Motor skill learning is one of the key components of motor function recovery after stroke, especially recovery driven by neurorehabilitation. Transcranial direct current stimulation can enhance neurorehabilitation and motor skill learning in stroke patients. However, the neural mechanisms underlying the retention of stimulation-enhanced motor skill learning involving a paretic upper limb have not been resolved. These neural substrates were explored by means of functional magnetic resonance imaging. Nineteen chronic hemiparetic stroke patients participated in a double-blind, cross-over randomized, sham-controlled experiment with two series. Each series consisted of two sessions: (i) an intervention session during which dual transcranial direct current stimulation or sham was applied during motor skill learning with the paretic upper limb; and (ii) an imaging session 1 week later, during which the patients performed the learned motor skill.

  1. Bk Precision Manuals

The motor skill learning task, called the ‘circuit game’, involves a speed/accuracy trade-off and consists of moving a pointer controlled by a computer mouse along a complex circuit as quickly and accurately as possible. Relative to the sham series, dual transcranial direct current stimulation applied bilaterally over the primary motor cortex during motor skill learning with the paretic upper limb resulted in (i) enhanced online motor skill learning; (ii) enhanced 1-week retention; and (iii) superior transfer of performance improvement to an untrained task. The 1-week retention’s enhancement driven by the intervention was associated with a trend towards normalization of the brain activation pattern during performance of the learned motor skill relative to the sham series. A similar trend towards normalization relative to sham was observed during performance of a simple, untrained task without a speed/accuracy constraint, despite a lack of behavioural difference between the dual transcranial direct current stimulation and sham series. Finally, dual transcranial direct current stimulation applied during the first session enhanced continued learning with the paretic limb 1 week later, relative to the sham series. This lasting behavioural enhancement was associated with more efficient recruitment of the motor skill learning network, that is, focused activation on the motor-premotor areas in the damaged hemisphere, especially on the dorsal premotor cortex.

Dual transcranial direct current stimulation applied during motor skill learning with a paretic upper limb resulted in prolonged shaping of brain activation, which supported behavioural enhancements in stroke patients. IntroductionStroke is a devastating pathology that causes restrictions in daily life activities, such as motor limitations due to upper limb hemiparesis in a majority of patients (; ). Neurorehabilitation aims to improve residual motor function and restore independence, but its impact is still limited.

Hence, innovative strategies for enhancing neurorehabilitation have been developed, including non-invasive brain stimulation (NIBS) techniques such as transcranial direct current stimulation (DCS) (; ). NIBS can modulate transiently brain excitability and behaviour in healthy individuals, as well as in stroke patients (;;; ). Pilot experiments have shown that NIBS has the potential to enhance neurorehabilitation (;; ). Upon which neural substrates NIBS acts in the brains of stroke patients and how exactly NIBS modulates brain activity is still poorly understood.Researchers started to explore the neural substrates associated with the enhancement of neurorehabilitation interventions by NIBS with functional MRI (;; ).

Typically, over a period of several days, hemiparetic stroke patients received occupational therapy coupled with real or sham NIBS; functional MRI data were acquired before and after the therapy programme. Motor performance enhancement of the paretic upper limb after neurorehabilitation with NIBS was associated with a reorganization of the functional MRI pattern, namely a transfer of brain activation from a bilateral network towards the ipsilesional sensori-motor-premotor areas (;; ). Recently, explored the brain activation associated with the after-effects of transcranial DCS in chronic stroke patients performing a simple response time task.

Immediately after applying anodal transcranial DCS over the primary motor cortex in the damaged hemisphere (M1 damH), improved performance was associated with increased activation in M1 damH and the supplementary motor area (SMA damH), as well as increased activation bilaterally in the dorsal premotor cortex (PMd). Furthermore, after applying cathodal transcranial DCS over M1 in the undamaged hemisphere (M1 undamH), activation was increased contralaterally in PMd damH and SMA damH as well as bilaterally in M1.In these pioneer studies, the tasks used to elicit brain activation were, for obvious practical reasons, relatively simple (e.g. Repetitive fingers/elbow/wrist flexion/extension, simple reaction time task). These tasks are distinct from the scales commonly used to quantify motor impairment and recovery i.e.

The modified Rankin Scale, National Institutes of Health Stroke Score (NIHSS), Fugl-Meyer assessment, Action Research Arm Test, Barthel index, etc. Furthermore, during neurorehabilitation interventions, the stroke patients were not trained specifically to perform the functional MRI tasks. Therefore, these functional MRI tasks represent a generic exemplification of the overall improvement of paretic upper limb function.

From a clinical point of view, such a transfer from neurorehabilitation interventions towards generic enhancement of motor control in the paretic upper limb is both highly satisfactory and promising. However, the specific components underlying neurorehabilitation-induced enhancements are still poorly understood.Overall, post-stroke motor recovery driven by neurorehabilitation relies most obviously on restored muscle strength, reduced spasticity, increased endurance, resolution of metabolic events in the (sub)acute stroke phase, and neural plasticity (i.e. Reorganization of the spared neuronal networks and connections).

However, beyond these crucial components, any lasting improvement gained through training and experience (e.g. Through neurorehabilitation) depends necessarily upon long-term motor memory retention. To some extent, recovering from hemiparesis can be conceptualized as a particular form of motor learning; in other words, learning to use the reconfigured motor network to optimize planning, execution, and control of movement with the affected limb. The idea that motor skill learning plays a central role in post-stroke motor recovery is becoming a focus of interest in neurorehabilitation (;;; ). Motor skill learning is defined as a practice-dependent motor performance improvement that persists over time; it is characterized by a shift in the speed/accuracy trade-off (SAT), some degree of automatization, and a reduction in variability (;; ).It was demonstrated recently that NIBS can enhance online motor skill learning and overnight retention in stroke patients (; ) and, more importantly, long-term retention of a motor skill involving a SAT. The demonstration that NIBS enhances motor skill learning and its long-term retention in stroke patients establishes a crucial link between bench observations transient enhancement of motor function (; ) and clinical implementation enhanced neurorehabilitation (;; ). The science of neurorehabilitation will be advanced by elucidation of how NIBS boosts the effects of neurorehabilitation (i.e.

Through enhanced motor skill learning), and which neural substrates underlie (i) specific motor skill learning; and (ii) generic improvement.Studies of the neural substrates underlying motor skill learning in healthy subjects have demonstrated activations in a network encompassing M1, SMA, premotor cortex, the dorsolateral prefrontal cortex (PFC), the cerebellum, and the basal ganglia (;;; ). Based on recent concepts in motor learning, we designed a motor skill learning paradigm involving a SAT and demonstrated that efficient motor skill learning, characterized by a shift of this SAT, depends upon recruitment of the SMA in healthy individuals.The few studies that have explored the neural substrates of motor skill learning after stroke have shown a decreased activation in the undamaged hemisphere and an increased specific activity in the damaged hemisphere compared to pre-training activation (;;; ). Also, motor skill learning in chronic stroke patients induced a recruitment of additional areas compared to healthy individuals, such as dorsolateral PFC (;;; ).

