ORIGINAL RESEARCH ARTICLE
published: 16 December 2014
doi: 10.3389/fpsyg.2014.01403
Engagement in community music classes sparks
neuroplasticity and language development in children from
disadvantaged backgrounds
Nina Kraus1,2 *, Jane Hornickel1,3 , Dana L. Strait1 , Jessica Slater1 and Elaine Thompson1
1
Auditory Neuroscience Laboratory, Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL, USA
Department of Otolaryngology, Neurobiology & Physiology and Northwestern University Interdepartmental Neuroscience Program, Northwestern University,
Chicago, IL, USA
3
Data Sense LLC, Chicago, IL, USA
2
Edited by:
Mireille Besson, Centre National de la
Recherche Scientifique – Institut de
Neurosciences Cognitives de la
Méditerranée, France
Reviewed by:
Lutz Jäncke, University of Zurich,
Switzerland
Johannes Ziegler, Université
Aix-Marseille, France
Sumitava Mukherjee, Indian Institute
of Management, India
*Correspondence:
Nina Kraus, Auditory Neuroscience
Laboratory, Department of
Communication Sciences and
Disorders, Northwestern University,
2240 Campus Drive, Evanston,
IL 60208, USA
e-mail: nkraus@northwestern.edu
Children from disadvantaged backgrounds often face impoverished auditory environments,
such as greater exposure to ambient noise and fewer opportunities to participate in
complex language interactions during development. These circumstances increase their
risk for academic failure and dropout. Given the academic and neural benefits associated
with musicianship, music training may be one method for providing auditory enrichment
to children from disadvantaged backgrounds. We followed a group of primary-school
students from gang reduction zones in Los Angeles, CA, USA for 2 years as they
participated in Harmony Project. By providing free community music instruction for
disadvantaged children, Harmony Project promotes the healthy development of children
as learners, the development of children as ambassadors of peace and understanding,
and the development of stronger communities. Children who were more engaged in the
music program—as defined by better attendance and classroom participation—developed
stronger brain encoding of speech after 2 years than their less-engaged peers in the
program. Additionally, children who were more engaged in the program showed increases
in reading scores, while those less engaged did not show improvements. The neural gains
accompanying music engagement were seen in the very measures of neural speech
processing that are weaker in children from disadvantaged backgrounds. Our results
suggest that community music programs such as Harmony Project provide a form of
auditory enrichment that counteracts some of the biological adversities of growing up in
poverty, and can further support community-based interventions aimed at improving child
health and wellness.
Keywords: low socioeconomic status/poverty, community music training, electrophysiology, reading, speech,
auditory training
INTRODUCTION
Over 16 million children in the US live in families with incomes
below the federal poverty level, with a disproportionate number (68%) being members of minority racial and ethnic groups
(Jiang et al., 2014). Parental income, occupation, and education are commonly combined to define a child’s socio-economic
status [SES (Sirin, 2005)]. A child’s SES can interact with
other personal factors (race, ethnicity, gender, etc.) to lead
to weaker academic achievement (Bradley and Corwyn, 2002;
Sirin, 2005; Hernandez, 2011; Skoe et al., 2013) and lower high
school graduation rates (Ensminger and Slusarcick, 1992; Bradley
and Corwyn, 2002), with lower academic achievement persisting even for low SES students who attend college (Walpole,
2003).
Auditory processing skills, known to be important for language development (Benasich et al., 2002, 2008; Boets et al., 2011;
Goswami et al., 2011), may contribute to the link between SES
and academic achievement. Children from low SES backgrounds
have greater daily exposure to noise (Adler and Newman, 2002;
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Evans and Kantrowitz, 2002) and tend to be less concerned with
using hearing protection in excessively noisy contexts like concerts
(Vogel et al., 2007). Chronic noise exposure in children has been
linked to weaker reading proficiency and cognitive skills (Maxwell
and Evans, 2000; Haines et al., 2001; Clark et al., 2005) and can lead
to delayed auditory neural development and greater spontaneous
neural activity in animals (Chang and Merzenich, 2003; Seki and
Eggermont, 2003; Zhu et al., 2014). Additionally, children from
low SES backgrounds hear less complex language and fewer words
overall during early language development, which contributes to
weaker vocabularies when entering school (Bradley and Corwyn,
2002; Hoff, 2003; Cartmill et al., 2013). Similarly, a host of neural systems important for language, memory, and cognition are
impacted by SES background (Raizada et al., 2008; Hackman et al.,
2010; Noble et al., 2012), including those implicated in auditory
attention (D’Angiulli et al., 2008; Stevens et al., 2009) and speech
encoding (Skoe et al., 2013). Encouragingly though, community
and home based interventions may reverse these effects; children
from low SES families participating in Head Start and attention
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Kraus et al.
training with their parents showed improved language, cognition,
and neural measures of auditory attention relative to children in
Head Start programs alone (Neville et al., 2013).
Musical training is another avenue of enrichment that may
counteract some of the auditory deprivation endemic to low
SES environments. A number of studies have revealed that children undergoing music training have stronger cognitive abilities,
vocabulary, rhythm perception and production (linked to reading
skill), perception of vocal pitch, and perception of speech in noisy
backgrounds than non-musician children (Ho et al., 2003; Schellenberg, 2004, 2006; Magne et al., 2006; Forgeard et al., 2008b;
Hyde et al., 2009; Moreno et al., 2009, 2011; Strait et al., 2011; Dege
and Schwarzer, 2012; Strait et al., 2012; Slater et al., 2013; Chobert
et al., 2014; Seither-Preisler et al., 2014; Strait and Kraus, 2014).
Additionally, musical practice can strengthen children’s auditory encoding of speech (Magne et al., 2006; Besson et al., 2007;
Chobert et al., 2011; Strait et al., 2011, 2013; Tierney et al., 2013;
Chobert et al., 2014; see Strait and Kraus, 2014 for a review), auditory discrimination and attention (Koelsch et al., 2003; Moreno
et al., 2009; Chobert et al., 2011; Putkinen et al., 2013), and lead
to structural changes in auditory cortical areas (Hyde et al., 2009;
Seither-Preisler et al., 2014). The auditory benefits of music training have direct implications for language skills and academic
achievement (Hetland and Winner, 2010; Corrigall and Trainor,
2011; see Tierney and Kraus, 2013 for a review); accordingly, music
may serve as an effective training tool for children with learning and attention impairments (Overy, 2003; Bhide et al., 2013;
Seither-Preisler et al., 2014).
Community music programs, such as El Sistema and Harmony Project, provide students from low SES backgrounds with
music opportunities that enrich the students and their communities. El Sistema, founded almost 40 years ago, provides over
500,000 Venezuelan children with free musical training in their
community (for a review of the program, see Majno, 2012).
