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Musical Training Shapes Structural Brain Development

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  • McLean Hospital / Harvard Medical School

Abstract and Figures

The human brain has the remarkable capacity to alter in response to environmental demands. Training-induced structural brain changes have been demonstrated in the healthy adult human brain. However, no study has yet directly related structural brain changes to behavioral changes in the developing brain, addressing the question of whether structural brain differences seen in adults (comparing experts with matched controls) are a product of "nature" (via biological brain predispositions) or "nurture" (via early training). Long-term instrumental music training is an intense, multisensory, and motor experience and offers an ideal opportunity to study structural brain plasticity in the developing brain in correlation with behavioral changes induced by training. Here we demonstrate structural brain changes after only 15 months of musical training in early childhood, which were correlated with improvements in musically relevant motor and auditory skills. These findings shed light on brain plasticity and suggest that structural brain differences in adult experts (whether musicians or experts in other areas) are likely due to training-induced brain plasticity.
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Musical Training Shapes Structural Brain Development
Krista L. Hyde1, Jason Lerch2, Andrea Norton4, Marie Forgeard4, Ellen Winner3, Alan C.
Evans1, and Gottfried Schlaug4
1 McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal,
Quebec, Canada H3A 2B4
2 Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, Canada M5T 3H7
3 Department of Psychology, Boston College, Chestnut Hill, Massachusetts 02467
4 Music and Neuroimaging Laboratory, Department of Neurology, Beth Israel Deaconess Medical
Center and Harvard Medical School, Boston, Massachusetts 02215
Abstract
The human brain has the remarkable capacity to alter in response to environmental demands.
Training-induced structural brain changes have been demonstrated in the healthy adult human
brain. However, no study has yet directly related structural brain changes to behavioral changes in
the developing brain, addressing the question of whether structural brain differences seen in adults
(comparing experts with matched controls) are a product of “nature” (via biological brain
predispositions) or “nurture” (via early training). Long-term instrumental music training is an
intense, multisensory, and motor experience and offers an ideal opportunity to study structural
brain plasticity in the developing brain in correlation with behavioral changes induced by training.
Here we demonstrate structural brain changes after only 15 months of musical training in early
childhood, which were correlated with improvements in musically relevant motor and auditory
skills. These findings shed light on brain plasticity and suggest that structural brain differences in
adult experts (whether musicians or experts in other areas) are likely due to training-induced brain
plasticity.
Introduction
Studies comparing adult musicians with matched nonmusicians have revealed structural and
functional differences in musically relevant brain regions such as sensorimotor brain areas
(Elbert et al., 1995; Hund-Georgiadis and von Cramon, 1999; Schlaug, 2001; Gaser and
Schlaug, 2003b), auditory areas (Pantev et al., 1998; Zatorre, 1998; Schneider et al., 2002;
Gaab and Schlaug, 2003; Bermudez and Zatorre, 2005; Lappe et al., 2008), and multimodal
integration areas (Münte et al., 2001; Sluming et al., 2002, 2007; Gaser and Schlaug, 2003a;
Lotze et al., 2003; Bangert and Schlaug, 2006; Zatorre et al., 2007). While some research
has investigated functional brain correlates of musical training in childhood (Overy et al.,
2004; Koelsch et al., 2005; Fujioka et al., 2006; Shahin et al., 2008), no studies have yet
examined structural brain and behavioral changes in the developing brain in response to
long-term music training to specifically address the question of whether structural brain
differences seen in adults (comparing experts with matched controls) are a product of
“nature” or “nurture.”
Correspondence should be addressed to either Krista L. Hyde or Gottfried Schlaug at the above addresses. krista.hyde@mail.mcgill.ca
or gschlaug@bidmc.harvard.edu.
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Author Manuscript
J Neurosci. Author manuscript; available in PMC 2010 December 2.
Published in final edited form as:
J Neurosci
. 2009 March 11; 29(10): 3019–3025. doi:10.1523/JNEUROSCI.5118-08.2009.
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Such a study could also examine cognitive and behavioral changes in parallel with brain
changes in response to music training. There is a widespread view that learning to play a
musical instrument in childhood stimulates cognitive development and leads to the
enhancement of skills in a variety of extramusical areas, which is commonly referred to as
transfer (Bangerter and Heath, 2004). The most commonly observed form of transfer occurs
when there is a close resemblance between the training domain and the transfer domain
(typically referred to as “near transfer,” e.g., fine motor skills that develop while learning to
play a musical instrument lead to increased speed and accuracy in typing). While near-
transfer effects are relatively common, it is notoriously difficult to demonstrate “far
transfer,” where the resemblance between training and transfer domains is much less
obvious (e.g., learning to read and perform with precision from musical rhythm notation and
understanding fractions in math). There are some claims for far transfer from instrumental
music training in the areas of verbal, spatial, mathematical, and intelligence quotient (IQ)
performance (Rauscher et al., 1993, 1997, 1998; Chan et al., 1998; Ho et al., 2003;
Schellenberg, 2004; Forgeard et al., 2008), but such findings have also been controversial
(Steele et al., 1999).
