Posts Tagged motor learning

[ARTICLE] Combining Upper Limb Robotic Rehabilitation with Other Therapeutic Approaches after Stroke: Current Status, Rationale, and Challenges – Full Text

Abstract

A better understanding of the neural substrates that underlie motor recovery after stroke has led to the development of innovative rehabilitation strategies and tools that incorporate key elements of motor skill relearning, that is, intensive motor training involving goal-oriented repeated movements. Robotic devices for the upper limb are increasingly used in rehabilitation. Studies have demonstrated the effectiveness of these devices in reducing motor impairments, but less so for the improvement of upper limb function. Other studies have begun to investigate the benefits of combined approaches that target muscle function (functional electrical stimulation and botulinum toxin injections), modulate neural activity (noninvasive brain stimulation), and enhance motivation (virtual reality) in an attempt to potentialize the benefits of robot-mediated training. The aim of this paper is to overview the current status of such combined treatments and to analyze the rationale behind them.

1. Introduction

Significant advances have been made in the management of stroke (including prevention, acute management, and rehabilitation); however cerebrovascular diseases remain the third most common cause of death and the first cause of disability worldwide [16]. Stroke causes brain damage, leading to loss of motor function. Upper limb (UL) function is particularly reduced, resulting in disability. Many rehabilitation techniques have been developed over the last decades to facilitate motor recovery of the UL in order to improve functional ability and quality of life [710]. They are commonly based on principles of motor skill learning to promote plasticity of motor neural networks. These principles include intensive, repetitive, task-oriented movement-based training [1119]. A better understanding of the neural substrates of motor relearning has led to the development of innovative strategies and tools to deliver exercise that meets these requirements. Treatments mostly target the neurological impairment (paresis, spasticity, etc.) through the activation of neural circuits or by acting on peripheral effectors. Robotic devices provide exercises that incorporate key elements of motor learning. Advanced robotic systems can offer highly repetitive, reproducible, interactive forms of training for the paretic limb, which are quantifiable. Robotic devices also enable easy and objective assessment of motor performance in standardized conditions by the recording of biomechanical data (i.e., speed, forces) [2022]. This data can be used to analyze and assess motor recovery in stroke patients [2326]. Since the 1990s, many other technology-based approaches and innovative pharmaceutical treatments have also been developed for rehabilitation, including virtual reality- (VR-) based systems, botulinum neurotoxin (BoNT) injections, and noninvasive brain stimulation (NIBS) (Direct Current Stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS)). There is currently no high-quality evidence to support any of these innovative interventions, despite the fact that some are used in routine practice [27]. By their respective mechanisms of action, each of these treatments could potentiate the effects of robotic therapy, leading to greater improvements in motor capacity. The aim of this paper is to review studies of combined treatments based on robotic rehabilitation and to analyze the rationale behind such approaches.[…]

 

Continue —> Combining Upper Limb Robotic Rehabilitation with Other Therapeutic Approaches after Stroke: Current Status, Rationale, and Challenges

Advertisements

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

Leave a comment

[Abstract] The application of virtual reality in neuro-rehabilitation: motor re-learning supported by innovative technologies

Abstract

The motor function impairment resulting from a stroke injury has a negative impact on autonomy, the activities of daily living thus the individuals affected by a stroke need long-term rehabilitation. Several studies have demonstrated that learning new motor skills is important to induce neuroplasticity and functional recovery. Innovative technologies used in rehabilitation allow one the possibility to enhance training throughout generated feedback. It seems advantageous to combine traditional motor rehabilitation with innovative technology in order to promote motor re-learning and skill re-acquisition by means of enhanced training. An environment enriched by feedback involves multiple sensory modalities and could promote active patient participation. Exercises in a virtual environment contain elements necessary to maximize motor learning, such as repetitive and diffe-rentiated task practice and feedback on the performance and results. The recovery of the limbs motor function in post-stroke subjects is one of the main therapeutic aims for patients and physiotherapist alike. Virtual reality as well as robotic devices allow one to provide specific treatment based on the reinforced feedback in a virtual environment (RFVE), artificially augmenting the sensory information coherent with the real-world objects and events. Motor training based on RFVE is emerging as an effective motor learning based techniques for the treatment of the extremities.

 

via The application of virtual reality in neuro-rehabilitation: motor re-learni

, , , ,

Leave a comment

[ARTICLE] Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another – Full Text

Abstract

As far as acquiring motor skills is concerned, training by voluntary physical movement is superior to all other forms of training (e.g. training by observation or passive movement of trainee’s hands by a robotic device). This obviously presents a major challenge in the rehabilitation of a paretic limb since voluntary control of physical movement is limited. Here, we describe a novel training scheme we have developed that has the potential to circumvent this major challenge. We exploited the voluntary control of one hand and provided real-time movement-based manipulated sensory feedback as if the other hand is moving. Visual manipulation through virtual reality (VR) was combined with a device that yokes left-hand fingers to passively follow right-hand voluntary finger movements. In healthy subjects, we demonstrate enhanced within-session performance gains of a limb in the absence of voluntary physical training. Results in healthy subjects suggest that training with the unique VR setup might also be beneficial for patients with upper limb hemiparesis by exploiting the voluntary control of their healthy hand to improve rehabilitation of their affected hand.

Introduction

Physical practice is the most efficient form of training. Although this approach is well established1, it is very challenging in cases where the basic motor capability of the training hand is limited2. To bypass this problem, a large and growing body of literature examined various indirect approaches of motor training.

One such indirect training approach uses physical practice with one hand to introduce performance gains in the other (non-practiced) hand. This phenomenon, known as cross-education (CE) or intermanual transfer, has been studied extensively 3,4,5,6,7,8,9 and used to enhance performance in various motor tasks 10,11,12. For instance, in sport skill settings, studies have demonstrated that training basketball dribbling in one hand transfers to increased dribbling capabilities in the other, untrained hand 13,14,15.

In another indirect approach, motor learning is facilitated through the use of visual or sensory feedback. In learning by observation, it has been demonstrated that significant performance gains can be obtained simply by passively observing someone else perform the task16,17,18,19,20. Similarly, proprioceptive training, in which the limb is passively moved, was also shown to improve performance on motor tasks 12,21,22,23,24,25,26.

Together, these lines of research suggest that sensory input plays an important role in learning. Here, we demonstrate that manipulating online sensory feedback (visual and proprioceptive) during physical training of one limb results in augmented performance gain in the opposite limb. We describe a training regime that yields optimal performance outcome in a hand, in the absence of its voluntary physical training. The conceptual novelty of the proposed method resides in the fact that it combines the three different forms of learning – namely, learning by observation, CE, and passive movement. Here we examined whether the phenomenon of CE, together with mirrored visual feedback and passive movement, can be exploited to facilitate learning in healthy subjects in the absence of voluntary physical movement of the training limb.

The concept in this setup differs from direct attempts to physically train the hand. At the methodological level – we introduce a novel setup including advanced technologies such as 3D virtual reality, and custom built devices that allow manipulating visual and proprioceptive input in a natural environmental setting. Demonstrating improved outcome using the proposed training has key consequences for real-world learning. For example, children use sensory feedback in a manner that is different from that of adults27,28,29 and in order to optimize motor learning, children may require longer periods of practice. The use of CE together with manipulated sensory feedback might reduce training duration. Furthermore, acquisition of sport skills might be facilitated using this kind of sophisticated training. Finally, this can prove beneficial for the development of a new approach for rehabilitation of patients with unilateral motor deficits such as stroke.[…]

Continue —> Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another

An external file that holds a picture, illustration, etc.
Object name is jove-127-55965-1.jpg

, , , , , , ,

Leave a comment

[Abstract+References] Non-invasive Cerebellar Stimulation: a Promising Approach for Stroke Recovery?

Abstract

Non-invasive brain stimulation (NIBS) combined with behavioral training is a promising strategy to augment recovery after stroke. Current research efforts have been mainly focusing on primary motor cortex (M1) stimulation. However, the translation from proof-of-principle to clinical applications is not yet satisfactory. Possible reasons are the heterogeneous properties of stroke, generalization of the stimulation protocols, and hence the lack of patient stratification. One strategy to overcome these limitations could be the evaluation of alternative stimulation targets, like the cerebellum. In this regard, first studies provided evidence that non-invasive cerebellar stimulation can modulate cerebellar processing and linked behavior in healthy subjects. The cerebellum provides unique plasticity mechanisms and has vast connections to interact with neocortical areas. Moreover, the cerebellum could serve as a non-lesioned entry to the motor or cognitive system in supratentorial stroke. In the current article, we review mechanisms of plasticity in the cortico-cerebellar system after stroke, methods for non-invasive cerebellar stimulation, and possible target symptoms in stroke, like fine motor deficits, gait disturbance, or cognitive impairments, and discuss strategies for multi-focal stimulation.

