Posts Tagged chronic stroke

[ARTICLE] Influence of physician empathy on the outcome of botulinum toxin treatment for upper limb spasticity in patients with chronic stroke: A cohort study – Full Text


Objective: To examine the relationship between patient-rated physician empathy and outcome of botulinum toxin treatment for post-stroke upper limb spasticity.

Design: Cohort study.

Subjects: Twenty chronic stroke patients with upper limb spasticity.

Methods: All patients received incobotulinumtoxinA injection in at least one muscle for each of the following patterns: flexed elbow, flexed wrist and clenched fist. Each treatment was performed by 1 of 5 physiatrists with equivalent clinical experience. Patient-rated physician empathy was quantified with the Consultation and Relational Empathy Measure immediately after botulinum toxin treatment. Patients were evaluated before and at 4 weeks after botulinum toxin treatment by means of the following outcome measures: Modified Ashworth Scale; Wolf Motor Function Test; Disability Assessment Scale; Goal Attainment Scaling.

Results: Ordinal regression analysis showed a significant influence of patient-rated physician empathy (independent variable) on the outcome (dependent variables) of botulinum toxin treatment at 4 weeks after injection, as measured by Goal Attainment Scaling (p < 0.001).

Conclusion: These findings support the hypothesis that patient-rated physician empathy may influence the outcome of botulinum toxin treatment in chronic stroke patients with upper limb spasticity as measured by Goal Attainment Scaling.


Stroke is a leading cause of adult disability (1, 2). Damage to the descending tracts and sensory-motor networks results in the positive and negative signs of the upper motor neurone syndrome (UMNS) (1–3). The upper limb is commonly involved after stroke, with up to 69% of patients having arm weakness on admission to hospital (4). Recovery of upper limb function has been found to correlate with the degree of initial paresis and its topical distribution according to the cortico-motoneuronal representation of arm movements (5–9).

Spasticity is a main feature of UMNS. It is defined as a state of increased muscle tone with exaggerated reflexes characterized by a velocity-dependent increase in resistance to passive movement (10). Upper limb spasticity has been found to be associated with reduced arm function, low levels of independence and high burden of direct care costs during the first year post-stroke (11). It affects nearly half of patients with initial impaired arm function, with a prevalence varying from 17% to 38% of all patients at one year post-stroke (11). Up to 13% of patients with stroke need some form of spasticity treatment (drug therapy, physical therapy or other rehabilitation approaches) within 6–12 months post-onset (11, 12). Botulinum toxin type A (BoNT-A) has been proven safe and effective for reducing upper limb spasticity and improving arm passive function in adult patients (13, 14). While current literature reports highly patient-specific potential gains in function after BoNT-A treatment, there is inadequate evidence to determine the efficacy of BoNT-A in improving active function associated with adult upper limb spasticity (13).

Empathy refers to the ability to understand and share the feelings, thoughts or attitudes of another person (15). It is an essential component of the physician-patient relationship and a key dimension of patient-centred care (15, 16). This is even more important in rehabilitation medicine, where persons with disabilities often report encountering attitudinal and environmental barriers when trying to obtain rehabilitative care and express the need for better communication with their healthcare providers (17).

To the best of our knowledge, no previous research has investigated the influence of physician empathy on patient outcome after spasticity treatment. The aim of this study was to examine the relationship between patient-rated physician empathy and clinical outcome of BoNT-A treatment for upper limb spasticity due to chronic stroke. […]

Continue —> Journal of Rehabilitation Medicine – Influence of physician empathy on the outcome of botulinum toxin treatment for upper limb spasticity in patients with chronic stroke: A cohort study – HTML


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[ARTICLE] Effects of repeated vibratory stimulation of wrist and elbow flexors on hand dexterity, strength, and sensory function in patients with chronic stroke: a pilot study – Full Text PDF


[Purpose] The aim of this study was to investigate the effects of repeated vibratory stimulation to muscles related to hand functions on dexterity, strength, and sensory function in patients with chronic stroke.

