Posts Tagged motor learning

[Abstract+References] Virtual reality software package for implementing motor learning and rehabilitation experiments


Virtual reality games for rehabilitation are attracting increasing growth. In particular, there is a demand for games that allow therapists to identify an individual’s difficulties and customize the control of variables, such as speed, size, distance, as well as visual and auditory feedback. This study presents and describes a virtual reality software package (Bridge Games) to promote rehabilitation of individuals living with disabilities and highlights preliminary researches of its use for implementing motor learning and rehabilitation. First, the study presents seven games in the software package that can be chosen by the rehabilitation team, considering the patient’s needs. All game characteristics are described including name, function presentation, objective and valuable measurements for rehabilitation. Second, preliminary results illustrate some applications of two games, considering 343 people with various disabilities and health status. Based on the results, in the Coincident Timing game, there was a main effect of movement sensor type (in this instance the most functional device was the keyboard when compared with Kinect and touch screen) on average time reached by sample analyzed, F(2, 225) = 4.42, p < 0.05. Similarly, in the Challenge! game, a main effect was found for movement sensor type. However, in this case, touch screen provided better performance than Kinect and Leap Motion, F(2, 709) = 5.90, p < 0.01. Thus, Bridge Games is a possible software game to quantify motor learning. Moreover, the findings suggest that motor skills might be practiced differently depending on the environmental interface in which the game may be used.


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Source: Virtual reality software package for implementing motor learning and rehabilitation experiments | SpringerLink


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A better understanding of the neural substrates that underlie motor recovery after stroke has led to the development of innovative rehabilitation strategies and tools that incorporate key elements of motor skill re-learning, i.e. intensive motor training involving goal-oriented repeated movements. Robotic devices for the upper limb are increasingly used in rehabilitation. Studies have demonstrated the effectiveness of these devices in reducing motor impairments, but less so for the improvement of upper limb function. Other studies have begun to investigate the benefits of combined approaches that target muscle function (functional electrical stimulation and Botulinum Toxin injections), modulate neural activity (Noninvasive Brain stimulation) and enhance motivation (Virtual Reality) in an attempt to potentialize the benefits of robot-mediated training. The aim of this paper is to overview the current status of such combined-treatments and to analyze the rationale behind them.

1. Introduction
Significant advances have been made in the management of stroke (including prevention, acute management and rehabilitation), however cerebrovascular diseases remain the third most common cause of death and the first cause of disability worldwide[1–6]. Stroke causes brain damage, leading to loss of motor function. Upper limb (UL) function is particularly reduced, resulting in disability. Many rehabilitation techniques have been developed over the last decades to facilitate motor recovery of the UL in order to improve functional ability and quality of life [7–10]. They are commonly based on principles of motor skill learning to promote plasticity of motor neural networks. These principles include intensive, repetitive, task-oriented movement-based training [11–19]. A better understanding of the neural substrates of motor re-learning has led to the development of innovative strategies and tools to deliver exercise that meets these requirements. Treatments mostly target the neurological impairment (paresis, spasticity etc.) through the activation of neural circuits or by acting on peripheral effectors. Robotic devices provide exercises that incorporate key elements of motor learning. Advanced robotic systems can offer highly repetitive, reproducible, interactive forms of training for the paretic limb, which are quantifiable. Robotic devices also enable easy and objective assessment of motor performance in standardized conditions by the recording of biomechanical data (i.e., speed, forces, etc.) [20–22]. This data can be used to analyze and assess motor recovery in stroke patients [23–26]. Since the 1990’s, many other technology-based approaches and innovative pharmaceutical treatments have also been developed for rehabilitation, including virtual reality (VR)-based systems, Botulinum neurotoxin (BoNT) injections and Non Invasive Brain stimulation (NIBS) (Direct Current Stimulation (tDCS) and repetitive Transcranial Magnetic Stimulation (rTMS)). There is currently no high-quality evidence to support any of these innovative interventions, despite the fact that some are used in routine practice [27]. By their respective mechanisms of action, each of these treatments could potentiate the effects of robotic therapy, leading to greater improvements in motor capacity. The aim of this paper is to review studies of combined treatments based on robotic rehabilitation, and to analyze the rationale behind such approaches. […]

