Clinical Assessment of Motor Imagery After Stroke

Department of Rehabilitation, Laval University, Quebec City, Quebec, Canada.
Neurorehabilitation and neural repair (Impact Factor: 3.98). 04/2008; 22(4):330-40. DOI: 10.1177/1545968307313499
Source: PubMed


The aim of this study was to investigate: (1) the effects of a stroke on motor imagery vividness as measured by the Kinesthetic and Visual Imagery Questionnaire (KVIQ-20); (2) the influence of the lesion side; and (3) the symmetry of motor imagery.
Thirty-two persons who had sustained a stroke, in the right (n = 19) or left (n = 13) cerebral hemisphere, and 32 age-matched healthy persons participated. The KVIQ-20 assesses on a 5-point ordinal scale the clarity of the image (visual scale) and the intensity of the sensations (kinesthetic scale) that the subjects are able to imagine from the first-person perspective.
In both groups, the visual scores were higher (P = .0001) than the kinesthetic scores and there was no group difference. Likewise, visual scores remained higher than kinesthetic scores irrespective of the lesion side. The visual scores poststroke were higher (P = .001) when imagining upper limb movements on the unaffected side than those on the affected side. When focusing on the lower limb only, however, the kinesthetic scores were higher (P = .001) when imagining movements of the unaffected compared to those on the affected side.
The vividness of motor imagery poststroke remains similar to that of age-matched healthy persons and is not affected by the side of the lesion. However, after stroke motor imagery is not symmetrical and motor imagery vividness is better when imagining movements on the unaffected than on the affected side, indicating an overestimation possibly related to a hemispheric imbalance or a recalibration of motor imagery perception.

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Available from: Julien Doyon, Aug 04, 2014
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    • "We used the Motor Imagery Questionnaire-Revised second version to classify our patients into high or low imagers. Although MI questionnaires have been shown to be reliable and valid tools to screen for MI vividness and allow us to distinguish between high and low imagers, the scores remain a subjective reflection of the motor imagery capacity of the individual and this subjectivity remains an important disadvantage of this motor imagery measure (Hall, 1997;Guillot and Collet, 2005;Malouin et al., 2008). Nevertheless, a study byLorey et al. (2011), examining brain activation patterns during the imaging of movements, has shown a close relationship between the motor imagery vividness scores and the level of brain activation. "
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    ABSTRACT: Background: mental practice with motor imagery has been shown to promote motor skill acquisition in healthy subjects and patients. Although lesions of the common motor imagery and motor execution neural network are expected to impair motor imagery ability, functional equivalence appears to be at least partially preserved in stroke patients. Aim: to identify brain regions that are mandatory for preserved motor imagery ability after stroke. Method: thirty-seven patients with hemiplegia after a first time stroke participated. Motor imagery ability was measured using a Motor Imagery questionnaire and temporal congruence test. A voxelwise lesion symptom mapping approach was used to identify neural correlates of motor imagery in this cohort within the first year post-stroke. Results: poor motor imagery vividness was associated with lesions in the left putamen, left ventral premotor cortex and long association fibres linking parieto-occipital regions with the dorsolateral premotor and prefrontal areas. Poor temporal congruence was otherwise linked to lesions in the more rostrally located white matter of the superior corona radiata. Conclusion: This voxel-based lesion symptom mapping study confirms the association between white matter tract lesions and impaired motor imagery ability, thus emphasizing the importance of an intact fronto-parietal network for motor imagery. Our results further highlight the crucial role of the basal ganglia and premotor cortex when performing motor imagery tasks.
    Full-text · Article · Mar 2016 · Frontiers in Behavioral Neuroscience
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    • "Motor imagery ability was assessed with the Kinesthetic and Visual Imagery Questionnaire (KVIQ – Malouin et al., 2007) in all participants. Individual SCI participant characteristics are summarized in Table 1 "

    Full-text · Article · Sep 2015
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    • "Selecting the subject-specific channels was especially important for stroke patients whose motor imagery were not symmetrical for the unaffected and affected sides (Malouin et al., 2008). A total of 9 frequency bands from 4 Hz to 36 Hz covering theta, mu (alpha), beta and low gamma frequency rhythms were employed to filter the EEG signal, which were generally employed for the detection of motor imagery of limb movements (Ramoser et al., 2000; Ang et al., 2011; Arvaneh et al., 2011; Blankertz et al., 2008; Lotte and Guan, 2011). "
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    ABSTRACT: Rehabilitation of lower limbs is equally as important as that of upper limbs. This paper presented a study to detect motor imagery of walking (MI-Walking) from background idle state. Broad overlapping neuronal networks involved in reorganization following motor imagery introduces redundancy. We hypothesized that MI-Walking could be robustly detected by constraining dependency among selected features and class separations. Hence, we proposed to jointly select channels and frequency bands involved in MI-Walking by optimizing the objective function formulated on the dependency between features and class labels, redundancy between to-be-selected with selected features, and separations between classes, namely, "regularized maximum dependency with minimum redundancy-based joint channel and frequency band selection (RMDR-JCFS)". Evaluated on electroencephalography (EEG) data of 11 healthy subjects, the results showed that the selected channels were mainly located at premotor cortex, mid-central area overlaying supplementary motor area (SMA), prefrontal cortex, foot area sensory cortex and leg and arm sensorimotor representation area. Broad frequencies of alpha, mu and beta rhythms were involved. Our proposed method yielded an averaged accuracy of 76.67%, which was 9.08%, 5.03%, 7.03%, 14.15% and 3.88% higher than that obtained by common spatial pattern (CSP), filter-bank CSP, sliding window discriminate CSP, filter-bank power and maximum dependency and minimum redundancy methods, respectively. Further, our method yielded significantly superior performance compared with other channel selection methods, and it yielded an averaged session-to-session accuracy of 70.14%. These results demonstrated the potentials of detecting MI-Walking using proposed method for stroke rehabilitation.
    Full-text · Article · Jun 2014 · Journal of Neuroscience Methods
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