The dynamic motor imagery of locomotion is task-dependent in patients with stroke

Abstract

Purpose: Recently, Motor Imagery (MI) has been associated with the execution of movements miming in part the mentally represented action (dynamic MI, dMI). Preliminary studies have reported as dMI may improve trainings in sport, with imagery timing close to the physical execution one. This study was aimed to investigate time and spatial parameters of dMI with actual locomotion in people with stroke.

Methods: Twelve patients (stroke group, SG) were compared with twelve healthy elderly (elderly group, EG) and twenty young adults (young group, YG). Subjects performed mental representations of different walking (forward, FW; lateral, LW, backward, BW), accompanied or not by movements imitating walking (dMI and static MI, sMI). Then, they performed actual locomotion (AL). Outcome measures were related to the time and the number of steps spent for completing the tasks for all the given locomotor conditions.

Results: Significant differences were found in patients with respect to healthy subjects, with time in sMI significantly shorter than in dMI (p < 0.004) and AL (p < 0.002), but not between dMI and AL in FW (p = 0.806). In patients, times obtained in sMI and dMI was significantly shorter with respect to those of AL in LW and BW. Patients performed imagery tasks with similar times in all locomotion. Healthy groups did not reveal differences among tasks in BW, while significant differences were found in LW. Analogous results were found in terms of number of performed steps.

Conclusions: In patients with stroke, a spatiotemporal functional equivalence with AL was found only for dMI, and not for sMI, in forward walking. This could be due to familiarity with this task. These results might have implications for the rehabilitative techniques based on MI.

Keywords

Walking dynamic motor imagery locomotor body schema locomotion task-dependent activity

1 Introduction

Use of Motor Imagery (MI) for the restoration of motor function in neurological disorders is one of the most innovative rehabilitative techniques. In general, the term “imagery” concerns about the cognitive experiences representing an organic sensation (auditory, visual, tactile, olfactory, gustatory, kinesthetic) or an action of own human being (Dickstein & Deutsch, 2007). The study of the processes of MI is related to mental representations of the motor actions. Hence, MI is defined as the brain’s ability to mentally simulate an action without actually performing it (Decety et al., 1989; Jeannerod, 1994, 1995).

In the last decades, it has been demonstrated as mental motor activities maintain a temporal congruence with executed motor actions, recruiting similar neural patterns (Decety et al., 1989; Decety, 1996; Guillot et al., 2008). These findings are the basis of the Functional Equivalence, i.e. the covariation between mental and physical practice, and for the PETTLEP model, in which it is indicated the sources and modalities of interactions among factors influencing MI (Holmes & Collins, 2001). It has been showed as MI may induce a cortical facilitation susceptible of modification for training effects, activating the same brain areas of motor execution when accurate (Bianco et al., 2012). All these reports provide the principles for the clinical applications of rehabilitation based on MI.

In patients with stroke, rehabilitation should start as soon as possible after the acute event, even if subjects are still unable to actively perform movements. For this reason, it has been suggested to use mental imagery in this phase, favoring an early reactivation of the same brain areas involved in motor execution (Page et al., 2001a). The first studies about MI were mainly focused on upper limb (Page et al., 2001b), but more recently researchers put their attention also to locomotion (Malouin et al., 2004a; Malouin & Richards, 2010). It has been showed as during MI of locomotion, the functional equivalence can be affected by many factors: aging (Personnier et al., 2010), impairment (Malouin et al., 2004b; Di Rienzo et al., 2014), type of required task (Kalicinski & Raab, 2014), body configuration during imagination task (Saimpont et al., 2012), and also if MI is accompanied or not by movements (Fusco et al., 2014).

Although the definition of MI entails the absence of movements, a subliminal muscular activity is frequently recorded in MI tasks, even if neither perceived nor voluntary [Guillot et al., 2007]. The presence of this activity of the motor system has led some researchers to investigate MI in conjunction with movements actually performed. This form of MI has been called dynamic motor imagery (dMI) (MacIntyre & Moran, 2010). The dMI is related to MI processes accompanied by external movements miming in part those mentally represented, with similar temporal and spatial features of the imagined action (Guillot et al., 2013). This is conceptually different by the original definition of MI, a condition occurring in the absence of any overt movement. This alternative approach can be easily performed for tasks related to locomotion. For example, subjects could be asked to statically imagine to walk without performing any movement, or to imagine to walk accompanying this imagination miming it by stepping in place (Fusco et al., 2014).

Preliminary reports have tested the dMI on athletes (skiers, jumpers) and healthy subjects. In these trials, dMI was shown to enhance quality of the mental representations with respect to MI, and when performed before actual execution of an athletic gesture, it could improve the actual performance (Guillot et al., 2013). Then, it has been showed as dMI obtained temporal performances closer to the actual physical execution with respect to the MI performed without any overt movement (Fusco et al., 2014). Despite the promising results obtained by dMI in sport, no studies have investigated dMI in pathological conditions. This aspect could be important for the implications in developing rehabilitative protocols, especially for people with stroke in subacute phase that need to recover locomotion for achieving independency.

The aim of this study was to evaluate the dynamic motor imagery in people with stroke in a subacute phase in terms of behavioral parameters. Our hypothesis was that the functional equivalence with actual locomotion could be superior in dMI than in sMI also in patients with stroke, such as previously shown for healthy subjects and athletes. The importance of testing this hypothesis is related to the potential impact on rehabilitative protocols based on motor imagery. However, the comparison of dMI and sMI should be tested taking into account also the factors affecting MI, such as age, impairment and familiarity with the task. We have hence designed a multivariate study aiming at comparing the dynamic motor imagery with the mental representation of different walking conditions in patients with subacute stroke and healthy subjects.

2 Material and methods

2.1 Participants

Twelve subjects affected by stroke in subacute phase (stroke group, SG; 7 males and 5 females; mean age: 65.7 ± 10.4 years old) were included in this pilot study (see Table 1 for demographic and clinical features of the enrolled patients). As control groups, twelve age/gender-matched healthy volunteers were enrolled (elderly group, EG; 7 males and 5 females; mean age: 65.6 ± 10.7 years old; p = 0.981). A group of healthy young subjects was also enrolled for obtaining a set of normative data representing the baseline of MI capacities for our protocol. In fact, MI functional equivalence could be reduced by aging, especially for movements more difficult or unusual (Saimpont et al., 2013; Kalicinski et al., 2015). This last group was formed by twenty healthy young adults (young group, YG; 9 males and 11 females; 29.3 ± 5.1 years; p < 0.001 with respect to both groups).

Patients were selected according to the following criteria: first-ever ischemic stroke in a subacute phase (time from the stroke event ranging between 2 and 12 weeks) with a presence of cortical or cortical-subcortical lesion, as confirmed by computed tomography or magnetic resonance imaging. Age of the patients ranged between 18 and 80 years and cognition had to be preserved (confirmed by a Mini-Mental State Examination score not less than 24, evaluated by a neuropsychologist) (Folstein et al., 1975).

For verifying the abilities to perform imagery tasks by patients, we administered the Time-Dependent Motor Imagery test (TDMI) (Malouin et al., 2008). The TDMI test is a simple and valid chronometric screening test that can be used to quickly check the maintenance of the mental imagery abilities in stroke population. It consists in the comparison of imagined with executed movements, carried out in three different times (15, 25, 45 s). The examiner recorded the number of imagined movements reported by the patient. This number had to increase in parallel with the increasing time period. The test indicates the temporal congruency of the patients’ MI. Patients unable to increase the number of imagined movements for the longer times were excluded due to their poor abilities related to MI. Other exclusion criteria were: presence of chronic disabling pathologies or severe spasticity that could have interfered with the locomotor function; presence of hemispatial neglect or psychiatric disorders that could have impeded the correct performance of the required tasks; presence of not modifiable visual deficits.

The enrolled patients were mildly affected in their motor abilities with an autonomous ambulation, as revealed by a score of Functional Ambulation Category equal or plus 4. This is a six-points hierarchical rating scale that allows to easily classify patients with respect to their walking ability, with scores 4 and 5 identifying a person able to ambulate independently on level and non-level surfaces, respectively (Holden et al., 1986).

2.2 Protocol

All the enrolled participants (patients and healthy subjects) started the experiment, standing on a line marked on the floor. In front of them, another line was taped on the ground at a distance (unknown by the participants) of 10 m. They were asked to imagine moving towards the visualized target, according to given different locomotion (forward walking, FW, lateral walking, LW, and backward walking, BW). We refer to this task as static Motor Imagery (sMI), in accordance with the classic definition of MI (Decety et al., 1989; Jeannerod, 1994). Then, subjects were requested to imagine the same tasks accompanied them by a stepping in place, miming the physical movements of walking (forward, backward or lateral). We refer to it as dynamic Motor Imagery (dMI), in accordance with previous studies (MacIntyre & Moran, 2010; Guillot et al., 2013). Subjects were requested to imagine themselves to move towards the target (first-person perspective). They were asked to perform the imagery tasks with open eyes and with a body configuration congruent to the task (implying that the target line resulted in front of them for FW, on their side for LW, and back of them in BW, despite in all conditions they were asked to fixing the target line). Congruent position was chosen for minimizing the effects of body posture on the imagery performance (Saimpont et al., 2012) and open eyes were chosen because patients could have problem in maintaining posture with closed eyes. The distance of 10 m has been selected in accordance to the most common walking test based on a fixed distance used for subjects with subacute stroke: the ten meter walk test (Bohannon, 1992).

Locomotor conditions and imagery tasks were performed according to a previously randomized sequence. After having performed sMI and dMI tasks, individuals were asked to actually perform the task (Actual Locomotion, AL), achieving the target according to the same randomization sequence related to the given locomotion. Patients performed all the tasks close to a researcher to avoid any risk of falls. They were also permitted to use an aid (cane or a walker with four swivel wheels) if needed, in accordance with their residual locomotor abilities. All the participants performed the experimental trial wearing a comfortable pair of shoes, as they usually used.

To avoid some possible learning effects, we tested naïve subjects to the requested tasks in a single trial. Because of possible cognitive influences, no verbal information were furnished to the participants about the target distance and their performances, as similarly done in previous studies (Iosa et al., 2012; Fusco et al., 2014). The entire experiment was conducted in the same indoor environment. In fact, the modification of the experimental settings has been shown to influence motor performances and distance-estimation (Lappin et al., 2006; Iosa et al., 2012).

This protocol was approved by the independent ethics committee of the hosting institution (Rehabilitation Hospital). All participants were aware of the protocol and provided a written informed consent.

2.3 Main outcome measures

The main outcome measure was related to the temporal performances obtained in the sMI, dMI and AL tasks, for all the conditions of performed locomotion. Consistently with previous studies (Guillot et al., 2013; Fusco et al., 2014), we have compared the duration of the performances, being chronometric tests shown as a reliable technique for measuring MI both for healthy subjects and for patients (Collet et al., 2011; Malouin et al., 2008). Mental chronometry involves the study of temporal sequences in the activities of information processing in the human brain (Guillot & Collet, 2005). It has been shown to be closely related to the time actually required to perform the movement (Papaxanthis et al., 2002).

In sMI, time was measured by means of a digital professional stopwatch (JUNSD^® JS-320, JUNSD Industry Shenzhen, Shenzhen, China), with a time resolution of 0.1 s. Healthy subjects were instructed to use the chronograph alone, as in previous trial [Lebon et al., 2012]. Timer was activated when the hip was imagined to move for the beginning of the first step and it was stopped when the subject imagined to have achieved the target. In patients with stroke, the investigator marked the time of performance. In this case, the words “go” and “stop” indicated the initial phase and the end of the trial. This difference in the protocol administered to subjects with stroke was due to their difficulties in self-pressing the chronometer button. This choice may generate a bias between healthy subjects and patients due to the delays introduced by the fact that the button was pressed by the researcher. However, we preferred this solution for three reasons. Firstly, the time to complete the task was computed as the difference between start and stop pressing, and it is hence poorly affected by the delay introduced by researcher, if it was similar in the two button pressing, as conceivable. Then, we were especially interested into the within subject differences, more than the between subjects differences. Finally, the alternative solution was to ask to healthy subjects to adopt the same protocol of patients, but in that case we obtained reference data poorly comparable with those of literature, mainly focused on healthy subjects adopting self-pressing protocols.

In dMI, we have also studied the spatial features of the imagery walking by counting the number of steps performed in place. In the estimation of spatiotemporal parameters for dMI and AL, we have used accelerometers. Accelerometric signals record movements along the three axes of the body, objectively measuring the dynamic stability and the spatio-temporal parameters of gait during walking (Kavanagh & Menz, 2006). Many studies have determined the reliability of this technique, in healthy subjects (Iosa et al., 2014a), but also in different neurological conditions, stroke included (Iosa et al., 2012a, 2013). The enrolled healthy subjects and the patients wore an elastic belt contains a wireless accelerometer (FreeSense^®, Sensorize, Rome, Italy; sampling frequency = 100 Hz), placed on the spinous process of the third lumbar vertebra (close to center of body mass). Anteroposterior acceleration peaks were used for computing the number of performed steps.

2.4 Statistical analyses

Means and standard deviations were computed for all the investigated parameters (demographic data of subjects, spatio-temporal results obtained by tests). The Imagery Performance Index was also computed as follows: | actual performance – MI performance |/ actual performance * 100. A t-test was performed to compare the demographic data among groups before the start of the protocol. Repeated measure analysis of variance (RM-ANOVA) were performed using time and number of steps as dependent variables, group as between subjects independent variable (SG, EG, YG), type of locomotion as within subjects independent factor (FW, BW, LW), and task as another within subjects independent variable (sMI, dMI, AL for time, dMI and AL for number of steps). As results of RM-ANOVA we reported the F-value accompanied by degrees of freedom, p-value and effect size. Then, similar analyses were performed for each single type of locomotion. Post-hoc analyses were performed when needed, correcting the level of significance in accordance to Bonferroni correction (three comparisons, p < 0.0167); for all the other analyses this level was set at 0.05.

3 Results

When taken into account all groups, the analyses of RM-ANOVA showed a significant difference in the temporal performances for every analyzed main factor: group, locomotion and task (p < 0.001). Furthermore, also all the possible interactions among these factors resulted significantly affecting the performances (see Table 2).

Due to significant interactions, post-hoc analysis between groups was performed. This analysis revealed a significant longer time spent by subjects with stroke with respect to healthy elderly and young adults (p < 0.001 for both). These last two groups did not show any significant difference between sMI and dMI in the FW (p = 0.050, not significant for the Bonferroni correction).

Conversely, post-hoc analyses revealed that patients spent a shorter time during sMI than during dMI (p < 0.004) and AL (p < 0.002) in FW, while similar performances were observed for dMI and AL (p = 0.806). In healthy subjects, the analyses showed a significant difference only between sMI and dMI (p < 0.001 in young adults; p = 0.002 in elderly) but neither between sMI and AL nor between dMI and AL (Fig. 1).

When patients were asked to actually walk, they achieved the target in different times with respect to the given locomotor conditions. Patients needed nearly double time to achieve the target in BW and LW respect to FW, as shown in Fig. 2. Conversely, in imagined tasks (sMI, dMI), they spent similar times independently by the locomotor conditions. In the post-hoc analyses for patients group, temporal performances were not significantly different between sMI and dMI both in LW and BW, while they were significantly different between sMI and AL (LW: p < 0.001; BW: p = 0.001) and dMI and AL (LW: p = 0.004; BW: p = 0.006).

In BW, healthy groups did not reveal significant differences among tasks. In LW, a significant difference was found between sMI and AL in EG (p = 0.002) and between sMI and dMI and between dMI and AL in YG (p < 0.001). Results of the healthy groups in the different locomotor condition are summarized in Fig. 3.

Figure 4 shows analogous results in terms of performed steps between dMI and AL, with patients not taking into account that in real they need many more steps for walking, dependently by the type of locomotion. Table 3 also reports the data for the other two groups of subjects. RM-ANOVA showed significant differences between groups (p < 0.001, in all locomotor conditions), and between tasks (dMI vs. AL) (p < 0.001). In LW and BW, the number of performed steps were significantly different (p < 0.001 in both), while in FW, the steps performed in dMI and AL were similar (p = 0.350). Analogous results were found for the interactions between group and task (FW: p = 0.108; LW and BW: p = 0.001). Among groups’ comparisons, young adults performed a lower number of steps with respect to patients (p < 0.001 for each locomotion) and also with respect to elderly, even if in this last case the difference was not significant at the post-hoc analyses (FW: p = 0.062; LW: p = 0.446; BW: p = 0.081).

Finally, the Imagery Performance Index showed a significant reduction of the absolute error when dMI was performed with respect to sMI (F(1,41) = 6.608, p = 0.014). The effect of task was only close to statistically significant threshold (F(2,82) = 2.678, p = 0.075), such as that of group (F(2,41) = 2.137, p = 0.131). Among all the possible interaction, only that between MI and group resulted statistically significant (F(2,41) = 3.453, p = 0.041). Post-hoc analyses revealed that the absolute error resulted significantly different between sMI and dMI for patients with stroke (44% vs. 30% , respectively, post-hoc analysis: p < 0.001), whereas this difference was only close to statistically significant threshold for elderly (39% vs. 28% , p = 0.066), and far from statistical significance for young subjects (26% vs. 28% , p = 0.425). We computed also the IPI on the number of steps, comparing dMI with AL, and we found significant correlations for patients during LW (R = 0.777, p = 0.003) and BW (R = 0.761, p = 0.004), but not for FW (R = 0.194, p = 0.546). The other two groups did not show any significant correlation between IPIs evaluated on time and that evaluated on number of steps.

4 Discussion

The aim of this study was to evaluate in terms of temporal and spatial features, the dynamic motor imagery in stroke population. The dMI is a recent form of MI in which the mental simulation of an action is simultaneously associated with a physical execution of movements imitating some temporal and spatial invariances of this action (Guillot et al., 2013).

A general result of our study was that the temporal equivalence with AL was more respected in dMI than in sMI. For all the subjects, in fact, the time imagined for completing the task in sMI resulted significantly shorter than that really needed independently by the type of locomotion. The presence of movement, even if only limited in place, might have facilitated the correct estimation of the time and the number of steps needed to cover the given distance providing sensorial feedback and highly activating the motor areas involved in the so-called locomotor body schema (Dominici et al., 2009; Ivanenko et al., 2011).

This aspect was particularly important for patients with stroke facing with a new physical impaired condition that could have led to the significant difference between sMI and AL. Higher differences were found for BW and LW in this group. Subjects with stroke were able to consider their deficits only when they accompain the motor mental action with an actual stepping in place, and only for a familiar task such as forward walking (as clearly shown in Fig. 1). Surprisingly, they seem to imagine for BW and LW the same time and the same number of steps needed for FW. This result could be related to the fact that motor imagery is a task-dependent process (Fusco et al., 2014), that is preserved in patients only for FW. It maybe related to the presence of an internal model of natural forward walking (Iosa et al., 2014b).

When applied to the healthy subjects, neither the comparison between sMI and AL nor that between dMI and AL showed statistically significant differences for FW and BW. The differences between imagery tasks and physical execution resulted significant only for sMI in LW (for both healthy groups). The results obtained for FW and BW could be due to the fact that BW retains some kinematic features of FW in physiological condition (Viviani et al., 2011), but this correspondence could be altered by stroke. In fact, only in patients a higher time was needed for actually achieving the target in backward walking with respect to forward one.

As the temporal errors performed by patients in imaging unusual walking, also the imagined spatial performances during BW and LW resulted completely inadequate, with similar number of imagined steps among locomotion conditions despite the differences in actual performances. The significant correlations found between the imagery performance index evaluated on time and those obtained on number of steps for lateral and backward walking confirmed as the patients, who imagined to spend shorter time, also imagined the need of less steps.

The reason could be due to the fact that patients were more familiar to FW than to BW and LW, implying the possible involvement of more neural structures for natural walking and of activation of patterns belonging to the locomotor internal model, probably lacking in BW and LW. In other studies, it was shown as the use of constraints increasing the complexity of the task can alter the duration of mental motor representation of the movement, leading to an under- or an over-estimation (Wu et al., 2010). Another reason could be related to the fact that patients are not prepared for mental simulation of the tasks, maybe because they were not adequately involved in the use of mental rehearsal of these tasks.

In patients with stroke, sMI failed to temporally match AL even in FW. It could be due to the location of cerebral lesion, influencing the mental representation of walking. A previous study showed that lesions of the parietal cortex led to a difficulty to mentally represent the time required to perform various movements of the fingers and pointing gestures, suggesting that this brain area is important in the generation process of MI (Sirigu et al., 1996). In other experiments, the slowing down of the velocity of physical and imagined movements of the upper limb permitted the maintenance of the temporal congruence (Sirigu et al., 1995). In our trial, the imitating movements during dMI could be resulted in a longer imagined time. Hence, they obtained more congruent times with actual locomotion on the basis of the functional impairments affecting movements (Di Rienzo et al., 2014).

In accordance with our results, other studies have showed an inconsistency of functional equivalence of time motor performances after cerebrovascular injury, both for the upper limbs (Malouin et al., 2004b; Stinear et al., 2007) and the lower limbs (Malouin et al., 2004a, 2012). In these studies, patients overestimated times of the imagined motor acts, indicating a slowdown in the processes involving MI. Some authors have related this aspect to right cerebral hemisphere lesions, demonstrating also a reduction of corticomotor excitability after a transcranial magnetic stimulation (Sabatè et al., 2004). Our results suggest that MI could greatly depend on task as well as previous studies also showed as MI can be context and experience dependent (Reed, 2002; Calmels et al., 2006).

At the same time, it is possible to interpret the results about subjects with stroke in two different manners. The first one is that both motor system and the relevant mental representation of movements are altered, being related to the same brain injured areas. The second hypothesis is that the mental representation of their locomotion is preserved but fixed to the movements before the stroke, and it is not updated in a congruent manner with their motor performance decline. If this second hypothesis was true, independently by their motor decline, they should had recognized that for LW and BW they need more time and steps, as healthy subjects did. Conversely, during sMI they just imagined the need of time and steps similar to FW even for the other two unfamiliar and more complex tasks. Hence, our results seem to support the idea that also motor imagery is altered in patients with stroke (probably because it refers to the same brain areas involved in motor deficits), especially during static motor imagery of an unfamiliar task. The imagery performance could be preserved for FW for the presence of a locomotor body schema conceivably located into the cerebellum (Dominici et al., 2009; Ivanenko et al., 2011).

The fact that our patients showed a better dynamic than static motor imagery, in fact, may suggest that the actual muscular activation, together with the sensori-motor and cognitive feedback obtained with these activations may help patients in re-activate the areas more related to the actual movement.

All these conditions should carefully be taken in account for the clinical use of mental practice for neurorehabilitation. In general, the use of mental imagery practice (i.e. training with the use of MI) is underrated as rehabilitative technique. Most of the intervention have been related to the recovery of the upper limb functions (Malouin et al., 2004b), while the application for the gait rehabilitation is still limited (Dunsky et al., 2008). Trainings with dMI have been never reported in any pathological condition. In this study, temporal congruence in the FW for dMI has provided promising results for developing treatment protocols. In fact, a better ability of dMI than sMI into represent AL may suggest to apply a protocol based on a dynamic form of motor imagery, when possible, and the need of a task-oriented motor imagery protocol, because the performances resulted task-dependent.

The findings of this study have to be read at the light of their limits. One of them is the small sample size, despite it has to be noted as our sample is similar to that of previous analogous studies in patients and healthy subjects (Papaxanthis et al., 2002; Guillot et al., 2013; Wu et al., 2010). Then, we have chosen a 10 m single path walk. This distance was considered among those at medium range, within which the correspondence of MI and physical execution was maintained (Schott & Munzert, 2007). In future studies, it will be intriguing to assess the differences between dMI and AL thresholds in terms of length. Another limitation of our study can be related to the use of accelerometer just as a step counter. It could be used also to identify step variability, and to put this parameter in relationship to MI and other factors important for patients with stroke such as the risk of fall. The last potential lack is related to the absence of the use of questionnaires to investigate the quality of mental motor representations of movements, to measure characteristics such as sharpness (clarity and sensory richness of a mental image) and controllability (ease and precision with which an image can be manipulated mentally) (Collet et al., 2011). We cannot use these questionnaires because not yet validated for the Italian language. We used only a test in order to verify the maintenance of the abilities to perform imagery tasks in patients. However, the use of these questionnaires used in sMI could be useful also in dMI, providing valuable insights for the development of trainings based on dMI. Although few studies have reported a positive relationship between the scores of questionnaires and increases arising from the mental practice in the face of a greater number of studies that have not found such a connection, the use of dMI could provide elements able to exploit the properties of the MI, which could result important for the success of rehabilitative protocols.

As well, in future studies, it will be necessary investigating how psychological features of patients impact on rehabilitative outcomes. It has been shown as recovery can be more difficult in some type of personality or with the presence of psychological distress as anxiety or locus of control, especially when using innovative techniques more than conventional neurorehabilitation (Bragoni et al., 2013), as well as in other common diseases (Paolucci et al., 2012).

5 Conclusion

In conclusion, dynamic motor imagery resulted more effective than static motor imagery in estimating the locomotor temporal and spatial performance to achieve a target in patients with stroke. Both imagery tasks resulted significantly related to the type of locomotion in patients probably due to a familiarity with the locomotor condition. Finally, patients were not able to discriminate temporal and spatial differences among types of locomotion, resulting in similar behavior independently by the type of adopted walking. Our results suggest to design rehabilitative protocols based on dynamic motor imagery of familiar tasks.

Declaration of interest

All authors state that any financial and personal interest have influenced this work. No funding for research has been used.

References

Bianco

, Feurra

, Fadiga

, Rossi

, & Rossi

(2012). Bi-hemispheric effects on corticospinal excitability induced by repeated sessions of imagery versus observation of actions. Restorative Neurology and Neuroscience, 30, 481–489.

Bohannon

R.W.

(1992). Walking after stroke: Comfortable versus maximum safe speed. International Journal of Rehabilitation Research, 15, 246–248.

Bragoni

, Broccoli

, Iosa

, Morone

, De Angelis

, Venturiero

, Coiro

, Pratesi

, Mezzetti

, Fusco

, & Paolucci

(2013). Influence of Psychological Features on Rehabilitation Outcomes in Patients with Subacute Stroke Trained with Robotic-aided Walking Therapy: A Controlled Longitudinal Observational Pilot Study. e. American Journal of Physical Medicine and Rehabilitation, 92, 16–25.

Calmels

, Holmes

, Lopez

, & Naman

(2006). Chronometric comparison of actual and imaged complex movement patterns. Journal of Motor Behaviour, 38, 339–348.

Collet

, Guillot

, Lebon

, MacIntyre

, & Moran

(2011). Measuring motor imagery: Combining psychometric, qualitative, chronometric and psychophysiological techniques. Exercise and Sport Science Reviews, 39, 89–92.

Decety

(1996). The neurophysiological basis of motor imagery. Behavioral Brain Research, 77, 45–52.

Decety

, Jeannerod

, & Prablanc

(1989). The timing of mentally represented actions. Behavioral Brain Research, 34, 35–42.

Dickstein

, & Deutsch

J.E.

(2007). Motor imagery in physical therapist practice. Physical Therapy, 87, 942–953.

Di Rienzo

, Collet

, Hoyek

, & Guillot

Impact of neurologic deficits on motor imagery: A systematic review of clinical evaluations. Neuropsychological Reviews, 24(2), 116–147.

10.

Dominici

, Daprati

, Nico

, Cappellini

, Ivanenko

Y.P.

, & Lacquaniti

(2009). Changes in the limb kinematics and walking-distance estimation after shank elongation: Evidence for a locomotor body schema?. Journal of Neurophysiology, 101, 1419–1429.

11.

Dunsky

, Dickstein

, Marcovitz

, Levy

, & Deutsch

J.E.

(2008). Home-based motor imagery training for gait rehabilitation of people with chronic poststroke hemiparesis. Archives of Physical Medicine and Rehabilitation, 89, 1580–1588.

12.

Folstein

M.F.

, Folstein

S.E.

, & McHugh

P.R.

(1975). Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189–198.

13.

Fusco

, Iosa

, Gallotta

M.C.

, Paolucci

, Baldari

, & Guidetti

(2014). Different performances in static and dynamic imagery and real locomotion. An exploratory trial. Frontiers in Human Neuroscience, 8, 760.

14.

Guillot

, Collet

, Nguyen

V.A.

, Malouin

, Richards

, & Doyon

(2008). Functional neuroanatomical networks associated with expertise in motor imagery ability. Neuroimage, 41, 1471–1483.

15.

Guillot

, Lebon

, Rouffet

, Champely

, Doyon

, & Collet

(2007). Muscular responses during motor imagery as a function of muscle contraction types. International Journal of Psychophysiology, 66, 18–27.

16.

Guillot

, Moschberger

, & Collet

(2013). Coupling movement with imagery as a new perspective for motor imagery practice. Behavioral Brain Functions, 9, 8.

17.

Holden

M.K.

, Gill

K.M.

, & Magliozzi

M.R.

(1986). Gait assessment for neurologically impaired patients. Standards for outcome assessment. Physical Therapy, 66, 1530–1539.

18.

Holmes

P.S.

, & Collins

D.J.

(2001). The PETTLEP approach to motor imagery: A functional equivalence model for sport psychologists. Journal of Applied Sport Psychology, 13, 60–83.

19.

Jeannerod

(1994). The representing brain: Neural correlates of motor intention and imagery. Behavioral Brain Science, 17, 187–245.

20.

Jeannerod

(1995). Mental imagery in the motor cortex. Neuropsychologia, 33, 1419–1432.

21.

Iosa

, Fusco

, Morone

, & Paolucci

(2012). Walking there: Environmental influence on walking-distance estimation. Behavioral Brain Research, 226, 124–132.

22.

Iosa

, Fusco

, Morone

, & Paolucci

(2014a). Development and decline of upright gait stability. Frontiers in Aging Neuroscience, 6, 14.

23.

Iosa

, Fusco

, Morone

, Pratesi

, Coiro

, Venturiero

, De Angelis

, Bragoni

, & Paolucci

(2012). Assessment of upper-body dynamic stability during walking in patients with subacute stroke. Journal of Rehabilitation Research and Development, 49, 439–450.

24.

Iosa

, Morelli

, Marro

, Paolucci

, & Fusco

(2013). Ability and stability of running and walking in children with cerebral palsy. Neuropediatrics, 44, 147–154.

25.

Iosa

, Zoccolillo

, Montesi

, Morelli

, Paolucci

, & Fusco

(2014b). The brain’s sense of walking: A study on the intertwine between locomotor imagery and internal locomotor models in healthy adults, typically developing children and children with cerebral palsy. Frontiers in Human Neuroscience, 8, 859.

26.

Ivanenko

Y.P.

, Dominici

, Daprati

, Nico

, Cappellini

, & Lacquaniti

(2011). Locomotor body scheme. Human Movement and Science, 30, 341–351.

27.

Kalicinski

, Kempe

, & Bock

(2015). Motor imagery: Effects of age, task complexity, and task setting. Experimental Aging Research, 41, 25–38.

28.

Kalicinski

, & Raab

(2014). Task requirements and their effects on imagined walking in elderly. Aging Clinical and Experimental Research, 26(4), 387–393.

29.

Kavanagh

J.J.

, & Menz

H.B.

(2008). Accelerometry: A technique for quantifying movement patterns during walking. Gait and Posture, 28, 1–15.

30.

Lappin

J.S.

, Shelton

A.L.

