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Music drives brain plasticity
Lutz Jäncke
Address: Division of Neuropsychology, Psychological Institute, University of Zurich, Binzmühlestrasse 14, 8050 Zürich, Switzerland
Email: l.jaencke@psychologie.uzh.ch
F1000 Biology Reports 2009, 1:78 (doi:10.3410/B1-78)
The electronic version of this article is the complete one and can be found at: http://F1000.com/Reports/Biology/content/1/78
Abstract
Music is becoming more and more of an issue in the cognitive neurosciences. A major finding in this
research area is that musical practice is associated with structural and functional plasticity of the
brain. In this brief review, I will give an overview of the most recent findings of this research area.
Introduction and context
Professional musicians have been used over the last
15 years as a model for brain plasticity [1,2]. Why are
musicians so interesting for plasticity research? First of
all, they are experts in playing musical instruments. To
play the demanding two three-second segments of the
11th variation from the sixth Paganini Etude by Franz
Liszt, for example, requires the production of 30 notes
per second. A tremendous amount of training is needed
to achieve this kind of finger speed . Ericsson and
colleagues [3] were among the first to show how much
professional musicians do in fact practice. The authors
showed that professional pianists and violinists practice
for 7,500 hours before reaching the age of 18 years,
whereas music teachers can look back on a total practice
time of approximately 3,500 hours. This differen ce was
unaffected by the quality of musical education since all
musicians in this study had graduated from the
prestigious Berlin Academy of Music. Thus, the amount
of practice is one of the most important factors
influencing musical expertise, at least in terms of the
skill required to play a musical instrument. If musicians
practice that much, it is hypothesised, they should show
some kind of neuroanatomical and neurophysiological
adaptations. P rofessional, semi-professional, and
non-professional musicians have no w been studied
extensively in terms of the neuroanatomical and
neurophysiological underpinnings of their expertise. In
principle, three different approaches to studying plastic
processes in musicians are possible: (a) Th e first
approach is cross-sectional in nature and mostly employs
quasi-experimental designs (‘post-test-only designs with
non-equivalent groups’ in the terminology of Cook and
Campbell [4]). With this design, musicians and non-
musicians are studied at the same point in time in
terms of anatomical or functional brain measures. This
approach has been widely used because it is relatively
easy to collect the data. Differences between both groups
are attributed mostly to the different learning histories of
musicians and non-musicians. However, the interpreta-
tion of these data is limited since this approach does not
allow the inference of strong causation because it cannot
be ruled out that selection differences between the two
groups or the different treatments (here, music lessons)
are responsible for the results. To enhance the interpret-
ability of such design s, several research groups have
employed pretest measures related to musical expertise
to control for pretest between-group differences. This
design, which is called the ‘untreated control group
design with proxy pretest measures’ [4], allows stronger
causation about the influence of musical training. (b)
The second approa ch used in this research context
consists of short-term longitudinal studies in which
subjects have undergone a specific training intervention.
These studies are typically designed according to a pre-
post design, and the subjects are enrolled in training
programs lasting from several hours to several months.
(c) Finally, long-term longitudinal studies in which
subjects have undergone a longer (at least a period of
years) training are also used. Longitudinal studies are
more co mplicated in terms o f organisation of the
experiments, they take longer, and they are more
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expensive. In addition, longitudinal studies are repeated
measurements studies, implying some methodological
problems (for example, unwanted practice effects). To
understand the influence of music practice on brain
plasticity more precisely, it is necessary to combine these
different approaches.
A general finding of the studies published thus far is that
nearly all of those brain areas involved in the control of
musical expertise (motor cortex, auditory cortex, cerebel-
lum, and other areas) show specific anatomical and
functional features in professional and semi-professional
musicians. In the following, I will review most of the
recent papers (after 2002) supporting the idea of brain
plasticity driven by musical expertise and musical training.
Major recent advances
Structural brain plasticity
Recently, Hyde and colleagues [5] published a paper
strongly supporting the idea of use-dependent brain
plasticity driven by musical training. In summary, this
study demonstrates that 6-year-old children receiving
instrumental musical training for 15 months (compared
with children receiving non-musical training) not only
learned to play their musical instrument but also showed
changed anatomical features in brain areas known to be
involved in the control of playing a musical instrument.
