Posts Tagged Upper Extremity

[Abstract+References] Restoring Motor Functions After Stroke: Multiple Approaches and Opportunities

More than 1.5 million people suffer a stroke in Europe per year and more than 70% of stroke survivors experience limited functional recovery of their upper limb, resulting in diminished quality of life. Therefore, interventions to address upper-limb impairment are a priority for stroke survivors and clinicians. While a significant body of evidence supports the use of conventional treatments, such as intensive motor training or constraint-induced movement therapy, the limited and heterogeneous improvements they allow are, for most patients, usually not sufficient to return to full autonomy. Various innovative neurorehabNIBSilitation strategies are emerging in order to enhance beneficial plasticity and improve motor recovery. Among them, robotic technologies, brain-computer interfaces, or noninvasive brain stimulation (NIBS) are showing encouraging results. These innovative interventions, such as NIBS, will only provide maximized effects, if the field moves away from the “one-fits all” approach toward a “patient-tailored” approach. After summarizing the most commonly used rehabilitation approaches, we will focus on  and highlight the factors that limit its widespread use in clinical settings. Subsequently, we will propose potential biomarkers that might help to stratify stroke patients in order to identify the individualized optimal therapy. We will discuss future methodological developments, which could open new avenues for poststroke rehabilitation, toward more patient-tailored precision medicine approaches and pathophysiologically motivated strategies.

References

Adeyemo BOSimis MMacea DDFregni F. 2012Systematic review of parameters of stimulation, clinical trial design characteristics, and motor outcomes in non-invasive brain stimulation in stroke. Front Psychiatry 3:88Google Scholar Crossref,

MedlineAltschuler ELWisdom SBStone LFoster CGalasko DLlewellyn DM, and others. 1999Rehabilitation of hemiparesis after stroke with a mirror. Lancet 353(9169):20356Google Scholar CrossrefMedline

