Content » Vol 46, Issue 9

Original report

Motor imagery during movement activates the brain more than movement alone after stroke: A pilot study

Lucy Dodakian, MA, OTR/L, Jill Campbell Stewart, PT, PhD and Steven C. Cramer, MD

From the Departments of Neurology and Anatomy and Neurobiology, University of California, Irvine, USA

OBJECTIVE: To examine the neural correlates of motor imagery performed in conjunction with movement of the paretic arm after stroke.

DESIGN: Cross-sectional, cohort study.

SUBJECTS: Seven individuals in the chronic phase of stroke recovery (median (range): age: 58 years (37–73); time post-stroke: 9 months (4–42); upper extremity Fugl-Meyer motor score: 48 (36–64)).

METHODS: Participants actively moved the paretic/right arm under two conditions while undergoing functional magnetic resonance imaging. In the motor condition, pronation/supination movements were made in response to a visual cue. In the motor + imagery condition, the same movements were performed in response to a visual cue but the participants were instructed to imagine opening and closing a doorknob during performance of the movement.

RESULTS: For the motor condition, the anticipated motor network was activated and included left sensorimotor cortex and right cerebellum. For performance of the same movements during the motor + imagery condition, additional brain regions were significantly engaged including the left inferior parietal lobule and right dorsolateral prefrontal cortex.

CONCLUSIONS: The addition of motor imagery to movement may provide a practical, accessible way to modulate activity in both the planning and execution components of the motor network after stroke.

Key words: stroke; motor imagery; upper extremity; functional neuroimaging.

J Rehabil Med 2014; 46: 843–848

Correspondence address: Steven C. Cramer, MD, University of California, Irvine Medical Center, 200 S. Manchester Ave., Suite 206, Orange, CA 92868, USA. E-mail:

Accepted Apr 8, 2014; Epub ahead of print Sep 2, 2014


Stroke continues to be a leading cause of disability (1) with persistent motor deficits being one of the most common long-term sequela (2). An inability to incorporate the hemiparetic arm and hand into the performance of functional activities has a negative impact on overall function and quality of life (3, 4). Currently available interventions that address upper extremity function vary in their effectiveness (2) and may be optimal for only a subgroup of individuals recovering from stroke (5, 6). Novel interventions are needed that include an understanding of how the stroke-damaged brain responds to behavioral manipulations.

Task-oriented training is a key component of many stroke rehabilitation programs aimed at improving motor function (7). One way to improve motor function during task-oriented training may be through movement execution and repetition. Several recent studies have suggested that increased training intensity via high levels of task repetition is an important driver of motor recovery after stroke (8–10). A parallel strategy to improve motor function may be through task conditions that target motor planning and the cognitive aspects of motor control which are important for the learning or relearning of novel motor skills (11). Motor imagery provides one avenue to target motor cognition during movement training (12).

In a strict sense, motor imagery is defined as the activation of a mental representation of a motor action without overt movement (13, 14). Motor imagery practice has shown promise as an intervention in rehabilitation after stroke (14, 15). A variety of brain regions that support movement are activated during motor imagery in nondisabled individuals. A recent meta-analysis of imaging studies in nondisabled adults (16) identified several motor planning and movement execution regions on both sides of the brain that were consistently active during imagery including premotor, prefrontal, and parietal cortices, basal ganglia, thalamus, and cerebellum. While the literature on individuals recovering from stroke is less robust, similar brain regions have been reported as playing a role in motor imagery after stroke, specifically bilateral premotor, prefrontal, and parietal cortices (17–20). If the goal of motor imagery in individuals post-stroke is to improve motor function, motor imagery performed concurrently with movement execution may be particularly attractive as a tool for motor rehabilitation, especially in situations where the ability to use real objects during movement is limited. However, the neural correlates of motor imagery performed during movement execution after stroke have not been reported.

The purpose of this pilot study was to examine the neural correlates of motor imagery performed in conjunction with a movement execution task with the paretic arm after stroke. We hypothesized that motor imagery during movement execution, compared with movement execution alone, would lead to additional brain activation in regions that participate in motor planning.



The participants in the current analysis were part of a larger study that investigated the effects of a robot-based therapy on hand motor function (21). The study protocol included 15 1.5-h training sessions over 3 weeks that focused on paretic hand grasp and release using a 3-degree-of-freedom, active-assist, pneumatic robot (22). Motor imagery was not part of the training protocol for any participant. All training sessions were supervised by a physical or occupational therapist. Eight participants in this study completed the functional magnetic resonance imaging (fMRI) protocol included in the current analysis, which took place immediately after the completion of training. All participants provided written informed consent in accordance with the University Institutional Review Board.