Children are commonly enrolled as young as 2 or 3 years old
and are supported through their teen years. Graduates of the
program commonly return to teach at their community music
center and parents are continually educated by the organization
on how to support and encourage their child’s music as they
advance. Since 2009, 62 El Sistema inspired music programs have
been started in the United States. Harmony Project (Los Angeles, CA, USA) similarly promotes the development of healthy
children and communities by providing free music training to children from low SES backgrounds in gang-reduction zones of Los
Angeles, CA, USA (www.harmony-project.org). Children learn
basic music skills in a preparatory class, eventually receive a free
instrument to participate in group classes, and have opportunities for performance and ensemble playing throughout their
enrollment from early grade school to high school. Between 2010
and 2014, 93% of Harmony Project alumni enrolled in postsecondary education, versus 67.6% of students graduating from
public schools within Los Angeles County [most recent data from
California Department of Education: Educational Demographics
Unit (2008–2009) and Harmony Project Whole Notes 2013–2014
(2014)].
Our laboratory has shown that participation in music training
through Harmony Project can reinforce literacy skills, enhance
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Musical engagement in disadvantaged youth
the perception of speech in background noise, and strengthen
the neural encoding of speech sounds in children from low SES
backgrounds (Kraus et al., 2014a,b; Slater et al., 2014a,b; Kraus
and Strait, in press). Here we explore how the extent of student engagement in instrumental classes mediates music training’s
effects on neural speech processing. Although we do not employ
an active control group, our aim is to show within a group undergoing musical training how engagement may influence the benefits
seen for students. Music engagement was defined by a student’s
percent attendance in class and teacher ratings of classroom participation. We investigated whether greater engagement in music
instruction can positively impact the neural encoding of speech.
We focused on measures of subcortical brain activity reflecting
speech harmonics, the consistency of the response, and spontaneous neural activity, all of which are weaker in teens from
low SES backgrounds relative to higher-SES peers (Skoe et al.,
2013).
MATERIALS AND METHODS
PARTICIPANTS
Twenty-six children participated in the study (13 males, ages
6 years 11 months – 9 years 3 months; M = 8 years 5 months). All
children were public elementary school students living in the gang
reduction zones of Los Angeles, CA, USA. Participants attended
schools where ≥90% of students qualify for free or reduced lunch.
Families qualify for free or reduced lunch when their income is
below 185% of the federal poverty level (U.S. Department of Education, 2014). The average education of the participants’ mothers
was 10.7 years (SD = 4.2) and the median and modal maternal
educational attainment level was completion of high school/GED.
Maternal education is one of the strongest predictors of SES (Hoff
et al., 2012), and 76% of children whose parents have a high school
degree or less live in low-income families (Jiang et al., 2014). Average maternal education was equivalent across the four Harmony
Project sites in which students were enrolled (Kruskal Wallis Test:
χ2 = 6.252, p = 0.100).
Participants were excluded if they had a history of neurological
disorders, IQ less than 80 [Wechsler Abbreviated Scale of Intelligence (Woerner and Overstreet, 1999)], previous musical training,
or failed a hearing screening (air-conduction thresholds above
20 dB HL for octaves 125–8000 Hz). All procedures were approved
by the Northwestern University Institutional Review Board and
informed parental consent was obtained for all participants.
MUSIC INSTRUCTION
Students were followed as they participated in Harmony Project for
2 years. The project curriculum started students in music appreciation class, where they learned pitch-matching and rhythm skills,
musical styles and notation, and basic vocal performance and
recorder playing. Participants attended these classes twice weekly
for 3–10 months (M = 5).
As musical instruments became available and students were
judged to be ready, they progressed to instrumental instruction.
Students were given their own instruments and participated in a
mix of group-based instrumental classes for approximately 4 h
per week. Students in this study participated in one of four Harmony Project sites. The description of weekly classes at each site
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Musical engagement in disadvantaged youth
and the number of students at each site is presented in Table 1.
Students played a number of different instruments (viola, cello,
bass, French horn, clarinet, or trumpet) and the number of students playing each type of instrument is also reported in Table 1.
Harmony Project has a standard curriculum for each instrument
type (woodwinds, brass, and strings), including rigorous mastery benchmarks required for advancement to the next level of
the curriculum. For example, Level 1 benchmarks require students to: demonstrate basic knowledge of music concepts such
as rhythm and intonation, exhibit instrument specific skills and
knowledge such as proper posture and correct clef identification, and present evidence of commitment through practice at
home. As students progress, expectations shift to more instrument specific skills (e.g., proper bowing or breathing techniques)
and knowledge and use of dynamics, key signatures and deviations, and articulations (e.g., staccato). Students are required to
learn to read music and play from memory throughout the program. All students in the study transitioned to instrument use
during their first year, so at the end of their second year in Harmony Project, students had an average of 165 h of school-based
instrumental training (SD = 40) and over 210 h of music training
overall.
Teacher ratings of music engagement
At the end of each instrumental class, teachers reported the percent attendance for each student (hours attended/total hours
possible) and rated the students on their level of participation
in class (1-not at all, 2-little, 3-moderate, 4-fairly good, 5complete). We averaged percent attendance and teacher ratings
of class participation across all the instrumental classes taken by
each child over the 2 years. Both attendance and teacher perception of student effort predict ‘behavioral engagement’ in school,
reflecting a student’s involvement in the participatory aspects of
school (Glanville and Wildhagen, 2007). Because neither percent
attendance nor participation were significantly correlated with
the number of instrumental classes taken (p-values > 0.310), we
are confident that higher levels of class participation or attendance reflect student motivation and are not simply artifacts of
being enrolled in more classes. Additionally, percent attendance
and participation ratings did not differ across Harmony Project
sites (Kruskal Wallis Test: χ2 = 5.366, p = 0.147; χ2 = 4.781,
p = 0.189, respectively), suggesting no rating bias among the
sites.
READING ASSESSMENT
Before enrolling in Harmony Project and after 2 years of Harmony participation, students were administered the Test of Oral
Word Reading Efficiency (TOWRE; Torgesen et al., 1999). The
TOWRE, a measure of reading fluency, comprises word and nonword reading subtests. Children are required to read a list aloud
as quickly as possible and the number of words (or non-words)
read correctly in 45 s is tallied and combined to form the Total
score.
NEUROPHYSIOLOGICAL MEASURES
Evoked potentials were collected from the auditory brainstem in response to a 40-ms synthesized speech syllable [da],
using an Intelligent Hearing Systems SmartEP system equipped
with a cABR module (Miami, FL, USA). Stimuli were presented to the right ear at 80 dB SPL at a rate of 10.9 Hz
through electromagnetically-shielded earphones (ER-3A, Etymotic Research, Elk Grove Village, IL, USA). Responses to
opposing polarities were subtracted and filtered online from 0.1 to
1.5 kHz in the test session before music training began and from
0.05 to 3 kHz in the test session after 2 years of music training.
Due to the differences in filter settings, which were expanded as
the longitudinal project evolved, within-subject comparisons over
the two test dates are not possible.