Ang KKChua KSPhua KSWang CChin ZYKuah CW, and others. 2015A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke. Clin EEG Neurosci 46(4):31020Google Scholar Link
Basmajian JVGowland CAFinlayson MAHall ALSwanson LRStratford PW, and others. 1987Stroke treatment: comparison of integrated behavioral-physical therapy vs traditional physical therapy programs. Arch Phys Med Rehabil 68(5 Pt 1):26772Google Scholar Medline
Bergmann TOGroppa SSeeger MMolle MMarshall LSiebner HR2009Acute changes in motor cortical excitability during slow oscillatory and constant anodal transcranial direct current stimulation. J Neurophysiol 102(4):230311Google Scholar CrossrefMedline
Bhagat NAVenkatakrishnan AAbibullaev BArtz EJYozbatiran NBlank AA, and others. 2016Design and optimization of an EEG-based brain machine interface (BMI) to an upper-limb exoskeleton for stroke survivors. Front Neurosci 10:122Google Scholar CrossrefMedline
Birbaumer NMurguialday ARCohen L. 2008Brain-computer interface in paralysis. Curr Opin Neurol 21(6):6348Google Scholar CrossrefMedline
Borich MRWheaton LABrodie SMLakhani BBoyd LA2016Evaluating interhemispheric cortical responses to transcranial magnetic stimulation in chronic stroke: a TMS-EEG investigation. Neurosci Lett618:2530Google Scholar
Brown JALutsep HLWeinand MCramer SC2008Motor cortex stimulation for the enhancement of recovery from stroke: a prospective, multicenter safety study. Neurosurgery 62(Suppl 2):85362Google Scholar CrossrefMedline
Buetefisch CM2015Role of the contralesional hemisphere in post-stroke recovery of upper extremity motor function. Front Neurol 6:214Google Scholar CrossrefMedline
Burke Quinlan EDodakian LSee JMcKenzie ALe VWojnowicz M, and others. 2015Neural function, injury, and stroke subtype predict treatment gains after stroke. Ann Neurol 77(1):13245Google Scholar CrossrefMedline
Butler AJShuster MO’Hara EHurley KMiddlebrooks DGuilkey K. 2013A meta-analysis of the efficacy of anodal transcranial direct current stimulation for upper limb motor recovery in stroke survivors. J Hand Ther 26(2):16270Google Scholar CrossrefMedline
Capogrosso MMilekovic TBorton DWagner FMoraud EMMignardot JB, and others. 2016A brain-spine interface alleviating gait deficits after spinal cord injury in primates. Nature 539(7628):2848Google ScholarCrossrefMedline
Carey JRDeng HGillick BTCassidy JMAnderson DCZhang L, and others. 2014Serial treatments of primed low-frequency rTMS in stroke: characteristics of responders vs. nonresponders. Restor Neurol Neurosci 32(2):32335Google Scholar Medline
Carter ARShulman GLCorbetta M. 2012Why use a connectivity-based approach to study stroke and recovery of function? Neuroimage 62(4):227180Google Scholar CrossrefMedline
Cassidy JMGillick BTCarey JR. 2014Priming the brain to capitalize on metaplasticity in stroke rehabilitation. Phys Ther 94(1):13950Google Scholar CrossrefMedline
Cecere RRees GRomei V. 2015Individual differences in alpha frequency drive crossmodal illusory perception. Curr Biol 25(2):2315Google Scholar CrossrefMedline
Chang WDLai PT2015New design of home-based dynamic hand splint for hemiplegic hands: a preliminary study. J Phys Ther Sci 27(3):82931Google Scholar CrossrefMedline
Chaudhary UBirbaumer NCurado MR2015Brain-machine interface (BMI) in paralysis. Ann Phys Rehabil Med 58(1):913Google Scholar CrossrefMedline
Chechlacz MHumphreys GWSotiropoulos SNKennard CCazzoli D. 2015Structural organization of the corpus callosum predicts attentional shifts after continuous theta burst stimulation. J Neurosci 35(46):1535368Google Scholar CrossrefMedline
Collinger JLWodlinger BDowney JEWang WTyler-Kabara ECWeber DJ, and others. 2013High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381(9866):55764Google Scholar CrossrefMedline
Conde VVollmann HSehm BTaubert MVillringer ARagert P. 2012Cortical thickness in primary sensorimotor cortex influences the effectiveness of paired associative stimulation. Neuroimage 60(2):86470Google Scholar CrossrefMedline
Corbetta DSirtori VCastellini GMoja LGatti R. 2015Constraint-induced movement therapy for upper extremities in people with stroke. Cochrane Database Syst Rev (10):CD004433Google Scholar Medline
Coupar FPollock ARowe PWeir CLanghorne P. 2012Predictors of upper limb recovery after stroke: a systematic review and meta-analysis. Clin Rehabil 26(4):291313Google Scholar Link
Dahl AEAskim TStock RLangorgen ELydersen SIndredavik B. 2008Short- and long-term outcome of constraint-induced movement therapy after stroke: a randomized controlled feasibility trial. Clin Rehabil 22(5):43647Google Scholar Link
De Vico Fallani FRichiardi JChavez MAchard S2014Graph analysis of functional brain networks: practical issues in translational neuroscience. Philos Trans R Soc Lond B Biol Sci 369(1653):0521Google Scholar Crossref
Di Carlo A2009Human and economic burden of stroke. Age Ageing 38(1):45Google Scholar CrossrefMedline
Dickstein RHocherman SPillar TShaham R. 1986Stroke rehabilitation. Three exercise therapy approaches. Phys Ther 66(8):12338Google Scholar CrossrefMedline
Donati ARShokur SMorya ECampos DSMoioli RCGitti CM, and others. 2016Long-term training with a brain-machine interface-based gait protocol induces partial neurological recovery in paraplegic patients. Sci Rep 6:30383Google Scholar CrossrefMedline
Dromerick AWEdwardson MAEdwards DFGiannetti MLBarth JBrady KP, and others. 2015Critical periods after stroke study: translating animal stroke recovery experiments into a clinical trial. Front Hum Neurosci 9:231Google Scholar CrossrefMedline
Dromerick AWLang CEBirkenmeier RLWagner JMMiller JPVideen TO, and others. 2009Very Early Constraint-Induced Movement during Stroke Rehabilitation (VECTORS): a single-center RCT. Neurology 73(3):195201Google Scholar CrossrefMedline
Duncan PW1997Synthesis of intervention trials to improve motor recovery following stroke. Top Stroke Rehabil 3(4):120Google Scholar CrossrefMedline
Duque JHummel FCelnik PMurase NMazzocchio RCohen LG2005Transcallosal inhibition in chronic subcortical stroke. Neuroimage 28(4):9406Google Scholar CrossrefMedline
Feigin VLNorrving BMensah GA2017Global burden of stroke. Circ Res 120(3):43948Google ScholarCrossrefMedline
Fox MDBuckner RLLiu HChakravarty MMLozano AMPascual-Leone A. 2014Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases. Proc Natl Acad Sci U S A 111(41):E436775Google Scholar CrossrefMedline
Fox MDLiu HPascual-Leone A. 2013Identification of reproducible individualized targets for treatment of depression with TMS based on intrinsic connectivity. Neuroimage 66:15160Google Scholar CrossrefMedline
Friston KJHarrison LPenny W. 2003Dynamic causal modelling. Neuroimage 19(4):1273302Google Scholar CrossrefMedline
Fritz SLLight KEPatterson TSBehrman ALDavis SB2005Active finger extension predicts outcomes after constraint-induced movement therapy for individuals with hemiparesis after stroke. Stroke 36(6):11727Google Scholar CrossrefMedline
Gaggioli AMorganti FWalker RMeneghini AAlcaniz MLozano JA, and others. 2004Training with computer-supported motor imagery in post-stroke rehabilitation. Cyberpsychol Behav 7(3):32732Google Scholar CrossrefMedline
Gamito POliveira JCoelho CMorais DLopes PPacheco J, and others. 2017Cognitive training on stroke patients via virtual reality-based serious games. Disabil Rehabil 39(4):3858Google Scholar CrossrefMedline
Geranmayeh FLeech RWise RJ2016Network dysfunction predicts speech production after left hemisphere stroke. Neurology 86(14):1296305Google Scholar Crossref
Gerardin ESirigu ALehericy SPoline JBGaymard BMarsault C, and others. 2000Partially overlapping neural networks for real and imagined hand movements. Cereb Cortex 10(11):1093104Google Scholar CrossrefMedline
Gharabaghi AKraus DLeao MTSpuler MWalter ABogdan M, and others. 2014a. Coupling brain-machine interfaces with cortical stimulation for brain-state dependent stimulation: enhancing motor cortex excitability for neurorehabilitation. Front Hum Neurosci 8:122Google Scholar CrossrefMedline
Gharabaghi ANaros GWalter AGrimm FSchuermeyer MRoth A and others. 2014b. From assistance towards restoration with epidural brain-computer interfacing. Restor Neurol Neurosci 32(4):51725Google Scholar Medline
Giraux PSirigu A. 2003Illusory movements of the paralyzed limb restore motor cortex activity. Neuroimage 20(Suppl 1):S10711Google Scholar CrossrefMedline
Graef PDadalt MLRodrigues DAStein CPagnussat Ade S. 2016Transcranial magnetic stimulation combined with upper-limb training for improving function after stroke: a systematic review and meta-analysis. J Neurol Sci 369:14958Google Scholar CrossrefMedline
Grefkes CNowak DAEickhoff SBDafotakis MKust JKarbe H, and others. 2008Cortical connectivity after subcortical stroke assessed with functional magnetic resonance imaging. Ann Neurol 63(2):23646Google Scholar CrossrefMedline
Grimm FNaros GGharabaghi A. 2016Closed-loop task difficulty adaptation during virtual reality reach-to-grasp training assisted with an exoskeleton for stroke rehabilitation. Front Neurosci 10:518Google Scholar CrossrefMedline
Groisser BNCopen WASinghal ABHirai KKSchaechter JD2014Corticospinal tract diffusion abnormalities early after stroke predict motor outcome. Neurorehabil Neural Repair 28(8):75160Google Scholar Link
Groppa SOliviero AEisen AQuartarone ACohen LGMall V, and others. 2012A practical guide to diagnostic transcranial magnetic stimulation: report of an IFCN committee. Clin Neurophysiol 123(5):85882Google Scholar CrossrefMedline
Hamzei FLiepert JDettmers CWeiller CRijntjes M. 2006Two different reorganization patterns after rehabilitative therapy: an exploratory study with fMRI and TMS. Neuroimage 31(2):71020Google Scholar
Hao ZWang DZeng YLiu M. 2013Repetitive transcranial magnetic stimulation for improving function after stroke. Cochrane Database Syst Rev 5:CD008862Google Scholar
Harris JHebert A. 2015Utilization of motor imagery in upper limb rehabilitation: a systematic scoping review. Clin Rehabil 29(11):1092107Google Scholar Link
Harvey RLWinstein CJ, Everest Trial Group. 2009Design for the everest randomized trial of cortical stimulation and rehabilitation for arm function following stroke. Neurorehabil Neural Repair 23(1):3244Google Scholar Link
Hatem SMSaussez GDella Faille MPrist VZhang XDispa D, and others. 2016Rehabilitation of motor function after stroke: a multiple systematic review focused on techniques to stimulate upper extremity recovery. Front Hum Neurosci 10:442Google Scholar CrossrefMedline
Helfrich RFSchneider TRRach STrautmann-Lengsfeld SAEngel AKHerrmann CS2014Entrainment of brain oscillations by transcranial alternating current stimulation. Curr Biol 24(3):3339Google Scholar CrossrefMedline
Hoffman HBBlakey GL2011New design of dynamic orthoses for neurological conditions. NeuroRehabilitation 28(1):5561Google Scholar Medline
