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CEREBROVASCULAR DISEASE: Edited by Ralph L. Sacco and Tatjana Rundek

Promoting neuroplasticity and recovery after stroke

future directions for rehabilitation clinical trials

Bowden, Mark G.a,b; Woodbury, Michelle L.a,c; Duncan, Pamela W.d

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Current Opinion in Neurology: February 2013 - Volume 26 - Issue 1 - p 37-42
doi: 10.1097/WCO.0b013e32835c5ba0
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Stroke is the leading cause of long-term disability in the US, affecting approximately 795 000 people each year, with a surviving cohort of nearly 6.5 million [1]. Whereas rehabilitation is generally considered critical to the restoration of functional activities after stroke, there remains a paucity of data indicating which physical rehabilitation strategies are most beneficial in promoting functional independence. Randomized controlled trials (RCTs) are considered to be the gold standard in establishing efficacy of a therapeutic intervention, but very few large, multicenter, federally funded RCTs have been conducted in the area of physical rehabilitation after stroke. In the past 6 years, however, three such trials have been conducted: Extremity Constraint Induced Therapy Evaluation (EXCITE), which tested the effect of constraint therapy in 224 patients 3–9 months poststroke and with mild–moderate upper extremity impairment; Robot-Assisted Upper-Limb Neurorehabilitation in Stroke Patients (UL-Robot), which tested the effect of robot-assisted therapy in 127 patients more than 6 months poststroke and with moderate-severe upper extremity impairment; and Locomotor Experience Applied Post-Stroke (LEAPS), which tested the effect of locomotor training in 408 patients more than 2 months poststroke and with moderate–severe walking impairment.

Of these three studies, only the EXCITE trial demonstrated a clear superiority of the experimental intervention compared to the control group. However, the control group in EXCITE was not an active control, only usual and customary care which could have been highly variable. EXCITE demonstrated that 2 weeks of intense (6 h per day) rehabilitation focused on guided use of the paretic upper extremity with behavioral shaping enabled participants to perform functional tasks faster (Wolf Motor Function Test, WMFT) and increased self-reported arm use and movement quality (Motor Activity Log, MAL) when compared to a control group of individuals undergoing usual and customary care [2]. The UL-Robot trial compared two groups receiving 36 1-h therapy sessions over 12 weeks (robot-assisted therapy and intensive comparison therapy) to usual care. At 12 and 36 weeks the robotic-assisted and intensive therapy both yielded significantly reduced upper extremity motor impairment (Fugl-Meyer Assessment, FMA) compared to the usual care group; however, there was no difference between the robot-assisted and intensive comparison groups [3]. The LEAPS trial compared a group initiating locomotor training (utilizing body-weight support and a treadmill) at 2 months after stroke to those receiving a home exercise program (HEP) at 2 months and those initiating locomotor training at 6 months after stroke [4]. There was no significant difference between any of the groups in the percentage of participants who progressed to a higher speed-defined functional category, nor was there a difference in self-selected walking speed at 12 months [5▪▪]. At the 6-month assessment, both early locomotor training and HEP groups walked significantly faster than those initiating locomotor training at 6 months who had received usual care to that point [5▪▪].

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These results demonstrate a lack of superiority of experimental interventions over control groups designed to match intensity and structured progression of the therapeutic activity. Additionally, all three interventions demonstrate clear superiority over control groups consisting of usual care, indicating that intensity of the activity may be a critical factor contributing to rehabilitation success. However, there is more to learn. The primary outcome measures used in these three RCTs reflected functional endpoints (e.g. WMFT and gait speed) and therefore provide an overall view of ability. However, the outcome measures did not reveal how neuroplastic mechanisms or other mechanistic factors may have contributed to the treatment response. Therefore, it is unclear if the outcomes were associated with recovery of premorbid behaviors or acquisition of compensatory behaviors.