Most of these brain areas are part of the cortical motor
system, but there were also structural changes in the
auditory system and in the corpus callosum. This is the
first longitudinal study demonstrating brain plasticity in
children in the context of learning a musical instrument.
Although longitudinal studies are the gold standard in
plasticity research, several cross-sectional studies demon-
strating specific anatomical features in musicians have
recently been published. For example, Bangert and
Schlaug [6] reported that pianists atypically showed the
‘omega sign’ (indicative of a larger hand motor area) on
both hemispheres, where as violinists showed the omega
sign on only the right hemisphere controlling the left
hand. This specific anatomical feature is possibly related
to the fact that pianists practice a lot with both hands,
whereas violinists practice a lot with their left hand
(manipulating the strings) and the ir right arm (manip-
ulating the bow). Thus, violinists might drive only the
right-sided hand motor area, whereas pianists drive the
hand motor areas on both hemispheres. This interesting
finding is in strong concordance with older studies
reporting specific anatomical features in the hand motor
area in pianists and violinists [7,8].
Using a voxel-based morphometry approach, Gaser and
Schlaug [9] identified grey matter volume differences in
motor, auditory, and visual-spatial brain regions when
comparing professional musicians (keyboard players in
this study) with a matched group of amateur musicians
and non-musicians. Most interestingly, they found a
strong association between structural differences (grey
matter density), musician status, and practice intensity,
supporting the view that practice (in this case, practicing
to play a musical instrument) has an impact on brain
anatomy. Increased grey matter density (and volume) is
currently taken as evidence of an increase in capillary
density as well as smaller changes in synapse and glial
cell density . Thus, these changes might reflect neuroana-
tomical adaptations in order to improve the cognitive
and motor functions controlled by these particular brain
areas.
Most recently, a Swedish group used diffusion tensor
imaging (D TI) to measure the integrity of fiber tracts
(association fibers and commissures) in eight profes-
sional pianists and found a strong positive co rrelation
between the measure of fractional anisotropy (FA)
(indicating the integrity of the fiber system) and time
spent practicing the piano [10,11]. Thus, the pianists
who practi ced more often showed higher FA values
(indicating a higher integrity of the fiber system). This
finding is of outstanding importance because it brings to
light morphometric differences even within a highly
specialised group of skilled pianists and indicates that
these differences are due to practice time (‘specialisation
of the specialised’). In 2002, Schneider and colleagues
[12], of Heidelberg, Germany, reported a remarkable
anatomical finding in musicians. Using magnetoence-
phalography (MEG) and sophisticated anatomical
analyses, the authors found neurophysiological and
anatomical differences between musicians and non-
musicians. First, the neurophysiological activity in the
primary auditory cortex 19-30 ms after tone onset was
more than 100% larger. In addition, the grey matter
volume of the anteromedial part of Heschl’s gyrus
(which covers most of the primary auditory cortex) was
130% larger in musicians. Both measures were also
highly correlated with musical aptitude. This study is one
of the first to indicate that both the morphology and
neurophysiology of Heschl’s gyrus have an essential
influence on musical aptitude [12]. The second paper of
the same group was even more spectacular [13]. In this
paper, they found a strong relationship between the
strategy used in processing complex tones and anatomi-
cal features in the primary auditory cortex. Professional
musicians who preferentially analyse the fundamental
pitch (the fundamental tone, abbreviated f0 or F0, is the
lowest frequency in a harmonic series) of complex tones
were found to have a leftward asymmetry of grey matter
volume in Heschl’s gyrus, whereas those who prefer to
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analyse the spectral pitch of complex tones show a
rightward asymmetry of grey matter volume of Heschl’s
gyrus. Thus, a marked anatomical feature of the auditory
system correlated with a particular tone-processing
strategy within a group of professional musicians.