Hoyer EHCelnik PA2011Understanding and enhancing motor recovery after stroke using transcranial magnetic stimulation. Restor Neurol Neurosci 29(6):395409Google Scholar Medline
Huang MHarvey RLStoykov MERuland SWeinand MLowry D, and others. 2008Cortical stimulation for upper limb recovery following ischemic stroke: a small phase II pilot study of a fully implanted stimulator. Top Stroke Rehabil 15(2):16072Google Scholar CrossrefMedline
Hummel FCCelnik PPascual-Leone AFregni FByblow WDBuetefisch CM, and others. 2008Controversy: noninvasive and invasive cortical stimulation show efficacy in treating stroke patients. Brain Stimul 1(4):37082Google Scholar CrossrefMedline
Hummel FCCohen LG2006Non-invasive brain stimulation: a new strategy to improve neurorehabilitation after stroke? Lancet Neurol 5(8):70812Google Scholar CrossrefMedline
Ibrahim IKBerger WTrippel MDietz V. 1993Stretch-induced electromyographic activity and torque in spastic elbow muscles. Differential modulation of reflex activity in passive and active motor tasks. Brain 116(Pt 4):97189Google Scholar CrossrefMedline
Ietswaart MJohnston MDijkerman HCJoice SScott CLMacwalter RS, and others. 2011Mental practice with motor imagery in stroke recovery: randomized controlled trial of efficacy. Brain 134(Pt 5):137386Google Scholar CrossrefMedline
Jackson AMavoori JFetz EE2006Long-term motor cortex plasticity induced by an electronic neural implant. Nature 444(7115):5660Google Scholar CrossrefMedline
Jackson PLLafleur MFMalouin FRichards CDoyon J. 2001Potential role of mental practice using motor imagery in neurologic rehabilitation. Arch Phys Med Rehabil 82(8):113341Google Scholar CrossrefMedline
Jiang RJansen BHSheth BRChen J. 2013Dynamic multi-channel TMS with reconfigurable coil. IEEE Trans Neural Syst Rehabil Eng 21(3):3705Google Scholar CrossrefMedline
Jo JYLee AKim MSPark EChang WHShin YKim YH2016Prediction of motor recovery using quantitative parameters of motor evoked potential in patients with stroke. Ann Rehabil Med 40(5):80615Google Scholar CrossrefMedline
Jongbloed LStacey SBrighton C. 1989Stroke rehabilitation: sensorimotor integrative treatment versus functional treatment. Am J Occup Ther 43(6):3917Google Scholar CrossrefMedline
Kamke MRRyan AESale MVCampbell MERiek SCarroll TJ, and others. 2014Visual spatial attention has opposite effects on bidirectional plasticity in the human motor cortex. J Neurosci 34(4):147580Google Scholar CrossrefMedline
Karabanov AJin SHJoutsen APoston BAizen JEllenstein A, and others. 2012Timing-dependent modulation of the posterior parietal cortex-primary motor cortex pathway by sensorimotor training. J Neurophysiol 107(11):31909Google Scholar CrossrefMedline
Karabanov ASiebner HR2012Unravelling homeostatic interactions in inhibitory and excitatory networks in human motor cortex. J Physiol 590(Pt 22):55578Google Scholar CrossrefMedline
Karabanov AThielscher ASiebner HR2016Transcranial brain stimulation: closing the loop between brain and stimulation. Curr Opin Neurol 29(4):397404Google Scholar CrossrefMedline
Karabanov ANRaffin ESiebner HR2015The resting motor threshold—restless or resting? A repeated threshold hunting technique to track dynamic changes in resting motor threshold. Brain Stimul 8(6):11914Google Scholar CrossrefMedline
Koch PJSchulz RHummel FC2016Structural connectivity analyses in motor recovery research after stroke. Ann Clin Transl Neurol 3(3):23344Google Scholar CrossrefMedline
Koch PJHummel FC2017Toward precision medicine: tailoring interventional strategies based on noninvasive brain stimulation for motor recovery after stroke. Curr Opin Neurol 30(4):38897Google ScholarCrossrefMedline
Kraus DNaros GBauer RLeao MTZiemann UGharabaghi A. 2016Brain-robot interface driven plasticity: distributed modulation of corticospinal excitability. Neuroimage 125:522–-32Google Scholar CrossrefMedline
Kwakkel Gvan Peppen RWagenaar RCWood Dauphinee SRichards CAshburn A, and others. 2004Effects of augmented exercise therapy time after stroke: a meta-analysis. Stroke 35(11):252939Google Scholar CrossrefMedline
Lang CEBland MDBailey RRSchaefer SYBirkenmeier RL2013Assessment of upper extremity impairment, function, and activity after stroke: foundations for clinical decision making. J Hand Ther 26(2):10414Google Scholar CrossrefMedline
Langhorne PLegg L. 2003Evidence behind stroke rehabilitation. J Neurol Neurosurg Psychiatry 74(Suppl 4):iv1821Google Scholar CrossrefMedline
Lannin NACusick AHills CKinnear BVogel KMatthews K, and others. 2016Upper limb motor training using a Saebo orthosis is feasible for increasing task-specific practice in hospital after stroke. Aust Occup Ther J 63(6):364372Google Scholar CrossrefMedline
Laver BDiwan MNobrega JNHamani C. 2014Augmentative therapies do not potentiate the antidepressant-like effects of deep brain stimulation in rats. J Affect Disord 161:8790Google Scholar CrossrefMedline
Lee KBLim SHKim KHKim KJKim YRChang WN and others. 2015Six-month functional recovery of stroke patients: a multi-time-point study. Int J Rehabil Res 38(2):17380Google Scholar CrossrefMedline
Legg LAQuinn TJMahmood FWeir CJTierney JStott DJ and others. 2011Non-pharmacological interventions for caregivers of stroke survivors. Cochrane Database Syst Rev (10):CD008179Google Scholar Medline
Levy RMHarvey RLKissela BMWinstein CJLutsep HLParrish TB, and others. 2016Epidural electrical stimulation for stroke rehabilitation: results of the prospective, multicenter, randomized, single-blinded Everest Trial. Neurorehabil Neural Repair 30(2):10719Google Scholar Link
Li LMUehara KHanakawa T. 2015The contribution of interindividual factors to variability of response in transcranial direct current stimulation studies. Front Cell Neurosci 9:181Google Scholar CrossrefMedline
Li WLi YZhu WChen X. 2014Changes in brain functional network connectivity after stroke. Neural Regen Res 9(1):5160Google Scholar CrossrefMedline
Liew SLRana MCornelsen SFortunato de Barros Filho MBirbaumer NSitaram R, and others. 2016Improving motor corticothalamic communication after stroke using real-time fMRI connectivity-based neurofeedback. Neurorehabil Neural Repair 30(7):6715Google Scholar Link
Lindenberg RRenga VZhu LLBetzler FAlsop DSchlaug G. 2010a. Structural integrity of corticospinal motor fibers predicts motor impairment in chronic stroke. Neurology 74(4):2807Google Scholar CrossrefMedline
Lindenberg RRenga VZhu LLNair DSchlaug G. 2010b. Bihemispheric brain stimulation facilitates motor recovery in chronic stroke patients. Neurology 75(24):217684Google Scholar CrossrefMedline
Lindenberg RZhu LLRuber TSchlaug G. 2012Predicting functional motor potential in chronic stroke patients using diffusion tensor imaging. Hum Brain Mapp 33(5):104051Google Scholar CrossrefMedline
Liu KPChan CCWong RSKwan IWYau CSLi LS and others. 2009A randomized controlled trial of mental imagery augment generalization of learning in acute poststroke patients. Stroke 40(6):22225Google Scholar CrossrefMedline
Lo ACGuarino PDRichards LGHaselkorn JKWittenberg GFFederman DG, and others. 2010Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med 362(19):177283Google Scholar CrossrefMedline
Lum PSGodfrey SBBrokaw EBHolley RJNichols D. 2012Robotic approaches for rehabilitation of hand function after stroke. Am J Phys Med Rehabil 91(11 Suppl 3):S24254Google Scholar CrossrefMedline
Masiero SCelia ARosati GArmani M. 2007Robotic-assisted rehabilitation of the upper limb after acute stroke. Arch Phys Med Rehabil 88(2):1429Google Scholar CrossrefMedline
Massie CLWhite CPruit KFreel AStaley KBackes M. 2017Influence of motor cortex stimulation during motor training on neuroplasticity as a potential therapeutic intervention. J Mot Behav 49(1):1116Google Scholar CrossrefMedline
McKeown MJHansen LKSejnowsk TJ2003Independent component analysis of functional MRI: what is signal and what is noise? Curr Opin Neurobiol 13(5):6209Google Scholar CrossrefMedline
Memberg WDPolasek KHHart RLBryden AMKilgore KLNemunaitis GA, and others. 2014Implanted neuroprosthesis for restoring arm and hand function in people with high level tetraplegia. Arch Phys Med Rehabil 95(6):12011211.e1Google Scholar CrossrefMedline
Minjoli SSaturnino GBBlicher JUStagg CJSiebner HRAntunes A, and others. 2017The impact of large structural brain changes in chronic stroke patients on the electric field caused by transcranial brain stimulation. Neuroimage Clin 15:10617Google Scholar CrossrefMedline
Morganti FGaggioli ACastelnuovo GBulla DVettorello MRiva G. 2003The use of technology-supported mental imagery in neurological rehabilitation: a research protocol. Cyberpsychol Behav 6(4):4217Google Scholar CrossrefMedline
Morishita THummel FC2017Non-invasive brain stimulation (NIBS) in motor recovery after stroke: concepts to increase efficacy. Curr Behav Neurosci Rep 4:2809Google Scholar Crossref
Mulder T2007Motor imagery and action observation: cognitive tools for rehabilitation. J Neural Transm (Vienna) 114(10):126578Google Scholar CrossrefMedline
Murase NDuque JMazzocchio RCohen LG2004Influence of interhemispheric interactions on motor function in chronic stroke. Ann Neurol 55(3):4009Google Scholar CrossrefMedline
Muratori LMLamberg EMQuinn LDuff SV2013Applying principles of motor learning and control to upper extremity rehabilitation. J Hand Ther 26(2):94102Google Scholar CrossrefMedline
Nakazono HOgata KKuroda TTobimatsu S. 2016Phase and frequency-dependent effects of transcranial alternating current stimulation on motor cortical excitability. PLoS One 11(9):e0162521Google Scholar CrossrefMedline
Naros GGharabaghi A. 2015Reinforcement learning of self-regulated beta-oscillations for motor restoration in chronic stroke. Front Hum Neurosci 9:391Google Scholar CrossrefMedline
Nitsche MACohen LGWassermann EMPriori ALang NAntal A, and others. 2008Transcranial direct current stimulation: state of the art 2008. Brain Stimul 1(3):20623Google Scholar CrossrefMedline
Nouri SCramer SC2011Anatomy and physiology predict response to motor cortex stimulation after stroke. Neurology 77(11):107683Google Scholar CrossrefMedline
Nudo RJ2003Functional and structural plasticity in motor cortex: implications for stroke recovery. Phys Med Rehabil Clin N Am 14(1 Suppl):S5776Google Scholar CrossrefMedline
O’Doherty JELebedev MAHanson TLFitzsimmons NANicolelis MA2009A brain-machine interface instructed by direct intracortical microstimulation. Front Integr Neurosci 3:20Google Scholar CrossrefMedline
Opitz APaulus WWill SAntunes AThielscher A. 2015Determinants of the electric field during transcranial direct current stimulation. Neuroimage 109:14050Google Scholar CrossrefMedline
Page SJLevine PLeonard A. 2007Mental practice in chronic stroke: results of a randomized, placebo-controlled trial. Stroke 38(4):12937Google Scholar CrossrefMedline
Penny WDStephan KEMechelli AFriston KJ2004Modelling functional integration: a comparison of structural equation and dynamic causal models. Neuroimage 23(Suppl 1):S26474Google Scholar CrossrefMedline