As part of an ongoing longitudinal study on the effects of music training on brain,
behavioral, and cognitive development in young children (Norton et al., 2005; Schlaug et al.,
2005), here we investigated structural brain changes in relation to behavioral changes in
young children who received 15 months of instrumental musical training relative to a group
of children who did not. We used deformation-based morphometry (DBM), an unbiased and
automated approach to brain morphology, to search throughout the whole brain on a
voxelwise basis for local brain size or shape differences between groups (Collins et al.,
1994; Robbins et al., 2004). The DBM technique is useful for measuring morphometric
brain changes longitudinally, as in the present study, where the DBM metric of interest, the
Jacobian determinant, yields a measure of relative voxel size change over time in terms of
voxel expansion (growth) or contraction (shrinkage). To investigate a brain–behavioral
relationship, we correlated the brain deformation changes after 15 months with performance
changes on behavioral tests.
Materials and Methods
Participants
We tested two groups of children that were recruited from Boston area public schools and
who had no prior formal musical training (see Table 1). The “instrumental” group consisted
of 15 children (mean age at start of study 6.32 years old, SD 0.82 years) beginning weekly
half-hour private keyboard lessons (outside of the school system), and who continued
lessons for a mean interval of 15 months. The “control” group consisted of 16 children
(mean age at start of study 5.90 years old, SD 0.54 years) who did not receive any
instrumental music training during this 15 month period, but did participate in a weekly 40
min group music class in school consisting of singing and playing with drums and bells. The
instrumental and control children were all right handed and matched as closely as possible in
gender, age at the start of the study, and socioeconomic status (SES). SES was defined by
parental education on a six-point scale, with a score of 1, for children whose parents had
some high school education, to a score of 6, for those whose parents had a doctoral degree
(see Norton et al., 2005).
At time 1, all children were tested on a series of behavioral tests (described below), and
underwent a magnetic resonance imaging (MRI) scan (scan 1). At time 2 (15 months later),
all children were retested on the behavioral tests and underwent a second MRI scan (scan 2).
The children whose results are reported here are drawn from a slightly larger group of
instrumental and control children (see Norton et al., 2005). Here we only report the results
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from the children who completed both the behavioral tests and MRI scanning at times 1 and
2. We tested the hypothesis that brain and behavioral changes after 15 months should be
greater in instrumental than in control children; this time period allows us to compare our
results with those of other studies using a similar observation period.
Behavioral tests and MRI scanning
Children were tested individually at times 1 and 2 on measures of handedness and SES, and
on two near-transfer measures: a four-finger motor sequencing test for the left and right
hands assessing fine finger motor skills, and a custom-made “melodic and rhythmic
discrimination test battery” assessing music listening and discrimination skills. Five far-
transfer measures were also administered: the object assembly, block design, and vocabulary
subtests of the WISC-III (Wechsler, 1991), the Raven’s progressive matrices (colored
progressive matrices and standard progressive matrices) (Raven, 1976a,b), and the auditory
analysis test (Rosner and Simon, 1971), assessing phonemic awareness. The vocabulary
subtest of the WISC was used as a proxy for verbal IQ. For a detailed description of these
tests and their administration to this group of children, see Norton et al. (2005) and Forgeard
et al. (2008).
The two musically relevant (near transfer) behavioral tests are described in more detail
below, since these were the only tests that showed significant between group differences
after 15 months (see below, Results). Both of these tests are related to musical activity, but
can also be performed by children who do not have any instrumental music training. In the
four-finger motor sequencing test, children pressed a particular number sequence (e.g.,
5-2-4-3-5) corresponding to fingers 2–5 of their left or right hand on the number keys of a
computer keyboard as often, accurately, and fast as possible over a 30 s period. In the
“melodic and rhythmic discrimination test battery,” children heard pairs of five-tone musical
phrases differing only in melody and pairs of phrases differing only in rhythm. The task was
to indicate whether the two musical phrases were the same or different. These musical
phrases were designed for this study and have been described in more detail previously
(Overy et al., 2004; Norton et al., 2005; Forgeard et al., 2008). The melodic and rhythmic
subtest scores were combined to form one single behavioral measure of auditory–musical
discrimination. Behavioral “difference scores” measuring the difference in performance on
the behavioral tests from time 1 to time 2 were calculated and then correlated with the brain
deformation measures.