Patricia Sluming and colleagues [14], of Liverpool, UK,
published a paper in which they reported anatomical
differences in Broca’s area between musicians and non-
musicians. In particular, the authors reported increased
grey matter in Broca’s area in the left inferior frontal
gyrus in musicians. In addition, they obse rved significant
age-related volume reduction s in cerebral hemispheres,
dorsolateral prefrontal cortex bilaterally, and grey matter
density in the left inferior frontal gyrus in controls but
not in musicians! In other words, musicians showed no
or a smaller decrease in grey matter density in the frontal
cortex compared with non-musicians with increasing
age. (This is very important for aging research since the
volume of the frontal cortex has been shown to correlate
negatively with age [15,16].) This anatomical study
suggests that orchestral musical performance might
promote use-dependent retention, and possibly expan-
sion, of grey matter within Broca’s area (a brain area that
is responsible for speech production, language proces-
sing, and language comprehension as well as controlling
facial neurons; it is named after Pierre Paul Broca, who
discovered the area after studying the postmortem brain
of a patient with a speech impairment). In addition, this
study emphasises the significant point that shared neural
networks (within Broca’s area) are involved in the
control of language and music. In a more recent study,
the same group showed that Broca’s area is also involved
in the control of mental rotation, but only in musicians
[17]. They relate this extraordinary finding to the sight-
reading skills of musicians. In sight reading, visuospatial
cognition is related to some ki nd of language decoding.
Broca’s area might be involved in the control of this
specific inter-relationship.
The most recent study to use DTI techniques was
published by Imfeld et al. [18]. These authors measu red
the training effects on FA in the corticosp inal tract (CST)
of professional musicians and co ntrol non-musicians
and found significantly lower FA values in both the left
and the right CST in the musician group. Diffusivity, a
parameter indicating the amount of water that diffuses
along and across the axon, was negatively correlated with
the onset of musical training in childhood in the
musician group. A subsequently performed median
split into an early- and a late-onset musician group
(median of 7 years) revealed increased diffusivity in the
CST of the early-onset group as compared with both the
late-onset group and the controls. In conclusion, DTI was
successfully applied in revealing plastic changes in white
matter arc hitecture of the CST in professional musicians.
The present results challenge the notion that increased
myelination induced by sensorimotor practice leads to
an increase in FA, as has been sugg ested previously.
Instead, training- induced changes in diffusion character-
istics of the axonal membrane may lead to increased
radial diffusivity reflected in decreased FA values.
However, this issue deserves more intensiv e discussion
about the methodological aspects associated with FA and
diffusivity measurements.
Functional brain plasticity
Besides the above-mentioned specific anatomical fea-
tures in musicians, several recent (and older) studies
have shown specific neurophysiological adaptations.
Recently, Lappe et al. [19] dem onstrated particular
changes with respect to the neurophysiological responses
of the auditory cortex in non-musicians who trained for
2 weeks to play the piano. These authors randomly
assigned subjects to one of two groups: one group
learned to play a musical sequence on the piano, whereas
the control group listened to the music that had been
played by the other group. The authors demonstrated
training-induced cortical plasticity using the musically
elicited mismatch negativity (MMNm) from MEG
measurements before and after training. The MMNm
is a neurophysiological response reflecting the pre-
attentive processing of auditory stimuli [20,21].
The subjects who learned to play piano showed
significant enlargement of MMNm after training com-
pared with the group who only listened to the music.
Thus, practicing to play the piano improves not only
hand motor skills but also the auditor y representation of
the musical tones that are generated by the pi ano keys.
Thus, a strong crossmodal link between motor com-
mands and the representation of auditory information is
established, causing a stronger representation of musical
information in the auditory cortex. Several years ago,
Bangert and Altenmüller [22] demonstrated a similar
finding using electroencephalogaphy (EEG). They iden-
tified changed activations in frontal brain regions of
subjects who had just 20 minutes of piano training, but
only in the learning conditions during which the subjects
could easily associate a particular piano key with a note.
In situations during which this association was random,
there was no cortical crossmodal plasticity. Effects of
training have also been shown to be instrument-specific
[23-25], and the EEG responses of children taking music
lessons have been shown to change differently over the
course of a year compared with those of children not
studying music [26]. Thus, in summary, musicians or
musically experienced subjects respond differently to
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musical stimuli even if top-down factors like attention
are controlled for [27]. There is also ample evidence of
change in the auditory system due to musical practice
and in the entire sensorimotor system [28-32].
Interestingly, most of the recent findings indicate that
even neurophysiological responses at the level of the
brainstem are dependent on experie nce-dependent
influences. Neural activity generated from the brainstem
can be measured using frequency following responses
(FFRs). The FFR is an electrophysiological scalp-recorded
electrical response that reflects processing stages of
auditory information at the level of the brainstem.
Specifically, Wong et al. [33] first showed music-related
plasticity in FFRs elicited by speech. Later, Musacchia
et al. [34] found that musicians had more robust FFRs to
auditory and audiovisual speech and music sounds. The
latter study also strengthened the notion that musical
experience shapes not only auditory processing but
multisensory mechanisms as well. However, both studies
indicate that playing music enhances the fidelity of the
earliest stage of auditory response, not only to musical
stimuli but also to speech and multi sensory cues.
More recently, Krishnan et al. [35] analysed the FFRs
from Chinese and English subjects in response to four
Mandarin tonal contours presente d in a non-speech
context. The FFR analysis revealed that the Chinese group
exhibited st ronger representati on of multiple pitch-
relevant harmonics relative to the English group across
all four tones. The authors concluded that long-term
experience (here, experience with Mandarin) enhanced
the sensitivity to linguistically relevant variations in
pitch. Thus, specific language experience changes the FFR
in a manner similar to that of music experience.
Future directions
The preceding findings give rise to the question of
whether there is transfer from musical to non-musical
skills. A well-trained auditory system might support the
perception of auditory speech information and thus
auditory speech information might be processed more
efficiently. In addition, when learning to play a musical
instrument, the trainee also practices attention, planning
functions, memory, and self-discipline. It is thus
hypothesised that musical experience would positively
influence executive functions, lan guage functions, or
even intelligence in general. Several recently published
papers are in line with this hypothesis. For example, one
paper demonstrates that extended musical experience
enhances executive control on a non-verbal spatial task
and auditory tasks [36]. Glenn Schellenberg [37]
uncovered a greater IQ increase in children enrolled in
music classes compared with well-matched children who
received no musical lessons, and Ho et al. [38] uncovered
an enhancement of verbal memory skills (but not visual
memory skills) in children enrolled in musical lessons.
There is therefore mounting evidence on the behavioural
level of positive transfer from musical expertise to non-
musical domains. Recen tly, Moreno et al. [39] estab-
lished that musical training (not longer than 6 months)
improves non-musical functions such as reading and
linguistic perception. These non-musical enhancements
are also accompanied by changed cortical activation
patterns. This study is one of the very few longitudinal
studies to hav e been conducted in the context of musical
training.
If music has such a strong infl uence on brain plasticity,
this raises the question of whether this effect can be used
to enhance brain plasticity and cognitive performance in
general and clinical settings. In a recent single-blind
randomised controlled study, Särkämö et al.[40]
examined whether daily music listening enhances the
recovery of cognitive functions and mood after stroke.
This study demonstrates that recovery of verbal memory
and focused attention improved significantly and sub-
stantially in the group of patients who listened to their
favourite music on a daily basis compared with patients
who listened to audio books or received no listening
material. Besides the cognitive improvement in the
context of listening to music, there was a substantial
mood improvement in the patients who listened to
music. Thus, music could be used as a non-i nvasive tool
for neuropsychological and neurological therapies. In
addition, musical elements could be used to improve
specific cognitive functions for which positive transfer
effects have been demonstrated. For exam ple, reading
and writing skills as well as memory function s are
possible candidates for functions that might benefit from
musical training elements. Recent evidence shows that
writing and reading can be improved when dyslexic
children learn to associate graphemes and phonemes
with musical notes [41] and that many memory
elements are linked to music [42,43]. Hopefully, the
current trend in the use of musicians as a model for brain
plasticity will continue in future experiments and extend
to the field of neuropsychological rehabilitation.
Abbreviations
CST, corticospinal tract; DTI, diffusion tensor imaging;
EEG, electroencephalogaphy; FA, fractional ani sotropy;
FFR, frequency following response; MEG, magnetoence-
phalography; MMNm, musically el icited mismat ch
negativity.
Competing interests
The author declares that he has no compe ting interests.
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