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[WHITE PAPER] Virtual and augmented reality based balance and gait training – Full Text PDF

The use of virtual and augmented reality for rehabilitation has become increasingly popular and has received much attention in scientific publications (over 1,000 papers). This white paper aims to summarize the scientific background and efficacy of using virtual and augmented reality for balance and gait training. For many patients with movement disorders, balance and gait training is an important aspect of their rehabilitation process and physical therapy treatment. Indications for such training include, among others, stroke, Parkinson’s disease, multiple sclerosis, cerebral palsy, vestibular disorders, neuromuscular diseases, low back pain, and various orthopedic complaints, such as total hip or knee replacement. Current clinical practice for balance training include exercises, such as standing on one leg, wobble board exercises and standing with eyes closed. Gait is often trained with a treadmill or using an obstacle course. Cognitive elements can be added by asking the patient to simultaneously perform a cognitive task, such as counting down by sevens. Although conventional physical therapy has proven to be effective in improving balance and gait,1,2 there are certain limitations that may compromise treatment effects. Motor learning research has revealed some important concepts to optimize rehabilitation: an external focus of attention, implicit learning, variable practice, training intensity, task specificity, and feedback on performance.3 Complying with these motor learning principles using conventional methods is quite challenging. For example, there are only a limited number of exercises, making it difficult to tailor training intensity and provide sufficient variation. Moreover, performance measures are not available and thus the patient usually receives little or no feedback. Also, increasing task specificity by simulating everyday tasks, such as walking on a crowded street, can be difficult and time consuming. Virtual and augmented reality could provide the tools needed to overcome these challenges in conventional therapy. The difference between virtual and augmented reality is that virtual reality offers a virtual world that is separate from the real world, while augmented reality offers virtual elements as an overlay to the real world (for example virtual stepping stones projected on the floor). In the first part of this paper we will explain the different motor learning principles, and how virtual and augmented reality based exercise could help to incorporate these principles into clinical practice. In the second part we will summarize the scientific evidence regarding the efficacy of virtual reality based balance and gait training for clinical rehabilitation.

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[ARTICLE] The application of virtual reality in neurorehabilitation: motor re-learning supported by innovative technologies – Full Text

The motor function impairment resulting from a stroke injury has a negative impact on autonomy, the activities of daily living thus the individuals affected by a stroke need long-term rehabilitation. Several studies have demonstrated that learning new motor skills is important to induce neuroplasticity and functional recovery. Innovative technologies used in rehabilitation allow one the possibility to enhance training throughout generated feedback. It seems advantageous to combine traditional motor rehabilitation with innovative technology in order to promote motor re-learning and skill re-acquisition by means of enhanced training. An environment enriched by feedback involves multiple sensory modalities and could promote active patient participation. Exercises in a virtual environment contain elements necessary to maximize motor learning, such as repetitive
and diffe-rentiated task practice and feedback on the performance and results. The recovery of the limbs motor function in post-stroke subjects is one of the main therapeutic aims for patients and physiotherapist alike. Virtual reality as well as robotic devices allow one to provide specific treatment based on the reinforced feedback in a virtual environment (RFVE), artificially augmenting the sensory information coherent with the real-world objects and events. Motor training based on RFVE is emerging as an effective motor learning based techniques for the treatment of the extremities.

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[Abstract] Bilateral sequential motor cortex stimulation and skilled task performance with non-dominant hand


  • Both, contralateral M1 iTBS and ipsilateral M1 cTBS improved non-dominant skilled-task performance.
  • Bilateral sequential M1 TBS (contralateral cTBS followed by ipsilateral iTBS) improved skilled-task performance more than unilateral or sham TBS.
  • Bilateral sequential M1 TBS may be particularly effective in improving motor learning, also in the neurorehabilitation setting.



To check whether bilateral sequential stimulation (BSS) of M1 with theta burst stimulation (TBS), using facilitatory protocol over non-dominant M1 followed by inhibitory one over dominant M1, can improve skilled task performance with non-dominant hand more than either of the unilateral stimulations do. Both, direct motor cortex (M1) facilitatory non-invasive brain stimulation (NIBS) and contralateral M1 inhibitory NIBS were shown to improve motor learning.


Forty right-handed healthy subjects were divided into 4 matched groups which received either ipsilateral facilitatory (intermittent TBS [iTBS] over non-dominant M1), contralateral inhibitory (continuous TBS [cTBS] over dominant M1), bilateral sequential (contralateral cTBS followed by ipsilateral iTBS), or placebo stimulation. Performance was evaluated by Purdue peg-board test (PPT), before (T0), immediately after (T1), and 30 min after (T2) an intervention.


In all groups and for both hands, the PPT scores increased at T1 and T2 in comparison to T0, showing clear learning effect. However, for the target non-dominant hand only, immediately after BSS (at T1) the PPT scores improved significantly more than after either of unilateral interventions or placebo.


M1 BSS TBS is an effective intervention for improving motor performance.


M1 BSS TBS seems as a promising tool for motor learning improvement with potential uses in neurorehabilitation.

Source: Bilateral sequential motor cortex stimulation and skilled task performance with non-dominant hand – Clinical Neurophysiology

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[ARTICLE] A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training – Full Text

Although motor learning theory has led to evidence-based practices, few trials have revealed the superiority of one theory-based therapy over another after stroke. Nor have improvements in skills been as clinically robust as one might hope. We review some possible explanations, then potential technology-enabled solutions.

Over the Internet, the type, quantity, and quality of practice and exercise in the home and community can be monitored remotely and feedback provided to optimize training frequency, intensity, and progression at home. A theory-driven foundation of synergistic interventions for walking, reaching and grasping, strengthening, and fitness could be provided by a bundle of home-based Rehabilitation Internet-of-Things (RIoT) devices.

A RIoT might include wearable, activity-recognition sensors and instrumented rehabilitation devices with radio transmission to a smartphone or tablet to continuously measure repetitions, speed, accuracy, forces, and temporal spatial features of movement. Using telerehabilitation resources, a therapist would interpret the data and provide behavioral training for self-management via goal setting and instruction to increase compliance and long-term carryover.

On top of this user-friendly, safe, and conceptually sound foundation to support more opportunity for practice, experimental interventions could be tested or additions and replacements made, perhaps drawing from virtual reality and gaming programs or robots. RIoT devices continuously measure the actual amount of quality practice; improvements and plateaus over time in strength, fitness, and skills; and activity and participation in home and community settings. Investigators may gain more control over some of the confounders of their trials and patients will have access to inexpensive therapies.

Neurologic rehabilitation has been testing a motor learning theory for the past quarter century that may be wearing thin in terms of leading to more robust evidence-based practices. The theory has become a mantra for the field that goes like this. Repetitive practice of increasingly challenging task-related activities assisted by a therapist in an adequate dose will lead to gains in motor skills, mostly restricted to what was trained, via mechanisms of activity-dependent induction of molecular, cellular, synaptic, and structural plasticity within spared neural ensembles and networks.

This theory has led to a range of evidence-based therapies, as well as to caricatures of the mantra (eg, a therapist says to patient, “Do those plasticity reps!”). A mantra can become too automatic, no longer apt to be reexamined as a testable theory. A recent Cochrane review of upper extremity stroke rehabilitation found “adequately powered, high-quality randomized clinical trials (RCTs) that confirmed the benefit of constraint-induced therapy paradigms, mental practice, mirror therapy, virtual reality paradigms, and a high dose of repetitive task practice.”1 The review also found positive RCT evidence for other practice protocols. However, they concluded, no one strategy was clearly better than another to improve functional use of the arm and hand. The ICARE trial2 for the upper extremity after stroke found that both a state-of-the-art Accelerated Skill Acquisition Program (motor learning plus motivational and psychological support strategy) compared to motor learning-based occupational therapy for 30 hours over 10 weeks led to a 70% increase in speed on the Wolf Motor Function Test, but so did usual care that averaged only 11 hours of formal but uncharacterized therapy. In this well-designed RCT, the investigators found no apparent effect of either the dose or content of therapy. Did dose and content really differ enough to reveal more than equivalence, or is the motor-learning mantra in need of repair?

Walking trials after stroke and spinal cord injury,38 such as robot-assisted stepping and body weight-supported treadmill training (BWSTT), were conceived as adhering to the task-oriented practice mantra. But they too have not improved outcomes more than conventional over-ground physical therapy. Indeed, the absolute gains in primary outcomes for moderate to severely impaired hemiplegic participants after BWSTT and other therapies have been in the range of only 0.12 to 0.22 m/s for fastest walking speed and 50 to 75 m for 6-minute walking distance after 12 to 36 training sessions over 4 to 12 weeks.3,9 These 15% to 25% increases are just as disappointing when comparing gains in those who start out at a speed of <0.4 m/s compared to >0.4 to 0.8 m/s.3

Has mantra-oriented training reached an unanticipated plateau due to inherent limitations? Clearly, if not enough residual sensorimotor neural substrate is available for training-induced adaptation or for behavioral compensation, more training may only fail. Perhaps, however, investigators need to reconsider the theoretical basis for the mantra, that is, whether they have been offering all of the necessary components of task-related practice, such as enough progressively difficult practice goals, the best context and environment for training, the behavioral training that motivates compliance and carryover of practice beyond the sessions of formal training, and blending in other physical activities such as strengthening and fitness exercise that also augment practice-related neural plasticity? These questions point to new directions for research….

Continue —> A Rehabilitation-Internet-of-Things in the Home to Augment Motor Skills and Exercise Training – Mar 01, 2017

Figure 1. Components of a Rehabilitation-Internet-of-Things: wireless chargers for sensors (1), ankle accelerometers with gyroscopes (2) and Android phone (3) to monitor walking and cycling, and a force sensor (4) in line with a stretch band (5) to monitor resistance exercises.


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[ARTICLE] Effects of two-handed task training on upper limb function of chronic hemiplegic patients after stroke – Full Text PDF


[Purpose] The purpose of this study was to determine whether two-handed task training is effective on motor learning of injured cerebral cortex activation and upper extremity function recovery after stroke.

[Subjects and Methods] Two hemiplegic subjects participated in this study: one patient was affected on the dominant side of the body and the other was affected on the non-dominant side of the body, and both scored in the range of 58–66 in the Fugl-Meyer assessment. The excitability of the corticospinal tract and Manual Function Test were examined.

[Results] The excitability of the corticospinal tract and the Manual Function Test showed significant differences in the activation of both sides of the cerebral cortex and in the variation in learning effect of upper extremity motor function recovery in patients with hemiplegic non-dominant hand (left).

[Conclusion] The results suggested that two-handed task training had a different influence on dominant hand (right) and non-dominant hand (left) motor recovery.


The dominant hand is defined as the hand that is usually used in performing activities of daily living (ADL). The development of the motor function of the cerebral cortex is asymmetrical to the dominant hand1) . Based on such asymmetrical development of the cerebral cortex, when the left hand is performing a task, the cerebral cortex motor area of the right cerebral hemisphere activates. However, a more interesting fact is that when the right hand is used functionally, the nerve cells of the entire cerebral cortex motor area of the right and left cerebral hemispheres activate2) . This finding supports the evidence of lateralization of the cerebral hemisphere and implies that the left cerebral hemisphere acts the role of the dominant cerebral hemisphere when performing ADL2, 3) . Thus, the left hemisphere, which is the dominant cerebral hemisphere due to the lateralization of the cerebral hemisphere, is more closely related with motor planning in ADL performance, and the same relationships were shown after cerebral hemisphere injury due to stroke3, 4) . Characteristically, the patient with stroke-damaged dominant left cerebral hemisphere reports a time delay on the exercise performance of both right and left hands, whereas the patient with damaged right cerebral hemisphere reports a mild motor function disorder confined to the left hand3) . This means that, consequently, after stroke onset, the patient with hemiplegic dominant hand (right) experiences more difficulty in performing ADL5) . However, regarding upper extremity rehabilitation, there is no study that differentiated the motor function recovery of the upper extremity of patients who are affected in the dominant cerebral hemisphere (with the hemiplegic right hand) or in the non-dominant cerebral hemisphere (with the hemiplegic left hand). A specific rehabilitation approach based on laterality through classification of cerebral damage on the right or left side is needed to achieve a more successful rehabilitation of the upper extremity. Thus, the purpose of this study is to determine whether two-handed task training is effective in the motor learning of the injured cerebral cortex activation and upper extremity function recovery after stroke.

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[ARTICLE] The effectiveness of reinforced feedback in virtual environment in the first 12 months after stroke – Full Text HTML/PDF

Background and purpose: Reinforced feedback in virtual environment (RFVE) therapy is emerging as an innovative method in rehabilitation, which may be advantageous in the treatment of the affected arm after stroke. The purpose of this study was to investigate the impact of assisted motor training in a virtual environment for the treatment of the upper extremity (UE) after stroke compared to traditional neuromotor rehabilitation (TNR), studying also if differences exist related to the type of stroke (haemorrhagic or ischaemic).
Material and methods: Eighty patients affected by a stroke (48 ischaemic and 32 haemorrhagic) that occurred at least 1 year before were enrolled. The clinical assessment comprising the Fugl-Meyer UE (F-M UE), modified Ashworth (Bohannon and Smith) and Functional Independence Measure scale (FIM) was administered before and after the treatment.
Results: A statistically significant difference between RFVE and TNR groups (Mann-Whitney U-test) was observed in the clinical outcomes of F-M UE and FIM (both p < 0.001), but not Ashworth (p = 0.053). The outcomes of F-M UE and FIM improved in the RFVE haemorrhagic group and in the TNR haemorrhagic group with a significant difference between groups (both p < 0.001), but not for Ashworth (p = 0.651). Comparing the RFVE ischaemic group to the TNR ischaemic group, statistically significant differences emerged in F-M UE (p < 0.001), FIM (p < 0.001), and Ashworth (p = 0.036).
Conclusions: The RFVE therapy in combination with TNR showed better improvements compared to the TNR treatment only. The RFVE therapy combined with the TNR treatment was more effective than the TNR double training, in both post-ischaemic and post-haemorrhagic groups. We observed improvements in both groups of patients: post-haemorrhagic and post-ischaemic stroke after RFVE training.
Stroke is one of the main causes of death and disability
in all classes and ethnic origins worldwide. Disability and
motor deficit could be particularly evident in upper
extremities. Indeed, the loss of mobility of the upper
extremity is a major source of impairment in neuro-
muscular disorders, frequently preventing effective oc-
cupational performance and autonomy in daily life [1].
Recent studies demonstrated that the traditional con-
cept of one-to-one rehabilitation [2], where the physi-
cal therapist (or more frequently several ones) interacts
directly with a single patient, could be advantageously
implemented with the use of strategies based on speci –
fic kinematic feedback to improve the motor performance
[3-7]. Patients affected by a stroke represent a consi –
derable number among those patients suffering from
nervous system disorders who need rehabilitation. Epi-
demiological data indicate a mortality rate of 30% in the
first month after stroke independently from the type of
cerebrovascular accident, while 10% of patients were dis-
charged from the hospital without serious functional
impairment [8]. At least 60% of patients affected by stroke
present severely reduced ability to perform activities of
daily living (ADL), with persistent symptoms of focal
brain lesion [1,8,9].
Reinforced feedback in virtual environment (RFVE)
for arm motor training, as demonstrated in previous stud-
ies [3,4,6,10-16], represents a possibility in the field of
the motor learning based technique for the upper limb.
The treatment in the virtual environment with augmented
feedback promotes learning in normal subjects and in
some post-stroke patients with motor deficit involving the
upper extremity [3,16,17]. After a stroke, patients can
improve movement ability with regular, intensive and su-
pervised training [2,12,18-20].
The central nervous system (CNS) shows regene-
ra tive capacities in post-stroke patients [21,22]. It is also
noted that the plasticity of the CNS, thus its adaptabi –
lity to natural developmental changes, is maintained
throughout all the life of a subject regardless of age [23].
Magnetic resonance (MR) imaging and transcranial
magnetic stimulation tests in humans provide evidence
for functional adaptation of the motor cortex following
injury [1,21,24-27]. Neuroimaging has shown evidence
of cortical plasticity after task-oriented motor exercises
[24,26,28]. Furthermore, many studies have demonstra –
ted that neuroplasticity can occur even in the chronic phase
after stroke [1,25,29].
Our study aims to investigate whether the repetition
of tasks (intended as oriented movements of the upper
extremity performed in interaction with a virtual envi-
ronment) could improve motor function in post-ischaemic
and post-haemorrhagic stroke subjects with hemipare-
sis, in comparison to the traditional neuromotor reha-
bilitation (TNR) treatment. The first aim of the study
was to determine the effectiveness of RFVE therapy com-
bined with TNR training compared to the double TNR
in the treatment of patients after stroke. The second ob-
jective was to study the effect of the RFVE therapy,
depending on the kind of stroke (haemorrhagic, ischae –
mic), between patients undergoing the RFVE and
TNR therapy compared to the double TNR training.

Continue (HTML) —> The effectiveness of reinforced feedback in virtual environment in the first 12 months after stroke


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[TEDx Talk] After watching this, your brain will not be the same | Lara Boyd | TEDxVancouver


In a classic research-based TEDx Talk, Dr. Lara Boyd describes how neuroplasticity gives you the power to shape the brain you want. Recorded at TEDxVancouver at Rogers Arena on November 14, 2015.

YouTube Tags: brain science, brain, stroke, neuroplasticity, science, motor learning, identity, TED, TEDxVancouver, TEDxVancouver 2015, Vancouver, TEDx, Rogers Arena, Vancouver speakers, Vancouver conference, ideas worth spreading, great idea,

Our knowledge of the brain is evolving at a breathtaking pace, and Dr. Lara Boyd is positioned at the cutting edge of these discoveries. In 2006, she was recruited by the University of British Columbia to become the Canada Research Chair in Neurobiology and Motor Learning. Since that time she has established the Brain Behaviour Lab, recruited and trained over 40 graduate students, published more than 80 papers and been awarded over $5 million in funding.

Dr. Boyd’s efforts are leading to the development of novel, and more effective, therapeutics for individuals with brain damage, but they are also shedding light on broader applications. By learning new concepts, taking advantage of opportunities, and participating in new activities, you are physically changing who you are, and opening up a world of endless possibility.

This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at

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[ARTICLE] Neural Network Underlying Intermanual Skill Transfer in Humans- Full Text




Unimanual training also enhances performance in the untrained hand (cross-education)

Real-time manipulation of visual feedback enhances magnitude of cross-education
Yoking movement of untrained to trained hand further increases cross-education
Functional connectivity with SPL during training predicts cross-education


Physical practice with one hand results in performance gains of the other (un-practiced) hand, yet the role of sensory feedback and underlying neurophysiology is unclear. Healthy subjects learned sequences of finger movements by physical training with their right hand while receiving real-time movement-based visual feedback via 3D virtual reality devices as if their immobile left hand was training. This manipulation resulted in significantly enhanced performance gain with the immobile hand, which was further increased when left-hand fingers were yoked to passively follow right-hand voluntary movements. Neuroimaging data show that, during training with manipulated visual feedback, activity in the left and right superior parietal lobule and their degree of coupling with motor and visual cortex, respectively, correlate with subsequent left-hand performance gain. These results point to a neural network subserving short-term motor skill learning and may have implications for developing new approaches for learning and rehabilitation in patients with unilateral motor deficits.