 References

  1. 1.
    Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, et al. Heart disease and stroke statistics—2016 update: a report from the American Heart Association. Circulation. 2016;133(4):e38–360. https://doi.org/10.1161/CIR.0000000000000350.PubMedCrossRefGoogle Scholar
  2. 2.
    Blackburn DJ, Bafadhel L, Randall M, Harkness KA. Cognitive screening in the acute stroke setting. Age Ageing. 2013;42(1):113–6. https://doi.org/10.1093/ageing/afs116.PubMedCrossRefGoogle Scholar
  3. 3.
    Kotila M, Waltimo O, Niemi ML, Laaksonen R, Lempinen M. The profile of recovery from stroke and factors influencing outcome. Stroke. 1984;15(6):1039–44. https://doi.org/10.1161/01.STR.15.6.1039.PubMedCrossRefGoogle Scholar
  4. 4.
    Ramsey LE, Siegel JS, Lang CE, Strube M, Shulman GL, Corbetta M. Behavioural clusters and predictors of performance during recovery from stroke. Nat Hum Behav. 2017;1(3):38. https://doi.org/10.1038/s41562-016-0038.CrossRefGoogle Scholar
  5. 5.
    Rathore SS, Hinn AR, Cooper LS, Tyroler HA, Rosamond WD. Characterization of incident stroke signs and symptoms: findings from the atherosclerosis risk in communities study. Stroke. 2002;33(11):2718–21. https://doi.org/10.1161/01.STR.0000035286.87503.31.PubMedCrossRefGoogle Scholar
  6. 6.
    Stinear CM, Barber PA, Petoe M, Anwar S, Byblow WD. The PREP algorithm predicts potential for upper limb recovery after stroke. Brain. 2012;135(8):2527–35. https://doi.org/10.1093/brain/aws146.PubMedCrossRefGoogle Scholar
  7. 7.
    Hummel FC, Cohen LG. Drivers of brain plasticity. Curr Opin Neurol. 2005;18(6):667–74. https://doi.org/10.1097/01.wco.0000189876.37475.42.PubMedCrossRefGoogle Scholar
  8. 8.
    Hummel F, Celnik P, Giraux P, Floel A, Wu WH, Gerloff C, et al. Effects of non-invasive cortical stimulation on skilled motor function in chronic stroke. Brain. 2005;128(3):490–9. https://doi.org/10.1093/brain/awh369.PubMedCrossRefGoogle Scholar
  9. 9.
    Lefaucheur JP, Antal A, Ayache SS, Benninger DH, Brunelin J, Cogiamanian F, et al. Evidence-based guidelines on the therapeutic use of transcranial direct current stimulation (tDCS). Clin Neurophysiol. 2017;128(1):56–92. https://doi.org/10.1016/j.clinph.2016.10.087.PubMedCrossRefGoogle Scholar
  10. 10.
    Wessel MJ, Zimerman M, Hummel FC. Non-invasive brain stimulation: an interventional tool for enhancing behavioral training after stroke. Front Hum Neurosci. 2015;9:265. https://doi.org/10.3389/fnhum.2015.00265.
  11. 11.
    Tedesco Triccas L, Burridge JH, Hughes AM, Pickering RM, Desikan M, Rothwell JC, et al. Multiple sessions of transcranial direct current stimulation and upper extremity rehabilitation in stroke: a review and meta-analysis. Clin Neurophysiol. 2016;127(1):946–55. https://doi.org/10.1016/j.clinph.2015.04.067.PubMedCrossRefGoogle Scholar
  12. 12.
    Rossi C, Sallustio F, Di Legge S, Stanzione P, Koch G. Transcranial direct current stimulation of the affected hemisphere does not accelerate recovery of acute stroke patients. Eur J Neurol. 2013;20(1):202–4. https://doi.org/10.1111/j.1468-1331.2012.03703.x.PubMedCrossRefGoogle Scholar
  13. 13.
    Kapoor A, Lanctôt KL, Bayley M, Kiss A, Herrmann N, Murray BJ, et al. “Good outcome” isn’t good enough: cognitive impairment, depressive symptoms, and social restrictions in physically recovered stroke patients. Stroke. 2017;48(6):1688–90. https://doi.org/10.1161/STROKEAHA.117.016728.PubMedCrossRefGoogle Scholar
  14. 14.
    das Nair R, Cogger H, Worthington E, Lincoln NB. Cognitive rehabilitation for memory deficits after stroke: an updated review. Stroke. 2017;48(2):e28–9. https://doi.org/10.1161/STROKEAHA.116.015377.PubMedCrossRefGoogle Scholar
  15. 15.
    Miniussi C, Cappa SF, Cohen LG, Floel A, Fregni F, Nitsche MA, et al. Efficacy of repetitive transcranial magnetic stimulation/transcranial direct current stimulation in cognitive neurorehabilitation. Brain Stimulat. 2008;1(4):326–36. https://doi.org/10.1016/j.brs.2008.07.002.CrossRefGoogle Scholar
  16. 16.
    Elsner B, Kugler J, Pohl M, Mehrholz J. Transcranial direct current stimulation (tDCS) for improving activities of daily living, and physical and cognitive functioning, in people after stroke. Cochrane Database Syst Rev. 2016;3:CD009645. https://doi.org/10.1002/14651858.CD009645.pub3.
  17. 17.
    Ameli M, Grefkes C, Kemper F, Riegg FP, Rehme AK, Karbe H, et al. Differential effects of high-frequency repetitive transcranial magnetic stimulation over ipsilesional primary motor cortex in cortical and subcortical middle cerebral artery stroke. Ann Neurol. 2009;66(3):298–309. https://doi.org/10.1002/ana.21725.PubMedCrossRefGoogle Scholar
  18. 18.
    Carey JR, Deng H, Gillick BT, Cassidy JM, Anderson DC, Zhang L, et al. Serial treatments of primed low-frequency rTMS in stroke: characteristics of responders vs. nonresponders. Restor Neurol Neurosci. 2014;32(2):323–35. https://doi.org/10.3233/RNN-130358.PubMedPubMedCentralGoogle Scholar
  19. 19.
    Wagner T, Fregni F, Fecteau S, Grodzinsky A, Zahn M, Pascual-Leone A. Transcranial direct current stimulation: a computer-based human model study. NeuroImage. 2007;35(3):1113–24. https://doi.org/10.1016/j.neuroimage.2007.01.027.PubMedCrossRefGoogle Scholar
  20. 20.
    Lindenberg R, Zhu LL, Ruber T, Schlaug G. Predicting functional motor potential in chronic stroke patients using diffusion tensor imaging. Hum Brain Mapp. 2012;33(5):1040–51. https://doi.org/10.1002/hbm.21266.PubMedCrossRefGoogle Scholar
  21. 21.
    Demirtas-Tatlidede A, Alonso-Alonso M, Shetty RP, Ronen I, Pascual-Leone A, Fregni F. Long-term effects of contralesional rTMS in severe stroke: safety, cortical excitability, and relationship with transcallosal motor fibers. NeuroRehabilitation. 2015;36(1):51–9. https://doi.org/10.3233/NRE-141191.PubMedGoogle Scholar
  22. 22.
    O’Shea J, Boudrias MH, Stagg CJ, Bachtiar V, Kischka U, Blicher JU, et al. Predicting behavioural response to TDCS in chronic motor stroke. NeuroImage. 2014;85(Pt 3):924–33. https://doi.org/10.1016/j.neuroimage.2013.05.096.PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Bradnam LV, Stinear CM, Barber PA, Byblow WD. Contralesional hemisphere control of the proximal paretic upper limb following stroke. Cereb Cortex. 2012;22(11):2662–71. https://doi.org/10.1093/cercor/bhr344.PubMedCrossRefGoogle Scholar
  24. 24.
    Wang CC, Wang CP, Tsai PY, Hsieh CY, Chan RC, Yeh SC. Inhibitory repetitive transcranial magnetic stimulation of the contralesional premotor and primary motor cortices facilitate poststroke motor recovery. Restor Neurol Neurosci. 2014;32(6):825–35. https://doi.org/10.3233/RNN-140410.PubMedGoogle Scholar
  25. 25.
    Fregni F, Boggio PS, Mansur CG, Wagner T, Ferreira MJ, Lima MC, et al. Transcranial direct current stimulation of the unaffected hemisphere in stroke patients. Neuroreport. 2005;16(14):1551–5. https://doi.org/10.1097/01.wnr.0000177010.44602.5e.PubMedCrossRefGoogle Scholar
  26. 26.
    Kwon TG, Kim YH, Chang WH, Bang OY, Shin YI. Effective method of combining rTMS and motor training in stroke patients. Restor Neurol Neurosci. 2014;32(2):223–32. https://doi.org/10.3233/RNN-130313.PubMedGoogle Scholar
  27. 27.
    Cho JY, Lee A, Kim MS, Park E, Chang WH, Shin YI, et al. Dual-mode noninvasive brain stimulation over the bilateral primary motor cortices in stroke patients. Restor Neurol Neurosci. 2017;35(1):105–14. https://doi.org/10.3233/RNN-160669.PubMedGoogle Scholar
  28. 28.
    Boggio PS, Nunes A, Rigonatti SP, Nitsche MA, Pascual-Leone A, Fregni F. Repeated sessions of noninvasive brain DC stimulation is associated with motor function improvement in stroke patients. Restor Neurol Neurosci. 2007;25(2):123–9.PubMedGoogle Scholar
  29. 29.
    Carey MR. Synaptic mechanisms of sensorimotor learning in the cerebellum. Curr Opin Neurobiol. 2011;21(4):609–15. https://doi.org/10.1016/j.conb.2011.06.011.PubMedCrossRefGoogle Scholar
  30. 30.
    Cheron G, Dan B, Marquez-Ruiz J. Translational approach to behavioral learning: lessons from cerebellar plasticity. Neural Plast. 2013;2013:853654. https://doi.org/10.1155/2013/853654.
  31. 31.
    Bostan AC, Dum RP, Strick PL. Cerebellar networks with the cerebral cortex and basal ganglia. Trends Cogn Sci. 2013;17(5):241–54. https://doi.org/10.1016/j.tics.2013.03.003.PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Manto MU. On the cerebello-cerebral interactions. The Cerebellum. 2006;5:286–8. https://doi.org/10.1080/14734220601003955.
  33. 33.
    Galea JM, Vazquez A, Pasricha N, de Xivry JJ, Celnik P. Dissociating the roles of the cerebellum and motor cortex during adaptive learning: the motor cortex retains what the cerebellum learns. Cereb Cortex. 2011;21(8):1761–70. https://doi.org/10.1093/cercor/bhq246.PubMedCrossRefGoogle Scholar
  34. 34.
    Theoret H, Haque J, Pascual-Leone A. Increased variability of paced finger tapping accuracy following repetitive magnetic stimulation of the cerebellum in humans. Neurosci Lett. 2001;306(1-2):29–32. https://doi.org/10.1016/S0304-3940(01)01860-2.PubMedCrossRefGoogle Scholar
  35. 35.
    Baron JC, Bousser MG, Comar D, Castaigne P. “Crossed cerebellar diaschisis” in human supratentorial brain infarction. Trans Am Neurol Assoc. 1981;105:459–61.PubMedGoogle Scholar
  36. 36.
    Szilagyi G, Vas A, Kerenyi L, Nagy Z, Csiba L, Gulyas B. Correlation between crossed cerebellar diaschisis and clinical neurological scales. Acta Neurol Scand. 2012;125(6):373–81. https://doi.org/10.1111/j.1600-0404.2011.01576.x.PubMedCrossRefGoogle Scholar
  37. 37.
    Gold L, Lauritzen M. Neuronal deactivation explains decreased cerebellar blood flow in response to focal cerebral ischemia or suppressed neocortical function. Proc Natl Acad Sci U A. 2002;99(11):7699–704. https://doi.org/10.1073/pnas.112012499.CrossRefGoogle Scholar
  38. 38.
    Kamouchi M, Fujishima M, Saku Y, Ibayashi S, Iida M. Crossed cerebellar hypoperfusion in hyperacute ischemic stroke. J Neurol Sci. 2004;225(1-2):65–9. https://doi.org/10.1016/j.jns.2004.07.004.PubMedCrossRefGoogle Scholar