Future rehabilitation RCTs require a better understanding of the interaction of interventions and neurophysiological recovery in order to target interventions at specific neurophysiologic substrates, develop a more clear understanding of the impact of intervention parameters (e.g. dose, intensity), and advance discussions regarding optimal ways to partner medical and rehabilitation interventions in order to improve outcomes. The purpose of this review is to establish a theoretical framework by which new therapies may be developed, analyze the critical ingredients to successful rehabilitation, and provide a contemporary example of how these considerations are guiding development of a novel therapy.


Current rehabilitation theories are based on an understanding of how behavioral activities and experiences modulate neural plasticity and promote ‘recovery’. However, the term ‘recovery’ is confusing because it is used to describe both the amelioration of neural deficits and functional improvements [6]. The two are intertwined, however, because functional recovery is likely associated with neurophysiological recovery. This recovery involves three acute overlapping phases: reversal of diaschisis (decreased function of remote brain regions due to hypometabolism, neurovascular uncoupling, and aberrant neurotransmission) [7], cell genesis, and repair; altering existing neuronal pathways; and formation of new neural connections [8]. These neurophysiologic events are due to varying degrees of spontaneous recovery [9], experience-dependent motor training [10], increased involvement of the contralateral hemisphere [11,12], and axonal remodeling of the corticospinal system [13]. The processes underlying functional recovery after stroke are also involved in normal learning, and subsequent recovery of functional movements is essentially a relearning process, the potential for which extends many years after stroke [14].

The critical question therefore, is how to promote these processes in patients poststroke. We suggest that continued advancements in the field require a more clear understanding of a rehabilitation program's ‘active ingredients’ [15]. Active ingredients are the fundamental parameters, or essential aspects, of a rehabilitation program that drive beneficial neural plasticity leading to positive functional outcomes. Two important active ingredients are intensity and progression.


The three RCTs described above demonstrate that intervention providing intense therapy is more beneficial than usual care. However, the term ‘intensity’ has several different connotations. First, in the locomotor literature, intensity has been defined as the ‘amount of work per unit time’ (i.e. rate of work or power) [16▪] and is separate from frequency of training sessions and repetitions of step count. Intensity of training leads to increased neuromotor and aerobic demands requiring improved cardiorespiratory output to meet this demand [16▪]. Critical to developing this rate of work is the speed of walking, and training at a faster than self-selected speed is a critical factor in the attainment of faster over-ground walking speeds [5▪▪,17,18]. A 2008 critical review of locomotor rehabilitation interventions demonstrated that intensity of walking was a ‘common denominator’ of interventions that facilitated functional recovery, and was more important for recovery than the treatment mode [19].

Deriving such cardiovascular benefits during task-practice upper extremity rehabilitation sessions [e.g. constraint-induced movement therapy (CIMT)] may be difficult because reaching training involves discrete, open-chain movements performed at sub-maximal levels. Therefore in the upper extremity literature intensity typically refers to the duration of therapy. However, an emerging, more precise understanding of intensity has to do with the movement repetitions per session [20]. The importance of upper limb movement repetition as a driver of beneficial neural plasticity is strongly supported by contemporary neuroscience. Extensive movement repetition alters cortical motor representations, such that the territory representing the repeated movement expands and shows dendritic branching, synaptic growth, and increased synaptic response. In contrast, territories representing nonrepeated movements do not expand and may even shrink [21–25]. In rat and primate models of cortical stroke, upper limb motor representation loss was prevented and cortical reorganization was promoted with repetitive limb use [26,27]. Human poststroke imaging studies suggest that repetitive limb use increases the excitability of the damaged hemisphere and contributes to the restoration of balanced inter-hemispheric facilitation and/or inhibition [28]. Moreover, repetitive attempts to achieve task goals engage a problem-solving process which provides implicit feedback to learning-based neural plasticity [29,30]. Although repetition is critical for producing lasting changes in motor system networks, the optimal number of repetitions required for learning is unknown. In animal models, neural adaptations are observed after hundreds of movement repetitions; however, in human rehabilitation studies this number may be far less. Achieving repetition may be easier in gait retraining when compared to upper extremity reach retraining because of the cyclical nature of lower extremity movements during gait.