Anatomical MRI scans were obtained for all children on a 3T General Electric MRI scanner
using a T1-weighted, magnetization-prepared gradient-echo volume acquisition with a voxel
resolution of 0.93 × 0.93 × 1.5 mm. This research was approved by the ethics committees of
the Beth Israel Deaconess Medical Center. Written informed consent was obtained from the
parents of all the children, and the children themselves gave assent to participate in this
study.
Brain deformation-based morphometry analyses
Automated deformation brain analyses were performed on the T1 MRI data for each child
(see supplemental Fig. 1, available at www.jneurosci.org as supplemental material). All
MRI scans were first nonuniformity corrected (Sled et al., 1998), and registered to MNI
space with a nine-parameter linear transform using mni_autoreg tools (Collins et al., 1994;
Robbins et al., 2004). Next, brain deformation measures in terms of the Jacobian
determinants (yielding a measure of relative voxel expansion or contraction) were calculated
so that we could perform three different statistical analyses. First, to test for any brain
deformation differences at baseline (before musical training), for each group, all time 1 MRI
scans (0 months) iteratively underwent nonlinear registration toward the previous group
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average (starting with the linear group average). The Jacobian determinants of the final
nonlinear registration were computed and blurred with a 10 mm Gaussian kernel. Statistical
analyses were then performed comparing the Jacobian determinant data between groups at
baseline, at each voxel. Second, to test for brain deformation differences between groups
over time, each child’s time 1 scan (at 0 months) was nonlinearly aligned to his or her time 2
scan (15 months later). The resulting displacement field was blurred with a 10 mm Gaussian
kernel and the Jacobian determinant of the blurred displacement field was computed.
Statistical analyses were then performed comparing the longitudinal Jacobian determinant
data between groups, at each voxel. Third, to test for a brain–behavioral relationship, brain
deformation differences (Jacobian determinants of scan 2 – scan 1 as above) were regressed
on the behavioral difference scores (difference in test performance time 1 to time 2), for
each subject, at each voxel. Last we checked for T1-weighted intensity differences between
groups. All scans were intensity normalized, each subject’s time 1 scan was subtracted from
their time 2 scan, and the resulting intensity differences were compared between groups in a
linear model.
The general linear model was used in the group statistical analyses with age at time 1,
gender, and SES entered as covariates. The results from the group comparison were
thresholded using random field theory cluster thresholding (Friston et al., 1994; Worsley et
al., 2004), with a p < 0.05 cluster corresponding to at least 904 connected voxels with an
uncorrected p < 0.001, or at an a priori cluster threshold of p < 0.1 (at least 240 connected
voxels at an uncorrected p < 0.001) for strongly predicted regions that were not significant at
the whole-brain threshold. The significant brain deformation differences from the group
comparison were then used to define a volume of interest in which to test for brain–behavior
correlations with the scores on the motor and auditory–musical tests. The results from this
volume of interest were thresholded using the false discovery rate theory (Genovese et al.,
2002) at q = 0.05.
Results
Behavioral changes
An initial χ2 analysis showed no significant difference between the instrumental and control
groups in gender distribution ( p > 0.1). Initial ANOVAs showed no significant difference
between the groups in vocabulary scores at baseline ( p > 0.1), replicating the results initially
reported in Norton et al. (2005). There was a significant difference between groups in SES,
with the instrumental group (mean 5.1 points, SD 0.63) having a higher average SES than
the control group (mean 4.47 points, SD 0.87). The two groups also differed slightly in age
at baseline (time 1), with the instrumental group (mean 6.32 years, SD 0.82) ~5 months
older than the control group (mean 5.90 years, SD 0.54). Although this age difference only
approached significance ( p = 0.1), we chose to be conservative and covaried age along with
SES in our subsequent analyses.
A multiple analysis of covariance (MANCOVA), covarying age and SES, was conducted to
determine that there were no preexisting group differences at time 1 on either near- or far-
transfer outcomes. Missing values were replaced by the series’ mean (2.42% of all values).
The MANCOVA revealed no significant overall difference between groups (Wilks’ λ =
0.85, F(8,20)= 0.44, p = 0.88). Follow-up univariate tests also indicated that the two groups
did not differ significantly on any of the outcomes (all p > 0.1). Furthermore, the groups did
not differ significantly in interval length (in months) between baseline (time 1) and time 2
testing ( p > 0.1)
To determine whether the instrumental group progressed more than the control group on any
of the outcomes between times 1 and 2, another MANCOVA was performed using the
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behavioral difference scores (performance difference from time 1 to 2) as our dependent
variable, and age at baseline and SES as our covariates. Missing values were replaced by the
series’ mean (for 6.85% of all values). As predicted, there was a significant overall
difference in the behavioral difference scores between the two groups (Wilks’ λ = 0.50,
F(8,20) = 2.55, p = 0.04, partial η2 = 0.51). Univariate tests revealed differences in the two
near-transfer outcomes (motor and melody/rhythm tests) but not in any far-transfer
outcomes.