It is common wisdom that “practice makes perfect”; however, what constitutes an optimal practice regime when learning a new skill is not clear. In the domain of motor skills, for example, when learning to dribble a basketball, physical training with the relevant effector obviously plays a crucial role. Nonetheless, research over the past decades has recognized that sensory feedback and mental imagery play a significant role in the learning process (Nyberg et al., 2006, Sigrist et al., 2013, Wolpert et al., 2011). In the case of vision, it has been shown that even in the absence of physical training, mere observation of someone else performing a motor task is sufficient to introduce significant gains in subsequent performance of the observer (Bird et al., 2005, Cross et al., 2009, Kelly et al., 2003, Mattar and Gribble, 2005, Nojima et al., 2015, Vogt and Thomaschke, 2007, Ossmy and Mukamel, 2016). Furthermore, passive limb movement has also been shown to facilitate learning (Beets et al., 2012, Darainy et al., 2013, Vahdat et al., 2014, Wong et al., 2012). Finally, physical training with one hand is known to result in significant performance gains in the opposite (untrained) hand—a phenomenon termed intermanual transfer or cross-education (Ruddy and Carson, 2013). Intermanual transfer has been reported as early as 1894, showing that unilateral strength training of a single limb increases the strength of the contralateral (untrained) homologous muscle group (Scripture et al., 1894). Since then, this effect has been demonstrated across multiple motor tasks (Anguera et al., 2007, Brass et al., 2001, Camus et al., 2009, Carroll et al., 2006, Criscimagna-Hemminger et al., 2003, Farthing et al., 2007, Lee et al., 2010, Malfait and Ostry, 2004, Perez and Cohen, 2008, Perez et al., 2007, Sainburg and Wang, 2002) and is suggested to occur through plastic changes in the brain that are not confined to the specific neural networks controlling the physically trained effector (e.g., plastic changes also in motor cortex ipsilateral to the active hand [Duque et al., 2008, Hortobágyi et al., 2003, Muellbacher et al., 2000, Obayashi, 2004]). Enhancing the behavioral effect of intermanual transfer and elucidating its underlying neural mechanism has important implications for rehabilitation of patients with unimanual deficits (Hendy et al., 2012, Ramachandran and Altschuler, 2009) in which direct training of the affected hand is difficult.

Given that visual input, physical training, and passive movement play a significant role in performance and intermanual transfer of motor skills, research in recent years examined the behavioral and neural consequences of training with manipulated visual feedback (Halsband and Lange, 2006). In particular, unimanual training with mirrored visual feedback (as if the opposite, passive hand, is training) has been shown to enhance transfer to the opposite hand and increase excitability of primary motor cortex (M1) ipsilateral to the physically trained hand (Garry et al., 2005, Hamzei et al., 2012, Nojima et al., 2012). Nonetheless, much less is known at the whole-brain network level and how inter-regional coupling during such training correlates with subsequent behavioral changes in performance. Additionally, at the behavioral level, the interaction between manipulated visual feedback and passive movement during training is unknown.

In the present study, we examined intermanual transfer using a novel setup employing 3D virtual reality (VR) devices to control visual feedback of finger movements during unimanual training of healthy adults (experiment 1). By using a novel device, we also examined whether the addition of passive finger movement of the non-physically training hand further enhances the intermanual transfer effect (experiment 2). Finally, we used whole-brain functional magnetic resonance imaging (fMRI) to probe the relevant brain regions engaged during such training and examined their degree of inter-regional coupling with respect to subsequent behavioral changes in performance of individual subjects (experiment 3).

Continue —>  Neural Network Underlying Intermanual Skill Transfer in Humans: Cell Reports

(A) Schematic illustration of one experimental condition. A unique sequence of five digits was presented together with a sketch of the mapped fingers (instructions). Subjects performed the sequence as accurately and rapidly as possible using their right hand (RH) and their left hand (LH) separately for initial evaluation of performance. Next, subjects were trained under a specific training type and finally repeated the evaluation test again. (B) Subjects wore a headset and motion sensitive gloves and received visual feedback of virtual hands. The VR devices allowed visual manipulation of online visual feedback. A camera mounted on the headset allowed embedding the virtual hands and subject’s view inside a natural environment. (C) Experiment 1 results. Physical training with the right hand while receiving online visual feedback as if the left hand is moving (RH-LH) resulted in highest left-hand performance gains relative to all other training conditions. Error bars indicate SEM across subjects. For condition acronyms, see Table 1.

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