Posts Tagged Neurorehabilitation

[WEB PAGE] Ekso Bionics Unveils the EksoNR Neurorehabilitation Device

EksoNR, the latest exoskeleton from Ekso Bionics, features EksoView, a new touchscreen controller that allows therapists to intuitively adapt assistance to challenge patients using real-time feedback. (Photo courtesy of Ekso Bionics Holdings Inc)

EksoNR, the latest exoskeleton from Ekso Bionics, features EksoView, a new touchscreen controller that allows therapists to intuitively adapt assistance to challenge patients using real-time feedback. (Photo courtesy of Ekso Bionics Holdings Inc)

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The EksoNR is a next-generation EksoGT exoskeleton device developed by Ekso Bionics Holdings Inc to aid the neurorehabilitation of patients recovering from stroke and spinal cord injury, and to help them learn to walk again with a more natural gait.

Among the EksoNR’s new features and enhancements is EksoView, a new touchscreen controller that allows therapists to intuitively adapt assistance to challenge patients using real-time feedback and perform outcome measures during use.

Held in the palm of a therapists’ hand, EksoView provides visualization of various exercises beyond gait training, such as balancing, squatting from sit-to-stand positioning, lifting one leg, or standing in place, to actively engage patients and enhance the use of these beneficial features.

Another feature is the optimized SmartAssist software, developed to enable EksoNR to have a smoother and more natural gait path when transitioning between steps.

SmartAssist also gives gait symmetry and posture feedback and allows therapists to track patient progress with the upgraded EksoPulse, a cloud-based analytics solution. EksoPulse now uses rehabilitation data to generate insightful metrics and graphs for therapists and administrators to monitor patient progress and outcomes, Ekso Bionics notes in a media release.

“Ekso Bionics is committed to developing the latest exoskeleton advances for rehabilitation. We continue to innovate to ensure physical therapists have access to the latest tools to deliver better patient outcomes and superior care in neurorehabilitation,” says Jack Peurach, chief executive officer and president of Ekso Bionics, in the release.

“EksoNR is a full neurorehabilitation tool that is effective, intuitive, and differentiating. There is an increasing demand for adoption, as our technology sets rehabilitation centers apart,” he adds.

EksoNR is cleared by the US Federal Drug Administration for stroke and spinal cord injury rehabilitation. The device is also CE-marked and available in Europe.

Ekso Bionics will begin taking orders for EksoNR immediately. Existing customers will have the option to upgrade, the release continues.

[Source: Ekso Bionics]

 

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[ARTICLE] Paired Associative Stimulation as a Tool to Assess Plasticity Enhancers in Chronic Stroke – Full Text

Background and Purpose: The potential for adaptive plasticity in the post-stroke brain is difficult to estimate, as is the demonstration of central nervous system (CNS) target engagement of drugs that show promise in facilitating stroke recovery. We set out to determine if paired associative stimulation (PAS) can be used (a) as an assay of CNS plasticity in patients with chronic stroke, and (b) to demonstrate CNS engagement by memantine, a drug which has potential plasticity-modulating effects for use in motor recovery following stroke.

Methods: We examined the effect of PAS in fourteen participants with chronic hemiparetic stroke at five time-points in a within-subjects repeated measures design study: baseline off-drug, and following a week of orally administered memantine at doses of 5, 10, 15, and 20 mg, comprising a total of seventy sessions. Each week, MEP amplitude pre and post-PAS was assessed in the contralesional hemisphere as a marker of enhanced or diminished plasticity. Strength and dexterity were recorded each week to monitor motor-specific clinical status across the study period.

Results: We found that MEP amplitude was significantly larger after PAS in baseline sessions off-drug, and responsiveness to PAS in these sessions was associated with increased clinical severity. There was no observed increase in MEP amplitude after PAS with memantine at any dose. Motor threshold (MT), strength, and dexterity remained unchanged during the study.

Conclusion: Paired associative stimulation successfully induced corticospinal excitability enhancement in chronic stroke subjects at the group level. However, this response did not occur in all participants, and was associated with increased clinical severity. This could be an important way to stratify patients for future PAS-drug studies. PAS was suppressed by memantine at all doses, regardless of responsiveness to PAS off-drug, indicating CNS engagement.

Introduction

The capacity of the brain to make structural, physiological, and genetic adaptations following stroke, otherwise known as plasticity, is likely to be critical for improving sensorimotor impairments and functional activities. Promotion of adaptive plasticity in the central nervous system (CNS) leading to sustained functional improvement is of paramount importance, given the personal suffering and cost associated with post-stroke disability (Ma et al., 2014). In addition to rehabilitation therapies to retrain degraded motor skills, animal and human studies have tried to augment recovery with neuropharmacologic interventions. Unfortunately, few if any have had a notable effect in patients or have come into routine use (Martinsson et al., 2007Chollet et al., 2011Cramer, 2015Simpson et al., 2015). Methods to screen drugs based on their presumed mechanism of action on plasticity in human motor systems could speed translation to patients. However, there is currently no accepted method in stroke patients for evaluating the potential effectiveness or individual responsiveness to putative “plasticity enhancing” drugs in an efficient, low-cost, cross-sectional manner, in order to establish target engagement in humans and to avoid the extensive time and cost of protracted clinical trials.

Paired associative stimulation (PAS) is a safe, painless, and non-invasive technique known to result in short-term modulation of corticospinal excitability in the adult human motor system, lasting ∼90 min (Stefan et al., 2000Wolters et al., 2003). Post-PAS excitability enhancement has been considered an LTP-like response thought to relate to transient changes in synaptic efficacy in the glutamatergic system at the N-methyl-D-aspartate (NMDA) receptor, since both human NMDA receptor deficiency (Volz et al., 2016) and pharmacological manipulation with dextromethorphan (Stefan et al., 2002) can block the effect. While PAS has been explored as a potential therapeutic intervention in patients with residual motor deficits after stroke (Jayaram and Stinear, 2008Castel-Lacanal et al., 2009), it has not previously been investigated for its potential use as an assay of motor system plasticity in this context. Prior studies have suggested that motor practice and PAS share the same neuronal substrates, modulating LTP and LTD-like plasticity in the human motor system (Ziemann et al., 2004Jung and Ziemann, 2009); therefore, as an established non-invasive human neuromodulation method (Suppa et al., 2017), we reasoned that PAS would be a suitable assay in the present study to examine the effect of a drug on motor system plasticity.

Here, we examine the effect of memantine, a drug used for treatment of Alzheimer’s disease, on the PAS response in patients with chronic stroke. Memantine is described pharmacologically as a low affinity, voltage dependent, non-competitive, NMDA antagonist (Rogawski and Wenk, 2003). At high concentrations, like other NMDA-R antagonists, it can inhibit synaptic plasticity. At lower, clinically relevant concentrations, memantine can, under some circumstances, promote synaptic plasticity by selectively inhibiting extra-synaptic glutamate receptor activity while sparing normal synaptic transmission, and hence may have clinical utility for rehabilitation (Xia et al., 2010). Interest in specifically using the drug for its interaction with stroke pathophysiology stems from animal models of both prevention (Trotman et al., 2015), in which pre-conditioning reduced infarct size, as well as for functional recovery, in which chronic oral administration starting >2 h post-stroke resulted in improved function through a non-neuroprotective mechanism (López-Valdés et al., 2014). In humans, memantine taken over multiple days has been used to demonstrate that the NMDA receptor is implicated in specific transcranial magnetic paired-pulse measures (Schwenkreis et al., 1999), and short-term training-induced motor map reorganization (Schwenkreis et al., 2005). In studies of neuromodulation, memantine blocked the facilitatory effect of intermittent theta-burst stimulation (iTBS) (Huang et al., 2007). Similarly, LTP-like plasticity induced by associative pairing of painful laser stimuli and TMS over primary motor cortex (M1) can also be blocked by memantine (Suppa et al., 2013). The effects of memantine on the PAS response have not yet been demonstrated, including examination of potential dose-response effects, which would be important for the potential clinical application of memantine for stroke recovery.

In our study, we set out to determine whether PAS might be a useful tool to probe the potential for plasticity after stroke in persons with chronic hemiparesis and apply PAS as an assay to look at drug effects on motor system plasticity using memantine. We hypothesized that (a) PAS would enhance corticospinal excitability in the contralesional hemisphere of stroke patients, and that (b) since PAS-induced plasticity is thought to involve a short-term change in glutamatergic synaptic efficacy, memantine would have a dose-dependent effect on PAS response. We predicted that at low doses, memantine would enhance PAS-induced plasticity through selective blockade of extrasynaptic NMDA receptors, whereas higher doses would inhibit PAS-induced plasticity.[…]

 

Continue —> Frontiers | Paired Associative Stimulation as a Tool to Assess Plasticity Enhancers in Chronic Stroke | Neuroscience

Figure 1. Axial MR/CT images for individual patients illustrating the stroke lesion. Images are displayed in radiological convention. Images are labeled by participant number.

 

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[WEB PAGE] An expert opinion: upper limb rehabilitation after stroke

Key take home messages

  1. Clinically meaningful improvements are possible in chronic stroke patients
  2. The dose of rehabilitation treatment needs to be larger than currently delivered
  3. Rehabilitation is a complex intervention that cannot be reduced to a single element

Somewhere between 50-80% of stroke survivors have upper limb symptoms after acute stroke1 and persistent difficulty in using the upper limb is a major contributor to ongoing physical disability.2 A commonly held view is that most recovery from stroke occurs over the first three to six months after which little improvement is possible, especially at the level of impairment.3-6 We argue that this may be a self-fulfilling prophecy resulting in lack of provision of potentially helpful rehabilitation.

What is the best way to promote upper limb recovery after stroke? Most studies of behavioural interventions have investigated forms of constraint induced movement therapy (CIMT),7,8 repetitive task training (RTT)9 or robotics,10 each of which focuses on increasing the activity of the affected limb. Kwakkel et al8suggested that motor function, arm-hand activities and self-reported arm-hand functioning in daily life, improved immediately after CIMT and at long-term follow-up, but the comparison was often with usual care. It is worth noting that CIMT approaches were said to be more likely to be successful in promoting long term benefits if the protocol included shaping, massed practice and a behavioural transfer package, whereas simple forced use therapy was ineffective.8 RTT also has some evidence to support benefits over what is described as usual care, but the evidence for benefits over ‘matched therapy’ is less strong.9 The use of robotics can increase the number of movement repetitions, but has failed to produce clinically meaningful effects.10 Indeed, the recent RATULS study showed that compared with usual care, approximately 23 hours of robot-assisted training and matched dose ‘upper limb therapy’ did not improve upper limb function.11Overall, it would appear that asking patients to make simple repetitions of movement, however meaningful the task, is relatively ineffective without some way of actively translating any improvements into activities of daily living. Simply increasing the number of repetitions does not appear to be effective,12 and this in itself should give us pause for thought.

A few studies have tested more complex therapies incorporating a number of different elements. The ICARE study13 of upper limb treatment after stroke went beyond simple repetitions, using a structured, task-oriented motor training programme that was impairment focused, task specific, intense, engaging, collaborative, self-directed, and patient centred, starting about six weeks post-stroke. Outcomes were not improved by this approach, but on reflection it is likely that, as with many of the studies, the dose of 30 hours over ten weeks was too low (the usual care group received 11.2 hours over ten weeks). Despite scepticism that stroke patients would be able to ‘tolerate’ much higher doses,12 one study managed to deliver 300 hours of upper limb therapy to chronic stroke patients over twelve weeks and reported changes in measures of both impairment and activity that were far greater than those in lower dose studies,14 and in fact the findings of this study have recently been replicated by the same group.15 We recently reported the findings of the Queen Square Upper Limb (QSUL) Neurorehabilitation programme,16 a single centre clinical service that provides 90 hours of treatment focusing on the post-stroke upper limb. Most patients entering the programme were in the chronic stage (> 6 months post-stroke), but were able to complete the 90 hours of the programme, even though they exhibited a wide range of impairments and fatigue levels. Despite the time since stroke (median = 18 months) we observed (i) large clinically meaningful improvements in upper limb impairment and activity (of a magnitude similar to those reported by McCabe et al.), and importantly (ii) that these changes were maintained, or even improved upon, six months after treatment.

The first lesson to take from these studies is that post-stroke rehabilitation programmes and clinical trials are almost certainly under dosing patients. In future, clinical trials must investigate the effects of much higher doses than are currently being used. The second question to be raised is what are the key ‘active ingredients’ of an upper limb rehabilitation treatment? Whilst it is not clear what the optimal behavioural approach for promoting upper limb recovery should be, it is clear that simple protocol driven approaches have not led to large or sustained effects,17 both of which are necessary to produce a step change in stroke recovery. Successful post-stroke neurorehabilitation is likely to require a combination of complimentary approaches. If we accept this premise, then we are unlikely to determine the optimal combination of active ingredients simply by studying each approach in isolation, because the interactions between these elements will also have to be considered.

So how do we work out what the ‘active ingredients’ of upper limb rehabilitation are? A more sensible way forward is to look at interventions that have already demonstrated a high level of efficacy and then begin to work out their key components. Here, it is important to say that we need to start with treatments that have a high chance of achieving minimum clinically important differences (MCID) rather than small changes that might be statistically significant. Both McCabe et al14 and Daly et al,15 as well as the QSUL programme,16 produced large improvements on both impairment and activity limitation and both involved more complex treatment approaches, not restricted to one element. It is worth considering these in more detail.

  • Analysis of movement and performance in activities of daily living. The initial assessment is crucial. The question, ‘why does this person’s hand and arm not work’ should never be answered with ‘because they have had a stroke’. There needs to be an appreciation of the range of potential contributory impairments (patterns of weakness, spasticity, loss of joint range, shoulder restriction and pain, sensory loss, apraxia, cognitive deficits, depression, apathy, fatigue etc.) because each of these becomes a therapeutic target. Our view is that without informed clinical reasoning based on the presence or absence of specific impairments, the correct treatment approach is unlikely to be selected.
  • Identify and treat barriers. Avoid complications that will prevent participation in an active rehabilitation programme. We commonly see loss of passive joint range preventing people accessing finger or thumb movement, due to either spasticity or non-neural shortening. This can happen at most joints, but particularly in the hand. As well as increased finger flexion, be alert to loss of flexion at MCP joints which makes it difficult to shape the hand properly. Treatment involves splinting and optimal spasticity management. We also see pain and restriction of range in the shoulder. Restriction of external rotation in particular should raise the possibility of adhesive capsulitis. Despite the lack of a clear evidence base for treating post-stroke adhesive capsulitis, anecdotally we have had success with capsular hydrodilatation followed by physiotherapy.
  • Preparation. Manual techniques are used to optimise and improve baseline at an impairment level, for example mobilising joints to improve range, lengthening and strengthening muscles to ensure they are at a biomechanical advantage to generate force, training sensory discrimination and improving postural control and balance.
  • Reduction of impairment and re-education of quality and control of movement within activities of daily living. Individualised meaningful tasks are practiced repeatedly in order to facilitate task mastery with a focus on quality of movement. This is achieved through (i) adaptation of the task, e.g. decomposing tasks into individual components to be practiced; (ii) adaptation of the environment, e.g. fabrication of functional splints and adaptation of tools such as cutlery or screwdrivers, to enable integration of the affected hand in meaningful activities; (iii) assistance, e.g. de-weighting the arm to allow strengthening and training of movement quality and control through increased range.
  • Coaching (involving instruction, supervision, reinforcement) was considered a key component of the QSUL programme, used throughout to embed new skills and knowledge into individual daily routines. Consequently, individuals increase participation and confidence in their desired goals, enhancing self-efficacy and motivation to sustain behavioural change beyond the end of the active treatment period.
  • Sustaining change. Our view is that the approach described, delivered at a high dose is most likely to achieve clinically meaningful improvement together with improved self-efficacy and behaviour change that results in retention of gains or further improvement (something not routinely seen with many upper limb interventions that have been investigated).

Rehabilitation is often criticised for not following standardised approaches that lend themselves to investigation through clinical trials. However, when single elements are then studied in isolation the results are often not clinically meaningful and are not sustained.18,19 Looking at the difference between these approaches and those taken by McCabe et al14, Daly et al15 and QSUL16 should be informative, with a view to formally describing the key elements of a successful treatment. Whilst approaches at the activity and participation level will vary as they are tailored to an individual’s specific meaningful goals, the overall therapeutic approach taken towards specific impairments should be the same across all patients. Ideally, it should be possible to describe the principles of an optimal intervention using a format such as the TIDIER guidelines.18,19

There is a way to go before we can really say we understand both the treatment itself and the effects of the treatment on individuals. This will require careful assessment of both the ‘input’ (the nature of the behavioural intervention) and of the ‘output’ (the resulting behavioural change) at a level of fine-grained detail that is not currently achieved on a regular basis, for example using kinematic20 or neurophysiological21 assessment. In addition, this input-output relationship will be modulated by a number of patient characteristics, which could relate to behavioural characteristics (e.g. severity, presence of multiple impairments) or to biological characteristics (e.g. the nature and extent of brain damage, time since stroke, age, medication).

Overall, our experience suggests that much higher doses and intensity of upper limb neurorehabilitation can be delivered with beneficial effects. We have highlighted the need to consider the dose and the nature of the intervention as well as appropriate patient stratification in informing future clinical trial design.

Figure 1. Outcome scores for all patients on the Queen Square Upper Limb Rehabilitation programme. Each data point represents a single patient. Top row shows individual scores at admission, discharge, six weeks and six months after discharge. Bottom row shows the individual difference scores for admission to discharge, admission to six weeks post-discharge, and admission to six months post-discharge. Scores are shown for modified Fugl-Meyer (upper limb), Action Research Arm Test and Chedoke Arm and Hand Activity Inventory (CAHAI). Median (solid line) and upper and lower quartiles (dotted lines) are shown. (Reproduced with permission from Ward et al, J Neurol Neurosurg Psychiatry. 2019 May;90(5):498-506).


References

  1. Lawrence ES et al. Estimates of the prevalence of acute stroke impairments and disability in a multiethnic population. Stroke. 2001;32:1279–1284.
  2. Broeks JG, Lankhorst GJ, Rumping K, Prevo AJ. The long-term outcome of arm function after stroke: results of a follow-up study. Disabil Rehabil. 1999;21:357–364.
  3. Kwakkel G, Kollen BJ, van der Grond J, Prevo AJH. Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke. Stroke. 2003;34:2181–2186.
  4. Nakayama H, Jørgensen HS, Raaschou HO, Olsen TS. Recovery of upper extremity function in stroke patients: the Copenhagen Stroke Study. Arch Phys Med Rehabil. 1994;75:394–398.
  5. Sunderland A et al. Enhanced physical therapy for arm function after stroke: a one year follow up study. J. Neurol. Neurosurg. Psychiatr. 1994;57:856–858.
  6. Wade DT, Langton-Hewer R, Wood VA, Skilbeck CE, Ismail HM. The hemiplegic arm after stroke: measurement and recovery. J. Neurol. Neurosurg. Psychiatr. 1983;46:521–524 .
  7. Corbetta D, Sirtori V, Castellini G, Moja L, Gatti R. Constraint-induced movement therapy for upper extremities in people with stroke. Cochrane Database Syst Rev CD004433 (2015). doi:10.1002/14651858.CD004433.pub3
  8. Kwakkel G, Veerbeek J, van Wegen EEH, Wolf SL. Constraint-induced movement therapy after stroke. Lancet Neurol. 2015;14:224–234.
  9. French B et al. Repetitive task training for improving functional ability after stroke. Cochrane Database Syst Rev. 2016;11:CD006073.
  10. Veerbeek JM, Langbroek-Amersfoort AC, van Wegen, EEH, Meskers CGM, Kwakkel G. Effects of Robot-Assisted Therapy for the Upper Limb After Stroke. Neurorehabil Neural Repair. 2017;31: 107–121.
  11. Rodgers H et al. Robot assisted training for the upper limb after stroke (RATULS): a multicentre randomised controlled trial. Lancet (2019). doi:10.1016/S0140-6736(19)31055-4.
  12. Lang CE et al. Dose response of task-specific upper limb training in people at least 6 months poststroke: A phase II, single-blind, randomized, controlled trial. Ann. Neurol. 2016;80:342–354.
  13. Winstein CJ et al. Effect of a Task-Oriented Rehabilitation Program on Upper Extremity Recovery Following Motor Stroke: The ICARE Randomized Clinical Trial. JAMA. 2016;315:571–581.
  14. McCabe J, Monkiewicz M, Holcomb J, Pundik S, Daly JJ. Comparison of robotics, functional electrical stimulation, and motor learning methods for treatment of persistent upper extremity dysfunction after stroke: a randomized controlled trial. Arch Phys Med Rehabil. 2015; 96:981–990.
  15. Daly JJ et al. Long-Dose Intensive Therapy Is Necessary for Strong, Clinically Significant, Upper Limb Functional Gains and Retained Gains in Severe/Moderate Chronic Stroke. Neurorehabil Neural Repair. 1545968319846120 (2019). doi:10.1177/1545968319846120.
  16. Ward NS, Brander F, Kelly K. Intensive upper limb neurorehabilitation in chronic stroke: outcomes from the Queen Square programme. J Neurol Neurosurg Psychiatry jnnp-2018-319954 (2019). doi:10.1136/jnnp-2018-319954
  17. Pollock A et al. Interventions for improving upper limb function after stroke. Cochrane Database Syst Rev. CD010820 (2014). doi:10.1002/14651858.CD010820.pub2
  18. Hoffmann TC et al. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ. 2014;348;g1687.
  19. Walker MF et al. Improving the Development, Monitoring and Reporting of Stroke Rehabilitation Research: Consensus-Based Core Recommendations from the Stroke Recovery and Rehabilitation Roundtable. Neurorehabil Neural Repair. 2017;31:877–884.
  20. Balasubramanian S, Colombo R, Sterpi I, Sanguineti V, Burdet E. Robotic assessment of upper limb motor function after stroke. Am J Phys Med Rehabil. 2012;91:S255-269.
  21. Cheung VCK et al. Muscle synergy patterns as physiological markers of motor cortical damage. Proc. Natl. Acad. Sci. U.S.A. 2012;109:14652–14656.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Correspondence to: Nick Ward, The National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG.
Conflict of interest statement: None declared.
Provenance and peer review: Submitted and externally reviewed.
Date first submitted: 15/4/19
Date resubmitted after peer review: 10/6/19
Acceptance date: 11/6/19
To cite: Ward NS, Kelly K, Brander F. ACNR 2019;18(4):20-22
Published online: 1/8/19

via An expert opinion: upper limb rehabilitation after stroke | ACNR | Online Neurology Journal

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[WEB PAGE] The Use of Noninvasive Brain Stimulation, Specifically Transcranial Direct Current Stimulation After Stroke

Motor impairment is a leading cause of disability after stroke. Approaches such as noninvasive brain stimulation are being investigated to attempt to increase effectiveness of stroke rehabilitation interventions. There are several types of noninvasive brain stimulation: repetitive transcranial magnetic stimulation, transcranial direct stimulation (tDCS), transcranial alternative current stimulation, and transcranial pulsed ultrasound to name a few. Of the types of noninvasive brain stimulation, repetitive transcranial magnetic stimulation and tDCS have been most extensively tested to modulate brain activity and potentially behavior. These two techniques have distinctive modes of action. Repetitive transcranial magnetic stimulation directly stimulates neurons in the brain and, given the appropriate conditions, leads to new action potentials. On the other hand, tDCS polarizes neuronal tissue including neurons and glia modulating ongoing firing patterns. There are also differences in cost, utility, and knowledge skill required to apply tDCS and repetitive transcranial magnetic stimulation. Transcranial direct stimulation is relatively inexpensive, easy to administer, portable, and may be applied while undergoing therapy, with lasting excitability changes detectable up to 90 minutes after administration. Repetitive transcranial magnetic stimulation equipment is bulkier, expensive, technically more challenging, and a patient’s head must remain still when treatment is being applied therefore needs to be administered before or after a session of rehabilitation. Because of these differences, tDCS has been more accessible and has rapidly grew as a potential tool to be used in neurorehabilitation to facilitate retraining of activities of daily living (ADL) capacity and possibly to improve restoration of neurological function after stroke.

There are three current stimulation approaches using tDCS to modulate corticomotor regions after stroke. In anodal stimulation mode, the anode electrode is placed over the lesioned brain area and a reference electrode is applied over the contralateral orbitofrontal cortex. Anodal tDCS is placed over the ipsilesional hemisphere to improve the responses of perilesional areas to training protocols. In cathodal stimulation, the cathode electrode is placed over the nonlesioned brain area and reference electrode over the contralateral (ipsilesional) orbitofrontal cortex. This approach has been predicated on the hypothesis that the nonstroke hemisphere will be inhibited by tDCS resulting in an increased activation of the ipsilesional hemisphere due to rebalancing of a presumably abnormal interhemispheric interaction. Although some studies have shown this approach to be beneficial, the causative role of interhemispheric interaction imbalance has been recently challenged and refuted.1 Thus, if cathodal stimulation approaches are beneficial, the behavioral effect cannot be explained by a presumed correction of abnormal interhemispheric connectivity. Finally, dual tDCS approach involves simultaneous application of the anode over the ipsilesional and the cathode over the contralesional side. Here again, the intended mechanism of action is to rebalance the presumably abnormal interhemispheric interaction.

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CLINICAL QUESTIONS ADDRESSED

What is the best tDCS type and electrical configuration? What are the effects of tDCS with rehabilitation program for upper limb recovery after stroke?

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RESEARCH FINDINGS OF tDCS

This short article discusses data obtained from a network meta-analysis of randomized controlled trials and a recent meta-analysis. The network meta-analysis included 12 randomized controlled trials including 284 participants examining the effect of tDCS on ADL function in the acute, subacute, and chronic phases after stroke.2 The meta-analysis included 9 studies with 371 participants in any stage after stroke.3

The network meta-analysis found evidence of a significant moderate effect in favor of cathodal tDCS without significant effects of dual tDCS, anodal tDCS, or sham tDCS. There was no difference in safety (as assessed by dropouts and adverse events) between sham tDCS, physical rehabilitation, cathodal tDCS, dual tDCS, and anodal tDCS. Elsner in a previous review of tDCS in 2016 found an effect on improving ADL, as well as function of the arm and lower limb, muscle strength, and cognition. Thus, the findings from the most recent meta-analysis indicating cathodal that tDCS improves ADL capacity are in line with previous meta-analyses. Of note, there was no evidence of an effect of either cathodal or other tDCS stimulation approaches on upper paretic limb impairment after stroke as measured by the Fugl-Meyer scale.

A meta-analysis that included participants in any stage after the stroke showed that tDCS in conjunction with multiple sessions of rehabilitation had no significant effect over delivering therapy alone for upper limb impairment and activity after stroke. This negative finding might be due to patient’s being in an acute, subacute, or chronic stage after stroke as well as variations in the type of therapy performed paired with tDCS (ie, conventional vs. constraint-induced movement therapy vs. robot protocol).

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RECOMMENDATIONS FOR PHYSIATRIC PRACTICE

There seems to be a modest effect supporting the use of tDCS as a co-adjuvant of rehabilitation interventions to improve ADLs after stroke. Cathodal tDCS seems to be the most promising approach, especially when applied early after the stroke. However, the evidence remains preliminary and does not warrant a widespread change in clinical rehabilitation practice at this time.

There is no evidence supporting the use of tDCS to improve motor impairment (as measured by the FMS) at this point.

Importantly, tDCS remains as a very safe intervention, with no differences in safety when real vs. control tDCS is applied.

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REFERENCES

1. Xu J, Branscheidt M, Schambra H, et al: Rethinking interhemispheric imbalance as a target for stroke neurorehabilitation. Ann Neurol 2019;85:502–13

2. Elsner B, Kwakkel G, Kugler J, et al: Transcranial direct current stimulation (tDCS) for improving capacity in activities and arm function after stroke: a network meta-analysis of randomised controlled trials. J Neuroeng Rehabil 2017;14:

3. Tedesco Triccas L, Burridge J, Hughes A, et al: Multiple sessions of transcranial direct current stimulation and upper extremity rehabilitation in stroke: a review and meta-analysis. Clin Neurophysiol2016;127:946–55

via The Use of Noninvasive Brain Stimulation, Specifically Trans… : American Journal of Physical Medicine & Rehabilitation

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[ARTICLE] Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling – Full Text

Abstract

Background

Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery.

Methods

We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time.

Results

We demonstrated patients’ control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients.

Conclusions

Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots.

Background

The ability to walk directly relates to quality of life. Neurological lesions such as those underlying stroke and spinal cord injury (SCI) often result in severe motor impairments (i.e., paresis, spasticity, abnormal joint couplings) that compromise an individual’s motor capacity and health throughout the life span. For several decades, scientific effort in rehabilitation robotics has been directed towards exoskeletons that can help enhance motor capacity in neurologically impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had limited performance when tested in healthy individuals [1] and have achieved only modest clinical impact in neurologically impaired patients [2], e.g., stroke [34], SCI patients [5]. […]

 

Continue —>  Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1

Fig. 1 Enter aSchematic representation of the real-time modeling framework and its communication with the robotic exoskeleton. The whole framework is operated by a Raspberry Pi 3 single-board computer. The framework consists of five main components: a The EMG plugin collects muscle bioelectric signals from wearable active electrodes and transfers them to the EMG-driven model. b The B-spline component computes musculotendon length (Lmt) and moment arm (MA) values from joint angles collected via robotic exoskeleton sensors. c The EMG-driven model uses input EMG, Lmt and MA data to compute the resulting mechanical forces in 12 lower-extremity musculotendon units (Table 1) and joint moment about the degrees of freedom of knee flexion-extension and ankle plantar-dorsiflexion. d The offline calibration procedure identifies internal parameters of the model that vary non-linearly across individuals. These include optimal fiber length and tendon slack length, muscle maximal isometric force, and excitation-to-activation shape factors. eThe exoskeleton plugin converts EMG-driven model-based joint moment estimates into exoskeleton control commands. Please refer to the Methods section for an in-depth description caption

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[ARTICLE] Efficacy and Brain Imaging Correlates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report – Full Text

To maximize brain plasticity after stroke, a plethora of rehabilitation strategies have been explored. These include the use of intensive motor training, motor-imagery (MI), and action-observation (AO). Growing evidence of the positive impact of virtual reality (VR) techniques on recovery following stroke has been shown. However, most VR tools are designed to exploit active movement, and hence patients with low level of motor control cannot fully benefit from them. Consequently, the idea of directly training the central nervous system has been promoted by utilizing MI with electroencephalography (EEG)-based brain-computer interfaces (BCIs). To date, detailed information on which VR strategies lead to successful functional recovery is still largely missing and very little is known on how to optimally integrate EEG-based BCIs and VR paradigms for stroke rehabilitation. The purpose of this study was to examine the efficacy of an EEG-based BCI-VR system using a MI paradigm for post-stroke upper limb rehabilitation on functional assessments, and related changes in MI ability and brain imaging. To achieve this, a 60 years old male chronic stroke patient was recruited. The patient underwent a 3-week intervention in a clinical environment, resulting in 10 BCI-VR training sessions. The patient was assessed before and after intervention, as well as on a one-month follow-up, in terms of clinical scales and brain imaging using functional MRI (fMRI). Consistent with prior research, we found important improvements in upper extremity scores (Fugl-Meyer) and identified increases in brain activation measured by fMRI that suggest neuroplastic changes in brain motor networks. This study expands on the current body of evidence, as more data are needed on the effect of this type of interventions not only on functional improvement but also on the effect of the intervention on plasticity through brain imaging.

Introduction

Worldwide, stroke is a leading cause of adult long-term disability (Mozaffarian et al., 2015). From those who survive, an increased number is suffering with severe cognitive and motor impairments, resulting in loss of independence in their daily life such as self-care tasks and participation in social activities (Miller et al., 2010). Rehabilitation following stroke is a multidisciplinary approach to disability which focuses on recovery of independence. There is increasing evidence that chronic stoke patients maintain brain plasticity, meaning that there is still potential for additional recovery (Page et al., 2004). Traditional motor rehabilitation is applied through physical therapy and/or occupational therapy. Current approaches of motor rehabilitation include functional training, strengthening exercises, and range of movement exercises. In addition, techniques based on postural control, stages of motor learning, and movement patterns have been proposed such as in the Bobath concept and Bunnstrom approach (amongst others) (Bobath, 1990). After patients complete subacute rehabilitation programs, many still show significant upper limb motor impairment. This has important functional implications that ultimately reduce their quality of life. Therefore, alternative methods to maximize brain plasticity after stroke need to be developed.

So far, there is growing evidence that action observation (AO) (Celnik et al., 2008) and motor imagery (MI) improve motor function (Mizuguchi and Kanosue, 2017) but techniques based on this paradigm are not widespread in clinical settings. As motor recovery is a learning process, the potential of MI as a training paradigm relies on the availability of an efficient feedback system. To date, a number of studies have demonstrated the positive impact of virtual-reality (VR) based on neuroscientific grounds on recovery, with proven effectiveness in the stroke population (Bermúdez i Badia et al., 2016). However, patients with no active movement cannot benefit from current VR tools due to low range of motion, pain, fatigue, etc. (Trompetto et al., 2014). Consequently, the idea of directly training the central nervous system was promoted by establishing an alternative pathway between the user’s brain and a computer system.

This is possible by using electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs), since they can provide an alternative non-muscular channel for communication and control to the external world (Wolpaw et al., 2002), while they could also provide a cost-effective solution for training (Vourvopoulos and Bermúdez, 2016b). In rehabilitation, BCIs could offer a unique tool for rehabilitation since they can stimulate neural networks through the activation of mirror neurons (Rizzolatti and Craighero, 2004) by means of action-observation (Kim et al., 2016), motor-intent and motor-imagery (Neuper et al., 2009), that could potentially lead to post-stroke motor recovery. Thus, BCIs could provide a backdoor to the activation of motor neural circuits that are not stimulated through traditional rehabilitation techniques.

In EEG-based BCI systems for motor rehabilitation, Alpha (8–12 Hz) and Beta (12–30 Hz) EEG rhythms are utilized since they are related to motor planning and execution (McFarland et al., 2000). During a motor attempt or motor imagery, the temporal pattern of the Alpha rhythms desynchronizes. This rhythm is also named Rolandic Mu-rhythm or the sensorimotor rhythm (SMR) because of its localization over the sensorimotor cortices. Mu-rhythms are considered indirect indications of functioning of the mirror neuron system and general sensorimotor activity (Kropotov, 2016). These are often detected together with Beta rhythm changes in the form of an event-related desynchronization (ERD) when a motor action is executed (Pfurtscheller and Lopes da Silva, 1999). These EEG patterns are primarily detected during task-based EEG (e.g., when the participant is actively moving or imagining movement) and they are of high importance in MI-BCIs for motor rehabilitation.

A meta-analysis of nine studies (combined N = 235, sample size variation 14 to 47) evaluated the clinical effectiveness of BCI-based rehabilitation of patients with post-stroke hemiparesis/hemiplegia and concluded that BCI technology could be effective compared to conventional treatment (Cervera et al., 2018). This included ischemic and hemorrhagic stroke in both subacute and chronic stages of stoke, between 2 to 8 weeks. Moreover, there is evidence that BCI-based rehabilitation promotes long-lasting improvements in motor function of chronic stroke patients with severe paresis (Ramos-Murguialday et al., 2019), while overall BCI’s are starting to prove their efficacy as rehabilitative technologies in patients with severe motor impairments (Chaudhary et al., 2016).

The feedback modalities used for BCI motor rehabilitation include: non-embodied simple two-dimensional tariffs on a screen (Prasad et al., 2010Mihara et al., 2013), embodied avatar representation of the patient on a screen or with augmented reality (Holper et al., 2010Pichiorri et al., 2015), neuromuscular electrical stimulation (NMES) (Kim et al., 2016Biasiucci et al., 2018). and robotic exoskeletal orthotic movement facilitation (Ramos-Murguialday et al., 2013Várkuti et al., 2013Ang et al., 2015). In addition, it has been shown that multimodal feedback lead to a significantly better performance in motor-imagery (Sollfrank et al., 2016) but also multimodal feedback combined with motor-priming, (Vourvopoulos and Bermúdez, 2016a). However, there is no evidence which modalities are more efficient in stroke rehabilitation are.

Taking into account all previous findings in the effects of multimodal feedback in MI training, the purpose of this case study is to examine the effect of the MI paradigm as a treatment for post-stroke upper limb motor dysfunction using the NeuRow BCI-VR system. This is achieved through the acquisition of clinical scales, dynamics of EEG during the BCI treatment, and brain activation as measured by functional MRI (fMRI). NeuRow is an immersive VR environment for MI-BCI training that uses an embodied avatar representation of the patient arms and haptic feedback. The combination of MI-BCIs with VR can reinforce activation of motor brain areas, by promoting the illusion of physical movement and the sense of embodiment in VR (Slater, 2017), and hence further engaging specific neural networks and mobilizing the desired neuroplastic changes. Virtual representation of body parts paves the way to include action observation during treatment. Moreover, haptic feedback is added since a combination of feedback modalities could prove to be more effective in terms of motor-learning (Sigrist et al., 2013). Therefore, the target of this system is to be used by patients with low or no levels of motor control. With this integrated BCI-VR approach, severe cases of stroke survivors may be admitted to a VR rehabilitation program, complementing traditional treatment.

Methodology

Patient Profile

In this pilot study we recruited a 60 years old male patient with left hemiparesis following cerebral infarct in the right temporoparietal region 10 months before. The participant had corrected vision through eyewear, he had 4 years of schooling and his experience with computers was reported as low. Moreover, the patient was on a low dose of diazepam (5 mg at night to help sleep), dual antiplatelet therapy, anti-hypertensive drug and metformin. Hemiparesis was associated with reduced dexterity and fine motor function; however, sensitivity was not affected. Other sequelae of the stroke included hemiparetic gait and dysarthria. Moreover, a mild cognitive impairment was identified which did not interfere with his ability to perform the BCI-VR training. The patient had no other relevant comorbidities. Finally, the patient was undergoing physiotherapy and occupational therapy at the time of recruitment and had been treated with botulinum toxin infiltration 2 months before due to focal spasticity of the biceps brachii.

Intervention Protocol

The patient underwent a 3-weeks intervention with NeuRow, resulting in 10 BCI sessions of a 15 min of exposure in VR training per session. Clinical scales, motor imagery capability assessment, and functional -together with structural- MRI data had been gathered in three time-periods: (1) before (serving as baseline), (2) shortly after the intervention and (3) one-month after the intervention (to assess the presence of long-term changes). Finally, electroencephalographic (EEG) data had been gathered during all sessions, resulting in more than 20 datasets of brain electrical activity.

The experimental protocol was designed in collaboration with the local healthcare system of Madeira, Portugal (SESARAM) and approved by the scientific and ethic committees of the Central Hospital of Funchal. Finally, written informed consent was obtained from the participant upon recruitment for participating to the study but also for the publication of the case report in accordance with the 1964 Declaration of Helsinki.

Assessment Tools

A set of clinical scales were acquired including the following:

1. Montreal Cognitive Assessment (MoCA). MoCA is a cognitive screening tool, with a score range between 0 and 30 (a score greater than 26 is considered to be normal) validated also for the Portuguese population, (Nasreddine et al., 2005).

2. Modified Ashworth scale (MAS). MAS is a 6-point rating scale for measuring spasticity. The score range is 0, 1, 1+, 2, 3, and 4 (Ansari et al., 2008).

3. Fugl-Meyer Assessment (FMA). FMA is a stroke specific scale that assesses motor function, sensation, balance, joint range of motion and joint pain. The motor domain for the upper limb has a maximum score of 66 (Fugl-Meyer et al., 1975).

4. Stroke Impact Scale (SIS). SIS is a subjective scale of the perceived stroke impact and recovery as reported by the patient, validated for the Portuguese population. The score of each domain of the questionnaire ranges from 0 to 100 (Duncan et al., 1999).

5. Vividness of Movement Imagery Questionnaire (VMIQ2). VMIQ2 is an instrument that assess the capability of the participant to perform imagined movements from external perspective (EVI), internal perspective imagined movements (IVI) and finally, kinesthetic imagery (KI) (Roberts et al., 2008).

NeuRow BCI-VR System

EEG Acquisition

For EEG data acquisition, the Enobio 8 (Neuroelectrics, Barcelona, Spain) system was used. Enobio is a wearable wireless EEG sensor with 8 EEG channels for the recording and visualization of 24-bit EEG data at 500 Hz and a triaxial accelerometer. The spatial distribution of the electrodes followed the 10–20 system configuration (Klem et al., 1999) with the following electrodes over the somatosensory and motor areas: Frontal-Central (FC5, FC6), Central (C1, C2, C3, C4), and Central-Parietal (CP5, CP6) (Figure 1A). The EEG system was connected via Bluetooth to a dedicated desktop computer, responsible for the EEG signal processing and classification, streaming the data via UDP through the Reh@Panel (RehabNet Control Panel) for controlling the virtual environment. The Reh@Panel is a free tool that acts as a middleware between multiple interfaces and virtual environments (Vourvopoulos et al., 2013).

FIGURE 1

Figure 1. Experimental setup, including: (A) the wireless EEG system; (B) the Oculus HMD, together with headphones reproducing the ambient sound from the virtual environment; (C) the vibrotactile modules supported by a custom-made table-tray, similar to the wheelchair trays used for support; (D) the visual feedback with NeuRow game. A written informed consent was obtained for the publication of this image.

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Continue —->  Frontiers | Efficacy and Brain Imaging Correlates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report | Frontiers in Human Neuroscience

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[ARTICLE] Effects of a Brain-Computer Interface With Virtual Reality (VR) Neurofeedback: A Pilot Study in Chronic Stroke Patients – Full Text

Rehabilitation for stroke patients with severe motor impairments (e.g., inability to perform wrist or finger extension on the affected side) is burdensome and difficult because most current rehabilitation options require some volitional movement to retrain the affected side. However, although these patients participate in therapy requiring volitional movement, previous research has shown that they may receive modest benefits from action observation, virtual reality (VR), and brain-computer interfaces (BCIs). These approaches have shown some success in strengthening key motor pathways thought to support motor recovery after stroke, in the absence of volitional movement. The purpose of this study was to combine the principles of VR and BCI in a platform called REINVENT and assess its effects on four chronic stroke patients across different levels of motor impairment. REINVENT acquires post-stroke EEG signals that indicate an attempt to move and drives the movement of a virtual avatar arm, allowing patient-driven action observation neurofeedback in VR. In addition, synchronous electromyography (EMG) data were also captured to monitor overt muscle activity. Here we tested four chronic stroke survivors and show that this EEG-based BCI can be safely used over repeated sessions by stroke survivors across a wide range of motor disabilities. Finally, individual results suggest that patients with more severe motor impairments may benefit the most from EEG-based neurofeedback, while patients with more mild impairments may benefit more from EMG-based feedback, harnessing existing sensorimotor pathways. We note that although this work is promising, due to the small sample size, these results are preliminary. Future research is needed to confirm these findings in a larger and more diverse population.

Introduction

Stroke is a leading cause of adult long-term disability worldwide (Mozaffarian et al., 2015), and an increasing number of stroke survivors suffer from severe cognitive and motor impairments each year. This results in a loss of independence in their daily life, such as decreased ability to perform self-care tasks and decreased participation in social activities (Miller et al., 2010). Rehabilitation following stroke focuses on maximizing restoration of lost motor and cognitive functions and on relearning skills to better perform activities of daily living (ADLs). There is increasing evidence that the brain remains plastic at later stages after stroke, suggesting additional recovery remains possible (Page et al., 2004Butler and Page, 2006). To maximize brain plasticity, several rehabilitation strategies have been exploited, including the use of intensive rehabilitation (Wittenberg et al., 2016), repetitive motor training (Thomas et al., 2017), mirror therapy (Pérez-Cruzado et al., 2017), motor-imagery (Kho et al., 2014), and action observation (Celnik et al., 2008), amongst others.

Recently, growing evidence of the positive impact of virtual reality (VR) techniques on recovery following stroke has accumulated (Bermúdez i Badia et al., 2016). When combined with conventional therapy, VR is able to effectively incorporate rehabilitation strategies such as intensity, frequency, and duration of therapy in a novel and low-cost approach in the stroke population (Laver et al., 2017). However, patients with low levels of motor control cannot benefit from current VR tools due to their low volitional motor control, range of motion, pain, and fatigue. Rehabilitation for these individuals is challenging because most current training options require some volitional movement to train the affected side, however, research has shown that individuals with severe stroke may receive modest benefits from action observation and brain-computer interfaces (BCIs) (Silvoni et al., 2011).

Merging BCIs with VR allows for a wide range of experiences in which patients can feel immersed in various aspects of their environment. This allows patients to control their experiences in VR using only brain activity, either directly (e.g., movement in VR through explicit control) or indirectly (e.g., modulating task difficulty level based on workload as implicit control) (Vourvopoulos et al., 2016Friedman, 2017). This direct brain-to-VR communication can induce a sensorimotor contingency between the patient’s internal intentions and the environment’s responsive actions, increasing the patient’s sense of embodiment of their virtual avatar (Slater, 2009Ramos-Murguialday et al., 2013).

After a stroke resulting in severe motor impairments (e.g., inability to perform wrist or finger extension on the affected side), research shows that action observation combined with physical training enhances the effects of motor training (Celnik et al., 2008), eliciting motor-related brain activity in the lesioned hemisphere, leading to modest motor improvements (Ertelt et al., 2007Garrison et al., 2013). Moreover, action observation in a head-mounted VR increases motor activity in both healthy and the post-stroke brains (Ballester et al., 2015Vourvopoulos and Bermúdez i Badia, 2016a).

In addition, neurofeedback through BCIs has been proposed for individuals with severe stroke because BCIs do not require active motor control. Research on BCIs for rehabilitation has shown that motor-related brain signals are reinforced by rewarding feedback so they can be used to strengthen key motor pathways that are thought to support motor recovery after stroke (Wolpaw, 2012). Such feedback has previously shown modest success in motor rehabilitation for severe stroke patients (Soekadar et al., 2015).

The most common brain signal acquisition technology used with BCIs in stroke patients is non-invasive electroencephalography (EEG) (Wolpaw, 2012), which provide a cost-effective BCI platform (Vourvopoulos and Bermúdez i Badia, 2016b). In BCI paradigms for motor rehabilitation, EEG signals related to motor planning and execution are utilized. During a motor attempt, the temporal pattern of the Alpha rhythm (8–12 Hz) desynchronizes. The Alpha rhythm is also termed Rolandic mu or the sensorimotor rhythm (SMR) when it is localized over the sensorimotor cortices of the brain. Mu rhythms (8–12 Hz) are considered indirect indications of the action observation network (Kropotov, 2016) and reflect general sensorimotor activity. Mu rhythms are often detected with changes in the Beta rhythm (12–30 Hz) in the form of event-related desynchronization (ERD), in which a motor action is executed (Pfurtscheller and Lopes da Silva, 1999). These EEG rhythms, or motor-related EEG signatures, are primarily detected during task-based EEG (i.e., when the patient is actively moving or imagining movement) and used for neurofeedback.

Further, neurofeedback-induced changes in brain activity have also been linked to changes in brain activity at rest. That is, after training one’s brain activity using neurofeedback, the intrinsic, resting brain activity (i.e., EEG activity in the absence of a task) may also show changes. This resting brain activity can be used to assess more generalized brain changes, and baseline resting-state signatures may be used to predict recovery (Wu et al., 2015) or response to treatments (Zhou et al., 2018). When combined with neural injury information, resting EEG parameters can also help predict the efficacy of stroke therapy.

In this study, our goal was to combine the principles of virtual reality and BCIs to elicit optimal rehabilitation gains for stroke survivors. We hypothesized that merging BCIs with VR should induce illusions of movement and a strong feeling of embodiment within a virtual body via the action observation network, activating brain areas that overlap with those controlling actual movement, which is important for mobilizing neuroplastic changes (Dobkin, 2007). Using a VR-based BCI, those with severe stroke impairments can trigger voluntary movements of the virtual arm in a closed neurofeedback loop. This helps to increase the illusion of one’s own movements through the coordination between one’s intention and the observed first-person virtual action. Therefore, we developed a training platform called REINVENT, which uses post-stroke brain signals that indicate an attempt to move and then drives the movement of a virtual avatar arm, providing patient-driven action observation in head-mounted VR (Spicer et al., 2017). Our previous work using REINVENT with healthy individuals indeed showed that the combination of VR integrated into a BCI encouraged greater embodiment, and greater embodiment was related to greater neurofeedback performance (Anglin et al., 2019).

For this study, we recruited four chronic stroke survivors to undergo a longitudinal BCI-VR intervention using REINVENT to provide EEG-based neurofeedback with simultaneous EMG acquisition. We assessed intervention results using clinical measures, Transcranial Magnetic Stimulation (TMS) and Magnetic Resonance Imaging (MRI) and compared these measures before and after the intervention. The purpose of this study was twofold. First, we sought to determine whether REINVENT is feasible for stroke patients to use across repeated sessions and second, whether REINVENT might be able to strengthen motor-related brain signals in individuals with differing levels of motor impairment after stroke.[…]

 

Continue —>  Frontiers | Effects of a Brain-Computer Interface With Virtual Reality (VR) Neurofeedback: A Pilot Study in Chronic Stroke Patients | Frontiers in Human Neuroscience

Figure 1. System architecture of a closed neurofeedback loop. From left, (1) the evoked physiological responses are captured at the interfacing layer through the data acquisition clients, (2) sent to the processing layer where the signals are filtered and logged, and then, (3) the extracted features (e.g., EEG bands) are sent to the interaction layer where VR training occurs. Written permission to use this photo was obtained from the individual.

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[Abstract] Differential Poststroke Motor Recovery in an Arm Versus Hand Muscle in the Absence of Motor Evoked Potentials

Background. After stroke, recovery of movement in proximal and distal upper extremity (UE) muscles appears to follow different time courses, suggesting differences in their neural substrates.

Objective. We sought to determine if presence or absence of motor evoked potentials (MEPs) differentially influences recovery of volitional contraction and strength in an arm muscle versus an intrinsic hand muscle. We also related MEP status to recovery of proximal and distal interjoint coordination and movement fractionation, as measured by the Fugl-Meyer Assessment (FMA).

Methods. In 45 subjects in the year following ischemic stroke, we tracked the relationship between corticospinal tract (CST) integrity and behavioral recovery in the biceps (BIC) and first dorsal interosseous (FDI) muscle. We used transcranial magnetic stimulation to probe CST integrity, indicated by MEPs, in BIC and FDI. We used electromyography, dynamometry, and UE FMA subscores to assess muscle-specific contraction, strength, and inter-joint coordination, respectively.

Results. Presence of MEPs resulted in higher likelihood of muscle contraction, greater strength, and higher FMA scores. Without MEPs, BICs could more often volitionally contract, were less weak, and had steeper strength recovery curves than FDIs; in contrast, FMA recovery curves plateaued below normal levels for both the arm and hand.

Conclusions. There are shared and separate substrates for paretic UE recovery. CST integrity is necessary for interjoint coordination in both segments and for overall recovery. In its absence, alternative pathways may assist recovery of volitional contraction and strength, particularly in BIC. These findings suggest that more targeted approaches might be needed to optimize UE recovery.

 

via Differential Poststroke Motor Recovery in an Arm Versus Hand Muscle in the Absence of Motor Evoked Potentials – Heidi M. Schambra, Jing Xu, Meret Branscheidt, Martin Lindquist, Jasim Uddin, Levke Steiner, Benjamin Hertler, Nathan Kim, Jessica Berard, Michelle D. Harran, Juan C. Cortes, Tomoko Kitago, Andreas Luft, John W. Krakauer, Pablo A. Celnik, 2019

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[ARTICLE] Personalized upper limb training combined with anodal-tDCS for sensorimotor recovery in spastic hemiparesis: study protocol for a randomized controlled trial – Full Text

Abstract

Background

Recovery of voluntary movement is a main rehabilitation goal. Efforts to identify effective upper limb (UL) interventions after stroke have been unsatisfactory. This study includes personalized impairment-based UL reaching training in virtual reality (VR) combined with non-invasive brain stimulation to enhance motor learning. The approach is guided by limiting reaching training to the angular zone in which active control is preserved (“active control zone”) after identification of a “spasticity zone”. Anodal transcranial direct current stimulation (a-tDCS) is used to facilitate activation of the affected hemisphere and enhance inter-hemispheric balance. The purpose of the study is to investigate the effectiveness of personalized reaching training, with and without a-tDCS, to increase the range of active elbow control and improve UL function.

Methods

This single-blind randomized controlled trial will take place at four academic rehabilitation centers in Canada, India and Israel. The intervention involves 10 days of personalized VR reaching training with both groups receiving the same intensity of treatment. Participants with sub-acute stroke aged 25 to 80 years with elbow spasticity will be randomized to one of three groups: personalized training (reaching within individually determined active control zones) with a-tDCS (group 1) or sham-tDCS (group 2), or non-personalized training (reaching regardless of active control zones) with a-tDCS (group 3). A baseline assessment will be performed at randomization and two follow-up assessments will occur at the end of the intervention and at 1 month post intervention. Main outcomes are elbow-flexor spatial threshold and ratio of spasticity zone to full elbow-extension range. Secondary outcomes include the Modified Ashworth Scale, Fugl-Meyer Assessment, Streamlined Wolf Motor Function Test and UL kinematics during a standardized reach-to-grasp task.

Discussion

This study will provide evidence on the effectiveness of personalized treatment on spasticity and UL motor ability and feasibility of using low-cost interventions in low-to-middle-income countries.

Background

Stroke is a leading cause of long-term disability. Up to 85% of patients with sub-acute stroke present chronic upper limb (UL) sensorimotor deficits [1]. While post-stroke UL recovery has been a major focus of attention, efforts to identify effective rehabilitation interventions have been unsatisfactory. This study focuses on the delivery of personalized impairment-based UL training combined with low-cost state-of-the-art technology (non-invasive brain stimulation and commercially available virtual reality, VR) to enhance motor learning, which is becoming more readily available worldwide.

A major impairment following stroke is spasticity, leading to difficulty in daily activities and reduced quality of life [2]. Studies have identified that spasticity relates to disordered motor control due to deficits in the ability of the central nervous system to regulate motoneuronal thresholds through segmental and descending systems [34]. In the healthy nervous system, the motoneuronal threshold is expressed as the “spatial threshold” (ST) or the specific muscle length/joint angle at which the stretch reflex and other proprioceptive reflexes begin to act [567]. The range of ST regulation in the intact system is defined by the task-specific ability to activate muscles anywhere within the biomechanical joint range of motion (ROM). However, to relax the muscle completely, ST has to be shifted outside of the biomechanical range [8].

After stroke, the ability to regulate STs is impaired [3] such that the upper angular limit of ST regulation occurs within the biomechanical range of the joint resulting in spasticity (spasticity zone). Thus, resistance to stretch of the relaxed muscle has a spatial aspect in that it occurs within the defined spasticity zone. In other joint ranges, spasticity is not present and normal reciprocal muscle activation can occur (active control zone; [4] Fig. 1). This theory-based intervention investigates whether recovery of voluntary movement is linked to recovery of ST control.[…]

Continue —> Personalized upper limb training combined with anodal-tDCS for sensorimotor recovery in spastic hemiparesis: study protocol for a randomized controlled trial | Trials | Full Text

Fig. 3Jintronix virtual reality (VR) games used in the intervention. a Fish Frenzy game requires the player to trace a three-dimensional (3D) trajectory by moving a fish on the screen in different shapes. b Kitchen Cleanup game requires forward reaching towards kitchen cutlery and returning them to shelves and drawers. c Garden Grab game requires lateral reaching while planting seeds, harvesting and transferring tomatoes to baskets. d Catch, Carry, Drop game requires bilateral coordination while catching apples, carrying and dropping them into a container

 

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[Case Report] Improving neuropsychiatric symptoms following stroke using virtual reality – Full Text

Abstract

Rationale: Post-stroke cognitive impairment occurs frequently in patients with stroke, with a 20% to 80% prevalence. Anxiety is common after stroke, and is associated with a poorer quality of life. The use of standard relaxation techniques in treating anxiety in patients undergoing post-stroke rehabilitation have shown some positive effects, whereas virtual reality seems to have a role in the treatment of anxiety disorders, especially when associated to neurological damage.

Patients concerns: A 50-year-old woman, smokers, affected by hypertension and right ischemic stroke in the chronic phase (i.e., after 12 months by cerebrovascular event), came to our observation for a severe anxiety state and a mild cognitive deficit, mainly involving attention and visuo-executive processes, besides a mild left hemiparesis.

Diagnosis: Anxiety in a patient with ischemic stroke.

Interventions: Standard relaxation techniques alone in a common clinical setting or the same psychological approach in an immersive virtual environment (i.e., Computer Assisted Rehabilitation Environment – CAREN).

Outcomes: The patient’s cognitive and psychological profile, with regard to attention processes, mood, anxiety, and coping strategies, were evaluated before and after the 2 different trainings. A significant improvement in the functional and behavioral outcomes were observed only at the end of the combined approach.

Lessons: The immersive virtual reality environment CAREN might be useful to improve cognitive and psychological status, with regard to anxiety symptoms, in post-stroke individuals.

1 Introduction

Stroke is a neurological syndrome caused by a focal disruption in the cerebral blood flow due to occlusion (ischemic stroke) or rupture of a blood vessel (hemorrhagic stroke). Stroke is the leading cause of disability worldwide and the third cause of death in the western countries.[1]Following stroke, especially in right hemisphere lesions, several psychological changes may arise, being depression and anxiety the most common.[2] The right hemisphere plays an important role in verbal communication, as it is mostly responsible for speech prosody and its emotional aspects.[3] Moreover, previous studies indicate that post-stroke anxiety is also common and persistent,[4,5] and this is attributable to a feeling of impotence and uncertainty about the future. Some personality factors, as coping strategies, can contribute to reduce or increase the anxiety’s level. The prevalence of post-stroke cognitive dysfunctions varies from 23% to 55% within three months from the stroke onset, and declines to a percentage between 11% and 31% after 1 year.[6,7] It has been found that after stroke most of the patients may have enduring difficulties in specific cognitive domains, such as attention process and concentration, memory abilities, spatial awareness, perception, praxis and executive functioning.[8,9] Thus, a proper psychometric evaluation should be the mainstay of post-stroke patient’s treatment. Limited evidences showed the relationship between cognition processes, emotions and anxiety. Anxiety disorders frequently coexist with depression, and may be more common in women and younger stroke survivors.[10]

Patients with a ‘probable anxiety disorder’ at 3-months had a poorer quality of life at 1, 3, and 5-years post-stroke after adjusting for age, gender, and stroke severity. Moreover, anxiety symptoms persisted for up to 10 years.[11]

Relaxation techniques can be considered a useful tool, determining a positive emotional and psychological well-being.[12,13] Among the different relaxation techniques, diaphragmatic breathing (DB), progressive muscle relaxation,[12,13] and autogenic relaxing training[14] are characterized by a significant positive association between physical and cognitive dimensions. The use of these techniques in treating anxiety in patients undergoing post-stroke rehabilitation have shown some positive effects.[15]

In the last years, virtual reality (VR) and interactive video gaming are emerging as promising treatment approaches in stroke rehabilitation, both for cognitive rehabilitation and mood/anxiety disorder treatment.[16] VR can provide exposure to nature for those living in isolated confined environments, and it has been demonstrated to reduce stress and improving mood.[17] Virtual Reality Therapy with an Interactive Semi-Immersive Program (i.e., Bts-Nirvana System) can be considered a useful complementary treatment to potentiate functional recovery, with regard to attention, visual-spatial deficits, and motor function in patients affected by stroke.[18] Moreover, relaxation and respiratory techniques in a semi-immersive virtual reality environment, using Bts-Nirvana, may be a promising tool in improving attention process, coping strategies, and anxiety in individuals with neurological disorders, including stroke.[19]

Aim of this case study is to evaluate the effects of a combined rehabilitative approach, using conventional relaxation and respiratory techniques in a virtual immersive rehabilitative environment, that is, Computer Assisted Rehabilitation ENvironment (CAREN), in a patient with chronic stroke.[…]

 

Continue —>  Improving neuropsychiatric symptoms following stroke using v… : Medicine

Figure 1 It shows the combined rehabilitative approach with the CAREN System. CAREN = Computer Assisted Rehabilitation ENvironment.
Source
Improving neuropsychiatric symptoms following stroke using virtual reality: A case report
Medicine98(19):e15236, May 2019.

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