  39. 39.
    Miura H, Nagata K, Hirata Y, Satoh Y, Watahiki Y, Hatazawa J. Evolution of crossed cerebellar diaschisis in middle cerebral artery infarction. J Neuroimaging. 1994;4(2):91–6. https://doi.org/10.1111/jon19944291.PubMedCrossRefGoogle Scholar
  40. 40.
    Takasawa M, Watanabe M, Yamamoto S, Hoshi T, Sasaki T, Hashikawa K, et al. Prognostic value of subacute crossed cerebellar diaschisis: single-photon emission CT study in patients with middle cerebral artery territory infarct. AJNR Am J Neuroradiol. 2002;23(2):189–93.PubMedGoogle Scholar
  41. 41.
    Bindman LJ, Lippold OC, Redfearn JW. Long-lasting changes in the level of the electrical activity of the cerebral cortex produced by polarizing currents. Nature. 1962;196(4854):584–5. https://doi.org/10.1038/196584a0.PubMedCrossRefGoogle Scholar
  42. 42.
    Lang N, Siebner HR, Ward NS, Lee L, Nitsche MA, Paulus W, et al. How does transcranial DC stimulation of the primary motor cortex alter regional neuronal activity in the human brain? Eur J Neurosci. 2005;22(2):495–504. https://doi.org/10.1111/j.1460-9568.2005.04233.x.PubMedPubMedCentralCrossRefGoogle Scholar
  43. 43.
    Schulz R, Frey BM, Koch P, Zimerman M, Bönstrup M, Feldheim J, et al. Cortico-cerebellar structural connectivity is related to residual motor output in chronic stroke. Cereb Cortex. 2017;27:635–45. https://doi.org/10.1093/cercor/bhv251.
  44. 44.
    Ugawa Y, Uesaka Y, Terao Y, Hanajima R, Kanazawa I. Magnetic stimulation over the cerebellum in humans. Ann Neurol. 1995;37(6):703–13. https://doi.org/10.1002/ana.410370603.PubMedCrossRefGoogle Scholar
  45. 45.
    Rothwell JC. Using transcranial magnetic stimulation methods to probe connectivity between motor areas of the brain. Hum Mov Sci. 2011;30(5):906–15. https://doi.org/10.1016/j.humov.2010.07.007.PubMedCrossRefGoogle Scholar
  46. 46.
    Kikuchi S, Mochizuki H, Moriya A, Nakatani-Enomoto S, Nakamura K, Hanajima R, et al. Ataxic hemiparesis: neurophysiological analysis by cerebellar transcranial magnetic stimulation. Cerebellum. 2012;11(1):259–63. https://doi.org/10.1007/s12311-011-0303-0.PubMedCrossRefGoogle Scholar
  47. 47.
    Ugawa Y, Terao Y, Hanajima R, Sakai K, Furubayashi T, Machii K, et al. Magnetic stimulation over the cerebellum in patients with ataxia. Electroencephalogr Clin Neurophysiol. 1997;104(5):453–8. https://doi.org/10.1016/S0168-5597(97)00051-8.PubMedCrossRefGoogle Scholar
  48. 48.
    Galea JM, Jayaram G, Ajagbe L, Celnik P. Modulation of cerebellar excitability by polarity-specific noninvasive direct current stimulation. J Neurosci. 2009;29(28):9115–22. https://doi.org/10.1523/JNEUROSCI.2184-09.2009.PubMedPubMedCentralCrossRefGoogle Scholar
  49. 49.
    Krakauer JW. Motor learning: its relevance to stroke recovery and neurorehabilitation. Curr Opin Neurol. 2006;19(1):84–90. https://doi.org/10.1097/01.wco.0000200544.29915.cc.PubMedCrossRefGoogle Scholar
  50. 50.
    Nudo RJ, Wise BM, SiFuentes F, Milliken GW. Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science. 1996;272(5269):1791–4. https://doi.org/10.1126/science.272.5269.1791.PubMedCrossRefGoogle Scholar
  51. 51.
    Askim T, Indredavik B, Vangberg T, Haberg A. Motor network changes associated with successful motor skill relearning after acute ischemic stroke: a longitudinal functional magnetic resonance imaging study. Neurorehabil Neural Repair. 2009;23(3):295–304. https://doi.org/10.1177/1545968308322840.PubMedCrossRefGoogle Scholar
  52. 52.
    Doyon J, Benali H. Reorganization and plasticity in the adult brain during learning of motor skills. Curr Opin Neurobiol. 2005;15(2):161–7. https://doi.org/10.1016/j.conb.2005.03.004.PubMedCrossRefGoogle Scholar
  53. 53.
    Hardwick RM, Rottschy C, Miall RC, Eickhoff SB. A quantitative meta-analysis and review of motor learning in the human brain. NeuroImage. 2013;67:283–97. https://doi.org/10.1016/j.neuroimage.2012.11.020.PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Cantarero G, Spampinato D, Reis J, Ajagbe L, Thompson T, Kulkarni K, et al. Cerebellar direct current stimulation enhances on-line motor skill acquisition through an effect on accuracy. J Neurosci. 2015;35(7):3285–90. https://doi.org/10.1523/JNEUROSCI.2885-14.2015.PubMedPubMedCentralCrossRefGoogle Scholar
  55. 55.
    Wessel MJ, Zimerman M, Timmermann JE, Heise KF, Gerloff C, Hummel FC. Enhancing consolidation of a new temporal motor skill by cerebellar noninvasive stimulation. Cereb Cortex. 2016;26(4):1660–7. https://doi.org/10.1093/cercor/bhu335.PubMedCrossRefGoogle Scholar
  56. 56.
    Di Lazzaro V, Restuccia D, Molinari M, Leggio MG, Nardone R, Fogli D, et al. Excitability of the motor cortex to magnetic stimulation in patients with cerebellar lesions. J Neurol Neurosurg Psychiatry. 1994;57(1):108–10. https://doi.org/10.1136/jnnp.57.1.108.PubMedPubMedCentralCrossRefGoogle Scholar
  57. 57.
    Liepert J, Kucinski T, Tuscher O, Pawlas F, Baumer T, Weiller C. Motor cortex excitability after cerebellar infarction. Stroke. 2004;35(11):2484–8. https://doi.org/10.1161/01.STR.0000143152.45801.ca.PubMedCrossRefGoogle Scholar
  58. 58.
    De Vico FF, Clausi S, Leggio M, Chavez M, Valencia M, Maglione AG, et al. Interhemispheric connectivity characterizes cortical reorganization in motor-related networks after cerebellar lesions. Cerebellum. 2017;16:358–75. https://doi.org/10.1007/s12311-016-0811-z.
  59. 59.
    Koziol LF, Budding D, Andreasen N, D’Arrigo S, Bulgheroni S, Imamizu H, et al. Consensus paper: the cerebellum’s role in movement and cognition. Cerebellum Lond Engl. 2014;13(1):151–77. https://doi.org/10.1007/s12311-013-0511-x.CrossRefGoogle Scholar
  60. 60.
    Sui R, Zhang L. Cerebellar dysfunction may play an important role in vascular dementia. Med Hypotheses. 2012;78:162–5. https://doi.org/10.1016/j.mehy.2011.10.017.
  61. 61.
    Chida K, Ogasawara K, Aso K, Suga Y, Kobayashi M, Yoshida K, et al. Postcarotid endarterectomy improvement in cognition is associated with resolution of crossed cerebellar hypoperfusion and increase in 123I-iomazenil uptake in the cerebral cortex: a SPECT study. Cerebrovasc Dis Basel Switz. 2010;29(4):343–51. https://doi.org/10.1159/000278930.CrossRefGoogle Scholar
  62. 62.
    Rastogi A, Cash R, Dunlop K, Vesia M, Kucyi A, Ghahremani A, et al. Modulation of cognitive cerebello-cerebral functional connectivity by lateral cerebellar continuous theta burst stimulation. NeuroImage. 2017;158:48–57. https://doi.org/10.1016/j.neuroimage.2017.06.048.PubMedCrossRefGoogle Scholar
  63. 63.
    Desmond JE, Chen SHA, Shieh PB. Cerebellar transcranial magnetic stimulation impairs verbal working memory. Ann Neurol. 2005;58(4):553–60. https://doi.org/10.1002/ana.20604.PubMedCrossRefGoogle Scholar
  64. 64.
    Balsters JH, Ramnani N. Cerebellar plasticity and the automation of first-order rules. J Neurosci. 2011;31(6):2305–12. https://doi.org/10.1523/JNEUROSCI.4358-10.2011.PubMedCrossRefGoogle Scholar
  65. 65.
    van Dun K, Bodranghien F, Manto M, Marien P. Targeting the cerebellum by noninvasive neurostimulation: a review. Cerebellum. 2017;16(3):695–741. https://doi.org/10.1007/s12311-016-0840-7.PubMedCrossRefGoogle Scholar
  66. 66.
    Fritsch B, Reis J, Martinowich K, Schambra HM, Ji Y, Cohen LG, et al. Direct current stimulation promotes BDNF-dependent synaptic plasticity: potential implications for motor learning. Neuron. 2010;66(2):198–204. https://doi.org/10.1016/j.neuron.2010.03.035.PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Antal A, Paulus W. Transcranial alternating current stimulation (tACS). Front Hum Neurosci. 2013;7:317. https://doi.org/10.3389/fnhum.2013.00317.
  68. 68.
    Valero-Cabre A, Payne BR, Pascual-Leone A. Opposite impact on 14C-2-deoxyglucose brain metabolism following patterns of high and low frequency repetitive transcranial magnetic stimulation in the posterior parietal cortex. Exp Brain Res. 2007;176(4):603–15. https://doi.org/10.1007/s00221-006-0639-8.PubMedCrossRefGoogle Scholar
  69. 69.
    Huang YZ, Chen RS, Rothwell JC, Wen HY. The after-effect of human theta burst stimulation is NMDA receptor dependent. Clin Neurophysiol. 2007;118(5):1028–32. https://doi.org/10.1016/j.clinph.2007.01.021.PubMedCrossRefGoogle Scholar
  70. 70.
    Naro A, Bramanti A, Leo A, Manuli A, Sciarrone F, Russo M, et al. Effects of cerebellar transcranial alternating current stimulation on motor cortex excitability and motor function. Brain Struct Funct. 2017;222(6):2891–906. https://doi.org/10.1007/s00429-016-1355-1.PubMedCrossRefGoogle Scholar
  71. 71.
    Morellini N, Grehl S, Tang A, Rodger J, Mariani J, Lohof AM, et al. What does low-intensity rTMS do to the cerebellum? Cerebellum. 2015;14(1):23–6. https://doi.org/10.1007/s12311-014-0617-9.PubMedCrossRefGoogle Scholar
  72. 72.
    Koch G, Mori F, Marconi B, Codeca C, Pecchioli C, Salerno S, et al. Changes in intracortical circuits of the human motor cortex following theta burst stimulation of the lateral cerebellum. Clin Neurophysiol. 2008;119(11):2559–69. https://doi.org/10.1016/j.clinph.2008.08.008.PubMedCrossRefGoogle Scholar
  73. 73.
    Doeltgen SH, Young J, Bradnam LV. Anodal direct current stimulation of the cerebellum reduces cerebellar brain inhibition but does not influence afferent input from the hand or face in healthy adults. Cerebellum. 2016;15(4):466–74. https://doi.org/10.1007/s12311-015-0713-5.PubMedCrossRefGoogle Scholar
  74. 74.
    Naro A, Leo A, Russo M, Cannavo A, Milardi D, Bramanti P, et al. Does transcranial alternating current stimulation induce cerebellum plasticity? Feasibility, safety and efficacy of a novel electrophysiological approach. Brain Stimul. 2016;9(3):388–95. https://doi.org/10.1016/j.brs.2016.02.005.PubMedCrossRefGoogle Scholar
  75. 75.
    Popa T, Russo M, Meunier S. Long-lasting inhibition of cerebellar output. Brain Stimul. 2010;3(3):161–9. https://doi.org/10.1016/j.brs.2009.10.001.PubMedCrossRefGoogle Scholar
