Since your doctors and therapists KNOW NOTHING on how to make neuroplasticity repeatable on demand, it is your responsibility to have musical training as a kid before you have your stroke.
Moderating variables of music training-induced neuroplasticity: a review and discussion
- 1Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, VIC, Australia
- 2Department of Psychology, Université de Montréal, Montréal, QC, Canada
A large body of literature now exists to substantiate the long-held idea that musicians' brains differ structurally and functionally from non-musicians' brains. These differences include changes in volume, morphology, density, connectivity, and function across many regions of the brain. In addition to the extensive literature that investigates these differences cross-sectionally by comparing musicians and non-musicians, longitudinal studies have demonstrated the causal influence of music training on the brain across the lifespan. However, there is a large degree of inconsistency in the findings, with discordance between studies, laboratories, and techniques. A review of this literature highlights a number of variables that appear to moderate the relationship between music training and brain structure and function. These include age at commencement of training, sex, absolute pitch (AP), type of training, and instrument of training. These moderating variables may account for previously unexplained discrepancies in the existing literature, and we propose that future studies carefully consider research designs and methodologies that control for these variables.
In the last few decades, a considerable body of research has accrued on differences in the brains of musicians and non-musicians, as well as the changes created in the brain when becoming a musician. The findings of over 100 neuroimaging studies have been variously reviewed in a number of previous publications (Schlaug, 2001; Münte et al., 2002; Johansson, 2006; Altenmüller, 2008; Stewart, 2008; Habib and Besson, 2009; Jäncke, 2009; Tervaniemi, 2009; Kraus and Chandrasekaran, 2010; Wan and Schlaug, 2010; Herholz and Zatorre, 2012; Merrett and Wilson, 2012) and demonstrate convincingly that music has a significant impact on brain structure and function. Musicians and non-musicians' brains appear to have differences in volume, morphology, density, connectivity, and functional activity across a range of brain regions and structures. In addition to numerous cross-sectional studies, longitudinal music training studies in both children and adults have provided the most powerful evidence of music-induced neuroplasticity.
However, when the literature is sampled extensively, it becomes apparent that there are a number of contradictory findings. For example, a number of studies have looked at differences between musicians and non-musicians in the size or latency of electrical or magnetic potentials evoked in response to a variety of auditory stimuli. For some waveform components, such as the N1(m), there appear to be as many studies that have not found differences as those that have reported musician-non-musician differences (Pantev et al., 1998, 2001; Schneider et al., 2002; Schulz et al., 2003; Shahin et al., 2003, 2005; Kuriki et al., 2006; Lütkenhöner et al., 2006; Baumann et al., 2008). Another example can be seen in diffusion tensor imaging (DTI) studies of the corticospinal tract. Two studies found that fractional anisotropy (FA) in this tract was greater in musicians (Bengtsson et al., 2005; Han et al., 2009), while two other studies found that it was greater in non-musicians (Schmithorst and Wilke, 2002; Imfeld et al., 2009). Furthermore, there has been suprisingly little concordance between the results of whole-brain voxel-based morphometry (VBM) studies comparing musicians and non-musicians. Apart from the inferior frontal gyrus in the left hemisphere, no other brain regions were consistently found to be different between musicians and non-musicians across the five known VBM studies (Sluming et al., 2002; Gaser and Schlaug, 2003; Bermudez and Zatorre, 2005; Bermudez et al., 2009; Han et al., 2009). Although each study showed differences, the nature and location of these differences varied across studies. Even in the inferior frontal gyrus, which was significantly different in each of the VBM studies, the differences varied in their location on this gyrus across the anterior-posterior dimension.
These types of discrepancies have seldom been discussed in the literature to date. This could be due to the fact that until recently, many researchers were sceptical that music training would lead to differences in brain structure and function and/or were cautious in attributing causality to cross-sectional and correlational research. Previous studies and reviews have focused primarily on collating sufficient evidence that musicians' brains are different from non-musicians' brains, and that this reflects their experiences and not just their genetics. Because of the need to establish beyond doubt the very existence of music-induced neuroplasticity, these reviews have not focused on critically evaluating the concordance of the evidence. We would suggest that the field has matured to the point that this type of critical analysis is necessary to advance our understanding of music-induced neuroplasticity and to drive future research. It is clear that music training does induce changes in the brain, but there are numerous factors that influence when, where, and how neuroplasticity occurs in response to music training.
A number of variables that moderate the relationship between music training and neuroplasticity have been proposed in the literature. Our aim is to review these putative moderating variables and to discuss whether they could account for some of the discrepancies in the results of existing studies, including the examples given above. In addition, we will look at the implications of these moderators for future research designs and methodologies.
Age at Commencement of Training
In 1995b, Schlaug et al., published a highly influential paper that showed that the anterior half of the corpus callosum was larger in musicians than in non-musicians, but only for those musicians who commenced music training prior to 7 years of age. Since that time, a number of additional studies have reported similar findings in the corpus callosum for early trained musicians (Öztürk et al., 2002; Lee et al., 2003). Musician-specific effects in other motor regions, such as the sensorimotor cortices and pyramidal tracts, have also been correlated with age at commencement of training (Elbert et al., 1995; Amunts et al., 1997; Li et al., 2010) or practice hours in childhood (Bengtsson et al., 2005). This fits well with the behavioral literature that shows that motor skill attainment in musicians is negatively correlated with the age at which they started training. For example, an earlier age of commencement is associated with less asymmetry in hand tapping speed (Jäncke et al., 1997). Even when total years of study are accounted for, early-trained musicians outperform later-trained musicians on motor tasks (Watanabe et al., 2007).
These findings suggest that age at commencement of training is an important moderating variable of music-induced neuroplasticity. While neuroplasticity can occur throughout the lifespan, the evidence suggests that there is a sensitive period for motor learning that music training may interact with (Penhune, 2011). Based on the findings in the literature, training commenced before age seven has become a marker for early training. Those musicians who begin training prior to age seven may show greater capacity for neuroplastic changes than those who take up an instrument later in childhood or in adulthood. However, it should be noted that these types of correlations have not been found consistently across all brain regions related to motor function or related to other sensory modalities known to be influenced by music training. Although the cerebellum is a key part of the motor system, and differences between musicians and non-musicians have been found in cerebellar volume, there does not appear to be a relationship between age at commencement of training and volume in the cerebellum (Hutchinson et al., 2003). Similarly, the results in the auditory domain are mixed, with some studies finding a correlation between age of commencement of training and measures of auditory function (Pantev et al., 1998, 2001; Trainor et al., 1999; Wong et al., 2007; Musacchia et al., 2008), while another study looking at the structure of auditory cortex showed no difference (Keenan et al., 2001).
Given that this variable has not been accounted for in many studies comparing musicians and non-musicians, its impact is not fully understood. However, it could possibly account for some of the discordant findings in the literature. For example, in studies looking at FA of the corpus callosum, one study that used early-trained musicians found musician-non-musicians differences (Schmithorst and Wilke, 2002), while other studies that included musicians who commenced training after aged seven did not (Han et al., 2009; Imfeld et al., 2009). While this is a highly plausible explanation in light of the existing literature, other explanations could include differences in imaging acquisition and analysis, as the specific techniques used in these studies can have a significant impact on the results (Jones, 2010). Thus, while accounting for age at commencement of training may not solve all the existing discrepancies, it is clear that it has a significant effect on outcomes and should be reported and analyzed in future research. It might also interact with other moderating variables, such as sex and absolute pitch (AP) ability, which are discussed in more detail below.
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