Entry criteria for the study included age > 18 years, stroke at least 3 months prior that resulted in right-hand weakness, a minimum of 10° of range of motion in the right index finger metacarpophalangeal (MCP) joint, score of 2 to 10 out of 24 total points on the hand motor function section of the upper extremity Fugl-Meyer (UE FM) (23), and time to complete the 9-hole pegboard test (24) that was at least 25% longer in the paretic hand (right) compared with the nonparetic (left) hand. Individuals were excluded if they presented with apraxia (score > 2.5 on the Alexander scale (25)), reduced attention (score > 0 on National Institutes of Health Stroke Scale (NIHSS) (26) questions 1a–c), substantial sensory loss (right hand Nottingham sensory score (27) < 75% of normal), severe increase in muscle tone (modified Ashworth spasticity score (28) of 4 for the right elbow, wrist, or MCP), severe aphasia (score of 2 on NIHSS question 9), major depression (Geriatric Depression Scale (29) score > 8), or another diagnosis having a major effect on hand function.

Motor task

During fMRI, each participant completed two task conditions that involved the same movement, pronation/supination of the right forearm, in response to the same visual cue. In the motor condition, individuals moved the forearm in pronation/supination in concert with movements of a stick-figure hand shown in a video moving at a rate of 0.125 Hz. In the motor + imagery condition, individuals performed the same movement along with the same video, however, immediately prior to scanning, additional imagery instructions were provided that cued the participant to focus on the functional intent of the task. Specifically, participants were instructed to make the same movement as in the motor condition but to “Imagine you are turning a doorknob when you are doing the movement. Imagine feeling the doorknob in your hand, imagine turning it like you are turning a real doorknob, feel your hand turning the doorknob to open it, then turning it back to close the knob”. During both conditions, a non-actuated, magnetic resonance imaging (MRI) compatible splint was worn to provide wrist support, and the range of motion was self-determined.

The motor condition was practiced in the laboratory immediately prior to the MRI session. This practice session included the same arm splint, video, and posture used during fMRI in order to closely mimic testing conditions. The video alternated between periods of rest (30 s, red cues) and periods of pronation/supination (30 s, green cues). Participants completed a full video (length of an fMRI run) accurately before moving to the MRI. The motor + imagery condition was not practiced prior to fMRI; individuals were only provided instructions for this condition while in the MRI, just prior to task completion, to insure that the imagery component would not contaminate the motor condition scan.

Magnetic resonance imaging data acquisition

All brain imaging sessions were performed on the same 1.5 Tesla MRI scanner (Philips Healthcare, Best, Netherlands). First, a high-resolution structural image was acquired, which included 150 1-mm thick slices with no interslice gap (acquisition voxel size 1 mm × 1 mm × 1 mm). Next, a total of 6 functional runs were completed, two runs for the motor condition and two for the motor + imagery condition; two additional runs were focused on a grasp/release condition and are not included in the current analysis (see Takahashi et al. (21)). Functional MRI data were acquired using a block design while participants moved (pronation/supination) in response to a visual cue presented via video. Periods of movement (30 s) during which visual cues were green alternated with periods of rest (30 s) during which visual cues were red. Each functional run lasted 2 min 5 s, during which 50 brain volumes were collected (repetition time = 2,500 ms, echo time = 40 ms); each volume included 25 axial slices that were 5 mm thick with no interslice gap (acquisition voxel size 1.8 mm × 1.8 mm × 5 mm).

The data included in the current analysis were collected during the post-training assessment session (immediately following 3 weeks of hand training). The two functional runs for the motor condition were completed first, followed by the two runs for the motor + imagery condition, in all participants. An investigator observed participant movement during scanning to verify task compliance.

Data analysis

All functional imaging data was analyzed using SPM8 (Wellcome Trust Centre for Neuroimaging, London, UK). The first two volumes from each run were discarded due to tissue non-saturation. Volumes from each run were realigned to the first volume and resliced to account for motion artifact. The mean image for each participant was then normalized to the standard Montreal Neurological Institute (MNI) echo planar image template in SPM. The normalization parameters were applied to all of the functional volumes for that participant, and the normalized images were resampled to 2 mm × 2 mm × 2 mm voxels. All MNI transformed images were visually inspected to confirm that no distortions were introduced by the spatial normalization process. Images were then spatially smoothed with an isotropic Gaussian filter (FWHM = 8 mm).