, & Rieser

J.J.

(2006). Environmental context influences visually perceived distance. Perception and Psychophysics, 68, 571–581.

31.

Lebon

, Byblow

W.D.

, Collet

, Guillot

, & Stinear

C. M.

(2012). The modulation of motor cortex excitability during motor imagery depends on imagery quality. European Journal of Neuroscience, 35, 323–331.

32.

MacIntyre

, & Moran

(2010) Meta-imagery processes among elite sports performers. In: Collet C, Guillot

, editors. The Neurohysiological Foundations of Mental and Motor Imagery. NewYork, NY, USA:Oxford University Press. pp. 227–244.

33.

Malouin

, & Richards

C.L.

(2010). Mental practice for relearning locomotor skills. Physical Therapy, 90(2), 240–251.

34.

Malouin

, Richards

C.L.

, Desrosiers

, & Doyon

(2004b). Bilateral slowing of mentally simulated actions after stroke. Neuroreport, 15(8), 1349–1353.

35.

Malouin

, Richards

C.L.

, Doyon

, Desrosiers

, & Belleville

(2004a). Training mobility tasks after stroke with combined mental and physical practice: A feasibility study. Neurorehabilitaion and Neural Repair, 18, 66–75.

36.

Malouin

, Richards

C.L.

, & Durand

(2012). Slowing of motor imagery after a right hemispheric stroke. Stroke Research and Treatment, 2012, 297217.

37.

Malouin

, Richards

C.L.

, Durand

, & Doyon

(2008). Reliability of mental chronometry for assessing motor imagery ability after stroke. Archives of Physical Medicine and Rehabilitation, 89, 311–319.

38.

Page

S.J.

, Levine

, Sisto

, & Johnston

M.V.

(2001a). A randomized efficacy and feasibility study of imagery in acute stroke. Clinical Rehabilitation, 15(3), 233–240.

39.

Page

S.J.

, Levine

, Sisto

, & Johnston

M.V.

(2001b). Mental practice combined with physical practice for upper-limb motor deficit in subacute stroke. Physical Therapy, 81(8), 1455–1462.

40.

Papaxanthis

, Pozzo

, Skoura

, & Schieppati

(2002). Does order and timing in performance of imagined and actual movements affect the motor imagery process? The duration of walking and writing task. Behavioral Brain Research, 134, 209–215.

41.

Paolucci

, Morone

, Iosa

, Fusco

, Alcuri

, Matano

, Bureca

, Saraceni

V.M.

, & Paolucci

(2012). Psychological features and outcomes of the Back School treatment in patients with chronic non-specific low back pain. A randomized controlled study. European Journal of Physical and Rehabilitation Medicine, 48, 245–253.

42.

Personnier

, Kubicki

, Laroche

, & Papaxanthis

(2010). Temporal features of imagined locomotion in normal aging. Neuroscience Letters, 476(3), 146–149.

43.

Reed

C.L.

(2002). Chronometric comparisons of imagery to action: Visualizing versus physically performing springboard dives. Memory and Cognition, 30, 1169–1178.

44.

Sabaté

, González

, & Rodríguez

(2004). Brain lateralization of motor imagery: Motor planning asymmetry as a cause of movement lateralization. Neuropsychologia, 42, 1041–1049.

45.

Saimpont

, Malouin

, Tousignant

, & Jackson

P.L.

(2013). Motor imagery and aging. J Motor Behaviour, 45, 21–28.

46.

Schott

, & Munzert

(2007). Temporal accuracy of motor imagery in older women. International Journal of Sport Psychology, 38, 304–320.

47.

Sirigu

, Cohen

, Duhamel

J.R.

, Pillon

, Dubois

, Agid

, & Pierrot-Deseilligny

(1995). Congruent unilateral impairments for real and imagined hand movements. Neuroreport, 6, 997–1001.

48.

Sirigu

, Duhamel

J.R.

, Cohen

, Pillon

, Dubois

, & Agid

(1996). The mental representation of hand movements after parietal cortex damage. Science, 273, 1564–1568.

49.

Stinear

C.M.

, Fleming

M.K.

, Barber

P.A.

, & Byblow

W.D.

(2007). Lateralization of motor imagery following stroke. Clinical Neurophysiology, 118, 1794–1801.

50.

Viviani

, Figliozzi

, Campione

G.C.

, & Lacquaniti

(2011). Detecting temporal reversals in human locomotion. Experimental Brain Research, 214, 93–103.

51.

A.J.

, Hermann

, Ying

, & Page

S.J.

(2010). Chronometry of mentally versus physically practiced tasks in people with stroke. American Journal of Occupational Therapy, 64, 929–934.