[Subjects and Methods] A total of 10 stroke patients with hemiplegia participated in this study. They were divided into two groups: a) Experimental and b) Control, with five randomly selected subjects for each group. The experimental group received vibratory stimulation, while the control group received the traditional physical therapy. Both interventions were performed for 30 minutes each session, three times a week for four weeks.

[Results] There was a significant within-group improvement in the box and block test results in both groups for dexterity. Grip strength improved in both groups but the improvement was not statistically significant.

[Conclusion] The vibratory stimulation activated the biceps brachii and flexor carpi radialis, which increased dexterity to grasp and lift the box and block from the surface. Therefore, repeated vibratory stimulation to muscles related to hand functions improved hand dexterity equality to the traditional physical therapy in patients with chronic stroke.

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[ARTICLE] The impact of large structural brain changes in chronic stroke patients on the electric field caused by transcranial brain stimulation – Full Text


Transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (TDCS) are two types of non-invasive transcranial brain stimulation (TBS). They are useful tools for stroke research and may be potential adjunct therapies for functional recovery. However, stroke often causes large cerebral lesions, which are commonly accompanied by a secondary enlargement of the ventricles and atrophy. These structural alterations substantially change the conductivity distribution inside the head, which may have potentially important consequences for both brain stimulation methods. We therefore aimed to characterize the impact of these changes on the spatial distribution of the electric field generated by both TBS methods. In addition to confirming the safety of TBS in the presence of large stroke-related structural changes, our aim was to clarify whether targeted stimulation is still possible. Realistic head models containing large cortical and subcortical stroke lesions in the right parietal cortex were created using MR images of two patients. For TMS, the electric field of a double coil was simulated using the finite-element method. Systematic variations of the coil position relative to the lesion were tested. For TDCS, the finite-element method was used to simulate a standard approach with two electrode pads, and the position of one electrode was systematically varied. For both TMS and TDCS, the lesion caused electric field “hot spots” in the cortex. However, these maxima were not substantially stronger than those seen in a healthy control. The electric field pattern induced by TMS was not substantially changed by the lesions. However, the average field strength generated by TDCS was substantially decreased. This effect occurred for both head models and even when both electrodes were distant to the lesion, caused by increased current shunting through the lesion and enlarged ventricles. Judging from the similar peak field strengths compared to the healthy control, both TBS methods are safe in patients with large brain lesions (in practice, however, additional factors such as potentially lowered thresholds for seizure-induction have to be considered). Focused stimulation by TMS seems to be possible, but standard tDCS protocols appear to be less efficient than they are in healthy subjects, strongly suggesting that tDCS studies in this population might benefit from individualized treatment planning based on realistic field calculations.

1. Introduction

Transcranial brain stimulation (TBS) methods are useful tools to induce and to quantify neural plasticity, and as such are increasingly being used in stroke research and as potential adjunct therapies in stroke rehabilitation. The cerebral lesions caused by stroke result in persisting physical or cognitive impairments in around 50% of all survivors (Di Carlo, 2008Leys et al., 2005 ;  Young and Forster, 2007), meaning that new therapies are urgently needed. Transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (TDCS) are two TBS approaches which are being increasingly utilised in stroke research. Single-pulse TMS combined with electromyography (EMG) or electroencephalography (EEG) can be used to assess cortical excitability, for example to index the functional state of the perilesional tissue. The neuromodulatory effects of repetitive TMS protocols (rTMS) may, in association with neuro-rehabilitative treatments, enhance motor recovery (Liew et al., 2014). Similar results have been demonstrated for TDCS. For example, anodal TDCS of the hand area in the primary motor cortex has been shown to improve motor performance of the affected hand (Allman et al., 2016Hummel et al., 2005 ;  Stagg et al., 2012) and anodal TDCS applied over the left frontal cortex enhanced naming accuracy in patients with aphasia (Baker et al., 2010). However, not all studies report a clear-cut positive impact of TBS on the stroke symptoms. Rather, the observed effects are often weak and not consistent across patients, demonstrating the need for a better understanding of the underlying biophysical and physiological mechanisms.