First-level statistical analysis was performed separately for each individual participant to determine regions of significant activation using a general linear model (30). To determine the regions active during each of the two conditions (motor, motor + imagery), movement was contrasted with rest (Move > Rest); both runs for each condition were weighted equally in all contrasts. For first-level analyses, the first derivative of head motion for all 6 directions, which was uncorrelated with stimulus presentation, was added as a regressor of no interest in order to account for the effect of head motion in the data.

The contrast maps for each participant and each condition were moved to a second-level group random effects analysis to identify brain regions that were active during movement across the group of participants. To determine brain regions active during each condition, a one-sample t-test was used on the Move > Rest contrasts separately for the motor and motor+imagery conditions. Next, a paired t-test was used to determine differences in brain activation between conditions (motor > motor + imagery; motor + imagery > motor). For group comparisons, statistical significance was set at p < 0.001 at the voxel level (magnitude of activation) and p < 0.05 at the cluster level (extent of activation), both uncorrected for multiple comparisons.



A total of 8 participants completed both the motor and motor + imagery conditions during the post-training fMRI session. Imaging data from one participant had excess head motion artifact and was excluded from further analysis. Therefore, data from 7 participants were included in the analysis (Table I). All 7 presented with hemiparesis on the right side per entry criteria, with 6 individuals being right-hand dominant and one left-hand dominant. The group had a median age of 58 years (range 37–73), median time post-stroke of 9 months (range 4–42), and a median UE FM motor score of 48 (range 38–64) at the end of training (time point that corresponded with fMRI data in current analysis). All 7 participants demonstrated improvement over the 3 week course of training, showing a median increase in UE FM motor score of 8 points (range 3–12) and in the number of blocks moved with the paretic arm on the Box & Blocks test of 6 (range 0–10).

Table I. Participant demographics


Months post-stroke

UE FM motor

Box & Blocks

SIS hand section

Stroke location












Corona radiata, PLIC






Corona radiata












Corona radiata, putamen, PLIC, temporal lobe












Putamen, PLIC, insula

UE FM: Upper Extremity Fugl Meyer (max score = 66); Box & Blocks: number of blocks moved in one minute with right hand; SIS: Stroke Impact Scale (score = mean of 5 items in hand subsection; max score = 5); PLIC: posterior limb of the internal capsule; M: male; F: female.

Brain activation during task performance

All participants actively moved in pronation/supination during the Move epochs for all functional runs. One participant had small visible mirror movements in the left hand and 5 participants had visible movement of the foot or elbow at least one time. Brain activation during the motor and motor + imagery conditions is shown in Fig. 1 and Table II. For the motor condition, activation was focused in the motor system: left sensorimotor cortex and right cerebellum. For the motor + imagery condition, activation was overall greater and included the same regions as the motor condition, however, additional regions were significantly recruited (right inferior parietal lobule (IPL), left cerebellum). When a lower statistical threshold (p < 0.01) was used for exploratory purposes, left IPL and bilateral supplementary motor area were additionally found to be active during motor + imagery.


Fig. 1. Group brain activation. Results of the one-sample t-tests generated for each of the two task conditions (motor, motor + imagery), as well as results of the paired t-test that directly contrasted the motor condition with the motor + imagery condition; activation maps have been overlayed onto a T1-weighted template in MNI space. The paired t-test shows motor + imagery > motor; no regions were significantly more active during the opposite contrast (i.e., motor > motor + imagery). Short arrow = activation in inferior parietal lobule; long arrows = activation in dorsolateral prefrontal cortex.

Table II. Location of significant clusters for each condition and their comparison


Brain region


Peak Z

MNI Coordinates





R Cerebellum






L Sensorimotor cortex






Motor + imagery

R Cerebellum






R Sensory cortex






L Cerebellum






R Inferior parietal lobule






L Sensorimotor cortex






Motor + imagery > motor

R Dorsolateral prefrontal cortex






L Inferior parietal lobule






L Inferior parietal lobule






All regions were significant at p < 0.001 (corresponding to Z > 3.09), uncorrected for multiple comparisons. Volume: number of 8 mm3-voxels in the cluster; Peak Z: peak Z-value within the cluster; R: right; L: left. Note that there were no significant clusters for the comparison motor > motor + imagery.