Plow EBCarey JRNudo RJPascual-Leone A. 2009Invasive cortical stimulation to promote recovery of function after stroke: a critical appraisal. Stroke 40(5):192631Google Scholar CrossrefMedline
Pollock ABaer GCampbell PChoo PLForster AMorris J, and others. 2014Physical rehabilitation approaches for the recovery of function and mobility following stroke. Cochrane Database Syst Rev (4):CD001920Google Scholar Medline
Posteraro FMazzoleni SAliboni SCesqui BBattaglia ADario P, and others. 2009Robot-mediated therapy for paretic upper limb of chronic patients following neurological injury. J Rehabil Med 41(12):97680Google Scholar CrossrefMedline
Qiu MDarling WGMorecraft RJNi CCRajendra JButler AJ2011White matter integrity is a stronger predictor of motor function than BOLD response in patients with stroke. Neurorehabil Neural Repair 25(3):27584Google Scholar Link
Ramachandran VSRogers-Ramachandran D. 1996Synaesthesia in phantom limbs induced with mirrors. Proc Biol Sci 263(1369):37786Google Scholar CrossrefMedline
Ramos-Murguialday ABroetz DRea MLaer LYilmaz OBrasil FL, and others. 2013Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol 74(1):1008Google ScholarCrossrefMedline
Rebesco JMStevenson IHKörding KPSolla SAMiller LE2010Rewiring neural interactions by micro-stimulation. Front Syst Neurosci 4:39Google Scholar CrossrefMedline
Rossi SHallett MRossini PMPascual-Leone A, Safety of TMS Consensus Group. 2009Safety, ethical considerations, and application guidelines for the use of transcranial magnetic stimulation in clinical practice and research. Clin Neurophysiol 120(12):200839Google Scholar CrossrefMedline
Rossiter HEBoudrias MHWard NS2014Do movement-related beta oscillations change after stroke? J Neurophysiol 112(9):20538Google Scholar CrossrefMedline
Ruber TSchlaug GLindenberg R. 2012Compensatory role of the cortico-rubro-spinal tract in motor recovery after stroke. Neurology 79(6):51522Google Scholar CrossrefMedline
Ruffini GFox MDRipolles OMiranda PCPascual-Leone A. 2014Optimization of multifocal transcranial current stimulation for weighted cortical pattern targeting from realistic modeling of electric fields. Neuroimage 89:21625Google Scholar CrossrefMedline
Rusjan PMBarr MSFarzan FArenovich TMaller JJFitzgerald PB, and others. 2010Optimal transcranial magnetic stimulation coil placement for targeting the dorsolateral prefrontal cortex using novel magnetic resonance image-guided neuronavigation. Hum Brain Mapp 31(11):164352Google Scholar Medline
Saposnik GCohen LGMamdani MPooyania SPloughman MCheung D, and others. 2016Efficacy and safety of non-immersive virtual reality exercising in stroke rehabilitation (EVREST): a randomised, multicentre, single-blind, controlled trial. Lancet Neurol 15(10):101927Google Scholar CrossrefMedline
Saposnik GLevin M, Outcome Research Canada Working G. 2011Virtual reality in stroke rehabilitation: a meta-analysis and implications for clinicians. Stroke 42(5):13806Google Scholar CrossrefMedline
Sato SBergmann TOBorich MR2015Opportunities for concurrent transcranial magnetic stimulation and electroencephalography to characterize cortical activity in stroke. Front Hum Neurosci 9:250Google ScholarCrossrefMedline
Sauseng PKlimesch WGerloff CHummel FC2009Spontaneous locally restricted EEG alpha activity determines cortical excitability in the motor cortex. Neuropsychologia 47(1):2848Google Scholar CrossrefMedline
Schulz RBraass HLiuzzi GHoerniss VLechner PGerloff C, and others. 2015a. White matter integrity of premotor-motor connections is associated with motor output in chronic stroke patients. Neuroimage Clin 7:826Google Scholar CrossrefMedline
Schulz RFrey BMKoch PZimerman MBonstrup MFeldheim J, and others. 2017a. Cortico-cerebellar structural connectivity is related to residual motor output in chronic stroke. Cereb Cortex 27(1):63545Google Scholar Medline
Schulz RKoch PZimerman MWessel MBonstrup MThomalla G, and others. 2015b. Parietofrontal motor pathways and their association with motor function after stroke. Brain 138(Pt 7):194960Google Scholar CrossrefMedline
Schulz RPark CHBoudrias MHGerloff CHummel FCWard NS2012Assessing the integrity of corticospinal pathways from primary and secondary cortical motor areas after stroke. Stroke 43(8):224851Google Scholar CrossrefMedline
Schulz RPark ELee JChang WHLee AKim YH, and others. 2017b. Synergistic but independent: the role of corticospinal and alternate motor fibers for residual motor output after stroke. Neuroimage Clin 15:11824Google Scholar CrossrefMedline
Shafi MMBrandon Westover MOberman LCash SSPascual-Leone A. 2014Modulation of EEG functional connectivity networks in subjects undergoing repetitive transcranial magnetic stimulation. Brain Topogr 27(1):17291Google Scholar CrossrefMedline
Sitaram RVeit RStevens BCaria AGerloff CBirbaumer N, and others. 2012Acquired control of ventral premotor cortex activity by feedback training: an exploratory real-time FMRI and TMS study. Neurorehabil Neural Repair 26(3):25665Google Scholar Link
Smajlovic D2015Strokes in young adults: epidemiology and prevention. Vasc Health Risk Manag 11:15764Google Scholar CrossrefMedline
Small SLBuccino GSolodkin A. 2012The mirror neuron system and treatment of stroke. Dev Psychobiol 54(3):293310Google Scholar CrossrefMedline
Stevens JAStoykov ME2003Using motor imagery in the rehabilitation of hemiparesis. Arch Phys Med Rehabil 84(7):10902Google Scholar CrossrefMedline
Stinear CMBarber PAPetoe MAnwar SByblow WD2012The PREP algorithm predicts potential for upper limb recovery after stroke. Brain 135(Pt 8):252735Google Scholar CrossrefMedline
Stinear CMByblow WD2014Predicting and accelerating motor recovery after stroke. Curr Opin Neurol 27(6):62430Google Scholar Medline
Stinear CMByblow WDAckerley SJBarber PASmith MC2017Predicting recovery potential for individual stroke patients increases rehabilitation efficiency. Stroke 48(4):10119Google Scholar CrossrefMedline
Strens LHAsselman PPogosyan ALoukas CThompson AJBrown P. 2004Corticocortical coupling in chronic stroke: its relevance to recovery. Neurology 63(3):47584Google Scholar CrossrefMedline
Stuck RAMarshall LMSivakumar R. 2014Feasibility of SaeboFlex upper-limb training in acute stroke rehabilitation: a clinical case series. Occup Ther Int 21(3):10814Google Scholar CrossrefMedline
Takeuchi NIzumi S. 2015Combinations of stroke neurorehabilitation to facilitate motor recovery: perspectives on Hebbian plasticity and homeostatic metaplasticity. Front Hum Neurosci 9:349Google Scholar CrossrefMedline
Tang CZhao ZChen CZheng XSun FZhang X and others. 2016Decreased functional connectivity of homotopic brain regions in chronic stroke patients: a resting state fMRI study. PLoS One 11(4):e0152875Google Scholar CrossrefMedline
Taub EMiller NENovack TACook EW3rdFleming WCNepomuceno CS, and others. 1993Technique to improve chronic motor deficit after stroke. Arch Phys Med Rehabil 74(4):34754Google Scholar Medline
Thut GVeniero DRomei VMiniussi CSchyns PGross J. 2011Rhythmic TMS causes local entrainment of natural oscillatory signatures. Curr Biol 21(14):117685Google Scholar CrossrefMedline
Truelsen TPiechowski-Jozwiak BBonita RMathers CBogousslavsky JBoysen G. 2006Stroke incidence and prevalence in Europe: a review of available data. Eur J Neurol 13(6):58198Google Scholar CrossrefMedline
Turolla ADam MVentura LTonin PAgostini MZucconi C, and others. 2013Virtual reality for the rehabilitation of the upper limb motor function after stroke: a prospective controlled trial. J Neuroeng Rehabil 10:85Google Scholar CrossrefMedline
Uswatte GTaub E. 2013Constraint-induced movement therapy: a method for harnessing neuroplasticity to treat motor disorders. Prog Brain Res 207:379401Google Scholar CrossrefMedline
Van Peppen RPKwakkel GWood-Dauphinee SHendriks HJVan der Wees PJDekker J. 2004The impact of physical therapy on functional outcomes after stroke: what’s the evidence? Clin Rehabil 18(8):83362Google Scholar Link
Varkuti BGuan CPan YPhua KSAng KKKuah CW, and others. 2013Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. Neurorehabil Neural Repair 27(1):5362Google Scholar Link
Veerbeek JMKwakkel Gvan Wegen EEKet JCHeymans MW2011Early prediction of outcome of activities of daily living after stroke: a systematic review. Stroke 42(5):14828Google Scholar
Vourvopoulos ABermudez IBS2016Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis. J Neuroeng Rehabil 13(1):69Google Scholar CrossrefMedline
Wagner TFregni FEden URamos-Estebanez CGrodzinsky AZahn M, and others. 2006Transcranial magnetic stimulation and stroke: a computer-based human model study. Neuroimage 30(3):85770Google Scholar CrossrefMedline
Westlake KPNagarajan SS2011Functional connectivity in relation to motor performance and recovery after stroke. Front Syst Neurosci 5:8Google Scholar CrossrefMedline
Winstein CJStein JArena RBates BCherney LRCramer SC, and others. 2016Guidelines for adult stroke rehabilitation and recovery: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 47(6):e98e169Google Scholar CrossrefMedline
Wolf SLThompson PAWinstein CJMiller JPBlanton SRNichols-Larsen DS, and others. 2010The EXCITE stroke trial: comparing early and delayed constraint-induced movement therapy. Stroke 41(10):230915Google Scholar CrossrefMedline
Wolf SLWinstein CJMiller JPTaub EUswatte GMorris D, and others. 2006Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. JAMA 296(17):2095104Google Scholar CrossrefMedline
World Health Organization. 2016World health statisticshttp://www.who.int/gho/publications/world_health_statistics/2016/en/Google Scholar
Wu CYChen YALin KCChao CPChen YT2012Constraint-induced therapy with trunk restraint for improving functional outcomes and trunk-arm control after stroke: a randomized controlled trial. Phys Ther 92(4):48392Google Scholar CrossrefMedline
Wu CYChuang LLLin KCChen HCTsay PK2011Randomized trial of distributed constraint-induced therapy versus bilateral arm training for the rehabilitation of upper-limb motor control and function after stroke. Neurorehabil Neural Repair 25(2):1309Google Scholar Link
Young BMNigogosyan ZRemsik AWalton LMSong JNair VA, and others. 2014Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device. Front Neuroeng 7:25Google Scholar Medline
Zeiler SRKrakauer JW2013The interaction between training and plasticity in the poststroke brain. Curr Opin Neurol 26(6):60916Google Scholar CrossrefMedline
Zimerman MHeise KFHoppe JCohen LGGerloff CHummel FC2012Modulation of training by single-session transcranial direct current stimulation to the intact motor cortex enhances motor skill acquisition of the paretic hand. Stroke 43(8):218591Google Scholar CrossrefMedline
Zrenner CBelardinelli PMuller-Dahlhaus FZiemann U. 2016Closed-loop neuroscience and non-invasive brain stimulation: a tale of two loops. Front Cell Neurosci 10:92Google Scholar CrossrefMedline