  76. 76.
    Oliveri M, Koch G, Torriero S, Caltagirone C. Increased facilitation of the primary motor cortex following 1 Hz repetitive transcranial magnetic stimulation of the contralateral cerebellum in normal humans. Neurosci Lett. 2005;376(3):188–93. https://doi.org/10.1016/j.neulet.2004.11.053.PubMedCrossRefGoogle Scholar
  77. 77.
    Fierro B, Giglia G, Palermo A, Pecoraro C, Scalia S, Brighina F. Modulatory effects of 1 Hz rTMS over the cerebellum on motor cortex excitability. Exp Brain Res. 2007;176(3):440–7. https://doi.org/10.1007/s00221-006-0628-y.PubMedCrossRefGoogle Scholar
  78. 78.
    Langguth B, Eichhammer P, Zowe M, Landgrebe M, Binder H, Sand P, et al. Modulating cerebello-thalamocortical pathways by neuronavigated cerebellar repetitive transcranial stimulation (rTMS). Neurophysiol Clin. 2008;38(5):289–95. https://doi.org/10.1016/j.neucli.2008.08.003.PubMedCrossRefGoogle Scholar
  79. 79.
    Torriero S, Oliveri M, Koch G, Caltagirone C, Petrosini L. Interference of left and right cerebellar rTMS with procedural learning. J Cogn Neurosci. 2004;16(9):1605–11. https://doi.org/10.1162/0898929042568488.PubMedCrossRefGoogle Scholar
  80. 80.
    Hoffland BS, Bologna M, Kassavetis P, Teo JT, Rothwell JC, Yeo CH, et al. Cerebellar theta burst stimulation impairs eyeblink classical conditioning. J Physiol. 2012;590(4):887–97. https://doi.org/10.1113/jphysiol.2011.218537.PubMedCrossRefGoogle Scholar
  81. 81.
    Li Voti P, Conte A, Rocchi L, Bologna M, Khan N, Leodori G, et al. Cerebellar continuous theta-burst stimulation affects motor learning of voluntary arm movements in humans. Eur J Neurosci. 2014;39(1):124–31. https://doi.org/10.1111/ejn.12391.PubMedCrossRefGoogle Scholar
  82. 82.
    Sebastian R, Saxena S, Tsapkini K, Faria AV, Long C, Wright A, et al. Cerebellar tDCS: a novel approach to augment language treatment post-stroke. Front Hum Neurosci. 2017;10:695. https://doi.org/10.3389/fnhum.2016.00695.
  83. 83.
    Kim WS, Jung SH, Oh MK, Min YS, Lim JY, Paik NJ. Effect of repetitive transcranial magnetic stimulation over the cerebellum on patients with ataxia after posterior circulation stroke: a pilot study. J Rehabil Med. 2014;46(5):418–23. https://doi.org/10.2340/16501977-1802.PubMedCrossRefGoogle Scholar
  84. 84.
    Bonni S, Ponzo V, Caltagirone C, Koch G. Cerebellar theta burst stimulation in stroke patients with ataxia. Funct Neurol. 2014;29(1):41–5. https://doi.org/10.11138/FNeur/2014.29.1.041.
  85. 85.
    Bikson M, Inoue M, Akiyama H, Deans JK, Fox JE, Miyakawa H, et al. Effects of uniform extracellular DC electric fields on excitability in rat hippocampal slices in vitro. J Physiol. 2004;557(1):175–90. https://doi.org/10.1113/jphysiol.2003.055772.PubMedPubMedCentralCrossRefGoogle Scholar
  86. 86.
    Creutzfeldt OD, Fromm GH, Kapp H. Influence of transcortical d-c currents on cortical neuronal activity. Exp Neurol. 1962;5(6):436–52. https://doi.org/10.1016/0014-4886(62)90056-0.PubMedCrossRefGoogle Scholar
  87. 87.
    Nitsche MA, Paulus W. Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. J Physiol. 2000;527(Pt 3):633–9. https://doi.org/10.1111/j.1469-7793.2000.t01-1-00633.x.PubMedPubMedCentralCrossRefGoogle Scholar
  88. 88.
    Ferrucci R, Brunoni AR, Parazzini M, Vergari M, Rossi E, Fumagalli M, et al. Modulating human procedural learning by cerebellar transcranial direct current stimulation. Cerebellum. 2013;12(4):485–92. https://doi.org/10.1007/s12311-012-0436-9.PubMedCrossRefGoogle Scholar
  89. 89.
    Pope PA, Miall RC. Task-specific facilitation of cognition by cathodal transcranial direct current stimulation of the cerebellum. Brain Stimul. 2012;5(2):84–94. https://doi.org/10.1016/j.brs.2012.03.006.PubMedPubMedCentralCrossRefGoogle Scholar
  90. 90.
    Oldrati V, Schutter DJLG. Targeting the human cerebellum with transcranial direct current stimulation to modulate behavior: a meta-analysis. Cerebellum. 2017. https://doi.org/10.1007/s12311-017-0877-2.
  91. 91.
    Block HJ, Celnik P. Can cerebellar transcranial direct current stimulation become a valuable neurorehabilitation intervention? Expert Rev Neurother. 2012;12(11):1275–7. https://doi.org/10.1586/ern.12.121.PubMedPubMedCentralCrossRefGoogle Scholar
  92. 92.
    Celnik P. Understanding and modulating motor learning with cerebellar stimulation. Cerebellum. 2015;14(2):171–4. https://doi.org/10.1007/s12311-014-0607-y.PubMedPubMedCentralCrossRefGoogle Scholar
  93. 93.
    Ferrucci R, Cortese F, Priori A. Cerebellar tDCS: how to do it. Cerebellum. 2015;14(1):27–30. https://doi.org/10.1007/s12311-014-0599-7.PubMedCrossRefGoogle Scholar
  94. 94.
    Grimaldi G, Argyropoulos GP, Bastian A, Cortes M, Davis NJ, Edwards DJ, et al. Cerebellar transcranial direct current stimulation (ctDCS): a novel approach to understanding cerebellar function in health and disease. Neuroscientist. 2016;22(1):83–97. https://doi.org/10.1177/1073858414559409.PubMedPubMedCentralCrossRefGoogle Scholar
  95. 95.
    van Dun K, Bodranghien FC, Marien P, Manto MU. tDCS of the cerebellum: where do we stand in 2016? Technical issues and critical review of the literature. Front Hum Neurosci. 2016;10:199. https://doi.org/10.3389/fnhum.2016.00199.
  96. 96.
    Antal A, Boros K, Poreisz C, Chaieb L, Terney D, Paulus W. Comparatively weak after-effects of transcranial alternating current stimulation (tACS) on cortical excitability in humans. Brain Stimul. 2008;1(2):97–105. https://doi.org/10.1016/j.brs.2007.10.001.PubMedCrossRefGoogle Scholar
  97. 97.
    Moliadze V, Antal A, Paulus W. Boosting brain excitability by transcranial high frequency stimulation in the ripple range. J Physiol. 2010;588(24):4891–904. https://doi.org/10.1113/jphysiol.2010.196998.PubMedPubMedCentralCrossRefGoogle Scholar
  98. 98.
    Helfrich RF, Schneider TR, Rach S, Trautmann-Lengsfeld SA, Engel AK, Herrmann CS. Entrainment of brain oscillations by transcranial alternating current stimulation. Curr Biol. 2014;24(3):333–9. https://doi.org/10.1016/j.cub.2013.12.041.PubMedCrossRefGoogle Scholar
  99. 99.
    Zaehle T, Rach S, Herrmann CS. Transcranial alternating current stimulation enhances individual alpha activity in human EEG. PLoS One. 2010;5(11):e13766. https://doi.org/10.1371/journal.pone.0013766.PubMedPubMedCentralCrossRefGoogle Scholar
  100. 100.
    Polania R, Nitsche MA, Korman C, Batsikadze G, Paulus W. The importance of timing in segregated theta phase-coupling for cognitive performance. Curr Biol. 2012;22(14):1314–8. https://doi.org/10.1016/j.cub.2012.05.021.PubMedCrossRefGoogle Scholar
  101. 101.
    Antal A, Herrmann CS. Transcranial alternating current and random noise stimulation: possible mechanisms. Neural Plast. 2016;2016:3616807. http://doi.org/10.1155/2016/3616807.
  102. 102.
    Hallett M. Transcranial magnetic stimulation: a primer. Neuron. 2007;55(2):187–99. https://doi.org/10.1016/j.neuron.2007.06.026.PubMedCrossRefGoogle Scholar
  103. 103.
    Rossi S, Hallett M, Rossini PM, Pascual-Leone A. Safety of TMSCG. Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. Clin Neurophysiol. 2009;120(12):2008–39. https://doi.org/10.1016/j.clinph.2009.08.016.PubMedPubMedCentralCrossRefGoogle Scholar
  104. 104.
    Huang YZ, Edwards MJ, Rounis E, Bhatia KP, Rothwell JC. Theta burst stimulation of the human motor cortex. Neuron. 2005;45(2):201–6. https://doi.org/10.1016/j.neuron.2004.12.033.PubMedCrossRefGoogle Scholar
  105. 105.
    Miall RC, Christensen LO. The effect of rTMS over the cerebellum in normal human volunteers on peg-board movement performance. Neurosci Lett. 2004;371(2-3):185–9. https://doi.org/10.1016/j.neulet.2004.08.067.PubMedCrossRefGoogle Scholar
  106. 106.
    Koch G. Repetitive transcranial magnetic stimulation: a tool for human cerebellar plasticity. Funct Neurol. 2010;25(3):159–63.PubMedGoogle Scholar
  107. 107.
    Minks E, Kopickova M, Marecek R, Streitova H, Bares M. Transcranial magnetic stimulation of the cerebellum. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 2010;154(2):133–9. https://doi.org/10.5507/bp.2010.020.PubMedCrossRefGoogle Scholar
  108. 108.
    Ivry RB, Keele SW, Diener HC. Dissociation of the lateral and medial cerebellum in movement timing and movement execution. Exp Brain Res. 1988;73(1):167–80. https://doi.org/10.1007/BF00279670.PubMedCrossRefGoogle Scholar
  109. 109.
    Stoodley CJ, MacMore JP, Makris N, Sherman JC, Schmahmann JD. Location of lesion determines motor vs. cognitive consequences in patients with cerebellar stroke. NeuroImage Clin. 2016;12:765–75. https://doi.org/10.1016/j.nicl.2016.10.013.PubMedPubMedCentralCrossRefGoogle Scholar
  110. 110.
    Machado AG, Cooperrider J, Furmaga HT, Baker KB, Park HJ, Chen Z, et al. Chronic 30-Hz deep cerebellar stimulation coupled with training enhances post-ischemia motor recovery and peri-infarct synaptophysin expression in rodents. Neurosurgery. 2013;73(2):344–53. https://doi.org/10.1227/01.neu.0000430766.80102.ac.
  111. 111.
    Jorgensen HS. The Copenhagen Stroke Study experience. J Stroke Cerebrovasc Dis. 1996;6(1):5–16. https://doi.org/10.1016/S1052-3057(96)80020-6.PubMedCrossRefGoogle Scholar
  112. 112.
    Beyaert C, Vasa R, Frykberg GE. Gait post-stroke: pathophysiology and rehabilitation strategies. Neurophysiol Clin. 2015;45(4-5):335–55. https://doi.org/10.1016/j.neucli.2015.09.005.PubMedCrossRefGoogle Scholar
  113. 113.
    Chieffo R, Comi G, Leocani L. Noninvasive neuromodulation in poststroke gait disorders: rationale, feasibility, and state of the art. Neurorehabil Neural Repair. 2015;30:71–82. https://doi.org/10.1177/1545968315586464.
  114. 114.
    Jayaram G, Tang B, Pallegadda R, Vasudevan EV, Celnik P, Bastian A. Modulating locomotor adaptation with cerebellar stimulation. J Neurophysiol. 2012;107(11):2950–7. https://doi.org/10.1152/jn.00645.2011.PubMedPubMedCentralCrossRefGoogle Scholar
  115. 115.
