Neuroscience and Biobehavioral Reviews 35 (2011) 2140–2154
Contents lists available at ScienceDirect
Neuroscience and Biobehavioral Reviews
journal homepage: www.elsevier.com/locate/neubiorev
Review
Plasticity of the human auditory cortex related to musical training
Christo Panteva,∗
, Sibylle C. Herholzb
a
Institute for Biomagnetism and Biosignalanalysis, University of Münster, Malmedyweg 15, 48129 Münster, Germany
b
Montreal Neurological Institute, McGill University, and International Laboratory for Brain, Music and Sound Research (BRAMS), Montreal, Quebec, Canada
a r t i c l e i n f o
Article history:
Received 31 August 2010
Received in revised form 21 June 2011
Accepted 24 June 2011
Keywords:
Uni-modal and multimodal cortical
plasticity
Auditory cortex
Neuroscience of music, Tinnitus
Notched music
a b s t r a c t
During the last decades music neuroscience has become a rapidly growing field within the area of neuroscience.
Music is particularly well suited for studying neuronal plasticity in the human brain because
musical training is more complex and multimodal than most other daily life activities, and because
prospective and professional musicians usually pursue the training with high and long-lasting commitment.
Therefore, music has increasingly been used as a tool for the investigation of human cognition and
its underlying brain mechanisms. Music relates to many brain functions like perception, action, cognition,
emotion, learning and memory and therefore music is an ideal tool to investigate how the human brain
is working and how different brain functions interact. Novel findings have been obtained in the field of
induced cortical plasticity by musical training. The positive effects, which music in its various forms has
in the healthy human brain are not only important in the framework of basic neuroscience, but they also
will strongly affect the practices in neuro-rehabilitation.
© 2011 Elsevier Ltd. All rights reserved.
Contents
1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2140
2. Long-term cortical plasticity as result of long-term musical training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2142
2.1. Increased auditory cortical representation in musicians for instrumental tones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2142
2.2. Timbre-specific enhancement of auditory cortical representations in musicians . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2142
2.3. Enhanced automatic encoding of melodic contour and interval structures in musicians . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2144
2.4. Music imagery and cortical plasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2144
2.5. Effects of musical training on the development of auditory cortical-evoked fields in young children . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2145
2.6. Effects of musical training on pitch processing in language. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2146
2.7. Effects of passive exposure to music . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2147
3. Short-term cortical plasticity due to musical training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2147
3.1. Plasticity of the human auditory cortex induced by auditory training. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2147
3.2. Cortical plasticity induced by short-term uni-modal and multi-modal musical training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2149
4. Tinnitus: the dark side of the auditory cortex plasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2150
5. General discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2152
Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2152
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2152
1. Introduction
The structural and functional organization of the human brain
becomes increasingly differentiated during child development. In
higher mammals, including humans, neurons are formed prenatally
as are some of their interconnections formed into neural
∗ Corresponding author. Tel.: +49251 8356885; fax: +49251 8356874.
E-mail address: pantev@uni-muenster.de (C. Pantev).
networks. For many years the prevailing opinion was that network
connections between neurons are built primarily during cerebral
maturation processes in the childhood and it was thought that
this network pattern, almost like a connection diagram, would not
change later. However, humans respond with considerable flexibility
to new challenges throughout their entire life. Since the early
eighties, increasing experimental evidence demonstrated that the
connectivity of the adult brain is in fact only partially determined by
genetics and early development, and may be substantially modified
through sensory experiences. Neuroscience research over the last
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C. Pantev, S.C. Herholz / Neuroscience and Biobehavioral Reviews 35 (2011) 2140–2154 2141
three decades has revealed that even in the adult brain the functional
organization in the mammalian cortex adjusts in response
to alteration of behaviorally relevant input and processing. The
knowledge that cortical representations are dynamic and continuously
modified by experience is based on a series of classical animal
studies. Several previous experiments have demonstrated that the
functional organization of sensory maps is not statically fixed in
adult cortex (Gilbert and Wiesel, 1992; Irvine and Rajan, 1995;
Jenkins et al., 1990; Kaas et al., 1990; Merzenich et al., 1983a,b;
Rauschecker et al., 1995, 1997; Recanzone et al., 1993). Early studies
in animals utilized large and permanent changes of afferent
sensory input, such as amputation of a forelimb (Irvine and Rajan,
1995; Kaas, 1991; Merzenich et al., 1984; Pons et al., 1991; Rajan
et al., 1993; Rajan and Irvine, 1998; Schwaber et al., 1993) or in
the auditory system, injuries such as deafferentation through damage
of a section of hair cells in the cochlea (Irvine and Rajan, 1995;
Rajan et al., 1993; Rajan and Irvine, 1998; Schwaber et al., 1993).
These experiments have shown that cortical regions that have lost
their normal input take over and serve functions found in adjacent
cortical areas (Rajan and Irvine, 1998; Stanton and Harrison, 1996).
The development of new, non-invasive scientific methods for
recording neuronal activity has made it possible to prove the
hypothesis of plasticity in functional neuronal networks in humans.
The electrical activity of single neurons can only be recorded invasively
in animals or in special cases in patients undergoing brain
surgery (at least local field potentials, e.g. in patients with deep
brain electrodes). However, derived potentials that reflect the
activity of a group of neurons can be recorded non-invasively on
the scalp surface by means of electroencephalography (EEG). The
magnetic counterpart of the EEG is the magnetoencephalography
(MEG), which has become an established method for noninvasive
study of the activity of the human cortex (Hari, 1990). The main
sources of cortical evoked magnetic fields are the pyramidal cells,
which produce currents flowing tangentially to the surface of the
head. Though MEG measurements provide only a macroscopic view
of the function of the brain, the spatial resolution achieved with
this technique is sufficient to give indications of functional organization
and reorganizational plasticity of the human cortex by
localizing the sources of evoked magnetic fields, which are elicited
by defined peripheral excitation. Thus, using MEG, which allows
non-invasive measurement in human subjects, changes in cortical
maps similar to those observed in primate cortex have been
demonstrated in patients with limb amputations and finger syndactyly
(Elbert et al., 1994; Flor et al., 1995; Mogilner et al., 1993;
Yang et al., 1994).
During the last years music has increasingly been used as a tool
for the investigation of human cognition and its underlying brain
mechanisms. Music relates to many brain functions and therefore
music is an ideal tool to investigate how the human brain is
working. As described by (Zatorre et al., 2007), playing a musical
instrument, for example the violin, is a highly complex task. The
whole body and almost all sensory systems are involved and have
to be coordinated at high degree of synchrony and accuracy. The
arms support the violin and move the bow, the left hand fingers
the strings, and feedback from the somatosensory perception about
the body posture and from the fingertips is constantly integrated to
fine-tune each and every movement. The auditory system analyses
the correctness of the sounds and the auditory feedback is used to
improve the sound quality. The visual system analyses the musical
score and translates the musical symbols into meaningful information
involved in the music production. Apart from the motor
and sensory systems, memory and attentional as well as emotional
systems are also involved. However, not only the brain of the performing
musician is highly active but also the brains of the listeners.
Even the passive act of listening to music activates a broad range of
cortical functions: perception and analysis of the pitches, timbres,
harmonics, rhythms, meter and of higher-order structures, attention
and memory systems, and emotional responses to the music
heard.
Over the last ten years enough scientific evidence has been
accumulated demonstrating that musical training has pronounced
effects on functional and structural human brain plasticity. On the
structural level, larger brain volume has been demonstrated in
musicians compared to non-musicians in several brain areas (auditory
processing; Bermudez et al., 2009; Gaser and Schlaug, 2003;
Schlaug et al., 1995b; Schneider et al., 2002), right superior parietal
gyrus (visuo-spatial processing; Gaser and Schlaug, 2003), corpus
callosum involved in motor control (Schlaug et al., 1995a), precentral
gyrus (Bangert and Schlaug, 2006; Gaser and Schlaug, 2003)
and cerebellum (Hutchinson et al., 2003). Not only in gray matter
but also in white matter in different fiber tracts differences
in nonmusicians and musicians have been found (Oechslin et al.,
2010a). Also, a recent longitudinal study (Hyde et al., 2009) in children
who received keyboard lessons over the course of 15 months
showed anatomical changes in primary motor and auditory areas
as well as in the corpus callosum, thus providing further support for
the interpretation that such changes are indeed due to the musical
training.
Also on the electrophysiological and functional level, fundamental
differences between musicians and non-musicians regarding
the processing of sounds have been demonstrated in various studies
– on the level of simple tones as well as in the processing of
complex melodic stimuli (Fujioka et al., 2004, 2005; Pantev et al.,
1998; Tervaniemi et al., 2001); chord sequences (Koelsch et al.,
2002b), non-melodic tone patterns (van Zuijen et al., 2004, 2005)
and speech stimuli (Besson et al., 2007). Differences between musically
trained and untrained persons cannot only be seen in the
auditory, but also in the somatosensory and the motor domains
(Elbert et al., 1995; Lotze et al., 2003).
However, not only processing within one modality but also
interactions between modalities are enhanced in musically trained
people (Schulz et al., 2003). The interplay and the integration
of several modalities is a key element of musical training and
performance, and the multimodal integration and co-activation
of the cortical areas involved during the training might be an
important mechanism supporting the training effects within each
modality, which are observed after musical training (Zatorre et al.,
2007).
As a complete review on all aspects of structural and functional
plasticity through musical training is beyond the scope of
this paper, we focus mainly on the electrophysiological literature
on plasticity in the auditory modality caused by musical training
(please refer to Jancke, 2009; Pascual-Leone et al., 2005 for
two excellent reviews on brain plasticity with a broader scope).
By means of EEG and MEG it is possible to observe electric potentials
and magnetic field distributions, respectively, over the scalp
surface, which result from neuronal activity mainly from cortical
areas of the human brain. These techniques are very well suited
to study auditory cortical processing, as the measurements are
completely non-invasive and silent. In addition, the high temporal
resolution is adequate to resolve the rapid processing of auditory
signals. The recorded cortical evoked responses correspond
to different major components of the auditory evoked fields: the
wave N1, having a latency of about 100 ms after stimulus onset
(Pantev et al., 1988), the mismatch negativity (MMN) with latency
of 150–250 ms (Naatanen and Alho, 1995), the transient auditory
evoked gamma-band fields (Pantev et al., 1991) as well as the
auditory steady state responses (ASSR) that follow the modulation
frequency of the auditory stimulus with a maximum around 40 Hz
(Pantev et al., 1996). All these components display stimulus-related
neuronal activity that occurs mainly in circumscribed auditory cortical
areas. In many of our own studies, this activity as measured
2142 C. Pantev, S.C. Herholz / Neuroscience and Biobehavioral Reviews 35 (2011) 2140–2154
with MEG has been analyzed using equivalent current dipole (ECD)
model, that can account for most of the variance of the observed
magnetic field distributions. Subsequently, the source space projection
technique (Tesche et al., 1995) is applied to compute the time
course of the corresponding dipole moment, resulting in so-called
source waveforms. Other analysis techniques such as distributed
source models have also been developed and provide information
about the underlying sources without assumptions about the number
of sources. The amplitude of early auditory evoked responses
can be used to measure differences in cortical source activity resulting
from musical training as a parameter that indicates functional
cortical plasticity. Such differences might result from newly formed
synapses or newly recruited additional neurons or from increased
neuronal activity corresponding to increased synchronization in
an existing cortical network. Whereas structural measures of brain
anatomy (e.g. voxel based morphometry, cortical thickness) have
been used to investigate differences in long-term reorganization
of the brain, but have rarely been used to assess short-term adaptation
processes, functional measures do not distinguish between
the different causes for increased or decreased cortical activity,
but have been used to assess both long- and short-term plasticity
changes.
Experiments on cerebral cortical organization in musicians
demonstrated an astonishing plasticity. Results of such studies
showing the influence of long-term and short-term musical training
on the cortical reorganizational processes to complex musical
sounds within the human auditory cortex are presented in this
review. In general the plasticity of the auditory cortex in musicians
due to their intensive musical training is a positive phenomenon.
The training induced alterations in the cortical map correspond to
perceptual correlates indicating superior performance, an example
of the “bright side” of the cortical plasticity. However, the plasticity
of the auditory cortex has also a “dark side”. It occurs when a
peripheral lesion, by itself manageable, is triggered by an unusually
intense and stressful experience to cause negative and even catastrophic
auditory cortex reorganization, such as that underlying the
tinnitus. At the end of this review the results of a study with modified
notched music to reverse mal-adaptive cortical reorganization
in tinnitus are presented.
2. Long-term cortical plasticity as result of long-term
musical training
2.1. Increased auditory cortical representation in musicians for
instrumental tones
In an early study on auditory cortex plasticity we investigated a
group of musicians and compared them to a control group of nonmusicians
matched in age. From each subject the N1 component
of auditory evoked fields to piano tones and to pure tones with
corresponding fundamental frequencies were recorded by means
of MEG. The N1–P2 complex of auditory evoked responses reflects
processing of stimulus features that can be modulated by factors
such as task demands and attention. In this experiment (Pantev
et al., 1998) we found a significant difference between musicians
and nonmusicians in the strength of the cortical sources activated
by the piano tones compared to the pure tones (Fig. 1). Both
for musicians with absolute or relative pitch the cortical source
strength (ECD) was by 21–28% significantly larger for piano tones
than for pure tones. This effect was found although the two types of
tones were matched for loudness. For the nonmusicians no significant
difference was found in ECD between piano and pure tones.
The increase in cortical source strength in musicians reflects either
an expansion of the number of cortical neurons processing the
musical tones, i.e. more neurons were engaged in the processing
of these tones in skilled musicians, or increased synchronization of
those neurons, or most probably both.
The observed increase in cortical source strength was correlated
with the age at which the musicians began to practice. This result
indicates that the younger the subjects started playing their instrument,
the larger was the cortical representation for the piano tones
compared to the pure tones.
This original finding was challenged by later studies that did not
find such a differential effect in musicians compared to nonmusicians.
Lutkenhoner et al. (2006) reported increased N1 responses
to piano tones compared to sine tones both in musicians and
nonmusicians. Meyer et al. (2006) investigated auditory evoked
responses to instrumental (piano, trumpet and violin) tones versus
sine tones using the analysis method of low-resolution electromagnetic
tomography (LORETA) on EEG data, an alternative approach
to ECD fitting that shows sources of evoked potentials without a
priori assumptions about the number of sources. All of their participants
were nonmusicians, but showed increased N1 and P2
responses to the instrumental tones compared to the sine tones
nevertheless. Similarly, in one of our own studies (Shahin et al.,
2005), no difference in N1 amplitude to instrumental tones compared
to sine tones was found in musicians and nonmusicians.
However, in this study, the musicians showed a stronger increase
of P2 amplitude to instrumental tones vs. sine tones than the nonmusicians.
This somewhat inconsistent pattern of findings might be
partially explained by attentional effects. Depending on the context
of the experiment, musicians might pay more attention to instrumental
tones because they are of high professional relevance. This
issue was recently addressed in a paper by Baumann et al. (2008).
Again, they presented instrumental tones and sine tones to musicians
and nonmusicians while the participants listened passively
or attended to the tones. Both musicians and nonmusicians performed
well on the detection tasks that controlled attention, and
both showed increased amplitudes of the N1/P2 complex in their
auditory evoked responses to the instrumental tones compared to
the sine tones. Regarding the effect of musical training the authors
reported enhanced N1 responses in musicians compared to nonmusicians
while controlling for attention, thus excluding attention
as a potential explanation for group differences, and partly confirming
earlier findings of training-related enhancements of cortical
representations of instrumental timbre. However, this group effect
was nonspecific, as both responses to both instrumental and sine
tones were enhanced in musicians, and therefore the controversy
remains unsettled.
However, an alternative way of testing if musicians show an
enhanced response to instrumental tones as a result of their training
is to test if responses are more pronounced for the timbre of the
specific instrument that they trained on.
2.2. Timbre-specific enhancement of auditory cortical
representations in musicians
In a further study we investigated (Pantev et al., 2001) whether
cortical representations for tones of different timbre (violin and
trumpet) are enhanced compared to sine tones in violinists and
trumpeters, preferentially for timbres of the instrument on which
the musician was trained. Seventeen highly skilled musicians
were recruited. Participants in both groups had practiced their
instrument for 15 years on average, and had practiced intensely
in the five years preceding the study. Importantly, they had
never practiced the respective other instrument. The N1 component
was recorded for violin and trumpet tones of corresponding
pitches.
The time course of the N1 cortical source strength evoked
by violin and trumpet tones is portrayed separately for the two
hemispheres in Fig. 2a, for one representative violinist and one
C. Pantev, S.C. Herholz / Neuroscience and Biobehavioral Reviews 35 (2011) 2140–2154 2143
Fig. 1. Equivalent dipole moment (strength of neuronal activation during the N1 component of the auditory evoked field) as shown for pure tones and piano tones in control
subjects and musicians with absolute or relative pitch (from Pantev et al., 1998).
representative trumpeter demonstrating clear timbre specificity.
Group data are depicted in Fig. 2b, where it can be seen that in each
hemisphere the N1 cortical strength was larger for timbres of the
instrument of training.
a left hemisphere right hemisphere
violinist
trumpeter
0
20
40
60
80
corticalstrength(nAm)
0
20
40
60
80
0
20
40
60
80
2001000
0
20
40
60
80
2001000
latency (ms)
p<0.01
0
10
20
30
40
50
60
70
corticalstrength(nAm)
b
p=0.0282
p=0.0014
trumpetersviolinists
Fig. 2. (a) Timbre specificity. Cortical responses evoked by string tones (thick line)
and trumpet tones (thin line) are shown for a representative violinist (upper panel)
and trumpeter (lower panel) for the left and right hemispheres separately. (b) Mean
ECD evoked by the string tones and trumpet tones are shown for the trumpeter and
violinist groups (hemispheres combined) (from Pantev et al., 2001).
Complementary evidence of timbre-specific enhancements of
cortical activity with different methods was also more recently
reported in professional flute and piano players (Margulis et al.,
2009), as well as in violin and piano players (Shahin et al., 2008).
Using fMRI, Margulis et al. (2009) found activity in an extensive network
of cortical areas that relate to processing of musical syntax (BA
44), timbre (auditory association cortex), and sound–motor interactions
(precentral gyrus). Shahin et al. (2008) reported increases
of induced oscillatory gamma band activity specifically in response
to tones of the trained instrument in violinists and pianists that are
interpreted to reflect highly learned perceptual template matching.
Taken together, these findings show that highly skilled musicians
exhibit enhanced auditory cortical representations for
musical timbres associated with their principal instrument, compared
to timbres associated with instruments on which they have
not been trained. Timbre specificity is predicted by neuroplastic
accounts of brain development, when musical training has been
specific to one or the other of these instruments. It is difficult to
explain such differential effects on the basis of genetic disposition
or environmental factors. In order to explain timbre specificity,
nativistic accounts would have to be elaborated to propose that
genetic mechanisms code for complex tones of specific spectral
structure, and that the genetic code for spectral structure is sufficiently
constraining as to determine who trains as a trumpeter and
who as a violinist. Additionally, Shahin et al. (2008) also reported
similar increases of induced oscillatory gamma band activity in 4and
5-year old children before and after one year of piano training,
whereas a age-matched control group of children who did not
receive piano training did not show such an effect. Although in
this case group assignment was not random, these data provide
additional and compelling evidence for training-induced plastic
changes of the auditory cortex.
These findings of timbre-specific enhancements of neuronal
responses among musicians shed new light on the controversy
about possible differences between musicians and nonmusicians
that was discussed earlier, because the selection of musicians
who took part in the studies might have influenced the results.
Whereas in both our studies that found enhanced auditory evoked
responses to instrumental tones in musicians most subjects played
the piano (which was the instrumental timbre used) as principal
or secondary instrument. In contrast, musicians in the studies
by Lutkenhoner et al. (2006) and Baumann et al. (2008) played a
2144 C. Pantev, S.C. Herholz / Neuroscience and Biobehavioral Reviews 35 (2011) 2140–2154
variety of instruments. Based on the recent findings of selectively
enhanced responses to the timbre of the instrument of training,
it seems plausible that the partial discrepancy of presented timbres
(piano timbre in Lutkenhoner et al.; trumpet, violin and piano
in Baumann et al.) and participating musicians’ instruments of
training might account for the null-findings regarding differences
between musicians and nonmusicians in these studies.
2.3. Enhanced automatic encoding of melodic contour and
interval structures in musicians
In the studies presented so far we have focused on the processing
of single tones. Meaning in music, however, involves how tones
are put together. The question asked here concerns the effect of
extensive musical training on the processing of sequences of tones
or melodic information. From a perceptual point of view, melodic
pitch structure has two aspects, a contour and an interval code. The
contour representation consists of information about the up and
down pattern of pitch changes, regardless of their exact size, and is
common to both speech prosody and musical melody. The interval
representation consists of more analytic structure of exact pitch
distances between successive tones. Pitch interval is specific to
music and is crucial for scales and harmony, where exact intervals
define the structure. We investigated the effect of musical expertise
on contour and interval processing as compared to nonmusicians.
The stimuli were designed to clearly separate contour and interval
encoding.
In this study (Fujioka et al., 2004) we have shown that musicians’
automatic encoding and discrimination of pitch contour and
interval information in melodies is specifically enhanced compared
to non-musicians. Twelve musicians and twelve non-musicians
matched in age participated in the experiment. The stimuli for both
the contour and the interval melodic conditions were composed of
sequences of 5-note standard and deviant melodies, with standard
melodies occurring 80% of the time. Standard melodies in the contour
condition were ascending, whereas the last note of deviant
melodies in this condition was descending, thus altering the contour
of the melody, which was noticeable regardless of the specific
pitch interval.
In contrast, in the interval condition, the pitch of the last tone of
deviant melodies was altered so that the contour was preserved but
the interval between the last and second-to-last note changed. Factors
of deviation in musical context involving out-of-key changes,
familiarity of melody, and range of pitch leaps that might cause
additional cognitive processing, were carefully controlled. The control
condition, in which no difference between the groups was
expected, consisted of two successive blocks of pure tones of two
different pitches in a classical oddball paradigm where one tone
occurs more often than the other. In all of the conditions, deviating
tones were expected to elicit a MMN, a neuronal response that is
elicited by unexpected violations of regularity in the auditory input,
and that reflects auditory discrimination ability on a pre-attentive
level of processing.
As shown in Fig. 3, after the onset of the fifth note, clear and
significant MMN responses were observed in both hemispheres
for both contour and interval conditions in musicians. In contrast,
non-musicians showed no clear responses in both contour
and interval conditions. However, in the single tone control condition
both musicians and non-musicians have clear MMN responses
to the frequency change of the stimulus, indicating that the lack
of MMN in nonmusicians in the melodic conditions is not due
to a general inability to discriminate pitches. The results of the
behavioral tests that were also conducted showed better behavioral
discrimination performance in the musicians and thus confirmed
the MEG results. The findings strongly support the hypothesis that
the musical experience of musicians leads to specific changes in
the neural mechanisms for processing melodic information, and
that long-term musical training particularly enhances the processing
of pitch relations between the successive notes of melodies.
These findings were confirmed and extended in a similar studies
using polyphonic melodies, in which again musicians showed a
larger MMN response to unexpected tones in short melodies compared
to nonmusicians (Fujioka et al., 2005). Moreover, similar to
findings regarding specificity to instrumental timbre, the individual
training biographies of musicians also reflect in their processing of
melodies. Seppänen et al. (2007) showed that musicians who prefer
aural training strategies (such as playing by ear and improvisation)
show better discrimination performance to contour and interval
changes in short melodies compared to musicians who mainly use
non-aural strategies (e.g. playing from sheet music), and that this
is also reflected in a larger MMN amplitude to these changes after
a short, focused (aural) training session.
Other recent studies have shown that musicians not only show
enhanced processing of melodies, but also of rather abstract tonal
material. van Zuijen et al. (2004, 2005) showed that nonmusicians
and musicians show similar processing of relatively simple,
unmelodic patterns of tones that were grouped according to similarity
of pitch, or a temporal regularity (van Zuijen et al., 2004,
2005, respectively). However, musicians show enhanced mismatch
responses to deviant and unexpected tones in slightly more
complex patterns of tones that were based on Gestalt features
(ascending tones; van Zuijen et al., 2004) or numerical regularities
(van Zuijen et al., 2005). Also, in one of our own recent studies, we
were able to show that musicians are better able than nonmusicians
to extract temporal regularities from abstract sequences of tones
based on global probabilities of patterns in tone sequences (Herholz
et al., 2009), which complements evidence of enhanced and differently
lateralized processing of musical meter in highly rhythmic,
musical stimuli in musicians compared to nonmusicians (Vuust
et al., 2005). Also, passive exposure to musical stimuli can modulate
neuronal responses as early as the brainstem even in nonmusicians
(Skoe and Kraus, 2010). Within 1.5 h of passive listening, auditory
evoked brainstem responses to a five tone melody increased, indicating
on-line plasticity based on global stimulus probabilities even
on such an early processing level. Also, the response to a repeated
note within the melody increased over time, suggesting an influence
of local stimulus probabilities on subcortical auditory plastic
changes as well. Most likely, such plastic changes in the responses
in the brainstem are the result of modulation by cortical feedback
via cortico-fugal pathways (Kraus and Chandrasekaran, 2010).
2.4. Music imagery and cortical plasticity
Although the influence of long-term musical training on the
processing of heard music has been the subject of many studies,
the neural basis of music imagery and the effect of musical
expertise remain still insufficiently understood. In a recent experiment
(Herholz et al., 2008) we compared the neuronal processing
of imagined music in musicians and non-musicians, who listened
to the beginnings (first six tones) of familiar melodies. Subjects
were explicitly instructed to imagine the following six tones of
the melody in their mind within the corresponding silent period.
Then they judged via button press if an actually presented test tone
was a correct further continuation of the melody or not. This could
only be identified based on the preceding correct imagination of
the individual melody and not solely on the basis of the first six
tones. As expected, musicians were far better than non-musicians
in the behavioral task. The MEG data showed that a significantly
increased MMN, i.e. an enhanced negativity in response to the
incorrect tones compared to the correct tones of the imagined
melody was observed only in the musicians’ group and this MMNlike
response was termed imagery mismatch negativity (iMMN).
C. Pantev, S.C. Herholz / Neuroscience and Biobehavioral Reviews 35 (2011) 2140–2154 2145
Fig. 3. The source space waveforms of MMN. For both contour and interval condition the time scale on the x-axis refers to the onset of the 5th note. The thick lines represents
the MMN response and thin lines above and below zero line shows the upper and lower limit of the corresponding 95% confidence interval. The mean of confidence interval
was subtracted from the response to adjust the baseline of MMN waveform to the zero line (from Fujioka et al., 2004).
Based on findings of largely overlapping activity during imagery
and perception of music (cf. review by Zatorre and Halpern, 2005),
we interpreted the observed difference between groups in both
the behavioral and MEG data as further evidence that the intense
musical training has modified cortical networks for auditory processing,
resulting a superior ability for imagery of music in trained
musicians.
2.5. Effects of musical training on the development of auditory
cortical-evoked fields in young children
Although differences between musicians and nonmusicians are
usually interpreted as evidence for plastic reorganization of the
brain, and correlations with years of training or age of onset of
training as well as training-specific effects such as the reported
timbre-specific responses provide further support for this interpretation,
such correlational evidence from cross-sectional studies is
indirectand has been challenged(Monaghanet al., 1998). For example,
musicians might be more likely to start and pursue a musical
career, because they have better auditory or other relevant skills
already before the training due to genetic factors or because they
were more strongly exposed to music or encouraged in their practice
due to socio-economical factors. Therefore, longitudinal studies
and training studies are of high importance to provide conclusive
causal evidence for training-induced effects.
In this vein, we addressed more directly the effects of musical
training on neuronal correlates of musical processing. In a longitudinal
study following Suzuki violin students over the first period of
one year, we observed a stronger increase in the neuronal responses
to violin as compared to noise stimuli, whereas such a differential
effect was not found in a group of children of the same age,
who did not take violin classes (Fujioka et al., 2006). Both groups
were comparable regarding other extra-curricular activities, time
spent on listening to music and parents’ musical background. Auditory
evoked responses to a violin tone A4 (fundamental frequency
of 440 Hz) and a 500 ms noise-burst stimulus were measured by
MEG in four repeated measurements over the one-year period.
The corresponding cortical source strengths showed prominent
bilateral peaks of various transient components of the auditory
evoked field: P100, N250, P320, and N450 peaks (cf. Fig. 4), which
in children has different configuration as compared to adults. Significant
changes in the peak latencies of all components except
P100 were observed over time. Larger P100 and N450 amplitude
as well as rapid change of N250 amplitude and latency was associated
to the violin rather than the noise stimuli. Larger P100
and P320 peak amplitude in the left hemisphere than in the right
2146 C. Pantev, S.C. Herholz / Neuroscience and Biobehavioral Reviews 35 (2011) 2140–2154
Fig. 4. Grand averaged source waveforms at each session with its standard deviation across all sessions in each hemisphere in response to violin and noise stimuli from (left)
Suzuki-music-trained subjects and (right) non-musically trained subjects (from Fujioka et al., 2006).
were consistent with left-lateralized cortical development in this
age group.
A clear musical training effect was expressed in a larger and earlier
N250 peak in the left hemisphere in response to the violin sound
in musically trained children compared to untrained children. This
difference coincided with pronounced change in the waveform
morphology during the time window between 100 and 400 ms,
which was observed in musically trained children in response to
violin stimuli only, whereas in untrained children a similar change
was present regardless of stimulus type. This transition could be
related to establishing a neural network associated with sound categorization
and/or involuntary attention, which can be altered by
music learning experience.
2.6. Effects of musical training on pitch processing in language
Music and language share cortical networks, and therefore it
is plausible that the plastic changes caused by musical training
affect language processing and acquisition as well (Koelsch et al.,
2002a; Maess et al., 2001; Zatorre et al., 2002). One aspect of
language processing that is highly related to music processing
is pitch (please see review by Kraus and Chandrasekaran, 2010,
for a discussion on effects of musical training on other auditory
skills). Pitch cues are important not only in tonal languages such
as Mandarin Chinese, but also in non-tonal languages to indicate
sentence meaning via the pitch contour of the sentence (prosody).
In a study investigating pitch processing of Mandarin pitch contours
in speech stimuli, Wong et al. (2007) showed that musicians
compared to nonmusicians have a more robust and faithful representation
of pitch contours at a neuronal processing stage as early
as the brainstem, as assessed in the frequency following response
(FFR) in EEG. Significant correlations with years of musical training
and age of onset of musical training again indicate a trainingbased
effect. Also, learning success in learning an artificial language
in which words had to be distinguished based on Mandarin-like
pitch contours can be predicted by the volume of left-hemispheric
Heschl’s gyrus, the first cortical stage in which pitch is encoded
(Wong et al., 2008), as well as by the amount of musical training
of the participants (Wong et al., 2009a). Learning success is also
related to bilateral activity in posterior superior temporal lobe during
sound-pattern classification already before language training
started.
C. Pantev, S.C. Herholz / Neuroscience and Biobehavioral Reviews 35 (2011) 2140–2154 2147
This confirms the important role of auditory cortex in the analysis
of pitch cues in language. In a very recent study, Marie et al.
(2010) provided evidence that processing of pitch information in
Mandarin words is enhanced in musicians compared to nonmusicians,
and that this is related to event-related potentials in auditory
cortex. Musicians and nonmusicians who did not speak Mandarin
had to discriminate sequences of Mandarin words that differed in
terms of their tonal or segmental (vowel or consonant, respectively)
information. Musicians performed better than nonmusicians in
both conditions, and also showed an increased and earlier N2/N3
component to tonal variations that reflects stimulus discrimination,
as well as an earlier and enhanced P3b (categorization and decision
processes; also modulated by task difficulty and confidence
in response) to both variations. Importantly, the N3a component
that reflects attention was similar in both groups, and therefore
the group differences are likely not caused by different attention.
Another line of research indicates that musicians not only have
enhanced processing for foreign language sounds, but also for pitch
variations in their own language (Besson et al., 2007). A study on
French speakers showed that musicians can better perceive subtle
pitch incongruities in short melodies and in spoken sentences,
where the fundamental frequency (F0) that corresponds to the perceived
pitch was altered in the last tone or word (Schon et al., 2004).
The superior detection performance for these weak incongruities
was reflected in a larger positivity to weak compared to congruous
endings, that was observed only in musicians.
Furthermore, in two longitudinal studies with kids, the effect of
musical training was compared to the effect of a painting training.
Importantly, children were comparable regarding intelligence and
socio-economic status, and were randomly assigned to the trainings.
In the first study that has been conducted in France, music
and painting training of 8 weeks duration did not lead to clear
differences regarding pitch processing in language between the
groups behaviorally, but there was a reduction of late positivity
to strong pitch incongruities in speech stimuli in the music group
(Moreno and Besson, 2006). In a second study that has been conducted
in Portugal and that involved music and painting training
of 15 months duration, children in the music group, but not in
the painting group, showed better detection of pitch incongruities
in music and language, associated with enhanced N300 and P600
to subtle pitch incongruities (Moreno et al., 2009). As both trainings
were comparable regarding the amount of time that children
spent on the respective activity, and the motivation and joy they
showed in the course of the training, these factors can be excluded
as explanations for the observed effects. Another recent study by
Oechslin et al. (2010b) reported somewhat contradictory results, as
they found a deactivation in auditory areas in response to speech
prosody in musicians compared to nonmusicians. This observation
was interpreted by the authors as a sign of more efficient processing
associated with higher proficiency in processing of auditory information.
Different methodology, trainings, age groups and stimuli
might lead to activity changes in different parts of auditory cortex.
Further studies will have to reveal how interactions between
different auditory areas during speech processing are modulated
by musical training and how this changes in the course of brain
development.
2.7. Effects of passive exposure to music
There is now evidence that even passive exposure to music
in everyday life shapes the way we perceive and process music.
Not only are even nonmusicians able to process complex musical
syntax in chord progressions (Koelsch et al., 2000), but studies by
Wong and colleagues also showed that implicit knowledge about
musical scales and meters is specific to the listener’s own musical
culture (Wong et al., 2009a,b). Bimusical subjects, that is, persons
who are familiar with two different musical cultures (Indian and
Western style) show similar cognitive (recognition) and affective
(tension judgements) responses to Indian and Western music
excerpts, whereas monomusical subjects showed differential patterns,
favoring the musical style that they were familiar with.
Passive exposure to music (in some cases during many years)
may be sufficient for some level of musicality and plastic neuronal
changes, but studies showing differences between musicians and
nonmusicians seem to suggest that more fine-grained auditory discrimination
skills can only be acquired through active training. In
the following sections we will discuss studies that investigated the
short-term effects of different types of training on auditory processing,
from purely auditory to instrumental training.
3. Short-term cortical plasticity due to musical training
3.1. Plasticity of the human auditory cortex induced by auditory
training
Most studies of musically induced plasticity of the auditory cortex
have compared musicians with years of extensive training to
non-musicians. However, the differences between musicians and
non-musicians described above may, however, not only be the
result of life-long training. Becoming a musician may also be related
to innately driven musical talent, socio-economic factors or different
learning skills (Monaghan et al., 1998). Therefore, training
musically naive subjects in a laboratory environment and comparing
different kinds of training is a method that is even better suited
to directly evaluate the effects of training. Therefore, the question
arises whether it is possible to induce plastic changes through
specific and relatively short-term training. In a first study we investigated
this question by having adults learn in the laboratory to
discriminate ambiguous “virtual” melodies (Schulte et al., 2002). A
short melody of eight harmonic complex tones with missing fundamental
frequencies was composed. The perceived pitch of those
complex tones corresponded to the beginning of the tune “Frère
Jacques” (virtual melody) whereas the harmonics were chosen so
that the spectral melody had an inverse contour to the virtual one.
Perception of the virtual melody involves auditory binding of the
harmonics into one auditory Gestalt.
Ten subjects who were initially only able to perceive the spectral
pitch melody were investigated in this experiment. They were
intensively trained over the course of several days until they gained
the ability to perceive the virtual pitch melody, and we were able to
investigate the involvement of plastic re-organizational processes
in virtual pitch formation and perception in the auditory cortex
(Fig. 5).
Auditory evoked fields were recorded over both hemispheres
before and after training in a passive listening paradigm by means
of MEG. In all 10 subjects, the training resulted in a sudden switch
from the spectral to the virtual mode of pitch perception. The clear
change in perception was accompanied by a distinct increase of
the transient gamma band response that has been found to be
associated with integrative cognitive functions, such as the binding
process during object recognition, and by a medial shift of the
underlying sources. Also, independent component analysis indicated
higher synchronization of the cortical networks involved in
the generation of the evoked gamma band activity after achieving
the ability to perceive the virtual melody. In summary, the question
of learning-induced plasticity in the perception of the virtual
pitch of complex tones was directly addressed in this study. The
enhancement of the auditory evoked responses can be interpreted
either by higher synchronization, or by enlargement of the involved
cortical networks, or most likely by both. As the latency of the transient
gamma band response is about 30–70 ms after stimulus onset
2148 C. Pantev, S.C. Herholz / Neuroscience and Biobehavioral Reviews 35 (2011) 2140–2154
Fig. 5. (a) Stimulus sequence of the virtual melody. The sequence consists of three different complex tones played in the order 12311231. The fundamental frequencies (f0i)
were chosen as follow: f01: 200 Hz; f02: 225 Hz and f03:250 Hz. The virtual melody follows the tune ‘Frère Jacques’ (shown by the dotted line), whereas the spectral melody
given by the center frequencies of three harmonics, has an inverse pitch contour. (b) Averaged tGBF of about 600 epochs of one representative subject to tone 1 (band pass
filtered: 24–48 Hz). Data are plotted together for the session before (left) and after training (right). (c) Location of the estimated cortical sources of the tGBF integrated into
MRI overlay of one individual subject. Circles denote the source location before training and squares denote the estimated location after training. (d) Estimated tGBF cortical
source strengths for the left and the right hemispheres, cross-averaged over all subjects. Black bars show the values after training and white bars before training. Error bars
indicate the standard error of the mean (from Schulte et al., 2002).
this plastic reorganization of auditory neural networks is likely to
take place at the level of the primary auditory cortex.
Several studies have now investigated the effects of pitch
discrimination training (Jancke et al., 2001; Menning et al., 2000;
Zarate et al., 2010). We showed that a frequency discrimination
training over the course of three weeks led to fast behavioral
improvements that were accompanied by enhanced N1 and
mismatch responses to pitch deviations, thus showing fast plastic
changes in auditory cortex (Menning et al., 2000). However, a
follow-up measurement 3 weeks after the end of the training
revealed that these changes are not lasting, as the amplitudes of
the evoked responses decreased again. In a similar fMRI study two
randomly assigned groups were compared, one of which received
auditory discrimination training over the course of one week
(Jancke et al., 2001). The results showed a differential pattern even
for subjects within the training group. Subjects who improved
in the course of the training and showed improved auditory
acuity also showed decreased activity in auditory areas (planum
temporale and superior temporal sulcus) in fMRI. Subjects who
did not improve in the course of the training and the control group
(no training) did not show different activity before and after one
week. The seemingly contradicting finding of decreased activity
after successful learning was interpreted by the authors as a sign
for more efficient processing within auditory cortex and was also
explained by differences in methodology (fMRI versus MEG/EEG
in most other studies).
The studies reported so far investigated effects of auditory
training on auditory perceptual skills. But does auditory training
also lead to improvements in productive skills? Zarate et al.
(2010) recently addressed this question in a study on the effects
of auditory discrimination training on singing proficiency. Based
on the hypothesis that poor singers might lack the auditory skill
to match their own singing to the required pitches, they trained
poor singers on an auditory discrimination task over the course of
two weeks. However, whereas their auditory discrimination performance
improved markedly, no improvements were seen in their
C. Pantev, S.C. Herholz / Neuroscience and Biobehavioral Reviews 35 (2011) 2140–2154 2149
Fig. 6. (a) Tone sequences for the standard and deviant stimuli that were used in the MEG measurements before and after training. (b) Musical score of the I–IV–V–I chord
progression in c-major in broken chords that was used as training sequence for SA and A training. (c) Visual templates for the SA training for each broken chord of the training
sequence. Numbers represent the fingers (thumb, 1; index finger, 2; etc.) with which the subjects were supposed to press the corresponding piano keys. On each template,
the image of the piano keyboard was depicted and the finger placement was marked. For each chord, the notes were to be played in ascending order first, and then descending
again (compare score in b) (from Lappe et al., 2008).
vocal proficiency–participants still sang poorly after the training as
assessed by a pitch singing and a melody singing task. Also, activity
in the cortical network for audio-vocal integration during singing
as measured with fMRI did not change before and after training.
Thus, it can be concluded that (at least in a short timeframe of two
weeks) mere auditory training is not sufficient, but that active vocal
training is required to improve vocal accuracy and to change the
underlying cortical networks.
As an interim conclusion, short-term auditory training leads to
plasticity within auditory cortex regarding the task that is trained,
but it might not be sufficient for other auditory functions, and the
gained skills are not easily transferable to other tasks. As already
described in the introduction, making music is all about multimodal
integration of different sensory modalities and motor functions.
Therefore, in a recent study we asked the question if it is this multimodal
integration during active instrumental practice that makes
musical training so effective for cortical plasticity.
3.2. Cortical plasticity induced by short-term uni-modal and
multi-modal musical training
The interaction, as well as the integration among different sensory
modalities is especially important when playing a musical
instrument. Sensory modalities interact, functionally reorganize,
and contribute to new qualities of perception that convey information
not inherent in each single modality and therefore the strong
effects of musical training on cortical reorganization might be due
to this multimodal nature of the training. Specifically, we hypothesized
that sensorimotor-auditory training in the context of piano
playing leads to greater plasticity in the human auditory cortex
compared with a mere auditory training. We investigated this using
a paradigm that was similar to the melody MMN study by Fujioka
et al. (2004) that was described above.
Twenty-three non-musicians with no formal musical training,
except for their compulsory school lessons, participated in the
study (Lappe et al., 2008). These subjects were assigned randomly
to either the sensorimotor-auditory or to the auditory experimental
group. The auditory MMN responses from all participants were
measured before and after training. Training-induced plasticity
was evaluated by comparing the MMN differences and the performance
in an auditory melody discrimination test before and
after training between the sensorimotor-auditory and auditory
groups.
For the MEG measurements before and after training, we used a
three- and a six-tone piano sequence (Fig. 6a). Deviant sequences
differed regarding the last tone of the sequence. Whereas the threetone
sequence was included in the sequence that was subsequently
trained, the three-tone sequence was not. During a two-week training,
the sensorimotor-auditory group learned to play the I–IV–V–I
chord progression (Fig. 6b) in broken chords using both hands from
a visual template that was easy to read for musical novices (Fig. 6c).
In contrast, subjects in the auditory group merely listened to
all of the training sessions of one randomly assigned subject from
the sensorimotor-auditory group and the training sessions were
scheduled in the same way. In order to ensure that the subjects
of the auditory group also participated actively in the experiment
and listened carefully they had to judge the correctness of the heard
sequences.
As expected, auditory discrimination of short melodies
improved more strongly in the group that received the piano training
compared to the listening group, as assessed by the behavioral
test and by the melody MMN to deviant melodies in the MEG. The
group averages of the MMN source waveforms before and after
training for both groups are displayed in Fig. 7. As can be seen from
the waveforms, and as confirmed by the statistical analysis, the
training effect, as seen in the increase from pre- to post-training
sessions, was much larger in the sensorimotor-auditory group than
in the auditory group. In line with a dominant role of the right
hemisphere in pitch processing, the overall training effect was
stronger in the right hemisphere. Interestingly, this was especially
true for the longer six-tone sequence, although this sequence was
not exactly contained in the training stimulus, indicating that the
generalization of the training effect to other stimuli is especially
right lateralized.
Most importantly, we showed that multimodal sensorimotorauditory
training in non-musicians results in greater plastic
changes in auditory cortex than auditory-only training. Since MMN
is primarily generated in auditory cortex (Picton et al., 2000),
the results indicate strong effects of sensory-motor practice on
auditory representations. In the present study, we manipulated
experience in a well-controlled laboratory setting, group assignment
was on a random basis, and auditory input was identical
for both groups. Therefore, this study enables us to conclude that
instrumental training involving both the sensorimotor and the
auditory system leads to stronger functional changes in auditory
cortical areas than mere auditory training. Certainly sensorimotor
2150 C. Pantev, S.C. Herholz / Neuroscience and Biobehavioral Reviews 35 (2011) 2140–2154
Fig. 7. Group averages of the source waveforms obtained after performing sourcespace
projection before and after training for both groups, stimulus conditions, and
hemispheres. Data for the three-tone sequences are shown in the top four panels
and data for the six-tone sequences in the bottom four panels. Within each set of
four panels, SA group data are shown in the top row, and A group data are shown in
the bottom row. Data from the left hemisphere (LH) are presented on the left and
those of the right hemisphere (RH) on the right. Thin lines indicate pretraining (pre)
data and thick lines posttraining (post) data (from Lappe et al., 2008).
training is more demanding and motivating, causing more attentional
resources to be spent on the perception of the tones. Thus,
the increased value of attention during the sensorimotor-auditory
training is a further factor leading to the increased neural activity
in the auditory system.
Further research will have to address the questions of how far
such short-term instrumental training generalizes on other auditory
and higher cognitive abilities. Also, the respective effects of
motivation, attention and the multimodality of the training should
be further investigated. All the training studies so far had very different
durations and training intensities, from long-term training
over the course of many months to short-term training of as little
as a week. In order to better evaluate the effectiveness of different
types of training, more research on the optimal duration of training
and on the long-term effects will be needed.
Plastic changes of the auditory cortex that have been observed
in healthy kids and adults hold a lot of potential for therapeutic
applications. We will now turn to one very recent example on how
plastic reorganization of auditory cortex can help recovery from
auditory disorders, specifically tinnitus.
4. Tinnitus: the dark side of the auditory cortex plasticity
Tinnitus is one of the most prevalent symptoms of hearing
disorders in the industrialized countries (Heller, 2003; Lockwood
et al., 2002) that can severely worsen a patient’s quality of life
(Eggermont and Roberts, 2004). The tinnitus perception arises in
auditory cortex, and tinnitus generation and maintenance have
been associated with maladaptive auditory cortex reorganization
(Eggermont, 2006; Eggermont and Roberts, 2004; Saunders, 2007).
Maladaptive cortical plasticity in the motor system has been
shown to be reversible by behavioral training (Elbert and Rockstroh,
2004; Taub et al., 2002). In this study, we developed and evaluated
a customized music training procedure that aims at the reduction
of subjective tinnitus loudness in patients suffering from chronic,
tonal tinnitus (Okamoto et al., 2010). This training was intended to
reverse maladaptive plastic processes in auditory cortex supposedly
contributing to the tinnitus perception. For the training, the
patients selected their favorite music. Then, the frequency spectrum
of the music was modified for each patient individually by
digitally filtering out the frequency band of one octave width centered
at the individual tinnitus frequency (i.e., the frequency that
sounds like the tinnitus). The patients were instructed to listen to
their customized music regularly with pleasure.
The results of this training evaluation study demonstrated that
those patients who had listened to their pleasant customized music
(target group) daily for approximately 1–2 h over the course of
12 months experienced significantly reduced subjective tinnitus
loudness. Moreover, subjective tinnitus annoyance as well as experienced
handicapping by the tinnitus decreased significantly.
In contrast, matched patients who had instead listened to pleasant
placebo music (placebo group) that had moving notch at a
frequencies that did not correspond to the tinnitus frequency did
not experience any significant changes in these variables over time,
and the same holds true for matched patients of the monitoring
group who listened to normal, non-modified music throughout this
time period (monitoring group).