Neural measures were those previously shown to index SES
(Skoe et al., 2013). Measures of response consistency, speech
harmonics, and spontaneous neural activity were generated in
MATLAB (Mathworks, Natick, MA, USA). Response consistency reflects the replicability of the response over the first
half and second half of the recording on a scale from −1
to 1. Values were Fisher-transformed for analyses, but Pearson’s r-values are shown in Figures 1 and 2. Speech harmonics comprise the average amplitude of the frequency following response over the first-formant range of the stimulus
Table 1 | Student instrument experience by site and by instrument.
Harmony Project site
Typical class participation
Number of students by instrument type
Alexandria elementary school
−1 h instrumental class twice per week
4 (bass)
−2 h string ensemble rehearsal weekly
Beyond the bell
−2 h ensemble rehearsals twice per week
12 (2 clarinet, 3 flute, 7 trumpet)
(includes pull-out sectional rehearsals)
EXPO enter (YOLA)
−1 h instrumental class each week
5 (2 cello, 1 French horn, 1 viola, 1 trumpet)
−3 h ensemble rehearsal weekly
Hollywood
−1 h instrumental class twice per week
5 (trumpet)
−3 h ensemble rehearsal (concert band) weekly
Participants were enrolled in music classes in one of four sites and played one of seven different instruments. Details about the standard class schedule of each site
is included.
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Kraus et al.
(264–656 Hz). Spontaneous neural activity reflects the amplitude of brainstem activity in the absence of the stimulus, and
likely reflects neural noise. Please see Banai et al. (2009), Hornickel and Kraus (2013), and Skoe et al. (2013) for additional
details.
Musical engagement in disadvantaged youth
STATISTICAL ANALYSES
direct measure of effect size, reflecting the proportion of variance in the dependent variable (neural measures) accounted
for by the independent variable (music engagement variables).
Correlation coefficients with magnitudes of 0.1 are considered small effect sizes, 0.3 medium, and 0.5 large (Cohen,
1992). Reading fluency scores were compared using a paired
t-test.
The relationships among instrumental class attendance, class
participation, and neural measures were evaluated at each
of the two test dates using Spearman’s correlations due to
the non-normal distributions of the music engagement variables. Spearman’s correlations are more conservative than Pearson’s correlations because they reduce the impact of outlying data points and are most appropriate for smaller sample
sizes. Similar to Pearson’s correlations, Spearman’s rho is a
RESULTS
Children who had better attendance in instrumental music classes
over 2 years had stronger neural encoding of speech harmonics and better response consistency, two measures previously
linked to SES (see Figure 1; Table 2A). Additionally, there was
a positive association between class participation and response
consistency (see Figure 2 for two representative participants
FIGURE 1 | Children who regularly attended instrumental classes
had stronger neural encoding of speech after 2 years, particularly
for measures of speech harmonics and response consistency.
Neural measures before music training began did not predict
attendance or level of class participation, suggesting greater
engagement in music classes may lead to stronger neural encoding
of speech and not vice versa. Speech Harmonics is measured as
the average amplitude of harmonic encoding in the frequency
following response (µV), Response Consistency is measured as the
Pearson’s correlation coefficient between response replications (r), and
Spontaneous Neural Activity is measured as the root-mean-square
magnitude of the pre-stimulus (µV).
FIGURE 2 | Children who were most engaged during instrument classes
had more consistent neural responses to speech after 2 years.
(A) (red/maroon), a representative subject who had “complete” participation
as rated by multiple teachers. The [da] stimulus is plotted in black in top panel
of A for reference, shifted in time to account for neural delay. (B) (black/gray),
a representative subject who had “moderate” participation as rated by
multiple teachers. Both students completed four instrumental classes over
the course of 2 years. Before music training, (top) the participants do not
differ greatly in the consistency of their neural response to speech. After
2 years of music training (bottom), however, the child who participated more
in class has a more consistent response to speech than the child who
participated less. The two traces in each panel represent the two replications
of the response, collected as the recording procedure dictates. The time
region of the analysis is marked with vertical hashed lines in each panel.
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Musical engagement in disadvantaged youth
and Table 2A). Interestingly, the least engaged participants
in the study still had “moderate” class participation, suggesting that even small variations in student engagement can have
implications for success with music training. No relationship
was found between music engagement and spontaneous neural
activity.
Importantly, our results suggest that greater engagement in
music classes predicts stronger speech encoding, not vice versa.
Neural measures before music training did not predict subsequent attendance or class participation (p-values > 0.126, see
Table 2B), which suggests that participants with strong speech
encoding to begin with did not necessarily go on to be the most
engaged students in the study. Additionally, the small variation in
years of maternal education did not predict attendance or class
participation (ρ = −0.302, p = 0.142; ρ = 0.126, p = 0.548).
Participants overall showed a subclinical yet statisticallysignificant decline in reading fluency over the 2 years
[M1(SD1) = 109.27(12.76), M2(SD2) = 106.04(15.22),
t 25 = 2.456, p = 0.021]. However, change in reading fluency
scores was strongly correlated with engagement in instrumental
classes. Children who participated more in class were more likely
to show an improvement in reading fluency, while those who participated less were more likely to show a decrease in reading fluency
(ρ = 0.443, p = 0.023).
DISCUSSION
Here we found that greater engagement in group music instruction
by children from low SES backgrounds predicted stronger neural
encoding of speech for measures negatively influenced by low SES.
Children who attended class more regularly and had better classroom participation had stronger neural encoding of speech after
2 years of music training than did their less-engaged peers. These
results were found within a group of children undergoing music
training, and without an active control group. It is likely that
greater engagement in other extracurricular activities could also
yield stronger neural outcomes. Perhaps the novel experience of
Table 2 | (A) Children who are more engaged in music classes have
stronger neural encoding of speech after music training for measures
previously linked to SES (speech harmonics and response consistency).
(B) Neural measures before beginning musical training do not predict
subsequent engagement in music classes.
Music engagement
Percent attendance
Class participation
A
Speech harmonics
Response consistency
Spontaneous neural activity
0.385 (0.052)
0.242 (0.235)
0.391 (0.048)
0.389 (0.049)
−0.201 (0.326)
0.013 (0.949)
B
Speech harmonics
Response consistency
Spontaneous neural activity
−0.150 (0.464)
0.119 (0.562)
0.280 (0.166)
0.138 (0.502)
−0.308 (0.126)
−0.212 (0.299)
Values are Spearman’s rho (p-value). Significant relationships are bolded.
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participating in an extracurricular activity, interacting with the
researchers, etc., influenced our findings. However, previous studies that have employed active control groups indicate that music
training but not art training strengthens auditory skills (Moreno
et al., 2009, 2011). Moreover, child musicians differ from nonmusician children on auditory working memory and attention
but not visual analogs (Strait et al., 2014). Similar to our analyses within a group of children participating in music, Hyde et al.