    Fernandez L, Albein-Urios N, Kirkovski M, McGinley JL, Murphy AT, Hyde C, et al. Cathodal transcranial direct current stimulation (tDCS) to the right cerebellar hemisphere affects motor adaptation during gait. Cerebellum. 2017;16(1):168–77. https://doi.org/10.1007/s12311-016-0788-7.PubMedCrossRefGoogle Scholar
  116. 116.
    Naro A, Milardi D, Cacciola A, Russo M, Sciarrone F, La Rosa G, et al. What do we know about the influence of the cerebellum on walking ability? Promising findings from transcranial alternating current stimulation. Cerebellum. 2017;16(4):859–67. https://doi.org/10.1007/s12311-017-0859-4.PubMedCrossRefGoogle Scholar
  117. 117.
    Nijsse B, Visser-Meily JM, van Mierlo ML, Post MW, de Kort PL, van Heugten CM. Temporal evolution of Poststroke cognitive impairment using the Montreal Cognitive Assessment. Stroke. 2017;48(1):98–104. https://doi.org/10.1161/STROKEAHA.116.014168.PubMedCrossRefGoogle Scholar
  118. 118.
    Dichgans M, Leys D. Vascular cognitive impairment. Circ Res. 2017;120(3):573–91. https://doi.org/10.1161/CIRCRESAHA.116.308426.PubMedCrossRefGoogle Scholar
  119. 119.
    Brainin M, Tuomilehto J, Heiss WD, Bornstein NM, Bath PM, Teuschl Y, et al. Post-stroke cognitive decline: an update and perspectives for clinical research. Eur J Neurol. 2015;22(2):229–238, e13-6https://doi.org/10.1111/ene.12626.PubMedCrossRefGoogle Scholar
  120. 120.
    Bodranghien F, Bastian A, Casali C, Hallett M, Louis ED, Manto M, et al. Consensus paper: revisiting the symptoms and signs of cerebellar syndrome. Cerebellum. 2016;15(3):369–91. https://doi.org/10.1007/s12311-015-0687-3.PubMedPubMedCentralCrossRefGoogle Scholar
  121. 121.
    Schmahmann JD, Sherman JC. The cerebellar cognitive affective syndrome. Brain. 1998;121(Pt 4):561–79. https://doi.org/10.1093/brain/121.4.561.PubMedCrossRefGoogle Scholar
  122. 122.
    Ferrucci R, Giannicola G, Rosa M, Fumagalli M, Boggio PS, Hallett M, et al. Cerebellum and processing of negative facial emotions: cerebellar transcranial DC stimulation specifically enhances the emotional recognition of facial anger and sadness. Cogn Emot. 2012;26(5):786–99. https://doi.org/10.1080/02699931.2011.619520.PubMedCrossRefGoogle Scholar
  123. 123.
    Turkeltaub PE, Swears MK, D’Mello AM, Stoodley CJ. Cerebellar tDCS as a novel treatment for aphasia? Evidence from behavioral and resting-state functional connectivity data in healthy adults. Restor Neurol Neurosci. 2016;34(4):491–505. https://doi.org/10.3233/RNN-150633.PubMedPubMedCentralGoogle Scholar
  124. 124.
    Boehringer A, Macher K, Dukart J, Villringer A, Pleger B. Cerebellar transcranial direct current stimulation modulates verbal working memory. Brain Stimul. 2013;6(4):649–53. https://doi.org/10.1016/j.brs.2012.10.001.PubMedCrossRefGoogle Scholar
  125. 125.
    Ferrucci R, Marceglia S, Vergari M, Cogiamanian F, Mrakic-Sposta S, Mameli F, et al. Cerebellar transcranial direct current stimulation impairs the practice-dependent proficiency increase in working memory. J Cogn Neurosci. 2008;20(9):1687–97. https://doi.org/10.1162/jocn.2008.20112.PubMedCrossRefGoogle Scholar
  126. 126.
    Macher K, Bohringer A, Villringer A, Pleger B. Cerebellar-parietal connections underpin phonological storage. J Neurosci. 2014;34(14):5029–37. https://doi.org/10.1523/JNEUROSCI.0106-14.2014.PubMedCrossRefGoogle Scholar
  127. 127.
    Grimaldi G, Oulad Ben Taib N, Manto M, Bodranghien F. Marked reduction of cerebellar deficits in upper limbs following transcranial cerebello-cerebral DC stimulation: tremor reduction and re-programming of the timing of antagonist commands. Front Syst Neurosci. 2014;8:9. https://doi.org/10.3389/fnsys.2014.00009.
  128. 128.
    Ramnani N. The primate cortico-cerebellar system: anatomy and function. Nat Rev Neurosci. 2006;7(7):511–22. https://doi.org/10.1038/nrn1953.PubMedCrossRefGoogle Scholar
  129. 129.
    Manto M, Marien P. Schmahmann’s syndrome—identification of the third cornerstone of clinical ataxiology. Cerebellum Ataxias. 2015;2(1):2. https://doi.org/10.1186/s40673-015-0023-1.PubMedPubMedCentralCrossRefGoogle Scholar
  130. 130.
    Stoodley CJ, Schmahmann JD. Functional topography in the human cerebellum: a meta-analysis of neuroimaging studies. NeuroImage. 2009;44(2):489–501. https://doi.org/10.1016/j.neuroimage.2008.08.039.PubMedCrossRefGoogle Scholar
  131. 131.
    Schutter DJ, van Honk J. The cerebellum on the rise in human emotion. Cerebellum. 2005;4(4):290–4. https://doi.org/10.1080/14734220500348584.PubMedCrossRefGoogle Scholar
  132. 132.
    Kelly RM, Strick PL. Cerebellar loops with motor cortex and prefrontal cortex of a nonhuman primate. J Neurosci. 2003;23(23):8432–44.PubMedGoogle Scholar
  133. 133.
    Jurgens U. The efferent and afferent connections of the supplementary motor area. Brain Res. 1984;300(1):63–81. https://doi.org/10.1016/0006-8993(84)91341-6.PubMedCrossRefGoogle Scholar
  134. 134.
    Akkal D, Dum RP, Strick PL. Supplementary motor area and presupplementary motor area: targets of basal ganglia and cerebellar output. J Neurosci. 2007;27(40):10659–73. https://doi.org/10.1523/JNEUROSCI.3134-07.2007.PubMedCrossRefGoogle Scholar
  135. 135.
    Brodal P. The corticopontine projection in the rhesus monkey. Origin and principles of organization. Brain. 1978;101(2):251–83. https://doi.org/10.1093/brain/101.2.251.PubMedCrossRefGoogle Scholar
  136. 136.
    Hashimoto M, Takahara D, Hirata Y, Inoue K, Miyachi S, Nambu A, et al. Motor and non-motor projections from the cerebellum to rostrocaudally distinct sectors of the dorsal premotor cortex in macaques. Eur J Neurosci. 2010;31(8):1402–13. https://doi.org/10.1111/j.1460-9568.2010.07151.x.PubMedCrossRefGoogle Scholar
  137. 137.
    Middleton FA, Strick PL. Dentate output channels: motor and cognitive components. Prog Brain Res. 1997;114:553–66. https://doi.org/10.1016/S0079-6123(08)63386-5.PubMedCrossRefGoogle Scholar
  138. 138.
    Clower DM, Dum RP, Strick PL. Basal ganglia and cerebellar inputs to “AIP”. Cereb Cortex. 2005;15(7):913–20. https://doi.org/10.1093/cercor/bhh190.PubMedCrossRefGoogle Scholar
  139. 139.
    Prevosto V, Graf W, Ugolini G. Cerebellar inputs to intraparietal cortex areas LIP and MIP: functional frameworks for adaptive control of eye movements, reaching, and arm/eye/head movement coordination. Cereb Cortex. 2010;20(1):214–28. https://doi.org/10.1093/cercor/bhp091.PubMedCrossRefGoogle Scholar
  140. 140.
    Anand BK, Malhotra CL, Singh B, Dua S. Cerebellar projections to limbic system. J Neurophysiol. 1959;22(4):451–7.PubMedGoogle Scholar
  141. 141.
    Snider RS, Maiti A. Cerebellar contributions to the Papez circuit. J Neurosci Res. 1976;2(2):133–46. https://doi.org/10.1002/jnr.490020204.PubMedCrossRefGoogle Scholar
  142. 142.
    Zimerman M, Nitsch M, Giraux P, Gerloff C, Cohen LG, Hummel FC. Neuroenhancement of the aging brain: restoring skill acquisition in old subjects. Ann Neurol. 2013;73(1):10–5. https://doi.org/10.1002/ana.23761.PubMedCrossRefGoogle Scholar
  143. 143.
    Samaei A, Ehsani F, Zoghi M, Hafez Yosephi M, Jaberzadeh S. Online and offline effects of cerebellar transcranial direct current stimulation on motor learning in healthy older adults: a randomized double-blind sham-controlled study. Eur J Neurosci. 2017;45(9):1177–85. https://doi.org/10.1111/ejn.13559.PubMedCrossRefGoogle Scholar
  144. 144.
    Ehsani F, Bakhtiary AH, Jaberzadeh S, Talimkhani A, Hajihasani A. Differential effects of primary motor cortex and cerebellar transcranial direct current stimulation on motor learning in healthy individuals: a randomized double-blind sham-controlled study. Neurosci Res. 2016;112:10–9. https://doi.org/10.1016/j.neures.2016.06.003.PubMedCrossRefGoogle Scholar
  145. 145.
    Fregni F, Boggio PS, Nitsche M, Bermpohl F, Antal A, Feredoes E, et al. Anodal transcranial direct current stimulation of prefrontal cortex enhances working memory. Exp Brain Res. 2005;166(1):23–30. https://doi.org/10.1007/s00221-005-2334-6.PubMedCrossRefGoogle Scholar
  146. 146.
    Miler JA, Meron D, Baldwin DS, Garner M. The effect of prefrontal transcranial direct current stimulation on attention network function in healthy volunteers. Neuromodulation. 2017. https://doi.org/10.1111/ner.12629.
  147. 147.
    Hulst T, John L, Kuper M, van der Geest JN, Goricke SL, Donchin O, et al. Cerebellar patients do not benefit from cerebellar or M1 transcranial direct current stimulation during force field reaching adaptation. J Neurophysiol. 2017;118(2):732–48. https://doi.org/10.1152/jn.00808.2016.PubMedCrossRefGoogle Scholar
  148. 148.
    Jalali R, Miall RC, Galea JM. No consistent effect of cerebellar transcranial direct current stimulation (tDCS) on visuomotor adaptation. J Neurophysiol. 2017;118(2):655–65. https://doi.org/10.1152/jn.00896.2016.PubMedCrossRefGoogle Scholar
  149. 149.
    Spielmann K, van der Vliet R, van de Sandt-Koenderman WM, Frens MA, Ribbers GM, Selles RW, et al. Cerebellar cathodal transcranial direct stimulation and performance on a verb generation task: a replication study. Neural Plast. 2017;2017:1254615. https://doi.org/10.1155/2017/1254615.
  150. 150.
    Verhage MC, Avila EO, Frens MA, Donchin O, van der Geest JN. Cerebellar tDCS does not enhance performance in an implicit categorization learning task. Front Psychol. 2017;8:476. https://doi.org/10.3389/fpsyg.2017.00476.
  151. 151.
    Cooper IS. Twenty-five years of experience with physiological neurosurgery. Neurosurgery. 1981;9(2):190–200. https://doi.org/10.1227/00006123-198108000-00017.PubMedCrossRefGoogle Scholar
  152. 152.
    Oulad Ben Taib N, Manto M. Trains of epidural DC stimulation of the cerebellum tune corticomotor excitability. Neural Plast. 2013;2013:613197. https://doi.org/10.1155/2013/613197.
  153. 153.
    Teixeira MJ, Cury RG, Galhardoni R, Barboza VR, Brunoni AR, Alho E, et al. Deep brain stimulation of the dentate nucleus improves cerebellar ataxia after cerebellar stroke. Neurology. 2015;85(23):2075–6. https://doi.org/10.1212/WNL.0000000000002204.PubMedCrossRefGoogle Scholar