The network activated while learning with the paretic upper limb a motor skill involving a SAT has been described only recently (Lefebvre et al., personal communication); it seems that efficient motor skill learning relied upon the recruitment of the PMd damH in chronic stroke patients (S. Lefebvre, personal communication).A finer knowledge of the neural substrates underlying motor learning in stroke patients and the neural substrates upon which NIBS acts to enhance neurorehabilitation and motor learning after stroke is of key importance for the implementation of NIBS in routine clinical practice. The aim of the present study was to explore by means of functional MRI the neural substrates underlying the long-term retention of specific motor skill enhancement driven by motor skill learning with a paretic upper limb under dual-transcranial DCS versus sham (intervention), generic enhancement of untrained movements performed with the paretic limb, and continued motor skill learning 1 week post-intervention. Study designThe stroke patients participated in a crossover experiment with two series. Each series consisted of two sessions: (i) an intervention session during which dual-transcranial DCS or sham was applied during motor skill learning of the paretic upper limb (using a double-blind, crossover randomized method); and (ii) an imaging session 1 week later (retention session), during which the patients performed the learned motor skill. The retention session permitted the exploration of the mean overall level of motor performance retention.

Because, during the retention session, the stroke patients performed eight blocks of the circuit learned 1 week before, the performance evolution during this session was also analysed as continued learning. The general design was similar to our previous study exploring the impact of dual-transcranial DCS on motor skill learning in chronic hemiparetic stroke patients , except that in the current study the intervention sessions were performed in the supine position with the circuit projected on the ceiling, to accommodate the patient’s position in the MRI scanner 1 week later during the retention session. Study design. During the intervention, each stroke patient trained in the supine position, matching their position during the MRI retention session 1 week later. They participated in two separate series of two sessions each in a double-blind, cross-over randomized fashion.

Each series contained one intervention session (one with dual-transcranial DCS, the other with sham) and a retention session 1 week after. Ten patients were enrolled in the first series (dual-transcranial DCS as the first intervention) and nine patients were enrolled in the second series (sham transcranial DCS as the first intervention).

FMRI = functional MRI; tDCS = transcranial DCS. Functional MRI design, acquisition and preprocessingThe functional MRI sessions consisted of one habituation run (2 min 40 s; four blocks of practice on a simple square circuit alternating with four blocks of rest) and two runs of the circuit learned the previous week (i.e.

During the intervention sessions) (8 min 41 s, 172 scans). Each run contained three conditions which occurred four times each: (i) Learning (performing the learned circuit as quickly and accurately as possible); (ii) Easy (simple motor condition without speed or accuracy constraints); and (iii) Replay (visual-visuomotor condition: with a video clip of the last Learning performance displayed, patients were instructed to follow the cursor’s displacement with their eyes while keeping their hands motionless). The practice blocks were separated by rest blocks during which a fixation cross was visible.The images were acquired with a 3-T scanner attached to a 32-channel head coil (Siemens Verio). Functional MRI scans were acquired by repeated single-shot echo-planar imaging with the following parameters: repetition time = 3000 ms, echo time = 23 ms, flip angle = 90°, matrix size = 64 × 64, field of view = 220 × 220 mm 2, slice order descending and interleaved, slice thickness = 2 mm (no gap) and number of slices = 59 (whole-brain).

A 3D T 1-weighted data set covering the whole brain was acquired (1 mm 3, repetition time = 1600 ms, echo time = 2.39 ms, flip angle = 9°, matrix size = 512 × 512, field of view = 256 × 256 mm 3, 176 slices, slice thickness = 1 mm, no gap).Functional MRI data were preprocessed and analysed with BrainVoyager QX (Version 2.4.2.2070) software; the data were processed as described previously (; S. Lefebvre, personal communication), except that for patients with stroke lesions on the right side of the brain, the 3D-T 1 and functional data were flipped: the 3D-T 1 by flipping the x-axis and the functional data by flipping the data horizontally. Briefly, the preprocessing of the functional data consisted of a slice-time scan correction, temporal high-pass filtering, and 3D motion correction. A general linear model was used to analyse the functional MRI data. Co-registrations between functional runs and 3D-T 1 weighted scans of each patient were performed automatically, and corrected manually when careful visual inspection identified imperfect co-registration. All anatomical and functional volumes were normalized in talairach space to allow group analysis. Functional runs were smoothed in the spatial domain with a 5-mm Gaussian filter.

Whole-brain random effectThree whole-brain random effects were constructed and included the 19 stroke patients. The first random effect involved the two retention sessions and was used in the ANOVA and for the regions of interest analyses. The two others random effects were computed for each session separately and also used for regions of interest analyses. Correlations analysis with motor skill retentionIn the regions of interest with significant activation found in the two separate random effects (one for each intervention), Pearson correlation analyses were performed to identify the area(s) whose activation correlated most strongly with retained motor skill performance. For this analysis, the learning indices of each patient were averaged across the two runs (overall mean learning index, reflecting the general level of performance of the retained motor skill) and correlated with the mean beta weights of the condition of interest for each stroke patient across the two runs.

The LEARNING – REPLAY was the contrast of interest for this analysis comparing the two retention sessions; a complementary region of interest analysis was performed on REPLAY. Individual contribution of each patient to the main (whole-group) patternThe following predefined regions of interest were drew bilaterally in Talairach space: using both the Talairach Daemon ( and the third edition of the ‘Atlas Of the Human Brain’ : M1, PMd, primary somatosensory cortex (S1), posterior parietal cortex (PPC), dorsolateral PFC, visual areas; based on for SMA proper and pre-SMA; and cerebellum. These regions of interest were used to explore the individual contribution of each patient to the main (whole-group) pattern: the numbers of activated voxels with an uncorrected P-value of 0.05 (in order to reveal all areas involved) were counted inside each region of interest for EASY, REPLAY and LEARNING − REPLAY. Each stroke patient was compared between the retention sessions for each predefined region of interest with paired Student t-tests. Continued learningFinally, the brain activations associated with continued learning were explored in three steps using the two separate whole-brain random effect analyses, including only the patients who achieved learning.

For each session, a conjunction analysis (LEARNING – REPLAY ∩ LEARNING – EASY) was performed to explore the activation common to both contrasts. Then, Pearson correlation analyses were performed between the evolution of the beta weights and that of the learning index values and performance index values in the regions of interest obtained with the conjunction analysis at each functional MRI session.

Bk Precision 1590 Manual Dexterity

BehaviourCompared to the sham procedure, dual-transcranial DCS improved both the magnitude (, and ) and the quality of motor skill learning with the paretic arm ( and ). The overall mean learning index 1 week after dual-transcranial DCS 52% ± 29, mean ± standard deviation (SD) was statistically superior to that observed after the sham procedure 12% ± 20, t(18) = 3.57; P = 0.002; Cohen’s d effect size (d) = 1.61. During the performance of simple, untrained movements (EASY condition), the two functional MRI sessions did not differ in speed 17 ± 3 u/s after sham versus 18 ± 4 u/s after dual-transcranial DCS, P = 0.28, total amount of movement 479 ± 92 u versus 503 ± 112 u, P = 0.50, or normalized jerk 353 070 ± 201 347 versus 513 756 ± 513 766, P = 0.18. Differential evolution of motor skill learning under sham and dual-transcranial DCS. Evolution of learning index, expressed as a%, change from baseline during the intervention session (baseline, training, after 0 min, 30 min and 60 min) and 1 week later (overall learning index during the functional MRI session).

