Archive for category REHABILITATION

[WEB SITE] Our Newest Acquisitions – National Rehabilitation Information Center

If you’re not already a subscriber, learn how you can join the FREE REHABDATA-Connection monthly literature awareness service.

The list below highlights 342 document abstracts added within the last 30 days.   Click the new link to read the latest additions for a given topic.   Some abstracts are related to several topics.

These keyword searches were developed specifically for REHABDATA Connection.   They are not a fool-proof method for topic searches, but will give a comprehensive listing of the latest abstract.   You can learn about how these search strategies were created by visiting the Search Strategies document.

Records are organized by when they were added to the database, not their publication date.

Autoimmune disorders 3 new
Blindness/visual impairments 7 new
Brain injuries 32 new
Deafness/hearing impairments 19 new
Developmental disabilities 68 new
Neurological/neuromuscular disorders 68 new
Poliomyelitis
Psychological disabilities 32 new
Spinal cord injury 19 new
Stroke 28 new
ADA 11 new
Advocacy/self help 14 new
Aging 30 new
Any legislation/policy 175 new
Assistive technology/devices 43 new
Attitudes/feelings 15 new
Burn injury 39 new
Caregiving 12 new
Case administration and management/counseling 19 new
Children/youth/infants 78 new
Disability Studies 3 new
Education/school 112 new
Emergency Preparedness
Employment/transition to work 109 new
Evaluation/needs assessment/tests 147 new
Family issues 49 new
Home modification 1 new
Independent living/community integration 99 new
Information resources 50 new
International rehabilitation 22 new
Knowledge translation 1 new
Learning disabilities 2 new
Medical or rehabilitation facilities 38 new
Medical rehabilitation/rehabilitation medicine 56 new
Mental health/self concept 22 new
Mobility issues 46 new
Money I: Organizations 21 new
Money II: Consumers 17 new
Organization management/project administration 48 new
Participatory action research 1 new
Physical/Occupational/Speech Therapy 37 new
Recreation/leisure/sports 45 new
Rehabilitation success/outcome 23 new
Research methodology 27 new
Research utilization 5 new
Self care/daily living 30 new
Service delivery/rehabilitation services 58 new
Special populations: ethnic groups/rural services 9 new
Statistics/demographics/epidemiology 47 new
Surveys/questionnaires 30 new
Transportation/travel 14 new
Universal design 5 new
All NIDILRR documents (by funded and defunded projects) 205 new
Fellowships (H133F) 3 new
Field-Initiated Projects (H133G) 1 new
Model Spinal Cord Injury Systems (H133N) 4 new
NIDILRR Contracts (HN)
Rehabilitation Engineering Research Centers (H133E) 9 new
Rehabilitation Research and Training Centers (H133B) 38 new
Research and Demonstration Projects (H133A) 76 new
Research Training Grants (H133P) 14 new
Small Business Innovative Research (RW) 1 new
State Technology Assistance (H224A)
Utilization Projects/ADA projects (H133D) 9 new

via Our Newest Acquisitions | National Rehabilitation Information Center

, ,

Leave a comment

[Abstract + References] Electromyography as a Suitable Input for Virtual Reality-Based Biofeedback in Stroke Rehabilitation – Conference paper

Abstract

Virtual reality (VR)-based biofeedback of brain signals using electroencephalography (EEG) has been utilized to encourage the recovery of brain-to-muscle pathways following a stroke. Such models incorporate principles of action observation with neurofeedback of motor-related brain activity to increase sensorimotor activity on the lesioned hemisphere. However, for individuals with existing muscle activity in the hemiparetic arm, we hypothesize that providing biofeedback of muscle signals, to strengthen already established brain-to-muscle pathways, may be more effective. In this project, we aimed to understand whether and when feedback of muscle activity (measured using surface electromyography (EMG)) might more effective compared to EEG biofeedback. To do so, we used a virtual reality (VR) training paradigm we developed for stroke rehabilitation (REINVENT), which provides EEG biofeedback of ipsilesional sensorimotor brain activity and simultaneously records EMG signals. We acquired 640 trials over eight 1.5-h sessions in four stroke participants with varying levels of motor impairment. For each trial, participants attempted to move their affected arm. Successful trials, defined as when their EEG sensorimotor desynchronization (8–24 Hz) during a time-limited movement attempt exceeded their baseline activity, drove a virtual arm towards a target. Here, EMG signals were analyzed offline to see (1) whether EMG amplitude could be significantly differentiated between active trials compared to baseline, and (2) whether using EMG would have led to more successful VR biofeedback control than EEG. Our current results show a significant increase in EMG amplitude across all four participants for active versus baseline trials, suggesting that EMG biofeedback is feasible for stroke participants across a range of impairments. However, we observed significantly better performance with EMG than EEG for only the three individuals with higher motor abilities, suggesting that EMG biofeedback may be best suited for those with better motor abilities.