<via Restoring Motor Functions After Stroke: Multiple Approaches and OpportunitiesThe Neuroscientist – Estelle Raffin, Friedhelm C. Hummel, 2017>

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[BLOG POST] SaeboFlex Helps Client Regain Hand Function 23 Years After Stroke

SaeboFlexStroke survivor exhibits remarkable improvement in hand function more than two decades after stroke, disproving theories that recovery window is limited to 6 months. 

Charlotte, N.C. – Tuesday, July 25, 2017 – Until recently, researchers believed that if a stroke survivor exhibited no improvement within the first 6 months, then he or she would have little to no chance of regaining motor function in the future. This assumed end of recovery is called a plateau. However, a groundbreaking new article published in the Journal of Neurophysiology discusses a stroke patient’s remarkable improvement decades after suffering a stroke at the age of 15. Doctors Peter Sörös, Robert Teasell, Daniel F. Hanley, and J. David Spence formally dismiss previous theories that stroke recovery occurs within 6 months, reporting that the patient experienced “recovery of hand function that began 23 years after the stroke.”

The patient’s stroke resulted in paralysis on the left side of his body, rendering his left hand completely nonfunctional, despite regular physical therapy. More than twenty years after his stroke, the patient took up swimming when his doctor recommended he lose weight. A year later, he began to show signs of movement on his affected side and returned to physical therapy. Therapists fitted the patient with the SaeboFlex, a mechanical device shown to improve hand function and speed up recoveryand, after only a few months of therapy, he began picking up coins with his previously nonfunctional hand. He also saw notable improvement in hand strength and control with the SaeboGlove, a low-profile hand device recently patented by Saebo.

Functional MRI studies showed the reorganization of sensorimotor neurons in both sides of the patient’s brain more than two decades after his stroke, resulting in a noticeable recovery in both hemispheres and improved motor function. “The marked delayed recovery in our patient and the widespread recruitment of bilateral areas of the brain indicate the potential for much greater stroke recovery than is generally assumed,” the doctors reported. “Physiotherapy and new modalities in development might be indicated long after a stroke.”

“This article highlights what we have seen for the last 15 years with many of our clients,” states Saebo co-founder, Henry Hoffman. “Oftentimes, stroke survivors are told that they have plateaued and no further progress is possible. We believe it is not the client that has plateaued but failed treatment options have plateaued them. In other words, traditional therapy interventions that lack scientific evidence can be ineffective and can actually facilitate the plateau.”

“The SaeboFlex device is a life-changing treatment designed for clients that lack motor recovery and function,” Hoffman continues. “Whether the client recently suffered a stroke or decades later, they can immediately begin using their hand with this device and potentially make significant progress over time. I agree with the authors that the neurorehabilitation community needs to take a hard look at traditional beliefs with respect to the window of recovery following stroke. It is my hope that this article will spark more interest by researchers to investigate upper limb function with clients at the chronic stage using Saebo’s hand technology.”

The abstract and article in its entirety can be viewed at the Journal of Neurophysiology’s website, jn.physiology.org.