via Non-invasive Cerebellar Stimulation: a Promising Approach for Stroke Recovery? | SpringerLink

, , , , ,

Leave a comment

[Abstract+References] Virtual reality software package for implementing motor learning and rehabilitation experiments

Abstract

Virtual reality games for rehabilitation are attracting increasing growth. In particular, there is a demand for games that allow therapists to identify an individual’s difficulties and customize the control of variables, such as speed, size, distance, as well as visual and auditory feedback. This study presents and describes a virtual reality software package (Bridge Games) to promote rehabilitation of individuals living with disabilities and highlights preliminary researches of its use for implementing motor learning and rehabilitation. First, the study presents seven games in the software package that can be chosen by the rehabilitation team, considering the patient’s needs. All game characteristics are described including name, function presentation, objective and valuable measurements for rehabilitation. Second, preliminary results illustrate some applications of two games, considering 343 people with various disabilities and health status. Based on the results, in the Coincident Timing game, there was a main effect of movement sensor type (in this instance the most functional device was the keyboard when compared with Kinect and touch screen) on average time reached by sample analyzed, F(2, 225) = 4.42, p < 0.05. Similarly, in the Challenge! game, a main effect was found for movement sensor type. However, in this case, touch screen provided better performance than Kinect and Leap Motion, F(2, 709) = 5.90, p < 0.01. Thus, Bridge Games is a possible software game to quantify motor learning. Moreover, the findings suggest that motor skills might be practiced differently depending on the environmental interface in which the game may be used.