On the finger motor sequencing test, the instrumental group significantly outperformed the
control group in terms of the right-hand motor performance improvement over time (F(1,27)
= 7.25, p = 0.01, partial η2 = 0.21), and the difference between groups approached
significance for the left hand (F(1,27) = 3.81, p = 0.06, partial η2 = 0.12). The instrumental
group also significantly outperformed the control group in improvement on the custom-
made melodic/rhythmic discrimination test battery (F(1,27) = 13.20, p < 0.01, partial η2 =
0.33). No between-group differences in improvement over time (time 1 to 2) were found for
the far-transfer measures of block design, vocabulary, object assembly, Raven’s progressive
matrices, and auditory analysis (all p > 0.1).
Brain deformation changes
With regard to between-group brain differences, we did not see any differences between
groups at time 1. In terms of brain deformation changes in typical development that occurred
in our controls (n = 15) over the 15 month period, brain deformations were found in frontal,
temporal, and parieto-occipital brain areas (supplemental Fig. 2, available at
www.jneurosci.org as supplemental material). In terms of between-group differences
between the two time points, instrumental children showed significantly different brain
deformation changes over the 15 months (time 2 scan at 15 months minus time 1 scan at 0
months) compared with controls (see Table 2 for all significant results). Instrumental
children showed areas of greater relative voxel size than those of controls in motor areas,
such as the right precentral gyrus (motor hand area) (Fig. 1a), and the corpus callosum
(fourth and fifth segment/midbody) (Fig. 2a), that were significant at a whole-brain cluster
threshold at p < 0.05, as well as in a right primary auditory region (lateral aspect of Heschl’s
gyrus) (Fig. 3a) that was significant at an a priori cluster threshold at p < 0.1. Some
significant brain deformation differences were also found outside auditory and motor brain
areas. Instrumental children showed areas of greater relative voxel size than those of
controls in bilateral frontolateral and frontomesial regions and a left posterior pericingulate
region. In comparison, instrumental children showed only one area of lesser relative voxel
size than that of controls in the left middle occipital gyrus. Last, no differences in
normalized MR intensities were found between the two groups.
Correlations between brain and behavioral changes
Brain deformation changes in motor-related brain areas, including the right precentral gyrus
and the corpus callosum, were predicted by left-hand motor test improvement scores. To
illustrate the relationship between brain morphometry and behavior, we plotted the
longitudinal brain deformation change over 15 months (in terms of relative voxel size) for
each child as a function of his or her behavioral difference score on the left-hand motor
sequencing test at the most significant (peak) voxel in the right precentral gyrus and the
corpus callosum. The relative voxel size significantly increased with increasing left-hand
motor improvement score at peak voxels in the right precentral gyrus (Fig. 1b) and the
corpus callosum (Fig. 2b), but not in the right primary auditory region. Brain deformation
changes in the right auditory area (Fig. 3b) were predicted by improvements on the melodic/
rhythmic discrimination test. However, brain deformation changes in the right primary
motor region were not predicted by improvements on the melodic/rhythmic discrimination
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test battery, and brain deformation changes in the right primary auditory region were not
predicted by motor improvement scores. No other significant correlations were found
between brain deformations and either near or far-transfer behavioral measures.
Discussion
In the present study, we demonstrate regional structural brain plasticity in the developing
brain that occurred with only 15 months of instrumental musical training in early childhood.
Structural brain changes in motor and auditory areas (of critical importance for instrumental
music training) were correlated with behavioral improvements on motor and auditory–
musical tests. This study is the first longitudinal investigation to directly correlate brain
structure and behavioral changes over time in the developing brain.
The lack of brain and behavioral differences between the instrumental and control children
at baseline (before any music training) is consistent with previous findings from a larger
sample that included the present subset of children tested here (Norton et al., 2005). It is not
possible from these findings to completely rule out that musicians may be born with
preexisting biological predictors of musicality or that some children may have a certain
genetically determined trajectory of cerebral development that may lead them to more likely
continue to practice music relative to other children without this same predisposition.
However, our findings do support the view that brain differences seen in adult musicians
relative to nonmusicians are more likely to be the product of intensive music training
(Norton et al., 2005; Schlaug et al., 2005). Children who played and practiced a musical
instrument showed greater improvements in motor ability (as measured by finger dexterity
in both left and right hands) and in auditory melodic and rhythmic discrimination skills.
Contrary to previous findings, however (Chan et al., 1998; Vaughn, 2000; Ho et al., 2003;
Schellenberg, 2004; Rauscher et al., 1997, 2000), children who studied an instrument for 15
months did not show superior progress in visual–spatial and verbal transfer domain
outcomes than children who did not receive instrumental training. We propose three reasons
why 15 months of instrumental music training may not have been sufficient to result in far
transfer: (1) 15 months of instrumental lessons may be too short a period of time (duration
explanation); (2) children in our instrumental group may have practiced too little (intensity
explanation); or (3) a larger sample may be required to demonstrate far transfer (power
explanation).