The paired t-test between conditions revealed 3 significant clusters that were more active during the motor + imagery condition compared to the motor condition located in right dorsolateral prefrontal cortex (DLPFC) and left IPL (Fig.1, Table II). These two regions were not reported as being significantly active in the one-sample t-test of the motor + imagery condition alone. For both of these regions, the significant difference between conditions was driven by two factors: (i) little to no activation (IPL) or decreased activation (DLPFC) during the motor task, plus (ii) increased activation during the motor + imagery task (Fig. 2). Thus, while the absent or decreased activation in (i) and the increased activation in (ii) were not significant by themselves, the difference between them (as measured by the paired t-test) was significant. Note that when a lower statistical threshold (p < 0.01) was used for exploratory purposes, right IPL and right posterior cingulate cortex were also found to be more active during the motor + imagery condition. No brain regions were more active in the motor condition compared with the motor + imagery condition.


Fig. 2. Group means with standard error bars are presented for the percent signal change within right dorsolateral prefrontal cortex (DLPFC) and left inferior parietal lobule (IPL) for each condition. The percent signal change was extracted from a 6-mm radius sphere that was centered on the activation peaks, defined using results of the paired t-test (see Table II).


To our knowledge, this is the first study to investigate the neural correlates of motor imagery done in conjunction with movement in individuals with hemiparesis after stroke. Motor imagery during movement activated similar brain regions as movement alone but also engaged additional regions previously shown to play a role in motor planning and motor imagery, specifically IPL and DLPFC. Task conditions that alter cognitive context (11) and modulate activation of the motor network during movement may be useful tools for rehabilitation. In young, nondisabled adults, conditions of motor task practice that benefit learning on a delayed retention test often show greater utilization of brain regions that support movement planning during practice (31, 32). Integration of motor planning via the addition of motor imagery to movement may provide a practical, accessible way to increase engagement of planning regions during movement after stroke.

Activation of the IPL and DLPFC during motor imagery has been commonly reported in nondisabled adults (14, 16). The IPL plays a role in the performance of goal-directed, grasping actions (33, 34). The addition of motor imagery to movement may increase the functional meaning of the movement and, therefore, lead to increased activity in this region. The DLPFC has been shown to play a variety of roles in the cognitive control of movement (35). The role of DLPFC for the control of movement may increase with age (36, 37) and after stroke (38). Activation of DLPFC during motor imagery done simultaneously with movement suggests an increased utilization of attentional resources to movement similar to motor imagery without movement. It cannot be determined from the current study whether increased activation in IPL and DLPFC would be beneficial or detrimental to the learning or relearning of motor skills after stroke. Task conditions that engage a larger component of the motor network may provide a level of challenge that benefits learning for some individuals and is overwhelming for others. However, the findings in the current study suggest that motor imagery during movement may provide a tool to modulate this activation during motor practice.

The functional intent of movement has been shown to have a behavioral impact on movement. In general, when movement includes a functional context (e.g., is goal-directed and involves a functional object), movement quality improves and is kinematically closer to that of nondisabled controls (39, 40). In individuals who lack the hand function to be able to physically interact with objects or during the performance of rote exercises, however, the ability to add functional context during movement can be limited. The addition of motor imagery to motor practice under these conditions may add some degree of functional meaning and, concordantly, have an effect on movement quality and neural activation. Additionally, the attentional valence and salience of motor practice are key factors in experience-dependent learning (41, 42). Motor imagery performed during movement may introduce increased attention and saliency in a manner that benefits learning and recovery. Based on the findings of the current study, future research could investigate the behavioral and neural effects of a period of motor imagery done in combination with motor task practice in individuals post-stroke.

There are a number of limitations and opportunities for future research in the current study. This was a small pilot study. While significant differences in activation between conditions were found, the small sample size may limit generalization of these results to individuals post-stroke who have a clinical presentation similar to the current patient cohort. Movement of the limbs during fMRI was not explicitly measured in the current study. Future research could benefit from quantitative measurement of movement to ensure compatibility between task conditions. The motor + imagery condition always followed the motor condition, an approach that was used to insure that brain events related to imagery did not in any way influence the motor condition, but which introduced a potential ordering effect that might have influenced results. The participants in the current study were all in the chronic phase of stroke recovery. Access to the motor system via motor imagery during moment may be different in the acute or subacute phases of stroke recovery.