The learning index is plotted as means ± SDs of five consecutive blocks of the circuit game, except for the functional MRI retention session for which the overall learning index is plot as means ± SD of the eight blocks. Insert: Continued learning is plotted as the learning index evolution compared the first block of the functional MRI session (‘new baseline’).

Numbers on the x-axis refer these blocks. Open triangles = sham; filled squares = dual-transcranial DCS. Post hoc analysesThe post hoc analyses contrasted interventions for each functional MRI condition. There was no significant difference between the two interventions for REPLAY t(74) = 2.30; q(FDR) = 0.05. With LEARNING and EASY, several areas were more activated during the retention session 1 week after sham compared to dual-transcranial DCS t(74) = 2.30,. By contrast, no area was more activated during the retention session 1 week after dual-transcranial DCS compared to sham at the same t. Activation patterns 1 week post-intervention.

Whole-brain activation of the 19 stroke patients with the LEARNING, EASY, REPLAY and LEARNING − REPLAY contrasts, for both sessions t(74) = 2.30 and for each session separately i.e. 1 week after sham/dual-transcranial DCS; t(18) = 2.13. The damaged hemisphere is on the right. Easy and Replay conditions evoked more consistent activation than the Learning condition. In fact, during Easy, the patients performed similar and consistent movements, associated with a larger amount of consistent brain activation. During Replay, as no movement was performed, the blood oxygen level-dependent signal observed was minimally contaminated by motion artefacts and resulted thus in higher levels of brain activation. Effect of intervention: regions of interest found with the first random effectIn each region of interest activated during LEARNING, paired Student t-tests compared the blood oxygen level-dependent (BOLD) activation between the two sessions.

Correlations with retention: regions of interest found with the two separate random effectA week after sham, a significant correlation between the overall mean learning index and mean beta weights of LEARNING – REPLAY for each patient was observed exclusively in M1 undamH (r = 0.61, P = 0.005). By contrast, 1 week after dual-transcranial DCS, there was a significant correlation only in PMd damH (r = 0.63, P = 0.004). There were no statistically significant correlations between overall mean learning index and mean beta weights of the regions of interest activated with REPLAY. Correlation analysisCorrelation analyses between the learning index or performance index after sham intervention and beta weights of the activated regions of interest showed a statistically significant positive correlation in the M1 damH learning index: r = 0.81, P = 0.01; performance index: r = 0.74, P = 0.04, M1 undamH learning index: r = 0.84, P. Neural substrates underlying the retention of dual-transcranial DCS enhanced motor skill performanceThis study confirms, in a new cohort of stroke patients, our previous observation that dual-transcranial DCS enhances not only online motor skill learning with the paretic upper limb, but also improves long-term retention of the learned motor skill. The retention level of the learned motor skill was strikingly comparable across the two studies: mean learning index values 1 week after the sham intervention were 12% in the present study ( n = 19) and 6% in the previous study ( n = 18), and the analogous mean learning index values after dual-transcranial DCS were 52% and 51%, respectively.

Thus, behaviourally, there was no major impact of the experimental modifications specific to the current study (e.g. Supine position without direct visual feedback of the paretic hand, in the scanner environment during the retention session, and the addition of the Replay and Easy conditions).It is worth noting that the brain activation related to visual-visuomotor processes (Replay) 1 week after the interventions was similar in both series and did not correlate with skill performance. Thus, the Replay condition did not participate directly in motor skill retention (e.g. Through rehearsal or by acting with additional feedback), which justifies its subtraction from the activation data during motor skill performance (Learning).The less efficient 1-week retention of the learned motor skill after the sham intervention was associated with extensive recruitment of both hemispheres and prominent involvement of M1 undamH; which might be interpreted as compensatory based on the positive correlation with motor skill retention. By contrast, the highly focused functional MRI pattern observed after dual-transcranial DCS tended towards normalization with lesser activation in both hemispheres.

It is particularly interesting to note that for PMd damH, the ANOVA and the predefined regions of interest analysis showed more widespread activation 1 week after sham compared to dual-transcranial DCS whereas the other regions of interest analyses revealed another activation peak 23 mm away in PMd damH, which activation was more intense 1 week after dual-transcranial DCS and correlated with retention, suggesting a key role for PMd damH in the efficient long-term retention of motor skills learned with a paretic limb. This is the first study to unveil specific functional MRI activation supporting long-term retention of a motor skill learned with dual-transcranial DCS facilitation. As dual-transcranial DCS was applied 1 week before the functional MRI retention session, the brain activation patterns cannot be attributed to neuronal or vascular after-effects of transcranial DCS.

Several hypotheses may explain this overall lesser activation found 1 week after dual-transcranial DCS compared to sham such as diminished need for sensory feedback processing, online error correction and/or attentional resources once the skill is learned, or enhanced neural efficiency, but the present experiment was not designed to explore this issue. Behavioural and neurophysiological transfers 1 week post-interventionWe observed two types of transfer: (i) a behavioural improvement on an untrained dexterity task; and (ii) an improvement of the functional MRI pattern underlying simple movements even in the absence of a performance difference. First, the improvement of the learned motor skill facilitated by dual-transcranial DCS transferred to improvement of general paretic hand’s dexterity as evidence by performance in an untrained motor task (the Purdue Pegboard Test). The former type of behavioural transfer following NIBS has been associated with increased functional MRI activation in the damaged hemisphere during performance of a generic (untrained) motor task in previous studies (;; ).

Such a transfer of behavioural enhancement to an untrained dexterity task 1 week after dual-transcranial DCS is very promising for neurorehabilitation. Motor skill learning boosted by dual-transcranial DCS could reshape activity in the motor system enduringly and lead to more efficient recruitment of neural resources.In the latter form of transfer, a striking change of brain activation pattern was observed in the absence of behavioural difference. During the retention functional MRI sessions, the stroke patients performed simple untrained movements with the paretic upper limb, without a SAT constraint (Easy). One week after the intervention, the kinematic parameters of these simple back and forth movements did not differ between the sham and dual-transcranial DCS series.

However, after the sham intervention, activation was more widespread (and thus likely less efficient), especially in the premotor-motor areas, compared to that observed in the dual-transcranial DCS series. As the Easy condition was interleaved with the performance of the learned motor skill, we cannot conclude whether this activation pattern change (i.e.

A much focused activation pattern in the absence of a behavioural difference) was independent of the practice of the learned motor skill. Two different, but equally interesting, mechanisms could explain this observation. The most optimistic interpretation is that learning a complex visuomotor skill with concurrent dual-transcranial DCS shapes the motor system in such an efficient and lasting way that, subsequently, even simple and untrained movements are performed with a less widespread activation pattern, suggesting lesser neural activation. The most restrictive interpretation would be that reperforming the motor skill acquired with dual-transcranial DCS facilitation primes the current activity of the motor system and enhances its efficiency, even for untrained movements. Either mechanism is promising but entails different implications for implementation in neurorehabilitation. It has to be acknowledged that these interpretations are speculative and that more work is needed before implementing transcranial DCS in routine clinical practice, determining whether patients with a cortical or subcortical stroke would equally respond, etc. Neural substrates underlying the enhancement of continued motor skill learning after interventionThis study is the first to explore continued motor skill learning 1 week after NIBS.

One week after the sham intervention, six of the hemiparetic stroke patients did not achieve continued learning (non-learners). In striking contrast, 1 week after dual-transcranial DCS, there were only three non-learners. The amount of continued motor skill learning was superior 1 week after dual-transcranial DCS compared to that of the sham intervention, although the rate of continued motor skill learning did not differ. Hence, the advantage yielded by dual-transcranial DCS since the first block of motor skill learning persisted 1 week later, which is obviously appealing for neurorehabilitation. For example, if applying dual-transcranial DCS during motor skill learning on Monday could enhance neurorehabilitation and continued skill learning for the rest of the week, then weekly transcranial DCS treatments would be easier to organize than daily sessions. However, it would first need to be demonstrated that such a ‘dual-transcranial DCS priming’ regimen is as effective as daily sessions during motor skill learning/neurorehabilitation.This study is also the first to explore the neural substrates supporting continued motor skill learning after NIBS in stroke patients. In sharp contrast to the widely distributed activation observed after the sham intervention, 1 week after dual-transcranial DCS, efficient continued motor skill learning was supported by a less widespread network focused on the damaged hemisphere, which resembled the activation pattern observed in healthy individuals (i.e.

Free bk precision manuals

M1 damH, SMA damH, PMd damH, and the contralesional cerebellum). Moreover, a significant correlation between activation and performance was found exclusively in PMd damH 1 week after dual-transcranial DCS, compared to the numerous significant correlations observed after sham (M1 damH, M1 undamH, PPC damH, PPC undamH, IPC damH, and S1 damH). These differences suggest that the less efficient continued motor skill learning after the sham intervention required a larger amount of bilateral neural resources.

As the after-effects of dual-transcranial DCS are unlikely to persist for an entire week, such long-lasting enhancements suggest that a (durable) modification of synaptic and neural activity had consolidated in the motor network after dual-transcranial DCS. One can thus safely infer that it is precisely this lasting enhancement of brain activation (i.e. The trend towards normalization of the functional MRI pattern and recruitment of PMd damH), which supported more efficient continued learning 1 week after dual-transcranial DCS. LimitationsThe current experiment has several limitations. First, the sample of patients with hemiparetic chronic stroke was relatively heterogeneous, as in several other recent studies (;;; ). However, we think this apparent weakness might be considered as strength (generalization) when considering the implementation of transcranial DCS in clinical neurorehabilitation settings with a diversity of stroke patients.Second, before implementing NIBS in standard neurorehabilitation, larger multi-centre trials should be performed. The number of stroke patients recruited in the current experiment compares fairly with previous studies.Third, it has to be acknowledged that the subgroups of patients who achieved continued learning 1 week post-intervention were not identical between the dual-transcranial DCS and sham series.

Indeed, some patients could not achieve continued motor skill learning and were thus excluded from the analysis as non-learners (three after dual-transcranial DCS, six after the sham intervention). Despite this limitation, the striking focusing of functional MRI activation during continued motor skill learning after dual-transcranial DCS suggests that the more efficient recruitment of neural resources lasted at least 1 week.Fourth, a previous study reported that transcranial DCS targeting M1 exclusively in the damaged (anodal) or undamaged (cathodal) hemisphere improved simple reaction time task in chronic stroke patients whereas dual-transcranial DCS failed to do so. Furthermore, cathodal transcranial DCS over the undamaged hemisphere can worsen residual function of the paretic upper limb in severely impaired stroke patients. In contrast to these experiments that used relatively simple motor tasks, in the current experiment and in previous ones (, ), we used challenging motor tasks and found consistent enhancement of digital dexterity and motor skill learning with dual-transcranial DCS in a large number of chronic stroke patients with mild to moderate hemiparesis (modified Rankin Scale 0–4, NIHSS 0–7), with worsening of neither the paretic nor non-paretic upper limb.

Future studies shall aim to compare different transcranial DCS protocols in stroke patients formally using challenging motor tasks and to identify surrogate markers of responsiveness, such as markers based on the lesion burden of the corticospinal tract (; ) or magnetic resonance spectroscopy.Fifth, this study has several statistical limitations. The thresholds used for the functional MRI analyses were voluntarily liberal ( P uncorrected. General conclusionThe combination of motor skill learning and dual-transcranial DCS resulted in lasting enhancements of paretic upper limb function in chronic stroke patients, both in the form of improvement specific to the learned motor skill benefit and of generic enhancement (transfer).

The enhancement specific to motor skill learning was supported by a (relative) normalization of the brain activation 1 week after dual-transcranial DCS (i.e. Compared to sham, less activation in the undamaged hemisphere and a focusing in the damaged hemisphere, especially in PMd damH). Thus, dual-transcranial DCS combined with motor skill learning gave rise to a durable modification of brain activation pattern in stroke patients, which resulted in enhanced retention and continued motor skill learning.The generic enhancement driven by dual-transcranial DCS benefitted both dexterity of the paretic hand on an untrained task (behavioural transfer) and less widespread brain activation pattern when performing simple, untrained movements with the paretic limb. It remains an open question as to whether these generic enhancements resulted from a lasting shaping of brain activation or from a priming of the motor system after performing the motor skill learned 1 week before during dual-transcranial DCS. Both interpretations are promising for neurorehabilitation but imply different approaches. Overall, the functional MRI patterns observed 1 week after the intervention tended towards a normalization of brain activation and an apparently adaptive recruitment of PMd damH, suggesting that dual-transcranial DCS combined with motor skill learning induced a prolonged shaping of brain activation. Boggio PS, Nunes A, Rigonatti SP, Nitsche MA, Pascual-Leone A, Fregni F.

Repeated sessions of noninvasive brain DC stimulation is associated with motor function improvement in stroke patients. Restor Neurol Neurosci. 2007; 25:123–9. Bolognini N, Vallar G, Casati C, Latif LA, El-Nazer R, Williams J, et al. Neurophysiological and behavioral effects of tDCS combined with constraint-induced movement therapy in poststroke patients. Neurorehabil Neural Repair.

2011; 25:819–29. Bonita R, Beaglehole R. Recovery of motor function after stroke. 1988; 19:1497–500. Bosnell RA, Kincses T, Stagg CJ, Tomassini V, Kischka U, Jbabdi S, et al. Motor practice promotes increased activity in brain regions structurally disconnected after subcortical stroke. Neurorehabil Neural Repair.

2011; 25:607–16. Boyd LA, Vidoni ED, Wessel BD.

Motor learning after stroke: is skill acquisition a prerequisite for contralesional neuroplastic change? Neurosci Lett. 2010; 482:21–5.

Bradnam LV, Stinear CM, Barber PA, Byblow WD. Contralesional hemisphere control of the proximal paretic upper limb following stroke.

Cereb Cortex. 2012; 22:2662–71. Caimmi M, Carda S, Giovanzana C, Maini ES, Sabatini AM, Smania N, et al. Using kinematic analysis to evaluate constraint-induced movement therapy in chronic stroke patients. Neurorehabil Neural Repair.

2008; 22:31–9. Carey JR, Kimberley TJ, Lewis SM, Auerbach EJ, Dorsey L, Rundquist P, et al. Analysis of fMRI and finger tracking training in subjects with chronic stroke. 2002; 125:773–88.

Contreras-Vidal JL, Buch ER. Effects of Parkinson's disease on visuomotor adaptation. Exp Brain Res. 2003; 150:25–32. Dayan E, Cohen LG.

Neuroplasticity subserving motor skill learning. 2011; 72:443–54. Dipietro L, Krebs HI, Volpe BT, Stein J, Bever C, Mernoff ST, et al. Learning, not adaptation, characterizes stroke motor recovery: evidence from kinematic changes induced by robot-assisted therapy in trained and untrained task in the same workspace. IEEE Trans Neural Syst Rehabil Eng. 2012; 20:48–57.

Halsband U, Lange RK. Motor learning in man: a review of functional and clinical studies. J Physiol Paris. 2006; 99:414–24. Hardwick RM, Rottschy C, Miall RC, Eickhoff SB. A quantitative meta-analysis and review of motor learning in the human brain. 2013; 67:283–97.

Hummel FC, Cohen LG. Non-invasive brain stimulation: a new strategy to improve neurorehabilitation after stroke? Lancet Neurol. 2006; 5:708–12. Hummel FC, Voller B, Celnik P, Floel A, Giraux P, Gerloff C, et al.

Effects of brain polarization on reaction times and pinch force in chronic stroke. BMC Neurosci. Karni A, Meyer G, Jezzard P, Adams MM, Turner R, Ungerleider LG. Functional MRI evidence for adult motor cortex plasticity during motor skill learning. 1995; 377:155–8.

Kasner SE. Clinical interpretation and use of stroke scales. Lancet Neurol. 2006; 5:603–12. Kasner SE, Chalela JA, Luciano JM, Cucchiara BL, Raps EC, Mcgarvey ML, et al. Reliability and validity of estimating the NIH stroke scale score from medical records. 1999; 30:1534–7.

Kitago T, Krakauer JW. Motor learning principles for neurorehabilitation. Handb Clin Neurol.

2013; 110:93–103. Krakauer JW. Motor learning: its relevance to stroke recovery and neurorehabilitation. Curr Opin Neurol. 2006; 19:84–90.

Krakauer JW, Mazzoni P. Human sensorimotor learning: adaptation, skill, and beyond.

Curr Opin Neurobiol. 2011; 21:636–44. Kwakkel G, Kollen BJ, Van Der Grond J, Prevo AJ. Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke. 2003; 34:2181–6. Lai SM, Studenski S, Duncan PW, Perera S. Persisting consequences of stroke measured by the stroke impact scale.

2002; 33:1840–4. Lefebvre S, Dricot L, Gradkowski W, Laloux P, Vandermeeren Y. Brain activations underlying different patterns of performance improvement during early motor skill learning.

Precision

2012; 62:290–9. Lefebvre S, Laloux P, Peeters A, Desfontaines P, Jamart J, Vandermeeren Y. Dual-tDCS enhances online motor skill learning and long-term retention in chronic stroke patients. Front Human Neurosci. 2013a; 6:343. Lefebvre S, Thonnard JL, Laloux P, Peeters A, Jamart J, Vandermeeren Y.

Bk Precision Manuals

Single session of dual-tDCS transiently improves precision grip and dexterity of the paretic hand after stroke. Neurorehabil Neural Repair. 2013b; 28:100–10. Lindenberg R, Nachtigall L, Meinzer M, Sieg MM, Floel A. Differential effects of dual and unihemispheric motor cortex stimulation in older adults. 2013; 33:9176–83. Lindenberg R, Renga V, Zhu LL, Nair D, Schlaug G.

Bihemispheric brain stimulation facilitates motor recovery in chronic stroke patients. 2010; 75:2176–84. Madhavan S, Shah B. Enhancing motor skill learning with transcranial direct current stimulation - a concise review with applications to stroke. Front Psychiatry. Mai J, Paxinos G, Voss T.

Atlas of the Human Brain. 3rd edn 2007. Matthews PM, Johansen-Berg H, Reddy H. Non-invasive mapping of brain functions and brain recovery: applying lessons from cognitive neuroscience to neurorehabilitation. Restor Neurol Neurosci.

2004; 22:245–60. Meehan SK, Dao E, Linsdell MA, Boyd LA. Continuous theta burst stimulation over the contralesional sensory and motor cortex enhances motor learning post-stroke. Neurosci Lett. 2011a; 500:26–30. 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. 2011b; 32:290–303. Murase N, Duque J, Mazzocchio R, Cohen LG. Influence of interhemispheric interactions on motor function in chronic stroke. 2004; 55:400–9. Nair DG, Renga V, Lindenberg R, Zhu L, Schlaug G. Optimizing recovery potential through simultaneous occupational therapy and non-invasive brain-stimulation using tDCS.

Restor Neurol Neurosci. 2011; 29:411–20. O'shea J, Boudrias MH, Stagg CJ, Bachtiar V, Kischka U, Blicher JU, et al. Predicting behavioural response to TDCS in chronic motor stroke. 2014; 85(Pt 3):924–33. Penta M, Tesio L, Arnould C, Zancan A, Thonnard JL. The ABILHAND questionnaire as a measure of manual ability in chronic stroke patients: rasch-based validation and relationship to upper limb impairment.

2001; 32:1627–34. Picard N, Strick PL. Imaging the premotor areas. Curr Opin Neurobiol. 2001; 11:663–72. Reis J, Schambra HM, Cohen LG, Buch ER, Fritsch B, Zarahn E, 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; 106:1590–5.

Rosso C, Valabregue R, Attal Y, Vargas P, Gaudron M, Baronnet F, et al. Contribution of corticospinal tract and functional connectivity in hand motor impairment after stroke.

2013; 8:e73164. Sehm B, Kipping J, Schafer A, Villringer A, Ragert PA. Comparison between Uni- and bilateral tDCS effects on functional connectivity of the human motor cortex. Front Hum Neurosci.

Stagg CJ, Bachtiar V, O'shea J, Allman C, Bosnell RA, Kischka U, et al. Cortical activation changes underlying stimulation-induced behavioural gains in chronic stroke. 2012; 135:276–84. Stagg CJ, Nitsche MA. Physiological basis of transcranial direct current stimulation.

2011; 17:37–53. Talairach JTP. Co-planar Stereotaxic Atlas of the Human Brain. 3-Dimensional Proportional System: An Approach to Cerebral Imaging. Stuttgart: Thieme Verlag; 1988. Tiffin J, Asher EJ.

The purdue pegboard; norms and studies of reliability and validity. J Appl Psychol. 1948; 32:234–47. Vines BW, Cerruti C, Schlaug G. Dual-hemisphere tDCS facilitates greater improvements for healthy subjects' non-dominant hand compared to uni-hemisphere stimulation.

BMC Neurosci. Waters-Metenier S, Husain M, Wiestler T, Diedrichsen J. Bihemispheric transcranial direct current stimulation enhances effector-independent representations of motor synergy and sequence learning. 2014; 34:1037–50. Yamada N, Kakuda W, Kondo T, Shimizu M, Mitani S, Abo M. Bihemispheric repetitive transcranial magnetic stimulation combined with intensive occupational therapy for upper limb hemiparesis after stroke: a preliminary study. Int J Rehabil Res.

2013; 36:323–9. Zhu LL, Lindenberg R, Alexander MP, Schlaug G. Lesion load of the corticospinal tract predicts motor impairment in chronic stroke. 2010; 41:910–5. Zimerman M, Heise KF, Hoppe J, Cohen LG, Gerloff C, Hummel FC.

Modulation of training by single-session transcranial direct current stimulation to the intact motor cortex enhances motor skill acquisition of the paretic hand. 2012; 43:2185–91.

A novel upper limb motor skill measure, task productivity rate (TPR) was developed integrating speed and spatial error, delivered by a practical motor skill rehabilitation task (MSRT). This prototype task involved placement of 5 short pegs horizontally on a spatially configured rail array. The stability of TPR was tested on 18 healthy right-handed adults (10 women, 8 men, median age 29 years) in a prospective single-session quantitative within-subjects study design. Manipulations of movement rate 10% faster and slower relative to normative states did not significantly affect TPR, F(1.387, 25.009) = 2.465, p =.121. A significant linear association between completion time and error was highest during the normative state condition (Pearson's r =.455, p.

The ability to learn and retain manual skills is fundamental to the achievement of physical goals in everyday life, from rehabilitation from injury to elite sporting endeavors (;; ). A number of objective clinical measures of motor skill are available to assess the level of upper limb manual dexterity or motor skill on continuous scales. The majority of these are derived from techniques for assessment of patient or employee dexterity. But tests can suffer from floor or ceiling effects, whereby the instrument applied proves insensitive to a meaningful change in the level of function performed by an individual or sample. Though studies have considered the relationship between movement rate and spatial accuracy in standardized motor tasks as a possible solution to the issue (;; ), there is as yet no objective means of capturing spatiotemporal performance within a univariate measure, which makes changes in these measured skill parameters difficult to interpret with confidence.Fundamentally, the lack of a working definition for motor skill that provides for the measurement of real-world tasks is a barrier both for clinical and laboratory-based study designs. Skill in any given task is both demonstrated and improved by practice. Whereas performance is concerned with the quality of the execution of a physical activity, skill is defined by the capability to achieve a goal with speed and reliability of precision (; ).

We therefore defined practical motor skill in the following terms: the ability to achieve a practical goal with spatial success over a limited quantity of time. Under this paradigm skill improvement is concerned with improving the accuracy rate, or productivity, in achieving the spatial goal target. It follows that, if participants are to be assessed on these criteria, the appropriate measurement system needs to detect and record both spatial and temporal domains with precision.In relation to human performance, Fitts and Radford considered the effect of movement rate on spatial variability with respect to a manual target with the upper limb. In general terms, for a standardized target of difficulty I D (unit of measure, bits) in an aiming task a subject must on average successfully commit sensorimotor control resources matching or exceeding I D to achieve reliable targeting accuracy. When repeated attempts at a sequence of n standardized targets were made with proportional success ( n-error count) over a mean movement time t, the parameter of performance index (I P) emerged as a mean rate of information transfer capacity, effectively an accuracy rate with unit of measure bits/second.

Varying the cognitive approach (speed-emphasis, accuracy-emphasis, or self-selected cognitive approaches) under which subjects carried out motor skill tasks it was shown that, although reductions in movement time resulted in increases in task error, the peak information carrying capacity of the human motor system appeared quite constant under different cognitive approaches. More recent experimental observations provided further statistical evidence that, in a sample of healthy humans carrying out a simple reciprocation task the information carrying capacity rate was not significantly disturbed within the limits of the movement rates imposed on subjects. This parameter might represent a ceiling of human performance which is, within limits, insensitive to variations in movement rate and, at least in terms of peak performances under standardized instruction, change in emphasis toward speed or accuracy of movement.We set out to investigate whether these concepts could be generalized as a practical skill measure that was responsive to practice but did not vary significantly across behavioral emphasis and hence might be considered as a bias-resistant metric of motor skill. In order to support the gathering of task movement rate and spatial accuracy outcomes a sequential motor skill rehabilitation task (MSRT) was developed.

This consisted of an array of standardized targets across each of which the volunteer was required to place a peg. Targeting variability exceeding the margins of each dichotomous target would be captured by the record of the number of errors incurred during each trial. Within a productivity centered interpretation of the Fitts paradigm the scalar task productivity rate (TPR) measure captured the successful utilization of targeting resources as an inverse function of Fitts’ I P, with units seconds per score (s/score). Motor skill rehabilitation task apparatus and procedure.

All activities were carried out with respect to the left upper limb. (A) Rail angles were adjusted for each participant: illustrated are orientation angles 120°, 60°, 90°, 30°, and 0° with respect to the centerline of the apparatus (black line), which was itself aligned to the left acromion process of the shoulder. Trial procedure: The participant triggered the start of the trial by tapping the start–stop button.

Pegs were grasped from the dish and placed on rail targets in consecutive order from left to right (1–5). The start–stop button was tapped once more to end the trial. ( B) The investigator tilted the rail mounting board to return the pegs to the receiver dish in pseudo-random orientation. ( C) Detail of rail and peg placement. Rails were engineered with a longitudinal groove to securely capture correctly placed pegs, and a central recession limits the effective target footprint dependent on relative orientation angle. An error was scored if a peg failed to retain contact with the upper surface of both raised rail areas following release.Fitts and others showed that endpoint spatial variability appears directly proportional to movement rate and vice versa (; ). We realized that Fitts’ law had potentially important implications for task design, to facilitate both measurement and regulation of motor skill during skill learning.

In accordance with Fitts’ law, if the difficulty of all targets in the task array were identical then, as movement rate increased beyond a single threshold level, the rate of error would rapidly increase at every target. Sensitivity of the measure to changes in spatial variability would be lost, constituting a floor effect. Furthermore, observation of spatial error is vital both for regulation and learning of motor skill. We predicted that, if the task were made up of identical targets then, operating at any given movement rate subjects would observe either very high or very low levels of error during successive trials which might impair behavioral reorganization during skill learning.As a solution to both of these problems we sought to create a marginal scaling of target difficulty within which subjects could develop motor skill and measures of the skill parameters could be taken. In order to achieve this we took the novel approach of manipulating target I D by varying the rotation angle of each target in the array over an incremental range because, as a generalization target intolerance rather than target dimension may be a valid means of quantitatively characterizing target difficulty.

Randomization was applied to the order of target angle orientation to control for possible order and positioning effects, which are known to affect movement times. To minimize a possible interference with declarative sequence learning this was designed to be attempted in consecutive order from left to right in all cases. Research Question 1We sought to answer the primary research question in relation to the importance of behavioral bias on skilled motor output: does the TPR univariate measure of motor skill vary significantly dependent on behavioral variation? Upholding the null hypothesis would not conclusively prove that this measure was stable across all conditions, but it would provide evidence that the TPR skill measure does not vary significantly due to changes in behavioral approach alone. Within the limitations of the study design, this would support our notion that, a common solution might exist to the speed/accuracy trade-off, which provides a stable metric of spatial motor skill. Research Question 3It was theorized that target difficulty, and hence both the sensorimotor resources required to achieve target matching, could be manipulated by varying the orientation of the rail target.

