1 Graduate School of Health Sciences, Graduate School of Kansai University of Health Sciences and Clinical Physical Therapy Laboratory, Faculty of Health Sciences, Kansai

2 Clinical Physical Therapy Laboratory, Faculty of Health Sciences, Kansai University of

[1] Guillot A, Rienzo FD, Maclntyre T, et al. Imagining is not doing but involves specific motor commands: a review of experimental data related to motor inhibition. Front Hum

[2] Yue G, Cole KJ. Strength increases from of motor program: comparison of training with maximal voluntary and imagined muscle contractions. J Neurophysiol. 1992;67:1114–1123.

[3] Ranganathan VK, Siemionow V, Liu JZ et al. From mental power to muscle power-

[4] Guillot A, Tolleron C, Collet C. Does motor imagery enhance stretching and flexibility?

[5] Luft AR, Skalej M, Stefanou A, et al. Comparing motion- and imagery-related activation in the human cerebellum: a functional MRI study. Hum Brain Mapp. 1998;6:105–113.

[6] Lotze M, Montoya P, Erb M, et al. Activation of cortical and cerebellar motor areas during executed and imagined hand movements: an fMRI study. J Cogn Neurosci. 1999;11:491–501.

[7] Matsuda T, Watanabe S, Kuruma H, et al. Neural correlates of chopsticks exercise for the non-dominant hand: comparison among the movement, images and imitations—a func-

[8] Stephan KM, Fink GR, Passingham RE, et al. Functional anatomy of the mental representation of upper extremity movements in healthy subjects. J Neurophysiol. 1995;73:373–386. [9] Kasai T, Kawai S, Kawanishi M, et al. Evidence for facilitation of motor evoked potentials

[10] Suzuki T, Bunno Y, Onigata C, et al. Excitability of spinal neural function during several motor imagery tasks involving isometric opponens pollicis activity. NeuroRehabilitation.

gaining strength by using mind. Neuropsychologia. 2004;42:944–956.

tional MRI study. Rigakuryoho Kagaku. 2011;26:117–122 (in Japanese).

(MEPs) induced by motor imagery. Brain Res. 1997;744:147–150.

University of Health Sciences, Wakaba, Kumatori, Sennan, Osaka, Japan

[43] Moritani T, Muro M, Nagata A. Intramuscular and surface electromyogram changes during muscle fatigue. J Appl Physiol. 1986;60:402–411.

[26] Aoyama T, Kaneko F. The effect of motor imagery on gain modulation of the spinal reflex.

[27] Park WH, Li S. No graded responses of finger muscles to TMS during motor imagery of

[28] Romero DH, Lacourse MG, Lawrence KE, et al. Event-related potentials as a function of movement parameter variations during motor imagery and isometric action. Behav Brain

[29] Oda S, Shibata M, Moritani T. Force-dependent changes in movement-related cortical

[30] Lorey B, Pilgramm S, Bischoff M, et al. Activation of the parieto-premotor network is associated with vivid motor imagery—a parametric fMRI study. PLoS One. 2011;6:

[31] Gentili R, Han CE, Schwelghofer N, et al. Motor learning without doing: Trial-by-Trial improvementinmotor performance duringmental training. J Neurophysiol. 2010;104:774–783.

[32] Driskell J, Copper C, Moran A. Does mental practice enhance performance? J Appl

[33] Hinshaw KE. The effects of mental practice on motor skill performance: critical evalua-

[34] Twining WE. Mental practice and physical practice in learning a motor skill. Res Q.

[35] Decety J. The neurophysiological basis of motor imagery. Behav Brain Res. 1996;77:45–52. [36] Tana MG, Montin E, Cerutti S, et al. Exploring cortical attentional system by using fMRI during a continuous perfomance test. Comput Intell Neurosci. 2010;2010:329213.

[37] Brunia CH, Zwaga HJ, van Boxtel A. Tendon reflex amplitude with increasing task

[38] Bray SR, Graham JD, Martin Ginis KA, et al. Cognitive task performance causes impaired maximum force production in human hand flexor muscles. Biol Psychol. 2012;89:195–200.

[39] Guillot A, Haguenauer M, Dittmar A, et al. Effect of a fatiguing protocol on motor

[40] Rozand V, Lebon F, Stapley PJ, et al. A prolonged motor imagery session alter imagined and actual movement durations: potential implications for neurorehabilitation. Behav

[41] Kluger BM, Palmer C, Shattuck JT, et al. Motor evoked potential depression following

[42] De Luca CJ. The use of surface electromyography in biomechanics. J Appl Biomechanics.

repetitive central motor initiation. Exp Brain Res. 2012;216:585–590.

isometric finger force. Neurosci Lett. 2011;494:255–259.

potentials. J Electromyogr Kinesiol. 1996;6:247–252.

tion and meta-analysis. Imagin Cogn Pers. 1991;11:3–35.

imagery accuracy. Eur J Appl Physiol. 2005;95:186–190.

difficulty. Ergonomics. 1973;16:495–499.

Brain Res. 2016;297:67–75.

1997;13:135–163.

Brain Res. 2011;1372:41–48.

Res. 2000;117:83–96.

Phychol. 1994;79:481–492.

1949;20:432–435.

e20368.

48 Neurological Physical Therapy

#### **Clinical Application of Motor Imagery Training Clinical Application of Motor Imagery Training**

Tsubasa Kawasaki Tsubasa Kawasaki

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/67518

#### **Abstract**

Motor imagery training is applied to a rehabilitation program based on previous studies regarding neuroscience and behavioral science. Motor imagery training is useful because it can be applied to almost all patients in clinical settings. However, because motor imag‐ ery training has some shortcoming, clinicians need to consider its shortcoming. The objec‐ tive of this chapter is to promote understanding about using motor imagery effectively.

**Keywords:** motor imagery, motor learning, rehabilitation

## **1. Introduction**

Motor imagery is defined as the process of mentally rehearsing a motor act without overt body movement [1]. Motor imagery basically involves an experience as of movement of body part and action, meaning that the motor imagery is influenced with based on motor represen‐ tations internally by working memory [2].

Motor imagery is called first‐perspective imagery (1PP imagery), that is, image originates from the same viewpoint experienced at encoding [3], different from visual imagery (third‐person perspective imagery or 3PP imagery) which is imagining physical movement from an external viewpoint as though observing other people's body movements as an onlooker. In comparison to brain activation during visual imagery, brain activation in the motor‐related area (e.g., pre‐ supplementary area, precuneus, and inferior parietal lobule) was great during 1PP imagery [4].

Many previous studies have shown that neural plasticity and brain activation during motor imagery training are similar to that of actual physical practice of motor learning. Most previ‐ ous studies demonstrated brain activation in the supplementary motor area, premotor cortex [5–7], and parietal cortex [6]. Additionally, as with changes in the motor neuron, enhancing

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

cortico‐spinal excitability during motor imagery led to an increase in the motor‐evoked poten‐ tial (MEP) by using transcranial magnetic stimulation [8, 9]. In behavioral data, there is a simi‐ larity between motor imagery and actual performance. Motor imagery is widely recognized as an effective method to enhance motor performance. Currently, motor imagery training is applied in rehabilitation programs in clinical settings. This training, which can be made avail‐ able to all patients because it does not impose a physical load on patients, was confirmed through clinical evidence from meta‐analysis. However, there are some problems with using motor imagery training for elderly people or stroke patients with reduced motor imagery ability and cognitive function. Based on this fact, physical therapists would be recommended to assess patients' motor imagery ability and to apply motor imagery training following the assessment. This chapter (a) addresses the effects of simple motor imagery training for patients such as those who have suffered a stroke and (b) offers three recommendations for resolving the problem of administering the training to patients with reduced cognitive function.

## **2. Motor imagery training**

The biggest advantage of motor imagery training is that, unlike general physical training, there is no limitation on the patient's ability to execute motions because motor imagery is a cognitive activity and does not require physical exertion. Because of this advantage, motor imagery training is currently applied for a wide range of body functions.

For example, Hamel et al. reported the availability of motor imagery training for postural con‐ trol. This study used imagery intervention for 6 months to enable patients to autonomously maintain a straight standing position, without body sway [10]. In a more recent study, Yasuda et al. reported the beneficial effect of decreased body sway after performing only 20 repeti‐ tions of imagining one's own body movement (dorsiflexion‐plantarflexion) [11]. For chronic post‐stroke patients, Cho et al. demonstrated that postural balances (Fugl‐Meyer score (FMS)), functional reach (distance), and gait ability (time required for up‐and‐go and 10‐m walk test) were improved after motor imagery training for normal gait [12].

Previous studies have also shown improved upper extremity function after stroke. Page and his colleagues have demonstrated that motor imagery training has beneficial effects on upper extremity function as measured by the FMS of motor recovery, motor activ‐ ity log, and action research arm (ARA) test in subacute [13] and chronic patients [14–16]. Particularly, Page's works used randomized control trial to verify the availability of motor imagery training, which contributed to demonstrate high clinical evidence for the enhance‐ ment of upper extremity function, measuring ARA test, motor assessment scale, and the FMS in stroke patients [17]. According to Langhorne's report, mental practice (motor imagery training) seems most effective for upper extremity function when conducting meta‐analysis. Therefore, this report has shown that motor imagery training had a greater effect than other interventions in established clinical evidence, such as constraint‐induced movement therapy [18–21] (where patients are asked to use an affected limb (execute a task) for a long time and for a large number of repetitions under the condition that the intact limb is constrained), robot‐assisted therapy [22, 23] (where robotic devices can assist patients' affected limb use in high‐intensity, repetitive, specific task by digital control), and electrical interface [24] (where electrostimulation can deliver electric impulses to the muscle through the skin surface and elicit muscle contraction by simulating the neuromuscular system; the intensity, frequency, and patterns of impulse delivery can be selected depending on the patient's condition). The reports investigating the effects of motor imagery training are increasing; however, it must be considered that there are still a very few number of reports verifying the beneficial effects of motor imagery training. This means that additional studies are necessary to establish and accumulate clinical evidence of availability of motor imagery training for improvement of upper extremity by high‐evidence level study design. Based on the clinical evidence, motor imagery training is a potential interventional approach for stroke disability [25]. In this report, intervention via motor imagery is an important tool to promote information process‐ ing for the improvement of actual motor function.

cortico‐spinal excitability during motor imagery led to an increase in the motor‐evoked poten‐ tial (MEP) by using transcranial magnetic stimulation [8, 9]. In behavioral data, there is a simi‐ larity between motor imagery and actual performance. Motor imagery is widely recognized as an effective method to enhance motor performance. Currently, motor imagery training is applied in rehabilitation programs in clinical settings. This training, which can be made avail‐ able to all patients because it does not impose a physical load on patients, was confirmed through clinical evidence from meta‐analysis. However, there are some problems with using motor imagery training for elderly people or stroke patients with reduced motor imagery ability and cognitive function. Based on this fact, physical therapists would be recommended to assess patients' motor imagery ability and to apply motor imagery training following the assessment. This chapter (a) addresses the effects of simple motor imagery training for patients such as those who have suffered a stroke and (b) offers three recommendations for resolving

the problem of administering the training to patients with reduced cognitive function.

imagery training is currently applied for a wide range of body functions.

were improved after motor imagery training for normal gait [12].

The biggest advantage of motor imagery training is that, unlike general physical training, there is no limitation on the patient's ability to execute motions because motor imagery is a cognitive activity and does not require physical exertion. Because of this advantage, motor

For example, Hamel et al. reported the availability of motor imagery training for postural con‐ trol. This study used imagery intervention for 6 months to enable patients to autonomously maintain a straight standing position, without body sway [10]. In a more recent study, Yasuda et al. reported the beneficial effect of decreased body sway after performing only 20 repeti‐ tions of imagining one's own body movement (dorsiflexion‐plantarflexion) [11]. For chronic post‐stroke patients, Cho et al. demonstrated that postural balances (Fugl‐Meyer score (FMS)), functional reach (distance), and gait ability (time required for up‐and‐go and 10‐m walk test)

Previous studies have also shown improved upper extremity function after stroke. Page and his colleagues have demonstrated that motor imagery training has beneficial effects on upper extremity function as measured by the FMS of motor recovery, motor activ‐ ity log, and action research arm (ARA) test in subacute [13] and chronic patients [14–16]. Particularly, Page's works used randomized control trial to verify the availability of motor imagery training, which contributed to demonstrate high clinical evidence for the enhance‐ ment of upper extremity function, measuring ARA test, motor assessment scale, and the FMS in stroke patients [17]. According to Langhorne's report, mental practice (motor imagery training) seems most effective for upper extremity function when conducting meta‐analysis. Therefore, this report has shown that motor imagery training had a greater effect than other interventions in established clinical evidence, such as constraint‐induced movement therapy [18–21] (where patients are asked to use an affected limb (execute a task) for a long time and for a large number of repetitions under the condition that the intact limb is constrained),

**2. Motor imagery training**

52 Neurological Physical Therapy

In almost all previous studies investigating the effects of motor imagery training on motor performance, subjects imagined their body movements while sitting or lying in a relaxed position, with eyes closed. This means that the effects of motor imagery training are influ‐ enced by the environmental context and participants' emotional condition. Considering these factors, Holmes and Collins devised the PETTLEP model as a guideline for applying motor imagery training for athletes [26]. PETTLEP is an acronym (Physical, Environment, Task, Timing, Learning, Emotion, and Perspective), with each letter representing an important fac‐ tor for practitioners to consider when conducting motor imagery training.

The author suggests that the PETTLEP model can be used for some patients in rehabilitation settings considering clinical application. Although PETTLEP model was originally proposed for improving athlete's performance using motor imagery training, athletes in sports settings and patients in clinical settings are in common that both of them improve motor performance via motor‐learning process, Therefore, contents in terms of clinical patients will be stated below. *Physical* refers to patients' physical experience when they imagine action (including body position, clothing, and sports equipment specific to the task/situation). In order to pro‐ duce effective imagery intervention, patients should attempt to physically replicate as much as possible their actual performance. *Environment* refers to the surroundings when the patients imagine their own movement. The surroundings should be replicas of the actual environment of action. *Task* refers to the identicalness of the task imaging to the actual task. This detail would need to be updated regularly as the patient's skill improves. *Timing* refers to the congruence between times for actual action and imagery of the action. Decety et al. reported the temporal coupling and the same neural substrate between actual and simulated movements measured by mental chronometry (refers to inferring the time course of information processing in the nervous system [2, 27]). Therefore, patients have to imagine the action as if they have temporal consistency between the two. *Learning* in the PETTLEP model refers to updating and reflecting the contents of their imagery with improvement of the skill. Some previous studies reported that the structural and functional changes are shown by improving motor skills or practice [28, 29]. Because of that, patients have to refine the contents of their imagery in association with learning processes instead of routine contents of imagery throughout their improvement of the skill. *Emotion* refers to the emotions with which patients perform the actual action. While patients are imagining action, they should attempt to accompany this with the emotions and arousal associated with the typical physical performance. *Perspective* refers to the direction from which the imagery is viewed. 1PP imagery is commonly used for motor imagery training because 1PP imagery involves shared neural mechanisms and functional equivalence, sug‐ gesting that 1PP imagery training would be most beneficial. However, some research has dem‐ onstrated that 3PP imagery training is more beneficial [30, 31]. Based on these studies, Holms and Collins stated that individuals should combine 3PP imagery with 1PP imagery [26]. These elements of the PLTTEP model would be most important to the improvement of motor func‐ tion; therefore, clinicians should consider them when applying motor imagery training.

Although it would be certain that the effects of motor imagery training would be influenced by the elements of PETTLEP model, because motor imagery is simple cognitive activity, it would have difficulty for precise and vividness motor imagery in some individuals. Binder pointed out that the clinician cannot comprehend how patients imagine their body move‐ ments, and the most concerning limitation of motor imagery is that it is not easy to objec‐ tively assess how the subject vividly imagines his or her action. Thus, indirect assessment methods were commonly used as a self‐rating scale, for example, MIQ‐RS [32, 33], KVIQ [34], mental chronometry, and a mental rotation (MR) task (see below for detail). In par‐ ticular, some disease conditions involving the central nervous system (CNS) are affected to imagine their body movements. For example, Personnier et al. showed that elderly people decline in motor imagery ability by using mental chronometry of various walking tasks (i.e., subjects actually or mentally walked (walking distance: 5 m) along three paths having dif‐ ferent widths (15, 25, and 50 cm)) [35]. In the report, functional changes in the aging brain cause reduced motor imagery ability in elderly people. Patients having upper limb motor dysfunction due to stroke have difficulty imagining their own body movement. Li et al. mea‐ sured the motor imagery ability of stroke patients by having them imagery performing a sequence of three kinds of movement and then choose from among four options (photo‐ graph) of the final posture of the three‐sequence movement. The result showed that 1PP imagery was disturbed in stroke patients, but 3PP imagery was not. This suggested that it is difficult for stroke patients to imagine their own actions (1PP imagery) [36]. Also, Decety et al. investigated motor imagery in patients with stroke and spinal cord injuries by measuring mental chronometry. Incongruence between the duration of actual body movement and its motor imagery was shown in stroke patients [37]. Moseley et al. demonstrated that patients with complex regional pain (refractory chronic pain) decrease low motor imagery ability by measuring MR task of hand stimuli. Notably, in the report, the low motor imagery ability is shown at affected body parts but not the unaffected limb. This means that patients who have difficulty imagining their body movement, such as stroke and chronic pain patients, may not benefit from motor imagery training. In fact, motor imagery does not enhance motor recovery in early post‐stroke patients [38]. Timmermans et al. reported that video‐based motor imag‐ ery training was not effective for subacute stroke patients, as measured by the FMS, Frenchay arm test, Wolf motor function test, and accelerometry [39]. Although Page et al. showed the improvement of hand function by motor imagery in acute stroke patients, Ietwaart et al. and Timmermans et al. reported no effect of the training. A possible reason for the inconsistency in results would be different in affected lesion due to brain stroke. The critical lesion site responsible for imagining one's own body movement was the inferior parietal lobule [40]. In this study, subjects were administered transcranial magnetic stimulation (TMS) using theta‐ burst stimulation to inhibit activity in the left inferior parietal lobule before they performed an implicit sequence‐learning task. In comparison with the sham stimulation (control condi‐ tion), the inhibition of the left IPL impaired the acquisition of motor skills. This study revealed the contribution of the left inferior parietal lobule to motor imagery. Moreover, patients with stroke‐disturbed motor imagery were investigated for responsible brain lesions to identify the area responsible for motor imagery. The results showed that stroke patients were dam‐ aged in the fronto‐parietal network, the left putamen, the left ventral premotor cortex, and long association fibers linking parieto‐occipital regions with the dorsolateral premotor and prefrontal areas. In addition to the results of brain activation in the left IPL resulting by Kraeutner's study, these areas indicated a responsible lesion for motor imagery. In any case, these previous studies provide the clinicians with valuable information about the importance of the left brain area during motor imagery. This indicates that the clinicians should consider some possibility of impaired motor imagery ability in patients with left brain damage when the clinicians apply motor imagery training to these patients.

More practical strategies using motor imagery rather than simple motor imagery were veri‐ fied for some patients having difficulty with motor imagery using a brain machine interface. Mihara et al. measured the changes in hemodynamic responses (associated with neuron behavior reflecting brain activity) using near infrared spectroscopy during motor imagery [41]. In this study, subjects were delivered information on the changes in hemodynamic responses in real time while subjects imagine the finger movements. The hemodynamic changes (i.e., brain activity) in real‐time monitoring were greater than in baseline and sham information.

## **3. Mental rotation task**

patients are imagining action, they should attempt to accompany this with the emotions and arousal associated with the typical physical performance. *Perspective* refers to the direction from which the imagery is viewed. 1PP imagery is commonly used for motor imagery training because 1PP imagery involves shared neural mechanisms and functional equivalence, sug‐ gesting that 1PP imagery training would be most beneficial. However, some research has dem‐ onstrated that 3PP imagery training is more beneficial [30, 31]. Based on these studies, Holms and Collins stated that individuals should combine 3PP imagery with 1PP imagery [26]. These elements of the PLTTEP model would be most important to the improvement of motor func‐ tion; therefore, clinicians should consider them when applying motor imagery training.

54 Neurological Physical Therapy

Although it would be certain that the effects of motor imagery training would be influenced by the elements of PETTLEP model, because motor imagery is simple cognitive activity, it would have difficulty for precise and vividness motor imagery in some individuals. Binder pointed out that the clinician cannot comprehend how patients imagine their body move‐ ments, and the most concerning limitation of motor imagery is that it is not easy to objec‐ tively assess how the subject vividly imagines his or her action. Thus, indirect assessment methods were commonly used as a self‐rating scale, for example, MIQ‐RS [32, 33], KVIQ [34], mental chronometry, and a mental rotation (MR) task (see below for detail). In par‐ ticular, some disease conditions involving the central nervous system (CNS) are affected to imagine their body movements. For example, Personnier et al. showed that elderly people decline in motor imagery ability by using mental chronometry of various walking tasks (i.e., subjects actually or mentally walked (walking distance: 5 m) along three paths having dif‐ ferent widths (15, 25, and 50 cm)) [35]. In the report, functional changes in the aging brain cause reduced motor imagery ability in elderly people. Patients having upper limb motor dysfunction due to stroke have difficulty imagining their own body movement. Li et al. mea‐ sured the motor imagery ability of stroke patients by having them imagery performing a sequence of three kinds of movement and then choose from among four options (photo‐ graph) of the final posture of the three‐sequence movement. The result showed that 1PP imagery was disturbed in stroke patients, but 3PP imagery was not. This suggested that it is difficult for stroke patients to imagine their own actions (1PP imagery) [36]. Also, Decety et al. investigated motor imagery in patients with stroke and spinal cord injuries by measuring mental chronometry. Incongruence between the duration of actual body movement and its motor imagery was shown in stroke patients [37]. Moseley et al. demonstrated that patients with complex regional pain (refractory chronic pain) decrease low motor imagery ability by measuring MR task of hand stimuli. Notably, in the report, the low motor imagery ability is shown at affected body parts but not the unaffected limb. This means that patients who have difficulty imagining their body movement, such as stroke and chronic pain patients, may not benefit from motor imagery training. In fact, motor imagery does not enhance motor recovery in early post‐stroke patients [38]. Timmermans et al. reported that video‐based motor imag‐ ery training was not effective for subacute stroke patients, as measured by the FMS, Frenchay arm test, Wolf motor function test, and accelerometry [39]. Although Page et al. showed the improvement of hand function by motor imagery in acute stroke patients, Ietwaart et al. and Timmermans et al. reported no effect of the training. A possible reason for the inconsistency in results would be different in affected lesion due to brain stroke. The critical lesion site

In order to overcome the shortcomings mentioned above (some patients find it difficult to imagine their body movement precisely and vividly), one idea was to use a mental rotation (MR) task. MR refers to the ability to imagine the rotation of an object in space [42]. In a semi‐ nal study by Shepard and Metzler [42], participants were required to judge whether the pairs of three‐dimensional objects were the same or different (**Figure 1a**, **b**). The results showed that the time required for the judgment (the MR reaction time) increased as the angle of rotation

#### **3.1. Mental rotation of body parts for motor imagery**

An MR task using a visual stimulus of a body part (typically a hand or a foot) requires par‐ ticipants to judge the laterality of a rotated body part (i.e., whether the stimulus is the right or the left hand/foot) (**Figure 2**).

**Figure 1.** (a) MR task for three‐dimensional objects: same‐different judgment task. (b) The schematic liner relationship between reaction times (vertical axis) disparity angle of two objects (horizontal axis) [42].

**Figure 2.** The MR task of body parts stimuli: making judgment of right or left parts.

In an MR task using body part stimuli, the time required for laterality judgment increased with the increasing rotation angle of the stimuli (**Figure 4**, unpublished work) as reaction times for MR task using object stimuli [42]. On the other hand, the time required for the simple reaction in the same MR task using body part stimuli was not significantly different. This suggested that the subject would imagine their own body movement of body parts of MR stimuli during MR task using body part stimuli. More directly, Parsons indicated that the MR of body parts involves cognitive processes used for both motor imagery and motor execution. The author reported that times required for the laterality judgment (i.e., the reaction time) of hand and foot stimuli were related to the times required for actual hand movement [43]. Participants were instructed to (a) judge the laterality as quickly and accurately as possible and (b) execute a hand/foot movement in a stimulus orientation. As a result, the reaction times for the hand/foot and the times required to execute these movements are nearly equiva‐ lent (e.g., the reaction time for 180° is similar to the time of actual hand movement of 180°). Parsons concluded that participants mentally rotate their own body image into congruence with the rotating stimuli during the MR task. Therefore, that study indicated that the MR of body parts can determine the ability to operate one's body image (i.e., the ability to operate motor imagery) by measuring the reaction time.

A more recent study by Ionta et al. showed that, during the MR of body parts, individu‐ als simulate to move their own body image so as to match the observed stimulus [44]. They compared the MR reaction times for hand and foot stimuli in two postural conditions: (a) an anatomical posture (i.e., participants positioned their hands on their knees) and (b) an unusual posture (i.e., they positioned their hands behind their back, with fingers intertwined). The results showed that the MR reaction times for hand stimuli, but not for foot stimuli, were delayed in the unusual posture condition. Even if a stimulus was presented with no rotation, the reaction time was delayed when individuals kept their hands behind their back, so that the orientation of the hands was different from that of the stimuli. Ionta et al. concluded that the MR of body parts was a cognitive task in which individuals simulate moving a specific body part image from its actual posture to that of the same observed or imagined body part [45].

Other previous studies investigating brain science strengthen the evidence that the MR of a body part promotes motor imagery involving the MR stimuli. Many previous reports dem‐ onstrated that execution‐related motor areas were activated during the MR of body parts (typically the hand). More specifically, brain activation in premotor area and parietal cortex was shown during the MR of body parts. Kawamichi et al. examined the time course of brain activation during the MR of body part (hand) stimuli. This article reported that neuronal activity in the visual cortex was observed approximately 100–200 ms from stimulus onset. Brain activation in the inferior parietal lobe followed (after 200 ms). Notably, brain activation in the inferior parietal lobe showed contralateral dominance in the visual stimulus hemi‐ field. Then premotor area activity started the inferior parietal lobe activity [46]. Moreover, to clarify the importance of the motor area's contribution to the MR of body parts, Ganis et al. examined whether MR performance (reaction time) was hindered when single‐pulse TMS was delivered to the representation of the hand in the left primary motor cortex during the MR of pictures of hands and feet [47]. The results showed that the response times for the judgment (MR performance) were slower when TMS was delivered, as compared with a peripheral magnetic stimulation (control stimulation). It was striking that the interference was obtained delivered at 650 ms after hand‐picture stimulus onset, and the effects were greater for hand stimuli in comparison with the foot‐picture stimulus. These findings indi‐ cated that the primary motor cortex is involved in the MR of body parts. In particular, the involvement of the primary motor cortex is (a) relatively late in the processing of MR and (b) stimulus specificity like a physical representation of the human body, located within the brain (the cortical homunculus). This physiological study directly revealed that the cortico‐ spinal tract (central nervous system in the primary motor cortex to the spinal cord) plays an essential role in performing MR.

In an MR task using body part stimuli, the time required for laterality judgment increased with the increasing rotation angle of the stimuli (**Figure 4**, unpublished work) as reaction times for MR task using object stimuli [42]. On the other hand, the time required for the simple reaction in the same MR task using body part stimuli was not significantly different. This suggested that the subject would imagine their own body movement of body parts of MR stimuli during MR task using body part stimuli. More directly, Parsons indicated that the MR of body parts involves cognitive processes used for both motor imagery and motor execution. The author reported that times required for the laterality judgment (i.e., the reaction time) of hand and foot stimuli were related to the times required for actual hand movement [43]. Participants were instructed to (a) judge the laterality as quickly and accurately as possible and (b) execute a hand/foot movement in a stimulus orientation. As a result, the reaction times for the hand/foot and the times required to execute these movements are nearly equiva‐ lent (e.g., the reaction time for 180° is similar to the time of actual hand movement of 180°). Parsons concluded that participants mentally rotate their own body image into congruence with the rotating stimuli during the MR task. Therefore, that study indicated that the MR of body parts can determine the ability to operate one's body image (i.e., the ability to operate

**Figure 1.** (a) MR task for three‐dimensional objects: same‐different judgment task. (b) The schematic liner relationship

between reaction times (vertical axis) disparity angle of two objects (horizontal axis) [42].

56 Neurological Physical Therapy

**Figure 2.** The MR task of body parts stimuli: making judgment of right or left parts.

motor imagery) by measuring the reaction time.

Brain activity during MR has been investigated in many previous studies. Zacks investigated brain activity in a meta‐analysis of neuroimaging study during MR. This reports showed brain activity in the superior parietal cortex, the motor region in the precentral cortex, and the lateral inferior prefrontal cortex when subjects were performing the MR of body parts [48]. These results support the view that MR depends on motor simulation of the body part stimulation in some situations. Enhanced cortico‐spinal excitability during the MR of hand stimuli indicates that the excitement of the motor area is involved in performing the MR of body parts [47]. However, since some studies show no brain activity in the primary motor area, its involvement remains unclear, that is, it is necessary to determine the involvement of primary motor area during the MR of body parts.

#### **3.2. Intervention of mental rotation**

Including the author's previous works, it is indicated that the intervention of the MR of body part stimuli would promote improvement of physical function. Kawasaki et al. showed that the reaction time for the MR of foot stimuli (see **Figure 3**) was related to the postural displace‐ ment of one foot while standing (the length of body sway), but there was no relationship between that and the reaction time for the MR of hand and car stimuli [49]. Jansen showed similar results in elderly people [50]. These results suggested the availability of an MR task using foot stimuli for the improvement of challenging postural stability, such as one‐foot standing, through motor imagery of feet of individuals themselves.

Based on previous research investigating neuroscientific mechanisms during the MR of body parts, the availability of MR using foot stimuli has immediate beneficial effects on postural sta‐ bility during a challenging posture, such as standing on one foot, but not during bipedal stand‐ ing. Notably, when the subject performed MR using hand stimuli, these beneficial effects were not obtained. After the report, the beneficial effects of the MR of foot stimuli on the postural stability while standing on one foot for a relatively long time (60 min <) were demonstrated. The long‐term effects would be helpful for a discussion about the mechanism of the effects of MR intervention. As stated above, Ganis determined the involvement of cortico‐spinal excit‐ ability performing the MR of body parts [47]. According to previous reports, the duration of the enhanced cortico‐spinal excitability is for a maximum of 30 min after finger movement [51, 52]. Therefore, the long‐term beneficial effects for a long time were not completely explained. A possi‐ ble explanation is motor consolidation, as previous studies have shown that motor memory was

**Figure 3.** Time required for (a) MR task using foot stimuli and (b) simple reaction for the foot stimuli. The reaction times for MR task using foot stimuli were delayed with rotation angle, but not simple reaction time.

consolidated for more than 24 h after repetitive motor imagery intervention [53]. Considering the findings, the beneficial effects of the MR of foot stimuli on postural stability are ascribed to factors such as the enhancement of cortico‐spinal excitability and memory consolidation.

## **4. Action observation therapy**

area, its involvement remains unclear, that is, it is necessary to determine the involvement of

Including the author's previous works, it is indicated that the intervention of the MR of body part stimuli would promote improvement of physical function. Kawasaki et al. showed that the reaction time for the MR of foot stimuli (see **Figure 3**) was related to the postural displace‐ ment of one foot while standing (the length of body sway), but there was no relationship between that and the reaction time for the MR of hand and car stimuli [49]. Jansen showed similar results in elderly people [50]. These results suggested the availability of an MR task using foot stimuli for the improvement of challenging postural stability, such as one‐foot

Based on previous research investigating neuroscientific mechanisms during the MR of body parts, the availability of MR using foot stimuli has immediate beneficial effects on postural sta‐ bility during a challenging posture, such as standing on one foot, but not during bipedal stand‐ ing. Notably, when the subject performed MR using hand stimuli, these beneficial effects were not obtained. After the report, the beneficial effects of the MR of foot stimuli on the postural stability while standing on one foot for a relatively long time (60 min <) were demonstrated. The long‐term effects would be helpful for a discussion about the mechanism of the effects of MR intervention. As stated above, Ganis determined the involvement of cortico‐spinal excit‐ ability performing the MR of body parts [47]. According to previous reports, the duration of the enhanced cortico‐spinal excitability is for a maximum of 30 min after finger movement [51, 52]. Therefore, the long‐term beneficial effects for a long time were not completely explained. A possi‐ ble explanation is motor consolidation, as previous studies have shown that motor memory was

**Figure 3.** Time required for (a) MR task using foot stimuli and (b) simple reaction for the foot stimuli. The reaction times

for MR task using foot stimuli were delayed with rotation angle, but not simple reaction time.

primary motor area during the MR of body parts.

standing, through motor imagery of feet of individuals themselves.

**3.2. Intervention of mental rotation**

58 Neurological Physical Therapy

Action observation therapy refers to learning through observing the behavior of another per‐ son as a model; this can be used in clinical settings to encourage motor performance without any physical activity. Action observation therapy involves bottom‐up processing based on visual information, which is different from motor imagery training (top‐down using a con‐ scious cognitive process) [54]. Several previous studies have reported that observational learn‐ ing is effective for younger participants [55, 56], and it has also been shown to improve motor performance in patients hospitalized due to stroke with upper or lower extremity hemiplegia [57–59] and in patients with Parkinson's disease [60, 61]. Thus, observational learning has been used for many kinds of patients in clinical settings.

The effect of observing the behavior of another person upon overt motor performance is most commonly attributed to the demonstration of brain activity in the ventral premotor areas, sul‐ cus temporalis superior, and inferior parietal lobe (i.e., mirror neuron system) during action observation [62, 63]. Historically, electrical activity in the rostral part of the inferior area 6 (area F5) of two macaque monkeys was shown both when the monkeys performed a given action and when they observed a similar action performed by the experimenter [64]. Particularly, the F5 area acutely responses to both the observation and execution of actions in terms of the goal (e.g., grasping) and how the goal is achieved (e.g., a precision grip). Human data also revealed that these areas are involved in imitating the actions of others [65–67] and understanding the intention of others' actions [63, 68]. When the beneficial effects of action observation are gained, high activation in the observer's mirror neuron system would be expected to create first‐person perspective (1PP) imagery [57, 76], that is, the mental process of the movement without any body movement [69–71]. Ertelt et al. reported that after the action observation therapy, brain activation in the mirror neuron system was increased [57], meaning that such activation is strongly related to the effects of action observation therapy.

Basically, action observation therapy has beneficial effects on motor performance through the activation of the central nervous system (CNS), which is involved in movement by first‐person perspective imagery. Actually, there is some condition for more effective activation of the CNS when applying action observation therapy. Based on a previous study by Gallese investigating the mirror neuron in monkeys, one condition is the observation of a goal‐oriented action. Fadiga et al. demonstrated that increasing MEP was shown when subjects observed the grasping of objects (observing movement of finger) compared with when they observed (a) the objects, (b) arm movement of the grasping objects, (c) and sham [72]. Muthukumaraswamy et al. examined whether mu rhythm modulates during observation of grasping to an object‐directed [73]. The electroencephalographic mu rhythm is an 8–13‐Hz rhythm generated by the sensorimotor cortex that is most prominent when subjects are resting and are attenuated or abolished when subjects move or observe biological movements [74, 75]; therefore, the hypothesis of the examination was to attenuate or abolish an 8–13‐Hz rhythm during these action observations. Consistent with the hypothesis, the result showed a lower mu rhythm magnitude for the object‐grip condition than for the empty‐grip condition. This result is fully reasonably taken together with the studies by Fadiga and Muthukumaraswamy, which suggested that the observation of movement with intention, such as tool manipulation or daily activities, can lead to brain modulation, and as a result, these observations would be expected to be more effective for motor learning.

Another way of the effective condition was that patients imagine body movement while they observed model's action (i.e., combined action observation and motor imagery). Vogt sug‐ gested that action observation with motor imagery is more effective for motor performance than action observation therapy or motor imagery training alone [76]. Tsukazaki et al. demon‐ strated that the MEP amplitude was significantly increased by observing a video clip of three‐ ball cascade juggling combined with motor imagery of it for novel motor learning [77]. This suggested the effectiveness of action observation combined with motor imagery. By contrast, they reported that for expert subjects increased the MEP amplitude when motor imagery only (without observation). This suggested that motor imagery alone is more effective than action observation therapy combined with motor imagery for novel motor learning. As evidence of the effectiveness of action observation plus motor imagery, in neuroscience data, Taube et al. have shown that greater activation in the mirror neuron system was obtained when subjects observed the movement of others during which time subjects imagined their own body move‐ ment in terms of the model's movement [78].

In addition, previous work has been focused on observing the model's skill. According to the studies mentioned above, although the beneficial effects of action observation therapy are robust, opinions vary as to the optimal model for observers (learners). For a typical example, previous studies have shown that motor learning was promoted both with a skilled model demonstrating movement quickly without error [56, 79, 80] and with an unskilled model demonstrating slowly with error [81–83]. Taken together, the effective model's skill has not been inconsistent among the previous studies, meaning that there is no evidence for pro‐ moting a model in terms of skill for a long time. Kawasaki et al. have begun to demonstrate effective learning with action observation therapy. To date, they have conducted two inves‐ tigations in young people. The results of the study were that the unskilled model demon‐ strating movement slowly with error was more effective than a skilled model demonstrating quickly without error [84]. Considering the clinical applications of action observation ther‐ apy, it is important to verify a model to promote for elderly persons for whom clinicians have many opportunities to care in clinical settings, because previous studies did not examine clinical settings. The ability to imagine their own body movements [35, 85] and to imitate movements after observation declines in elderly people [86]. Visual information processing of dynamic movement also declines with aging [87, 88]. Based on the previous study show‐ ing changes in body function with aging, Kawasaki and colleague investigated whether the unskilled model was effective for motor learning as the author's previous research in young people. Consistent with the hypothesis, the unskilled model showing quickness with some error has the advantage to acquire new motor skills (under review). Moreover, almost all of the subjects gained acquiring higher motor skills in subjects observed unskilled model than in subjects observed skilled model regardless of individuals' motor imagery ability; in other words, unskilled model provides observer (patients or learners) with positive effects on the motor learning with or without impairment of motor imagery ability. This suggested that motor imagery ability is not necessary when the clinicians use action observation therapy and unskilled models are readily available. In this research, positive information can be provided to apply action observation to elderly people in clinical settings.

Recently, the beneficial effects of action observation therapy have been established and applied in clinical settings. However, note that the effects of action observation therapy may be influenced by an individual's motor imagery ability. Lawrence et al. showed the effective‐ ness of gymnastic movement performance after observation of the movement. Additionally, the improved gymnastic movement performance in subjects having high motor imagery ability was greater than in subjects having low motor imagery ability, suggesting that the effects of action observation on motor performance are moderated by imagery ability [89]. Considering that the effects of action observation are based on imagining the individual's body movement, using the PETTPEP model would provide an advantage and lead to benefi‐ cial effects when using action observation therapy.

## **5. Verbalizing motor skills**

move or observe biological movements [74, 75]; therefore, the hypothesis of the examination was to attenuate or abolish an 8–13‐Hz rhythm during these action observations. Consistent with the hypothesis, the result showed a lower mu rhythm magnitude for the object‐grip condition than for the empty‐grip condition. This result is fully reasonably taken together with the studies by Fadiga and Muthukumaraswamy, which suggested that the observation of movement with intention, such as tool manipulation or daily activities, can lead to brain modulation, and as a

Another way of the effective condition was that patients imagine body movement while they observed model's action (i.e., combined action observation and motor imagery). Vogt sug‐ gested that action observation with motor imagery is more effective for motor performance than action observation therapy or motor imagery training alone [76]. Tsukazaki et al. demon‐ strated that the MEP amplitude was significantly increased by observing a video clip of three‐ ball cascade juggling combined with motor imagery of it for novel motor learning [77]. This suggested the effectiveness of action observation combined with motor imagery. By contrast, they reported that for expert subjects increased the MEP amplitude when motor imagery only (without observation). This suggested that motor imagery alone is more effective than action observation therapy combined with motor imagery for novel motor learning. As evidence of the effectiveness of action observation plus motor imagery, in neuroscience data, Taube et al. have shown that greater activation in the mirror neuron system was obtained when subjects observed the movement of others during which time subjects imagined their own body move‐

In addition, previous work has been focused on observing the model's skill. According to the studies mentioned above, although the beneficial effects of action observation therapy are robust, opinions vary as to the optimal model for observers (learners). For a typical example, previous studies have shown that motor learning was promoted both with a skilled model demonstrating movement quickly without error [56, 79, 80] and with an unskilled model demonstrating slowly with error [81–83]. Taken together, the effective model's skill has not been inconsistent among the previous studies, meaning that there is no evidence for pro‐ moting a model in terms of skill for a long time. Kawasaki et al. have begun to demonstrate effective learning with action observation therapy. To date, they have conducted two inves‐ tigations in young people. The results of the study were that the unskilled model demon‐ strating movement slowly with error was more effective than a skilled model demonstrating quickly without error [84]. Considering the clinical applications of action observation ther‐ apy, it is important to verify a model to promote for elderly persons for whom clinicians have many opportunities to care in clinical settings, because previous studies did not examine clinical settings. The ability to imagine their own body movements [35, 85] and to imitate movements after observation declines in elderly people [86]. Visual information processing of dynamic movement also declines with aging [87, 88]. Based on the previous study show‐ ing changes in body function with aging, Kawasaki and colleague investigated whether the unskilled model was effective for motor learning as the author's previous research in young people. Consistent with the hypothesis, the unskilled model showing quickness with some error has the advantage to acquire new motor skills (under review). Moreover, almost all of the subjects gained acquiring higher motor skills in subjects observed unskilled model than

result, these observations would be expected to be more effective for motor learning.

ment in terms of the model's movement [78].

60 Neurological Physical Therapy

To address the difficulty of motor imagery training mentioned above, verbalizing motor skills is also an effective tool for improving motor performance. Verbalizing motor skills promote to imagine own body movement because the process of verbalizing one's own motor skills would be induced by internal language [90, 91] through recalling one's own motor skills and, as a result, promoting motor imagery. The left frontal lobe is activated not only during the recalling of one's own body movement [92] but also when inducing internal language [93]. Additionally, the left frontal lobe is involved in motor programming [94]. This means that the neural circuit of verbalizing motor skill and its motor execution was shared, therefore, it is with regard to strangeness closeness relationship between verbalizing motor skill and its motor execution [95–97]. In fact, using verbal expression motor behavior leads to improved motor control and accuracy of hand grasp [98]. Considering these previous studies, we hypothesized that verbal‐ izing of own body skills involves a process of promoting motor imagery through recalling own motor performance, as a result, the verbalizing motor skills might improve motor learning.

Based on previous knowledge, we hypothesized that the effects of verbalizing motor skills pro‐ vide the subject with improved motor performance. One examination was conducted to inves‐ tigate the effect of verbalizing motor skills after practicing a ball‐rotation task compared with scientific read aloud (i.e., no verbalizing). The results showed the beneficial effects of verbalizing motor skills on acquiring the motor skill of finger coordination (under review, **Figure 4**). This showed that verbalizing would be effective for acquiring and improving motor skills. The author considered that verbalizing motor skills provide subjects' motor skills with beneficial effects through motor imagery. Further study is needed to analyze contents of subjects' verbalizing (qualitative research) as well as quantitative research, such as present data.

**Figure 4.** The changes in the number of ball rotation in all sessions. Results showed improvement of motor skills only after verbalizing.

#### **6. Summary**

There are some interventions and ideas to compensate for the shortcoming of traditional motor imagery training. This chapter studied that investigating methods of applied motor imagery training is important to develop clinical rehabilitation settings because the suggest‐ ing interventions and ideas are commonly based on motor imagery. Therefore, it is necessary to more deeply investigate motor imagery (e.g., the most effective procedure and method for individual disease characteristics), expecting that this will lead to future development of motor imagery training.

## **Acknowledgements**

Part of this work was supported by JSPS KAKENHI Grant Number 15K16402.

## **Author details**

Tsubasa Kawasaki

Address all correspondence to<EMAIL_ADDRESS>
Department of Physical Therapy, Faculty of Health Science, Ryotokuji University, Urayasu, Chiba, Japan

## **References**

**6. Summary**

after verbalizing.

62 Neurological Physical Therapy

motor imagery training.

**Acknowledgements**

**Author details**

Tsubasa Kawasaki

Chiba, Japan

There are some interventions and ideas to compensate for the shortcoming of traditional motor imagery training. This chapter studied that investigating methods of applied motor imagery training is important to develop clinical rehabilitation settings because the suggest‐ ing interventions and ideas are commonly based on motor imagery. Therefore, it is necessary to more deeply investigate motor imagery (e.g., the most effective procedure and method for individual disease characteristics), expecting that this will lead to future development of

**Figure 4.** The changes in the number of ball rotation in all sessions. Results showed improvement of motor skills only

Department of Physical Therapy, Faculty of Health Science, Ryotokuji University, Urayasu,

Part of this work was supported by JSPS KAKENHI Grant Number 15K16402.

Address all correspondence to<EMAIL_ADDRESS>

[29] Wright DJ, Holmes P, Di Russo F, Loporto M, Smith D. Reduced motor cortex activ‐ ity during movement preparation following a period of motor skill practice. PloS One. 2012;7(12):e51886.

[15] Page SJ, Levine P, Leonard A. Mental practice in chronic stroke results of a randomized,

[16] Page SJ, Levine P, Leonard AC. Effects of mental practice on affected limb use and function in chronic stroke. Archives of Physical Medicine and Rehabilitation. 2005;86(3):399–402.

[17] Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review. The

[18] Page SJ, Sisto S, Johnston MV, Levine P. Modified constraint‐induced therapy after subacute stroke: a preliminary study. Neurorehabilitation and Neural Repair. 2002;16(3):290–295.

[19] Page SJ, Sisto S, Levine P, McGrath RE. Efficacy of modified constraint‐induced move‐ ment therapy in chronic stroke: a single‐blinded randomized controlled trial. Archives

[20] Page SJ, Levine P, Leonard AC. Modified constraint‐induced therapy in acute stroke: a randomized controlled pilot study. Neurorehabilitation and Neural Repair.

[21] Page SJ, Levine P, Leonard A, Szaflarski JP, Kissela BM. Modified constraint‐induced therapy in chronic stroke: results of a single‐blinded randomized controlled trial.

[22] Amirabdollahian F, Loureiro R, Gradwell E, Collin C, Harwin W, Johnson G. Multivariate analysis of the Fugl‐Meyer outcome measures assessing the effectiveness of GENTLE/S robot‐mediated stroke therapy. Journal of NeuroEngineering and Rehabilitation.

[23] Volpe BT, Lynch D, Rykman‐Berland A, Ferraro M, Galgano M, Hogan N, et al. Intensive sensorimotor arm training mediated by therapist or robot improves hemiparesis in patients with chronic stroke. Neurorehabilitation and Neural Repair. 2008;22(3):305–310.

[24] Cauraugh J, Light K, Kim S, Thigpen M, Behrman A. Chronic motor dysfunction after stroke recovering wrist and finger extension by electromyography‐triggered neuromus‐

[25] Sharma N, Cohen LG. Recovery of motor function after stroke. Developmental

[26] Holmes PS, Collins DJ. The PETTLEP approach to motor imagery: A functional equivalence model for sport psychologists. Journal of Applied Sport Psychology.

[27] Malouin F, Richards CL, Durand A, Doyon J. Reliability of mental chronometry for assess‐ ing motor imagery ability after stroke. Archives of Physical Medicine and Rehabilitation.

[28] Pascual‐Leone A, Nguyet D, Cohen LG, Brasil‐Neto JP, Cammarota A, Hallett M. Modulation of muscle responses evoked by transcranial magnetic stimulation during the acquisition of new fine motor skills. Journal of Neurophysiology. 1995;74(3):1037–1045.

placebo‐controlled trial. Stroke. 2007;38(4):1293–1297.

of Physical Medicine and Rehabilitation. 2004;85(1):14–18.

Lancet Neurology. 2009;8(8):741–754.

Physical Therapy. 2008;88(3):333–340.

cular stimulation. Stroke. 2000;31(6):1360–1364.

Psychobiology. 2012;54(3):254–262.

2005;19(1):27–32.

64 Neurological Physical Therapy

2007;4(1):1–16.

2001;13(1):60–83.

2008;89(2):311–319.

[58] Franceschini M, Ceravolo MG, Agosti M, Cavallini P, Bonassi S, Dall'Armi V, et al. Clinical relevance of action observation in upper‐limb stroke rehabilitation a possible role in recovery of functional dexterity. A randomized clinical trial. Neurorehabilitation and Neural Repair. 2012;26(5):456–462.

[43] Parsons LM. Temporal and kinematic properties of motor behavior reflected in men‐ tally simulated action. Journal of Experimental Psychology: Human Perception &

[44] Fiorio M, Tinazzi M, Ionta S, Fiaschi A, Moretto G, Edwards MJ, et al. Mental rotation of body parts and non‐corporeal objects in patients with idiopathic cervical dystonia.

[45] Ionta S, Fourkas AD, Fiorio M, Aglioti SM. The influence of hands posture on mental

[46] Kawamichi H, Kikuchi Y, Endo H, Takeda T, Yoshizawa S. Temporal structure of implicit motor imagery in visual hand‐shape discrimination as revealed by MEG. Neuroreport.

[47] Ganis G, Keenan JP, Kosslyn SM, Pascual‐Leone A. Transcranial magnetic stimulation of primary motor cortex affects mental rotation. Cerebral Cortex. 2000;10(2):175–180. [48] Zacks JM. Neuroimaging studies of mental rotation: a meta‐analysis and review. Journal

[49] Kawasaki T, Yasuda K, Fukuhara K, Higuchi T. Relationship between mental rotation of body parts and postural stability during quiet stance. Journal of Imagery Research in

[50] Jansen P, Kaltner S. Object‐based and egocentric mental rotation performance in older adults: The importance of gender differences and motor ability. Aging, Neuropsychology,

[51] Kawasaki T, Higuchi T. Mental rotation intervention using foot stimuli has lasting effect on postural stability during quiet stance: a randomized controlled study. Journal of

[52] Classen J, Liepert J, Wise SP, Hallett M, Cohen LG. Rapid plasticity of human corti‐ cal movement representation induced by practice. Journal of Neurophysiology.

[53] Gentili R, Han CE, Schweighofer N, Papaxanthis C. Motor learning without doing: trial‐by‐trial improvement in motor performance during mental training. Journal of

[54] Holmes P, Calmels C. A neuroscientific review of imagery and observation use in sport.

[55] Aglioti SM, Cesari P, Romani M, Urgesi C. Action anticipation and motor resonance in

[56] Heyes C, Foster C. Motor learning by observation: Evidence from a serial reaction time task. The Quarterly Journal of Experimental Psychology: Section A. 2002;55(2):593–607.

[57] Ertelt D, Small S, Solodkin A, Dettmers C, McNamara A, Binkofski F, et al. Action obser‐ vation has a positive impact on rehabilitation of motor deficits after stroke. Neuroimage.

elite basketball players. Nature Neuroscience. 2008;11(9):1109–1116.

rotation of hands and feet. Experimental Brain Research. 2007;183(1):1–7.

Performance. 1994;20(4):709–730.

1998;9(6):1127–1132.

66 Neurological Physical Therapy

1998;79(2):1117–1123.

Neuropsychologia. 2007;45(10):2346–2354.

of Cognitive Neuroscience. 2008;20(1):1–19.

Sport and Physical Activity. 2014;9(1):39–46.

and Cognition. 2013;4(ahead‐of‐print):1–21.

Motor Behavior. 2016;48(357–364):357–364.

Neurophysiology. 2010;104(2):774–783.

2007;36(Suppl 2):T164–T173.

Journal of Motor Behavior. 2008;40(5):433–445.

[88] Ishigaki H, Miyao M. Implications for dynamic visual acuity with changes in age and sex. Perceptual and Motor Skills. 1994;78(2):363–369.

[74] Babiloni C, Carducci F, Cincotti F, Rossini PM, Neuper C, Pfurtscheller G, et al. Human movement‐related potentials vs desynchronization of EEG alpha rhythm: a high‐resolu‐

[75] Pineda JA, Allison B, Vankov A. The effects of self‐movement, observation, and imagi‐ nation on μ rhythms and readiness potentials (RP's): toward a brain‐computer interface

[76] Vogt S, Rienzo FD, Collet C, Collins A, Guillot A. Multiple roles of motor imagery dur‐

[77] Tsukazaki I, Uehara K, Morishita T, Ninomiya M, Funase K. Effect of observation com‐ bined with motor imagery of a skilled hand‐motor task on motor cortical excitability:

difference between novice and expert. Neuroscience Letters. 2012;518(2):96–100.

[78] Taube W, Mouthon M, Leukel C, Hoogewoud HM, Annoni JM, Keller M. Brain activ‐ ity during observation and motor imagery of different balance tasks: An fMRI study.

[79] Bird G, Heyes C. Effector‐dependent learning by observation of a finger movement sequence. Journal of Experimental Psychology: Human Perception and Performance.

[80] Hodges NJ, Chua R, Franks IM. The role of video in facilitating perception and action of a novel coordination movement. Journal of Motor Behavior. 2003;35(3):247–260.

[81] Buchanan JJ, Ryu YU, Zihlman K, Wright DL. Observational practice of relative but not absolute motion features in a single‐limb multi‐joint coordination task. Experimental

[82] Buchanan JJ, Dean NJ. Specificity in practice benefits learning in novice models and vari‐ ability in demonstration benefits observational practice. Psychological Research PRPF.

[83] Black CB, Wright DL. Can observational practice facilitate error recognition and move‐ ment production? Research Quarterly for Exercise and Sport. 2000;71(4):331–339. [84] Kawasaki T, Aramaki H, Tozawa R. An Effective Model for Observational Learning to Improve Novel Motor Performance. Journal of Physical Therapy Science. 2015;

[85] Mulder T, Hochstenbach JB, van Heuvelen MJ, den Otter AR. Motor imagery: the rela‐ tion between age and imagery capacity. Human Movement Science. 2007;26(2):203–211.

[86] Maryott J, Sekuler R. Age‐related changes in imitating sequences of observed move‐

[87] Muiños M, Ballesteros S. Sports can protect dynamic visual acuity from aging: A study with young and older judo and karate martial arts athletes. Attention, Perception, &

(BCI). IEEE Transactions on Rehabilitation Engineering. 2000;8(2):219–222.

ing action observation. Frontiers in Human Neuroscience. 2013;7:807.

tion EEG study. Neuroimage. 1999;10(6):658–665.

Cortex. 2014;64c:102–114.

Brain Research. 2008;191(2):157–169.

ments. Psychology and Aging. 2009;24(2):476.

Psychophysics. 2015;77(6):2061–2073.

2005;31(2):262.

68 Neurological Physical Therapy

2010;74(3):313–326.

27(12):3829–3832.

## **The Effects of Motor Imagery After a Variety of Motor Learning Times on Excitability of Spinal Motor Neurons and Accurate Motion**

Yuki Fukumoto and Yoshibumi Bunno

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/67470

#### Abstract

Purpose: This study aimed to examine the effects of motor imagery on the excitability of spinal motor neurons and accurate motion. Subjects and Methods: About 30 healthy volunteers were recruited. F-waves were recorded at rest, while touching a sensor and motor imagery conditions. Also, the pinch force was measured before and after motor imagery. Furthermore, the subjects mastered the 50% MVC pinch force with learning times of 10 s, 30 s, 1 min, and 2 min beforehand. Results: Spinal motor neuron excitability with motor imagery after motor learning for 10 s, 30 s, 1 min, and 2 min was significantly increased as compared to other conditions. Accurate motion in the pinch task after motor imagery was better maintained than in the pinch task before motor imagery with motor learning times of 30 s and 1 min. However, with learning times of 10s and 2 min, the subject's ability to sustain accurate motion in the pinch task after motor imagery was significantly decreased as compared to that of the pinch task before motor imagery. Conclusion: Motor imagery increases spinal motor neuron excitability. To maximally improve accurate motion using motor imagery, it is important to practice and master motor learning beforehand

Keywords: F-waves, spinal motor neuron, motor imagery, motor learning, accurate motion

## 1. Introduction

Motor imagery is reproduced by memory. Motor imagery especially involves the activation of cognitive processes from working memory [1]. In addition, motor imagery and the preparation for motion reportedly had mechanisms similar to those of the processes of managing motion

© 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

within the brain [2, 3]. Motor imagery might be applied to therapeutic exercise. Motor imagery may also serve as a therapeutic expedient for patients with restricted activities or in whom physical activity is contraindicated.

Effects of motor imagery on the central nervous system include the following: activations of the primary motor area, supplementary motor area, premotor area, primary somatosensory area, dorsolateral prefrontal area, cingulate cortex, and cerebellums occurred during motor imagery [4–7]. Accordingly, motor imagery increases the excitability of the central nervous system. Also, spinal motor neuron excitability was studied by using F-waves and the H-reflex. An F-wave is a compound action potential obtained as a result of re-excitation ("backfiring") of an antidromic impulse following distal electrical stimulation of motor nerve fibers in the anterior horn cells [8, 9] (Figure 1).

An F-wave is a compound action potential obtained as a result of re-excitation ("backfiring") of an antidromic impulse following distal electrical stimulation of motor nerve fibers in the anterior horn cells.

Figure 1. F-wave mechanism.

The H-reflex results from sub-maximal stimulation of type Ia sensory fibers. The potential enters the posterior horn of the spinal cord and passes through the synapses with alpha-motor neurons. Finally, a compound muscle action potential is generated and is recorded as H-waves [10]. Fwaves and the H-reflex are generally used as an index of spinal motor neuron function. Suzuki et al. [11] reported that persistence and the F/M amplitude ratio during motor imagery at 50% maximum voluntary contraction (MVC) pinch action were significantly increased than those at rest. Taniguchi et al. [12] reported that persistence and the F/M amplitude ratio were significantly decreased after a sustained rest for 3 h as compared to the preresting condition. However, persistence and the F/M amplitude ratio were maintained, showing similar values, after the sustained rest as compared to the preresting condition when rest and motor imagery were combined. Kasai et al. [13] reported that no significant differences were observed in the H-reflex amplitude between the resting condition and motor imagery involving flexion-extension movements at the wrist joint. Additionally, Oishi et al. [14] reported that the H-reflex amplitude was significantly increased, unchanged, or decreased with motor imagery involving skating, as compared to the resting condition, in a speed skater. Thus, studies have obtained a variety of results such as increased, unchanged, or decreased spinal motor neuron excitability during motor imagery. Given this wide range of observations, the only consistent result is an increase in activation of the central nervous system, while the excitability of spinal motor neurons may not always increase during motor imagery. To optimize improvement of motor function during physical therapy using motor imagery, it is necessary to enhance the excitability of spinal motor neurons as well as to activate the cerebellar cortex.

Next, we considered the effects of motor imagery on actual motion. Yue et al. [15] reported a comparison of muscular strengths after motor imagery of the little finger MVC abduction movement for 4 weeks among motor imagery, physical training, and control groups. They found that muscular strength was reinforced at 30% in the strength training group and at 22% in the motor imagery group. Guillot et al. [16] reported flexibility of the hamstrings and ankle joint muscles to be significantly improved in the postmotor imagery condition of stretching as compared to the premotor imagery condition in swimmers. Page et al. [17] reported that upper limb motor function was improved using a combination of physical therapy and motor imagery in poststroke hemiparesis patients. Dickstein et al. [18], likewise, reported gait speed to be improved using motor imagery in hemiparesis patients with cardiovascular disease. Thus, motor imagery improves muscular strength, range of motion, and motor function. However, it is unclear whether motor imagery affects the accuracy of motion. We use a tool and an object, manipulated by the upper limb, for activities of daily living. For example, buttoning and unbuttoning, using chopsticks, picking up coins, and so forth, are important motor activities. Therefore, the acquisition of accurate motion is crucial. Herein, we studied the effects of motor imagery after various motor learning times, that is, 10 s, 30 s, 1 min, and 2 min, on the accuracy of motion and the excitability of spinal motor neurons.

## 2. Subjects

within the brain [2, 3]. Motor imagery might be applied to therapeutic exercise. Motor imagery may also serve as a therapeutic expedient for patients with restricted activities or in whom

Effects of motor imagery on the central nervous system include the following: activations of the primary motor area, supplementary motor area, premotor area, primary somatosensory area, dorsolateral prefrontal area, cingulate cortex, and cerebellums occurred during motor imagery [4–7]. Accordingly, motor imagery increases the excitability of the central nervous system. Also, spinal motor neuron excitability was studied by using F-waves and the H-reflex. An F-wave is a compound action potential obtained as a result of re-excitation ("backfiring") of an antidromic impulse following distal electrical stimulation of motor nerve fibers in the

An F-wave is a compound action potential obtained as a result of re-excitation ("backfiring") of an antidromic impulse following distal electrical stimulation of motor nerve fibers in the

The H-reflex results from sub-maximal stimulation of type Ia sensory fibers. The potential enters the posterior horn of the spinal cord and passes through the synapses with alpha-motor neurons. Finally, a compound muscle action potential is generated and is recorded as H-waves [10]. Fwaves and the H-reflex are generally used as an index of spinal motor neuron function. Suzuki et al. [11] reported that persistence and the F/M amplitude ratio during motor imagery at 50% maximum voluntary contraction (MVC) pinch action were significantly increased than those at rest. Taniguchi et al. [12] reported that persistence and the F/M amplitude ratio were significantly decreased after a sustained rest for 3 h as compared to the preresting condition. However, persistence and the F/M amplitude ratio were maintained, showing similar values, after the sustained rest as compared to the preresting condition when rest and motor imagery were combined. Kasai et al. [13] reported that no significant differences were observed in the H-reflex amplitude between the resting condition and motor imagery involving flexion-extension movements at the wrist joint.

physical activity is contraindicated.

72 Neurological Physical Therapy

anterior horn cells [8, 9] (Figure 1).

anterior horn cells.

Figure 1. F-wave mechanism.

We included 30 healthy subjects (males, 15; females, 15; mean age, 20.3 1.0) in the group with a motor learning time of 10 s. This study was approved by the Research Ethics Committee at Kansai University of Health Sciences (Approval number: 15-04).

Next, we enrolled another 30 healthy subjects (males, 15; females, 15; mean age, 21.1 1.2) in the group with a motor learning time of 30 s. This study was approved by the Research Ethics Committee at Kansai University of Health Sciences (Approval number: 16-25).

Then, we included 30 healthy subjects (males, 15; females, 15; mean age, 19.7 1.3 years) in the group with a motor learning time of 1 min. This study was approved by the Research Ethics Committee at Kansai University of Health Sciences (Approval number: 16-26).

Finally, we enrolled 30 healthy subjects (males, 15; females, 15; mean age, 22.3 3.0 years) in the group with a motor learning time of 2 min. This study was approved by the Research Ethics Committee at Kansai University of Health Sciences (Approval number: 16-47).

All subjects provided informed consent prior to study commencement. The experiments were conducted in accordance with the Declaration of Helsinki.

## 3. Methods

The study process required three conditions: resting, touching a sensor, and motor imagery. We recorded F-waves during isometric contraction of the thenar muscle. We also measured the pinch force before and after motor imagery. The process is described in detail in below text.