Although movement repetition is critical, research conducted in animal models suggests that the greatest neuroplastic changes are associated with the generation and repetition of novel movements rather than repetition of already known movements. In a classic series of studies, Nudo et al.[23] showed that training a primate to retrieve a food pellet using a novel pattern of forelimb and digit coordination increased the size of cortical representational areas for the trained limb. In contrast, repeating an already known limb coordination pattern did not alter the movement representational area [31]. In a rat model of stroke, Kleim et al.[32] demonstrated that systematically progressing the difficulty of a reaching task resulted in reorganization of movement representations within the motor cortex, whereas repeating the task without progressive difficulty had no effect on cortical motor maps. Findings such as these suggest motor reorganization is dependent on the content of the practice sessions [31]. Progression requires monitoring of patient responses to gauge if the intervention is of appropriate intensity, and a well defined hierarchy of task requirements so that when one level is attained difficulty may be increased to maintain the appropriate challenge (Fig. 1).

Theoretical model for recovery after stroke. Our theoretical model is based on maximizing neuroplastic potential of the central nervous system to lead to functional and neurophysiologic recovery. This adaptive neuroplasticity is modulated by combining neurorehabilitation strategies with medical treatments informed by the active ingredients gleaned from recent randomized controlled trials in stroke. tDCS, transcranial direct current stimulation.


An example of how medical interventions targeting the augmentation of neural plasticity combine with experience-dependent interventions in early phase I and phase II trials is in the area of noninvasive brain stimulation. Recently, transcranial direct current stimulation (tDCS) presents as a relatively inexpensive, easy to use and well tolerated method of transferring a small current (1–2 milliamp) of electricity into cortical tissue. Current passes between the anode and the cathode and acts as either excitatory (anode over the motor cortex) or inhibitory (cathode over the motor cortex) [33]. Increased excitation is most likely due to the modulation of cellular N-methyl-D-aspartate receptors [34,35] and augmentation of synaptic plasticity that requires the presence of brain-derived neurotrophic factor [36]. This excitation also modulates regional blood-flow changes during voluntary movement; in particular activation-induced change in regional cerebral blood flow in M1 is significantly lower on the cathodal than the anodal side when compared with sham stimulation [37]. Improvements in motor control are observed in patients after stroke when the stimulus is excitatory to the lesioned hemisphere [38] and when inhibitory stimulation is applied to the contralateral hemisphere [39], and both techniques elicit improved motor control compared to sham stimulation [40]. In addition, tDCS augments skill acquisition when repeated over a 5-day period through an effect of consolidation of offline skill gains which are superior to controls 3 months after stimulation [41].

Recent evidence suggests that ‘positive, reproducible effects on motor learning’ are achieved by combining tDCS with therapeutic interventions, perhaps because tDCS modulates the motor cortical transmission system in a way consistent with motor rehabilitation [42]. A recent study demonstrated that 5 days of tDCS combined with occupational therapy and physical therapy significantly improved performance on the FMA and WMFT relative to therapy combined with sham stimulation, and performance remained superior at 1 week post-test [43]. Early trials indicate that tDCS and rehabilitation yield improvements in motor control that exceed those seen in rehabilitation alone [44▪]. In those with chronic stroke receiving both CIMT and tDCS, greater gains were achieved in the Jebsen Taylor Hand Function Test, handgrip strength, the MAL, and the FMA than in those who underwent CIMT alone [45▪]. Furthermore, decreases in transcallosal inhibition and increases in corticospinal excitability were only seen in those who received both interventions [45▪]. When partnered with locomotor rehabilitation interventions, tDCS likely improves mechanisms of learning, as illustrated by the fact that anodal tDCS over the cerebellum increases spatial adaptation of locomotor tasks [46]. Whereas investigations of tDCS and other noninvasive brain stimulation interventions are in the very early stages, a theoretical foundation has been well formulated by combining interventions aiming to increase neural excitation and neuroplastic recovery with the rehabilitation interventions designed to further drive neuroplasticity and promote motor learning. In doing so, these trials are maximizing the opportunity for success.