Compared with healthy subjects, several factors might contribute to a change in the neuroplastic response to TBS protocols in stroke patients, including changes in the neural responsiveness to the applied electric fields, as well as differences in the underlying physiology and metabolism (Blicher et al., 2009Blicher et al., 2015 ;  O’Shea et al., 2014). When the lesions are large, they may also substantially alter the generated electric field pattern, meaning that the assumptions on spatial targeting as derived from biophysical modelling and physiological experiments in healthy subjects might no longer be valid. Stroke lesions are often accompanied by secondary macrostructural changes such as cortical atrophy and enlargement of the ventricles (e.g., Skriver et al., 1990), which may further contribute to changes in the field pattern. In addition, the safety of TBS in patients with large lesions needs to be further clarified, as it is possible that the lesions might cause stimulation “hot spots”. In chronic patients, the stroke cavity becomes filled with corticospinal fluid (CSF), which might cause shunting of current, funnelling the generated currents towards the surrounding brain tissue and potentially causing localized areas of dangerously high field strengths.

Here, using finite-element calculations and individual head models derived from structural MR images, we focused on the impact of a large cortical lesion in chronic stroke on the electric field pattern generated in the brain by TMS and TDCS, respectively. Firstly, we assessed the safety of the stimulation by comparing the achieved field strengths with those estimated for a healthy control. Secondly, we tested how reliably we can accurately target the perilesional tissue, often the desired target for TBS, as reorganisation here is thought to underpin functional recovery (Kwakkel et al., 2004). Finally, we were also interested to see whether any observed changes in the field pattern were specific to a patient with a cortical lesion (which is connected to the CSF layer underneath the skull), or whether similar effects might occur in case of large chronic subcortical lesion. We therefore additionally tested the field distribution in a head model of a patient with a subcortical lesion occurring at a similar position as the cortical lesion.

2. Materials and methods

2.1. Selection of patients

The aim of this study was to characterize the effect of a large chronic cortical stroke lesion on the electric field distribution generated by TBS, and to compare the effects of this lesion to that caused by a large chronic subcortical lesion. MR images of several patients were visually inspected to select two datasets, which had a cortical [P01] and subcortical lesion [P02], respectively, within the same gross anatomical regions.

Patient P01 was a 36 year old female with episodic migraine; she was admitted with left hemiparalysis, fascial palsy and a total NIHSS score of 16 due to a right ICI/MCI occlusion. She was treated with IV thrombolysis and thrombectomy and recanalization was achieved 5 h after symptom onset. One year post-stroke she still suffered from motor impairment (Wolf Motor Function Test [WMFT] score of 30) and was scanned as part of a clinical study investigating the effect of combining Constraint-Induced Movement Therapy and tDCS (Figlewski et al., 2017; Clinical trials NCT01983319, Regional Ethics approval: 1-10-72-268-13). The structural scans showed a cortical lesion in the right parietal lobe (Fig. 1A). The lesion volume, delineated manually with reference to T1- and T2-weighted imaging, was 26,415 mm3.

Fig. 1:Fig. 1.

A) Coronal view of patient P01 with a cortical lesion in the right hemisphere. The top shows the T1-weighted MR image and the bottom the reconstructed head mesh. The view was chosen to include the lesion centre. The lesion is marked by red dashed circles. B) Corresponding view of patient P02 with a large subcortical lesion at a similar location in the right hemisphere. C) Corresponding view of the data set of the healthy control. D) The coil and electrode positions were systematically moved along two directions that were approximately perpendicular to each other. Five positions were manually placed every 2 cm in posterior – anterior direction symmetrically around the centre of the cortical lesion. The same was repeated along the lateral – medial direction. Both lines share the same centre position above the lesion, resulting in 9 positions in total. E) At each position, two coil orientations were tested which resulted in a current flow underneath the coil centre from anterior to posterior (top) and from lateral to medial, respectively (bottom). F) For each position of the yellow “stimulating” electrode, two positions of the blue return electrode were tested. First, the contralateral equivalent of the electrode position above the centre of the cortical lesion was used (top). In addition, a position on the contralateral forehead was tested (bottom).

