The Effects of Musical Training on Structural Brain Development - A Longitudinal Study Krista L. Hyde, Jason Lerch,Andrea Norton, Marie Forgeard,

Summary

This study examined the structural brain and behavioural changes in the developing brain in response to long-term music training and to specifically address the question of whether structural brain differences seen in adults are a product of “nature” or “nurture”. As part of an on going study, the researchers investigated the structural brain changes in relation to behavioural changes in young children who received 15 months of instrumental music (keyboard) training relative to a group of children who did not. The children who did not participate in keyboard lessons were still involved in singing and percussion lessons at their own schools.

The subjects performed a 4-finger motor sequencing test for the left and right hands assessing fine finger motor skills, music listening skills, and discrimination skills. 5 additional non-music tests were also administered as well as behavioural tests. MRI scans were also used to determine brain differences.

There were no behavourial or brain differences between the Instrumental and Control children at base line prior to any music training. Therefore the brain differences of adults who have musical training are more likely to be the product of intensive musical training rather than biological predispositions. The children who had instrumental music lessons showed greater behaviour improvement on the finger motor tasks but not the non-musical tasks. They did show an improvement in the right primary motor area, corpus callosum, and the right auditory processing areas. While these were somewhat expected, there were additional developments in various frontal areas and occipital regions.

These findings indicate that plasticity can occur in brain regions that control primary functions important for playing a musical instrument and also in brain regions that might be responsible for the kind of multimodal sensorimotor integration likely to underlie instrumental learning.

Reflection

I found it interesting that the control non-instrumental group was still participating in singing and drumming in regular mainstream school. The sensory motor areas were only activated when the students learned keyboarding, so we as educators need to look at what these students do in their private piano studios that is different from what we do with the whole class when singing and drumming. For one thing, students are required to use both hands when playing keyboard while reading two different staves of music. So this lead me to wonder about using body percussion in class, where students read two different lines of rhythms and play them simultenously on their own body. Or if singing and playing a rhythm would have the same benefits of keyboarding.

We know that brain plasticity in children occurs in regions related to playing a musical instrument. This study shows us that developing the brain through musical long-term experience leads to adult brain differences and promotes higher motor-skill functions. Music educators should therefore incorporate activities that promote this development in their own classrooms, and this is why we should advocate music education in the classroom all through elementary school, even if there is no direct correlation between performance on music tests and performance on other behavioural tests.

Functional neuroanatomical networks associated with expertise in motor imagery

Source:
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 <http://simplelink.library.utoronto.ca/url.cfm/198818>

Summary:
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.

Reflection:
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.


Reference:
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.

Music Training Causes Changes in the Brain – Catherine Applefeld Olson, Teaching Music, April 2010

Summary

In a recent study, researchers in Massachusetts found that changers are more pronounced in children who practice music more frequently. These changes did not correlate with improved performance in mathematics, spatial skills, or phonological ability. The study consisted of comparing two groups of six and seven year olds; one with musical training and one without. After three months the children who received musical instruction showed improvements in the following areas: the motor area, the corpus callosum, and the right primary auditory region.

Additionally, the changes became more pronounced over time and the musically trained students also performed better on motor sequencing tests involving patterning. The study did not detect any difference in performance in select academic areas between the two groups. The author makes a point that although the correlation between arts and other subjects is important, it does not justify music education. The arts are crucial in themselves, not because there may be a positive relationship between them and success in mathematical patterning.

Reflection

What I liked most about this article was the author’s stance on arts education advocacy. Too often we see music educators advocating for their program because musical training may benefit other subject areas (hence the “music makes you smarter” theory). But we need to support arts education as it’s own entity and promote the benefits on its own.

I think we should promote aesthetic education and the social connections inherent in music making while supporting these claims with scientific evidence. If we are going to make the claim that there is a positive correlation between playing music and success in math, then we must ensure that music stands out on its own instead of being dependent on another subject area.