Unfortunately task-practice programs may not be relevant across all levels of stroke severity. For example, the majority of poststroke patients (>75%) do not meet the minimum motor ability required for participation in CIMT, a type of task-oriented upper extremity rehabilitation, because they do not have requisite wrist and finger voluntary movements hence are unable to practice tasks involving reach, grasp, and manipulation of objects [47]. More severely impaired patients may require a unique therapeutic focus to develop movement capacity. Platz and coworkers studied an impairment-oriented training approach that specifically addressed body function impairments relevant to functional task performance [48,49].

The neurophysiologic factors associated with recovery are more robust in the acute stage after stroke, and medical interventions are increasingly focused on this period of optimized neuroplastic potential [50]. The goal of optimal recovery is to partner emerging medical interventions with rehabilitation during the poststroke phases when maximum plasticity and recovery potential may be appreciated. This partnership between medical interventions and experience-dependent learning requires joint knowledge of the principles of plasticity and the principles of rehabilitation. However, rehabilitation interventions, which are the behavioral engine required to drive learning, have primarily been conducted in the chronic phase, and to date the optimal therapy and parameters of training have not been identified for the acute population. Structured, repeatable, and theoretically sound programs must be developed to drive desired neuroplastic change, but in the absence of phase II and phase III data in acute stroke, best evidence must be gleaned from what we know drives functional change in the chronic population. The literature suggests that lessons learned in a chronic population cannot always be directly applied to an acute population. For example, whereas results from the EXCITE trial suggest that intensive therapy is beneficial in chronic poststroke patients, results from a study of constraint therapy for patients enrolled at less than 1 month poststroke suggest that less intense therapy may be optimal [51].

Whereas CIMT and locomotor training are promising interventions to promote motor recovery, some have criticized the practicality of the methodology and the limitations of the standardized model of CIMT for use in the clinic [52–55]. Moreover, it may be difficult for participants to comply with a research-based intervention due to the demands of daily life. For example, approximately 1/3 of participants comply with the schedule of home constraint in CIMT studies [52,56]. Therefore, alternatives to research-based protocols with modifications for clinical implementation should be investigated.


Whereas RCTs remain the gold standard for defining efficacy of rehabilitation interventions, future rehabilitation RCTs require a more detailed understanding of the interaction of interventions and neurophysiological recovery. An understanding of both neuroplasticity and potential recovery is critical in the design of rehabilitation interventions in order to select the most appropriate intervention for an individual based on: available neurophysiologic substrate that may be best targeted [50]; the parameters of the intervention (including intensity and progression of the intervention); and the optimal way to partner medical and rehabilitation interventions in order to facilitate recovery. Medical treatments designed to facilitate neurophysiological recovery, such as delivery of stem cells, growth factors, and small molecules, which are currently at a preclinical stage [57], will need to partner with the behavioral engines of rehabilitation to promote learning and maximize recovery at both the neurophysiologic and functional levels. As interventions progress to more acute stages where the rate of recovery is accelerated, the selection of the most appropriate rehabilitation intervention will need to be guided by a theoretical framework based on the active ingredients of rehabilitation as gleaned from the chronic stroke literature. Issues related to the severity of the stroke, comorbidities during the acute phase, and the pragmatics of the delivery of rehabilitation will need to be carefully explored.


This work was supported in part by the Ralph H. Johnson Veterans Affairs Medical Center and the Department of Veterans Affairs Office of Research and Development, Rehabilitation Research and Development; Career Development-1 Award (B-7177M), PI: M.G. Bowden; Career Development-2 Award (B-6332W), PI: M.L. Woodbury.

Conflicts of interest

None of the authors has a conflict of interest with this manuscript. Dr Duncan has been involved with private industry, notably she has received honorarium for consulting with Allergan to develop a post stroke disability measures and with Glaxo to design phase 2 trials to enhance stroke recovery. The authors are supported by the following grants: South Carolina Clinical and Translational Research Institute Discovery Grant; American Heart Association Innovative Research Grant.