Patient P02 was a 44 year old female. She woke up with a left hemiparesis and an acute CT scan showed no bleeding. No IV thrombolysis was given due to uncertain timing of symptom onset. An embolic stroke was suspect due to a patent foramen ovale, which was subsequently closed. She was scanned with MRI 9 months post stroke showing a right subcortical infarct, at which time she had a WMFT score of 8. The lesion volume, delineated as for P01, was 56,010 mm3. She was scanned as part of a clinical study investigating the effect of combining tDCS with daily motor training (Allman et al., 2016; Regional Ethics approval: Oxfordshire REC A; 10/H0604/98)….

Continue —> The impact of large structural brain changes in chronic stroke patients on the electric field caused by transcranial brain stimulation

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[Abstract] Experience of an upper limb training program with a non-immersive virtual reality system in patients after stroke: a qualitative study



The YouGrabber (YG) is a new virtual reality training system that focuses on unilateral and bimanual activities. This nested study was part of a larger multicentre randomised controlled trial and explored experiences of people with chronic stroke during a 4 week intensive upper limb training with YG.


A qualitative design using semi-structured, face-to-face interviews. A phenomenological descriptive approach was used, with data coded, categorized and summarized using a thematic analysis. Topics investigated included: the experience of YG training, perceived impact of YG training on arm function, and the role of the treating therapist.


Five people were interviewed (1 female, age range 55-75yrs, 1-6yrs post-stroke). Seven main themes were identified: (1) general experience, (2) expectations, (3) feedback, (4) arm function, (5) physiotherapist’s role, (6) fatigue, (7) motivation. Key experiences reported included feelings of motivation and satisfaction, with positive factors identified as challenge, competition, fun and effort. The YG training appeared to trigger greater effort, however fatigue was experienced at the end of the training. Overall, patients described positive changes in upper limb motor function and activity level, e.g. automatic arm use. While the opportunity for self-practice was appreciated, input from the therapist at the start of the intervention was deemed important for safety and confidence.


Reported experiences were mostly positive and the participants were motivated to practice intensively. They enjoyed the challenging component of the games.

Source: Experience of an upper limb training program with a non-immersive virtual reality system in patients after stroke: a qualitative study – Physiotherapy

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[Abstract] Quantitative EEG for Predicting Upper-limb Motor Recovery in Chronic Stroke Robot-assisted Rehabilitation – IEEE Xplore Document


Stroke is a leading cause for adult disability, which in many cases causes motor deficits. Despite the developments in motor rehabilitation techniques, recovery of upper limb functions after stroke is limited and heterogeneous in terms of outcomes, and knowledge of important factors that may affect the outcome of the therapy is necessary to make a reasonable prediction for individual patients.
In this study, we assessed the relationship between quantitative electroencephalographic (QEEG) measures and the motor outcome in chronic stroke patients that underwent a robot-assisted rehabilitation program to evaluate the utility of QEEG indices to predict motor recovery. For this purpose, we acquired resting-state electroencephalographic signals from which the Power Ratio Index (PRI), Delta/Alpha Ratio (DAR), and Brain Symmetry Index (BSI) were calculated. The outcome of the motor rehabilitation was evaluated using upper-limb section of the Fugl-Meyer Assessment.
We found that PRI was significantly correlated with the motor recovery, suggesting that this index may provide useful information to predict the rehabilitation outcome.

Source: Quantitative EEG for Predicting Upper-limb Motor Recovery in Chronic Stroke Robot-assisted Rehabilitation – IEEE Xplore Document

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[Abstract] Executive function is associated with off-line motor learning in people with chronic stroke

Provisional Abstract:
Background and Purpose: Sleep has been shown to promote off-line motor learning in individuals following stroke. Executive function ability has been shown to be a predictor of participation in rehabilitation and motor recovery following stroke. The purpose of this study was to explore the association between executive function and off-line motor learning in individuals with chronic stroke compared to healthy control participants.

Methods: Seventeen individuals with chronic stroke (> 6 months post stroke) and nine healthy adults were included in the study. Participants underwent three consecutive nights of polysomnography (PSG), practiced a continuous tracking task (CTT) the morning of the third day, and underwent a retention test the morning after the third night. Participants underwent testing on four executive function tests after the CTT retention test.

