Neurophysiological improvements in speech-in-noise task after

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INTRODUCTION

In the United States, 25% of adults over aged 64-74, and

50% of adults over the age of 75 experience hearing loss

[1]. Auditory difficulties can be due to sensorineural

hearing loss, conductive hearing loss, or central hearing

loss, which encompasses deterioration or damage to

ascending auditory pathways beyond the cochlea [2].

One consequence of central hearing loss is the reduction

in ability to understand speech in noisy environments.

Speech-in-noise (SIN) discrimination is notably difficult

to target with hearing aids [3, 4], and deficits may exist

even in the presence of a clinically normal audiogram [5].

Communication difficulties that result from hearing

loss produce strain on social relationships and quality of

life. Specifically, auditory decline is associated with

loneliness [6], depression [7, 8], substance abuse [9], and

reduced social functioning [7, 10, 11]. To address the

dramatic impact of speech-in-noise discrimination loss on

quality of life, it is relevant to both investigate ways to

prevent decline and to improve speech-in-noise abilities

in older adults. Music training is a reasonable candidate

to improve auditory abilities by fine-tuning perceptual

abilities of sound and enhancing discrimination between

streams of sound in a complex auditory scene.

Accordingly, adult musicians show enhanced

performance on sentence-in-noise [12–15], masked

sentence [16–19], word-in-noise [20], and gap-in-noise

www.aging-us.com AGING 2021, Vol. 13, No. 7

Research Paper

Neurophysiological improvements in speech-in-noise task after short-

term choir training in older adults

Sarah Hennessy

, Alison Wood

, Rand Wilcox

, Assal Habibi

Brain and Creativity Institute, University of Southern California, Los Angeles, CA 90089, USA

Department of Psychology, University of Southern California, Los Angeles, CA 90089, USA

Correspondence to: Assal Habibi; email: ah[email protected]

Keywords: auditory perception, aging, music, speech-in-noise, electroencephalography

Received: December 22, 2020 Accepted: March 26, 2021 Published: April 6, 2021

Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the

original author and source are credited.

ABSTRACT

Perceiving speech in noise (SIN) is important for health and well-being and decreases with age. Musicians show

improved speech-in-noise abilities and reduced age-related auditory decline, yet it is unclear whether short term

music engagement has similar effects. In this randomized control trial we used a pre-post design to investigate

whether a 12-week music intervention in adults aged 50-65 without prior music training and with subjective

hearing loss improves well-being, speech-in-noise abilities, and auditory encoding and voluntary attention as

indexed by auditory evoked potentials (AEPs) in a syllable-in-noise task, and later AEPs in an oddball task. Age

and gender-matched adults were randomized to a choir or control group. Choir participants sang in a 2-hr

ensemble with 1-hr home vocal training weekly; controls listened to a 3-hr playlist weekly, attended concerts,

and socialized online with fellow participants. From pre- to post-intervention, no differences between groups

were observed on quantitative measures of well-being or behavioral speech-in-noise abilities. In the choir group,

but not the control group, changes in the N1 component were observed for the syllable-in-noise task, with

increased N1 amplitude in the passive condition and decreased N1 latency in the active condition. During the

oddball task, larger N1 amplitudes to the frequent standard stimuli were also observed in the choir but not

control group from pre to post intervention. Findings have implications for the potential role of music training to

improve sound encoding in individuals who are in the vulnerable age range and at risk of auditory decline.

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[21] tasks as compared to non-musicians. Additionally,

Ruggles et al., [22] observed a significant correlation in

speech-in-noise abilities with years of music training in

adults. In older adults, musicians additionally out-

perform non-musicians in sentence-in-noise [23, 24]

and word-in-noise discrimination [23, 25]. Fostick,

2019 demonstrated that the musician advantage for

words-in-noise discrimination remained when

comparing older adult musicians to life-long card

players. Zendel and Alain [26] found that the rate of

speech-in-noise decline associated with age was less

steep in musicians as compared to non-musicians,

indicating that music training may protect against age-

related hearing difficulties.