We sought to establish whether target difficulty constituted a stable scale for observations of spatial error, hence providing a reliable feedback condition for modulation of movement rate. The null hypothesis was that, based on observations of error, the relative target difficulty did not vary significantly during free practice conditions. Research Question 4The skill parameters of motor output and sensory experience are intimately associated in optimization of goal-centric motor performance through adaption , which informs the development of more sophisticated motor engrams. As a parsimonious means of considering the relationship between the skill parameters we observed and analyzed the linear associations between the proxy skill parameters of MRST completion time and error rate during each condition. The null hypothesis was applied, that manipulation of behavior would not give rise to a significant difference in the strength of linear association between the skill parameters under the speed- or accuracy-emphasis conditions compared to the normative state. RecruitmentEighteen healthy, right-handed (modified Edinburgh Handedness Inventory; median = 100, range = 67–100) adult staff or student members of the university population (10 women, 8 men; median age = 29 years, age range = 22–67 years) who were free from history of neurological deficit, upper limb orthopedic condition, or uncorrected visual impairment provided written consent to participate in this study, which was approved by the Research Ethics Committee of the School of Health Sciences and Social Care, Brunei University, London.

Each volunteer carried out the study protocol during a single interval lasting around one hour, at a campus behavioral laboratory facility. All activities were designed and carried out in accordance with the Declaration of Helsinki. No financial or other inducement to take part in this study was provided. Study design schematic.

The mean completion time of two blocks of 20 Motor skill rehabilitation task trials provided the metronome tempo guiding the completion rate of the subsequent movement-rate guided blocks. Behavioral manipulation is applied in three blocks, which are presented in counterbalanced randomized order: each block emphasizes completion speed, task accuracy, or nominal (combined speed and accuracy) conditions. Subjects were allowed 4–6 practice trials before each practice block and condition. A final block of trials at self-selected speed were gathered in order to assess short-term learning effects. Practice BlocksParticipants were asked to carry out the task using any preferred grasp pattern or approach and using the left arm only, but as accurately and quickly as possible. This motivational statement was repeated once at the start and twice during the course of every block of 20 MSRT trials, with the terms quickly and accurately spoken in alternating order, in order to prevent biasing of behavioral approach to the task practice. First, after explaining the procedure, participants were directed to carry out practice 4–6 trials of the MSRT to demonstrate understanding of the instructions.

Immediately following this, each participant carried out two blocks of 20 practice trials, in blocks 1 and 2. The mean completion time from these 40 calibration trials was immediately calculated from the spreadsheet record and generated the movement rates for normative, speed- and accuracy-emphasis blocks as per. Because the motor skill rehabilitation task (MSRT) comprised 11 idealized movement intervals between 12 spatial point positions in a full trial, 11 metronome beats signal the start of successive movements with each trial ending on the 12th beat.

The calibration movement rates are derived from the mean MSRT completion time calculated from 40 trials over practice blocks 1 and 2. BPM = beats per minute/metronome cadence.Participants then carried out a total of 60 behavioral manipulation trials, with the behavior manipulated by guidance of movement rate as discussed subsequently. Following these, participants carried out a final free practice block of 20 trials under the same conditions at practice blocks 1 and 2 in order to evaluate the sensitivity of the outcome measure to motor learning over the duration of the protocol. Behavioral Manipulation: Effect of Speed on Spatial AccuracyAfter the approach of, we manipulated the movement rate as the independent variable. A metronome was used to impose a movement rate, which would reliably guide participants to complete MSRT trials at a rate of our choosing.In order to analyze the consequence of changing approaches to a task on spatial accuracy, we applied a movement rate (cadence) at the normative (guide) speed, 10% faster during speed-emphasis trials and 10% slower during accuracy emphasis trials. In order to entrain manual performance, participants were instructed to attend to the sound of an aural metronome tempo (Aroma Scroll-Wheel AM-703, Shenzhen City, China) as a guide to the desired movement rate between each of the 12 critical point-to-point reaching movements involved in a single full trial of the MSRT task.At each movement rate participants carried out 4–6 practice trials followed by a block of 20 trials at each metronome-guided movement rate in counterbalanced, randomized order.

Task completion times were monitored online and volunteers advised to adjust movement rate accordingly if this diverged from the target. In all behavioral conditions participants were asked to maintain the best accuracy possible while moving at the indicated cadence. Completion times were monitored online throughout and, if necessary, participants verbally motivated to attend more closely to the required movement rate. Data Capture and Calculation of TPR Skill MeasureThe on-off trigger switch on the MSRT task assembly (JellyBean Twist, art.

4088, Inclusive Technology Ltd., Oldham, England) was connected to a Simple Switch Adaptor (Inclusive Simple Switch Box, art. 3208, Inclusive Technology Ltd.) into the USB port of a computer running Microsoft Windows software and program Microsoft Office Excel 2007. A stopwatch application was utilized as a freeware add-in for Excel. The layout of the apparatus was as shown in. Layout of the motor skill rehabilitation task (MSRT) apparatus for left-handed training. The participant was seated with the MSRT apparatus (A) to the front.

The MSRT start-stop button was linked to the computer (B) via a switch box and USB link. The investigator sits to the right of the participant, which provides for good surveillance of performance and physical access to reset the task without physically disturbing the participant. Resetting of the software-driven stopwatch via the computer mouse, and administration of the error log (C) was carried out by the same investigator.During MSRT practice, repeated operation of the stopwatch button at the start and end of each trial automatically generated a spreadsheet output of trial durations, while the investigator recorded the number and the position of errors within the rail array as they occurred, for later analysis. The TPR score was then calculated as the completion time for each individual trial divided by the residual number of accurate placements achieved during that trial to provide a parsimonious measure of task-speciflc information carrying capacity over the sampling period. Trial-by-Trial SummaryThis was adopted as an approach to investigate the systematic association between the skill parameters underlying the skill measure during each of the free practice and behaviorally guided block conditions. The skill parameters of Task completion time and error score were separately summarized, creating datasets of 20 values for each practice block by taking the arithmetic mean of the raw values for each individual consecutive trial across the 18-strong participant sample.

It was reasoned that the effect of synchronous, non-zero mean associations between the skill parameters over each 20-trial sampling interval would emerge. Statistical TestsResearch questions 1 and 2 were tested by one-way repeated measures analysis of variance (ANOVA) with the main factor of practice/behavioral emphasis block to control for the possibility of rejecting a null hypothesis. Tests were applied to assess the effect of task practice on error rate, completion time and the TPR skill measure relative to the normative/baseline state. The factor of block (3 levels) was applied in each case.Approaching research question 3, separate analyses on the error distribution-summarized datasets across free practice and behaviorally manipulated blocks was made by two-way ANOVA, with main within-subjects factors of Block (3) and Angle (5) in each case.

The same analysis was applied to investigate variations in error distribution observed under behavioral manipulation. For further analysis of the differences in error distribution between paired behavioral conditions ANOVAs were carried out with Block (2) and Angle (5).

Mauchly's test of sphericity was applied to all analyses and, where significant, degrees of freedom were adjusted using the Greenhouse-Geisser epsilon correction. Bonferroni corrections were applied for paired and post hoc comparisons of main effects as indicated in the text.For research question 4, parametric associations between the error trial-by-trial summarized skill parameter datasets were calculated between the trial-by-trial summarized datasets using Pearson's product moment correlation coefficient (PMCC) to test the null hypothesis that the strength of association between paired correlations was not significantly different. Following this, comparisons of differences in correlation across paired conditions were made using the Steiger's test method advocated by, following r-to- z transformation.