(2009) found that the quality of musical training is critical for neuroplasticity. Children participating in private keyboard lessons for
15 months showed structural growth in primary auditory cortex,
correlated with their improvement in melody and rhythm perception tasks; however, a second group of children who participated in
school music classes each week learning basic singing and drumming skills did not show the same neural growth (Hyde et al.,
2009). Our results suggest that engagement is an important factor
mediating the benefits seen from musical training. It’s important
to note that we found these relationships between engagement
and neural function within a group of students whose attendance
was relatively high on average (88%), who were rated as having
‘moderate’ or better class participation, and whose families were
highly motivated for them to participate. The limited variance in
music engagement and probable influence of other factors such as
personality on student engagement may have contributed to the
modest statistical significance of our tests. Nevertheless, we do see
moderately strong relationships between engagement and neural
measures, suggesting that even if well-motivated to begin music
training, students may not make gains unless they are actively
engaged in the process. In the same vein, previous studies of musicians have revealed the important role of continued practice in
maintaining the benefits of musicianship (Pantev et al., 1998; Ho
et al., 2003; Norton et al., 2005; Forgeard et al., 2008b; see Strait
and Kraus, 2014 for a review).
Greater attendance and class participation were not predicted
by neural measures before musical training. This suggests that children with the strongest neural function before beginning musical
practice did not go on to be those most engaged in music classes.
Instead, it appears that greater engagement in music may have
resulted in stronger neural encoding. Cohort research revealing
neural differences between musicians and non-musicians is unable
to definitively say whether those differences were pre-existing,
although the oft observed relationship between length of practice and benefits of musicianship supports music experience as
the cause (Pantev et al., 1998; Ho et al., 2003; Norton et al., 2005;
Forgeard et al., 2008b; Seither-Preisler et al., 2014; see Strait and
Kraus, 2014 for a review). A host of recent longitudinal studies
report that musical training can selectively enhance auditory function in children without pre-existing differences. When children
are randomly assigned to music or art training, only those in musical training show enhancements in auditory neurophysiology and
attention (Moreno et al., 2009, 2011; Chobert et al., 2014). Additionally, children who elect to participate in music do not have
inherent differences in neural structure or function from their
peers (Norton et al., 2005; Hyde et al., 2009; Tierney et al., 2013),
but show enhancements in auditory system structure and function
after 1–2 years of musical training (Hyde et al., 2009; Tierney et al.,
2013; Kraus et al., 2014b). We were not able to conduct paired
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Kraus et al.
pre-post comparisons due to alterations to our recording scheme
as the study progressed. However, the lack of relationship between
pre-test neural measures and subsequent classroom engagement
in our study suggests our results are independent of pre-existing
differences in neural function.
Although some studies report no differences in cognition for
children who elect to participate in music versus those who do
not (Schlaug et al., 2005; Hyde et al., 2009), personality and preexisting skill may nevertheless influence a student’s engagement
and persistence in music. For example, using duration of music
lessons as the outcome variable, Corrigall et al. (2013) showed
that cognitive skills (along with parental income) predicted how
long students continued with music. They additionally showed
that children exhibiting high Openness to experience (part of
the Big Five personality traits) were more likely to continue with
music lessons after taking into account cognition and parental
income (Corrigall et al., 2013). In our group of students, personality factors may have influenced attendance and participation in
music classes. Those who were highly engaged in music classes
may have been highly engaged throughout their school day, had
a more encouraging home environment, may have been more
motivated individuals, etc. Indeed, Openness as a personality trait
is linked to factors reflecting academic engagement and achievement in college students, such as enjoying thinking and analyzing,
enjoying connecting with others in class, and enjoying working
hard (Komarraju and Karau, 2005). One limitation of our study
is the lack of measures to investigate which of these personality
factors may predict engagement in music. Importantly though,
duration of musical experience also predicts cognition and language skills (Schellenberg, 2006; Forgeard et al., 2008b), suggesting
the presence of a reinforcing loop. Children more open to new
experiences may elect to begin music lessons and continue to participate longer, which leads to larger cognitive benefits, predicting
continued retention in music, etc.
In addition to enhancing cognitive skills, music training can
lead to stronger reading, language, and academic skills in children
(Schellenberg, 2004, 2006; Hetland and Winner, 2010; Corrigall
and Trainor, 2011; Dege and Schwarzer, 2012; Francois et al.,
2013; Seither-Preisler et al., 2014; Slater et al., 2014a; see Tierney and Kraus, 2013 for a review), including those with reading
impairments (Overy, 2003; Bhide et al., 2013). As a group our participants showed a small decline in reading fluency scores, which
likely reflects a trend for the achievement gap to widen for children
from low SES backgrounds as they age (Sirin, 2005). However, the
change in reading scores varied with music engagement; reading
fluency improved in children with greater class participation. That
this relationship was seen despite the small variance in class participation ratings speaks to the importance of active engagement
in music for engendering benefits. Students who had the highest
class participation rankings were more likely to show an increase in
reading score after 2 years of participation in Harmony Project. On
the other hand, participants who had ‘moderate’ class participation on average were likely to show a decrease in reading score. As
the consistency of the auditory brainstem response to speech and
the strength of representations of the speech harmonics have been
repeatedly linked to reading ability (Banai et al., 2009; Hornickel
et al., 2011, 2012a; Hornickel and Kraus, 2013; White-Schwoch and
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Musical engagement in disadvantaged youth
Kraus, 2013), and musical aptitude can predict reading ability (Ho
et al., 2003; Forgeard et al., 2008a; Huss et al., 2011; Strait et al.,
2011), it is possible that stronger speech encoding arising from
greater participation in music classes contributed to improved
reading scores.
Animal models of auditory deprivation and enrichment yield
insight into the potential mechanisms involved in the neuroplasticity observed here. Similar to humans exposed to chronic
noise, animals reared in noisy environments show detrimental
effects on auditory processing, such as broader neural frequency
selectivity (Zhou et al., 2011; Zhu et al., 2014), greater spontaneous neural activity (Seki and Eggermont, 2003), and weaker
neural speech processing (Reed et al., 2014). Additionally, rats
with a knock-down of a dyslexia-linked gene show more variable cortical responses to speech and weaker neural differentiation
of speech (Centanni et al., 2014a). These deficits are thought to
be due to imbalances in excitatory and inhibitory neurotransmitters, also shown to be negatively affected by noise exposure
during development (Guo et al., 2012; Zhu et al., 2014). Auditory enrichment and behavioral training can reverse these effects,
yielding better neural frequency selectivity, more consistent neural responses to speech, better neural differentiation of speech
sounds, and can re-establish excitatory/inhibitory balances in
rats with early auditory deprivation (Guo et al., 2012; Centanni
et al., 2014b; Zhu et al., 2014). Although these exact mechanisms have not been investigated in humans, it is very likely
that similar ones are at play. Like the auditory enrichment
paradigms in animal studies, music training is a social, multisensory process that activates multiple neural networks during
repeated exposure to the enriching “environment” [please see
Patel (2011) for a review of the multi-faceted nature of music
training].