Results: Stroke participants showed a significant positive correlation between the off-line motor learning score and performance on the Trail Making Test (TMT D-KEFS) (r= .652 p= .005), while the healthy controls did not. Regression analysis showed that the TMT D-KEFS is a significant predictor of off-line motor learning (p= .008).

Discussion and Conclusions: This is the first study to demonstrate that better performance on an executive function test of attention and set-shifting predicts a higher magnitude of off-line motor learning in individuals with chronic stroke. This emphasizes the need to consider attention and set-shifting abilities of individuals following stroke as these abilities predict off-line motor learning. This in turn could affect learning of ADL’s and impact functional recovery following stroke.

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Source: JUST ACCEPTED: “Executive function is associated with off-line motor learning in people with chronic stroke”

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[Abstract+References] Predicting Motor Sequence Learning in Individuals With Chronic Stroke

Background. Conventionally, change in motor performance is quantified with discrete measures of behavior taken pre- and postpractice. As a high degree of movement variability exists in motor performance after stroke, pre- and posttesting of motor skill may lack sensitivity to predict potential for motor recovery.

Objective. Evaluate the use of predictive models of motor learning based on individual performance curves and clinical characteristics of motor function in individuals with stroke.

Methods. Ten healthy and fourteen individuals with chronic stroke performed a continuous joystick-based tracking task over 6 days, and at a 24-hour delayed retention test, to assess implicit motor sequence learning.

Results. Individuals with chronic stroke demonstrated significantly slower rates of improvements in implicit sequence-specific motor performance compared with a healthy control (HC) group when root mean squared error performance data were fit to an exponential function. The HC group showed a positive relationship between a faster rate of change in implicit sequence-specific motor performance during practice and superior performance at the delayed retention test. The same relationship was shown for individuals with stroke only after accounting for overall motor function by including Wolf Motor Function Test rate in our model.

Conclusion. Nonlinear information extracted from multiple time points across practice, specifically the rate of motor skill acquisition during practice, relates strongly with changes in motor behavior at the retention test following practice and could be used to predict optimal doses of practice on an individual basis.