If you are suffering from limited hand function or have been told you have plateaued, then schedule a call with a Saebo Specialist or click here to get started on the road to recovery.

via SaeboFlex Helps Client Regain Hand Function 23 Years After Stroke | Saebo

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[Abstract] Virtual Rehabilitation through Nintendo Wii in Poststroke Patients: Follow-Up

Objective

To evaluate in the follow-up the sensory-motor recovery and quality of life patients 2 months after completion of the Nintendo Wii console intervention and determine whether learning retention was obtained through the technique.

Methods

Five hemiplegics patients participated in the study, of whom 3 were male with an average age of 54.8 years (SD = 4.6). Everyone practiced Nintendo Wii therapy for 2 months (50 minutes/day, 2 times/week, during 16 sessions). Each session lasting 60 minutes, under a protocol in which only the games played were changed, plus 10 minutes of stretching. In the first session, tennis and hula hoop games were used; in the second session, football (soccer) and boxing were used. For the evaluation, the Fulg-Meyer and Short Form Health Survey 36 (SF-36) scales were utilized. The patients were immediately evaluated upon the conclusion of the intervention and 2 months after the second evaluation (follow-up).

Results

Values for the upper limb motor function sub-items and total score in the Fugl–Meyer scale evaluation and functional capacity in the SF-36 questionnaire were sustained, indicating a possible maintenance of the therapeutic effects.

Conclusion

The results suggest that after Nintendo Wii therapy, patients had motor learning retention, achieving a sustained benefit through the technique.

via Virtual Rehabilitation through Nintendo Wii in Poststroke Patients: Follow-Up

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[Brochure] THE FUTURE IS MOVING – Revolutionizing Functional Movement Therapy – HOCOMA

HOCOMA REVOLUTIONIZING REHABILITATION

Conventional therapy today is limited—by time, by number of repetitions, by
the lack of reproducible movement quality and by the fact that it is strenuous for both therapists and patients. In other words: there is a disbalance between the therapy we know we should provide according to motor learning principles and all the factors that prevent us from reaching this goal.[…]

Download Brochure (PDF file)

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[ARTICLE] Is two better than one? Muscle vibration plus robotic rehabilitation to improve upper limb spasticity and function: A pilot randomized controlled trial – Full Text

Abstract

Even though robotic rehabilitation is very useful to improve motor function, there is no conclusive evidence on its role in reducing post-stroke spasticity. Focal muscle vibration (MV) is instead very useful to reduce segmental spasticity, with a consequent positive effect on motor function. Therefore, it could be possible to strengthen the effects of robotic rehabilitation by coupling MV. To this end, we designed a pilot randomized controlled trial (Clinical Trial NCT03110718) that included twenty patients suffering from unilateral post-stroke upper limb spasticity. Patients underwent 40 daily sessions of Armeo-Power training (1 hour/session, 5 sessions/week, for 8 weeks) with or without spastic antagonist MV. They were randomized into two groups of 10 individuals, which received (group-A) or not (group-B) MV. The intensity of MV, represented by the peak acceleration (a-peak), was calculated by the formula (2πf)2A, where f is the frequency of MV and A is the amplitude. Modified Ashworth Scale (MAS), short intracortical inhibition (SICI), and Hmax/Mmax ratio (HMR) were the primary outcomes measured before and after (immediately and 4 weeks later) the end of the treatment. In all patients of group-A, we observed a greater reduction of MAS (p = 0.007, d = 0.6) and HMR (p<0.001, d = 0.7), and a more evident increase of SICI (p<0.001, d = 0.7) up to 4 weeks after the end of the treatment, as compared to group-B. Likewise, group-A showed a greater function outcome of upper limb (Functional Independence Measure p = 0.1, d = 0.7; Fugl-Meyer Assessment of the Upper Extremity p = 0.007, d = 0.4) up to 4 weeks after the end of the treatment. A significant correlation was found between the degree of MAS reduction and SICI increase in the agonist spastic muscles (p = 0.004). Our data show that this combined rehabilitative approach could be a promising option in improving upper limb spasticity and motor function. We could hypothesize that the greater rehabilitative outcome improvement may depend on a reshape of corticospinal plasticity induced by a sort of associative plasticity between Armeo-Power and MV.

Introduction

Spasticity is defined as a velocity-dependent increase in muscle tone due to the hyper-excitability of muscle stretch reflex [1]. Spasticity of the upper limb is a common condition following stroke and traumatic brain injury and needs to be assessed carefully because of the significant adverse effects on patient’s motor functions, autonomy, and quality of life [2].

Different pharmacological and non-pharmacological approaches are currently available for upper limb spasticity management, as physiotherapy (including magnetic stimulation, electromagnetic therapy, sensory-motor techniques, and functional electrical stimulation treatment) and robot-assisted therapy [34]. In this regard, several studies suggest robotic devices, including the Armeo® (a robotic exoskeleton for the rehabilitation of upper limbs), may help reducing spasticity by modifying spasticity-related synaptic processes at either the brain or spinal level [513], resulting in spasticity reduction in antagonist muscles through, e.g., a strengthening of spinal reciprocal inhibition mechanisms [11].

Growing research is proposing segmental muscle vibration (MV) as being a powerful tool for the treatment of focal spasticity in post-stroke patients [1415]. Mechanical devices deliver low-amplitude/high-frequency vibratory stimuli to specific muscles [1617], thus offering strong proprioceptive inputs by activating the neural pathway from muscle spindle annulospiral endings to Ia-fiber, dorsal column–medial lemniscal pathway, the ventral posterolateral nucleus of the thalamus (and other nuclei of the basal ganglia), up to the primary somatosensory area (postcentral gyrus and posterior paracentral lobule of the parietal lobe), and the primary motor cortex [1819]. At the cortical network level, proprioceptive inputs can alter the excitability of the corticospinal pathway by modulating intracortical inhibitory and facilitatory networks within primary sensory and motor cortex, and affecting the strength of sensory inputs to motor circuits [2022]. In particular, periods of focal MV delivered alone can modify sensorimotor organization within the primary motor cortex (i.e., can increase or decrease motor evoked potential—MEP—and short intracortical inhibition (SICI) magnitude in the vibrated muscles, while opposite changes occur in the neighboring muscles), thus reducing segmental hyper-excitability and spasticity [2022].

While focal MV is commonly used to reduce upper limb post-stroke spasticity, there is no conclusive evidence on the role of robotic rehabilitation in such a condition [1417,2327]. A strengthening of the effects of neurorobotics and MV on spasticity could be achieved by combining MV and neurorobotics. The rationale for combining Armeo-Power and MV to reduce spasticity could lie in the summation and amplification of their single modulatory effects on corticospinal excitability [28]. Specifically, it is hypothesizable that MV may strengthen the learning-dependent plasticity processes within sensory-motor areas that are in turn triggered by the intensive, repetitive, and task-oriented movement training offered by Armeo-Power [2930]. Such an amplification may depend on a sort of associative plasticity (i.e., the one generated by timely coupling two different synaptic inputs) between MV and Armeo-Power [3133].

To the best of our knowledge, this is the first attempt to investigate such approach. Indeed, a previous study combining MV with conventional physiotherapy used Armeo only as evaluating tool [14].

The aim of our study was to assess whether a combined protocol employing MV and Armeo-Power training, as compared to Armeo-Power alone, may improve upper limb spasticity and motor function in patients suffering from a hemispheric stroke in the chronic phase. To this end, we compared the clinical and electrophysiological after-effects of Armeo-Power with or without MV on upper limb spasticity. We also assessed the effects on upper limb motor function and muscle activation, disability burden, and mood, given that spasticity may have significant negative consequences on these outcomes. Further, it is important to evaluate mood, as it may negatively affect functional recovery [3436], increase mortality [37], and weaken the compliance of the patient to the rehabilitative training [3839].[…]

Continue —>  Is two better than one? Muscle vibration plus robotic rehabilitation to improve upper limb spasticity and function: A pilot randomized controlled trial

 

Fig 2. Combined rehabilitative approach. https://doi.org/10.1371/journal.pone.0185936.g002

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[ARTICLE] Design and Interaction Control of a New Bilateral Upper-Limb Rehabilitation Device – Full Text

Abstract

This paper proposed a bilateral upper-limb rehabilitation device (BULReD) with two degrees of freedom (DOFs). The BULReD is portable for both hospital and home environment, easy to use for therapists and patients, and safer with respect to upper-limb robotic exoskeletons. It was implemented to be able to conduct both passive and interactive training, based on system kinematics and dynamics, as well as the identification of real-time movement intention of human users. Preliminary results demonstrate the potential of the BULReD for clinical applications, with satisfactory position and interaction force tracking performance. Future work will focus on the clinical evaluation of the BULReD on a large sample of poststroke patients.