We failed to find significant relationships between spontaneous neural activity and musical engagement. Previous studies of
auditory training in humans, such as music training, computerbased training games, foreign language learning, and classroom
acoustic modification, have shown improvements in the evoked
response to speech, but none have reported changes in spontaneous neural activity (Besson et al., 2007; Song et al., 2008;
Stevens et al., 2008; Russo et al., 2010; Hornickel et al., 2012b;
Strait et al., 2013). Animals that are subjected to noise during
development have increased spontaneous neural activity, likely
due to decreased neural inhibition as a result of abnormal
excitatory/inhibitory neurotransmitter ratios (Seki and Eggermont, 2003; Zhu et al., 2014). However, auditory enrichment
can alleviate imbalances in excitatory and inhibitory neurotransmitters (Guo et al., 2012; Zhu et al., 2014) and possibly
reduce noise-induced spontaneous neural activity. Spontaneous
cortical activity thought to reflect a functional “resting-state network” can be altered with training in humans (Lewis et al.,
2009; Taubert et al., 2011). The present findings show that the
extent of music engagement tracks with changes in stimulusevoked, but not spontaneous background activity; trainingrelated influences on spontaneous activity should be explored
further.
Our results support the importance of active experience and
meaningful engagement with sound to engender neural changes.
December 2014 | Volume 5 | Article 1403 | 6
Kraus et al.
Even in a group of highly motivated students, small variations
in music engagement (attendance and class participation) predicted the strength of speech encoding after music training.
These measures of neural encoding are known to be weaker in
children from low SES backgrounds. Although personality features likely played a large role in students’ levels of engagement
in class, our results do support that greater levels of engagement are not prompted by pre-existing differences in neural
encoding. In our group of participants from low SES backgrounds, those who were the most engaged in music classes
had the strongest neural responses and were most likely to
show improvements in reading ability. Community music programs such as Harmony Project and El Sistema have proven
success in bolstering academic achievement in children from
disadvantaged backgrounds. We have also shown that participation in Harmony Project reinforces literacy skills and enhances
the neural encoding of speech cues important for reading and
the perception of speech in noisy backgrounds (Kraus et al.,
2014a,b; Slater et al., 2014a; Kraus and Strait, in press). Taken
together, we suggest that motivated engagement in community
music programs may counteract some of the auditory impoverishment that children from low SES backgrounds commonly
experience.
AUTHOR CONTRIBUTIONS
Nina Kraus and Dana L. Strait designed the study; Dana L.
Strait, Jessica Slater, and Elaine Thompson collected the data; Jane
Hornickel analyzed the data; Jane Hornickel and Nina Kraus prepared the manuscript; all authors contributed to the final version
of the manuscript.
ACKNOWLEDGMENTS
We thank Samantha R. O’Connell, Shivani Bhatia, Jason V.
Thompson, Emily Spitzer, Erika Skoe, Jen Krizman, Travis WhiteSchwoch, and Trent Nicol for their contributions throughout the
project. We are grateful to Harmony Project (www.harmonyproject.org) administrators and staff for facilitating this research,
especially Harmony Project founder Dr. Margaret Martin, Executive Director Myka Miller, Monk Turner, Sara Flores, and Jeremy
Drake. We thank the children and their families for participating.
This work was supported by the National Association of Music
Merchants, the GRAMMY Foundation, and the Knowles Hearing
Center of Northwestern University.
REFERENCES
Adler, N. E., and Newman, K. (2002). Socioeconomic disparities in health: pathways
and policies. Health Aff. 21, 60–76. doi: 10.1377/hlthaff.21.2.60
Banai, K., Hornickel, J., Skoe, E., Nicol, T., Zecker, S. G., and Kraus, N. (2009).
Reading and subcortical auditory function. Cereb. Cortex 19, 2699–2707. doi:
10.1093/cercor/bhp024
Benasich, A. A., Gou, Z., Choudhury, N., and Harris, K. (2008). Early cognitive and
language skills are linked to resting frontal gamma power across the first 3 years.
Behav. Brain Res. 195, 215–222. doi: 10.1016/j.bbr.2008.08.049
Benasich, A. A., Thomas, J. J., Choudhury, N., and Leppanen, P. H. T. (2002). The
importance of rapid auditory processing abilities to early language development:
evidence from converging methodologies. Dev. Psychobiol. 40, 278–292. doi:
10.1002/dev.10032
Besson, M., Schon, D., Moreno, S., Santos, A., and Magne, C. (2007). Influence of
musical expertise and musical training on pitch processing in music and language.
Restor. Neurol. Neurosci. 25, 399–410.
www.frontiersin.org
Musical engagement in disadvantaged youth
Bhide, A., Power, A., and Goswami, U. (2013). A rhythmic musical intervention
for poor readers: a comparison of efficacy with a letter-based intervention. Mind
Brain Educ. 7, 113–123. doi: 10.1111/mbe.12016
Boets, B., Vandermosten, M., Poelmans, H., Luts, H., Wouters, J., and Ghesquiere,
P. (2011). Preschool impairments in auditory processing and speech perception
uniquely predict future reading problems. Res. Dev. Disabil. 32, 560–570. doi:
10.1016/j.ridd.2010.12.020
Bradley, R. H., and Corwyn, R. F. (2002). Socioeconomic status and child development. Annu. Rev. Psychol. 53, 371–399. doi: 10.1146/annurev.psych.53.100901.
135233
California Department of Education: Educational Demographics Unit. (2008–
2009). High School Graduates’ College Enrollment. Available at: http://dq.cde.ca.
gov/dataquest/sfsf/PostsecondaryIndicatorC11.aspx?cChoice=C11Cnty&cYear=
2008-09&TheCounty = 19%2CLOSÂNGELES&cLevel=County&cTopic=C11&
myTimeFrame=S&submit1=Submit
Cartmill, E. A., Amstrong, B. F. I., Gleitman, L. R., Goldin-Meadow, S., Medina,
T. N., and Trueswell, J. C. (2013). Quality of early parent input predicts child
vocabulary 3 years later. Proc. Natl. Acad. Sci. U.S.A. 110, 11278–11283. doi:
10.1073/pnas.1309518110
Centanni, T. M., Booker, A. M., Sloan, A. M., Chen, F., Maher, B. J., Carraway, R.
S., et al. (2014a). Knockdown of the dyslexia-associated gene Kiaa0319 impairs
temporal responses to speech stimuli in rat primary auditory cortex. Cereb. Cortex
24, 1753–1766. doi: 10.1093/cercor/bht028
Centanni, T. M., Chen, F., Booker, A. M., Engineer, C., Sloan, A. M., Rennaker, R.
L., et al. (2014b). Speech sound processing deficits and training-induced neural
plasticity in rats with a dyslexia gene knockdown. PLoS ONE 9:e98439. doi:
10.1371/journal.pone.0098439
Chang, E., and Merzenich, M. M. (2003). Environmental noise retards
auditory cortical development. Science 300, 498–502. doi: 10.1126/science.