1. Muratori LM, Lamberg EM, Quinn L, Duff SV. Applying principles of motor learning and control to upper extremity rehabilitation. J Hand Ther. 2013;26:94102. Google Scholar Medline
2. Lohse KR, Lang CE, Boyd LA. Is more better? Using metadata to explore dose-response relationships in stroke rehabilitation. Stroke. 2014;45:20532058. Google Scholar CrossRef, Medline
3. Schmidt RA, Lee TD. Motor Control and Learning: A Behavioral Emphasis. 4th ed. Champaign, IL: Human Kinetics; 2005. Google Scholar
4. Boyd L, Winstein C. Explicit information interferes with implicit motor learning of both continuous and discrete movement tasks after stroke. J Neurol Phys Ther. 2006;30:4657. Google Scholar Medline
5. Boyd LA, Edwards JD, Siengsukon CS, Vidoni ED, Wessel BD, Linsdell MA. Motor sequence chunking is impaired by basal ganglia stroke. Neurobiol Learn Mem. 2009;92:3544. Google Scholar Medline
6. Boyd LA, Winstein CJ. Implicit motor-sequence learning in humans following unilateral stroke: the impact of practice and explicit knowledge. Neurosci Lett. 2001;298:6569. Google Scholar Medline
7. Boyd LA, Winstein CJ. Providing explicit information disrupts implicit motor learning after basal ganglia stroke. Learn Mem. 2004;11:388396. Google Scholar Medline
8. Vidoni ED, Boyd LA. Motor sequence learning occurs despite disrupted visual and proprioceptive feedback. Behav Brain Funct. 2008;4:32. Google Scholar Medline
9. Whitall J. Stroke rehabilitation research: time to answer more specific questions? Neurorehabil Neural Repair. 2004;18:38. Google Scholar Link
10. Doyon J, Bellec P, Amsel R, . Contributions of the basal ganglia and functionally related brain structures to motor learning. Behav Brain Res. 2009;199:6175. Google Scholar Medline
11. Deuschl G, Toro C, Zeffiro T, Massaquoi S, Hallett M. Adaptation motor learning of arm movements in patients with cerebellar disease. J Neurol Neurosurg Psychiatry. 1996;60:515519. Google Scholar Medline
12. Ioffe ME, Ustinova KI, Chernikova LA, Kulikov MA. Supervised learning of postural tasks in patients with poststroke hemiparesis, Parkinson’s disease or cerebellar ataxia. Exp Brain Res. 2006;168:384394. Google Scholar Medline
13. Lang CE, Bastian AJ. Cerebellar subjects show impaired adaptation of anticipatory EMG during catching. J Neurophysiol. 1999;82:21082119. Google Scholar Medline
14. Lang CE, Bastian AJ. Additional somatosensory information does not improve cerebellar adaptation during catching. Clin Neurophysiol. 2001;112:895907. Google Scholar Medline
15. Cousineau D, Hélie S, Lefebvre C. Testing curvatures of learning functions on individual trial and block average data. Behav Res Methods Instrum Comput. 2003;35:493503. Google Scholar Medline
16. Dite W, Langford ZN, Cumming TB, Churilov L, Blennerhassett JM, Bernhardt J. A phase 1 exercise dose escalation study for stroke survivors with impaired walking. Int J Stroke. 2015;10:10511056. Google Scholar Abstract
17. Karni A, Meyer G, Rey-Hipolito C, . The acquisition of skilled motor performance: fast and slow experience-driven changes in primary motor cortex. Proc Natl Acad Sci U S A. 1998;95:861868. Google Scholar Medline
18. Feldman LS, Cao J, Andalib A, Fraser S, Fried GM. A method to characterize the learning curve for performance of a fundamental laparoscopic simulator task: defining “learning plateau” and “learning rate”. Surgery. 2009;146:381386. Google Scholar Medline
19. Cousineau D, Lacroix GL. Getting parameters from learning data. Tutorials Quant Methods Psychology. 2006;2:7783. Google Scholar
20. Ritter FE, Schooler LJ. The learning curve. In: Smelser NJ, Baltes PB, eds. International Encyclopedia of the Social & Behavioral Sciences. Amsterdam, Netherlands: Pergamon; 2002:86028605.
21. Newell KM. Motor skill acquisition. Annu Rev Psychol. 1991;42:213237. Google Scholar Medline
22. Sampaio-Baptista C, Filippini N, Stagg CJ, Near J, Scholz J, Johansen-Berg H. Changes in functional connectivity and GABA levels with long-term motor learning. Neuroimage. 2015;106:1520. Google Scholar Medline
23. Sampaio-Baptista C, Khrapitchev AA, Foxley S, . Motor skill learning induces changes in white matter microstructure and myelination. J Neurosci. 2013;33:1949919503. Google Scholar CrossRef, Medline