1. Introduction

In the United States, more than 700,000 people suffer from stroke each year, and approximately two-thirds of these individuals survive and require rehabilitation [1]. In New Zealand (NZ), there are an estimated 60,000 stroke survivors, and many of them have mobility impairments [2]. Stroke is the third reason for health loss and takes the proportion of 3.9 percent, especially for the group starting on middle age, suffering the stroke as a nonfatal disease in NZ [3]. Professor Caplan who studies Neurology at Harvard Medical School describes stroke as a term which is a kind of brain impairment as a result of abnormal blood supply in a portion of the brain [4]. The brain injury is most likely leading to dysfunctions and disabilities. These survivors normally have difficulties in activities of daily living, such as walking, speaking, and understanding, and paralysis or numbness of the human limbs. The goals of rehabilitation are to help survivors become as independent as possible and to attain the best possible quality of life.

Physical therapy is conventionally delivered by the therapist. While this has been demonstrated as an effective way for motor rehabilitation [5], it is time-consuming and costly. Treatments manually provided by therapists require to take place in a specific environment (in a hospital or rehabilitation center) and may last several months for enhanced rehabilitation efficacy [6]. A study by Kleim et al. [7] has shown that physical therapy like regular exercises can improve plasticity of a nervous system and then benefits motor enrichment procedures in promoting rehabilitation of brain functional models. It is a truth that physical therapy should be a preferable way to take patients into regular exercises and guided by a physical therapist, but Chang et al. [8] showed that it is a money-consuming scheme. Robot-assisted rehabilitation solutions, as therapeutic adjuncts to facilitate clinical practice, have been actively researched in the past few decades and provide an overdue transformation of the rehabilitation center from labor-intensive operations to technology-assisted operations [9]. The robot could also provide a rich stream of data from built-in sensors to facilitate patient diagnosis, customization of the therapy, and maintenance of patient records. As a popular neurorehabilitation technique, Liao et al. [10] indicated that robot-assisted therapy presents market potential due to quantification and individuation in the therapy session. The quantification of robot-assisted therapy refers that a robot can provide consistent training pattern without fatigue with the given parameter. The characterization of individuation allows therapists to customize a specific training scheme for an individual.

Many robotic devices have been developed in recent years for stroke rehabilitation and show great potential for clinical applications [1112]. Typical upper-limb rehabilitation devices are MIME, MIT-Manus, ARM Guide, NeReBot, and ARMin [51321]. Relevant evidences demonstrated that these robots are effective for upper-limb rehabilitation but mostly for the one side of the human body. Further, upper-limb rehabilitation devices can be unilateral or bilateral [2224]. Despite the argument between these two design strategies, bilateral activities are more common than unilateral activities in daily living. Liu et al. [25] pointed that the central nervous system dominates the human movement with coordinating bilateral limb to act in one unit instead of independent unilateral actions. From this point, bilateral robots are expected to be more potential than unilateral devices. Robotic devices for upper-limb rehabilitation can be also divided into two categories in terms of structure: the exoskeleton and the end-effector device [26]. Two examples of upper-limb exoskeletons are the arm exoskeleton [27] and the RUPERT IV [28]. In addition, Lum et al. [13] incorporated a PUMA 560 robot (Staubli Unimation Inc., Duncan, South Carolina) to apply forces to the paretic limbs in the MIME system. This robotic device can be made for both unilateral and bilateral movements in a three-dimensional space. To summarize, existing robotic exoskeletons for upper-limb rehabilitation are mostly for unilateral training.

There are some devices that have been specially designed for bilateral upper-limb training for poststroke rehabilitation. van Delden et al. [29] conducted a systematic review to provide an overview and qualitative evaluation of the clinical applications of bilateral upper-limb training devices. A systematic search found a total of six mechanical devices and 14 robotic bilateral upper-limb training devices, with a comparative analysis in terms of mechanical and electromechanical characteristics, movement patterns, targeted part, and active involvement of the upper limb, training protocols, outcomes of clinical trials, and commercial availability. Obviously, these mechanical devices require the human limbs to actively move for training, while the robotic ones can be operated in both passive and active modes. However, few of these robotic bilateral upper-limb training devices have been commercially available with current technology. For example, the exoskeleton presented in [30] requires the development of higher power-to-weight motors and structural materials to make it mobile and more compact.

The University of Auckland developed an end-effector ReachHab device to assist bilateral upper-limb functional recovery [31]. However, this device suffered from some limitations, such as deformation of the frame leading to significant vibration, also hard to achieve satisfactory control performance. This paper presents the design and interaction control of an improved bilateral upper-limb rehabilitation device (BULReD). This device is portable for both hospital and home environment, easy to use for therapists and patients, and safer with respect to upper-limb robotic exoskeletons. This paper is organized as follows. Following Introduction, a detailed description of the BULReD is given, including mechanical design, electrical design, kinematics, and dynamics. Then, the control design is presented for both passive training and interactive training, as well as the fuzzy-based adaptive training. Experiments and Results is introduced next and the last is Conclusion.[…]

Continue —>  Design and Interaction Control of a New Bilateral Upper-Limb Rehabilitation Device

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[WEB SITE] SMARTmove – FES

Summary

SMARTmove is a £1.1 million Medical Research Council research project running for 30 months from September 2016 to February 2019, funded under the Development Pathway Funding Scheme (DPFS). The project brings together a multidisciplinary team with expertise in functional materials, direct printing fabrication, control algorithms, wireless electronics, sensors, and end user engagement to address stroke rehabilitation. Working together with the advisory board members from six institutions, we will deliver a personalised wearable device for home-based stroke upper limb rehabilitation.

     

The Need

Stroke is one of the largest causes of disability: 17 million strokes occur every year worldwide, meaning one stroke every two seconds. Half of stroke survivors lose the ability to perform everyday tasks with their upper limb, which affects their independence. The cost to society in the UK is nine billion pounds per year due to health and social care, informal care, productivity loss and benefit payments. As stroke is an age-related disease, these numbers are set to increase as the population ages.

Novelty

Current commercial devices using functional electrical stimulation (FES) have large electrodes that only stimulate a limited number of muscles, resulting in simple, imprecise movements and the rapid onset of fatigue. In addition, current commercial devices do not employ feedback control to account for the movement of patients, only reducing the level of precision in the resulting movements. In addition, devices are either bulky and expensive, or difficult to set-up due to trailing wires.

Our project uses bespoke screen printable pastes to print electrode arrays directly onto everyday fabrics, such as those used in clothing. The resulting garments will have cutting-edge sensor technologies integrated into them. Advanced control algorithms will then adjust the stimulation based on the patients’ limb motion to enable precise functional movements, such as eating, washing or dressing.

Impact

This project will deliver a fabric-based wearable FES for home based stroke rehabilitation. The beneficiaries include:

  1. Persons with stroke (PwS) and other neurological conditions. Stroke survivors are the direct beneficiaries of our research. The FES clothing can be adapted to also treat hand/arm disabilities resulting from other neurological conditions such as cerebral palsy, head injury, spinal cord injury, and multiple sclerosis. The use of the wearable training system increases the intensity of rehabilitation without an increase in clinical contact time. This leads to better outcomes such as reduced impairment, greater restoration of function, improved quality of life and increased social activity.
  2. The NHS. FES-integrated clothing is comfortable to wear and convenient to use for rehabilitation, enabling impaired people to benefit from FES at home. It will transfer hospital based professional care to home based self-care, and therefore will reduce NHS costs by saving healthcare professionals’ time and other hospital resources.
  3. Industry. Benefits include: bringing business to the whole supply chain; increasing the FES market demand by improving performance; benefiting other industry sectors such as rehabilitation for other neurological conditions.
  4. Research communities in related fields. Specifically, the fields of novel fabrication, control systems, design of medical devices, rehabilitation, smart fabrics, and remote healthcare will benefit from the highly transformative platform technology (e.g. direct write printing, fabric electrodes, iterative learning control systems) developed in this work.

