the absence of the therapist, with exercises mimiking the daily living activities in VR environment and a feedback information procedure. The system featured a passive gravity-balancing orthosis based on the Wilmington Robotic Esoskeleton (WREX) (Rahman et al, 2007), a hand grip sensor and the software needed for VR and performance evaluation, but was not a robotics/VR integration because WREX assisted patients only against gravity and by elastic bands. It focused on the re-training of function on a plane, therefore displaying the movement on the plane of interest. The system bypassed the problems of 3D complexity, but limitated the movements to be re-learned. Most patients nevertheless found T-WREX less boring than conventional therapy and their progress during rehabilitation easier to track. Reiteration of motor training by T-WREX reduced motor impairment (as measured by the Fugl-Meyer scale) in a preliminary randomized controlled study (Reinkensmaeyer & Housman, 2007).
4.2 Low-cost and open source systems for VR-supported tele-rehabilitation
Interest on tele-rehabilitation as a possible alternative to the traditional treatment of inpatients in hospital increased in recent years with the increment of costs and commitment by the private and public healthcare. Sugarman and colleagues assumed it is impossible for the therapist to monitor patients performing rehabilitation at home, emphasized the therapist’s role in motivating the patient and the need of efficient communication between the therapist and patients at any time and place, including home (Sugarman et al, 2006). Approaches combining VR and mechanical devices for rehabilitation (Fasoli et al, 2004; Coote & Sokes, 2005; Broeren et al, 2004; Reinkensmeyer et al, 2002; Jadhav & Krovi, 2004) appear encouraging, but the systems specifically developed for these purposes have high costs. In alternative, Sugarman and colleagues adopted a commercial feedback joystick in association with a specifically designed armrest and a PC with Internet connection. Their proposed VR solution could be operated in two different modes: stand-alone or cooperative. In the former, patients exercised at home without Internet connection; in the latter, the patient and therapist were online and the therapist could monitor and tutor the patient performing.