References

  1. Anderson F, Annett M, Bischof WF (2010) Lean on Wii: physical rehabilitation with virtual reality Wii peripherals. Stud Health Technol Inform 154:229–234. doi:10.3233/978-1-60750-561-7-229Google Scholar
  2. Anderson KR, Woodbury ML, Phillips K, Gauthier LV (2015) Virtual reality video games to promote movement recovery in stroke rehabilitation: a guide for clinicians. Arch Phys Med Rehabil 96(5):973–976. doi:10.1016/j.apmr.2014.09.008CrossRefGoogle Scholar
  3. Antunes TPC, de Oliveira ASB, Crocetta TB, Antao J, Barbosa RTD, Guarnieri R, Massetti T, Monteiro CBD, de Abreu LCs (2017) Computer classes and games in virtual reality environment to reduce loneliness among students of an elderly reference center Study protocol for a randomised cross-over design. Medicine 96(10):e5954. doi:10.1016/j.apmr.2014.09.008 CrossRefGoogle Scholar
  4. Bieryla KA (2016) Xbox Kinect training to improve clinical measures of balance in older adults: a pilot study. Aging Clin Exp Res 28:451–457. doi:10.1007/s40520-015-0452-yCrossRefGoogle Scholar
  5. Bonnechere B, Jansen B, Omelina L, Sholukha V, Jan SV (2017) Patients’ follow-up using biomechanical analysis of rehabilitation exercises. Int J Serious Games 4(1):3–13. doi:10.17083/ijsg.v4i1.121CrossRefGoogle Scholar
  6. Crabtree DA, Antrim LR (1988) Guidelines for measuring reaction-time. Percept Mot Skills 66(2):363–370CrossRefGoogle Scholar
  7. Crocetta TB, Andrade A (2015) Retrasos en la medición del tiempo con el uso de computadoras en la investigación del Tiempo de Reacción: Una revisión sistemática. Revista de Psicología del Deporte 24:341–349Google Scholar
  8. Crocetta TB, Oliveira SRD, Liz CMD, Andrade A (2015) Virtual and augmented reality technologies in human performance: a review. Fisioterapia em Movimento 28(4):823–835. doi:10.1590/0103-5150.028.004.ar01CrossRefGoogle Scholar
  9. Da Gama A, Chaves T, Figueiredo L, Teichrieb V (2012) Guidance and movement correction based on therapeutics movements for motor rehabilitation support systems. 14th symposium on virtual and augmented reality (SVR), 2012. IEEE, pp 191–200Google Scholar
  10. da Silva TD, de Monteiro CBM, Corrêa AGD, Alonso AC, Greve JMDA (2015) Realidade Virtual na Paralisia Cerebral – Definição Tipos e Possibilidades de Intervenção. In: da Monteiro CBM, de Abreu LC, Valenti VE (eds) Paralisia Cerebral – Teoria e Prática. São Paulo, Plêiade, p 484Google Scholar
  11. Herrero D, Crocetta T, Massetti T, de Moraes I, Trevizan I, Guarnieri R (2015) Total reaction time performance of individuals with autism after a virtual reality task. IJN an open access journal 2(2376–0281):1000189. doi:10.4172/2376-0281.1000189Google Scholar
  12. Hocine N, Gouaich A, Cerri SA, Mottet D, Froger J, Laffont I (2015) Adaptation in serious games for upper-limb rehabilitation: an approach to improve training outcomes. User Model User-Adap Inter 25(1):65–98. doi:10.1007/s11257-015-9154-6CrossRefGoogle Scholar
  13. Hondori HM, Khademi M (2014) A review on technical and clinical impact of microsoft kinect on physical therapy and rehabilitation. J Med Eng 2014:846514Google Scholar
  14. Hondori HM, Khademi M, Dodakian L, McKenzie A, Lopes CV, Cramer SC (2016) Choice of human–computer interaction mode in stroke rehabilitation. Neurorehabilitation Neural Repair 30(3):258–265. doi:10.1177/1545968315593805CrossRefGoogle Scholar
  15. Iwasaki Y, Kinoshita M, Ikeda K, Takamiya K, Shiojima T (1990) Cognitive impairment in amyotrophic lateral sclerosis and its relation to motor disabilities. Acta Neurol Scand 81(2):141–143CrossRefGoogle Scholar
  16. Juanes JA, Gomez JJ, Peguero PD, Ruisoto P (2016) Digital environment for movement control in surgical skill training. J Med Syst 40(6):133. doi:10.1007/s10916-016-0495-4CrossRefGoogle Scholar
  17. Kim R, Nauhaus G, Glazek K, Young D, Lin S (2013) Development of coincidence-anticipation timing in a catching task. Percept Mot Skills 117(1):319–338. doi:10.2466/10.23.PMS.117x17z9CrossRefGoogle Scholar
  18. Levac D, Espy D, Fox E, Pradhan S, Deutsch JE (2015) “Kinect-ing” with clinicians: a knowledge translation resource to support decision making about video game use in rehabilitation. Phys Ther 95(3):426–440. doi:10.2522/ptj.20130618CrossRefGoogle Scholar
  19. Lohse K, Shirzad N, Verster A, Hodges N, Van der Loos HFM (2013) Video games and rehabilitation: using design principles to enhance engagement in physical therapy. J Neurol Phys Ther 37(4):166–175. doi:10.1097/npt.0000000000000017CrossRefGoogle Scholar
  20. Lv ZH, Penades V, Blasco S, Chirivella J, Gagliardo P (2016) Evaluation of Kinect2 based balance measurement. Neurocomputing 208:290–298. doi:10.1016/j.neucom.2015.12.128CrossRefGoogle Scholar
  21. Malheiros SR, da Silva TD, Favero FM, de Abreu LC, Fregni F, Ribeiro DC, de Mello Monteiro CB (2016) Computer task performance by subjects with Duchenne muscular dystrophy. Neuropsychiatr Dis Treat 12:41–48. doi:10.2147/NDT.S87735Google Scholar
  22. Microsoft (2015) Timer class—.NET framework 4.6 and 4.5. In: Library, N. F. C., (ed) Microsoft Corporation. https://msdn.microsoft.com/en-us/library/system.threading.timer(v=vs.110).aspx. Accessed sept 2015
  23. Monteiro CBM, Massetti T, da Silva TD, van der Kamp J, de Abreu LC, Leone C, Savelsbergh GJ (2014) Transfer of motor learning from virtual to natural environments in individuals with cerebral palsy. Res Dev Disabil 35(10):2430–2437. doi:10.1016/j.ridd.2014.06.006CrossRefGoogle Scholar
  24. Monteiro CBD, da Silva TD, de Abreu LC, Fregni F, de Araujo LV, Ferreira F, Leone C (2017) Short-term motor learning through nonimmersive virtual reality task in individuals with down syndrome. BMC Neurol 17:71. doi:10.1186/s12883-017-0852-zCrossRefGoogle Scholar
  25. Nooijen CFJ, de Groot JF, Stam HJ, van den Berg-Emons RJG, Bussmann HBJ, Fit Future C (2015) Validation of an activity monitor for children who are partly or completely wheelchair-dependent. J Neuroeng Rehabil 12:11. doi:10.1186/s12984-015-0004-xCrossRefGoogle Scholar
  26. Pastor I, Hayes HA, Bamberg SJM, IEEE (2012) A feasibility study of an upper limb rehabilitation system using kinect and computer games. 34th annual international conference of the IEEE engineering-in-medicine-and-biology-society (EMBS). San Diego, pp 1286–1289Google Scholar
  27. Patrizia M, Claudio M, Leonardo G, Alessandro P, IEEE (2009) A robotic toy for children with special needs: from requirements to design 11th IEEE international conference on rehabilitation robotics. Kyoto, p 1070Google Scholar
  28. Rodrigues PC, Vasconcelos O, Barreiros J, Barbosa R, Trifilio F (2009) Functional asymmetry in a simple coincidence-anticipation task: effects of handedness. Eur J Sport Sci 9(2):115–123. doi:10.1080/17461390802603903CrossRefGoogle Scholar
  29. Stanmore E, Stubbs B, Vancampfort D, de Bruin ED, Firth J (2017) The effect of active video games on cognitive functioning in clinical and non-clinical populations: a meta-analysis of randomized controlled trials. Neurosci Biobehav Rev 78:34–43. doi:10.1016/j.neubiorev.2017.04.011CrossRefGoogle Scholar
  30. Thomson K, Pollock A, Bugge C, Brady M (2014) Commercial gaming devices for stroke upper limb rehabilitation: a systematic review. Int J Stroke 9(4):479–488. doi:10.1111/ijs.12263CrossRefGoogle Scholar

Source: Virtual reality software package for implementing motor learning and rehabilitation experiments | SpringerLink

, , , ,

Leave a comment

[ARTICLE] COMBINING UPPER LIMB ROBOTIC REHABILITATION WITH OTHER THERAPEUTIC APPROACHES AFTER STROKE: CURRENT STATUS, RATIONALE AND CHALLENGES – Full Text PDF

Abstract:

A better understanding of the neural substrates that underlie motor recovery after stroke has led to the development of innovative rehabilitation strategies and tools that incorporate key elements of motor skill re-learning, i.e. intensive motor training involving goal-oriented repeated movements. Robotic devices for the upper limb are increasingly used in rehabilitation. Studies have demonstrated the effectiveness of these devices in reducing motor impairments, but less so for the improvement of upper limb function. Other studies have begun to investigate the benefits of combined approaches that target muscle function (functional electrical stimulation and Botulinum Toxin injections), modulate neural activity (Noninvasive Brain stimulation) and enhance motivation (Virtual Reality) in an attempt to potentialize the benefits of robot-mediated training. The aim of this paper is to overview the current status of such combined-treatments and to analyze the rationale behind them.

1. Introduction
Significant advances have been made in the management of stroke (including prevention, acute management and rehabilitation), however cerebrovascular diseases remain the third most common cause of death and the first cause of disability worldwide[1–6]. Stroke causes brain damage, leading to loss of motor function. Upper limb (UL) function is particularly reduced, resulting in disability. Many rehabilitation techniques have been developed over the last decades to facilitate motor recovery of the UL in order to improve functional ability and quality of life [7–10]. They are commonly based on principles of motor skill learning to promote plasticity of motor neural networks. These principles include intensive, repetitive, task-oriented movement-based training [11–19]. A better understanding of the neural substrates of motor re-learning has led to the development of innovative strategies and tools to deliver exercise that meets these requirements. Treatments mostly target the neurological impairment (paresis, spasticity etc.) through the activation of neural circuits or by acting on peripheral effectors. Robotic devices provide exercises that incorporate key elements of motor learning. Advanced robotic systems can offer highly repetitive, reproducible, interactive forms of training for the paretic limb, which are quantifiable. Robotic devices also enable easy and objective assessment of motor performance in standardized conditions by the recording of biomechanical data (i.e., speed, forces, etc.) [20–22]. This data can be used to analyze and assess motor recovery in stroke patients [23–26]. Since the 1990’s, many other technology-based approaches and innovative pharmaceutical treatments have also been developed for rehabilitation, including virtual reality (VR)-based systems, Botulinum neurotoxin (BoNT) injections and Non Invasive Brain stimulation (NIBS) (Direct Current Stimulation (tDCS) and repetitive Transcranial Magnetic Stimulation (rTMS)). There is currently no high-quality evidence to support any of these innovative interventions, despite the fact that some are used in routine practice [27]. By their respective mechanisms of action, each of these treatments could potentiate the effects of robotic therapy, leading to greater improvements in motor capacity. The aim of this paper is to review studies of combined treatments based on robotic rehabilitation, and to analyze the rationale behind such approaches. […]