In conclusion, motor imagery during movement increased brain activation in regions that support goal-directed, complex movement, IPL and DLPFC. The addition of motor imagery to movement may provide a practical, accessible way to modulate brain activation in both the planning and execution components of the motor network after stroke. Selective engagement of distinct components of the motor network via motor imagery during movement warrants further investigation.


This research was supported by the National Center of Research Resources, 5M011 RR-00827-29 and NS059909, US Public Health Service (SCC) and by grant T32 AR047752 from the National Institutes of Health (JCS). Steve Cramer has served as a consultant for GlaxoSmith Kline and MicroTransponder.


1. Go AS, Mozaffarian D, Roger VL, Benjamin EJ, Berry JD, Borden WB, et al. Heart disease and stroke statistics – 2013 update: a report from the American Heart Association. Circulation 2013; 127: e6–e245.

2. Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review. Lancet Neurol 2009; 8: 741–754.

3. Lai SM, Studenski S, Duncan PW, Perera S. Persisting consequences of stroke measured by the Stroke Impact Scale. Stroke 2002; 33: 1840–1844.

4. Nichols-Larsen DS, Clark PC, Zeringue A, Greenspan A, Blanton S. Factors influencing stroke survivors’ quality of life during subacute recovery. Stroke 2005; 36: 1480–1484.

5. Chen SY, Winstein CJ. A systematic review of voluntary arm recovery in hemiparetic stroke: critical predictors for meaningful outcomes using the international classification of functioning, disability, and health. J Neurol Phys Ther 2009; 33: 2–13.

6. Coupar F, Pollock A, Rowe P, Weir C, Langhorne P. Predictors of upper limb recovery after stroke: a systematic review and meta-analysis. Clin Rehabil 2012; 26: 291–313.

7. Winstein CJ, Wolf SL. Task-oriented training to promote upper extremity recovery. In: Stein J, RL H, Macko R, Winstein C, Zorowitz R, editors. Stroke recovery & rehabilitation. New York: Demos Medical; 2008, p. 267–290.

8. 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.

9. Rose D, Paris T, Crews E, Wu SS, Sun A, Behrman AL, et al. Feasibility and effectiveness of circuit training in acute stroke rehabilitation. Neurorehabil Neural Repair 2011; 25: 140–148.

10. van Wijk R, Cumming T, Churilov L, Donnan G, Bernhardt J. An early mobilization protocol successfully delivers more and earlier therapy to acute stroke patients: further results from phase II of AVERT. Neurorehabil Neural Repair 2012; 26: 20–26.

11. Dennis A, Bosnell R, Dawes H, Howells K, Cockburn J, Kischka U, et al. Cognitive context determines dorsal premotor cortical activity during hand movement in patients after stroke. Stroke 2011; 42: 1056–1061.

12. McEwen SE, Huijbregts MP, Ryan JD, Polatajko HJ. Cognitive strategy use to enhance motor skill acquisition post-stroke: a critical review. Brain Inj 2009; 23: 263–277.

13. Dickstein R, Deutsch JE. Motor imagery in physical therapist practice. Phys Ther 2007; 87: 942–953.

14. Sharma N, Pomeroy VM, Baron JC. Motor imagery: a backdoor to the motor system after stroke? Stroke 2006; 37: 1941–1952.

15. Zimmermann-Schlatter A, Schuster C, Puhan MA, Siekierka E, Steurer J. Efficacy of motor imagery in post-stroke rehabilitation: a systematic review. J Neuroeng Rehabil 2008; 5: 8.

16. Hetu S, Gregoire M, Saimpont A, Coll MP, Eugene F, Michon PE, et al. The neural network of motor imagery: an ALE meta-analysis. Neurosci Biobehav Rev 2013; 37: 930–949.

17. Szameitat AJ, Shen S, Conforto A, Sterr A. Cortical activation during executed, imagined, observed, and passive wrist movements in healthy volunteers and stroke patients. Neuroimage 2012; 62: 266–280.

18. Sharma N, Simmons LH, Jones PS, Day DJ, Carpenter TA, Pomeroy VM, et al. Motor imagery after subcortical stroke: a functional magnetic resonance imaging study. Stroke 2009; 40: 1315–1324.

19. Confalonieri L, Pagnoni G, Barsalou LW, Rajendra J, Eickhoff SB, Butler AJ. Brain activation in primary motor and somatosensory cortices during motor imagery correlates with motor imagery ability in stroke patients. ISRN neurology 2012; 2012: 613595.