24. Sampaio-Baptista C, Scholz J, Jenkinson M, . Gray matter volume is associated with rate of subsequent skill learning after a long term training intervention. Neuroimage. 2014;96:158166. Google Scholar Medline
25. Ward NS. Does neuroimaging help to deliver better recovery of movement after stroke? Curr Opin Neurol. 2015;28:323329. Google Scholar Medline
26. Neva JL, Henriques DY. Visuomotor adaptation and generalization with repeated and varied training. Exp Brain Res. 2013;226:363372. Google Scholar Medline
27. Heathcote A, Brown S, Mewhort DJ. The power law repealed: the case for an exponential law of practice. Psychon Bull Rev. 2000;7:185207. Google Scholar CrossRef, Medline
28. Meehan SK, Randhawa B, Wessel B, Boyd LA. Implicit sequence-specific motor learning after subcortical stroke is associated with increased prefrontal brain activations: an fMRI study. Hum Brain Mapp. 2011;32:290303. Google Scholar Medline
29. Fugl-Meyer AR, Jaasko L, Leyman I, Olsson S, Steglind S. The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance. Scand J Rehabil Med. 1975;7:1331. Google Scholar Medline
30. Hodics TM, Nakatsuka K, Upreti B, Alex A, Smith PS, Pezzullo JC. Wolf Motor Function Test for characterizing moderate to severe hemiparesis in stroke patients. Arch Phys Med Rehabil. 2012;93:19631967. Google Scholar Medline
31. Wadden K, Brown K, Maletsky R, Boyd LA. Correlations between brain activity and components of motor learning in middle-aged adults: an fMRI study. Front Hum Neurosci. 2013;7:169. Google Scholar Medline
32. Brown S, Heathcote A. Averaging learning curves across and within participants. Behav Res Methods Instrum Comput. 2003;35:1121. Google Scholar Medline
33. Krakauer JW, Pine ZM, Ghilardi MF, Ghez C. Learning of visuomotor transformations for vectorial planning of reaching trajectories. J Neurosci. 2000;20:89168924. Google Scholar Medline
34. Modabber M, Neva J, Gill M, Budge I, Henriques D. Learning and retaining visuomotor adaptation across time. J Vision. 2008;8:610610. Google Scholar
35. Haibach P, Reid G, Collier D. Motor Learning and Development. Champaign, IL: Human Kinetics; 2011.Google Scholar
36. Field A. Discovering Statistics Using SPSS. Thousand Oaks, CA: Sage; 2009. Google Scholar
37. Nesselroade JR, Salthouse TA. Methodological and theoretical implications of intraindividual variability in perceptual-motor performance. J Gerontol B Psychol Sci Soc Sci. 2004;59:P49P55. Google Scholar Medline
38. Lee TD, Simon DA. Contextual interference. In: Williams AM, Hodges NJ, eds. Skill Acquisition in Sport: Research, Theory and Practice. London, England: Routledge; 2004:2944.
39. Guadagnoli MA, Lee TD. Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning. J Mot Behav. 2004;36:212224. Google Scholar Medline
40. Wright D, Verwey W, Buchanen J, Chen J, Rhee J, Immink M. Consolidating behavioral and neurophysiologic findings to explain the influence of contextual interference during motor sequence learning. Psychon Bull Rev. 2016;23:121. Google Scholar Medline
41. Haith AM, Krakauer JW. Motor learning: the great rate debate. Curr Biol. 2014;24:R386R388. Google Scholar Medline
42. Eversbusch A, Grantcharov T. Learning curves and impact of psychomotor training on performance in simulated colonoscopy: a randomized trial using a virtual reality endoscopy trainer. Surg Endosc. 2004;18:15141518. Google Scholar Medline
43. Flamme C, Stukenborg-Colsman C, Wirth C. Evaluation of the learning curves associated with uncemented primary total hip arthroplasty depending on the experience of the surgeon. Hip Int. 2005;16:191197. Google Scholar
44. Hernandez J, Bann S, Munz Y, . Qualitative and quantitative analysis of the learning curve of a simulated surgical task on the da Vinci system. Surg Endosc. 2004;18:372378. Google Scholar Medline
45. Lundy-Ekman L. Neuroscience: Fundamentals for Rehabilitation. Philadelphia, PA: WB Saunders; 1998.Google Scholar
46. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189198. Google Scholar CrossRef, Medline
47. Wulf G, Schmidt RA. Variability of practice and implicit motor learning. J Exp Psychol Learn Mem Cogn. 1997;23:9871006. Google Scholar
48. Wadden KP, Woodward TS, Metzak PD, . Compensatory motor network connectivity is associated with motor sequence learning after subcortical stroke. Behav Brain Res. 2015;286:136145. Google ScholarMedline