The brain deformations found over 15 months in our controls (see supplemental Fig. 2,
available at www.jneurosci.org as supplemental material) are consistent with previous
findings in normal development that have included similar age ranges (from 5 to 7 years old)
(e.g., Sowell et al., 2004). The consistency of the brain deformation found here in our
controls with other studies of typical brain development in frontal, temporal, and parieto-
occipital brain areas strengthens our conclusions that the brain deformations observed here
between instrumental and control children are due to musical training. The present findings
of structural brain changes in response to 15 months of instrumental music training are
consistent with previous findings of training-induced structural brain differences in adults in
various contexts (Draganski et al., 2004; Draganski and May, 2008). More specifically, the
brain deformation differences found in primary motor brain regions are consistent with
structural brain differences found between adult musicians and nonmusicians in the
precentral gyri (Gaser and Schlaug, 2003b) and the corpus callosum (Schlaug et al., 1995;
Oztürk et al., 2002; Schmithorst and Wilke, 2002; Lee et al., 2003). Although the right
auditory cluster was not significant at a whole-brain level, this result was strongly predicted
on the basis of findings of previous structural brain differences in right auditory cortex in
adult musicians (Schneider et al., 2002; Gaser and Schlaug, 2003b; Bermudez and Zatorre,
2005). Thus, we report this right primary auditory region at an a priori threshold.
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The brain–behavioral correlations found here in motor and auditory brain regions for
performance on motor and auditory (melodic/rhythmic) tests show that different motor and
auditory behavioral functions (both musically relevant) appear to be driving the group
differences in separate predicted brain regions. These results are important from a functional
perspective since these brain regions are known to be of critical importance in instrumental
music performance and auditory processing. For example, the primary motor area plays a
critical role in motor planning, execution, and control of bimanual sequential finger
movements as well as motor learning (Karni et al., 1995; Grodd et al., 2001). The
correlation found between the brain deformation measures and the motor test at the corpus
callosum is consistent with the fact that the peak voxel lies in the fourth and fifth segments
of the corpus callosum (Witelson, 1989) (also called mid-body), which contains fibers
connecting primary sensorimotor cortex (Wahl et al., 2007). Moreover, it has been
suggested that intense bimanual motor training of musicians could play an important role in
the determination of callosal fiber composition and size (Schlaug et al., 1995). Last, the
correlation found between the brain deformation measures and the melody/rhythmic test
battery in the right primary auditory region is consistent with functional brain mapping
studies that have found activity changes using auditory–musical tests in similar auditory
regions (Zatorre et al., 2002).
While structural brain differences were expected in motor and auditory brain areas,
unexpected significant brain deformation differences were also found in various frontal
areas, the left posterior pericingulate, and a left middle occipital region. However, none of
these unexpected deformation changes were correlated with motor or auditory test
performance changes. While we do not currently have an interpretation for some of these
unexpected brain findings since they did not correlate with the auditory and motor
behaviors, the left posterior pericingulate region warrants additional discussion since it
showed a highly significant deformation difference. This region lies in the vicinity of
Brodmann area 31 in the transition between posterior cingulate and occipital cortex and is
involved in the integration of sensory (mostly visual) information and the limbic system.
Such integration is involved in learning to read musical notation and relating music to its
emotional content. The relative voxel size increases in frontomesial regions also stand out,
although no obvious relationship with changes in motor and auditory performance was seen
in these regions. Overall, 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 the instrumental learning. None of the unexpected brain deformation differences
mentioned above were correlated with behavioral performance changes in any of the far-
transfer domains. This may indicate that brain structural changes in association areas and
multimodal integration regions may develop before the emergence of significant behavioral/
cognitive changes in far-transfer domains.
While we have discussed the functional significance of the brain–behavioral structural
changes, the underlying structural properties of the results are not trivial to explain. The
brain deformation techniques used here are key to localize brain size/shape changes over
time, but are not able to inform us on the microstructural nature of these changes. Overall,
instrumental children showed greater relative voxel size expansion than controls over the 15
months, and only one area of voxel size contraction. A voxel expansion or contraction may
reflect increased or decreased gray or white matter due to neural reorganization/pruning or
increased/decreased brain connectivity. Evidence from animal models investigating the
effects of long-term learning and practice of complex motor skills (Anderson et al., 2002) on
brain structure may shed light on the structural neural basis of the brain structural changes
seen here. Several groups have demonstrated microstructural brain changes as a function of
long-term motor learning, including an increased number of synapses and glial cells,
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increased density of capillaries in primary motor cortex and cerebellum, and new brain cells
in the hippocampus after long-term motor training in adult rats (Black et al., 1990; Isaacs et
al., 1992; Anderson et al., 1994; Kleim et al., 1996; Kempermann et al., 1997; Anderson et
al., 2002). The sum of these microstructural changes could amount to structural differences
that are detectable on a macrostructural level, such as those observed in the present study
(Anderson et al., 2002; Bangert and Schlaug, 2006). It is possible that the specific and
continuous engagement of a unimodal and multimodal sensorimotor network, and the
induced changes in this network across a musician’s career, may provide the neural basis for
some of the sensorimotor and cognitive enhancements attributed to musical training. Future,
even higher-resolution morphometric investigations with more direct measures of gray and
white matter will be key to developing a better understanding of the underlying nature of the
brain deformation differences found here. We also did not find any differences in MR
intensities between groups, though using T1-weighted sequences is clearly a limitation in
this regard. Future studies should examine quantitative sequences, such as diffusion tensor
imaging, magnetization transfer, etc., in more detail to see whether microstructural changes
can be captured separately from the volumetric differences described herein. Last, we wish
to point out that one of the potential confounds of deformation-based morphometry is that
the deformation procedure can sometimes result in changes being propagated to regions
distant from their actual origin. Given that the present results were predicted based on the
functional literature, we feel it is unlikely that such propagation accounts for the results
presented in this manuscript. In the future, converging results from additional structural and
functional analyses metrics will serve to strengthen our conclusions.
In summary, our findings show for the first time that musical training over only 15 months
in early childhood leads to structural brain changes that diverge from typical brain
development. Regional training-induced structural brain changes were found in musically
relevant regions that were driven by musically relevant behavioral tests. The fact there were
no structural brain differences found between groups before the onset of musical training
indicates that the differential development of these brain regions is induced by instrumental
practice rather by than preexisting biological predictors of musicality. These results provide
new evidence for training-induced structural brain plasticity in early childhood. These
findings of structural plasticity in the young brain suggest that long-term intervention
programs can facilitate neuroplasticity in children. Such an intervention could be of
particular relevance to children with developmental disorders and to adults with neurological
diseases.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
This work was supported by grants from the National Science Foundation (BCS0518837), the Dana Foundation,
and the NAMM Foundation. We thank our previous research assistants and postdoctoral fellows (K. Cronin, L.
Forbes, L. Blake, C. Alexander, M. Rosam, K. Brumm, A. Norton, L. Zhu, U. Iyengar, and K. Overy) for test
preparation, behavioral testing, and imaging data collection and the participating children and their families for
their cooperation in taking part in our experiments.
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Figure 1.
Longitudinal group brain deformation differences and brain– behavioral correlations in
primary motor area. The brain image (a horizontal slice) shows areas of significant
difference in relative voxel size over 15 months in instrumental (n = 15) versus control (n =
16) children in terms of a t-statistical color map of the significant clusters superimposed on
an average MR image of all children (n = 31). The yellow arrow points to the primary motor
area (right precentral gyrus). To illustrate the group differences, the relative voxel size
(expressed as the mean by the horizontal dark black line, 25% and 75% quartiles by the top
and bottom lines of the box, SDs by the errors bars, and outliers by circles) is plotted for
each group at the most significant (peak) voxel in the right precentral gyrus (x = 40, y = 7,
z = 57; t = 4.2, p < 0.05 at whole-brain cluster threshold) (a). A voxel with a relative voxel
size of 1 indicates no brain deformation change from time 1, values >1 indicate voxel
expansion, and values <1 indicate voxel contraction. For example, a value of 1.1 at voxel X
indicates a 10% expansion from time 1, whereas 0.9 indicates a 10% contraction (this also
applies to Figs. 2, 3). The significant positive correlation of relative voxel size with
behavioral difference scores (from time 1 to time 2) of each child on the left-hand motor test
that was found at the peak voxel in the right precentral gyrus is shown in b.
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Figure 2.
Longitudinal group brain deformation differences and brain– behavioral correlations in the
corpus callosum. The brain image (a sagittal slice) shows areas of significant difference in
relative voxel size over 15 months in instrumental (n = 15) versus control (n = 16) children
in terms of a t-statistical color map of the significant clusters superimposed on an average
MR image of all children (n = 31). The yellow arrow points to the corpus callosum. To
illustrate the group differences, the relative voxel size is plotted for each group at the most
significant (peak) voxel in the corpus callosum (x = 14, y = 24, z = 30; t = 5.2, p < 0.05 at
whole-brain cluster threshold) (a). The significant positive correlation of relative voxel size
with behavioral difference scores (from time 1 to time 2) of each child is shown for the left-
hand motor test at the peak voxel in the corpus callosum (b).
Hyde et al. Page 13
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Figure 3.
Longitudinal group brain deformation differences and brain– behavioral correlations in right
primary auditory area. The brain image (a horizontal slice) shows areas of significant
difference in relative voxel size over 15 months in instrumental (n = 15) versus control (n =
16) children in terms of a t-statistical color map of the significant clusters superimposed on
an average MR image of all children (n = 31). The yellow arrow points to the right primary
auditory region (lateral aspect of Heschl’s gyrus). To illustrate the group differences, the
relative voxel size is plotted for each group at the most significant (peak) voxel in the right
primary auditory region (x = 55, y = 8, z = 10; t = 4.9, p < 0.1 at a priori cluster threshold)
(a). The significant positive correlations of relative voxel size with behavioral difference
scores (from time 1 to time 2) of each child is shown for the melody/rhythm test at the peak
voxel in the right primary auditory area (b).
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NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript
Hyde et al. Page 15
Table 1
Subject characteristics
Characteristics Instrumentals (n = 15) Controls (n = 16)
Age at start of study (SD) 6.32 (0.82) years 5.90 (0.54) years
Time from MRI scan 1 to scan 2 (SD) 15.60 (3.30) months 14.80 (3.80) months
Socioeconomic standard*5.10 (0.60) 4.60 (0.80)
Gender 9 females; 6 males 7 females; 9 males
*Socioeconomic standard was defined on a six-point scale, with a score of 1, reflecting that the children’s parents had some high school education,
to a score of 6, reflecting that parents had a doctoral degree (Norton et al., 2005).
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Hyde et al. Page 16
Table 2
Significant between-group longitudinal brain deformation differences
Brain area Number of voxels in cluster RFT cluster p value Brodmann area
Relative voxel size increases
Corpus callosum 4744 0.0000
Left middle frontal gyrus 3145 0.0001 6
Left superior frontal gyrus 2177 0.0011 8
Right middle frontal gyrus 2152 0.0012 10
Left pericingulate 2094 0.0014 31
Right superior frontal gyrus 1575 0.0057 10
Left superior frontal gyrus 1394 0.0097 9
Right primary motor (precentral gyrus) 1250 0.0152, *0.0014 6
Bilateral medial frontal gyrus 1217 0.0170 10
Right middle frontal gyrus 940 0.0434 11
Right primary auditory (Heschl’s gyrus) 293 0.5458, *0.0717 41
Relative voxel size decreases
Left middle occipital gyrus 1095 0.0024 37
All results are significant with whole-brain random field theory (RFT) cluster thresholding at p < 0.05, with the exception of results with *, which
are significant at an a priori cluster threshold of p < 0.1 for strongly predicted regions.
J Neurosci. Author manuscript; available in PMC 2010 December 2.
... Above all when comparing groups of healthy subjects, methodological rigor (e.g., avoiding spurious samples, controlling for confounding factors etc.) is necessary as baseline cognitive performance is typically high and results are less definitive than comparisons between healthy and clinical populations. In addition, if participants are children, the dynamics of typical cognitive development may mask subtle effects of music training (Hyde et al., 2009). ...
... This predominance likely stems from the field's focus on precisely measuring neural plasticity in relation to training parameters. Neuroscientists have demonstrated that the brain's structural and functional responses to music training occur on a continuum (Zatorre, Chen & Penhune, 2007;Hyde et al., 2009;Wan & Schlaug, 2010;Herholz & Zatorre, 2012;Schlaug, 2015). Additionally, the neuroscience community's focus on identifying the neural mechanisms underlying skill acquisition naturally leads to more accurate measurements of training intensity, whereas psychological research has traditionally been more concerned with group-level comparisons or longitudinal intervention-like designs that may obscure individual variations in training engagement. ...
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Full-text available
Despite an extensive body of research on the effects of music training on cognition, consensus on the transfer effects remains elusive. This paper addresses the methodological challenges underlying conflicting findings by integrating insights from two different studies. Our systematic review and meta-analysis (Study 1) revealed that only 12 of 149 studies screened employed dynamic measures—variables whose values can change across test waves for the same participant—to capture training intensity, with most stemming from neuroscientific research. These dynamic variables exhibited larger effect sizes for both near and far transfer outcomes compared to traditional static measures. Then, our empirical study (Study 2) employed a longitudinal quasi-experimental design with 141 preadolescents enrolled in either a Music Curriculum (MC) or a Standard Curriculum (SC) over 18 months. We used a dynamic measure of individual practice through the Concurrent Musical Activities (CCM; Müllensiefen et al., 2015) questionnaire and assessed fluid intelligence (Gf) with the Matrix Reasoning (MIQ; Condon & Revelle, 2014) test and the Jack & Jill Working Memory (JAJ; Tsigeman et al., 2022) test for visuospatial working memory along with perceptual tasks to assess musical ability. Findings indicate that higher training intensity significantly predicted gains only in Gf, even after controlling for baseline abilities, musicality, SES and musical home environment. Our findings suggest that individual practice is a key variable for detecting far transfer effects of music training, highlighting the need for more consistent and precise measures in future research.