1082163
Chobert, J., Francois, C., Velay, J.-L., and Besson, M. (2014). Twelve months of active
musical training in 8-to10-year-old children enhances the preattentive processing of syllabic duration and voice onset time. Cereb. Cortex 24, 956–967. doi:
10.1093/cercor/bhs377
Chobert, J., Marie, C., Francois, C., Schon, D., and Besson, M. (2011). Enhanced
passive and active processing of syllables in musician children. J. Cogn. Neurosci.
23, 3874–3887. doi: 10.1162/jocn_a_00088
Clark, C., Martin, R., Van Kempen, E., Alfred, T., Head, J., Davies, H. W., et al.
(2005). Exposure-effect relations between aircraft and road traffic noise exposure at school and reading comprehension. Am. J. Epidemiol. 163, 27–37. doi:
10.1093/aje/kwj001
Cohen, J. (1992). A power primer. Q. Methods Psychol. 112, 155–159.
Corrigall, K. A., Schellenberg, E. G., and Misura, N. M. (2013). Music training,
cognition, and personality. Front. Psychol. 4:222. doi: 10.3389/fpsyg.2013.00222
Corrigall, K. A., and Trainor, L. J. (2011). Associations between length of
music training and reading skills in children. Music Percept. 29, 147–155. doi:
10.1525/mp.2011.29.2.147
D’Angiulli, A., Herdman, A., Stapells, D., and Hertzman, C. (2008). Children’s event-related potentials of auditory selective attention vary with their
socioeconomic status. Neuropsychology 22, 293–300. doi: 10.1037/0894-4105.
22.3.293
Dege, F., and Schwarzer, G. (2012). The effect of a music program on phonological
awareness in preschoolers. Front. Psychol. 2:124. doi: 10.3389/fpsyg.2011.00124
Ensminger, M. E., and Slusarcick, A. L. (1992). Paths to high school graduation or
dropout: a longitudinal study of a first-grade cohort. Sociol. Educ. 65, 95–113.
doi: 10.2307/2112677
Evans, G. W., and Kantrowitz, E. (2002). Socioeconomic status and health: the
potential role of environmental risk exposure. Annu. Rev. Public Health 23, 303–
331. doi: 10.1146/annurev.publhealth.23.112001.112349
Forgeard, M., Schlaug, G., Norton, A., Rosam, C., and Iyengar, U. (2008a). The
relation betwen music and phonological processing in normal-reading children
and children with dyslexia. Music Percept. 25, 383–390. doi: 10.1525/mp.2008.
25.4.383
Forgeard, M., Winner, E., Norton, A., and Schlaug, G. (2008b). Practicing a musical
instrument in childhood is associated with enhanced verbal ability and nonverbal
reasoning. PLoS ONE 3:e3566. doi: 10.1371/journal.pone.0003566
Francois, C., Chobert, J., Besson, M., and Schon, D. (2013). Music training for
the development of speech segmentation. Cereb. Cortex 23, 2038–2043. doi:
10.1093/cercor/bhs180
December 2014 | Volume 5 | Article 1403 | 7
Kraus et al.
Glanville, J. L., and Wildhagen, T. (2007). The measurement of school engagement:
assessing dimensionality and measurement invariance across race and ethnicity.
J. Psychol. Meas. 67, 1019–1041. doi: 10.1177/0013164406299126
Goswami, U., Wang, H. L., Cruz, A., Fosker, T., Mead, N., and Huss, M. (2011).
Language-universal sensory deficits in developmental dyslexia: English, Spanish,
and Chinese. J. Cogn. Neurosci. 23, 325–327. doi: 10.1162/jocn.2010.21453
Guo, F., Zhang, Y., Zhu, X., Cai, R., Zhou, X., and Sun, X. (2012). Auditory
discrimination training rescues developmentally degraded directional selectivity and restores mature expression of GABAA and AMPA receptor subunits
in rat auditory cortex. Behav. Brain Res. 229, 301–307. doi: 10.1016/j.bbr.
2011.12.041
Hackman, D. A., Farah, M. J., and Meaney, M. J. (2010). Socioeconomic status
and the brain: mechanistic insights from human and animal research. Nat. Rev.
Neurosci. 11, 651–659. doi: 10.1038/nrn2897
Haines, M. M., Stansfeld, S. A., Job, R. F. S., and Head, J. (2001). Chronic aircraft
noise exposure, stress responses, mental health and cognitive performance in
school children. Psychol. Med. 31, 265–277. doi: 10.1017/S0033291701003282
Harmony Project Whole Notes 2013–2014. (2014). Available at: http://www.harmony
-project.org/about-us/harmony-project-current-newsletter/
Hernandez, D. J. (2011). Double Jeopardy: How Third-Grade Reading Skills and
Povery Influence High School Graduation. Baltimore, MD: The Annie E. Casey
Foundation.
Hetland, L., and Winner, E. (2010). The arts and academic achievement: what the
evidence shows. Arts Educ. Policy Rev. 102, 3–6. doi: 10.1080/10632910109600008
Ho, Y.-C., Cheung, M.-C., and Chan, A. S. (2003). Music training improves verbal
but not visual memory: cross-sectional and longitudinal explorations in children.
Neuropsychology 17, 439–450. doi: 10.1037/0894-4105.17.3.439
Hoff, E. (2003). The specificity of environmental influence: socioeconomic status
affects early vocabulary development via maternal speech. Child Dev. 74, 1368–
1378. doi: 10.1111/1467-8624.00612
Hoff, E., Laursen, B., and Bridges, K. (2012). “Measurement and modeling in
studying the influence of socioeconomic status on child development,” in The
Cambridge Handbook of Environment in Human Development. eds L Mayes and
M Lewis (Cambridge: Cambridge University Press), 590–606.
Hornickel, J., Anderson, S., Skoe, E., Yi, H., and Kraus, N. (2012a). Subcortical
representation of speech fine structure relates to reading ability. Neuroreport 23,
6–9. doi: 10.1097/WNR.0b013e32834d2ffd
Hornickel, J., Zecker, S., Bradlow, A. R., and Kraus, N. (2012b). Assistive listening
devices drive neuroplasticity in children with dyslexia. Proc. Natl. Acad. Sci. U.S.A.
109, 16731–16736. doi: 10.1073/pnas.1206628109
Hornickel, J., Chandrasekaran, B., Zecker, S. G., and Kraus, N. (2011). Auditory brainstem measures predict reading and speech-in-noise perception in
school-aged children. Behav. Brain Res. 216, 597–605. doi: 10.1016/j.bbr.2010.
08.051
Hornickel, J., and Kraus, N. (2013). Unstable representation of sound: a biological
marker of dyslexia. J. Neurosci. 33, 3500–3504. doi: 10.1523/JNEUROSCI.420512.2013
Huss, M., Verney, J. P., Fosker, T., Mead, N., and Goswami, U. (2011).