Source: Predicting Motor Sequence Learning in Individuals With Chronic Stroke – Aug 10, 2016

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[Abstract] Role of corpus callosum integrity in arm function differs based on motor severity after stroke


    Corpus callosum structural integrity could impact motor function after stroke.Corpus callosum integrity was decreased and correlated with motor function.Correlation was strongest in the subgroup with relatively greater motor capacity.In subgroup with less motor capacity, only CST integrity correlated with motor function.


While the corpus callosum (CC) is important to normal sensorimotor function, its role in motor function after stroke is less well understood.

This study examined the relationship between structural integrity of the motor and sensory sections of the CC, as reflected by fractional anisotropy (FA), and motor function in individuals with a range of motor impairment level due to stroke.

Fifty-five individuals with chronic stroke (Fugl-Meyer motor score range 14 to 61) and 18 healthy controls underwent diffusion tensor imaging and a set of motor behavior tests. Mean FA from the motor and sensory regions of the CC and from corticospinal tract (CST) were extracted and relationships with behavioral measures evaluated. Across all participants, FA in both CC regions was significantly decreased after stroke (p < 0.001) and showed a significant, positive correlation with level of motor function. However, these relationships varied based on degree of motor impairment: in individuals with relatively less motor impairment (Fugl-Meyer motor score > 39), motor status correlated with FA in the CC but not the CST, while in individuals with relatively greater motor impairment (Fugl-Meyer motor score ≤ 39), motor status correlated with FA in the CST but not the CC.

The role interhemispheric motor connections play in motor function after stroke may differ based on level of motor impairment. These findings emphasize the heterogeneity of stroke, and suggest that biomarkers and treatment approaches targeting separate subgroups may be warranted.

Source: Role of corpus callosum integrity in arm function differs based on motor severity after stroke

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[Abstract] Chronic Stroke Survivors Improve Reaching Accuracy by Reducing Movement Variability at the Trained Movement Speed

Background. Recovery from stroke is often said to have “plateaued” after 6 to 12 months. Yet training can still improve performance even in the chronic phase. Here we investigate the biomechanics of accuracy improvements during a reaching task and test whether they are affected by the speed at which movements are practiced.

Method. We trained 36 chronic stroke survivors (57.5 years, SD ± 11.5; 10 females) over 4 consecutive days to improve endpoint accuracy in an arm-reaching task (420 repetitions/day). Half of the group trained using fast movements and the other half slow movements. The trunk was constrained allowing only shoulder and elbow movement for task performance.

Results. Before training, movements were variable, tended to undershoot the target, and terminated in contralateral workspace (flexion bias). Both groups improved movement accuracy by reducing trial-to-trial variability; however, change in endpoint bias (systematic error) was not significant. Improvements were greatest at the trained movement speed and generalized to other speeds in the fast training group. Small but significant improvements were observed in clinical measures in the fast training group.

Conclusions. The reduction in trial-to-trial variability without an alteration to endpoint bias suggests that improvements are achieved by better control over motor commands within the existing repertoire. Thus, 4 days’ training allows stroke survivors to improve movements that they can already make. Whether new movement patterns can be acquired in the chronic phase will need to be tested in longer term studies. We recommend that training needs to be performed at slow and fast movement speeds to enhance generalization.

Source: Chronic Stroke Survivors Improve Reaching Accuracy by Reducing Movement Variability at the Trained Movement Speed – Feb 01, 2017

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[Abstract] Determining the benefits of transcranial direct current stimulation on functional upper limb movement in chronic stroke. – International Journal of Rehabilitation Research


Transcranial direct current stimulation (tDCS) has been proposed as a tool to enhance stroke rehabilitation; however, evidence to support its use is lacking. The aim of this study was to investigate the effects of anodal and cathodal tDCS on upper limb function in chronic stroke patients. Twenty five participants were allocated to receive 20 min of 1 mA of anodal, cathodal or sham cortical stimulation in a random, counterbalanced order. Patients and assessors were blinded to the intervention at each time point. The primary outcome was upper limb performance as measured by the Jebsen Taylor Test of Hand Function (total score, fine motor subtest score and gross motor subtest score) as well as grip strength. Each outcome was assessed at baseline and at the conclusion of each intervention in both upper limbs. Neither anodal nor cathodal stimulation resulted in statistically significantly improved upper limb performance on any of the measured tasks compared with sham stimulation (P>0.05). When the data were analysed according to disability, participants with moderate/severe disability showed significantly improved gross motor function following cathodal stimulation compared with sham (P=0.014). However, this was accompanied by decreased key grip strength in the unaffected hand (P=0.003). We are unable to endorse the use of anodal and cathodal tDCS in the management of upper limb dysfunction in chronic stroke patients. Although there appears to be more potential for the use of cathodal stimulation in patients with severe disability, the effects were small and must be considered with caution as they were accompanied by unanticipated effects in the unaffected upper limb.

Source: Determining the benefits of transcranial direct current stim… : International Journal of Rehabilitation Research

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