... Above all when comparing groups of healthy subjects, methodological rigor (e.g., avoiding spurious samples, controlling for confounding factors etc.) is necessary as baseline cognitive performance is typically high and results are less definitive than comparisons between healthy and clinical populations. In addition, if participants are children, the dynamics of typical cognitive development may mask subtle effects of music training (Hyde et al., 2009). ...
... This predominance likely stems from the field's focus on precisely measuring neural plasticity in relation to training parameters. Neuroscientists have demonstrated that the brain's structural and functional responses to music training occur on a continuum (Zatorre, Chen & Penhune, 2007;Hyde et al., 2009;Wan & Schlaug, 2010;Herholz & Zatorre, 2012;Schlaug, 2015). Additionally, the neuroscience community's focus on identifying the neural mechanisms underlying skill acquisition naturally leads to more accurate measurements of training intensity, whereas psychological research has traditionally been more concerned with group-level comparisons or longitudinal intervention-like designs that may obscure individual variations in training engagement. ...
Preprint
Full-text available
Despite an extensive body of research on the effects of music training on cognition, consensus on the transfer effects remains elusive. This paper addresses the methodological challenges underlying conflicting findings by integrating insights from two different studies. Our systematic review and meta-analysis (Study 1) revealed that only 12 of 149 studies screened employed dynamic measures-variables whose values can change across test waves for the same participant-to capture training intensity, with most stemming from neuroscientific research. These dynamic variables exhibited larger effect sizes for both near and far transfer outcomes compared to traditional static measures. Then, our empirical study (Study 2) employed a longitudinal quasi-experimental design with 141 preadolescents enrolled in either a Music Curriculum (MC) or a Standard Curriculum (SC) over 18 months. We used a dynamic measure of individual practice through the Concurrent Musical Activities (CCM; Müllensiefen et al., 2015) questionnaire and assessed fluid intelligence (Gf) with the Matrix Reasoning (MIQ; Condon & Revelle, 2014) test and the Jack & Jill Working Memory (JAJ; Tsigeman et al., 2022) test for visuospatial working memory along with perceptual tasks to assess musical ability. Findings indicate that higher training intensity significantly predicted gains only in Gf, even after controlling for baseline abilities, musicality, SES and musical home environment. Our findings suggest that individual practice is a key variable for detecting far transfer effects of music training, highlighting the need for more consistent and precise measures in future research.
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The European philosophical and historical discourse is international Journal dedicated to researches on topical issues of modern philosophy, history of philosophical thought, cultural studies, theory and philosophy of history, analysis of historical processes in particular regions of the world, including history of Central and Eastern Europe countries. The Journal aims at publishing of high quality articles that may bring innovative and significant theoretical, conceptual, methodological and empirical contribution to relevant scientific fields. The Journal works with anonymous peer-review system that verifies scientific quality of submitted articles. The European philosophical and historical discourse has a particular interest in interdisciplinary researches in the field of philosophy, cultural studies and history, but by no means restricts its interests to these spaces. Researches in the field of advanced and promising directions of history such as oral history and historical computer science are also welcomed. Publication Frequency The European philosophical and historical discourse is published four times a year. Archiving To grant permanent access to its publications, European philosophical and historical discourse deposits Open Access articles in site of Journal. Authors are also permitted to post the final, published PDF of their article on a website, or other free public resources, immediately upon publication. Open Access Policy The European philosophical and historical discourse supports the Budapest Open Access Initiative. Abstracts and full texts of all papers published by the Journal are freely accessible to everyone immediately after publication. Publication Fee The journal’s editorial service fee for publishing an article online in English is €30; an article online in any other EU language or in Ukrainian is €40; an article processing fee covering costs associated with editing, efficient publishing service for authors, proofreading, typesetting, etc. Licensing The European philosophical and historical discourse works on the license Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International. It gives an opportunity to read, to load, to copy out, to expand, to print, to search, to quote or to refer to the full text of the article in this Journal. Indexing The Journal is indexed in the Index Copernicus International Journals Master List (impact-factor is ICV 2016: 59.90; ICV 2017: 79.30; ICV 2018: 81.50; ICV 2019: 70.93; ICV 2020: 72.38; ICV 2021: 72.6; ICV 2022: 72.83). Scilit, CrossRef` Publisher Till January 2024, the Journal was published by the BEROSTAV DRUŽSTVO. Official publisher of the Journal since February 2024 is ENIGMA CORPORATION.
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