Download Full Text PDF

, , , , , , , , ,

Leave a comment

[WHITE PAPER] Virtual and augmented reality based balance and gait training – Full Text PDF

The use of virtual and augmented reality for rehabilitation has become increasingly popular and has received much attention in scientific publications (over 1,000 papers). This white paper aims to summarize the scientific background and efficacy of using virtual and augmented reality for balance and gait training. For many patients with movement disorders, balance and gait training is an important aspect of their rehabilitation process and physical therapy treatment. Indications for such training include, among others, stroke, Parkinson’s disease, multiple sclerosis, cerebral palsy, vestibular disorders, neuromuscular diseases, low back pain, and various orthopedic complaints, such as total hip or knee replacement. Current clinical practice for balance training include exercises, such as standing on one leg, wobble board exercises and standing with eyes closed. Gait is often trained with a treadmill or using an obstacle course. Cognitive elements can be added by asking the patient to simultaneously perform a cognitive task, such as counting down by sevens. Although conventional physical therapy has proven to be effective in improving balance and gait,1,2 there are certain limitations that may compromise treatment effects. Motor learning research has revealed some important concepts to optimize rehabilitation: an external focus of attention, implicit learning, variable practice, training intensity, task specificity, and feedback on performance.3 Complying with these motor learning principles using conventional methods is quite challenging. For example, there are only a limited number of exercises, making it difficult to tailor training intensity and provide sufficient variation. Moreover, performance measures are not available and thus the patient usually receives little or no feedback. Also, increasing task specificity by simulating everyday tasks, such as walking on a crowded street, can be difficult and time consuming. Virtual and augmented reality could provide the tools needed to overcome these challenges in conventional therapy. The difference between virtual and augmented reality is that virtual reality offers a virtual world that is separate from the real world, while augmented reality offers virtual elements as an overlay to the real world (for example virtual stepping stones projected on the floor). In the first part of this paper we will explain the different motor learning principles, and how virtual and augmented reality based exercise could help to incorporate these principles into clinical practice. In the second part we will summarize the scientific evidence regarding the efficacy of virtual reality based balance and gait training for clinical rehabilitation.

Full Text PDF

, , , , ,

Leave a comment

[ARTICLE] The application of virtual reality in neurorehabilitation: motor re-learning supported by innovative technologies – Full Text

Abstract
The motor function impairment resulting from a stroke injury has a negative impact on autonomy, the activities of daily living thus the individuals affected by a stroke need long-term rehabilitation. Several studies have demonstrated that learning new motor skills is important to induce neuroplasticity and functional recovery. Innovative technologies used in rehabilitation allow one the possibility to enhance training throughout generated feedback. It seems advantageous to combine traditional motor rehabilitation with innovative technology in order to promote motor re-learning and skill re-acquisition by means of enhanced training. An environment enriched by feedback involves multiple sensory modalities and could promote active patient participation. Exercises in a virtual environment contain elements necessary to maximize motor learning, such as repetitive
and diffe-rentiated task practice and feedback on the performance and results. The recovery of the limbs motor function in post-stroke subjects is one of the main therapeutic aims for patients and physiotherapist alike. Virtual reality as well as robotic devices allow one to provide specific treatment based on the reinforced feedback in a virtual environment (RFVE), artificially augmenting the sensory information coherent with the real-world objects and events. Motor training based on RFVE is emerging as an effective motor learning based techniques for the treatment of the extremities.

Continue —> The application of virtual reality in neurorehabilitation: motor re-learning supported by innovative technologies (PDF Download Available)

, , , ,

Leave a comment

[Abstract] Bilateral sequential motor cortex stimulation and skilled task performance with non-dominant hand

Highlights

  • Both, contralateral M1 iTBS and ipsilateral M1 cTBS improved non-dominant skilled-task performance.
  • Bilateral sequential M1 TBS (contralateral cTBS followed by ipsilateral iTBS) improved skilled-task performance more than unilateral or sham TBS.
  • Bilateral sequential M1 TBS may be particularly effective in improving motor learning, also in the neurorehabilitation setting.

Abstract

Objective

To check whether bilateral sequential stimulation (BSS) of M1 with theta burst stimulation (TBS), using facilitatory protocol over non-dominant M1 followed by inhibitory one over dominant M1, can improve skilled task performance with non-dominant hand more than either of the unilateral stimulations do. Both, direct motor cortex (M1) facilitatory non-invasive brain stimulation (NIBS) and contralateral M1 inhibitory NIBS were shown to improve motor learning.

Methods

Forty right-handed healthy subjects were divided into 4 matched groups which received either ipsilateral facilitatory (intermittent TBS [iTBS] over non-dominant M1), contralateral inhibitory (continuous TBS [cTBS] over dominant M1), bilateral sequential (contralateral cTBS followed by ipsilateral iTBS), or placebo stimulation. Performance was evaluated by Purdue peg-board test (PPT), before (T0), immediately after (T1), and 30 min after (T2) an intervention.

Results

In all groups and for both hands, the PPT scores increased at T1 and T2 in comparison to T0, showing clear learning effect. However, for the target non-dominant hand only, immediately after BSS (at T1) the PPT scores improved significantly more than after either of unilateral interventions or placebo.

Conclusion

M1 BSS TBS is an effective intervention for improving motor performance.

Significance

M1 BSS TBS seems as a promising tool for motor learning improvement with potential uses in neurorehabilitation.

Source: Bilateral sequential motor cortex stimulation and skilled task performance with non-dominant hand – Clinical Neurophysiology

, , , , , , , , , , ,

Leave a comment

[ARTICLE] A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training – Full Text

Although motor learning theory has led to evidence-based practices, few trials have revealed the superiority of one theory-based therapy over another after stroke. Nor have improvements in skills been as clinically robust as one might hope. We review some possible explanations, then potential technology-enabled solutions.

Over the Internet, the type, quantity, and quality of practice and exercise in the home and community can be monitored remotely and feedback provided to optimize training frequency, intensity, and progression at home. A theory-driven foundation of synergistic interventions for walking, reaching and grasping, strengthening, and fitness could be provided by a bundle of home-based Rehabilitation Internet-of-Things (RIoT) devices.

A RIoT might include wearable, activity-recognition sensors and instrumented rehabilitation devices with radio transmission to a smartphone or tablet to continuously measure repetitions, speed, accuracy, forces, and temporal spatial features of movement. Using telerehabilitation resources, a therapist would interpret the data and provide behavioral training for self-management via goal setting and instruction to increase compliance and long-term carryover.

On top of this user-friendly, safe, and conceptually sound foundation to support more opportunity for practice, experimental interventions could be tested or additions and replacements made, perhaps drawing from virtual reality and gaming programs or robots. RIoT devices continuously measure the actual amount of quality practice; improvements and plateaus over time in strength, fitness, and skills; and activity and participation in home and community settings. Investigators may gain more control over some of the confounders of their trials and patients will have access to inexpensive therapies.

Neurologic rehabilitation has been testing a motor learning theory for the past quarter century that may be wearing thin in terms of leading to more robust evidence-based practices. The theory has become a mantra for the field that goes like this. Repetitive practice of increasingly challenging task-related activities assisted by a therapist in an adequate dose will lead to gains in motor skills, mostly restricted to what was trained, via mechanisms of activity-dependent induction of molecular, cellular, synaptic, and structural plasticity within spared neural ensembles and networks.

This theory has led to a range of evidence-based therapies, as well as to caricatures of the mantra (eg, a therapist says to patient, “Do those plasticity reps!”). A mantra can become too automatic, no longer apt to be reexamined as a testable theory. A recent Cochrane review of upper extremity stroke rehabilitation found “adequately powered, high-quality randomized clinical trials (RCTs) that confirmed the benefit of constraint-induced therapy paradigms, mental practice, mirror therapy, virtual reality paradigms, and a high dose of repetitive task practice.”1 The review also found positive RCT evidence for other practice protocols. However, they concluded, no one strategy was clearly better than another to improve functional use of the arm and hand. The ICARE trial2 for the upper extremity after stroke found that both a state-of-the-art Accelerated Skill Acquisition Program (motor learning plus motivational and psychological support strategy) compared to motor learning-based occupational therapy for 30 hours over 10 weeks led to a 70% increase in speed on the Wolf Motor Function Test, but so did usual care that averaged only 11 hours of formal but uncharacterized therapy. In this well-designed RCT, the investigators found no apparent effect of either the dose or content of therapy. Did dose and content really differ enough to reveal more than equivalence, or is the motor-learning mantra in need of repair?

Walking trials after stroke and spinal cord injury,38 such as robot-assisted stepping and body weight-supported treadmill training (BWSTT), were conceived as adhering to the task-oriented practice mantra. But they too have not improved outcomes more than conventional over-ground physical therapy. Indeed, the absolute gains in primary outcomes for moderate to severely impaired hemiplegic participants after BWSTT and other therapies have been in the range of only 0.12 to 0.22 m/s for fastest walking speed and 50 to 75 m for 6-minute walking distance after 12 to 36 training sessions over 4 to 12 weeks.3,9 These 15% to 25% increases are just as disappointing when comparing gains in those who start out at a speed of <0.4 m/s compared to >0.4 to 0.8 m/s.3

Has mantra-oriented training reached an unanticipated plateau due to inherent limitations? Clearly, if not enough residual sensorimotor neural substrate is available for training-induced adaptation or for behavioral compensation, more training may only fail. Perhaps, however, investigators need to reconsider the theoretical basis for the mantra, that is, whether they have been offering all of the necessary components of task-related practice, such as enough progressively difficult practice goals, the best context and environment for training, the behavioral training that motivates compliance and carryover of practice beyond the sessions of formal training, and blending in other physical activities such as strengthening and fitness exercise that also augment practice-related neural plasticity? These questions point to new directions for research….

Continue —> A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training – Mar 01, 2017

Figure 1. Components of a Rehabilitation-Internet-of-Things: wireless chargers for sensors (1), ankle accelerometers with gyroscopes (2) and Android phone (3) to monitor walking and cycling, and a force sensor (4) in line with a stretch band (5) to monitor resistance exercises.

 

, , , , , , , , , ,

Leave a comment

%d bloggers like this: