The Journal of Neuroscience, September 3, 2014 • 34(36):11913–11918 • 11913
Systems/Circuits
Music Enrichment Programs Improve the Neural Encoding
of Speech in At-Risk Children
Nina Kraus,1,2,3,4 Jessica Slater,1,2 Elaine C. Thompson,1,2 Jane Hornickel,1,5 Dana L. Strait,1,3 Trent Nicol,1,2
and Travis White-Schwoch1,2
Auditory Neuroscience Laboratory, 2Department of Communication Sciences, 3Neuroscience Program, and 4Departments of Neurobiology and Physiology,
Northwestern University, Evanston, Illinois 60208, and Department of Otolaryngology, Northwestern University, Chicago, Illinois 60611, and 5Data Sense
LLC, Chicago, Illinois 60660
1
Musicians are often reported to have enhanced neurophysiological functions, especially in the auditory system. Musical training is
thought to improve nervous system function by focusing attention on meaningful acoustic cues, and these improvements in auditory
processing cascade to language and cognitive skills. Correlational studies have reported musician enhancements in a variety of populations across the life span. In light of these reports, educators are considering the potential for co-curricular music programs to provide
auditory-cognitive enrichment to children during critical developmental years. To date, however, no studies have evaluated biological
changes following participation in existing, successful music education programs. We used a randomized control design to investigate
whether community music participation induces a tangible change in auditory processing. The community music training was a longstanding and successful program that provides free music instruction to children from underserved backgrounds who stand at high risk
for learning and social problems. Children who completed 2 years of music training had a stronger neurophysiological distinction of stop
consonants, a neural mechanism linked to reading and language skills. One year of training was insufficient to elicit changes in nervous
system function; beyond 1 year, however, greater amounts of instrumental music training were associated with larger gains in neural
processing. We therefore provide the first direct evidence that community music programs enhance the neural processing of speech in
at-risk children, suggesting that active and repeated engagement with sound changes neural function.
Key words: auditory brainstem; community enrichment; development; language; music; neuroplasticity
Introduction
Community music programs provide an exciting model to
offer widespread music training, especially to underserved
children. Whereas private music lessons are prohibitively expensive, community programs bring together groups of children, channeling their creativity and energy away from
damaging alternatives. Reports of programs such as El Sistema
(Caracas, Venezuela) suggest these programs accomplish
more than providing children with an enjoyable activity—
participants stay in school, do well in school, and pursue postsecondary education more frequently than their peers (Majno,
Received May 9, 2014; revised June 17, 2014; accepted July 22, 2014.
Author contributions: N.K. and D.L.S. designed research; J.S., E.C.T., and D.L.S. performed research; J.H., T.N., and
T.W.-S. analyzed data; N.K., J.H., T.N., and T.W.-S. wrote the paper.
This work is supported by the National Association of Music Merchants, the Grammy Foundation, and the Hugh
Knowles Center. We are grateful to S.R. O’Connell, S. Bhatia, J. Thompson, E. Spitzer, E. Skoe, and J. Krizman for their
assistance with the study. We also express our appreciation to Harmony Project founder Margaret Martin, Dr. P.H.,
M.P.H., executive director Myka Miller, and staff Monk Turner, Sara Flores, and Jeremy Drake (www.
harmony-project.org), and the children and their families for their participation.
The authors declare no competing financial interests.
Correspondence should be addressed to Nina Kraus, 2240 Campus Drive, Evanston, IL 60208. E-mail:
nkraus@northwestern.edu.
D. Strait’s present address: Neural Systems Laboratory, Institute for Systems Research, University of Maryland,
College Park, MD 20742.
DOI:10.1523/JNEUROSCI.1881-14.2014
Copyright © 2014 the authors 0270-6474/14/3411913-06$15.00/0
2012). To date, however, few studies have asked whether these
community music programs have a biological impact on the
developing nervous system.
Myriad cross-sectional studies have reported behavioral and
neurophysiological differences between musicians and nonmusicians (Bidelman et al., 2011; Parbery-Clark et al., 2012; Seppänen et al., 2012; for review see Strait and Kraus, 2014); these
“musician effects” are predominantly attributed to trainingrelated plasticity. This interpretation is supported by evidence
from humans and animals that the nervous system has profound
potential for functional reorganization following auditory training, imparting a positive impact on everyday communication
(Recanzone et al., 1993; Blake et al., 2006; Kilgard, 2012; Anderson et al., 2013; Anguera et al., 2013; Heim et al., 2013; Engineer
et al., 2014). It is thought that music training can effect structural
and functional neural changes (i.e., experience-dependent plasticity; Kraus and Chandrasekaran, 2010; Patel, 2011; Herholz and
Zatorre, 2012; Zatorre, 2013) because music engages widely distributed sensory, cognitive, and reward networks in the brain—
the very networks whose integration drives neuroplasticity.
However, only a small number of longitudinal studies have described a direct effect of music training (Fujioka et al., 2006;
Moreno et al., 2009; Johnson et al., 2013; Tierney et al., 2013;
Chobert et al., 2014) and debates persist concerning innate differences between musicians and non-musicians versus a causal role
11914 • J. Neurosci., September 3, 2014 • 34(36):11913–11918
for music training (Corrigall et al., 2013; Zatorre, 2013); although
there is encouraging longitudinal evidence for the potential of music
training to engender improvements in automatic sound processing
in children in this age range (Putkinen et al., 2014).
These music enhancements do not only manifest neurophysiologically: musicianship is associated with a host of cognitive
benefits for listening and learning. These include auditory memory and attention (Koelsch et al., 1999; Strait et al., 2010; Kraus et
al., 2012), general intelligence and executive functions (Schellenberg, 2004; Moreno et al., 2011), understanding speech in noisy
environments (Parbery-Clark et al., 2009b; Zendel and Alain,
2012), language processing (Milovanov et al., 2008), and literacy
skills (reviewed in Tierney and Kraus, 2013). Therefore, largescale community interventions have the potential to instill salient
behavioral benefits in children that can set them up for better
learning in and out of the classroom.
Motivated by cross-sectional studies of music training (Elbert
et al., 1995; Gaser and Schlaug, 2003; Bidelman et al., 2011), and
the overlap of biological mechanisms of speech and music (Patel,
2011, 2010), here we asked whether participation in an established community music program changes auditory neurophysiology. We hypothesized that participation improves the neural
processing of speech syllables. To test this hypothesis, we used a
randomized control design in collaboration with Harmony Project (Los Angeles, CA), a longstanding and successful community
music program that has provided free music instruction to
⬎1000 children from Los Angeles gang-reduction zones. We
measured neural responses to contrastive speech sounds before
and after training, and in light of cross-sectional studies of childhood musical training (Strait et al., 2014), we predicted that music training improves the neural differentiation of speech.
Materials and Methods
Subjects. Forty-four children, aged 80 –112 months (mean 99 months;
8.25 years; 25 girls) at Year 1, participated in a hybrid randomized control
design. All were public-school pupils living in Los Angeles gangreduction zones. Subjects were randomly assigned either to defer their
participation in music lessons for 1 year and then undergo training
(“Group 1,” N ⫽ 18, 1 year of total music) or begin music lessons immediately (“Group 2,” N ⫽ 26, 2 years of total music), all following Harmony Project’s curriculum (see below). Targeted group assignment was
conducted for the last few subjects to ensure that the two groups were
age- and sex-balanced. Thus at Year 2, Group 2 had 1 year of music
training; at Year 3, Group 2 had 2 years of music training and Group 1
had 1 year. At Year 1, groups were matched on age (t(42) ⫽ 1.196, p ⫽
0.239), hearing thresholds (t(42) ⫽ 0.289, p ⫽ 0.774), maternal education
(t(40) ⫽ 0.799, p ⫽ 0.429), IQ (t(41) ⫽ 0.419, p ⫽ 0.677), and proportion
of females and males ( p ⬎ 0.1). All subjects came from Harmony Project’s waitlist, meaning the groups were equally motivated to pursue music training.
Intervention. The musical training followed Harmony Project’s standard
curriculum. All children first attend group introductory musicianship
classes (1 h per session, 2 sessions per week) consisting of instruction in
fundamental skills such as pitch and rhythm identification, performance,
notation, and basic recorder playing. Subjects generally progress to group
instrumental instruction after 6 months or when instruments are available,
depending on instructor judgment of their proficiency in musicianship class
and access to instruments (provided at no cost to subjects). Instrumental and
ensemble training differ as a function of instructor/seat availability programmatically, but comprise ⱖ4 h/week of group instruction. Instruments include strings, woodwinds, and brass winds.
Neurophysiological protocol. At each test session (annually in July of
2011, 2012, and 2013) all subjects received a neurophysiological test
battery consisting of click and speech-evoked auditory brainstem responses administered using Intelligent Hearing System’s SmartEP platform (Intelligent Hearing Systems). The click-evoked response was
Kraus et al. • Neural Plasticity with Community Music
conventionally administered (Hall, 2006) and all children were within
normal limits for response latency. The speech-evoked responses combine neural responses to transients and sustained (frequency following)
features in speech that, together, offer insight into the precision of automatic auditory processing (Skoe and Kraus, 2010). Despite their subcortical origin, these responses reflect short- and long-term influences from
auditory cortical and nonauditory regions, because the brainstem is an
integrative “hub” of auditory processing (Kraus and Nicol, 2014). Two
synthesized, voiced consonant–vowel syllables, [ba] and [ga], differing
only in the onset frequency of the second formant, were delivered to the
right ear via insert earphones at 80 dB SPL. See (Hornickel et al., 2009) for
a complete acoustic description of the syllables. Six thousand presentations of each syllable were presented in alternating polarity at a rate of
4.35/s. Responses were recorded from vertex (Cz) referenced to right
earlobe, digitized at 13.333 kHz, and filtered on-line from 0.05 to 3 kHz.
Responses to the two presentation polarities were averaged separately
and subtracted to enhance the spectral component of the response (Aiken and Picton, 2008).
Cross-phaseogram procedure. A time-frequency cross-phaseogram approach, first described by Skoe et al. (2011), was used to quantify the
difference in response timing between the two evoking consonants. This
technique comprises computing a short-term cross-phase spectrum resulting in a time-frequency matrix of phase differences. With this pair of
stimuli, the response to [ga] phase leads the response to [ba] in a typically
operating auditory system. This is because [ga] has higher frequency
content in the first 50 ms of the syllable; higher frequencies activate more
basal regions of the cochlea initiating an earlier neural volley. When
depicted in graphical form as in Figure 1, the phaseogram’s abscissa is
time, in milliseconds (0 ⫽ stimulus onset), the ordinate is frequency, in
Hertz, and the phase difference in radians is depicted in color. Green
represents no phase difference; warm colors indicate the response to [ga]
leading the response to [ba] and cool colors indicate [ba] leading [ga].
Statistical analysis. The dependent variable was an arithmetic mean of
phase differences in a time-frequency “region of interest” (ROI) defined
as 15– 45 ms poststimulus onset and 0.9 –1.5 kHz (Strait et al., 2014). This
ROI corresponds to the second format frequency over the time of maximal difference between the stimuli. Outlying data (⬎2 SDs from the
group mean) were adjusted to exactly 2 SDs before analysis (Group 2,
N ⫽ 4; Group 1, N ⫽ 2). Repeated-measures analyses of covariance
(RMANCOVA) were computed, with age in months as a covariate. The
repeated factor was test time (Year 1, Year 2, and Year 3) and the
between-groups factor was participant group (Group 2, music training
between all test times; Group 1, music training only between Test 2 and
Test 3). Follow-up RMANCOVAs were conducted for each study group.
Sphericity was confirmed for all within-subjects comparisons (Mauchly’s ps ⬎ 0.750) and post hoc tests were Bonferroni corrected.
Results
We observed a progressive enhancement of neurophysiological
function with community music training when controlling for
age (i.e., development). Children with 2 years of training (Group
2) showed a marked improvement in the neural differentiation of the syllables [ba] and [ga]. Across both groups, more
music training was associated with larger enhancements in
neural function.
We found an improvement in the neurophysiological distinction of contrastive speech sounds in children who participated in
2 years of music lessons, but not those who participated in only 1
year (Group ⫻ Year interaction, F(2,80) ⫽ 3.709, p ⫽ 0.029).
Neurophysiological distinction of the syllables [ba] and [ga] is
displayed in Figure 1 for each group, at each session, in a crossphaseogram format. These figures provide an objective illustration of the timing differences between responses to the two
speech syllables. Both groups evinced a moderate distinction of
the syllables at Year 1, illustrated by the red swatch in a timefrequency bin corresponding to acoustic differences between the
syllables (i.e., in their consonant–vowel transitions in a frequency
Kraus et al. • Neural Plasticity with Community Music
J. Neurosci., September 3, 2014 • 34(36):11913–11918 • 11915
speech sounds. This is the first demonstration of biological changes in auditory
processing following participation in community music programs using a randomized longitudinal design. These changes
were in the neurophysiological distinction
of contrastive speech syllables during passive listening, after active music training
had stopped. This suggests that music
training transferred to non-music listening settings to influence automatic
auditory processing. Importantly, these
improvements were in processes that are
important for everyday communication:
previous investigations have revealed
that, as groups, children who are better
readers and children who hear better in
noise show stronger neural distinctions of
these same syllables (Hornickel et al., 2009;
Skoe et al., 2011; White-Schwoch and
Kraus, 2013). These findings therefore provide support for the efficacy of community
and co-curricular music programs to engender improvements in nervous system
function. These children are from underserved backgrounds and stand at high risk
for academic and social problems; this impoverishment carries concomitant biological insults (Bradley and Corwyn, 2002;
Figure 1. Two years of music training improves the neurophysiological distinction of consonants. Right, Cross-phaseogram Skoe et al., 2013). Our finding reveals the
difference plots for children in Group 2. After 2 years of training (bottom) these children show a stronger neural distinction of potential for neuroplasticity in the impovspeech, illustrated by the large red swatch. Children who first undergo a control year (left) do not show any year-to-year changes erished human brain (Neville et al., 2013),
in neurophysiological distinction. Black boxes represent the region of interest for statistical analysis (see Materials and Methods). paralleling an effect shown in a rat model
(Zhu et al., 2014). Moreover, our finding
has a clear pragmatic implication by
region corresponding to the second format; see Materials and
showing that community music programs may stave off certain
Methods). This distinction is strengthened after 2 years of trainlanguage-based challenges.
ing, illustrated by larger and deeper red contrast at Year 3 in
What mechanisms drive these changes? We propose that the imGroup 2 (within group main effect of year, F(2,48) ⫽ 6.670, p ⫽
provements observed in neurophysiological distinction of speech
0.003). This strengthening occurred following the second year of
sounds were driven by top-down modifications to automatic
music training (Year 2 vs Year 3, p ⫽ 0.010) with overall stronger
auditory processing, with music training directing children’s atdistinction after 2 years (Year 1 vs Year 3, p ⫽ 0.025).
tention to meaningful sounds of their environment. This interIn Group 1 there was no change in neurophysiological distincpretation is consistent with Patel’s OPERA hypothesis (overlap,
tion across the 3 years (within group main effect of year, F(2,32) ⫽
precision, emotion, repetition, and attention; Patel, 2011), which
1.634, p ⫽ 0.211). While there was no overall group difference
stresses the importance of attentional involvement during train(main effect of group, F(1,40) ⫽ 0.559, p ⫽ 0.459), there was a
ing. Patel also identifies the importance of repetition during
trending difference present at the third assessment (F(1,40) ⫽
training; we see a strong role for the prolonged repetition of
3.688, p ⫽ 0.062), with Group 2 having better neural differentimusic practice, because 1 year of training was insufficient to affect
ation than Group 1.
nervous system function. In addition to OPERA, our view is
The group analysis suggested that more music training led to
broadly consistent with other theories of learning that impute a
greater enhancements in neurophysiological function. We theremajor role for directed attention to modulate future automatic
fore asked whether there was a direct relationship between extent
sensory processing (Ahissar and Hochstein, 2004; Kraus and
of music training (i.e., total hours of instrumental music practice
Chandrasekaran, 2010; Green and Bavelier, 2012).
over the 2 years) and extent of neurophysiological improvement.
The neural responses we measured are generated predominantly
Indeed, we found that increasing hours of instrumental training
by auditory midbrain (Warrier et al., 2011). Midbrain plasticity is
predicted larger improvements in neural differentiation (r ⫽
mediated by a large network of descending corticofugal fibers (Bajo
0.481, p ⫽ 0.001; Fig. 2). Together, these results suggest that
et al., 2010) and other projections that cross-innervate midbrain and
community musical training improves neural differentiation of
brainstem nuclei with motor (Molinari et al., 2007), reward (Bajo
speech syllables and that more training leads to larger gains in
and King, 2012), and prefrontal cortices (Raizada and Poldrack,
neurophysiological function.
2007)—the very centers that are actively engaged by music (Kraus
Discussion
and Chandrasekaran, 2010; Chanda and Levitin, 2013; Salimpoor et
We show that 2 years of participation in a community music
al., 2013). These influences converge to make auditory midbrain a
program improves the neurophysiological distinction of similar
hub of cognitive, motor, and sensory processing. We speculate that
11916 • J. Neurosci., September 3, 2014 • 34(36):11913–11918
Kraus et al. • Neural Plasticity with Community Music
top-down attentional and cognitive modulations caused an activity-driven enhancement in midbrain function, which
progressively (i.e., with more training)
drove the changes we observed (Polley et
al., 2006; Hornickel et al., 2009; Bajo et al.,
2010; Kraus and Chandrasekaran, 2010;
Bajo and King, 2012). Uniquely, making
music engages these systems in a positive,
reinforcing, and active manner that offers
neuroplastic potential beyond everyday
listening experiences.
Since music integrates the perception
and production of meaningful sounds in a
communicative context, music training
has the potential to generalize to language
and speech, as has been argued previously
(Kraus and Chandrasekaran, 2010; Patel,
2011). By directing children’s attention to
meaningful acoustic cues in their environments, music training may have facilitated the sound-meaning connections
that drive neural plasticity, observed here
as an improvement in the neural distinction of speech syllables. Converging eviFigure 2. A correlation is observed between hours of music training over the course of the study and change in neurophysiodence from animals and humans suggests
logical distinction, with children undergoing more training having a larger improvement in this distinction when controlling for
that attention to past sounds influences their age. Children from Group 1 (circles) with zero hours of instrumental training did not move beyond group music skills classes
automatic processing of sounds during due to programmatic constraints and student readiness (see Materials and Methods). The zero line across the y-axis represents no
future listening experiences (Krishnan et change in neural distinction after training.
al., 2005; Zhou and Merzenich, 2008;
Threlkeld et al., 2009; Ortiz-Mantilla et
children to make sound-meaning connections that modulated
al., 2010; Sarro and Sanes, 2011; Krizman et al., 2012; Whiteneural function (Fritz et al., 2003; Kraus and Chandrasekaran,
Schwoch et al., 2013), such as the neurophysiological improve2010).
ment observed here.
A number of longitudinal studies have used scientifically develA previous cross-sectional study, using the same neurophysioped training materials based on the principles of perceptual learnological methods, showed that school-aged children with at least
ing elucidated in decades of animal and human studies (Tallal et al.,
3 years of music training had stronger distinctions of these speech
1996; Temple et al., 2003; Moore et al., 2005; Moreno et al., 2009;
syllables than non-musician children—a finding paralleled in
Anderson et al., 2013). These training regimens are carefully depreschool age children and adults (Parbery-Clark et al., 2012; Zuk
signed to be delivered in a short time span in the laboratory or on a
et al., 2013; Kraus and Nicol, 2014; Strait et al., 2014). Here we
computer, and are associated with improvements in perceptual and
show this enhancement with 2 years of training longitudinally,
neurophysiological functions after only a few short weeks of trainsuggesting that the musician enhancement established through
ing; yet training benefits often do not generalize far beyond the traincross-sectional differences is indeed, at least in part, due to music
ing material (Hayes et al., 2003; Song et al., 2012; Anderson et al.,
training, and not innate differences between musicians and non2013; Anderson et al., 2014). However, there have been studies that
musicians. Children who underwent only 1 year of music training
have found biological enhancements in auditory processing followdid not have stronger neural processing of these speech sound
ing participation in informal music activities during early childhood
differences. Neural changes from music training may take longer
(Putkinen et al., 2013).
to emerge than those from other forms of auditory training, such
Here, we show an improvement in auditory processing that
as computerized training programs. However, previous investiemerges
after a 2 year course of music. Neural enhancements that
gations suggest that these neural enhancements from music traingeneralize
to automatic processing of stimuli that were not exing persist for decades after training stops (Skoe and Kraus, 2012;
plicitly trained, such as we show here, may take longer to emerge
White-Schwoch et al., 2013). Therefore, even if these enhancements
than those from focused computer training. We still find merit in
take relatively long to emerge, they may be long lasting.
music training as a mechanism to improve neural function. After
Our finding is also evocative of research on training attenall, music is an inherently fun activity for most people, likely
tional systems using action video games: an interpretation of
providing children emotional satisfaction throughout their trainthis line of research is that video games allow individuals to
ing (Dube and Le Bel, 2003), even if that training continues over
“learn how to learn,” and functional enhancements follow this
several years. That said, it remains an open question whether and
prerequisite (Bavelier et al., 2011; Green and Bavelier, 2012).
how scientifically inspired training regimens may be combined
Here, the first year of music training may have facilitated more
with ecologically valid music programs to provide the most effecactive engagement with sound in a meaningful context to protive improvements in communicative skills. An additional quesmote efficient auditory processing (Strait et al., 2009; Parberytion is what would be seen with other types of enrichment. We
Clark et al., 2009a). During the second year this new mode of
did not have an active control group in this study, meaning some
active listening may have been brought to bear, allowing the
Kraus et al. • Neural Plasticity with Community Music
or all of the training-related enhancements we observed might be
attributed to providing these children with any kind of enrichment as opposed to a per se music effect (Moreno et al., 2009; but
see Anderson et al., 2013). It also bears mentioning that, although
significant, our training effects were relatively small. It will be
important to replicate these findings to strengthen the argument
of the potential for these sorts of community-based interventions. There are also several factors that may contribute to the
amount of music instruction a child received (Fig. 2), including
availability of instruments, if they missed classes (due to illness,
home trouble, etc.), and Harmony faculty’s judgments of their progress in the curriculum. And since Group 1 students started ⬃1 year
later, we cannot rule out interactions with development that may
have biased training benefits toward Group 2 (Bailey and Penhune,
2013). Future work will have to evaluate the intersections of age and
training that dictate final outcomes. However, in cross-sectional
studies of musicianship Strait et al. (2009, 2013) have found that
musician enhancements for timing aspects of neural processing, including the distinction of contrastive speech syllables, are linked to
the extent of music training and not age of onset.
Cross-sectional studies of musicians, on the one hand, and
longitudinal studies of computerized or private music training on
the other hand, offer little concrete evidence for policymakers
and community organizers interested in enacting broad-based
youth programs. By providing objective biological evidence that
music programs improve the neurophysiological processing of
speech sound contrasts, our findings support efforts to expand
community and co-curricular opportunities for at-risk children
during critical developmental years. Future work should follow
children in similar programs to ascertain whether these neurophysiological changes eventually lead to salient behavioral outcomes for learning, listening, and literacy skills, and whether
music training can counteract learning and auditory processing
difficulties in clinical populations. These efforts are especially
important for children from underserved populations, such as
those who participated in the current study. Our findings support
efforts to reintegrate music into public schooling as an important
complement to science, technology, math, and reading instruction (Rabkin and Hedberg, 2011; President’s Committee on the
Arts and the Humanities, 2011). In addition to providing children with a personally satisfying afterschool activity, community
music programs offer the potential to engender biological changes in
neural processes important for everyday communication.
References
Ahissar M, Hochstein S (2004) The reverse hierarchy theory of visual perceptual learning. Trends Cogn Sci 8:457– 464. CrossRef Medline
Aiken SJ, Picton TW (2008) Envelope and spectral frequency-following responses to vowel sounds. Hear Res 245:35– 47. CrossRef Medline
Anderson S, White-Schwoch T, Parbery-Clark A, Kraus N (2013) Reversal
of age-related neural timing delays with training. Proc Natl Acad Sci U S A
110:4357– 4362. CrossRef Medline
Anderson S, White-Schwoch T, Choi HJ, Kraus N (2014) Partial maintenance
of auditory-based cognitive training benefits in older adults. Neuropsychologia, in press.
Anguera JA, Boccanfuso JL, Rintoul J, Al-Hashimi O, Faraji F, Janowich J,
Kong E, Larraburo Y, Rolle C, Johnston E, Gazzaley A (2013) Video
game training enhances cognitive control in older adults. Nature 501:97–
101. CrossRef Medline
Bailey JA, Penhune VB (2013) The relationship between the age of onset of
musical training and rhythm synchronization performance: validation of
sensitive period effects. Front Neurosci 7:227. CrossRef Medline
Bajo VM, King AJ (2012) Cortical modulation of auditory processing in the
midbrain. Front Neural Circuits 6:114. CrossRef Medline
Bajo VM, Nodal FR, Moore DR, King AJ (2010) The descending corticocol-
J. Neurosci., September 3, 2014 • 34(36):11913–11918 • 11917
licular pathway mediates learning-induced auditory plasticity. Nat Neurosci 13:253–260. CrossRef Medline
Bavelier D, Green CS, Han DH, Renshaw PF, Merzenich MM, Gentile DA
(2011) Brains on video games. Nat Rev Neurosci 12:763–768. CrossRef
Medline
Bidelman GM, Gandour JT, Krishnan A (2011) Cross-domain effects of
music and language experience on the representation of pitch in the human auditory brainstem. J Cogn Neurosci 23:425– 434. CrossRef Medline
Blake DT, Heiser MA, Caywood M, Merzenich MM (2006) Experiencedependent adult cortical plasticity requires cognitive association between
sensation and reward. Neuron 52:371–381. CrossRef Medline
Bradley RH, Corwyn RF (2002) Socioeconomic status and child development. Annu Rev Psychol 53:371–399. CrossRef Medline
Chanda ML, Levitin DJ (2013) The neurochemistry of music. Trends Cogn
Sci 17:179 –193. CrossRef Medline
Chobert J, François C, Velay JL, Besson M (2014) Twelve months of active
musical training in 8-to 10-year-old children enhances the preattentive
processing of syllabic duration and voice onset time. Cereb Cortex 24:
956 –967. CrossRef Medline
Corrigall KA, Schellenberg EG, Misura NM (2013) Music training, cognition, and personality. Front Psychol 4:222. CrossRef Medline
Dube L, Le Bel J (2003) The content and structure of laypeople’s concept of
pleasure. Cogn Emot 17:263–295. CrossRef
Elbert T, Pantev C, Wienbruch C, Rockstroh B, Taub E (1995) Increased
cortical representation of the fingers of the left hand in string players.
Science 270:305–307. CrossRef Medline
Engineer CT, Perez CA, Carraway RS, Chang KQ, Roland JL, Kilgard MP
(2014) Speech training alters tone frequency tuning in rat primary auditory cortex. Behav Brain Res 258:166 –178. CrossRef Medline
Fritz J, Shamma S, Elhilali M, Klein D (2003) Rapid task-related plasticity of
spectrotemporal receptive fields in primary auditory cortex. Nat Neurosci
6:1216 –1223. CrossRef Medline
Fujioka T, Ross B, Kakigi R, Pantev C, Trainor LJ (2006) One year of musical
training affects development of auditory cortical-evoked fields in young
children. Brain 129:2593–2608. CrossRef Medline
Gaser C, Schlaug G (2003) Brain structures differ between musicians and
non-musicians. J Neurosci 23:9240 –9245. Medline
Green CS, Bavelier D (2012) Learning, attentional control, and action video
games. Curr Biol 22:R197–R206. CrossRef Medline
Hall III JW (2006) New handbook for auditory evoked responses. Boston:
Pearson.
Hayes EA, Warrier CM, Nicol TG, Zecker SG, Kraus N (2003) Neural plasticity following auditory training in children with learning problems. Clin
Neurophysiol 114:673– 684. CrossRef Medline
Heim S, Keil A, Choudhury N, Thomas Friedman J, Benasich AA (2013)
Early gamma oscillations during rapid auditory processing in children
with a language-learning impairment: changes in neural mass activity
after training. Neuropsychologia 51:990 –1001. CrossRef Medline
Herholz SC, Zatorre RJ (2012) Musical training as a framework for brain
plasticity: behavior, function, and structure. Neuron 76:486 –502.
CrossRef Medline
Hornickel J, Skoe E, Nicol T, Zecker S, Kraus N (2009) Subcortical differentiation of stop consonants relates to reading and speech-in-noise perception. Proc Natl Acad Sci U S A 106:13022–13027. CrossRef Medline
Johnson JK, Louhivuori J, Stewart AL, Tolvanen A, Ross L, Era P (2013)
Quality of life (QOL) of older adult community choral singers in Finland.
Int Psychogeriatr 25:1055–1064. CrossRef Medline
Kilgard MP (2012) Harnessing plasticity to understand learning and treat
disease. Trends Neurosci 35:715–722. CrossRef Medline
Koelsch S, Schröger E, Tervaniemi M (1999) Superior pre-attentive auditory
processing in musicians. Neuroreport 10:1309 –1313. CrossRef Medline
Kraus N, Chandrasekaran B (2010) Music training for the development of
auditory skills. Nat Rev Neurosci 11:599 – 605. CrossRef Medline
Kraus N, Nicol T (2014) The cognitive auditory system. In: Perspectives on
auditory research (Fay R, Popper A, eds), pp 299 –319. Heidleberg: Springer.
Kraus N, Strait DL, Parbery-Clark A (2012) Cognitive factors shape brain
networks for auditory skills: spotlight on auditory working memory. Ann
N Y Acad Sci 1252:100 –107. CrossRef Medline
Krishnan A, Xu Y, Gandour J, Cariani P (2005) Encoding of pitch in the
human brainstem is sensitive to language experience. Brain Res Cogn
Brain Res 25:161–168. CrossRef Medline
Krizman J, Marian V, Shook A, Skoe E, Kraus N (2012) Subcortical encod-
11918 • J. Neurosci., September 3, 2014 • 34(36):11913–11918
ing of sound is enhanced in bilinguals and relates to executive function
advantages. Proc Natl Acad Sci U S A 109:7877–7881. CrossRef Medline
Majno M (2012) From the model of El Sistema in Venezuela to current
applications: learning and integration through collective music education. Ann N Y Acad Sci 1252:56 – 64. CrossRef Medline
Milovanov R, Huotilainen M, Välimäki V, Esquef PA, Tervaniemi M (2008)
Musical aptitude and second language pronunciation skills in schoolaged children: neural and behavioral evidence. Brain Res 1194:81– 89.
CrossRef Medline
Molinari M, Leggio MG, Thaut MH (2007) The cerebellum and neural networks for rhythmic sensorimotor synchronization in the human brain.
Cerebellum 6:18 –23. CrossRef Medline
Moore DR, Rosenberg JF, Coleman JS (2005) Discrimination training of
phonemic contrasts enhances phonological processing in mainstream
school children. Brain Lang 94:72– 85. CrossRef Medline
Moreno S, Marques C, Santos A, Santos M, Castro SL, Besson M (2009) Musical
training influences linguistic abilities in 8-year-old children: more evidence for
brain plasticity. Cereb Cortex 19:712–723. CrossRef Medline
Moreno S, Bialystok E, Barac R, Schellenberg EG, Cepeda NJ, Chau T (2011)
Short-term music training enhances verbal intelligence and executive
function. Psychol Sci 22:1425–1433. CrossRef Medline
Neville HJ, Stevens C, Pakulak E, Bell TA, Fanning J, Klein S, Isbell E (2013)
Family-based training program improves brain function, cognition, and
behavior in lower socioeconomic status preschoolers. Proc Natl Acad Sci
U S A 110:12138 –12143. CrossRef Medline
Ortiz-Mantilla S, Choudhury N, Alvarez B, Benasich AA (2010) Involuntary
switching of attention mediates differences in event-related responses to
complex tones between early and late Spanish–English bilinguals. Brain
Res 1362:78 –92. CrossRef Medline
Parbery-Clark A, Skoe E, Kraus N (2009a) Musical experience limits the
degradative effects of background noise on the neural processing of
sound. J Neurosci 29:14100 –14107. CrossRef Medline
Parbery-Clark A, Skoe E, Lam C, Kraus N (2009b) Musician enhancement
for speech-in-noise. Ear Hear 30:653– 661. CrossRef Medline
Parbery-Clark A, Tierney A, Strait DL, Kraus N (2012) Musicians have finetuned neural distinction of speech syllables. Neuroscience 219:111–119.
CrossRef Medline
Patel AD (2010) Music, language, and the brain. Oxford, UK: Oxford UP.
Patel AD (2011) Why would musical training benefit the neural encoding of
speech? The OPERA hypothesis. Front Psychol 2:142. CrossRef Medline
Polley DB, Steinberg EE, Merzenich MM (2006) Perceptual learning directs
auditory cortical map reorganization through top-down influences.
J Neurosci 26:4970 – 4982. CrossRef Medline
President’s Committee on the Arts and the Humanities (2011) Re-investing
in arts education: sinning America’s future through creative schools.
Washington DC.
Putkinen V, Tervaniemi M, Huotilainen M (2013) Informal musical activities are linked to auditory discrimination and attention in 2–3-year-old
children: an event-related potential study. Eur J Neurosci 37:654 – 661.
CrossRef Medline
Putkinen V, Tervaniemi M, Saarikivi K, Ojala P, Huotilainen M (2014) Enhanced development of auditory change detection in musically trained
school-aged children: a longitudinal event-related potential study. Dev
Sci 17:282–297. CrossRef Medline
Rabkin N, Hedberg EC (2011) Arts education in America: what the declines mean
for arts participation. Washington DC: National Endowment for the Arts.
Raizada RD, Poldrack RA (2007) Challenge-driven attention: interacting frontal and brainstem systems. Front Hum Neurosci 1:3. CrossRef Medline
Recanzone GH, Schreiner CE, Merzenich MM (1993) Plasticity in the frequency representation of primary auditory cortex following discrimination training in adult owl monkeys. J Neurosci 13:87–103. Medline
Salimpoor VN, van den Bosch I, Kovacevic N, McIntosh AR, Dagher A,
Zatorre RJ (2013) Interactions between the nucleus accumbens and auditory cortices predict music reward value. Science 340:216 –219.
CrossRef Medline
Sarro EC, Sanes DH (2011) The cost and benefit of juvenile training on
adult perceptual skill. J Neurosci 31:5383–5391. CrossRef Medline
Schellenberg EG (2004) Music lessons enhance IQ. Psychol Sci 15:511–514.
CrossRef Medline
Seppänen M, Hämäläinen J, Pesonen AK, Tervaniemi M (2012) Music
Kraus et al. • Neural Plasticity with Community Music
training enhances rapid neural plasticity of N1 and P2 source activation
for unattended sounds. Front Hum Neurosci 6:43. CrossRef Medline
Skoe E, Kraus N (2010) Auditory brain stem response to complex sounds: a
tutorial. Ear Hear 31:302–324. CrossRef Medline
Skoe E, Kraus N (2012) A little goes a long way: how the adult brain is
shaped by musical training in childhood. J Neurosci 32:11507–11510.
CrossRef Medline
Skoe E, Nicol T, Kraus N (2011) Cross-phaseogram: objective neural index
of speech sound differentiation. J Neurosci Methods 196:308 –317.
CrossRef Medline
Skoe E, Krizman J, Kraus N (2013) The impoverished brain: disparities in
maternal education affect the neural response to sound. J Neurosci 33:
17221–17231. CrossRef Medline
Song JH, Skoe E, Banai K, Kraus N (2012) Training to improve hearing
speech in noise: biological mechanisms. Cereb Cortex 22:1180 –1190.
CrossRef Medline
Strait DL, Kraus N (2014) Biological impact of auditory expertise across the
life span: musicians as a model of auditory learning. Hear Res 308:109 –
121. CrossRef Medline
Strait DL, Kraus N, Skoe E, Ashley R (2009) Musical experience and neural
efficiency– effects of training on subcortical processing of vocal expressions of emotion. Eur J Neurosci 29:661– 668. CrossRef Medline
Strait DL, Kraus N, Parbery-Clark A, Ashley R (2010) Musical experience
shapes top-down auditory mechanisms: evidence from masking and auditory attention performance. Hear Res 261:22–29. CrossRef Medline
Strait DL, O’Connell S, Parbery-Clark A, Kraus N (2013) Musicians’ enhanced neural differentiation of speech sounds arises early in life: developmental evidence from ages three to thirty. Cereb Cortex. Advance
online publication. Retrieved April 18, 2013. doi: 10.1093/cercor/bht103.
CrossRef
Tallal P, Miller SL, Bedi G, Byma G, Wang X, Nagarajan SS, Schreiner C,
Jenkins WM, Merzenich MM (1996) Language comprehension in
language-learning impaired children improved with acoustically modified speech. Science 271:81– 84. CrossRef Medline
Temple E, Deutsch GK, Poldrack RA, Miller SL, Tallal P, Merzenich MM,
Gabrieli JD (2003) Neural deficits in children with dyslexia ameliorated
by behavioral remediation: evidence from functional MRI. Proc Natl
Acad Sci U S A 100:2860 –2865. CrossRef Medline
Threlkeld SW, Hill CA, Rosen GD, Fitch RH (2009) Early acoustic discrimination experience ameliorates auditory processing deficits in male rats
with cortical developmental disruption. Int J Dev Neurosci 27:321–328.
CrossRef Medline
Tierney A, Kraus N (2013) Music training for the development of reading
skills. Prog Brain Res 207:209 –241. CrossRef Medline
Tierney A, Krizman J, Skoe E, Johnston K, Kraus N (2013) High school music
classes enhance the neural processing of speech. Front Psychol 4:855.
CrossRef Medline
Warrier CM, Abrams DA, Nicol TG, Kraus N (2011) Inferior colliculus contributions to phase encoding of stop consonants in an animal model. Hear
Res 282:108 –118. CrossRef Medline
White-Schwoch T, Kraus N (2013) Physiologic discrimination of stop consonants relates to phonological skills in pre-readers: a biomarker for subsequent reading ability? Front Hum Neurosci 7:899. CrossRef Medline
White-Schwoch T, Carr KW, Anderson S, Strait DL, Kraus N (2013) Older adults
benefit from music training early in life: biological evidence for long-term
training-driven plasticity. J Neurosci 33:17667–17674. CrossRef Medline
Zatorre RJ (2013) Predispositions and plasticity in music and speech learning: neural correlates and implications. Science 342:585–589. CrossRef
Medline
Zendel BR, Alain C (2012) Musicians experience less age-related decline in central auditory processing. Psychol Aging 27:410 – 417. CrossRef Medline
Zhou X, Merzenich MM (2008) Enduring effects of early structured noise
exposure on temporal modulation in the primary auditory cortex. Proc
Natl Acad Sci U S A 105:4423– 4428. CrossRef Medline
Zhu X, Wang F, Hu H, Sun X, Kilgard MP, Merzenich MM, Zhou X (2014)
Environmental acoustic enrichment promotes recovery from developmentally degraded auditory cortical processing. J Neurosci 34:5406 –
5415. CrossRef Medline
Zuk J, Ozernov-Palchik O, Kim H, Lakshminarayanan K, Gabrieli JD, Tallal
P, Gaab N (2013) Enhanced syllable discrimination thresholds in musicians. PLoS One 8:e80546. CrossRef Medline