Papers of particular interest, published within the annual period of review, have been highlighted as:

  • ▪ of special interest
  • ▪▪ of outstanding interest

Additional references related to this topic can also be found in the Current World Literature section in this issue (p. 104).


1. Lloyd-Jones D, Adams R, Carnethon M, et al. Heart disease and stroke statistics: 2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 2009; 119:e21–e181.
2. Wolf SL, Winstein CJ, Miller JP, et al. Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. J Am Med Assoc 2006; 296:2095–2104.
3. Lo AC, Guarino PD, Richards LG, et al. Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med 2010; 362:1772–1783.
4. Duncan PW, Sullivan KJ, Behrman AL, et al. Protocol for the Locomotor Experience Applied Poststroke (LEAPS) trial: a randomized controlled trial. BMC Neurol 2007; 7:39.
5▪▪. Duncan PW, Sullivan KJ, Behrman AL, et al. Body-weight-supported treadmill rehabilitation after stroke. N Engl J Med 2011; 364:2026–2036.

This phase III randomized, control trial examined 408 individuals with sub-acute stroke and found no superiority of body-weight-supported treadmill rehabilitation, applied either at two months or six months after stroke, compared to a progressive home exercise program focusing on flexibility, balance, and strength.

6. Levin MF, Kleim JA, Wolf SL. What do motor ‘recovery’ and ‘compensation’ mean in patients following stroke? Neurorehabil Neural Repair 2009; 23:313–319.
7. Wieloch T, Nikolich K. Mechanisms of neural plasticity following brain injury. Curr Opin Neurobiol 2006; 16:258–264.
8. Pekna M, Pekny M, Nilsson M. Modulation of neural plasticity as a basis for stroke rehabilitation. Stroke 2012; 43:2819–2828.
9. Duncan PW, Goldstein LB, Matchar D, et al. Measurement of motor recovery after stroke. Outcome assessment and sample size requirements. Stroke 1992; 23:1084–1089.
10. Richards LG, Stewart KC, Woodbury ML, et al. Movement-dependent stroke recovery: a systematic review and meta-analysis of TMS and fMRI evidence. Neuropsychologia 2008; 46:3–11.
11. Zemke AC, Heagerty PJ, Lee C, Cramer SC. Motor cortex organization after stroke is related to side of stroke and level of recovery. Stroke 2003; 34:e23–e28.
12. Enzinger C, Johansen-Berg H, Dawes H, et al. Functional MRI correlates of lower limb function in stroke victims with gait impairment. Stroke 2008; 39:1507–1513.
13. Liu Z, Zhang RL, Li Y, et al. Remodeling of the corticospinal innervation and spontaneous behavioral recovery after ischemic stroke in adult mice. Stroke 2009; 40:2546–2551.
14. Warraich Z, Kleim JA. Neural plasticity: the biological substrate for neurorehabilitation. PM R 2010; 2 (12 Suppl 2):S208–S219.
15. Whyte J, Hart T. It's more than a black box; it's a Russian doll: defining rehabilitation treatments. Am J Phys Med Rehabil 2003; 82:639–652.
16▪. Hornby TG, Straube DS, Kinnaird CR, et al. Importance of specificity, amount, and intensity of locomotor training to improve ambulatory function in patients poststroke. Topics Stroke Rehabil 2011; 18:293–307.

This study describes the parameters of walking practice that can promote plasticity of neuromuscular and cardiopulmonary systems as they relate to locomotor rehabilitation after stroke.

17. Macko RF, Ivey FM, Forrester LW, et al. Treadmill exercise rehabilitation improves ambulatory function and cardiovascular fitness in patients with chronic stroke: a randomized, controlled trial. Stroke 2005; 36:2206–2211.
18. Pohl M, Mehrholz J, Ritschel C, Ruckriem S. Speed-dependent treadmill training in ambulatory hemiparetic stroke patients: a randomized controlled trial. Stroke 2002; 33:553–558.
19. Dickstein R. Rehabilitation of gait speed after stroke: a critical review of intervention approaches. Neurorehabil Neural Repair 2008; 22:649–660.
20. Birkenmeier RL, Prager EM, Lang CE. Translating animal doses of task-specific training to people with chronic stroke in 1-hour therapy sessions: a proof-of-concept study. Neurorehabil Neural Repair 2010; 24:620–635.
21. Cohen LG, Brasil-Neto JP, Pascual-Leone A, Hallett M. Plasticity of cortical motor output organization following deafferentation, cerebral lesions, and skill acquisition. Adv Neurol 1993; 63:187–200.
22. Liepert J, Bauder H, Wolfgang HR, et al. Treatment-induced cortical reorganization after stroke in humans. Stroke 2000; 31:1210–1216.
23. Nudo RJ, Milliken GW, Jenkins WM, Merzenich MM. Use-dependent alterations of movement representations in primary motor cortex of adult squirrel monkeys. J Neurosci 1996; 16:785–807.
24. Jones TA, Kleim JA, Greenough WT. Synaptogenesis and dendritic growth in the cortex opposite unilateral sensorimotor cortex damage in adult rats: a quantitative electron microscopic examination. Brain Res 1996; 733:142–148.
25. Kleim JA, Swain RA, Czerlanis CM, et al. Learning-dependent dendritic hypertrophy of cerebellar stellate cells: plasticity of local circuit neurons. Neurobiol Learn Memory 1997; 67:29–33.
26. Nudo RJ, Plautz EJ, Frost SB. Role of adaptive plasticity in recovery of function after damage to motor cortex. Muscle Nerve 2001; 24:1000–1019.
27. Kleim JA, Jones TA, Schallert T. Motor enrichment and the induction of plasticity before or after brain injury. Neurochem Res 2003; 28:1757–1769.
28. Harris-Love ML, Morton SM, Perez MA, Cohen LG. Mechanisms of short-term training-induced reaching improvement in severely hemiparetic stroke patients: a TMS study. Neurorehabil Neural Repair 2011; 25:398–411.
29. Timmermans AA, Spooren AI, Kingma H, Seelen HA. Influence of task-oriented training content on skilled arm-hand performance in stroke: a systematic review. Neurorehabil Neural Repair 2010; 24:858–870.
30. Guadagnoli MA, Lee TD. Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning. J Motor Behav 2004; 36:212–224.
31. Plautz EJ, Milliken GW, Nudo RJ. Effects of repetitive motor training on movement representations in adult squirrel monkeys: role of use versus learning. Neurobiol Learn Memory 2000; 74:27–55.
32. Kleim JA, Barbay S, Nudo RJ. Functional reorganization of the rat motor cortex following motor skill learning. J Neurophysiol 1998; 80:3321–3325.
33. Schlaug G, Renga V, Nair D. Transcranial direct current stimulation in stroke recovery. Arch Neurol 2008; 65:1571–1576.
34. Liebetanz D, Nitsche MA, Tergau F, Paulus W. Pharmacological approach to the mechanisms of transcranial DC-stimulation-induced after-effects of human motor cortex excitability. Brain 2002; 125:2238–2247.
35. Nitsche MA, Liebetanz D, Antal A, et al. Modulation of cortical excitability by weak direct current stimulation: technical, safety and functional aspects. Suppl Clin Neurophysiol 2003; 56:255–276.
36. Fritsch B, Reis J, Martinowich K, et al. Direct current stimulation promotes BDNF-dependent synaptic plasticity: potential implications for motor learning. Neuron 2010; 66:198–204.
37. Paquette C, Sidel M, Radinska BA, et al. Bilateral transcranial direct current stimulation modulates activation-induced regional blood flow changes during voluntary movement. J Cereb Blood Flow Metab 2011; 31:2086–2095.
38. Hummel F, Cohen LG. Improvement of motor function with noninvasive cortical stimulation in a patient with chronic stroke. Neurorehabil Neural Repair 2005; 19:14–19.
39. Fregni F, Boggio PS, Mansur CG, et al. Transcranial direct current stimulation of the unaffected hemisphere in stroke patients. Neuroreport 2005; 16:1551–1555.
40. Boggio PS, Nunes A, Rigonatti SP, et al. Repeated sessions of noninvasive brain DC stimulation is associated with motor function improvement in stroke patients. Restorative Neurol Neurosci 2007; 25:123–129.
41. Reis J, Schambra HM, Cohen LG, et al. Noninvasive cortical stimulation enhances motor skill acquisition over multiple days through an effect on consolidation. Proc Natl Acad Sci U S A 2009; 106:1590–1595.
42. Reis J, Fritsch B. Modulation of motor performance and motor learning by transcranial direct current stimulation. Curr Opin Neurol 2011; 24:590–596.
43. Kim DY, Lim JY, Kang EK, et al. Effect of transcranial direct current stimulation on motor recovery in patients with subacute stroke. Am J Phys Med Rehabil 2010; 89:879–886.
44▪. Nair DG, Renga V, Lindenberg R, et al. Optimizing recovery potential through simultaneous occupational therapy and noninvasive brain-stimulation using tDCS. Restorative Neurol Neurosci 2011; 29:411–420.

This randomized, double-blind, sham-controlled study of chronic stroke patients demonstrated improved motor control in those undergoing occupational therapy with cathodal (inhibitory to the nonlesioned hemisphere) tDCS compared to those combining occupational therapy with sham stimulation. Improvements in motor score were inversely related to fMRI activation in the stimulated region.

45▪. Bolognini N, Vallar G, Casati C, et al. Neurophysiological and behavioral effects of tDCS combined with constraint-induced movement therapy in poststroke patients. Neurorehabil Neural Repair 2011; 25:819–829.

This study assed the effect of combining tDCS with a standardized upper extremity rehabiliation program and found improvements in motor function when therapies were combined. Neurophysiological changes correlated with behavioral gains.

46. Jayaram G, Tang B, Pallegadda R, et al. Modulating locomotor adaptation with cerebellar stimulation. J Neurophysiol 2012; 107:2950–2957.
47. Wolf SL, Binder-MacLeod SA. Electromyographic biofeedback applications to the hemiplegic patient. Changes in upper extremity neuromuscular and functional status. Phys Ther 1983; 63:1393–1403.
48. Platz T. Impairment-oriented training (IOT): scientific concept and evidence-based treatment strategies. Restor Neurol Neurosci 2004; 22:301–315.
49. Platz T, van Kaick S, Mehrholz J, et al. Best conventional therapy versus modular impairment-oriented training for arm paresis after stroke: a single-blind, multicenter randomized controlled trial. Neurorehabil Neural Repair 2009; 23:706–716.
50. Cramer SC. Repairing the human brain after stroke. II. Restorative therapies. Ann Neurol 2008; 63:549–560.
51. Dromerick AW, Lang CE, Birkenmeier RL, et al. Very Early Constraint-Induced Movement during Stroke Rehabilitation (VECTORS): a single-center RCT. Neurology 2009; 73:195–201.
52. Daniel L, Howard W, Braun D, Page SJ. Opinions of constraint-induced movement therapy among therapists in southwestern Ohio. Top Stroke Rehabil 2012; 19:268–275.
53. Wolf SL. Revisiting constraint-induced movement therapy: are we too smitten with the mitten? Is all nonuse ‘learned’? and other quandaries. Phys Ther 2007; 87:1212–1223.
54. Reiss AP, Wolf SL, Hammel EA, et al. Constraint-induced movement therapy (CIMT): current perspectives and future directions. Stroke Res Treatment 2012; 2012:159391.
55. Taub E, Lum PS, Hardin P, et al. AutoCITE: automated delivery of CI therapy with reduced effort by therapists. Stroke 2005; 36:1301–1304.
56. Page SJ, Levine P, Sisto S, et al. Stroke patients’ and therapists’ opinions of constraint-induced movement therapy. Clin Rehabil 2002; 16:55–60.
57. Chollet F, Albucher JF. Strategies to augment recovery after stroke. Curr Treat Options Neurol 2012; 14:531–540.

neuroplasticity; recovery; rehabilitation; stroke

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