Speech-in-noise difficulties are thought to reflect

reduced synchrony of neuronal firing [27–29], and are

associated with alterations to both bottom-up and top-

down processing [30]. Perceiving speech in noise relies

on encoding acoustic features, such as frequency or

temporal structure, through bottom-up processes in

combination with recruiting attentional resources,

memory, and contextual prediction through top-down

processes. In age-related hearing decline, individuals

may compensate for bottom-up sensory deficits with

greater reliance on top-down mechanisms, filling in

missed pieces of information [31]. In situations of

cognitive decline, these compensatory resources may be

less available, resulting in further reduced speech-in-

noise perception [32, 33]. Thus, both top-down and

bottom-up mechanisms are important for supporting

speech-in-noise perception in older adults and can be

dissociated and assessed at the level of the brain.

Specifically, neural responses to speech-in-noise can be

measured with event-related potentials, voltage

recorded from scalp electrodes evoked by a stimulus

[34]. Specifically, the P1, N1, P2, and P3 components

are utilized to assess auditory processing, including

SIN, at a cortical level. The P1 potential (sometimes

referred to as P50) peaks around 70-100ms post-

stimulus onset, is the first cortical component of the

auditory response [35, 36] and has a fronto-central

distribution. It is thought to originate in the primary

auditory cortex and the reticular activating system [36,

37], and becomes more robust with age [38]. N1 is a

negative deflection peaking around 100ms after

stimulus onset and is most reliably has a frontal and

fronto-central distributions on the scalp [39]. N1 is

thought to originate in the primary auditory cortex,

specifically from the posterior supratemporal plane,

Heschl’s gyrus, and the planum temporal [37, 40, 41],

and may be modulated by prefrontal regions engaged in

attention processes [42]. A vertically-oriented or

“tangential” dipole in the primary auditory cortex, in

parallel with orientation of auditory cortex neurons, is

likely responsible for generating the negative potential

recorded in frontal and frontocentral sites [40, 41]. N1

response measured in frontal electrodes from this

tangential dipole, as compared to a horizontal dipole

originating in secondary auditory areas and recorded

more centrally, is more dependent on stimulus intensity

and on age [43]. N1 amplitude increases in the presence

of an unpredictable or change-related stimulus [44, 45].

P2, peaking around 200ms, is less studied but is known

to appear with the N1 response [46] and may, like P1,

originate in the reticular activation system [47]. P2 may

reflect attentional processing of sensory input after

initial detection marked by N1 (for review, see [48]).

The P3 component peaks from 300-700ms post-

stimulus onset, and is reflective of attentional

engagement [49], classically assessed utilizing the

Oddball task. P3 contains two main subcomponents,

P3a and P3b. P3a has a frontocentral distribution and is

elicited by novel, non-target stimuli and is largely

generated by the anterior cingulate cortex [50]. P3b,

often referred to as simply P3, occurs slightly later and

has a posterior parietal distribution. It is elicited in

response to an infrequent target sound and reflects

voluntary attention [51] and is largely generated by the

temporal-parietal junction [52]. Of particular relevance

to this study investigating speech in noise, it has been

demonstrated that early auditory event-related potentials

(AERPs) showing cortical responses to speech (e.g: N1,

P2) degrade with increased level of background noise

[53, 54], as well as with advancing age [55, 56].

Behavioral differences between musicians and non-

musicians in speech-in-noise abilities are paralleled by

differences in electrophysiological measures of auditory

processing. Adult musicians, compared to non-musicians,

show enhancements (earlier and larger peaks) of P1 and

N1 in response to syllables in silence [57], and P2 in

response to vowels [58]. Adult musicians, compared to

non-musicians, also exhibit less changes in N400 [15], a

component reflective of meaning representations [59],

and N1 [60] as a result of increasing background noise

level in a speech task, indicating less degrading effects of

noise on speech processing. In older adults, musicians

demonstrate enhanced N1, P2, and P3 response to vowels

as compared to non-musicians [61], suggesting more

robust encoding of and increased attention to speech

stimuli. At the subcortical level, both child [62] and adult

[13, 57, 58, 63] musicians show enhanced auditory

brainstem encoding, a measure of pre-attentive

processing, when compared to non-musicians.