In order to complement and corroborate the subjective change
measurements with neurophysiological data, we additionally measured
tinnitus related evoked neuronal activity change from
primary (ASSR) and non-primary auditory cortical areas (N1) corresponding
to the individual tinnitus frequency by means of MEG
(Fig. 8). After 12 months of training, tinnitus related neuronal
activity was significantly reduced (Fig. 9) in the target group both
regarding the ASSR and N1 responses, but did not significantly
change in the placebo and monitoring groups. Additionally, perceived
change of tinnitus loudness correlated with evoked neuronal
activity change in primary auditory cortex. Patients in whom the
tinnitus became less loud exhibited reduced tinnitus related primary
auditory cortex activity, and patients in whom the loudness
had not changed or had increased exhibited the corresponding
change in tinnitus related primary auditory cortex activity.
The observed reductions in tinnitus loudness, annoyance and
handicapping as well as the reductions of the evoked neural activity
generated in primary and secondary auditory structures appeared
cumulatively, indicating a long-term neuroplastic effect. There is
evidence in humans that tinnitus is associated with a relative
excitatory-inhibitory cortical neural network dysbalance, at the
expense of the inhibitory system (Diesch et al., 2010). This loss of
inhibition may lead to hyperactivity and/or spontaneous hypersynchrony
of a certain cortical neuronal population, which would
eventually contribute to the tinnitus perception (Weisz et al., 2005).
By means of our customized music modification, we intended to
C. Pantev, S.C. Herholz / Neuroscience and Biobehavioral Reviews 35 (2011) 2140–2154 2151
Fig. 8. Examples of measured auditory evoked magnetic fields and estimated underlying sources. Top: Examples of averaged measured auditory evoked steady-state response
(ASSR, originating from primary auditory cortex) and N1 response (originating from non-primary auditory cortex). Bottom: Measured contour maps and estimated dipolar
sources corresponding to the responses shown in the top.
6 12
−60
−40
−20
0
20
40
60
Target
NormalizedChange(%)
ASSR
N1m
6 12
Time (Months)
Placebo
6 12
Monitoring
Fig. 9. Normalized tinnitus-related auditory cortex evoked activity change after 6 and 12 months of treatment (or monitoring) relative to baseline (0) for the three patient
groups (target, placebo, and monitoring). Positive change values reflect increment, negative change values reflect decrement. The bars indicate group averages; each “×”
indicates an individual data point. The error bars denote confidence intervals. ASSR change values are reflected by white bars, N1 change values are reflected by gray bars. The
data were normalized as following: [(ASSR or N1 tinnitus frequency after 6 or 12 months/ASSR or N1 control frequency after 6 or 12 months)/(ASSR or N1 tinnitus frequency
baseline/ASSR or N1 control frequency baseline) − 1] × 100. As indicated by the confidence interval bars, only the changes in the target group were statistically significant
(from Okamoto et al., 2010).
2152 C. Pantev, S.C. Herholz / Neuroscience and Biobehavioral Reviews 35 (2011) 2140–2154
“re-attract” lateral inhibition into these neurons in order to reverse
their maladaptive hyperactivity and/or hypersynchrony. The consequence
of this induced reversion is that the tinnitus related
auditory cortex activity of the target group patients decreased, and
correspondingly their tinnitus became less loud (and therefore less
annoying and less handicapping). The customization of the training
is in several aspects important. One aspect is the customization of
the frequency spectrum of an acoustic stimulus in order to reverse
maladaptive cortical plasticity. Additionally, it is evident that cortical
plasticity benefits from focused attention and enjoyment (Polley
et al., 2006). Therefore, it should be advantageous to motivate the
brain to process the tailored acoustic input as actively and with as
much pleasure as possible, requirements that music, and especially
one’s favorite music, meets perfectly, as it can absorb attention, and
it can elicit positive emotions (Blood and Zatorre, 2001; Salimpoor
et al., 2011).
5. General discussion
Musical training perfectly match the conditions that are necessary
for studying brain plasticity in humans, since musical training
is more complex and multimodal than most other daily life activities
and the musicians perform the training with high attention
and long-lasting commitment. Learning to play a musical instrument
involves training of sensory and motor abilities as well as the
coordination and integration of several modalities (Zatorre et al.,
2007). Musical training has been shown to modulate uni-modal
(Elbert et al., 1998; Pantev et al., 1998) as well as multimodal
(Schulz et al., 2003) cortical processing and enhance neuronal
co-activation of involved cortical structures such as auditory and
motor cortices (Lahav et al., 2007). Investigating long- and shortterm
training effects in the musical domain yields important
information about how the brain is constantly reorganizing itself
when confronted with new demands or special environmental
influences.
The results presented in this overview open perspectives for
future studies, which should also answer open questions that could
not yet be resolved. The fact that short-term training of a complex
auditory function such as the analysis of melodies can be trained in
an experimentally controlled setup is very promising for the study
of training effects also on other complex auditory tasks. Comparisons
of training effects resulting from short-term musical training
and long-term training assessed in similar tasks will allow to differentiate
the effects of laboratory training of a specific function and
the more general effects of long-term training and to shed more
light on the effects of “nature” and “nurture” in musical training.
In the presented studies, the focus of the investigation laid
on neuronal processing within the auditory cortex. However, in
such complex functions like melodic discrimination, imagery, and
higher-order temporal analysis, other brain areas such as frontal
areas play an important role as well, and plastic effects of musical
training on areas other than auditory cortex can be expected. Using
complementary neuroimaging methods like fMRI and multiplesource
analysis methods in MEG will yield important information
not only on activity in other cortical areas involved, but also on
the interactions within the whole network supporting the instrumental
performance. This will enhance our understanding about
the mechanisms, through which the multimodal training exerts its
effects on uni- and cross-modal processing.
The effects of training on cortical plasticity involving music hold
promising prospects for neuronal rehabilitation, as shown in the
last tinnitus study. By means of a multimodal training, auditory or
motor skills impaired by lesions might be recovered more effectively
compared to classic rehabilitation programs, for example for
the recovery of motor function (Altenmuller et al., 2009; Schneider
et al., 2007) or speech (Schlaug et al., 2009) after stroke, but see also
the recent review by Koelsch (2009).
Apart from the fact that multimodal training seems to be more
effective than uni-modal training, making music can be a very
rewarding and positive experience, which might enhance motivation
and cooperation of patients in the training, and consequently
enhance the therapeutic effects.
Acknowledgements
We would like to thank Bernhard Ross, Takako Fujioka, Larry
Roberts, Laurel Trainor, Michael Schulte, Claudia Lappe, Hidehiko
Okamoto and Henning Teismann for the many useful and constructive
discussions; Andreas Wollbrink for technical assistance
and Karin Berning, Hildegard Deitermann, and Ute Trompeter for
data acquisition of the different experiments. This work has been
supported by “Deutsche Forschungsgemeinschaft” Pa 392/10-3, Pa
392/13-1, Pa 392/12-2 and He 6067/1-1.
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