What is FES?

Functional electrical stimulation (FES) is a technique used to facilitate the practice of therapeutic exercises and tasks. Intensive movement practice can restore the upper limb function lost following stroke. However, stroke patients often have little or no movement, so are unable to practice. FES activates muscles artificially to facilitate task practise and improve patients’ movement.

More…..

Source: SMARTmove

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[ARTICLE] The Relationship between Poststroke Depression and Upper Limb Recovery in Patients Admitted to a Rehabilitation Unit – Full Text PDF

Abstract

Objective: We sought to determine the relationship between poststroke depression and upper limb recovery in a cohort of patients admitted to a rehabilitation center in Singapore.

Method: We conducted a secondary analysis of an interventional study of 105 patients with a stroke. Depression was diagnosed using the Centre for Epidemiological Studies Depression Scale (CES-D) and this was correlated with the following measures: Fugl-Meyer Assessment of Upper Limb (FMA), Action Research Am Test (ARAT), Stroke Impact Scale – Upper Limb Items (SIS) and Functional Independence Measure-Selfcare (FIM-Selfcare) at 3, 7 and 15 weeks after admission to rehabilitation.

Results: Poststroke depression was present in 20% of patients on admission to rehabilitation. It was negatively correlated to SIS and FIM-Selfcare at 7 weeks and to FMA, ARAT, SIS and FIM-Selfcare at 15 weeks after rehabilitation admission. Depression on rehabilitation admission did not influence upper limb recovery at 3 weeks, 7 weeks, and 15 weeks after admission to rehabilitation.

Conclusion: Given the negative impact of depression on upper limb impairment, function and performance of selfcare, routine screening of depression should be considered in subacute stroke patients, especially in those with poorer upper limb function.

 Full Text PDF

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[ARTICLE] Adapting Tai Chi for Upper Limb Rehabilitation Post Stroke: A Feasibility Study

Abstract: 

Background: Tai chi (TC) has been reported as being beneficial for improving balance post stroke, yet its utility in upper limb rehabilitation remains unknown. Methods: Twelve chronic stroke survivors with persistent paresis of an upper limb underwent 60 minutes of adapted TC twice a week for eight weeks, with a 4-week follow up. A 10-min TC home program was recommended for the days without sessions. TC level of performance, attendance to the sessions, duration of self-practice at home, and adapted TC movements used were recorded. Results: Eleven participants completed the study. A clinical reasoning algorithm underlying the adaptation of TC was elaborated throughout the trial. Participants with varying profiles including a severely impaired upper limb, poor balance, shoulder pain, and severe spasticity were not only capable of practicing the adapted TC, but attended all 16 sessions and practiced TC at home for a total of 16.51 ± 9.21 h. The degree of self-practice for subgroups with low upper limb function, shoulder pain, or moderate-to-severe spasticity was similar to that of subgroups with greater upper limb function, no shoulder pain, and minimal-to-no spasticity. Conclusion: Adapted TC seems feasible for upper limb rehabilitation post stroke. Although the study was based on a small sample size and requires confirmation, low upper limb function, insufficient balance, spasticity, and shoulder pain do not appear to hinder the practice of TC.

1. Introduction

Stroke is a leading cause of serious, long-term disability among middle-aged and older adults worldwide [1]. Functional impairment of an upper limb is reported in approximately 85% of stroke survivors [2]. The effects of current treatments for arm weakness are shown to be suboptimal [3]. Though upper limb recovery has been found to continue even in the chronic stage [4], long-term rehabilitation services are limited for a large proportion of chronic stroke patients after returning home [5,6]. Therefore, novel and effective approaches are needed to provide timely and ongoing upper limb rehabilitation.
Tai chi is an ancient martial art originating from Chinese healing traditions. Typified by slow and gentle movements, tai chi is an “internal” martial art that cultivates the flow and balance of qi, which is translated as vital energy [7]. The relaxation of body and mind is a main feature which is said to facilitate the flow of qi [8]. Also, tai chi requires well-coordinated sequencing of segments to make the body move as a whole unit to help the flow of qi [9]. Thus, tai chi is a moving form of qigong, which is a practice focusing on cultivation, circulation, and harmonization of qi. To date, tai chi is accepted as a suitable, low impact, home-based exercise option for aged and patients with chronic diseases [10,11,12]. Since tai chi emphasizes slow and continuous weight transfer between the two feet, it has been widely shown as beneficial for improving balance and for fall prevention in the aged [13,14,15].
In recent years, some studies have also reported the benefits of tai chi in improving balance in chronic stroke patients [16,17,18]. However, the use of tai chi in upper limb rehabilitation post stroke remains unknown. Tai chi is not only an exercise of lower limb, but a whole-body exercise. Upper limb muscle strength and flexibility have been shown to improve in the aged following the practice of tai chi [19,20,21]. Tai chi practitioners have also demonstrated better arm movement control and eye–hand coordination in older adults [21,22,23,24]. Furthermore, the relaxation component of tai chi may have the potential to improve the motor function of the paretic upper limb. Therefore, tai chi may be a promising upper limb rehabilitation method.
However, the presence of hemiplegia may be an important barrier to using tai chi for upper limb rehabilitation post stroke, potentially limiting the ability to actually perform upper limb tai chi movements. Similarly, shoulder pain and severe spasticity of the affected arm may impact on the ability to perform tai chi movements. Furthermore, the standing position used in traditional tai chi styles poses difficulties for persons with poor balance. Adapting tai chi to take into account these limitations may need to be included in post-stroke rehabilitation. Although sitting tai chi has been reported to be used in persons with spinal cord injuries to improve muscle strength of the upper limbs [25], little is known about how to adapt tai chi with respect to paretic upper limbs. Moreover, the feasibility of using adapted tai chi movements for upper limb rehabilitation remains unknown.
Therefore, this study aimed to explore the use of adapted tai chi movements for upper limb rehabilitation. More specifically, the objective was to evaluate the feasibility of using adapted tai chi for upper limb rehabilitation post stroke, including: (1) whether the adapted tai chi was performable and acceptable by participants; and (2) whether the potential influencing factors such as impairment level of an upper limb, insufficient balance, shoulder pain, and spasticity constrained the practice of the adapted tai chi. A second objective was to document the clinical reasoning underlying the adaptations made to tai chi based on the participants’ characteristics when used for upper limb rehabilitation post stroke. The efficacy of adapted tai chi has been reported elsewhere [26]. […]

Continue —>  Medicines | Free Full-Text | Adapting Tai Chi for Upper Limb Rehabilitation Post Stroke: A Feasibility Study | HTML

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Figure 1. Individual self-practice hours per month of participants.

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[ARTICLE] Effect of repetitive wrist extension with electromyography-triggered stimulation after stroke: a preliminary randomized controlled study – Full Text PDF

Objective: The purpose of this study was to explore the effect of repetitive wrist extension task training with electromyography (EMG)-triggered neuromuscular electrical stimulation (NMES) for wrist extensor muscle recovery in patients with stroke.

Design: Randomized controlled trial.

Methods: Fifteen subjects who had suffered a stroke were randomly assigned to an EMG-triggered NMES group (n=8) or control group (n=7); subjects in both groups received conventional therapy as usual. Subjects in the experimental group received application of EMG-triggered NMES to the wrist extensor muscles for 20 minutes, twice per day, five days per week, for a period of four weeks, and were given a task to make a touch alarm go off by activity involving extension of their wrist. In the control group, subjects
performed wrist self-exercises for the same duration and frequency as those in the experimental group. Outcome measures included muscle reaction time and spectrum analysis. Assessments were performed during the pre- and post-treatment periods.

Results: In the EMG-triggered NMES group, faster muscle reaction time was observed, and median frequency also showed improvement, from 68.2 to 75.3 Hz, after training (p<0.05). Muscle reaction time was significantly faster, and median frequency was significantly higher in the experimental group than in the experimental group after training.

Conclusions: EMG-triggered NMES is beneficial for patients with hemiparetic stroke in recovery of upper extremity function.

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