While these cross-sectional studies provide valuable

information regarding differences between musicians and

musically untrained individuals, they do not establish a

causal relationship between musical experience and

speech-in-noise discrimination. Additionally, it has been

suggested that cognitive abilities and socioeconomic

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status [64] as well as inherent differences in auditory

abilities [65], may mediate the relationship between

music training and speech-in-noise perception. To

address this, several longitudinal studies have

investigated the effect of music training on speech-in-

noise perception. In a randomized waitlist-control study,

children aged 7-9 who received community-based music

training showed significant improvement in sentence-in-

noise discrimination after 2 years of training, and as

compared to controls [66]. Children aged 6-9 with

prelingual moderate-to-profound sensorineural hearing

loss showed advantages in sentence-in-noise ability as

compared to a passive control group after 12 weeks of

music training [67]. In older adults, individuals

randomly assigned to choir participation outperformed a

passive control group on a sentence-in-noise task after

10 weeks of training [68]. In this study, participants

assigned to the choir group additionally demonstrated

enhanced neural representation to temporal-fine

structure of auditory stimuli related to speech (i.e.:

fundamental frequency of the syllable \da\), and that

this training effect remained robust in individuals with

higher levels of peripheral hearing loss. In another

randomized-control study, older adults who participated

in 6 months of piano training performed better on a

words-in-noise task and showed enhanced N1 and mid-

latency responses, as compared to a videogame and no-

training group [69].

Overall, cross-sectional and longitudinal findings

demonstrate the potential for music training to affect

speech-in-noise perception across development.

However, more experimental work is needed to

continue disentangling the effects of music training

from pre-existing biological differences, both in terms

of behavior and neural response. Additionally, as our

global population ages, investigation of auditory decline

in relation to socio-emotional well-being in older adults

grows more significant. More research is needed to

assess effects of shorter-term music interventions

commencing later in life, as compared to life-long

learning. Lastly, it is unclear whether music training

may produce advantages in speech processing through

bottom-up processes, implying that music training

improves the neural encoding of sound, or through top-

down processes implying enhanced conscious

attentional network performance leading to improved

auditory discrimination. Studies on long-term music

training suggest that both mechanisms are at play,

where musicians as compared to non-musicians show

enhancements of attention-related P300 during a 2-

stimulus pure tone oddball task [70], but also enhanced

subcortical pitch encoding [57]. Working memory

additionally appears to mediate the relationship between

preservation of speech-in-noise abilities and lifelong

music training in older adults [71]. However, the

contribution of each of these mechanisms in short-term

music training is not known.

In this study, we expand upon existing literature to

examine the effects of a short-term, community-oriented

music training program on speech-in-noise abilities,

associated neural mechanisms, and well-being in older

adults with mild subjective hearing loss. We utilize a

randomized-control design with an active control group

to examine whether potential differences can be

attributed to active music engagement, or simply to any

music listening activity. Choir singing was chosen as the

active music intervention due to its practicality in short-

term application, potential for near-transfer, and

pervasiveness through human culture and evolution.

Additionally, as compared to instrument-learning, choir

singing is more accessible to larger communities as it

requires less equipment and financial resources. By

recruiting adults aged 50-65 with mild subjective hearing

loss, we examine the effects of music training on a

population vulnerable to age-related auditory decline.

Inclusion of EEG measurements provides information on

training-related changes in neural processing of speech

and sound. To parse the effects of bottom-up versus top-

down changes in auditory processing related to music

training, we include both a speech-in-noise, aimed to

target mostly bottom-up processing, and an auditory

attention (Oddball) task, aimed to target mostly top-down

processing, in our EEG assessments. Lastly, we address

the link between aging, hearing loss, and psychological

well-being by including measures of quality of life and

loneliness.

We hypothesized that after 12 weeks of training

participants in the choir group, as compared to the control

group, would show 1) greater improvements in behavioral

measures of speech-in-noise perception, 2) more robust

neural responses during EEG, and 3) improvements in

socioemotional well-being. Exploratory analyses between

EEG tasks were additionally assessed. We expected that

greater change in the P3 vs. early sensory components

(N1, P2) in the oddball task and/or the syllable in noise

task would support a top-down model of attentional

neuroplasticity associated with music training of this

type, indicating that training supports cognitive processes

(i.e. attention, memory) that support speech perception. If

the reverse (a greater change in N1, P2 vs. P3) a bottom-

up model in which music training enhances stimulus-

encoding would be supported.

RESULTS

Means and standard deviations for each behavioral

task, EEG task amplitude, and EEG task latency by

group are presented in Supplementary Tables 1–3,

respectively.

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Montreal cognitive assessment

At pre-test, no difference between groups was observed

for the MoCA (p > 0.05). Groups demonstrated nearly

identical distributions (Choir M = 26.11, SD = 2.25;

Control M = 26.48, SD = 2.06).

Sentence-in-noise task

In the BKB-SIN task, no effect of Group was observed

(p > 0.05).

Musical sophistication

At Pretest, no difference between groups was observed

in any subcategory of the Goldsmith MSI (p > 0.05).

Music-in-noise task

In the MINT, 3 participants from the control group had

incomplete or missing data from one or more time

points and were thus excluded from analysis, resulting

in 20 Control and 18 Choir participants. No main or

interaction effects of Condition or Group were observed

for accuracy or reaction time (all p > 0.05).

Well-being

No significant effects of Group were observed for any

subcategory of Ryff’s Psychological Well-being Scale

(all p > 0.05).

For the Dejong’s Loneliness Scale, no effect of group

was observed in emotional or social loneliness at post-

test (all p > 0.05).

For the open-ended prompt, “Do you think that music

intervention has had any impact on your social life or

feelings of connection with other people?”, 13

participants responded from the Control group and 15

participants responded from the Choir group. In the Choir

group, 62% reported that the intervention had an impact

on their social wellbeing, 19% reported an impact on

emotional well-being, and 19% reported no impact. In the

Control group, 8% reported that the intervention had an

impact on their social well-being, 54% reported impact

on emotional well-being, and 31% reported no impact. A

chi-squared test of independence indicated that response

category (social, emotional, none) was dependent on

group (X

(2, N = 30) = 11.02, p < 0.01).

Behavioral responses during EEG tasks

Syllable-in-noise

One participant from the Choir group was removed

from analysis due to excessive noise in EEG data, and 3

participants were removed from the Control group for

excessive noise or incomplete data. No main or

interaction effects were observed for accuracy (all p >

0.05). No main or interaction effects were observed for

reaction time (all p > 0.05).

Oddball

Three participants from the Control group were

removed from analysis due to excessive noise in EEG

data. No effect of Group was observed for accuracy or

reaction time (all p > 0.05).

Event-related potentials in active syllable-in-noise

task

P1 amplitude and latency

P1 reached peak latency at 35-70ms in the Silent SNR

condition, 50-85ms in the 10dB SNR condition, 65-

110ms (pre) and 55-95ms (post) in the 5dB SNR

condition, and 60-105ms in the 0dB SNR condition. No

significant effects between groups or interactions were

observed for P1 amplitude or latency (all p > 0.05). For

P1 latency, a main effect of SNR Condition was

observed (Test statistic: 7.50, p < 0.01, QS = 0.78),

where latency in the 5dB condition was earlier than in

the 0dB (p < 0.001), 10dB (p < 0.05), and silent (p <

0.01) conditions from Pretest to Posttest.

N1 amplitude

N1 reached peak amplitude at 90-125ms (pre) and 85-

130ms (post) during the Silent SNR condition, 105-

175ms in the 10dB SNR condition, 125-190 in the 5dB

condition, and 130-200 in the 0dB condition. No

significant effects related to intervention were observed

for N1 amplitude (p > 0.05). A main effect of Frontality

was observed (Test statistic = 4.15, p < 0.05, QS = 0.50)

where amplitude in frontal electrodes showed an

increase more than in central electrodes from Pretest to

Posttest (p < 0.01).

N1 latency

For N1 latency, a main effect of Group was observed

(Test statistic = 7.31, p < 0.05, QS = 0.31), where N1

latency in the Choir group decreased to a greater extent

than in the Control group from Pretest to Posttest (p <

0.01) across all SNR conditions (see Figure 1).

P2 amplitude and latency

P2 was observed only in the Silent SNR condition

around 160-245ms. For P2 amplitude and latency, no

significant effects between groups were observed (all p

> 0.05).

P3-like amplitude

A positive inflection varying from 275-400ms to 305-

445ms (latency dependent on SNR condition) was

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observed across SNR conditions of the active, but not the

passive, task. A Group x Laterality interaction was

observed for the P3-like amplitude (Test statistic = 3.10,

p < 0.05) where, in the right electrodes, the Control group

showed an increased amplitude from Pretest to Posttest

more than the Choir group (p < 0.05, QS = 0.41). A

Group x SNR Condition interaction approached

significance (Test statistic = 2.55, p = 0.05) where, in the

silent SNR condition only, the Control group showed an

increased amplitude from Pretest to Posttest more than

the Choir group. A main effect of Frontality was

observed (Test statistic = 7.51, p < 0.01, QS = 0.44),

where amplitude increased from Pretest to Posttest was

more pronounced in frontal than central electrodes (p <

0.01). After inspecting individual traces, we noted that

the group differences in amplitude were driven by a

single participant in the Control group and, when that

participant was removed, did not approach significance.

Figure 1. (A) N1 latency, difference score (post-test – pre-test) at Cz in the active condition of the syllable-in-noise task in choir and control

groups, across SNR conditions. (B) ERPs recorded at Cz during active condition of the syllable-in-noise task in the choir and control groups at

pre and post-test for each noise condition. (C) Topographic headplots for N1 during active condition of the syllable-in-noise task in the choir

and control groups at pre and post-test for 0dB and Silent conditions.

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P3-like latency

For Latency, no significant effects or interactions were

observed (p > 0.05).

Event related potentials in passive syllable-in-noise

task

P1 amplitude and latency

P1 reached peak amplitude at 40-75ms in the Silent

SNR condition, 50-100ms in the 10dB SNR condition,

55-105ms in the 5dB SNR condition, and 55-110ms

(pre) and 65-115ms (post) in the 0dB condition. No

significant effects between groups or interactions

were observed for P1 amplitude or latency (all

p > 0.05).

N1 amplitude

N1 reached peak amplitude at 90-130ms in the Silent

SNR condition, 125-195ms (pre) and 125-185ms

(post) in the 10dB SNR condition, 145-200ms in the

5dB SNR condition, and 144-215ms (pre) and 155-

200ms (post) in the 0dB SNR condition. A main effect

of Group was observed (Test statistic = 6.62, p < 0.05,

QS = 0.51), where the Choir group showed an increase

in N1 amplitude from Pretest to Posttest significantly

more than did the Control group (p < 0.001) (see

Figure 2) across SNR conditions. A Group X SNR

Condition X Frontality interaction was observed on N1

amplitude (Test statistic = 3.38, p < 0.05) but was not

significant after correcting for multiple comparison

(p > 0.05).

N1 latency

For N1 latency, no significant effects between groups or

interactions were observed (p > 0.05).

P2 amplitude and latency

P2 was observed only in the silent SNR condition and

reached peak amplitude at 160-230ms. No significant

effects related to intervention were observed for P2

amplitude (p > 0.05). A main effect of Laterality was

observed (Test statistic = 7.32, p < 0.01), but was not

significant after correcting for multiple comparisons (p

> 0.05). No significant effects between groups were

observed for P2 latency (all p > 0.05).

Event related potentials in oddball task

N1 amplitude

N1 reached peaked amplitude at 65-115ms at pretest

and 70-110 ms at posttest in the Oddball, Standard,

and Distractor conditions. During Standard trials, a

Group X Frontality interaction was observed (Test

statistic = 5.36, p < 0.05, QS = 0.64) where, in frontal

electrodes, amplitude in the Choir group increased

more than in the Control group (p < 0.01, QS = 0.37)

from Pretest to Posttest (see Figure 3). During Oddball

and Distractor trials, no effect of Group was observed

(p < 0.05). During Distractor trials, a main effect of

laterality was observed (Test statistic = 3.59, p < 0.05,

QS = 0.73), where amplitude at right electrodes

increased more than amplitude at left electrodes

(p < 0.01).

Figure 2. (A) N1 amplitude, difference score (post-test – pre-test) averaged across frontal and central in the passive condition of the syllable-

in-noise task in choir and control groups. (B) ERPs recorded at Cz during passive condition of the syllable-in-noise task in the choir and control

groups at pre and post-test for each noise condition.

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N1 latency

During Oddball, Standard, and Distractor trials, no

significant effects between groups or interactions were

observed on N1 latency (all p > 0.05).

P2 amplitude and latency

P2 reached peak amplitude at 145-250ms (pre) and 125-

155ms (post) in the Standard condition, 135-185ms

(pre) and 115-145ms (post) in the Oddball condition,

and 190-265ms (pre) and 115-145ms (post) in the

Distractor condition. However, no significant effects

between groups or interactions were observed for P2

amplitude or latency (all p > 0.05) for any of the

conditions.

P3a amplitude and latency

During the Distractor trials, P3a reached peak amplitude

at 345-495ms at pretest and 320-390 ms at posttest.

However, there were no observed significant amplitude

or latency effects between groups or interactions (all

p > 0.05).

P3b amplitude and latency

P3b reached peak amplitude at 300-625ms (pre)

and 315-610ms (post) during Oddball trials and 450-

660ms during Distractor trials. No significant effects

between groups were observed on P3b amplitude

or latency during Oddball or Distractor trials (all

p > 0.05).

Figure 3. (A) N1 amplitude, difference score (post-test – pre-test) in frontal and central electrodes in the standard condition of the oddball

task in choir and control groups. (B) ERPs recorded at Fz during standard condition of the oddball task in the choir and control groups at pre

and post-test. (C) Topographic headplots for N1 during oddball task in choir and control groups in the standard condition.

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DISCUSSION

In this study, we investigated the effects of participation

in a short-term choir program on perceiving speech in

noise (SIN), auditory attention, and their underlying

neurophysiological correlates using event-related

potentials (ERPs) in a randomized-control trial with

older adults between ages 50-65. We also assessed social

well-being as a result of participation in the choir. We

observed an effect of music training on the auditory

evoked potential N1 response in an Active and Passive

Syllable-in-Noise task, although no behavioral

differences were observed. An effect of training was also

observed on N1 response during the Oddball task, again

in the absence of behavioral differences. Lastly, well-

being measure qualitatively indicated that choir training

may have benefitted participants’ social well-being,

while passive music listening may have benefitted

control participants’ emotional well-being. These results

have implications for the use of a short-term music

program to mitigate the perceptual and socioemotional

effects of age-related auditory decline. We discuss these

findings in detail in the context of existing literature

below.

N1 is regarded as a correlate of initial stimulus

detection [72]. N1 is additionally enhanced by increased

attention, where larger amplitudes [73–75] and shorter

latencies [75] are observed with increasing attentional

engagement. In the presence of background noise, N1 is

attenuated, with decreased amplitude and increased

latency with falling signal-to-noise ratios [76–78]. Thus,

N1 is associated with encoding of physical properties of

sound and marks the arrival of potentially important

sounds to the auditory cortex. While N1 elicitation does

not require conscious processing [79, 80], it can be

modulated by attentional demands [74].