References

  1. 1.
    Langhorne, P., Bernhardt, J., Kwakkel, G.: Stroke rehabilitation. Lancet 377, 1693–1702 (2011).  https://doi.org/10.1016/S0140-6736(11)60325-5CrossRefGoogle Scholar
  2. 2.
    Ramos-Murguialday, A., et al.: Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann. Neurol. (2013).  https://doi.org/10.1002/ana.23879CrossRefGoogle Scholar
  3. 3.
    Shindo, K.: Effects of neurofeedback training with an electroencephalogram-based brain-computer interface for hand paralysis in patients with chronic stroke: a preliminary case series study. J. Rehabil. Med. 43, 951–957 (2016).  https://doi.org/10.2340/16501977-0859CrossRefGoogle Scholar
  4. 4.
    Armagan, O., Tascioglu, F., Oner, C.: Electromyographic biofeedback in the treatment of the hemiplegic hand: a placebo-controlled study. Am. J. Phys. Med. Rehabil. 82, 856–861 (2003).  https://doi.org/10.1097/01.PHM.0000091984.72486.E0CrossRefGoogle Scholar
  5. 5.
    Garrison, K.A., Aziz-Zadeh, L., Wong, S.W., Liew, S.L., Winstein, C.J.: Modulating the motor system by action observation after stroke. Stroke 44, 2247–2253 (2013).  https://doi.org/10.1161/STROKEAHA.113.001105CrossRefGoogle Scholar
  6. 6.
    Celnik, P., Webster, B., Glasser, D.M., Cohen, L.G.: Effects of action observation on physical training after stroke. Stroke. 39, 1814–1820 (2008).  https://doi.org/10.1161/STROKEAHA.107.508184CrossRefGoogle Scholar
  7. 7.
    Vourvopoulos, A., Bermúdezi Badia, S.: Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis. J. Neuroeng. Rehabil. 13, 1–14 (2016).  https://doi.org/10.1186/s12984-016-0173-2CrossRefGoogle Scholar
  8. 8.
    Spicer, R., Anglin, J., Krum, D.M., Liew, S.L.: REINVENT: a low-cost, virtual reality brain-computer interface for severe stroke upper limb motor recovery. In: Proceedings of IEEE Virtual Reality, pp. 385–386 (2017).  https://doi.org/10.1109/vr.2017.7892338
  9. 9.
    Klem, G.H., Lüders, H.O., Jasper, H.H., Elger, C.: The ten-twenty electrode system of the International Federation. Electroencephalogr. Clin. Neurophysiol. 10, 371–375 (1999).  https://doi.org/10.1016/0013-4694(58)90053-1CrossRefGoogle Scholar
  10. 10.
    Kothe, C.: Lab Streaming Layer (LSL). https://github.com/sccn/labstreaminglayer
  11. 11.
    Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods. 134, 9–21 (2004).  https://doi.org/10.1016/j.jneumeth.2003.10.009CrossRefGoogle Scholar

via Electromyography as a Suitable Input for Virtual Reality-Based Biofeedback in Stroke Rehabilitation | SpringerLink

, , , ,

Leave a comment

[Abstract + References] Multimodal Head-Mounted Virtual-Reality Brain-Computer Interface for Stroke Rehabilitation – Conference paper

Abstract

Rehabilitation after stroke requires the exploitation of active movement by the patient in order to efficiently re-train the affected side. Individuals with severe stroke cannot benefit from many training solutions since they have paresis and/or spasticity, limiting volitional movement. Nonetheless, research has shown that individuals with severe stroke may have modest benefits from action observation, virtual reality, and neurofeedback from brain-computer interfaces (BCIs). In this study, we combined the principles of action observation in VR together with BCI neurofeedback for stroke rehabilitation to try to elicit optimal rehabilitation gains. Here, we illustrate the development of the REINVENT platform, which takes post-stroke brain signals indicating an attempt to move and drives a virtual avatar arm, providing patient-driven action observation in head-mounted VR. We also present a longitudinal case study with a single individual to demonstrate the feasibility and potentially efficacy of the REINVENT system.

References

  1. 1.
    Mozaffarian, D., et al.: American heart association statistics committee and stroke statistics subcommittee: heart disease and stroke statistics–2015 update: a report from the American heart association. Circulation 131, e29–e322 (2015)Google Scholar
  2. 2.
    Miller, E.L., et al.: American heart association council on cardiovascular nursing and the stroke council: comprehensive overview of nursing and interdisciplinary rehabilitation care of the stroke patient: a scientific statement from the American heart association. Stroke 41, 2402–2448 (2010)CrossRefGoogle Scholar
  3. 3.
    Celnik, P., Webster, B., Glasser, D., Cohen, L.: Effects of action observation on physical training after stroke. Stroke J. Cereb. Circ. 39, 1814–1820 (2008)CrossRefGoogle Scholar
  4. 4.
    Ertelt, D., et al.: Action observation has a positive impact on rehabilitation of motor deficits after stroke. NeuroImage 36(Suppl 2), T164–T173 (2007)CrossRefGoogle Scholar
  5. 5.
    Garrison, K.A., Aziz-Zadeh, L., Wong, S.W., Liew, S.-L., Winstein, C.J.: Modulating the motor system by action observation after stroke. Stroke 44, 2247–2253 (2013)CrossRefGoogle Scholar
  6. 6.
    Ballester, B.R., et al.: The visual amplification of goal-oriented movements counteracts acquired non-use in hemiparetic stroke patients. J. Neuroeng. Rehabil. 12, 50 (2015)CrossRefGoogle Scholar
  7. 7.
    Vourvopoulos, A., Bermúdez i Badia, S.: Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis. J. Neuroeng. Rehabil. 13, 69 (2016)CrossRefGoogle Scholar
  8. 8.
    Maclean, N., Pound, P., Wolfe, C., Rudd, A.: Qualitative analysis of stroke patients’ motivation for rehabilitation. BMJ 321, 1051–1054 (2000)CrossRefGoogle Scholar
  9. 9.
    Paraskevopoulos, I., Tsekleves, E., Warland, A., Kilbride, C.: Virtual reality-based holistic framework: a tool for participatory development of customised playful therapy sessions for motor rehabilitation. In: 2016 8th International Conference on Games and Virtual Worlds for Serious Applications (VS-Games), September (2016)Google Scholar
  10. 10.
    Wolpaw, J.R.: Brain-Computer Interfaces: Principles and Practice. Oxford University Press, Oxford (2012)CrossRefGoogle Scholar
  11. 11.
    Vourvopoulos, A., Bermudez i Badia, S.: Usability and cost-effectiveness in brain-computer interaction: is it user throughput or technology related? In: Proceedings of the 7th Augmented Human International Conference. ACM, Geneva, Switzerland (2016)Google Scholar
  12. 12.
    Schomer, D.L., Lopes da Silva, F.H.: Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins, Philadelphia (2011)Google Scholar
  13. 13.
    Kropotov, J.D.: Chapter 2.2 – Alpha rhythms. In: Kropotov, J.D. (ed.) Functional Neuromarkers for Psychiatry, pp. 89–105. Academic Press, San Diego (2016)CrossRefGoogle Scholar
  14. 14.
    Pfurtscheller, G., Lopes da Silva, F.H.: Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol. 110, 1842–1857 (1999)CrossRefGoogle Scholar
  15. 15.
    Wu, J., et al.: Connectivity measures are robust biomarkers of cortical function and plasticity after stroke. Brain 138, 2359–2369 (2015)CrossRefGoogle Scholar
  16. 16.
    Zhou, R.J., et al.: Predicting gains with visuospatial training after stroke using an EEG measure of frontoparietal circuit function. Front. Neurol. 9, 597 (2018)CrossRefGoogle Scholar
  17. 17.
    Soekadar, S.R., Birbaumer, N., Slutzky, M.W., Cohen, L.G.: Brain–machine interfaces in neurorehabilitation of stroke. Neurobiol. Dis. 83, 172–179 (2015)CrossRefGoogle Scholar
  18. 18.
    Friedman, D.: Brain-computer interfacing and virtual reality. In: Nakatsu, R., Rauterberg, M., Ciancarini, P. (eds.) Handbook of Digital Games and Entertainment Technologies, pp. 151–171. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-4560-50-4_2CrossRefGoogle Scholar
  19. 19.
    Vourvopoulos, A., Ferreira, A., Bermúdez i Badia, S.: NeuRow: an immersive VR environment for motor-imagery training with the use of brain-computer interfaces and vibrotactile feedback. In: 3rd International Conference on Physiological Computing Systems, Lisbon (2016)Google Scholar
  20. 20.
    Slater, M.: Place illusion and plausibility can lead to realistic behaviour in immersive virtual environments. Philos. Trans. R. Soc. B Biol. Sci. 364, 3549–3557 (2009)CrossRefGoogle Scholar
  21. 21.
    Spicer, R., Anglin, J., Krum, D.M., Liew, S.L.: REINVENT: a low-cost, virtual reality brain-computer interface for severe stroke upper limb motor recovery. In: 2017 IEEE Virtual Reality (VR), pp. 385–386 (2017)Google Scholar
  22. 22.
    Klem, G.H., Luders, H.O., Jasper, H.H., Elger, C.: The ten-twenty electrode system of the international federation. Electroencephalogr. Clin. Neurophysiol. 52, 3–6 (1999). The International Federation of Clinical NeurophysiologyGoogle Scholar
  23. 23.
    Kothe, C.: Lab streaming layer (LSL). https://github.com/sccn/labstreaminglayer. Accessed 26 Oct 2015 (2014)
  24. 24.
    Fugl-Meyer, A.R., Jääskö, L., Leyman, I., Olsson, S., Steglind, S.: The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. Scand. J. Rehabil. Med. 7, 13–31 (1975)Google Scholar
  25. 25.
    Duncan, P.W., Wallace, D., Lai, S.M., Johnson, D., Embretson, S., Laster, L.J.: The stroke impact scale version 2.0: evaluation of reliability, validity, and sensitivity to change. Stroke 30, 2131–2140 (1999)CrossRefGoogle Scholar
  26. 26.
    Kennedy, R.S., Lane, N.E., Berbaum, K.S., Lilienthal, M.G.: Simulator sickness questionnaire: an enhanced method for quantifying simulator sickness. Int. J. Aviat. Psychol. 3, 203–220 (1993)CrossRefGoogle Scholar
  27. 27.
    Bailey, J.O., Bailenson, J.N., Casasanto, D.: When does virtual embodiment change our minds? Presence Teleoperators Virtual Environ. 25, 222–233 (2016)CrossRefGoogle Scholar
  28. 28.
    Witmer, B.G., Singer, M.J.: Measuring presence in virtual environments: a presence questionnaire. Presence Teleoperator Virtual Environ. 7, 225–240 (1998)CrossRefGoogle Scholar
  29. 29.
    Bouchard, S., Robillard, G., Renaud, P., Bernier, F.: Exploring new dimensions in the assessment of virtual reality induced side effects. J. Comput. Inf. Technol. 1, 20–32 (2011)Google Scholar
  30. 30.
    Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004)CrossRefGoogle Scholar
  31. 31.
    Makeig, S.: Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalogr. Clin. Neurophysiol. 86, 283–293 (1993)CrossRefGoogle Scholar
  32. 32.
    Neuper, C., Wörtz, M., Pfurtscheller, G.: ERD/ERS patterns reflecting sensorimotor activation and deactivation. Prog. Brain Res. 159, 211–222 (2006)CrossRefGoogle Scholar
  33. 33.
    Pfurtscheller, G., Aranibar, A.: Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. Electroencephalogr. Clin. Neurophysiol. 46, 138–146 (1979)CrossRefGoogle Scholar
  34. 34.
    Pfurtscheller, G., Brunner, C., Schlögl, A., Lopes da Silva, F.H.: Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage 31, 153–159 (2006)CrossRefGoogle Scholar
  35. 35.
    Liew, S.-L., et al.: Laterality of poststroke cortical motor activity during action observation is related to hemispheric dominance. Neural Plast. 2018, 14 (2018)CrossRefGoogle Scholar
  36. 36.
    Ritter, P., Moosmann, M., Villringer, A.: Rolandic alpha and beta EEG rhythms’ strengths are inversely related to fMRI-BOLD signal in primary somatosensory and motor cortex. Hum. Brain Mapp. 30, 1168–1187 (2009)CrossRefGoogle Scholar
  37. 37.
    Westlake, K.P., et al.: Resting state alpha-band functional connectivity and recovery after stroke. Exp. Neurol. 237, 160–169 (2012)CrossRefGoogle Scholar
  38. 38.
    Dubovik, S., et al.: EEG alpha band synchrony predicts cognitive and motor performance in patients with ischemic stroke. https://www.hindawi.com/journals/bn/2013/109764/abs/

via Multimodal Head-Mounted Virtual-Reality Brain-Computer Interface for Stroke Rehabilitation | SpringerLink

, , , , , , , , , , , , ,

Leave a comment

[Abstract + References] Preliminary Design of Soft Exo-Suit for Arm Rehabilitation – Conference paper

Abstract

Every year, millions of people experience a stroke but only a few of them fully recover. Recovery requires a working staff, which is time consuming and inefficient. Therefore, over the past few years rehabilitation robots like Exoskeletons have been used in the recuperation process for patients. In this paper we have designed an Exosuit which takes into considerations of the rigid Exo-Skeleton and its limitations for patients suffering from loss of function of the arm. This paper concentrates on enabling a stroke affected person to perform flexion-extension at elbow joint. Validation of the developed model on general population is still needed.

References

  1. 1.
    Mathers, C., Fat, D.M., Boerma, J.T., World Health Organization: The global burden of disease: 2004 update. World Health Organization (2008)Google Scholar
  2. 2.
    McPhee, S.J., Hammer, G.D.: Nervous system disorders. Pathophysiol. Dis. Introd. Clin. Med. 59, 177–180 (2010)Google Scholar
  3. 3.
    Committee on Nervous System Disorders in Developing Countries the Board on Global Health and the Institute of Medicine. Neurological, Psychiatric, and Develop-Mental Disorders. National Academies Press, Washington, DC (2001)Google Scholar
  4. 4.
    Zhang, Y., Arakalian, V.: Design of a passive robotic ExoSuit for carrying heavy loads. In: Proceedings of the IEEE-RAS, 18th Annual International Conference on Humanoid Robots, Lyon, France (2018)Google Scholar
  5. 5.
    Gross, R., et al.: Modulation of lower limb muscle activity induced by curved walking in typically developing children. Gait Posture 50, 34–41 (2016)CrossRefGoogle Scholar
  6. 6.
    Viteckova, S., Kutilek, P., Jirina, M.: Wearable lower limb robotics: a review. Biocybern. Biomed. Eng. 33(2), 96–105 (2013)CrossRefGoogle Scholar
  7. 7.
    Rupala, B.S., Singla, A., Virk, G.S.: Lower limb exoskeletons: a brief review. In: Proceedings of the Conference on Mechanical Engineering and Technology COMET, Varanasi, Utter Pradesh, pp. 18–24 (2016)Google Scholar
  8. 8.
    Collo, A., Bonnet, V., Venture, G.: A quasi-passive lower limb exoskeleton for partial body weight support. In: Proceedings of the 6th IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), UTown, Singapore, pp. 643–648 (2016)Google Scholar
  9. 9.
    Stewart, A.M., Pretty, C.G., Adams, M., Chen, X.: Review of upper limb hybrid exoskeletons. IFAC 50(1), 15169–15178 (2017)Google Scholar
  10. 10.
    Serea, F., Poboroniuc, M., Hartopanu, S., Olaru, R.: Exoskeleton for upper arm rehabilitation for disabled patients. In: International Conference and Exposition on Electrical and Power Engineering, (EPE 2014), pp. 153–157 (2014)Google Scholar
  11. 11.
    Perry, J.C., Rosen, J., Burns, S.: Upper-limb powered exoskeleton design. IEEE/ASM Trans. Mechatron. 12(4), 408–417 (2007)CrossRefGoogle Scholar
  12. 12.
    Li, B., Yuan, B., Chen, J., Zuo, Y., Yang, Y.: Mechanical design and human-machine coupling dynamic analysis of a lower extremity exoskeleton. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds.) ICIRA 2017. LNCS (LNAI), vol. 10462, pp. 593–604. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-65289-4_56CrossRefGoogle Scholar
  13. 13.
    Jarrasé, N.: Contributions à l’explotation d’exosquelettes actifs pour la rééducation neuromotrice. Ph.D. thesis of Pierre et Marie Curie University (UPMC) (2010)Google Scholar
  14. 14.
    Gunn, M., Shank, T.M., Epps, M., Hossain, J., Rahman, T.: User evaluation of a dynamic arm orthosis for people with neuromuscular disorders. IEEE Trans. Neural Syst. Rehabil. Eng. 24(12), 1277–1283 (2016)CrossRefGoogle Scholar
  15. 15.
    Seth, D., Chablat, D., Bennis, F., Sakka, S., Jubeau, M., Nordez, A.: New dynamic muscle fatigue model to limit musculo-skeletal disorder. In: Virtual Reality International Conference 2016, Article no. 26 (2016)Google Scholar
  16. 16.
    Seth, D., Chablat, D., Sakka, S., Bennis, F.: Experimental validation of a new dynamic muscle fatigue model. In: Duffy, V.G.G. (ed.) DHM 2016. LNCS, vol. 9745, pp. 54–65. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-40247-5_6CrossRefGoogle Scholar
  17. 17.
    Seth, D., Chablat, D., Bennis, F., Sakka, S., Jubeau, M., Nordez, A.: Validation of a new dynamic muscle fatigue model and DMET analysis. Int. J. Virtual Real. 2016(16), 2016 (2016)Google Scholar
  18. 18.
    Talaty, M., Esquenazi, A., Briceno, J.E.: Differentiating ability in users of the ReWalk(TM) powered exoskeleton: an analysis of walking kinematics. In: Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR), Seattle, USA, pp. 1–5 (2013).  https://doi.org/10.1109/icorr.2013.6650469
  19. 19.
    Aoustin, Y.: Walking gait of a biped with a wearable walking assist device. Int. J. of Humanoid Robotics 12(2), 1 550 018-1–11 550 018-20 (2015).  https://doi.org/10.1142/s0219843615500188CrossRefGoogle Scholar
  20. 20.
    Ktistakis, I.P., Bourbakis, N.G.: A survey on robotic wheelchairs mounted with robotic arms. In: National Aerospace and Electronics Conference (NAECON), pp. 258–262 (2015)Google Scholar
  21. 21.
    Aoustin, Y., Formalskii, A.: Walking of biped with passive exoskeleton: evaluation of energy consumption. Multibody Syst. Dyn. 43, 71–96 (2017).  https://doi.org/10.1007/s11044-017-9602-7MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Park, W., Jeong, W., Kwon, G., Kim, Y.H., Kim, L.: A rehabilitation device to improve the hand grasp function of stroke patients using a patient-driven approach. In: IEEE International Conference on Rehabilitation Robotics, Seattle Washington, USA (2013)Google Scholar
  23. 23.
    Akhmadeev, K., Rampone, E., Yu, T., Aoustin, Y., Le Carpentier, E.: A testing system for a real-time gesture classification using surface EMG. In: Proceedings of the 20th IFAC World Congress, Toulouse France (2017)Google Scholar
  24. 24.
    Schwartz, C., Lempereur, M., Burdin, V., Jacq, J.J., Rémy-Néris, O.: Shoulder motion analysis using simultaneous skin shape registration. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS, Lyon, France (2007)Google Scholar
  25. 25.
    National Stroke Association Brochure (2017)Google Scholar
  26. 26.
    Nef, T., Guidali, M., Riener, R.: ARMin III – arm therapy exoskeleton with an ergonomic shoulder actuation. Appl. Bionics Biomech. 6(2), 127–142 (2009)CrossRefGoogle Scholar
  27. 27.
    Krebs, H.I., Hogan, N., Volpe, B.T., Aisen, M.L., Edelstein, L., Diels, C.: Overview of clinical trials with MITMANUS: a robot-aided neuro-rehabilitation facility. Technol. Health Care 7(6), 419–423 (1999)Google Scholar
  28. 28.
    Ali, H.: Bionic exoskeleton: history, development and the future. IOSR J. Mechan. Civ. Eng. 58–62 (2014)Google Scholar
  29. 29.
    Banala, S.K., Agrawal, S.K., Scholz, J.P.: Active leg exoskeleton (ALEX) for gait rehabilitation of motor-impaired patients. In: IEEE 2007 Rehabilitation Robotics, pp. 401–407 (2007)Google Scholar
  30. 30.
    Fitle, K.D., Pehlivan, A.U., O’Malley, M.K.: A robotic exoskeleton for re-habilitation and assessment of the upper limb following incomplete spinal cord in-jury. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 4960–4966 (2015)Google Scholar
  31. 31.
  32. 32.
  33. 33.
    Plagenhoef, S., et al.: Anatomical data for analyzing human motion (1983)Google Scholar

via Preliminary Design of Soft Exo-Suit for Arm Rehabilitation | SpringerLink

, , , , , , , , ,

Leave a comment

[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.

[…]

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

, , , , , , , , , , , , ,

Leave a comment

[ARTICLE] Cell-Based Therapies for Stroke: Are We There Yet? – Full Text

Stroke is the second leading cause of death and physical disability, with a global lifetime incidence rate of 1 in 6. Currently, the only FDA approved treatment for ischemic stroke is the administration of tissue plasminogen activator (tPA). Stem cell clinical trials for stroke have been underway for close to two decades, with data suggesting that cell therapies are safe, feasible, and potentially efficacious. However, clinical trials for stroke account for <1% of all stem cell trials. Nevertheless, the resources devoted to clinical research to identify new treatments for stroke is still significant (53–64 million US$, Phase 1–4). Notably, a quarter of cell therapy clinical trials for stroke have been withdrawn (15.2%) or terminated (6.8%) to date. This review discusses the bottlenecks in delivering a successful cell therapy for stroke, and the cost-to-benefit ratio necessary to justify these expensive trials. Further, this review will critically assess the currently available data from completed stroke trials, the importance of standardization in outcome reporting, and the role of industry-led research in the development of cell therapies for stroke.

Introduction

Background

Stroke has a devastating effect on the society worldwide. In addition to its significant mortality rate of 50% as reported in 5-year survival studies (1), it affects as many as 1 in 6 people in their lifetimes, and is the leading cause of disability worldwide (2). A stroke results in a complex interplay of inflammation and repair with effects on neural, vascular, and connective tissue in and around the affected areas of the brain (3). Therefore, sequelae of stroke such as paralysis, chronic pain, and seizures can persist long term and prevent the patient from fully reintegrating into society. Stroke therefore remains the costliest healthcare burden as a whole (4). In 2012, the total cost of stroke in Australia was estimated to be about $5 billion with direct health care costs attributing to $881 million of the total (5).

Unfortunately, treatment options for stroke are still greatly limited. Intravenous recombinant tissue plasminogen activator (tPA) and endovascular thrombectomy (EVT) are currently the only effective treatments available for acute stroke. However, there is only a brief window of opportunity where they can be successfully applied. EVT is performed until up to 24 h of stroke onset (6), while tPA is applied within 4.5 h of stroke onset. Notably, the recent WAKE-UP (NCT01525290) (7) and EXTEND (NCT01580839) trials have shown that this therapeutic window can be safely extended to 9 h from stroke onset. Furthermore, advancements in acute stroke care and neurorehabilitation have shown to be effective in improving neurological function (8). However, there are no treatments that offer restoration of function and as a result, many patients are left with residual deficits following a stroke. Cell-based therapies have shown promising results in animal models addressing the recovery phase following stroke (9). This is encouraging as currently, there are no approved treatment options addressing the reversal of neurological damages once a stroke has occurred (10).

The majority of data from animal studies and clinical trials demonstrate the therapeutic potential of stem cells in the restoration of central nervous system (CNS) function (1112), applicable to neurodegenerative diseases as well as traumatic brain injury. Transplanted stem cells were reportedly able to differentiate into neurons and glial cells, whilst supporting neural reconstruction and angiogenesis in the ischemic region of the brain (13). Previous work demonstrated the ability of mesenchymal stem cells (MSCs) to differentiate into neurons, astrocytes (14), endothelial cells (1516), and oligodendrocyte lineage cells (17) such as NG2-positive cells (18in vitro, and undergo neuronal or glial differentiation in vivo (19). Bone marrow-derived mesenchymal stem cells (BMSCs) have shown potential to differentiate into endothelial cells in vitro (20). Additionally, both BMSCs and adipose stem cells (ASCs) have been shown to demonstrate neural lineage differentiation potential in vitro (2123). Furthermore, stem cells are able to modulate multiple cell signaling pathways involved in endogenous neurogenesis, angiogenesis, immune modulation and neural plasticity, sometimes in addition to cell replacement (3). The delivery of stem cells from the brain, bone marrow, umbilical cord, and adipose tissue, have been reported to reduce infarct size and improve functional outcomes regardless of tissue source (9). While these were initially exciting reports, they raise the question as to the validity of the findings to date since these preclinical reports are almost uniformly positive. The absence of scientific skepticism and robust debate may in fact have negated progress in this field.

Cell-based therapies have been investigated as a clinical option since the 1990s. The first pilot stroke studies in 2005 investigated the safety of intracranial delivery of stem cells (including porcine neural stem cells) to patients with chronic basal ganglia infarcts or subcortical motor strokes (2425). However, since the publication of these reports, hundreds of preclinical studies have shown that a variety of cell types including those derived from non-neural tissues can enhance structural and functional recovery in stroke. Cell therapy trials, mainly targeted at small cohorts of patients with chronic stroke, completed in the 2000s, showed satisfactory safety profiles and suggestions of efficacy (10). Current treatments such as tPA and EVT only have a narrow therapeutic window, limited efficacy in severe stroke and may be accompanied by severe side effects. Specifically, the side effects of EVT include intracranial hemorrhage, vessel dissection, emboli to new vascular territories, and vasospasm (26). The benefit of tPA for patients with a severe stroke with a large artery occlusion can vary significantly (27). This is mainly due to the failure (<30%) of early recanalisation of the occlusion. Thus, despite the treatment options stroke is still a major cause of mortality and morbidity, and there is need for new and improved therapies.

Stem cells have been postulated to significantly extend the period of intervention and target subacute as well as the chronic phase of stroke. Numerous neurological disorders such as Parkinson’s disease (1228), Alzheimer’s disease (29), age-related macular degeneration (30), traumatic brain injury (31), and malignant gliomas (32) have been investigated for the applicability of stem cell therapy. These studies have partly influenced the investigation of stem cell therapies for stroke. A small fraction of stem cell research has been successfully translated to clinical trials. As detailed in Table 1, most currently active trials use neuronal stem cells (NSCs), MSCs or BMSCs (3537), including conditionally immortalized neural stem-cell line (CTX-DP) CTX0E03 (38), neural stem/progenitor cells (NSCs/NPSCs) (e.g., NCT03296618), umbilical cord blood (CoBis2, NCT03004976), adipose (NCT02813512), or amnion epithelial cells (hAECs, ACTRN 1261800076279) (39).

Table 1. Challenges and bottlenecks of stem cell therapy and clinical trials using stem cells (3334).

[…]

 

Continue —>  Frontiers | Cell-Based Therapies for Stroke: Are We There Yet? | Neurology

, , , , , , ,

Leave a comment

[WEB SITE] How It Works – Motion Guidance

The Motion Guidance Concept is simple: maximize patient experience by adding real-time feedback with visual external cues to enhance virtually any aspect of rehabilitation. Whether you want to encourage a Gain in Range of Motion, Specific Control of Motion, or Isolate Motion,  the Motion Guidance “Clinician Kit” Offers Enhanced Solutions!

take a look at how our kit works!

Need more applications?

Below are some more video examples of Motion Guidance products in action.

 

CERVICAL APPLICATION

SHOULDER APPLICATION

TRUNK/CORE APPLICATION

LOWER CHAIN APPLICATION

 

The Clinician kit comes with a parallel and perpendicular mounting plate, so you can have a laser pointer visual cue projecting at any angle. You can simply strap the device on any body part, and adjust the aim where you like. This device is a game-changer for patient engagement, movement assessment and training. Further, it enhances learning by emphasizing external cues and instant recognition of body positional awareness. The laser’s remote switch allows you to remove or add in visual cues during traning, for advanced learning and increased engagement!


Benefits of Using Visual Feedback in Clinic:

  • Give clients the benefit of seeing how they move, and instantly recognizing where their body is in space.
  • Add a visual component to simple range of motion exercise, and allow clients to see progress.
  • Turn simple exercises such as cervical range of motion, hip hinges or squats into a visually motivating exercise.
  • Add proprioceptive awareness drills to any body part, and let your client visualize their motor control ability.
  • The application and benefit of Motion Guidance is only limited by your creativity!

via How It Works – Motion Guidance

, ,

Leave a comment

[Editorial] Introducing the thematic series on transcranial direct current stimulation (tDCS) for motor rehabilitation: on the way to optimal clinical use

Introduction

Transcranial direct current stimulation (tDCS) is a method of noninvasive brain stimulation that directs a constant low amplitude electric current through scalp electrodes. tDCS has been shown to modulate excitability in both cortical and subcortical brain areas [], with anodal tDCS leading to increased neuronal excitability and cathodal tDCS inversely leading to reduced neuronal excitability. tDCS can also modulate blood flow (i.e. oxygen supply to cortical and subcortical areas []) and neuronal synapsis strength [], triggering plasticity processes (i.e. long-term potentiation and long-term depression). There is growing interest in using tDCS as a low-cost, non-invasive brain stimulation option for a wide range of potential clinical applications. Advantages of tDCS over other methods of non-invasive brain stimulation include favorable safety and tolerability profiles and its portability and applicability.

The use of tDCS in motor rehabilitation for neurological diseases as well as in healthy ageing is a growing area of therapeutic use. Although the results of tDCS interventions for motor rehabilitation are still preliminary, they encourage further research to better understand its therapeutic utility and to inform optimal clinical use. Therefore, The Journal of NeuroEngineering and Rehabilitation (JNER. https://jneuroengrehab.biomedcentral.com/) is pleased to present the thematic series entitled “tDCS application for motor rehabilitation”.

The goal of this thematic series is to increase the awareness of academic and clinical communities to different potential applications of tDCS for motor rehabilitation. Experts in the field were invited to submit experimental or review studies. A call for papers was also announced to reach those interested in contributing to this thematic series. This collection of articles was thought to present the most recent advances in tDCS for motor rehabilitation, addressing topics such as theoretical, methodological, and practical approaches to be considered when designing tDCS-based rehabilitation. The targeted disorders include but are not limited to: stroke, Parkinson’s disease, Cerebral Palsy, cerebellar ataxia, trauma, Multiple Sclerosis.

tDCS – A promising clinical tool for motor rehabilitation

tDCS has been used in experimental and clinical neuroscience for the study of brain functions and treatment in a range of disorders of the central nervous system. Of particular interest to this thematic series, a growing body of evidence suggest that tDCS has potential to become a clinical tool for motor rehabilitation.

The existing tDCS protocols using well-defined montages, stimulus durations and intensities are safe and well tolerated by both healthy individuals and clinical populations. There are no reported indications of any serious adverse effects, such as damage of brain tissue or seizure induction, with the use of 1–2 mA protocols []. The most commonly reported adverse effects included redness, tingling and itching sensations under the electrodes, as well as headache []. Moreover, the overall adverse effect rates are similar between active and sham tDCS [], which suggests that the mild adverse effects are related to electrode positioning on the skin and not the stimulation itself.

As tDCS is portable, devices can easily be transported, which circumvents accessibility barriers to health care (i.e. tDCS can easily be moved into clinics or wards). It can be implemented in combination with other kinds of interventions, such as cognitive or physical training or exercise, with this pairing possibly leading to synergistic benefit []. Although accumulating evidence highlights potential benefits offered by tDCS for motor rehabilitation, further research is required for tDCS to become an approved clinical tool. The majority of existing clinical trials has involved a limited number of participants, which may imply underpowered analysis. Thus, large-scale studies are needed to overcome this major flaw.

Due to the potential for self- or caregiver-application, remotely supervised protocols have been developed and recently found feasible for those with motor impairment []. However, these studies employ highly structured protocols and rigorous criteria with real time supervision via teleconference, and do not support a “do-it-yourself” tDCS practice. Instead, the remotely supervised protocols can be used to facilitate the clinical trial designs that are necessary in order to advance tDCS towards therapeutic use.

Data on optimal protocols and predictors of response to tDCS are currently lacking in the literature. Future studies in this field should focus on determining the optimal stimulation parameters and predictors of response to tDCS in different clinical populations. It seems that one size does not fit all in tDCS. However, previous studies may be limited, as standard clinical assessments may miss subtle motor improvements. Future outcomes for determining the effectiveness of tDCS for motor rehabilitation need to be robust. Therefore, combining tDCS protocols with other validated mobile technologies to monitor motor performance, such as wearable inertial sensors or innovative Internet of Things devices, may provide important insight into effectiveness within clinic and beyond.

Despite the positive progression of research to clinical practice, there are still questions to be answered before tDCS can be extensively recommended for motor rehabilitation.

• What is the ideal intensity and duration of the session?

• How many sessions are required?

• What is the ideal interval between sessions?

• What about patients’ characteristics?

• Who will benefit from tDCS?

• Do specific demographic characteristics lead to greater benefits?

Final considerations

We hope the accepted papers will contribute meaningfully to the body of knowledge in the field of tDCS for motor rehabilitation and that they will motivate the development of further research. Additionally, we hope this thematic series will assist both researchers and clinical professionals in making decisions for the achievement of optimal benefits throughout tDCS.

References

  1. 1.
    Bolzoni F, Pettersson L-G, Jankowska E. Evidence for long-lasting subcortical facilitation by transcranial direct current stimulation in the cat. J Physiol [Internet]. 2013 [cited 2018 Nov 10];591:3381–3399. Available from: http://doi.wiley.com/10.1113/jphysiol.2012.244764.
  2. 2.
    Nitsche MA, Paulus W. Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. J Physiol [Internet]. 2000 [cited 2018 Nov 10];527 Pt 3:633–639. Available from: http://www.ncbi.nlm.nih.gov/pubmed/10990547.
  3. 3.
    Zheng X, Alsop DC, Schlaug G. Effects of transcranial direct current stimulation (tDCS) on human regional cerebral blood flow. Neuroimage [Internet]. 2011 [cited 2019 Feb 14];58:26–33. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21703350.
  4. 4.
    Polanía R, Paulus W, Antal A, Nitsche MA. Introducing graph theory to track for neuroplastic alterations in the resting human brain: a transcranial direct current stimulation study. Neuroimage [Internet]. 2011 [cited 2019 Feb 14];54:2287–2296. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1053811910012875.
  5. 5.
    Woods AJ, Antal A, Bikson M, Boggio PS, Brunoni AR, Celnik P, et al. A technical guide to tDCS, and related non-invasive brain stimulation tools. Clin Neurophysiol [Internet] 2016 [cited 2018 Nov 10];127:1031–1048. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26652115.
  6. 6.
    Moffa AH, Brunoni AR, Fregni F, Palm U, Padberg F, Blumberger DM, et al. Safety and acceptability of transcranial direct current stimulation for the acute treatment of major depressive episodes: Analysis of individual patient data. J Affect Disord [Internet]. 2017 [cited 2018 Nov 10];221:1–5. Available from: http://www.ncbi.nlm.nih.gov/pubmed/28623732.
  7. 7.
    Bikson M, Grossman P, Thomas C, Zannou AL, Jiang J, Adnan T, et al. Safety of transcranial direct current stimulation: evidence based update 2016. Brain Stimul [Internet] 2016 [cited 2018 Nov 10];9:641–661. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27372845.
  8. 8.
    Fertonani A, Ferrari C, Miniussi C. What do you feel if I apply transcranial electric stimulation? Safety, sensations and secondary induced effects. Clin Neurophysiol [Internet]. 2015 [cited 2018 Nov 10];126:2181–2188. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25922128.
  9. 9.
    Kaski D, Dominguez R, Allum J, Islam A, Bronstein A. Combining physical training with transcranial direct current stimulation to improve gait in Parkinson’s disease: a pilot randomized controlled study. Clin Rehabil [Internet]. 2014 [cited 2018 Nov 10];28:1115–24. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24849794.
  10. 10.
    Agarwal S, Pawlak N, Cucca A, Sharma K, Dobbs B, Shaw M, et al. Remotely-supervised transcranial direct current stimulation paired with cognitive training in Parkinson’s disease: An open-label study. J Clin Neurosci [Internet]. 2018 [cited 2018 Nov 10];57:51–57. Available from: http://www.ncbi.nlm.nih.gov/pubmed/30193898.

via Introducing the thematic series on transcranial direct current stimulation (tDCS) for motor rehabilitation: on the way to optimal clinical use | SpringerLink

, , ,

Leave a comment

[ARTICLE] A review of transcranial electrical stimulation methods in stroke rehabilitation – Full Text

 

Abstract

Transcranial electrical stimulation (TES) uses direct or alternating current to non-invasively stimulate the brain. Neuronal activity in the brain is modulated by the electrical field according to the polarity of the current being applied. TES includes transcranial direct current stimulation (tDCS), transcranial random noise stimulation, and transcranial alternating current stimulation (tACS). tDCS and tACS are the two non-invasive brain stimulation techniques that have been used alone or in combination with other rehabilitative therapies for the improvement of motor control in hemiparesis. Increasing research in these methods is being carried out to improvise on the existing technology because they have proven to exhibit a lasting effect, thereby contributing to brain plasticity and motor re-learning. Artificial stimulation of the lesioned or non-lesioned hemisphere induces participation of its cells when a movement is being performed. The devices are portable, stimulation is easy to deliver, and they are not known to cause any major side effects which are the foremost reasons for their trials in stroke rehabilitation. Recent research is focused on maximizing the outcome of stroke rehabilitation by combining them with other modalities. This review focuses on stimulation protocols, parameters, and the results obtained by these techniques and their combinations.

Key Message: Motor recovery and control poses a great challenge in stroke rehabilitation. Transcranial electrical stimulation methods look promising in this regard as they have been shown to augment long-term and short-term potentiation in the brain which may have a role in motor re-learning. This review discusses transcranial direct current stimulation and transcranial alternating current stimulation in stroke rehabilitation.

According to World Health Organization (WHO) statistics on 2016, cardiovascular diseases (CVD) are the foremost cause of death and adult disability worldwide.[1],[2] Stroke statistics in India show that the incidence of stroke was 435/100,000 population and only one in three stroke survivors are hospitalized and given further rehabilitation because treatment is expensive.[3]

Stroke survivors are faced with paralysis of one side of the body, that is, the side contra-lateral to the affected side in the brain. Rehabilitation aims at strengthening these muscles to prevent wastage and bring back function to the maximum possible extent. Taking the upper extremity into consideration, a combination of muscle over-activity (spastic muscle) in certain groups and weakening in other groups causes poor motor control leading to deformities and inability to reach, grasp, and release objects.

Various therapies such as splinting, stretching exercises, functional electrical stimulation (FES), and mirror therapy are being used to treat this condition, with varying degrees of success. In an ideal situation, the aim of stroke rehabilitation is to recover the paralyzed limb to an extent that it is functionally useful. In this context, recent research is being conducted in neuroplasticity or motor-relearning. Neuroplasticity refers to the brain being able to adapt to changes in response to its external environment and stimulation. TES and transcranial magnetic stimulation (TMS) are the non-invasive brain stimulation (NIBS) methods that invoke this type of re-learning.[4],[5]

NIBS methods include TMS and TES since they non-invasively stimulate the cortex. These methods are still under research for medical applications and were first introduced to treat psychiatric conditions such as insomnia, chronic anxiety, mild depression and post stroke aphasia.[6],[7],[8] Recently, tDCS has also been tried on normal individuals and was shown to improve cognition, working memory, and performance.[9],[10],[11] These methods are now gaining importance in stroke rehabilitation because they provide motor relearning probably through cortical reorganization, which occurs because the neural continuity between the brain and the periphery is intact.[12]

This article attempts to review the stimulation protocols used for TES by various research groups and the results obtained. The first section begins with an introduction to non-invasive methods of brain stimulation followed by a brief summary on the history that led to the use of TES for stroke rehabilitation. Later sections deal with tDCS and tACS. The section on tDCS is further subdivided into tDCS alone and tDCS with adjuvant therapy. The tables give a list of the studies that have been carried out for neurorehabilitation, although it is not meant to be an exhaustive list.[…]

Continue —> A review of transcranial electrical stimulation methods in stroke rehabilitation Solomons CD, Shanmugasundaram V Neurol India

Figure 1: Placement of electrodes for a-tDCS and c-tDCS

Figure 1: Placement of electrodes for a-tDCS and c-tDCS

, , , , , ,

Leave a comment

[Abstract] Vision-Based Serious Games and Virtual Reality Systems for Motor Rehabilitation: A Review Geared Toward a Research Methodology

ABSTRACT

Background

Nowadays, information technologies are being widely adopted to promote healthcare and rehabilitation. Owing to their affordability and use of hand-free controllers, vision-based systems have gradually been integrated into motor rehabilitation programs and have greatly drawn the interest of healthcare practitioners and the research community. Many studies have illustrated the effectiveness of these systems in rehabilitation. However, the report and design aspects of the reported clinical trials were disregarded.

Objective

In this paper, we present a systematic literature review of the use of vision-based serious games and virtual reality systems in motor rehabilitation programs. We aim to propose a research methodology that engineers can use to improve the designing and reporting processes of their clinical trials.

Methods

We conducted a review of published studies that entail clinical experiments. Searches were performed using Web of Science and Medline (PubMed) electronic databases, and selected studies were assessed using the Downs and Black Checklist and then analyzed according to specific research questions.

Results

We identified 86 studies and our findings indicate that the number of studies in this field is increasing, with Korea and USA in the lead. We found that Kinect, EyeToy system, and GestureTek IREX are the most commonly used technologies in studying the effects of vision-based serious games and virtual reality systems on rehabilitation. Findings also suggest that cerebral palsy and stroke patients are the main target groups, with a particular interest on the elderly patients in this target population. The findings indicate that most of the studies focused on postural control and upper extremity exercises and used different measurements during assessment.

Conclusions

Although the research community’s interest in this area is growing, many clinical trials lack sufficient clarity in many aspects and are not standardized. Some recommendations have been made throughout the article.

via Vision-Based Serious Games and Virtual Reality Systems for Motor Rehabilitation: A Review Geared Toward a Research Methodology – ScienceDirect

, , , , , , , , ,

Leave a comment

%d bloggers like this: