Evaluating Motor Imagery Capabilities in Fatigued Versus Non-Fatigued Individuals With Multiple Sclerosis Without Cognitive Impairment

Abstract

Background

Motor imagery is adversely affected by various factors in individuals with multiple sclerosis (MS). However, the impact of MS-related fatigue on motor imagery remains unclear. Our study aimed to compare motor imagery abilities between fatigued and non-fatigued individuals with MS without cognitive impairment.

Methods

This study included 73 individuals with MS, with Expanded Disability Status Scale scores from 0 to 4.5. Participants were divided into 2 groups based on Fatigue Severity Scale scores: ≥4 for Fatigued Group and <4 for Non-fatigued Group. Assessment of motor imagery vividness was done through Kinesthetic and Visual Imagery Questionnaire-20 (KVIQ-20). The Box and Block Test (BBT) and the Timed Up and Go (TUG) were employed for the temporal congruence component.

Results

The mean ages of the Fatigued Group (30.4 ± 9.2 years) and the Non-fatigued Group (31.5 ± 9.8 years) were similar (P = .650). The fatigued Group exhibited significantly lower kinesthetic imagery scores on the KVIQ-20 (P = .028) and significantly lower performance in the BBT (upper extremities) mental chronometry test for both the most affected and least affected sides of the upper extremities (P = .007 and .028, respectively). Additionally, the Fatigued Group showed significantly lower performance in the TUG (lower extremities) mental chronometry test (P = .006).

Conclusion

In fatigued individuals with MS, there is a greater impact on both temporal congruence components and kinesthetic motor imagery abilities. The difference in the temporal congruence component was observed in tests involving both TUG (lower extremities) and BBT (upper extremities), independent of the affected side.

Keywords

multiple sclerosis fatigue motor imagery ability temporal congruence vividness

Introduction

Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease affecting the central nervous system and is characterized by various clinical symptoms.¹ These symptoms, including fatigue, muscle weakness, spasticity, balance difficulties, vision impairments, bladder and bowel dysfunction, and cognitive deficits, interfere with daily life activities.²

Motor imagery is the capacity to generate cognitive models of movements without motor activity.^3,4 Motor imagery is commonly classified into 2 types: direct and indirect. Direct imagery is further divided into visual and kinesthetic imagery. In visual imagery, individuals imagine acting from an external perspective, while kinesthetic imagery involves perceiving the action from a primary viewpoint. Indirect imagery mainly involves the mental rotation of objects or body parts.^5,6 Motor imagery also consists of 3 main components: temporal congruence, accuracy, and vividness. It is widely known that brain lesions negatively affect motor imagery in neurological disorders due to their impact on cognitive functions and body schema.^5,7 Studies in the literature show that motor imagery ability is reduced in individuals with MS.^8,9

The influence of confounding factors on motor imagery components has been the focus of some studies. Previous research indicates a relationship between cognitive impairment in individuals with MS and the accuracy and temporal congruence of motor imagery components.^8
-10 It has been reported that not only cognitive impairments but also cognitive fatigue affect temporal congruence. Furthermore, anxiety influences both the vividness and temporal congruence components.^11,12

Functional Magnetic Resonance Imaging (fMRI) studies have shown heightened activation in the superior parietal and occipital regions while individuals perform visual motor imagery tasks, when kinesthetic imagery is utilized, there is increased activation in the bilateral basal ganglia (putamen and caudate nucleus), and cerebellum.^13,14 Research suggests that fatigue in individuals with MS may be linked to abnormalities in multiple brain structures, including the basal ganglia, thalamus, striatum, superior frontal gyrus, intraparietal gyrus, medial prefrontal cortex, parietal-temporal cortex, posterior parietal cortex, precuneus, and posterior cingulate cortex, encompassing regions also implicated in mental imagery.^15,16

To better understand motor imagery and improve rehabilitation methods, it is crucial to conduct new research aimed at uncovering the underlying mechanisms, taking into account the subcomponents of motor imagery. The literature is uncertain about the link between motor imagery and fatigue, which is the most debilitating symptom experienced by individuals with MS.¹⁷ Therefore, our study aimed to determine whether fatigue affects motor imagery ability and which components of imagery are affected by fatigue in individuals with MS without cognitive impairment.

Methods

Study Design and Participants

The research was conducted Faculty of Physical Therapy and Rehabilitation at Hacettepe University, Ankara, Turkey. This study received ethical approval from the Hacettepe University Non-Interventional Studies Ethics Committee on 18.10.2022 with the registration number GO 22/829. Before the study began, participants agreed to participate by signing a document that provided them with all the necessary information.

Participants had to meet the age criterion of being between 18 and 54 years old,¹⁸ having MS based on the McDonald criteria (2013 version),⁶ an Expanded Disability Status Scale (EDSS) score of less than 4.5, and no cognitive impairment (Symbol Digit Modalities Test score ≥38, and Montreal Cognitive Assessment ≥21).^19,20 Exclusion criteria included taking psychiatric medication, having MS attacks in the past 3 months, having comorbid neurologic or orthopedic problems, and having severe depressive symptoms (Beck Depression Inventory score >30).

The study participants were divided into 2 groups based on the Fatigue Severity Scale (FSS) cut-off score: individuals with MS who had an FSS score of ≥4 were assigned to the Fatigued Group, while those with an FSS score of <4 were assigned to the Non-fatigued Group.^21,22

Assessment Methods

Sociodemographic information including age (year), gender (female/male), and data on the clinical course of the disease (EDSS, duration of disease, and type of disease) were recorded. Following the initial assessments to determine whether individuals would be included in the study, the individuals deemed eligible for inclusion underwent the following evaluations.²²

Assessment of Fatigue

The FSS was used to measure fatigue. The degree of fatigue in individuals with chronic illnesses is often measured using this 9-item measure. Each item on this scale is scored between 1 and 7, with a total possible score of 63. The cut-off value for this scale is 4. The definition of severe fatigue is based on the cut-off value with a score of 4 or higher and mild fatigue is defined as a score below 4.²³ The Turkish validity and reliability of the FSS were assessed by Armutlu and colleagues.²⁴

Assessment of Motor Imagery Components

The Kinesthetic and Visual Imagery Questionnaire-20 (KVIQ-20) was used to assess the vividness of motor imagery. The KVIQ-20 comprises 20 items in total, with 10 items dedicated to visual imagery and 10 items to kinesthetic imagery. The test includes simple activities that can be performed while seated. In kinesthetic imagery, the intensity of the sensation is rated on a scale from 1 to 5, whereas in visual imagery, the vividness of the image is scored on a scale from 1 to 5.²⁵ Dilek et al²⁶ investigated the validity and reliability of the Turkish Version of the KVIQ-20.

Temporal congruence refers to the alignment or synchronization of the timing between imagined and actual motor actions. This concept is typically assessed through mental chronometry tests, where the time difference between the execution of a motor action and the mental imagery of that action is measured and normalized by the actual movement duration.²⁷ The motor function is typically tested first, followed by the imagery function. Performing actual motor tasks initially helps activate motor memory, which facilitates more efficient execution of the imagery task.²⁸ The Box and Block Test (BBT) was used to evaluate upper extremity mental chronometry, and the Timed Up and Go (TUG)-based mental chronometry test was used to assess lower extremity mental chronometry. The mental chronometry test based on TUG (lower extremities) was conducted the day following the BBT (upper extremities) test. Each test evaluating motor imagery was conducted as a single trial without the inclusion of a familiarization task.

In the BBT-based mental chronometry test, the upper extremities were categorized into the least affected side and the most affected side. “The most affected side was defined as the body side for which physical execution of the BBT was at least 10% slower than for the other side.”⁸ The BBT (upper extremities) based mental chronometry test requires the individual to transfer 20 cubes (2.5 cm³) using his dominant hand initially. Afterward, individuals are asked to transport it using imagery. They are asked to say verbally each time they pass the block to the opposite side. With the non-dominant hand, the same procedure is followed.²⁹

In the TUG (lower extremities) based mental chronometry test, participants first perform the physical performance upon the command “Start.” They are instructed to rise from a chair, walk a 3-m distance, and return to the chair. Subsequently, motor imagery durations are calculated. Participants are asked to close their eyes and mentally visualize the start and end points of the 3-m path within the room. Upon the “Start” command, participants begin to mentally walk this distance. When the participant indicates they have returned and are seated, the stopwatch is stopped. Both the actual physical performance times and the motor imagery times are recorded for subsequent analysis.¹⁰

The rates of mental chronometry ratio were calculated involving motor imagery time and executed motor functions. To analyze the mental chronometry data, the time difference between motor imagination and motor execution was taken as an absolute error, regardless of whether motor imagination duration was shorter or longer than the executed motor function time. The mental chronometry data were then normalized using the formula (Mental chronometry ratio = (Executed motor function time − Motor imagery time)/Executed motor function time) to account for performance differences due to confounding factors. The range of the mental chronometry rate is 0 to 1. When this ratio is close to 0, the executed motor function time and motor imagery time are almost identical.

Statistical Analysis

The sample size for this study was calculated based on the effect size reported in the study by Kahraman et al,¹¹ which utilized the TUG-based mental chronometry test. A pre-study sample size calculation was conducted using the G Power 3.1 program, based on the effect size (d = 0.696) derived from comparing the TUG (lower extremities) mental chronometry ratios between 2 independent groups. The significance criterion (α) was set at .05 (2-tailed), and the statistical power was established at a minimum of 80%. The target sample size for each group was established as 34 participants. Considering a 10% dropout rate, it was decided to include 38 (34 + 3, 4) participants in each group. IBM Statistical Package for Social Sciences 23.0 software (IBM Corp., Chicago, IL, USA) was employed to perform statistical analysis on the data. Normality was assessed both visually with histogram plots and analytically using the Shapiro–Wilk test. The Mann–Whitney U test was utilized to examine differences between 2 independent groups in the data’s non-parametric distribution. The Wilcoxon sign test was utilized to investigate differences between 2 dependent groups in the data’s non-parametric distribution. The Chi-Square test was employed to compare categorical data. For differences between 2 independent groups within a parametric data distribution, the Student’s t-test was utilized. A Multivariate Analysis of Covariance (MANCOVA) was conducted to examine differences in mental imagery ability between groups while controlling for the potential effects of EDSS and fatigue. Since the correlation data did not follow a normal distribution, Spearman’s correlation coefficient was calculated to examine the relationships between mental chronometry tests, overall cognitive status, and cognitive processing speed. A significance level of P < .05 was considered for all statistical analyses.

Results

Within the scope of the study, 93 individuals with MS were evaluated, and 81 of them met the inclusion criteria. Based on fatigue evaluations, 40 individuals with MS were assigned to the Fatigued Group and 41 to the Non-fatigued Group. Out of the 2 groups, 5 participants from the Fatigued Group and 3 participants from the Non-Fatigued Group voluntarily left the research. Consequently, the study was completed with a total of 73 participants (Figure 1).

Figure 1.

Flow chart of the study.

Sociodemographic and Disease-Related Variables

The age and duration of disease were identical for both groups (P > .05). However, the Fatigued Group reported a higher EDSS score compared to the Non-fatigued Group (P < .05). The types of MS and gender distribution were similar across the groups (P > .05). The sociodemographic and disease-related variables are presented in Table 1.

Table 1.

Sociodemographic and Disease Related Variables.

Variables	Fatigued MS (n = 35)	Non-fatigued MS (n = 38)	P
Age (y)	30.4 ± 9.2	31.5 ± 9.8	.650
Disease duration (y)	4.7 ± 4.2	5.6 ± 4.7	.297
EDSS median (IQR)	2 (1.5-2.5)	1.5 (1.5-2)	.002*
Gender (n/%)	27 F (77.1)/8 M (22.9)	27 F (71.1)/11 M (28.9)	.554
MS type (n/%)
RRMS	31 (88.6)	37 (97.4)	.067
SPMS	4 (11.4)	0 (0.0)
PPMS	0 (0.0)	1 (2.6)

Abbreviations: IQR, Inter quartile range; n, number of participants in the group; EDSS, Expanded Disability Status Scale; MS, multiple sclerosis; PPMS, primary progressive multiple sclerosis; RRMS, relapsing-remitting multiple sclerosis; SPMS, secondary progressive multiple sclerosis.

P < .05.

Result of Motor Imagery components

When comparing the groups in terms of the vividness components of motor imagery, the kinesthetic imagery score was considerably lower in the Fatigued Group (F = 5.052; P = .028). Conversely, the visual imagery scores were identical between the groups (F = 0.806; P = .372). In comparing the groups regarding the temporal congruence components of motor imagery, the Fatigued Group exhibited significantly lower performance in BBT most affected side (F = 7.744, P = .007), BBT least affected side (F = 5.003, P = .028), and TUG (F = 8.068, P = .006) mental chronometry tests. MANCOVA results indicated that EDSS did not have a significant effect on motor imagery abilities, including KVIQ-20 visual imagery (F = 0.034, P = .854) and kinesthetic imagery (F = 0.000, P = .985), as well as mental chronometry tests such as BBT on the most affected side (F = .220, P = .640), BBT on the least affected side (F = 0.910, P = .343), and TUG (F = 1.643, P = .204).

In comparing the groups regarding the execution duration of mental chronometry tests, the Fatigued Group exhibited significantly higher duration in upper extremity mental chronometry tests (P < .05). In the lower extremity mental chronometry test, the durations of the Fatigued Group and the Non-Fatigued Group are comparable (P > .05). A detailed comparison of vividness and temporal congruence components of motor imagery ability between groups is presented in Table 2.

Table 2.

Comparison of Vividness and Temporal Congruence Components of Motor Imagery Ability Between Groups.

Tests	Fatigued MS (n = 35)	Non-fatigued MS (n = 38)	P
KVIQ-20
VI (17-85)	56.03 ± 14.14	59.71 ± 19.97	.372
KI (17-85)	46.83 ± 13.25	56.32 ± 19.12	.028*
BBT (0-1)
Most affected side	0.19 ± 0.11	0.12 ± 0.09	.007*
Least affected side	0.23 ± 0.18	0.13 ± 0.09	.028*
TUG (0-1)	0.32 ± 0.14	0.23 ± 0.16	.006*
BBT most affected side execution duration	28.70 (25.30-32.22)	28.15 (21.85-32.08)	.242
BBT least affected side execution duration	26.81 ± 4.05	25.30 ± 5.54	.191
TUG execution duration	8.02 ± 1.93	7.18 ± 1.35	.034*

Abbreviations: KVIQ-20, Kinesthetic and Visual Imagery Questionnaire-20, VI, visual imagery; KI, kinesthetic imagery; BBT, Box and Block Test; TUG, Timed Up and Go Test.

P < .05

Comparison of Execution Duration and Motor Imagery Duration

BBT (upper extremities) most affected side execution duration and motor imagery duration were identical (P = .125). BBT (upper extremities) least affected side execution duration and motor imagery duration were identical (P = .658). TUG (lower extremities) execution duration is greater than motor imagery duration (P < .001). A comparison of execution duration and motor imagery duration is presented in Table 3.

Table 3.

Comparison of Motor Imagery Duration and Execution Duration in BBT and TUG.

Tests	Execution duration	Motor imagery duration	Between-group difference (P)
BBT most affected side	28.37 (24.00-32.09)	25.81 (21.98-32.05)	P = .125
BBT least affected side	26.14 (22.45-29.29)	25 (21.17-29.41)	P = .658
TUG	7.41 (6.42-8.62)	5.63 (4.23-6.88)	P = .001*

Abbreviations: BBT, Box and Block Test; TUG, Timed Up and Go Test.

P < .05.

Correlation Between Mental Chronometry Tests and Cognitive Screening Tests

The SDMT showed no correlation with the BBT most affected side, BBT least affected side, and TUG mental chronometry tests (BBT most affected side: rho = .165; BBT least affected side: rho = .035; TUG: rho = .247). Similarly, the MoCA demonstrated no correlation with the BBT most affected side, BBT least affected side, TUG mental chronometry tests (BBT most affected side: rho = −.087; BBT least affected side: rho = −.022; TUG: rho = −.091). Detailed correlations between mental chronometry and cognitive screening tests are presented in Table 4.

Table 4.

Correlations Between Mental Chronometry Tests and Cognitive Screening Tests.

Cognitive Tests	BBT most affected side (0-1)	BBT least affected side (0-1)	TUG (0-1)
SDMT
rho	0.165	0.037	0.247
P	0.161	0.751	0.035
MoCA
rho	−0.087	−0.022	−0.091
P	0.462	0.850	0.442

Abbreviations: rho, Spearman’s rank correlation coefficient; EDSS, Expanded Disability Status Scale; BBT, Box and Block Test; TUG, Timed Up and Go Test; SDMT, symbol digit modalities test; MoCA, montreal cognitive assessment.

P < .05.

Discussion

The present study aimed to compare motor imagery ability in individuals with MS without cognitive impairment, between fatigued and non-fatigued individuals. The findings of this study indicate that fatigue adversely affects the vividness of motor imagery ability, particularly kinesthetic motor imagery ability. Mental chronometry tests for upper and lower limbs revealed that fatigue in individuals with MS is linked to a decrease in performance on the temporal congruence component of motor imagery ability. The duration of actual motor function is similar to that of upper extremity motor imagery but longer than the time required for lower limb motor imagery. There is no relationship between the temporal congruence component of mental chronometry and cognitive processing speed, general cognitive status, and disability status.

The literature indicates that motor imagery ability in individuals with MS is influenced by various factors. A recent systematic review highlights that motor imagery is shaped by cognitive functioning, including aspects such as cognitive fatigue and impairments. Additionally, MS phenotype, depression, and anxiety have been identified as other potentially significant contributors.³⁰ Previous research has primarily concentrated on the influence of cognitive functioning on motor imagery, with its effects now well-established.^8,10,31 However, exploring the impact of fatigue, a condition that affects a significant proportion of individuals with MS, offers a promising avenue for advancing our understanding of motor imagery. In this context, we deliberately selected participants without cognitive impairments to ensure that the role of fatigue could be assessed more precisely and independently.¹⁷

The impact of cognitive fatigue on motor imagery ability in individuals with MS was investigated by Podda et al¹² According to the study’s findings, cognitive fatigue negatively affected the temporal congruence component of imagery. Similar to the research of Podda et al,¹² we found that fatigue could adversely affect the temporal congruence component.

Our study findings showed that while fatigue did not affect visual imaging, it did alter kinesthetic imagery. While direct supportive literature explicitly showing that kinesthetic imagery is more affected by fatigue due to its more complex organization is scarce, indirect studies exist. For instance, Dettmers et al³² reported that impaired proprioception may affect kinesthetic imagery in individuals with stroke. The kinesthetic aspect of motor imagery may have greater difficulties than the visual aspect. This suggests that this component may arise from impairment in proprioception.^32,33

Stins et al³³ investigated the vividness component of motor imagery during postural sway in Parkinson’s disease. Postural sway was found to be effectively influenced by kinesthetic imagery, but not by visual imagery. Grangeon et al³⁴ found that there is a greater increase in postural sway during kinesthetic imagery in comparison to visual imagery in individuals without neurological problems, which they attribute to the higher cognitive demand associated with kinesthetic imagery.³⁴

Kinesthetic and visual imagery exhibit distinct differences in neural connectivity. Guillot and colleagues investigated brain activity during these 2 types of imagery using fMRI. Their findings revealed that kinesthetic imagery is associated with increased activation in the bilateral basal ganglia (putamen and caudate nucleus) and the cerebellar regions. In contrast, visual imagery demonstrated greater activation in the occipital lobe and the superior parietal regions.¹⁴ Furthermore, visual imagery involves the mental visualization of movement guided by visual cues, whereas kinesthetic imagery relies on the perception of movement through proprioceptive feedback.³⁴

Internal models play a key role in predicting and simulating body movements and sensations based on past experiences.³⁵ In individuals with MS, these models can be more strongly affected by fatigue. Fatigue, particularly linked to proprioceptive (related to joint and tissue position) information, has a greater impact on kinesthetic imagery compared to visual imagery, leading to disruptions in the simulation of motor actions. This distinction helps explain how fatigue influences kinesthetic imagery differently from visual imagery.³⁶

In the literature, there are studies on the effect of some confounding factors on the temporal congruence component of motor imagery ability. The study by Allali et al¹⁰ evaluated the temporal congruence component of motor imagery using a TUG-based mental chronometry test. According to the study, there is a connection between the temporal congruence component and cognitive functions like memory, attention, and executive function. Kahraman et al¹¹ divided individuals with MS into 2 groups: those with anxiety and those without anxiety. They compared the motor imagery abilities of these groups using a TUG-based mental chronometry test to assess the temporal congruence component. According to their findings, the temporal congruence component of motor imagery ability may be adversely affected by anxiety. In our study, negative effects of fatigue were observed in mental chronometry tests conducted on both upper and lower extremities. These findings indicate that under conditions of fatigue, temporal accuracy is diminished in both upper and lower extremities.⁸

This suggests that fatigue affects not only physical performance but also motor imagination. Fatigue in MS patients may affects mental imagery processes by slowing nerve conduction, reducing cortical activity, and disrupting energy metabolism.^37
-39 Mental imagery relies on the synchronized functioning of areas such as the motor cortex, premotor regions, basal ganglia, and parietal lobes.⁴⁰ Fatigue reduces neural activity in these areas, weakening connections and slowing information processing. As a result, it becomes more difficult to mentally simulate movements, leading to decreased accuracy and speed.^37
-40 Therefore, management of fatigue in individuals with MS is critically important for enhancing both physical movements and the quality of motor imagination.

Motor imagery is not commonly used in clinical practice. However, there are experimental studies conducted in this field. For example, Seebacher et al conducted a study comparing non-cued and cued motor imagery interventions, and their findings showed that cued motor imagery was more effective in reducing fatigue. Similarly, Kahraman et al conducted a study examining the effects of tele-rehabilitation-based motor imagery training on fatigue, and they demonstrated that this method was effective in reducing fatigue. The literature indicates the existence of various motor imagery interventions focusing on reducing fatigue. Therefore, identifying confounding factors such as fatigue could contribute to therapeutic advancements.

This study encountered several limitations. As the exclusion criteria omitted individuals aged 55 years and older, the findings of the study cannot be generalized to the entire population of individuals with MS. This consideration is crucial for informing future research directions. There are approximately 82 000 individuals with MS in Turkey and considering that this study was conducted at a single center, the study population may not be fully representative of the Turkish population.⁴¹ Although our study does not include brain imaging techniques or physiological measurements, it is considered to provide a valuable contribution by evaluating the components of motor imagery separately. In future studies, it would be beneficial to utilize more comprehensive assessment methods and consider evaluating motor imagery components individually. Conversely, this study offered some advantages. The investigation successfully achieved the targeted sample size.

Conclusion

This study demonstrated that fatigue in individuals with MS may affect the vividness and temporal congruence components of motor imagery. One of the components of motor imagery, the accuracy component, was not assessed in this study. Future research should consider evaluating the accuracy component to provide a more comprehensive understanding of motor imagery skills. Although motor imagery applications are not frequently utilized in clinical practice, identifying influencing factors such as fatigue, as in this study, may contribute to the integration of motor imagery practices into rehabilitation in the future. In this context, the integration of motor imagery into rehabilitation strategies holds significant potential.

Footnotes

Acknowledgements

The authors acknowledge the late Prof. Dr. Kadriye Armutlu for her scientific contributions to the study.

Author Contributions

Gizem Şekercan: Conceptualization, Formal analysis, Investigation, Methodology, Writing—review & editing. Ayla Fil: Formal analysis. Mehmet F. Yetkin: Resources. Rana Karabudak: Resources. Aslı Tuncer: Resources. Yeliz Salcı: Conceptualization, Formal analysis, Methodology, Project administration, Writing—review & editing.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Gizem Şekercan

Ayla Fil

Yeliz Salcı

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