Music, rhythm, rise time perception and developmental dyslexia: perception
of musical meter predicts reading and phonology. Cortex 47, 674–689. doi:
10.1016/j.cortex.2010.07.010
Hyde, K. L., Lerch, J., Norton, A., Forgeard, M., Winner, E., Evans, A. C., et al. (2009).
Musical training shapes structural brain development. J. Neurosci. 29, 3019–3025.
doi: 10.1523/JNEUROSCI.5118-08.2009
Jiang, Y., Ekono, M., and Skinner, C. (2014). Basic Facts About Low-Income Children:
Children Under 18, 2012. New York, NY: National Center for Children in Poverty.
Available at: www.nccp.org/publications/pub_1047.html
Koelsch, S., Grossmann, T., Gunter, T. C., Hahne, A., Schroger, E., and Friederici,
A. D. (2003). Children processing music: electric brain responses reveal musical competence and gender differences. J. Cogn. Neurosci. 15, 683–693. doi:
10.1162/jocn.2003.15.5.683
Komarraju, M., and Karau, S. J. (2005). The relationship between the big five
personality traits and academic motivation. Pers. Individ. Differ. 39, 557–567.
doi: 10.1016/j.paid.2005.02.013
Kraus, N., Slater, J., Thompson, E., Hornickel, J., Strait, D. L., Nicol, T., et al.
(2014a). Auditory learning through active engagement with sound: biological
impact of community music lessons in at-risk children. Front. Neurosci. 8:351.
doi: 10.3389/fnins.2014.00351
Frontiers in Psychology | Cognitive Science
Musical engagement in disadvantaged youth
Kraus, N., Slater, J., Thompson, E., Hornickel, J., Strait, D. L., Nicol, T., et al. (2014b).
Music enrichment programs improve the neural encoding of speech in at-risk
children. J. Neurosci. 34, 11913–11918. doi: 10.1523/JNEUROSCI.1881-14.2014
Kraus, N., and Strait, D. L. (in press). Emergence of biological markers of
musicianship with school-based music instruction. Ann. N. Y. Acad. Sci.
Lewis, C. M., Baldassarre, A., Committeri, G., Romani, G. L., and Corbetta, M.
(2009). Learning sculpts spontaneous activity of the resting human brain. Proc.
Natl. Acad. Sci. U.S.A. 106, 17558–17563. doi: 10.1073/pnas.0902455106
Magne, C., Schon, D., and Besson, M. (2006). Musician children detect pitch
violations in both music and language better than nonmusician children: behavioral and electrophysiological approaches. J. Cogn. Neurosci. 18, 199–211. doi:
10.1162/jocn.2006.18.2.199
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. doi: 10.1111/j.1749-6632.2012.06498.x
Maxwell, L. E., and Evans, G. W. (2000). The effects of noise on pre-school children’s
pre-reading skills. J. Environ. Psychol. 20, 91–97. doi: 10.1006/jevp.1999.0144
Moreno, S., Bialystok, E., Barac, R., Schellenberg, E. G., Cepeda, N. J., and Chau, T.
(2011). Short-term musical training enhances verbal intelligence and executive
function. Psychol. Sci. 22, 1425–1433. doi: 10.1177/0956797611416999
Moreno, S., Marques, C., Santos, A., Santos, M., Castro, S. L., and Besson,
M. (2009). Musical training influences linguistic abilities in 8-year-old children: more evidence for brain plasticity. Cereb. Cortex 19, 712–723. doi:
10.1093/cercor/bhn120
Neville, H., Stevens, C., Pakulak, E., Bell, T. A., Fanning, J., Klein, S., et al. (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. doi: 10.1073/pnas.1304437110
Noble, K. G., Houston, S. M., Kan, E., and Sowell, E. R. (2012). Neural correlates of
socioeconomic status in the developing human brain. Dev. Sci. 15, 516–527. doi:
10.1111/j.1467-7687.2012.01147.x
Norton, A., Winner, E., Cronin, K., Overy, K., Lee, D. J., and Schlaug, G. (2005). Are
there pre-existing neural, cognitive, or motoric markers for musical ability? Brain
Cogn. 59, 124–134. doi: 10.1016/j.bandc.2005.05.009
Overy, K. (2003). Dyslexia and music: from timing deficits to musical intervention.
Ann. N. Y. Acad. Sci. 999, 497–505. doi: 10.1196/annals.1284.060
Pantev, C., Oostenveld, R., Engelien, A., Ross, B., Roberts, L. E., and Hoke, M. (1998).
Increased auditory cortical representation in musicians. Nature 392, 811–814. doi:
10.1038/33918
Patel, A. D. (2011). Why would musical training benefit the neural encoding of speech? The OPERA hypothesis. Front. Psychol. 2:142. doi:
10.3389/fpsyg.2011.00142
Putkinen, V., Tervaniemi, M., and Huotilainen, M. (2013). Informal musical
activities are linked to auditory discrimination and attenction in 2-3 year old
children: an event-related potential study. Eur. J. Neurosci. 37, 654–661. doi:
10.1111/ejn.12049
Raizada, R. D. S., Richards, T. L., Meltzoff, A., and Kuhl, P. K. (2008). Socioeconomic status predicts hemispheric specialisation of the left inferior frontal gyrus
in young children. Neuroimage 40, 1392–1401. doi: 10.1016/j.neuroimage.2008.
01.021
Reed, A. C., Centanni, T. M., Borland, M. S., Matney, C. J., Engineer, C., and
Kilgard, M. P. (2014). Behavioral and neural discrimination of speech sounds
after moderate or intense noise exposure in rats. Ear Hear. 35:e248–e261. doi:
10.1097/AUD.0000000000000062
Russo, N. M., Hornickel, J., Nicol, T., Zecker, S., and Kraus, N. (2010). Biological
changes in auditory function following training in children with autism spectrum
disorders. Behav. Brain Funct. 6, 60. doi: 10.1186/1744-9081-6-60
Schellenberg, E. G. (2004). Music lessons enhance IQ. Psychol. Sci. 15, 511–514. doi:
10.1111/j.0956-7976.2004.00711.x
Schellenberg, E. G. (2006). Long-term positive associations between music
lessons and IQ. J. Educ. Psychol. 98, 457–468. doi: 10.1037/0022-0663.98.
2.457
Schlaug, G., Norton, A., Overy, K., and Winner, E. (2005). Effects of music training
on the child’s brain and cognitive development. Ann. N. Y. Acad. Sci. 1060,
219–230. doi: 10.1196/annals.1360.015
Seither-Preisler, A., Parncutt, R., and Schneider, P. (2014). Size and synchronization
of auditory cortex promotes musical, literacy, and attentional skills in children.
J. Neurosci. 34, 10937–10949. doi: 10.1523/JNEUROSCI.5315-13.2014
December 2014 | Volume 5 | Article 1403 | 8
Kraus et al.