20. Kimberley TJ, Khandekar G, Skraba LL, Spencer JA, Van Gorp EA, Walker SR. Neural substrates for motor imagery in severe hemiparesis. Neurorehabil Neural Repair 2006; 20: 268–277.

21. Takahashi CD, Der-Yeghiaian L, Le V, Motiwala RR, Cramer SC. Robot-based hand motor therapy after stroke. Brain 2008; 131: 425–437.

22. Takahashi CD, Der-Yeghiaian L, Le VH, Cramer SC. A robotic device for hand motor therapy after stroke. IEEE 9th International Conference on Rehabilitation Robotics; 2005, June 28–July 1; Chicago, USA; 2005, p. 17–20.

23. 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: 13–31.

24. Oxford Grice K, Vogel KA, Le V, Mitchell A, Muniz S, Vollmer MA. Adult norms for a commercially available Nine Hole Peg Test for finger dexterity. Am J Occup Ther 2003; 57: 570–573.

25. Alexander MP, Baker E, Naeser MA, Kaplan E, Palumbo C. Neuropsychological and neuroanatomical dimensions of ideomotor apraxia. Brain 1992; 115: 87–107.

26. Brott T, Adams HP, Jr., Olinger CP, Marler JR, Barsan WG, Biller J, et al. Measurements of acute cerebral infarction: a clinical examination scale. Stroke 1989; 20: 864–870.

27. Lincoln NB, Jackson JM, Adams SA. Reliability and revision of the Nottingham sensory assessment for stroke patients. Physiotherapy 1998; 84: 358–365.

28. Katz RT, Rovai GP, Brait C, Rymer WZ. Objective quantification of spastic hypertonia: correlation with clinical findings. Arch Phys Med Rehabil 1992; 73: 339–347.

29. Burke WJ, Roccaforte WH, Wengel SP. The short form of the Geriatric Depression Scale: a comparison with the 30-item form. J Geriatr Psychiatry Neurol 1991; 4: 173–178.

30. Friston KJ, Holmes AP, Poline JB, Grasby PJ, Williams SC, Frackowiak RS, et al. Analysis of fMRI time-series revisited. Neuroimage 1995; 2: 45–53.

31. Cross ES, Schmitt PJ, Grafton ST. Neural substrates of contextual interference during motor learning support a model of active preparation. J Cogn Neurosci 2007; 19: 1854–1871.

32. Lin CH, Knowlton BJ, Chiang MC, Iacoboni M, Udompholkul P, Wu AD. Brain-behavior correlates of optimizing learning through interleaved practice. Neuroimage 2011; 56: 1758–1772.

33. Fogassi L, Luppino G. Motor functions of the parietal lobe. Curr Opin Neurobiol. 2005; 15: 626–631.

34. Tunik E, Rice NJ, Hamilton A, Grafton ST. Beyond grasping: representation of action in human anterior intraparietal sulcus. Neuroimage 2007; 36 Suppl 2: T77–T86.

35. Hoshi E. Functional specialization within the dorsolateral prefrontal cortex: a review of anatomical and physiological studies of non-human primates. Neurosci Res 2006; 54: 73–84.

36. Stewart JC, Tran X, Cramer SC. Age-related variability in performance of a motor action selection task is related to differences in brain function and structure among older adults. Neuroimage 2014; 86: 326–334.

37. Heuninckx S, Wenderoth N, Debaere F, Peeters R, Swinnen SP. Neural basis of aging: the penetration of cognition into action control. J Neurosci 2005; 25: 6787–6796.

38. 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: 290–303.

39. Trombly CA, Wu CY. Effect of rehabilitation tasks on organization of movement after stroke. Am J Occup Ther 1999; 53: 333–344.

40. Wu C, Trombly CA, Lin K, Tickle-Degnen L. A kinematic study of contextual effects on reaching performance in persons with and without stroke: influences of object availability. Arch Phys Med Rehabil 2000; 81: 95–101.

41. Woldag H, Hummelsheim H. Evidence-based physiotherapeutic concepts for improving arm and hand function in stroke patients: a review. J Neurol 2002; 249: 518–528.

42. Kleim JA, Jones TA. Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage. J Speech Lang Hear Res 2008; 51: S225–S239.


Do you want to comment on this paper? The comments will show up here and if appropriate the comments will also separately be forwarded to the authors. You need to login/create an account to comment on articles. Click here to login/create an account.