We recorded F-waves of the left thenar muscle and used the spinal motor neurons under the resting condition as an index. Suzuki et al. [11] reported that motor imagery is not simply a matter of carrying out an action, instead actually representing a combination involving maintenance of motion position. Accordingly, we recorded F-waves while the subjects not only simply touched the pinch meter sensor [Digital indicator F340A (Unipulse Inc.)] between the thumb and index finger (touching sensor condition) but also during the combination of touching the pinch meter sensor and performing motor imagery for 1 min (motor imagery condition). In advance, we determined the magnitude of MVC in subjects holding the pinch meter sensor while exerting maximum effort for 10 s. Furthermore, the subjects were required to learn 50% MVC beforehand with isometric contraction for the pinch action with various motor learning times, that is, 10 s, 30 s, 1 min, and 2 min. At this time, the subjects were instructed to maintain the 50% MVC while viewing the pinch meter display. Subsequently, the subjects were asked to subjectively determine the 50% MVC without using visual feedback before motor imagery. In addition, we measured the pinch force for 10 s (pinch task first trial). Again, the subjects were asked to subjectively estimate the 50% MVC without using visual feedback after motor imagery, and we measured pinch force for 10 s (pinch task second trial). On a different day, the control group, while not using motor imagery in a similar process in the motor imagery phase (without motor imagery condition), underwent F-wave recording. These tasks were performed randomly in the motor imagery and control groups (Figure 2).

Figure 2. Study process.

We recorded F-waves under resting, touching a sensor, and motor imagery conditions. Also, the subjects were instructed to learn the 50% MVC with visual feedback prior to motor imagery. Furthermore, the subjects were asked to subjectively estimate the 50% MVC without using visual feedback before and after motor imagery.

The testing conditions for measurement of F-waves were as follows. A Viking Quest electromyography machine (Natus Medical Inc.) was used to record F-waves. The subjects were comfortably placed in the supine position. We recorded the F-waves by stimulating the left median nerve at the wrist. Supramaximal shocks (adjusted up to the value 20% higher than the maximal stimulus) were delivered at 0.5 Hz and 0.2 ms for F-wave acquisition. We recorded F-waves of the left thenar muscles using a pair of disks attached with collodion to the skin over the eminence of the thumb and the bones of the metacarpophalangeal joint of the thumb. The stimulating electrodes were composed of a cathode placed over the left median nerve 3 cm proximal to the palmar crease of the wrist joint and an anode placed 2 cm more proximally (Figure 3).

Figure 3. The F-wave testing conditions.

All subjects provided informed consent prior to study commencement. The experiments were

The study process required three conditions: resting, touching a sensor, and motor imagery. We recorded F-waves during isometric contraction of the thenar muscle. We also measured the pinch force before and after motor imagery. The process is described in detail in below text.

We recorded F-waves of the left thenar muscle and used the spinal motor neurons under the resting condition as an index. Suzuki et al. [11] reported that motor imagery is not simply a matter of carrying out an action, instead actually representing a combination involving maintenance of motion position. Accordingly, we recorded F-waves while the subjects not only simply touched the pinch meter sensor [Digital indicator F340A (Unipulse Inc.)] between the thumb and index finger (touching sensor condition) but also during the combination of touching the pinch meter sensor and performing motor imagery for 1 min (motor imagery condition). In advance, we determined the magnitude of MVC in subjects holding the pinch meter sensor while exerting maximum effort for 10 s. Furthermore, the subjects were required to learn 50% MVC beforehand with isometric contraction for the pinch action with various motor learning times, that is, 10 s, 30 s, 1 min, and 2 min. At this time, the subjects were instructed to maintain the 50% MVC while viewing the pinch meter display. Subsequently, the subjects were asked to subjectively determine the 50% MVC without using visual feedback before motor imagery. In addition, we measured the pinch force for 10 s (pinch task first trial). Again, the subjects were asked to subjectively estimate the 50% MVC without using visual feedback after motor imagery, and we measured pinch force for 10 s (pinch task second trial). On a different day, the control group, while not using motor imagery in a similar process in the motor imagery phase (without motor imagery condition), underwent F-wave recording. These tasks

were performed randomly in the motor imagery and control groups (Figure 2).

conducted in accordance with the Declaration of Helsinki.

3. Methods

74 Neurological Physical Therapy

Figure 2. Study process.

F-waves were analyzed with respect to persistence and the F/M amplitude ratio using 30 stimuli. Persistence was defined as the number of measurable F-wave responses divided by 30 supramaximal stimuli. Persistence reflects the number of backfiring anterior horn cells (Figure 4). The F/M amplitude ratio was defined as the mean amplitude of all responses divided by the amplitude of the M-wave. The F/M amplitude ratio reflects the number of backfiring anterior horn cells and the excitability of individual anterior horn cells (Figure 5).

The number of measurable F-wave responses divided by 30 supramaximal stimuli. Persistence reflects the number of backfiring anterior horn cells. This case (17/30) · 100 = 56%

Figure 4. Excitability of spinal motor neurons examined for persistence.

The F/M amplitude ratio was defined as the mean amplitude of all responses divided by the amplitude of the M-wave. The F/M amplitude ratio reflects the number of backfiring anterior horn cells and the excitability of individual anterior horn cells.

Therefore, persistence and the F/M amplitude ratio are regarded as indices of the excitability of spinal motor neurons. In this study, provided that the excitability of spinal motor neurons in the motor imagery condition is significantly increased as compared to that in the touching a sensor condition, it may be improved by subjects performing motor imagery. Furthermore, we confirmed that no significant differences were observed in relative electromyogram integral values between the resting and touching a sensor conditions versus the motor imagery condition when using surface electromyography.

The Effects of Motor Imagery After a Variety of Motor Learning Times on Excitability of Spinal Motor Neurons… http://dx.doi.org/10.5772/67470 77

Figure 5. Excitability of spinal motor neurons examined for F/M amplitude ratio.

The F/M amplitude ratio was defined as the mean amplitude of all responses divided by the amplitude of the M-wave. The F/M amplitude ratio reflects the number of backfiring anterior

The number of measurable F-wave responses divided by 30 supramaximal stimuli. Persistence

The F/M amplitude ratio was defined as the mean amplitude of all responses divided by the amplitude of the M-wave. The F/M amplitude ratio reflects the number of backfiring anterior

Therefore, persistence and the F/M amplitude ratio are regarded as indices of the excitability of spinal motor neurons. In this study, provided that the excitability of spinal motor neurons in the motor imagery condition is significantly increased as compared to that in the touching a sensor condition, it may be improved by subjects performing motor imagery. Furthermore, we confirmed that no significant differences were observed in relative electromyogram integral values between the resting and touching a sensor conditions versus the motor imagery condi-

horn cells and the excitability of individual anterior horn cells.

Figure 4. Excitability of spinal motor neurons examined for persistence.

tion when using surface electromyography.

reflects the number of backfiring anterior horn cells. This case (17/30) · 100 = 56%

horn cells and the excitability of individual anterior horn cells (Figure 5).

76 Neurological Physical Therapy

An index reflecting the accuracy of motion was applied, as follows. In this study, we defined two indexes representing the accuracy of motion (Figure 6). Since the index representing the accuracy of motion was not defined in the past literature, the first index used herein was correction time, which was the total time for 50 5% MVC. Correction time reflects the ability to control the accuracy of muscle force in the pinch action. Blefari et al. [19] adopted an error range of 5% as the index reflecting the accuracy of motion. Based on the aforementioned considerations, we adopted an error range of 5%. We did this because our study and that of Blefari were similar in terms of adopting pinch action. The second index was the 50% MVC error, obtained by subtraction of the relative pinch force value at the 50% MVC from one. In addition, this index was converted to an absolute value, and then expressed as a percentage. The 50% MVC error reflects whether or not there is convergence on 50% MVC. The correction time and the 50% MVC error were calculated for the first trial and the second trial of the pinch task for the motor imagery and control groups. We measured the pinch force value using electromyogram recording software VitalRecorder2 (KISSEI COMTEC). We calculated two indexes reflecting the accuracy of motion using a versatile biological analysis system, the BIMUTAS-Video (KISSEI COMTEC). Provided that the index reflecting the accuracy of motion in the pinch task second trial is significantly improved as compared to that in the pinch task first trial, the effect of motor imagery is confirmed.

We defined two indexes reflecting the accuracy of motion. The first index was correction time (the total time of 50 5% MVC). The second index was the 50% MVC error (derived by subtraction of the relative pinch force value at the 50% MVC from one, followed by conversion to an absolute value, and then expressed as a percentage).

Figure 6. The index for the accuracy of motion.

Data analysis was carried out as follows. Statistical analysis for the normality of the distribution was performed using the Kolmogorov-Smirnov and Shapiro-Wilk tests. Because the data were not recognized as showing a normal distribution, the Friedman test was used to compare F-wave results among the resting, touching a sensor, motor imagery, and without motor imagery conditions. Thereafter, the Scheffe test was used to compare F-wave results across all conditions. Also, the Wilcoxon signed-rank test was used to compare correction time, the 50% MVC error between the first and second trials of the pinch task. The significance level was set at p < 0.05. We used SPSS ver. 19 for all statistical analyses.

#### 4. Results

#### 4.1. F-wave results

#### 4.1.1. F-wave results with a 10 s motor learning time

In the motor imagery group when examined for persistence, the resting condition was 63.4 22.7%, the touching a sensor condition was 78.1 17.2%, and the motor imagery condition was 90.5 9.6%. The persistence values under the touching a sensor and motor imagery conditions were significantly increased as compared to that of the resting condition. In addition, persistence was significantly increased in the motor imagery condition than in the touching a sensor condition (\*p < 0.05, \*\*p < 0.01, Figure 7). Accordingly, in the control group when examined for persistence, the resting condition was 47.4 19.1%, the touching a sensor condition was 72.1 16.5%, and in the condition without motor imagery was 67.5 17.6%. The persistence values in the touching a sensor and without motor imagery conditions were significantly increased compared to that in the resting condition. However, no significant differences in persistence values were observed between the touching a sensor and without motor imagery conditions (\*p < 0.05, \*\*p < 0.01, Figure 7).

Next, in the motor imagery group when examined for the F/M amplitude ratio, the resting condition was 1.2 0.6%, the touching a sensor condition was 1.8 0.8% and the motor imagery condition was 2.2 1.5%. The F/M amplitude ratio was significantly increased in the touching sensor and motor imagery conditions as compared to that in the resting condition. However, no significant differences in the F/M amplitude ratios were observed between the touching a sensor and the motor imagery conditions (\*p < 0.05, \*\*p < 0.01, Figure 7). Accordingly, in the control group when examined for the F/M amplitude ratio, the resting condition was 1.2 0.5%, the touching a sensor condition was 1.9 1.1%, and the condition without motor imagery was 1.8 1.1%. The F/M amplitude ratio in the touching a sensor and without motor imagery conditions were significantly increased compared to that in the resting condition. However, no significant differences in the F/M amplitude ratios were observed between the touching a sensor and without motor imagery conditions (\*p < 0.05, \*\*p < 0.01, Figure 7).

In the motor imagery group, persistence values when examined in the touching the sensor and motor imagery conditions were significantly increased as compared to that in the resting condition. In addition, persistence was significantly increased in the motor imagery condition compared to that in the touching a sensor condition. The F/M amplitude ratio was significantly increased in the touching a sensor and motor imagery conditions than in the

Figure 7. F-wave results.

Data analysis was carried out as follows. Statistical analysis for the normality of the distribution was performed using the Kolmogorov-Smirnov and Shapiro-Wilk tests. Because the data were not recognized as showing a normal distribution, the Friedman test was used to compare F-wave results among the resting, touching a sensor, motor imagery, and without motor imagery conditions. Thereafter, the Scheffe test was used to compare F-wave results across all conditions. Also, the Wilcoxon signed-rank test was used to compare correction time, the 50% MVC error between the first and second trials of the pinch task. The significance level was set

In the motor imagery group when examined for persistence, the resting condition was 63.4 22.7%, the touching a sensor condition was 78.1 17.2%, and the motor imagery condition was 90.5 9.6%. The persistence values under the touching a sensor and motor imagery conditions were significantly increased as compared to that of the resting condition. In addition, persistence was significantly increased in the motor imagery condition than in the touching a sensor condition (\*p < 0.05, \*\*p < 0.01, Figure 7). Accordingly, in the control group when examined for persistence, the resting condition was 47.4 19.1%, the touching a sensor condition was 72.1 16.5%, and in the condition without motor imagery was 67.5 17.6%. The persistence values in the touching a sensor and without motor imagery conditions were significantly increased compared to that in the resting condition. However, no significant differences in persistence values were observed between the touching a sensor and

Next, in the motor imagery group when examined for the F/M amplitude ratio, the resting condition was 1.2 0.6%, the touching a sensor condition was 1.8 0.8% and the motor

at p < 0.05. We used SPSS ver. 19 for all statistical analyses.

without motor imagery conditions (\*p < 0.05, \*\*p < 0.01, Figure 7).

4.1.1. F-wave results with a 10 s motor learning time

Figure 6. The index for the accuracy of motion.

78 Neurological Physical Therapy

4. Results

4.1. F-wave results

resting condition. In the control group, the persistence values in the touching a sensor and without motor imagery conditions were significantly increased than that in the resting condition. The F/M amplitude ratios in the touching a sensor and without motor imagery conditions were shown to be significantly increased as compared to that in the resting condition.

#### 4.1.2. F-wave results with a 30 s motor learning time

In the motor imagery group when examined for persistence, the resting condition was 55.6 17.2%, the touching a sensor condition was 72.8 14.3%, and the motor imagery condition was 84.5 12.8%. The persistence values in the touching a sensor and motor imagery conditions were significantly increased as compared to that in the resting condition. In addition, the persistence value was significantly increased in the motor imagery condition than in the touching a sensor condition (\*\*p < 0.01, Figure 8). Accordingly, in the control group when examined for persistence, the resting condition was 54.3 18.2%, the touching a sensor condition was 70.4 14.4%, and the condition without motor imagery was 70.1 17.7%. The persistence values in the touching a sensor and without motor imagery conditions were significantly increased as compared to that in the resting condition. However, no significant differences in the persistence values were observed between the touching a sensor and without motor imagery conditions (\*\*p < 0.01, Figure 8).

Next, in the motor imagery group when examined for the F/M amplitude ratio, the resting condition was 1.1 0.7%, the touching a sensor condition was 1.5 0.8%, and the motor imagery condition was 1.7 0.8%. The F/M amplitude ratio was significantly increased in the touching a sensor and motor imagery conditions than in the resting condition. However, no significant differences in the F/M amplitude ratios were observed between the touching a sensor and motor imagery conditions (\*\*p < 0.01, Figure 8). Accordingly, in the control group when examined for the F/M amplitude ratio, the resting condition was 1.2 0.7%, the touching a sensor condition was 1.5 0.8%, and the condition without motor imagery was 1.6 0.8%. The F/M amplitude ratios in the touching a sensor and without motor imagery conditions were significantly increased than that in the resting condition. However, no significant differences in the F/M amplitude ratios were observed between the touching a sensor and without motor imagery conditions (\*p < 0.05, \*\*p < 0.01, Figure 8).

In the motor imagery group, the persistence values in the touching a sensor and motor imagery conditions were significantly increased than that in the resting condition. In addition, the persistence value was significantly increased in the motor imagery condition as compared to that in the touching a sensor condition. The F/M amplitude ratio was observed to be significantly increased in the touching a sensor and motor imagery conditions as compared to the resting condition. In the control group, the persistence values in the touching a sensor and without motor imagery conditions were significantly increased than that in the resting condition. Next, the F/M amplitude ratios in the touching a sensor and without motor imagery conditions were observed to be significantly increased than that in the resting condition.

The Effects of Motor Imagery After a Variety of Motor Learning Times on Excitability of Spinal Motor Neurons… http://dx.doi.org/10.5772/67470 81

Figure 8. F-wave results.

resting condition. In the control group, the persistence values in the touching a sensor and without motor imagery conditions were significantly increased than that in the resting condition. The F/M amplitude ratios in the touching a sensor and without motor imagery conditions were shown to be significantly increased as compared to that in the resting

In the motor imagery group when examined for persistence, the resting condition was 55.6 17.2%, the touching a sensor condition was 72.8 14.3%, and the motor imagery condition was 84.5 12.8%. The persistence values in the touching a sensor and motor imagery conditions were significantly increased as compared to that in the resting condition. In addition, the persistence value was significantly increased in the motor imagery condition than in the touching a sensor condition (\*\*p < 0.01, Figure 8). Accordingly, in the control group when examined for persistence, the resting condition was 54.3 18.2%, the touching a sensor condition was 70.4 14.4%, and the condition without motor imagery was 70.1 17.7%. The persistence values in the touching a sensor and without motor imagery conditions were significantly increased as compared to that in the resting condition. However, no significant differences in the persistence values were observed between the touching a sensor and without motor imagery conditions (\*\*p < 0.01, Figure 8).

Next, in the motor imagery group when examined for the F/M amplitude ratio, the resting condition was 1.1 0.7%, the touching a sensor condition was 1.5 0.8%, and the motor imagery condition was 1.7 0.8%. The F/M amplitude ratio was significantly increased in the touching a sensor and motor imagery conditions than in the resting condition. However, no significant differences in the F/M amplitude ratios were observed between the touching a sensor and motor imagery conditions (\*\*p < 0.01, Figure 8). Accordingly, in the control group when examined for the F/M amplitude ratio, the resting condition was 1.2 0.7%, the touching a sensor condition was 1.5 0.8%, and the condition without motor imagery was 1.6 0.8%. The F/M amplitude ratios in the touching a sensor and without motor imagery conditions were significantly increased than that in the resting condition. However, no significant differences in the F/M amplitude ratios were observed between the touching a sensor and without motor

In the motor imagery group, the persistence values in the touching a sensor and motor imagery conditions were significantly increased than that in the resting condition. In addition, the persistence value was significantly increased in the motor imagery condition as compared to that in the touching a sensor condition. The F/M amplitude ratio was observed to be significantly increased in the touching a sensor and motor imagery conditions as compared to the resting condition. In the control group, the persistence values in the touching a sensor and without motor imagery conditions were significantly increased than that in the resting condition. Next, the F/M amplitude ratios in the touching a sensor and without motor imagery conditions were observed to be significantly increased than that in the resting

condition.

80 Neurological Physical Therapy

condition.

4.1.2. F-wave results with a 30 s motor learning time

imagery conditions (\*p < 0.05, \*\*p < 0.01, Figure 8).

#### 4.1.3. F-wave results with a 1 min motor learning time

In the motor imagery group when examined for persistence, the resting condition was 48.9 20.3%, the touching a sensor condition was 69.1 13.4%, and the motor imagery condition was 79.8 10.4%. Persistence values in the touching a sensor and motor imagery conditions were significantly increased than that in the resting condition. In addition, persistence was significantly increased in the motor imagery condition as compared to the touching a sensor condition (\*\*p < 0.01, ##p < 0.01, Figure 9). Accordingly, in the control group when examined for persistence, the resting condition was 57.6 20.5%, the touching a sensor condition was 74.5 16.7%, and the condition without motor imagery was 68.2 14.9%. Persistence values in the touching a sensor and without motor imagery conditions were significantly increased than that in the resting condition. No significant differences in the persistence values were observed between the touching a sensor and without motor imagery conditions (\*\*p < 0.01, Figure 9).

Next, in the motor imagery group when examined for the F/M amplitude ratio, the resting condition was 1.8 1.1%, the touching a sensor condition was 2.2 1.2%, and the motor imagery condition was 2.6 2.1%. The F/M amplitude ratio was significantly increased in the motor imagery than that in the resting condition (\*\*p < 0.01, Figure 9). Accordingly, in the control group when examined for the F/M amplitude ratio, the resting condition was 1.5 0.7%, the touching a sensor condition was 1.7 0.9%, and the condition without motor imagery was 1.6 0.9%. There were no significant differences in the F/M amplitude ratios among the three conditions (Figure 9).

In the motor imagery group, persistence values in the touching a sensor and motor imagery conditions were significantly increased as compared to that in the resting condition. In addition, persistence was significantly increased in the motor imagery condition as compared to the touching a sensor condition. The F/M amplitude ratio was observed to be significantly increased in the motor imagery condition as compared to that in the resting condition. In the control group, persistence values in the touching a sensor and without motor imagery conditions were significantly increased than that in the resting condition. There were no significant differences in the F/M amplitude ratios among the three conditions.

4.1.4. F-wave results with a 2 min motor learning time

In the motor imagery group when examined for persistence, the resting condition was 63.714.2%, the touching a sensor condition was 72.8 15.2%, and the motor imagery condition was 85.2 14.1%. Persistence values in the touching a sensor and motor imagery conditions were significantly increased as compared to that in the resting condition. In addition, persistence was significantly increased in the motor imagery condition as compared to the touching a sensor condition (\*\*p < 0.01, ## p < 0.01, Figure 10). Accordingly, in the control group when examined for persistence, the resting condition was 58.0 19.9%, the touching a sensor condition was 75.7 15.0% and the condition without motor imagery was 75.6 15.2%. Persistence values in the touching a sensor and without motor imagery conditions were significantly increased than that in the resting condition. No significant differences in the persistence values were observed between the touching a sensor and without motor imagery conditions (\*\*p < 0.01, Figure 10).

Next, in the motor imagery group when examined for the F/M amplitude ratio, the resting condition was 1.1 0.5%, the touching a sensor condition was 1.3 0.6%, and the motor imagery condition was 1.4 0.6%. The F/M amplitude ratios were significantly increased in the touching a sensor and motor imagery conditions than that in the resting condition. However, no significant differences in the F/M amplitude ratios were observed between the touching a sensor and motor imagery conditions (\*p < 0.05, \*\*p < 0.01, Figure 10). Accordingly, in the control group when examined for the F/M amplitude ratio, the resting condition was 1.1 0.7%, the touching a sensor condition was 1.3 0.7%, and the condition without motor imagery was 1.4 0.6%. The F/M amplitude ratios were significantly increased in the

Figure 10. F-wave results.

motor imagery than that in the resting condition (\*\*p < 0.01, Figure 9). Accordingly, in the control group when examined for the F/M amplitude ratio, the resting condition was 1.5 0.7%, the touching a sensor condition was 1.7 0.9%, and the condition without motor imagery was 1.6 0.9%. There were no significant differences in the F/M amplitude ratios among the three condi-

In the motor imagery group, persistence values in the touching a sensor and motor imagery conditions were significantly increased as compared to that in the resting condition. In addition, persistence was significantly increased in the motor imagery condition as compared to the touching a sensor condition. The F/M amplitude ratio was observed to be significantly increased in the motor imagery condition as compared to that in the resting condition. In the control group, persistence values in the touching a sensor and without motor imagery conditions were significantly increased than that in the resting condition. There were no significant

In the motor imagery group when examined for persistence, the resting condition was 63.714.2%, the touching a sensor condition was 72.8 15.2%, and the motor imagery condition was 85.2 14.1%. Persistence values in the touching a sensor and motor imagery

differences in the F/M amplitude ratios among the three conditions.

4.1.4. F-wave results with a 2 min motor learning time

Figure 9. F-wave results.

tions (Figure 9).

82 Neurological Physical Therapy

touching a sensor and without motor imagery conditions than that in the resting condition. However, no significant differences in the F/M amplitude ratios were observed between the touching a sensor and without motor imagery conditions (\*p < 0.05, \*\*p < 0.01, Figure 10).

In the motor imagery group, persistence values in the touching a sensor and motor imagery conditions were significantly increased as compared to that in the resting condition. In addition, persistence was significantly increased in the motor imagery condition as compared to the touching a sensor condition. The F/M amplitude ratios were significantly increased in the touching a sensor and motor imagery conditions as compared to the resting condition. In the control group, persistence values in the touching a sensor and without motor imagery conditions were significantly increased as compared to that in the resting condition. The F/M amplitude ratios were significantly increased in the touching a sensor and without motor imagery conditions as compared to the resting condition.

#### 4.2. The index for the accuracy of motion results

#### 4.2.1. The index for the accuracy of motion results with a 10 s motor learning time

In the motor imagery group when examined for the correction time, the pinch task first trial was 1.2 1.5 s and the pinch task second trial was 0.7 1.6 s. No significant differences were observed in the correction time between the first and second trials of the pinch task (Figure 11).

Figure 11. The index reflecting the accuracy of motion results.

Accordingly, in the control group when examined for the correction time, the pinch task first trial was 1.2 1.7 s and the pinch task second trial was 0.7 1.2 s. No significant differences were observed in the correction time between the first and second trials of the pinch task (Figure 11).

touching a sensor and without motor imagery conditions than that in the resting condition. However, no significant differences in the F/M amplitude ratios were observed between the touching a sensor and without motor imagery conditions (\*p < 0.05, \*\*p < 0.01, Figure 10).

In the motor imagery group, persistence values in the touching a sensor and motor imagery conditions were significantly increased as compared to that in the resting condition. In addition, persistence was significantly increased in the motor imagery condition as compared to the touching a sensor condition. The F/M amplitude ratios were significantly increased in the touching a sensor and motor imagery conditions as compared to the resting condition. In the control group, persistence values in the touching a sensor and without motor imagery conditions were significantly increased as compared to that in the resting condition. The F/M amplitude ratios were significantly increased in the touching a sensor and without motor

In the motor imagery group when examined for the correction time, the pinch task first trial was 1.2 1.5 s and the pinch task second trial was 0.7 1.6 s. No significant differences were observed in the correction time between the first and second trials of the pinch task (Figure 11).

imagery conditions as compared to the resting condition.

4.2.1. The index for the accuracy of motion results with a 10 s motor learning time

4.2. The index for the accuracy of motion results

84 Neurological Physical Therapy

Figure 11. The index reflecting the accuracy of motion results.

Next, in the motor imagery group at the 50% MVC error, the pinch task first trial was 25.7 21.9% and the pinch task second trial was 32.3 18.5%. The 50% MVC error was significantly increased in the second than in the first trial of the pinch task (\*p < 0.05, Figure 11). Accordingly, in the control group at the 50% MVC error, the pinch task first trial was 20.9 17.2% and the pinch task second trial was 36.3 44.3%. The 50% MVC error was significantly increased in the second than in the first trial of the pinch task (\*p < 0.05, Figure 11).

In the motor imagery group, no significant differences were observed in the correction time between the first and second trials of the pinch task. The 50% MVC error was significantly increased in the second as compared to the first trial of the pinch task. In the control group, no significant differences were observed in the correction time between the first and second trials of the pinch task. The 50% MVC error was significantly increased in the second than in the first trial of the pinch task.

#### 4.2.2. The index for the accuracy of motion results with a 30 s motor learning time

In the motor imagery group when examined for the correction time, the pinch task first trial was 1.8 1.9 s and the pinch task second trial was 1.8 1.8 s. No significant differences were observed in the correction times between the first and second trials of the pinch task (Figure 12). Accordingly, in the control group when examined for the correction time, the pinch task first trial was 1.5 1.6 s and the pinch task second trial was 0.9 1.3 s. The correction time was significantly decreased in the second than in the first trial of the pinch task (\*p < 0.05, Figure 12).

In the motor imagery group at the 50% MVC error, the pinch task first trial was 25.6 18.8% and the pinch task second trial was 27.4 22.3%. No significant differences were observed at the 50% MVC error between the first and second trials of the pinch task (Figure 12). Accordingly, in the control group at the 50% MVC error, the pinch task first trial was 21.1 17.2% and the pinch task second trial was 31.9 28.3%. The 50% MVC was significantly increased in the second than in the first trial of the pinch task (\*p < 0.05, Figure 12).

In the motor imagery group, no significant differences were observed in the correction time or the 50% MVC error between the first and second trials of the pinch task. In the control group, the correction time was significantly decreased in the second than in the first trial of the pinch task. The 50% MVC error was significantly increased in the second than in the first trial of the pinch task.

#### 4.2.3. The index for the accuracy of motion results with a 1 min motor learning time

In the motor imagery group when examined for the correction time, the pinch task first trial was 1.5 1.6 s and the pinch task second trial was 1.3 1.8 s. No significant differences were observed in correction time between the first and second trials of the pinch task (Figure 13). Accordingly, in the control group when examined for the correction time, the

Figure 12. The index for the accuracy of motion results.

pinch task first trial was 1.5 1.9 s and the pinch task second trial was 0.9 1.3 s. Correction time was significantly decreased in the second as compared to the first trial of the pinch task. (\*\*p < 0.01, Figure 13).

Next, in the motor imagery group at the 50% MVC error, the pinch task first trial was 21.5 16.7% and the pinch task second trial was 24.7 22.7%. No significant differences were observed in the 50% MVC error between the first and second trials of the pinch task (Figure 13). Accordingly, in the control group at the 50% MVC error, the pinch task first trial was 28.1 29.1% and the pinch task second trial was 38.9 40.4%. The 50% MVC error was significantly increased in the second than in the first trial of the pinch task (\*p < 0.05, Figure 13).

In the motor imagery group, no significant differences were observed in correction time or the 50% MVC error between the first and second trials of the pinch task. In the control group, correction time was significantly decreased in the second than in the first trial of the pinch task. The 50% MVC error was significantly increased in the second than in the first trial of the pinch task.

#### 4.2.4. The index for the accuracy of motion results with a 2 min motor learning time

In the motor imagery group when examined for the correction time, the pinch task first trial was 1.2 1.7 s and the pinch task second trial was 1.3 1.8 s. No significant differences were

The Effects of Motor Imagery After a Variety of Motor Learning Times on Excitability of Spinal Motor Neurons… http://dx.doi.org/10.5772/67470 87

Figure 13. The index for the accuracy of motion results.

pinch task first trial was 1.5 1.9 s and the pinch task second trial was 0.9 1.3 s. Correction time was significantly decreased in the second as compared to the first trial of the pinch task.

Next, in the motor imagery group at the 50% MVC error, the pinch task first trial was 21.5 16.7% and the pinch task second trial was 24.7 22.7%. No significant differences were observed in the 50% MVC error between the first and second trials of the pinch task (Figure 13). Accordingly, in the control group at the 50% MVC error, the pinch task first trial was 28.1 29.1% and the pinch task second trial was 38.9 40.4%. The 50% MVC error was significantly increased in the second than in the first trial of the pinch task (\*p < 0.05, Figure 13). In the motor imagery group, no significant differences were observed in correction time or the 50% MVC error between the first and second trials of the pinch task. In the control group, correction time was significantly decreased in the second than in the first trial of the pinch task. The 50% MVC error was significantly increased in the second than in the first trial of the pinch task.

In the motor imagery group when examined for the correction time, the pinch task first trial was 1.2 1.7 s and the pinch task second trial was 1.3 1.8 s. No significant differences were

4.2.4. The index for the accuracy of motion results with a 2 min motor learning time

(\*\*p < 0.01, Figure 13).

86 Neurological Physical Therapy

Figure 12. The index for the accuracy of motion results.

observed in correction time between the first and second trials of the pinch task (Figure 14). Accordingly, in the control group when examined for the correction time, the pinch task first trial was 1.6 2.2 s and the pinch task second trial was 0.8 1.2 s. Correction time was significantly decreased in the second than in the first trial of the pinch task (\*p < 0.05, Figure 14).

In the motor imagery group at the 50% MVC error, the pinch task first trial was 25.3 26.3% and the pinch task second trial was 36.8 36.8%. The 50% MVC error was significantly increased in the second than that in the first trial of the pinch task (\*p < 0.05, Figure 14). Accordingly, in the control group at the 50% MVC error, the pinch task first trial was 21.7 17.4% and the pinch task second trial was 29.6 25.7%. The 50% MVC error was significantly increased in the second as compared to that the first trial of the pinch task. (\*p < 0.05, Figure 14).

In the motor imagery group, no significant differences were observed in correction time between the first and second trials of the pinch task. The 50% MVC error was significantly increased in the second than that in the first trial of the pinch task. In the control group, correction time was significantly decreased in the second as compared to that in the first trial of the pinch task. The 50% MVC error was significantly increased in the second than that in the first trial of the pinch task.

Figure 14. The index for the accuracy of motion results.

### 5. Discussion

#### 5.1. The factor indicating increased spinal motor neuron excitability

The excitability of spinal motor neurons under the motor imagery condition was increased than that of spinal motor neurons at rest and in the touching a sensor condition. We attribute this to the influence of the descending pathways corresponding to the thenar muscle. In contrast, excitatory inputs travel through the corticospinal pathway and reticulospinal tract and from the corticospinal pathway and extrapyramidal tract to anterior horn cells. Suzuki et al. [11] reported the excitability of spinal motor neurons in the motor imagery condition to be influenced by the descending pathways from the cerebral nervous system. Furthermore, activation of the primary motor area, supplementary motor area, premotor area, primary somatosensory area, dorsolateral prefrontal area, cingulate cortex, and cerebellar regions occurred during motor imagery [4–7]. Therefore, activation of the cerebral cortex in the motor imagery condition presumably increased the excitability of spinal motor neurons via the corticospinal pathway and extrapyramidal tract. The subjects performed motor imagery while touching a pinch meter sensor. Therefore, the influences of tactile and proprioceptive inputs should be considered. Mizuguchi et al. [20, 21] reported that the responsiveness of afferent pathways to the primary somatosensory area during motor imagery while utilizing an object was modulated by a combination of tactile and proprioceptive inputs while touching the object. Tactile and proprioceptive inputs from the periphery are integrated after they have been hierarchically processed and then projected to the primary motor area. Furthermore, Suzuki et al. [11] reported that the excitability of spinal motor neurons with motor imagery under the "with sensor" condition was increased than that of the spinal motor neurons with motor imagery under the "without sensor" condition. Thus, tactile and proprioceptive inputs while touching the pinch meter sensor would presumably increase the excitability of spinal motor neurons as part of a synergistic effect. Therefore, we hypothesized that our subjects might perform some form of motor imagery.

#### 5.2. The effect of motor imagery on the accuracy of motion

5. Discussion

88 Neurological Physical Therapy

Figure 14. The index for the accuracy of motion results.

5.1. The factor indicating increased spinal motor neuron excitability

The excitability of spinal motor neurons under the motor imagery condition was increased than that of spinal motor neurons at rest and in the touching a sensor condition. We attribute this to the influence of the descending pathways corresponding to the thenar muscle. In contrast, excitatory inputs travel through the corticospinal pathway and reticulospinal tract and from the corticospinal pathway and extrapyramidal tract to anterior horn cells. Suzuki et al. [11] reported the excitability of spinal motor neurons in the motor imagery condition to be influenced by the descending pathways from the cerebral nervous system. Furthermore, activation of the primary motor area, supplementary motor area, premotor area, primary somatosensory area, dorsolateral prefrontal area, cingulate cortex, and cerebellar regions occurred during motor imagery [4–7]. Therefore, activation of the cerebral cortex in the motor imagery condition presumably increased the excitability of spinal motor neurons via the corticospinal pathway and extrapyramidal tract. The subjects performed motor imagery while touching a pinch meter sensor. Therefore, the influences of tactile and proprioceptive inputs should be considered. Mizuguchi et al. [20, 21] reported that the responsiveness of afferent pathways to the primary somatosensory area during With a motor learning time of 10 s, no significant differences were observed in the correction times between the first and second trials of the pinch task in either the motor imagery group or the control group. The correction time reflects the ability to control the accuracy of muscle force during the pinch action. We attributed this to numerous zero second correction times from the first trial and to the second trial of the pinch task. Surely, the subject could not be learning 50% MVC with a motor learning time of only 10 s. Accordingly, the correction time was not changed after versus before motor imagery or in the condition without motor imagery. The 50% MVC error was significantly increased in the second than that in the first trial of the pinch task in both the motor imagery and the control group. The error in 50% MVC reflects whether or not there is convergence on 50% MVC. Provided that the pinch value obtained represents convergence on 50% MVC, the 50% MVC error is decreased in the second trial as compared to the first trial of the pinch task. Conversely, provided that the pinch value obtained does not represent convergence on 50% MVC, the 50% MVC error is increased in the second than that in the first trial of the pinch task. Given these observations, our results suggest that motor imagery does not improve the ability to achieve accurate motion after motor learning for 10 s. Mulder et al. [22] reported that motor imagery improved the ability to achieve actual motion only in people with learning that corresponded to the motor imagery task. Accordingly, the subjects might not be able to learn the 50% MVC in only 10 s. Thus, it is necessary for the learning of subjects to correspond fully to the motor task. We conclude that adequate learning time should be provided in future studies.

With a motor learning time of 30 s, no significant differences were observed in the correction time between the first and second trials of the pinch task in the motor imagery group. Accordingly, the correction time was significantly decreased in the second than that in the first trial of the pinch task in the control group. These results suggest that the accuracy of motion was decreased in the control group. If the correction time is increased after motor imagery, the index of the accuracy of motion would be improved in the second trial as compared to the first trial of the pinch task. Conversely, the correction time should be decreased if there is no improvement in the accuracy of motion. Ronsse et al. [23] reported that the effectiveness of motor learning with the use of visual feedback was decreased over time with periodic flexion and extension at both wrist joints. Ohashi et al. [24] reported that the information pertaining to the intensity of force in an isometric contraction task was decreased over the course of time. In this study, the subjects carried out motor learning with isometric contraction and using visual feedback. Accordingly, if accurate motion was acquired with motor learning for 30 s, it was decreased over the course of time under inactive conditions. On the other hand, the accuracy of motion was not decreased between the first and second trials of the pinch task when performing motor imagery. Therefore, the accuracy of motion might be maintained by motor imagery after motor learning for 30 s. However, in our previous study, motor imagery after motor learning for 10 s failed to maintain accurate motion. It was concluded that a motor learning time of 10 s was insufficient, while 30 s was sufficient. In the motor imagery group, no significant differences were observed in the 50% MVC error between the first and second trials of the pinch task. However, in the control group, the 50% MVC error was significantly increased in the second than that in the first trial of the pinch task. Provided that the pinch value does not represent convergence on 50% MVC, the 50% MVC error would be increased in the second than that in the first trial of the pinch task. Consequently, the accuracy of motion was maintained only in the motor imagery group, results consistent with those for the correction time. Taken together, the present results suggest that motor imagery after 30 s of motor learning is found to be strongly involved in the accuracy of motion. In future studies, it will be necessary to extend the motor learning time before attempting motor imagery. Such a strategy will allow us to study the effects of motor imagery on the accuracy of motion.