The two-tailed significance of the z values was established from tables. R x dependencies between the predictor variable time series were calculated as the PMCC r between the time series for the relevant behavioral conditions.

A Bonferroni correction was applied for multiple comparisons such that the level of significance was 2.5%.Effect and sample size calculations were derived from Cohen's d where d values of 0.2, 0.5, and 0.8 represent small, moderate, and large effect sizes, respectively. All statistical tests were performed using SPSS (version 15). The Effect of Manipulating Behavioral Approach on TPRResults of the one-way ANOVA are given in.

Behavioral manipulation of mean movement rate had a highly significant effect on task completion time in the accuracy-emphasis condition at 109.9 ± 1.4% and speed-emphasis at 90.6 ± 0.5% completion times, respectively, compared to the norm condition, F(1.184, 20.133) = 144.96, p =.001. Pairwise, there were highly significant differences ( p. Results of separate one-way analyses of variance for the effect of free practice, or manipulation of behavioral conditions, across three blocks of 20 MSRT trials.

TPR = Task Productivity Rate. Significant at. p ≤.05.

p ≤.001.Compared to the normative state, when speed was emphasized to reduce the completion time by 10% the mean aggregate error approximately doubled to 200.6 ± 50.6% of the norm value. In contrast, when we increased completion time thereby allowing for increased accuracy, error was reduced to 78.8 ± 20.4% of that in the norm state. The main effect of varying movement rate on the occurrence of error was significant, F(1.055, 17.943) = 6.291, p =.021, with pairwise comparisons indicating a significant difference between the speed and accuracy conditions ( p =.005; 95% CI 0.36, 2.08). The Effects of Free Task Practice on TPRThe main effect of practice on task completion time was highly significant, F(2, 34) = 51.553, p. Proportional error distribution over successive blocks of free task practice.

Proportional scaling of observed error distribution did not vary significantly over successive practice intervals and was quasi-linear according to the angle of target rail orientation. Two-way analysis of variance. Rail angle in increasing order of observed error, left to right.

Distribution of error count per rail angle as a proportion of the total error count across all angles per interval ± standard error of the mean, over successive blocks. Angle graphic illustrates the respective rail orientation as seen by the participant. ## = significant main effect (p.

Highly significant variations in error distribution occur as a result of behavioral manipulation. Behavioral manipulations: normative speed, speed-emphasis and accuracy-emphasis conditions. Two-way analysis of variance.

Angle graphic illustrates the respective rail orientation as seen by the participant, ordered left to right as per. ### = highly significant main effect ( p. p ≤.001.The two-way ANOVA of the effect of behavioral manipulation on error distribution revealed a highly significant main effect of rail target angle, F(2.505, 42.588) = 10.475, p.

p ≤.05.No significant correlations between completion time and error rate were found to occur over any of the free practice blocks. Furthermore, associations between skill parameters under speed and accuracy conditions were found to be nonsignificant. TPR Did Not Vary Significantly Across Behavioral ConditionsDespite highly significant variations in guided movement rates the TPR skill measure did not vary significantly compared to the normative rate condition, upholding the null hypothesis. As variations in movement rate were imposed during behavioral guidance, there was a corresponding systematic impact on spatial accuracy such that TPR was not significantly affected. The results suggest that, when the variable of learning experience was controlled for, the sample of healthy subjects maintained a stable mean level of skill as we defined it, even when movement rates varied systematically by as much as 20%. The null hypothesis for research question 1 was upheld.The generalizable inference is that, within limits, around an optimal peak performance specific to the individual and the task, there exists a common solution to the speed/accuracy trade-off function. This result is consistent with Fitts’ law but is, we believe, the first time that the theory has been applied in respect of a practical manual visuomotor activity involving complex movement sequences.The results concur with those found in analysis of performance levels in a simple reciprocation task, which likewise did not significantly differ over a range of movement rates.

Though the mean scores between behavioral conditions were not significantly different the data did show that reducing or increasing movement rate relative to the normative level resulted in a negative impact on TPR scores. The ability to demonstrate information carrying capacity of the individual may fall off away from an optimal central value, which could be partially dictated by the spatial parameters of the target or, indeed the behavioral approach. In the interest of rigorously testing the limits of stability of the TPR outcome measure we further tested two different hypothetical scenarios. These results showed that TPR is only stable within limits and that the extent of behavioral manipulation alone may interact with the study design and sample size to give rise to large effect sizes. These findings are noteworthy when considering the statistical power of future study designs. Spatial Error as a Modulating Parameter in Skilled Motor ActivityIn the MSRT design paradigm target difficulty was manipulated, not by the conventional method of modifying component dimensions but by employing a reverse kinematic principle (; ) to enforce more or less complex grasp combinations across the motor sequence.

The aims of this design criterion were twofold: to provide for target difficulty scaling in a fashion designed both to improve the linearity of measurement, and to facilitate a naturalistic motor learning experience. The design also provided for control for order effects by implementing true randomization of target orientation across the sample.We found that the scale of target difficulty, as inferred from observations of error distribution, did not vary significantly when analyzed across free practice blocks and maintained a reliable distribution constituting a continuous quasilinear scale. In relation to research question 3, the null hypothesis was upheld.By varying the single parameter of target orientation, the difficulty of otherwise identical sub-task elements was modulated to increase the range and sensitivity of the aggregate error measure, which we theorize provided for explicit feedback of spatial error to inform aspects of future performances, including movement rate. The finding that over successive practice sessions the error distribution did not significantly vary also lends support to the theory that skill learning reflects improvements in global control parameters such as refinement of reaching kinematics rather than improvements based on the accuracy constraints of specific targets.

The behavioral advantage of varying movement rates based on recent feedback of targeting error may be that movement rates can be regulated over the short-term, around a level that achieves the task objective according to the behavioral emphasis under operation.Significant variations in the distribution of error were detected between the extremes of speed and accuracy emphasis behavioral conditions. It is thought motor learning results from reduction, not negation of net spatial variability with minimal correction of spatial error to optimally achieve the goal outcome while the variability of spatial trajectory in reaching increases as a function of movement planning both in relation to the accuracy demands of the task (; ) and the intensity of muscle activations.

Systematic Associations Between the Skill ParametersOur results in respect of the calculated TPR skill measure were consistent with Fitts’ law in showing that, despite modification of behavioral approach under parallel skill states the construct of motor skill based on information transfer (of which the TPR is a derivative) was statistically robust. We have suggested that the criterion governing movement rate in the MSRT task was observation of spatial error. But was there any direct evidence of co-regulation between the skill parameters and, furthermore, was there evidence that behavioral manipulation significantly altered coregulation behavior?A significant linear correlation between sample mean movement rate and spatial error scores, where error rate varied proportionally with completion time was only observed when behavior was constrained at the normative movement rate. Because normative movement rate was individually matched to the previously observed average of self-selected task completion times, it is appealing to generalize that the ideal movement rate emerged as a result of coregulation between the two skill parameters. ConclusionsThis study was concerned with investigating the properties of a novel univariate outcome measure inferential of task-dependent motor skill learning. In contrast to highly significant changes in the component skill parameter of task completion time, the TPR measure was statistically stable over large perturbations in behavioral approach.