Seki, S., and Eggermont, J. J. (2003). Changes in spontaneous firing rate and neural
synchrony in cat primary auditory cortex after localized tone-induced hearing
loss. Hear. Res. 180, 28–38. doi: 10.1016/S0378-5955(03)00074-1
Sirin, S. R. (2005). Socioeconomic status and academic achievement: a meta-analytic
review of research. Rev. Educ. Res. 75, 417–453. doi: 10.3102/00346543075003417
Skoe, E., Krizman, J., and Kraus, N. (2013). The impoverished brain: disparities in
maternal education affect the neural response to sound. J. Neurosci. 33, 17221–
17231. doi: 10.1523/JNEUROSCI.2102-13.2013
Slater, J., Strait, D. L., Skoe, E., O’connell, S., Thompson, E., and Kraus,
N. (2014a). Longitudinal effects of group music instruction on literacy skills
in low-income children. PLoS ONE. 9:e113383. doi: 10.1371/journal.pone.
0113383
Slater, J., Strait, D. L., Thompson, E., Hornickel, J., and Kraus, N. (2014b). “Longitudinal effects of group music instruction on speech and rhythm processing:
cognitive, perceptual, and neural evidence,” in Neurosciences and Music-V, Dijon.
Slater, J., Tierney, A., and Kraus, N. (2013). At-risk elementary school children with
one year of classroom music instruction are better at keeping a beat. PLoS ONE
8:e77250. doi: 10.1371/journal.pone.0077250
Song, J. H., Skoe, E., Wong, P. C. M., and Kraus, N. (2008). Plasticity in the
adult human auditory brainstem following short-term linguistic training. J. Cogn.
Neurosci. 20, 1892–1902. doi: 10.1162/jocn.2008.20131
Stevens, C., Fanning, J., Coch, D., Sanders, L., and Neville, H. (2008).
Neural mechanisms of selective auditory attention are enhanced by computerized training: electrophysiological evidence from language-impaired and
typically developing children. Brain Res. 1205, 55–69. doi: 10.1016/j.brainres.
2007.10.108
Stevens, C., Lauinger, B., and Neville, H. (2009). Differences in the neural
mechanisms of selective attention in children from different socioeconomic backgrounds: an event-related brain potential study. Dev. Sci. 12, 634–646. doi:
10.1111/j.1467-7687.2009.00807.x
Strait, D. L., Hornickel, J., and Kraus, N. (2011). Subcortical processing of speech
regularities predicts reading and music aptitude in children. Behav. Brain Funct.
7, 44. doi: 10.1186/1744-9081-7-44
Strait, D. L., and Kraus, N. (2014). Biological impact of auditory expertise across
the life span: musicians as a model of auditory learning. Hear. Res. 308, 109–121.
doi: 10.1016/j.heares.2013.08.004
Strait, D. L., O’connell, S., Parbery-Clark, A., and Kraus, N. (2014). Musicians’ enhanced neural differentiation of speech sounds arises early in life:
developmental evidence from ages 3 to 30. Cereb. Cortex 24, 2512–2521. doi:
10.1093/cercor/bht103
Strait, D. L., Parbery-Clark, A., Hittner, E., and Kraus, N. (2012). Musical training
during early childhood enhances the neural encoding of speech in noise. Brain
Lang. 123, 191–201. doi: 10.1016/j.bandl.2012.09.001
Strait, D. L., Parbery-Clark, A., O’connell, S., and Kraus, N. (2013). Biological impact
of preschool music classes on processing speech in noise. Dev. Cogn. Neurosci. 6,
51–60. doi: 10.1016/j.dcn.2013.06.003
Taubert, M., Lohman, G., Margulies, D. S., Villringer, A., and Ragert, P. (2011).
Long-term effects of motor training on resting-state networks and underlying
brain structure. Neuroimage 57, 1492–1498. doi: 10.1016/j.neuroimage.2011.
05.078
www.frontiersin.org
Musical engagement in disadvantaged youth
Tierney, A., and Kraus, N. (2013). “Music training for the development of reading
skills,” in Progress in Brain Research: Changing Brains, Applying Brain Plasticity to
Advance and Recovery Human Ability, eds M. M. Merzenich, M. Nahum, and T.
M. Van Vleet. (Burlington, VT: Academic Press), 209–241.
Tierney, A., Krizman, J., Skoe, E., Johnston, K., and Kraus, N. (2013). High school
music classes enhance the neural processing of speech. Front. Psychol. 4:855. doi:
10.3389/fpsyg.2013.00855
Torgesen, J. K., Wagner, R. K., and Rashotte, C. A. (1999). Test of Word Reading
Efficiency. Austin, TX: Pro-Ed.
U.S. Department of Education (2014). Children and Youth with Disabilities. Available
at: http://nces.ed.gov/programs/coe/indicator_cgg.asp
Vogel, I., Brug, J., Van Der Ploeg, C. P. B., and Raat, H. (2007). Young
people’s exposure to loud music. Am. J. Prev. Med. 33, 124–133. doi:
10.1016/j.amepre.2007.03.016
Walpole, M. (2003). Socioeconomic status and college: how SES affects college
experiences and outcomes. Rev. Higher Educ. 27, 45–73. doi: 10.1353/rhe.
2003.0044
White-Schwoch, T., and Kraus, N. (2013). Physiological discrimination of stop
consonantsrelates to phonological skills in pre-readers: a biomarker forsubsequent reading ability? Front. Hum. Neurosci. 7:899. doi: 10.3389/fnhum.2013.
00899
Woerner, C., and Overstreet, K. (1999). Wechsler Abbreviated Scale of Intelligence
(WASI). San Antonio, TX: The Psychological Corporation.
Zhou, X., Panizzutti, R., De Villers-Sidani, E., Madiera, C., and Merzenich,
M. M. (2011). Natural restoration of critical period plasticity in the juvenile and adult primary auditory cortex. J. Neurosci. 31, 5625–5634. doi:
10.1523/JNEUROSCI.6470-10.2011
Zhu, X., Wang, F., Hu, H., Sun, X., Kilgard, M. P., Merzenich, M. M., et al.
(2014). Environmental acoustic enrichment promotes recovery from developmentally degraded auditory cortical processing. J. Neurosci. 34, 5406–5415. doi:
10.1523/JNEUROSCI.5310-13.2014
Conflict of Interest Statement: The authors declare that the research was conducted
in the absence of any commercial or financial relationships that could be construed
as a potential conflict of interest.
Received: 21 August 2014; accepted: 17 November 2014; published online: 16 December
2014.
Citation: Kraus N, Hornickel J, Strait DL, Slater J and Thompson E (2014)
Engagement in community music classes sparks neuroplasticity and language development in children from disadvantaged backgrounds. Front. Psychol. 5:1403. doi:
10.3389/fpsyg.2014.01403
This article was submitted to Cognitive Science, a section of the journal Frontiers in
Psychology.
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