Research ReportMotor imagery-based brain activity parallels that of motor execution: Evidence from magnetic source imaging of cortical oscillations
Introduction
Motor learning is the process of acquiring or strengthening a skill through repetitive practice and the provision of feedback (Newell, 1991). Motor learning occurs via plastic changes in the brain that are driven by this repetitive practice. While physical practice is recognized as the ideal vehicle to drive brain plasticity and thus motor learning, other forms of practice have been shown to facilitate skill acquisition. Motor imagery (MI) is a form of practice in which an individual mentally rehearses a motor task (Jeannerod, 1995, Jeannerod, 2001). Motor imagery can take two forms, including first person or kinaesthetic imagery (i.e., imagining from “behind their own eyes” Munzert and Zentgraf, 2009), or third person visual imagery (i.e., imagining someone else performing the movement). Secondary to physical practice, MI has been used as a form of skill acquisition in numerous domains including surgery, sport, and music (Wulf et al., 2010, Moran et al., 2012). Motor imagery is suggested to facilitate skill acquisition in a manner similar to physical practice; that is plastic changes in the brain occur as a result of repetitive mental practice (Grezes and Decety, 2001, Miller et al., 2010, Schuster et al., 2011). The purported similarity in brain activity between MI and physical practice has led to an increased interest in the use of MI as an adjunct to physical practice in neurorehabilitation, particularly with regard to the treatment of upper limb impairment post-stroke (Sharma et al., 2009). If MI is to be used as an adjunct to physical practice to facilitate skill acquisition as outlined above, it is critical that similar brain regions are activated in both mental and physical practice.
At present, it remains unclear whether the neural correlates that underlie MI parallel those observed for physical practice (Grezes and Decety, 2001, Hétu et al., 2013), which we refer to throughout as motor execution (ME). In non-disabled individuals, the majority of neuroimaging studies have assessed task-related activation during MI using functional magnetic resonance imaging (fMRI) (Grezes and Decety, 2001, Hétu et al., 2013). Studies utilizing fMRI have largely determined that MI engages brain areas that overlap with ME, including the premotor (PMC), cingulate, and parietal cortices (Porro et al., 1996, Hanakawa et al., 2003, De Lange et al., 2008), however, this activation may be influenced by the type (kinaesthetic vs. visual) of MI employed (Guillot et al., 2009, Hétu et al., 2013). The primary motor cortex (referred to throughout as ‘motor cortex’) has also been proposed to play a critical role in MI, especially when employing kinaesthetic MI, though activation of the motor cortex during MI has not been consistently reported and thus has yet to be verified (De Lange et al., 2008, Hétu et al., 2013). In fact, a recent meta-analysis by Hétu et al. (2013) examining the brain regions that underlie MI concluded that while similar cortical areas were recruited across the studies, only 22 of 75 studies reported activation of the motor cortex. Furthermore, whether MI also recruits ventral and dorsal PMC (vPMC and dPMC, respectively), which are implicated in the preparation and guidance of movement (Rizzolatti et al., 1996, Binkofski et al., 2000), is conflicting in the literature (Lotze and Halsband, 2006, Munzert et al., 2009). Thus, due to the potential differences in areas recruited between ME and MI, MI is suggested to rely on a more widespread neural network in comparison with ME (Burianová et al., 2013).
Methodological approaches utilized in examining brain activity during MI may also contribute to variability in the results observed. In their meta-analysis, Hétu et al. (2013) reported that only two of 75 studies used electromyography (EMG) in addition to visual inspection to control for overt muscle activity. Thus, the resemblance in activation between MI and ME observed in many of these previous studies might be driven by brain activity associated with actual execution (i.e., cortical output to lower motor neurons). As such, it remains unclear whether ‘pure’ MI elicits the same spatial patterns as ME. Furthermore, the majority of studies examining brain activity underlying MI utilize either fMRI or positron emission tomography (PET) (Hétu et al., 2013), both of which rely on indirect measures of brain activity with low temporal resolution (Sutton et al., 2009; Cumming, 2014). Accordingly, these measures provide rich spatial information, but are limited in their ability to directly measure electrophysiological activity.
Unlike fMRI and PET, electroencephalography (EEG) provides a direct measure of brain activity with high temporal resolution (Niedermeyer, 1996). Analysis of EEG data during a sustained task (such as MI) can reveal changes in brain activity by assessing an increase or decrease in the magnitude of ongoing cortical oscillations, known as event-related synchronization and desynchronization (ERS/ERD) (Pfurtscheller and Lopes da Silva, 1999, Schoffelen and Gross, 2009). For example, EEG studies have shown that ERD occurs over contralateral sensorimotor areas in the beta frequency band (15–30 Hz) during MI and motor preparation and execution (Pfurtscheller and Neuper, 1997, Neuper et al., 2006, Formaggio et al., 2010). EEG is limited however in its ability to localize the underlying sources of beta ERD during MI and ME due mainly to spatial smearing of the electric potentials (Niedermeyer, 1996).
Similar to EEG, magnetoencephalography (MEG) obtains a direct measure of brain activity (Baillet et al., 2001). Unlike EEG however, MEG features considerably better spatial resolution (on the order of millimetres; Baillet et al., 2001) that provides the ability to more precisely identify source-level activity in the brain (Baillet et al., 2001, Schoffelen and Gross, 2009, Hari et al., 2010). For instance, Burianová et al. (2013) sought to identify the neural correlates of actual and imagined finger movements using both MEG and fMRI. In agreement with previous research, there was considerable overlap between the brain areas activated during MI and ME for both the MEG and fMRI data. Differences between MI and ME were noted however, with activity observed in brain areas crucial for visuospatial processing (e.g. left inferior parietal lobule, parahippocampus, right superior temporal gyrus and superior frontal gyrus) but not in areas related to somatosensory coordination during the MI condition. While this work capitalized on the ability of MEG to measure brain activity associated with MI with high temporal and spatial resolution, it is one of only a few studies to do so (Nakagawa et al., 2011, Di Rienzo et al., 2014). Moreover, this prior work examined brain activity associated with a simple motor task that was limited to flexion and extension movements of the fingers (Burianová et al., 2013). In light of the proposed use of MI to facilitate skill acquisition, ideally our understanding of how brain activity underlying MI parallels that of ME would be derived from a more complex, skilled movement.
The current study seeks to extend the literature examining the neural correlates of MI by measuring electrophysiological activity at the source level during the performance of a skilled motor task, and during imagery of the same task. By monitoring muscle activity throughout, brain activity will be isolated to MI only. We hypothesize that similar patterns of activity in MI and ME will be observed, including activity in contralateral motor cortex and posterior parietal areas. Establishing that spatial patterns of brain activation during rigorously controlled MI overlap with those of ME will provide further support for its use as an adjunct to physical practice in facilitating skill acquisition.
Section snippets
Behavioural results
A significant difference (p<0.05) was observed for the number of correct sequences between block 1 and block 2 of ME (37.3±12.3 and 47.1±13.4, respectively).
EMG results
Two participant׳s data was excluded from further analyses because less than 33% of MI trials were free from muscle activity. For the remaining 16 participants, a total of 144 trials were discarded, which corresponded to 30.0% of the total number of trials performed (480). Of note, at least 1 trial per participant was excluded from analysis,
Discussion
The purpose of this study was to establish an overlap in patterns of brain activity between MI and ME to further support the use of MI as an adjunct to physical practice in facilitating skill acquisition. Direct electrophysiological evidence was provided to show that similar brain regions are active during MI and ME of the same task. The removal of MI trials in which muscle activity was present ensured this comparison targets brain activity resulting from MI only.
Conclusions
The process of acquiring or strengthening a skill through repetitive practice is driven by underlying changes in the brain (Newell, 1991, Grezes and Decety, 2001); thus it is critical that brain regions activated in mental practice overlap with those activated in physical practice in order to use MI to facilitate skill acquisition. The current study provides direct electrophysiological evidence that extends upon previous PET/fMRI findings in support of using MI as an adjunct to physical
Participants
Eighteen right handed participants (8 male, 24.7±3.8 years) agreed to partake in the study. All participants were free of neurological disorders and each provided written, informed consent. Prior to the onset of the study, participants were screened for compatibility with MEG (e.g., magnetic artefacts) according to institutional procedure. Participants were part of a larger study examining the effect of neurofeedback on MI performance (Boe et al., 2014). Thus participants were randomly assigned
Acknowledgments
SB acknowledges salary support from the Heart and Stroke Foundation of Canada in the form of an Early Career Research Award. AG and SK are supported by NSERC training awards. This work was funded by grants awarded to SB and TB from the Nova Scotia Health Research Foundation (MED-DI 1551) and Faculty of Health Professions, Dalhousie University.
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