Potential Applications of Motor Imagery for Improving Standing Posture Balance in Rehabilitation

Abstract

Improving standing posture balance is an essential role of rehabilitation to prevent falls in the elderly and stroke victims. Recently, motor imagery has been reported to be an effective method to improve standing posture balance. Motor imagery is a simulation of a movement in the brain without actual movement. Motor imagery is believed to have a common neural basis with actual movement and is effective in reconstructing motor functions. Recently, it has also been shown that motor imagery can be enhanced through use in combination with neuromodulation techniques. In this chapter, motor imagery contributing to the improvement of standing postural balance and its combination with neuromodulation techniques are reviewed.

1. Introduction

An important role of rehabilitation is to improve the standing postural balance of the elderly and stroke victims to prevent falls. Recently, motor imagery has been reported to effectively improve standing postural balance and facilitate the effects of neuromodulation techniques. This chapter outlines how motor imagery contributes to the improvement of standing postural balance and reviews how this method can be used in combination with neuromodulation techniques.

2. Motor imagery

Motor imagery is the simulation of motion in the brain without actual motion [1]. According to the PubMed database, 3853 articles on motor imagery were reviewed from 1979 to 2021, and the number is increasing every year. In addition, 1178 articles on rehabilitation using motor imagery were reviewed from 1999 to 2021, and the number of articles in this subtopic is also increasing, indicating that the application of motor imagery in rehabilitation has been attracting attention in recent years (Figure 1). In addition, motor imagery has a common neural basis with actual movement and is considered to be effective in reconstructing motor function. Therefore, clarification of brain activity during motor imagery will enhance the validity of motor function reconstruction by comparing it with brain activity during the actual exercise. In this section, we review the neural basis and mechanisms of motor imagery based on previous studies.

Figure 1.

Progression of the number of publications on motor imagery and the number of publications on motor imagery and rehabilitation. The red line shows the number of publications on motor imagery from 1977 to 2021. The blue line shows the number of publications on motor imagery and rehabilitation from 1999 to 2021. Data were collected from the PubMed database (https://pubmed.ncbi.nlm.nih.gov/) by online searches with the terms “motor imagery” for the red lines and “motor imagery” and “rehabilitation” for the blue lines.

2.1 Brain function studies on motor imagery

In 1977, Ingvar and Philipson [2] introduced the first method for displaying the mean blood flow distribution in the brain as a two-dimensional color map in the study of brain function during motor imagery. They used this method to measure and compare regional cerebral blood flow at rest, during motor imagery, and during actual movement. Subjects performed a task in which they were asked to imagine a rhythmical clasping movement of the right hand during motor imagery and then perform a rhythmical movement of the right hand during actual movement. The measurement procedure in this study was the same for all subjects, with the resting state being measured first, followed by the motor imagery, and finally the actual exercise. The results showed that motor imagery increased blood flow in the entire frontal lobe, including the supraorbital region, as well as the parietal and temporal lobe regions. However, the actual movement of the right hand increased blood flow mainly in the central sulcus. Thus, the results of this study suggest that the centers of motor imagery are located in a different region of the cerebrum than the centers that control actual hand movements. However, medical science and technology have made obvious progress since 1977, and new techniques, such as functional magnetic resonance imaging (fMRI) [3] and positron emission tomography (PET) [4], have been used to detect brain activity. Moreover, Hétu et al. [5] performed an activation likelihood estimation (ALE) meta-analysis of 75 studies measuring brain activity during motor imagery using fMRI or PET reported up to 2011. They were the first to examine quantitative maps of structures activated during motor imagery. The results revealed that motor imagery depends on a network that includes motor-related areas, such as frontoparietal and subcortical structures. Therefore, studies from recent years have supported the view that motor imagery and actual movement share a common neural basis. In addition, a study from 2018 [6] that compared brain activity during motor imagery and actual movement in detail reported that there is effective connectivity between motor and cognitive networks. In that study, 20 healthy subjects were tested in a series of finger tapping trials, and electroencephalography (EEG) data throughout the task were validated using dynamic causal modeling. The results demonstrated effective connectivity between the dorsolateral prefrontal cortex (DLPFC) and secondary motor areas (M2), and between primary motor areas (M1) and M2, both during motor imagery and motor execution. Furthermore, DLPFC-premotor cortex (PMC) connectivity was more strongly activated during motor imagery than during actual movement. Additionally, PMC-supplementary motor areas (SMA) connectivity and M1-PMC connectivity were more strongly activated during motor imagery than during actual movement. Thus, in addition to supporting the recent view that motor imagery and actual movement share a common neural basis, the results of that study also suggest that although they share a common neural basis, they are distinct processes. In light of the above, reports on motor imagery are increasing annually, and subsequent studies are expected to elucidate brain activity during motor imagery.

2.2 Classification of motor imagery

Motor imagery can be divided into two types—muscular sensory imagery (KI) and visual imagery (VI). Because these methods of imagery differ, resulting in differing brain activity and training effects [7], the characteristics of each method must be understood to flexibly introduce motor imagery training in rehabilitation and elicit its effects. Guillot et al. [8] used fMRI to determine whether the neural networks formed by KI and VI are equivalent. In this study, 13 subjects were given a finger movement as a motor imagery task. The results of the comparison between KI and VI showed that movement-related structures and the inferior parietal lobule (IPL) were activated in KI, whereas the occipital lobule and superior parietal lobule (SPL) were mainly activated in VI. Figure 2 shows the results of the evoked responses obtained for KI and VI during the 5 s of the test using a FASTRAK digitizer (Polhemus, Colchester, Vermont, USA), based on the neurophysiological data measured by the magnetoencephalography system. From the figure, it can be seen that KI results in outstanding PMC activity, while VI results in an activated occipital lobe. In addition, Figure 3 shows the power spectra of brain activity measured by EEG for four activities—kinesthetic motor imagery (KMI), visual motor imagery (VMI), motor execution (ME), and visual observation (VO). Panel (a) shows that the normalized power of the KMI and VMI conditions was similar in the alpha and beta frequency bands. Panel (b) demonstrates that the neural networks were similar in KMI and ME due to their high connectivity to regions of interest (ROI) in the sensorimotor cortex. Furthermore, VMI and VO networks were similar, with a large number of networks distributed in the DLPFC and PMC. Moreover, Hétu et al. [5] reported in detail the brain regions that are consistently activated during KI and VI execution. KI showed consistent activation of the SMA, IPL, precentral gyrus (PcG), cerebellum (CB), left inferior frontal gyrus (IFG), supramarginal gyrus (SMG), temporal pole, putamen, anterior insula, right Rolandic operculum, angular gyrus, and pallidum. VI showed consistent activation of the bilateral SMA, left PcG, lingual gyrus, CB, light middle frontal gyrus (MFG), and postcentral gyrus (PocG). When KI and VI were combined, the left PcG, SMA, anterior insula, and bilateral putamen were consistently activated. Therefore, KI is employed during the execution of real movements, while VI is activated in the visual cortex, which processes visual information.

Figure 2.

Typical induced responses in KI and VI [9]. Right: induced response in KI. Left: induced response in VI.

Figure 3.

Power spectra and connectivity in ME, KMI, VMI, and VO brain activity [10]. (a) Grand average power spectra of the four groups. Each line represents the grand average of the normalized power of each group with all seven subjects and ROIs. (b) The average of the maximum 20% connectivity in each of the four frequency bands. The line represents the functional connectivity calculated from the mutual information, and the thickness of the edge represents the strength of the connectivity. The node represents the location of the ROI, and the size of the node represents the connectivity with other ROIs.

3. Motor imagery and standing postural balance

Improvement of standing postural balance consists of muscle strength [11, 12]; joint range of motion [13]; and somatosensory [14], visual [15], and brain function [16]. Moreover, standing postural balance and fall prevention are correlated, and it has been shown that control of lateral stability is significant for fall prevention interventions [17, 18]. Recently, motor imagery has been attracting attention as an intervention to improve balance function and the effects of motor imagery on standing postural balance have been reported in healthy subjects, the elderly, patients with stroke, and those with Parkinson’s disease, among others. In this section, we review the effects of motor imagery on standing postural balance compared by subject based on previous studies.

3.1 Motor imagery and standing postural balance in healthy and elderly subjects

In a study on healthy subjects, Jahn et al. [19] examined the activation/deactivation patterns of each brain region during motor imagery using fMRI in imagery tasks of standing posture, walking, running, and supine posture. In this study, 13 healthy adults with an average age of 27.3 years performed the above-mentioned four motor imagery tasks for 20 s each in the supine position with closed eyes. The results showed that different activation/deactivation patterns were detected in the three conditions of standing, walking, and running, respectively. During motor imagery of the standing posture, the thalamus, basal ganglia, and cerebellar mediastinum were activated. During motor imagery of walking, the parahippocampal gyrus and cuneiform gyrus, occipital visual area, and CB were significantly activated. Moreover, during motor imagery of running, the cerebellar vermis and adjacent cerebellar hemispheres in the CB were activated six times more than during motor imagery in the standing and walking conditions, while the parahippocampal gyrus and cuneus gyrus were not activated compared to the walking condition. These results support the concept of hierarchical organization of posture and movement and suggest that motor imagery activates low-intensity CB activity that controls standing postural balance and the sensory-motor control through the thalamus and basal ganglia. In addition, a study has examined the effects of nonphysical training on standing postural balance from the perspective of brain activity [20]. The intervention involved 16 healthy adults with an average age of 27.5 years. The study was conducted under three conditions: (1) a combination of action observation and motor imagery in which the subjects watched a video of a balance task being performed, (2) simple action observation in which they watched a video, and (3) simple motor imagery in which they imagined walking with their eyes closed. Two balance tasks were performed under each condition: static standing and dynamic standing with internal and external perturbations, which were measured four times for static trials and four times for dynamic trials in a randomly determined order. The results showed that the intervention of motor imagery during the dynamic balance task predominantly activated the putamen, CB, SMA, and M1, and the combination of action observation and motor imagery activated the PMC in addition to the brain regions activated in motor imagery alone. However, intervention with action observation did not significantly activate these brain regions. In other words, this study suggests that motor imagery training may be effective in controlling standing posture in the medial and lateral directions.

In a study of older adults, Oh et al. [21] examined the potential for effective training adaptations for fall prevention by assessing static and dynamic balance and fear of falling in older adults who have a history of falls, before and after motor imagery training or task-oriented training. This study included 34 elderly subjects aged 65 years or older, randomly assigned to three groups: a motor imagery (11 subjects), task-oriented training (11 subjects), and a control group (12 subjects). In motor imagery training, the subjects sat in a sitting posture with their eyes closed during a 10-min relaxation period. Then, they imagined movements to protect themselves in the event of a fall for 20 min. In task-oriented training, balance training focusing on daily activities was conducted. The results showed that dynamic balance and fear of falling were significantly improved in the motor imagery group compared to the other two groups. Therefore, motor imagery training for the elderly and those without disease improved balance function, suggesting that it is highly effective as an intervention for fall prevention.

3.2 Motor imagery and standing postural balance in stroke patients

In 2005, a systematic review of seven databases on the effectiveness of motor imagery interventions in stroke patients [22] revealed a significant effect of motor imagery training in the Fugl-Meyer Stroke Assessment (FMA). In another study, a 30-minute motor imagery task of daily activities was performed on the paralyzed upper limbs, and changes in the cortex were verified by fMRI after 10 weeks of intervention [23]. This study revealed significant activation in the bilateral PMC and M1, as well as in the superior parietal lobe of the paralyzed side for flexion or extension movements of the wrist on the paralyzed side. These studies suggest that intervention with motor imagery training is effective for improving function in stroke patients.

In addition, a meta-analysis of balance function in stroke patients was performed in 2016 by extracting randomized controlled trials of motor imagery intervention for gait ability and balance in stroke patients from 12 electronic databases [24]. This study reported that intervention with motor imagery is effective in improving gait performance, but no statistical difference was found concerning balance function. According to Oostra et al. [25], poor motor imagery after stroke is associated with lesions of the left putamen, left ventral premotor cortex (PMv), and long association fibers connecting the parietooccipital region and the DLPFC. In other words, the effect of motor imagery is less clearly defined when the frontoparietal network is impaired. It has also been reported that the effect of motor imagery in stroke patients depends on their ability to maintain and manipulate information in working memory [26]. Moreover, the working memory involves the frontoparietal network [27], and it is highly likely that the basal ganglia and PMC have a strong influence on motor imagery. Additionally, the frontoparietal network has been reported to be the same brain region that is activated in actual movement [5]. Thus, a part of the frontoparietal network that is related to motor imagery as well as the actual movement was impaired, which affected the result that balance function was not significantly improved in the stroke patients. However, many studies have reported statistically significant effects on walking ability and upper limb function, suggesting that screening for motor imagery effectiveness based on lesion localization is necessary.

3.3 Motor imagery and standing postural balance in patients with Parkinson’s disease

Parkinson’s disease (PD) presents with movement [28], cognitive [29], and psychiatric symptoms [30]. PD can be clinically classified into a tremor-dominant subtype and a postural instability gait disorder subtype, and it has been reported that balance function is more impaired and the risk of falling is higher in the postural instability gait disorder subtype than in the tremor-dominant subtype [31, 32]. In addition, the severity of the postural instability gait disturbance is a useful indicator of PD severity and prognosis [33], suggesting that improvement in balance function and walking ability may be attributed to a favorable prognosis.

Motor imagery interventions for PD have been reported in many studies. For example, Thobois et al. used PET to compare the brain activity of normal subjects and immobilized PD subjects who imagined continuous hand movements [34]. The results showed that the prefrontal cortex, SMA, superior parietal lobe, IFG, and CB were activated during motor imagery in normal healthy subjects, while M1 activation was only observed during the dominant hand trials. Furthermore, in PD patients, motor imagery of the immobile hand showed a lack of activation in the contralateral primary somatosensory cortex and CB, persistent activation in the SMA, and bilateral activation in the superior parietal lobes. Based on these results, this study reports that PD patients with immobility show abnormal brain activation during motor imagery and that ideal brain activation depends on the state of the imagery hand.

Another symptom of PD is the altered timing of continuous movements. It has been shown that movement timing in internally generated continuous movements is selectively deficient, and the defects can lead to problems in movement planning [35]. This symptom may limit the conduct and potential effectiveness of motor imagery in rehabilitation for PD patients. Therefore, Heremans et al. validated the effects of a goal-directed motor imagery task using visual and auditory cues [36]. The results showed that the motor imagery task with visual cues significantly reduced bradykinesia. Moreover, the results suggest that the effectiveness of motor imagery for restoring function in PD patients can be enhanced by employing VI, while the effectiveness of KI is low. A study of VI intervention in PD patients examined the effects on standing postural balance and walking ability [37]. In this study, VO and motor imagery were administered as VI for 6 weeks. The results showed improved balance function and gait velocity in PD patients with postural instability and gait impairment. Thus, the addition of VI to standing postural balance training in PD patients promoted specific functional reorganization of brain regions involved in motor control and executive-attentional abilities, which is expected to have a long-term effect.

4. Neuromodulation techniques facilitated motor imagery effects

Motor imagery can be easily introduced into clinical practice because it can improve performance without special equipment. However, mental practice using motor imagery is limited in that the quality of the motor imagery being performed is not feedbacked to the subject [38], which causes individual differences in the motor imagery effects [39]. Neuromodulation technology has recently attracted attention as a method to solve this problem. Neuromodulation is a technique used to regulate the nervous system by electrical or scientific measures and is applied for many diseases [40]. Using this technology to provide feedback to the subject on the quality of motor imagery may be effective in improving movement performance. In this section, we review various neuromodulation techniques that facilitate motor imagery.

4.1 Combined motor imagery and neurofeedback

Neurofeedback is a noninvasive tool for purposeful modulation of human brain function that has the potential to dramatically impact neuroscience and clinical treatment of neuropsychiatric disorders [41]. Boe et al. [42] investigated whether the combined use of neurofeedback during motor imagery tasks could modulate brain activity. In this study, 18 healthy subjects (eight males, ten females, 24.7 ± 3.8 years old) were randomly assigned to a neurofeedback group or a control group. The motor imagery task was a KI activity in which the subject continuously pressed buttons with the ineffective hand. Neurofeedback was based on event-related synchronization/desynchronization (ERS/ERD) in the β-band of the sensorimotor cortex and was provided in real-time during motor imagery from a bar graph on a projector. The results showed that neurofeedback from bilateral sensory-motor cortices increased the contralateral pattern of brain activity associated with motor imagery with each successive session compared to the control group. Thus, this study suggests that the provision of neurofeedback provides significant information about motor imagery training and an opportunity for patients to modulate their own regional brain activation. In addition, neurofeedback approaches have a background of dependence on a single brain imaging modality such as EEG or fMRI. However, a study validated breaking away from this dependency system by reporting the effects of bimodal neurofeedback with simultaneous EEG and fMRI feedback [43]. In this study, the effects of unimodal EEG- and fMRI-neurofeedback were compared with those of bimodal EEG-fMRI-neurofeedback. The results showed that EEG-fMRI-neurofeedback significantly modulated activity in the movement domain compared to the two groups of short-peaked neurofeedback, and specific mechanisms and their additional value were found. Other studies have examined the effects of EEG-fMRI-neurofeedback on motor imagery, and all have shown that neurofeedback can modulate brain activity better than unimodal neurofeedback [44, 45]. In conclusion, neurofeedback during motor imagery can modulate brain activity and improve performance. Furthermore, incorporating multimodal techniques, such as bimodal neurofeedback instead of unimodal neurofeedback, may enhance the effects obtained from motor imagery.

4.2 Motor imagery and tES

Transcranial electrical stimulation (tES) aims to noninvasively modulate brain function by applying current from the current source [46]. Cranial electro-stimulation therapy (CET), cranial electrotherapy stimulation (CES), and transcranial pulsed current noise stimulation (tRNS) are several methods of tES used as clinical treatment [47]. Several previous studies have reported the use of tES, especially transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation in combination with motor imagery. Moreover, Xie et al. [48] examined the effects of tES on brain activity during motor imagery. The results showed that ERD of μ and β rhythms during a motor imagery task was significantly enhanced by the combined use of tDCS with motor imagery. In addition, a study that examined the modulation of motor learning by transcranial alternating current stimulation [tACS] [49] suggested that 70 Hz tACS enhances motor learning ability by intermodulation activity in the β-wave band. Thus, tDCS and tACS are potential approaches to modulate brain activity during motor imagery and enhance effective functions to improve performance.

4.3 Motor imagery and rTMS

Transcranial magnetic stimulation (TMS) is a technique that noninvasively modulates brain activity through the induction of currents by rapidly changing magnetic field pulses [50]. In addition, by reviewing the literature through 2018, guidelines were established for treatment with repetitive transcranial magnetic stimulation (rTMS) in Europe in 2020. The guidelines established rTMS as a clinical treatment modality, although its efficacy has not reached Level A/B evidence. Moreover, rTMS can be divided into high frequency (HF) and low frequency (LF) rTMS. LF rTMS decreases the excitability of the nonaffected hemisphere [51], while HF rTMS increases it [52].

Many previous studies in which motor imagery and rTMS were combined have verified the therapeutic effects on upper limb function in stroke victims [53, 54]. For example, Pan et al. [54] investigated the effects of motor imagery and LF rTMS on upper limb motor function during stroke rehabilitation. They applied 1 Hz rTMS to the M1 of the nonaffected hemisphere; 10 sessions of 30 min were performed during a two-week intervention period. The results showed that upper limb motor function was significantly improved in the group that received motor imagery and LF rTMS in the second and fourth weeks after the intervention compared to the control group (LF rTMS-only group). Moreover, a study in which motor imagery was combined with HF rTMS [55] also revealed a significant improvement in pre- and post-stimulation performance. These results suggest that rTMS can enhance the effects obtained from motor imagery in subjects such as stroke survivors. Finally, it was suggested that there is no difference between high and low rTMS frequencies in terms of performance improvement.

5. Conclusion

In this chapter, we outlined motor imagery effects that contribute to the improvement of standing postural balance and the effects that can result from use in combination with neuromodulation techniques. We further discussed the consequences for healthy subjects and those with illnesses. Recently, the view that motor imagery constitutes the same neural basis as actual movement is gaining ground, and the main effects of motor imagery in improving standing postural balance have been demonstrated. In addition, neuromodulation technology has the potential to improve the effects of motor imagery and is expected to further contribute to rehabilitation. Thus, the combination of neuromodulation techniques with motor imagery training will be significant in improving the quality of rehabilitation.

Acknowledgments

This work was supported by the Yuumi Memorial Foundation for Home Health Care and JSPS KAKENHI Grant Number JP20K11173.

Conflict of interest

The authors declare no conflict of interest.

Appendices and nomenclature

fMRI

functional magnetic resonance imaging

PET

positron emission tomography

ALE

activation likelihood estimation

DLPFC

dorsolateral prefrontal cortex

M1

primary motor areas

M2

secondary motor areas

PMC

premotor cortex

SMA

supplementary motor areas

KI

kinesthetic imagery

VI

visual imagery

IPL

inferior parietal lobule

SPL

superior parietal lobule

ME

motor execution

VO

visual observation

EEG

electroencephalography

ROI

regions of interest

PcG

precentral gyrus

CB

cerebellum

IFG

inferior frontal gyrus

SMG

supramarginal gyrus

MFG

middle frontal gyrus

PocG

postcentral gyrus

FMA

Fugl-Meyer Stroke Assessment

PMv

ventral premotor cortex

PD

Parkinson’s disease

tES

transcranial electrical stimulation

CET

cranial electro stimulation therapy

CES

cranial electrotherapy stimulation

tPCS

transcranial pulsed current stimulation

tDCS

transcranial direct current stimulation

tACS

transcranial Alternating Current Stimulation

tRNS

transcranial Random Noise Stimulation

TMS

transcranial magnetic stimulation

rTMS

repetitive transcranial magnetic stimulation

HF

high frequency

LF

low frequency