Thursday, November 24, 2011

Functional neuroanatomical networks associated with expertise in motor imagery

Guillot, A., Collet, C., Nguyen, V.A., Malouin, F., Richards, C., & Doyon, J. (2008). Functional neuroanatomical networks associated with expertise in motor imagery ability. NeuroImage, 41, 1471-1783. Retrieved October 31, 2011, from Scholars Portal Journals <>

Guillot et al. defined motor imagery (MI) as “a dynamic state during which a subject simulates an action mentally without any body movement”. They noted that MI and motor performance share the same neural networks and that MI has even been found to produce the same neuroplastic changes as physical practice, pointing to the potential benefits of MI. However, they believed that these benefits are dependent on imagery ability, which varies among individuals. Therefore, in a study that was claimed to be the first of its kind, Guillot et al. attempted to find the functional neuroanatomical networks associated with MI expertise.

First, they had to conduct a series of pre-selection tests on 50 participants to distinguish those who could reach a high level of MI performance (“good imagers”) from those who were having trouble with MI (“poor imagers”). The participants were required to perform and imagine three motor actions, during which their autonomic nervous system (ANS) responses (as measured by skin resistance) and timings were recorded. Then, they had to rate their own imagery vividness and complete the revised Movement Imagery Questionnaire (MIQ-R) as well. By combining all these measures, a global imagery score was calculated for each participant.

Based on the global imagery score, the researchers selected 28 out of the 50 participants to take part in the fMRI experiment. The 28 participants were made up of 13 good imagers and 15 poor imagers (as determined by the global imagery score). The selected participants were asked to learn a finger sequence task and they were scanned during: 1. The physical execution of this task on a four-key keyboard that recorded their accuracy and timing, 2. The imagining of the task without any movement (MI), and 3. Perceptual control condition (simply remaining motionless).

Guillot et al. found that poor imagers generally showed more widely-distributed activations than good imagers during both MI and physical execution of the task. The good imagers showed increased bilateral activations in the superior parietal lobule and the lateral premotor cortex, as well as in the left cingulated cortex, the right inferior parietal lobule and the right inferior prefrontal region. Poor imagers showed exclusive activation of the posterior cingulated and orbito-frontal cortices, as well as both the anterior and posterior cerebellar hemispheres.

Thus, the researchers pointed out that, compared to skilled imagers, poor imagers not only needed to recruit the cortico-striatal system, but also to compensate with the cortico-cerebellar system during MI of sequential movements. Since much evidence points to the fact that the cerebellum is no longer necessary when a movement sequence is well-learned, the researchers speculated that good imagers may have a more efficient recruitment of movement engrams.

Even though this study does not directly concern music, I still think that it is highly relevant for musicians, as MI has been noted to be the “main component of mental rehearsal” (Bangert, 2006, p. 175).

If MI can produce the same neuroplastic changes as physical practice, then mental rehearsal seems to hold great promise for performers. And if the benefits of MI simply depend on MI ability, then the logical step toward maximizing these benefits would be to try to improve one’s MI ability. The implication for performers is therefore clear: effective mental rehearsal depends on the development of good imagery ability, and especially MI ability.

But it still amazes me that MI ability could actually be measured at all. Furthermore, I marvel at the complexity of this study – at the combined use of objective neurophysiological techniques and more subjective questionnaire-based testing to differentiate good imagers from poor imagers before conducting the fMRI experiment to determine the neural networks associated with MI expertise.

And now that the result reveals that good and poor imagers indeed show different patterns of brain activations, the more important question that arises is: Would it be possible for poor imagers to receive feedback through real-time fMRI and learn to change their pattern of activations to more closely resemble that of good imagers?

More fundamentally, however, I am curious about why imagery ability should vary among individuals. Given the functional equivalence between imagery and real perception and action, could it be that good imagers are also more “perceptive” and kinesthetically aware in their everyday life and, consequently, can create more vivid and accurate mental representations? This makes sense, considering the finding that poor imagers show more widely-distributed activations than good imagers during the physical execution of the finger sequence task as well as during MI.

Or maybe there is also a genetic component to imagery ability?

It is important to note that none of the participants in the fMRI experiment were musicians or professional typists, as the researchers wanted to “eliminate subjects with pre-existing skills requiring highly coordinated finger dexterities”. So I suppose that, in the future, it would be interesting to study precisely this group of people in order to see how the MI ability of such highly skilled individuals compares with the designated good and poor imagers.

Bangert, M. (2006). Brain activation during piano playing. In E. Altenmüller, M. Wiesendanger, & J. Kesselring (Eds.), Music, motor control and the brain. (pp. 173-184). Oxford: Oxford University Press.

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