N1 response is reduced in certain clinical populations

with disorders related to audition, including individuals

with misophonia [81] and sensorineural hearing loss

[82]. The effects of age on N1 are less clear. While

some report decreased amplitude [83], others report a

pattern of increased amplitude and longer N1 latency

in older adults [84–87] and older adults with hearing

loss [55] and many investigations report little or no

effects of age on either amplitude or latency [88–93].

Throughout the lifespan, however, N1 appears to be

mutable through experience-dependent plasticity. N1

is larger in adult musicians as compared to non-

musicians [94, 95]. N1 amplitude increases are

observed after short-term syllable [96], frequency

(using a tone-based oddball task) [97] and music

training [69, 98].

Effect of music training on N1

In the present study, participants involved in choir, as

compared to participants engaged in passive music

listening, demonstrated larger N1 amplitudes in a passive

syllable-in-noise task from pre- to post-training across all

noise conditions. This finding replicates that of [69], who

also showed larger N1 during a passive, but not active,

words-in-noise task after 6 months of piano training. Of

note, all participants in our study first completed the

active task followed by the passive task. The group

difference in N1 amplitude observed only in the passive

condition could be related to the order of task

administration and interaction with music training; where

during the active condition both groups equally attended

to the incoming auditory stimuli and due to a ceiling

effect, no group differences were evident- during the

passive task however, the participants in the choir group

continued to involuntarily attend to the incoming

auditory stimuli, due to a general re-organization of

attention to and encoding of sound in relation to their

music training.

In the oddball task, choir participants additionally

demonstrated larger N1 amplitudes from pre- to post-

training as compared to controls. This finding was

specific to the frontal electrode (Fz), during trials of

standard tones. This finding is similar to that of [97] who

observed that a short-term frequency discrimination

intervention led to increased N1 amplitude most

prominently during standard (as compared to deviant)

trials of an oddball task. The finding that N1 amplitude

was enhanced only in standard trials may simply reflect

the fact that standard tones were presented 4.7 times as

frequently as oddball or distractor tones, indicating that a

larger sample of trials was necessary to see an effect of

training. The observed frontality effect replicates

previous work showing the N1 response most reliably

observed at frontal or frontocentral sites [39], and further

demonstrates that the effect of training was most robust

in locations where N1 is classically observed.

Given that N1 amplitude is known to be enhanced by

attention [73–75], it is possible that observed changes in

N1 amplitude in the oddball and passive syllable-in-noise

tasks may be explained by, in addition to enhanced

encoding, increased attention to sound in general in the

choir group. Participating in music training may have in

part re-organized participants’ orientation towards sounds

and led to greater engagement of attention resources

towards tones and syllables. This, in conjunction with

improved basic auditory perception, may have

contributed to enhanced amplitudes of N1.

In contrast to amplitude, latency differences were

observed only in the active condition of the syllable-in-

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noise task, where choir participants demonstrated earlier

N1 latencies from pre- to post-training across all noise

conditions. Attention has been shown to decrease N1

latency, where latency is earlier in active as compared

to passive tasks [75, 99]. These findings support the

Prior Entry Hypothesis, which posits that attended

stimuli are perceived earlier than unattended stimuli

[100]. While it is expected that latencies will be shorter

in the active than the passive condition across

participants, the choir group’s latency decrease from pre

to post-test in the active condition here suggests that

music training impacted attentional processes. It could

be that music training led participants to be more

attentive during the task, or that it increased the

potential for acceleration in neural processing speed for

the same level of attentional engagement. Given that the

choir group did not demonstrate any improvements in

syllable-in-noise response time, which would also

indicate greater attentiveness during the task, we posit

that the latter explanation is more likely to be true.

Specifically, choir training increased the influence of

attention on the speed of neural processing which may

be not evident in the motor response as measured by

reaction time.

Of note, no effect of latency was observed during the

oddball task, even though it is also an active task and

latency effects were observed during the active

condition of the syllable-in-noise task. If attention

modulates latency of N1 response, and music training

further enhances this effect, then one would expect

latency during N1 to also decrease in the oddball task in

the choir-trained group. The lack of latency difference

between groups may relate to a ceiling effect on the

latency of the stimuli in the oddball task. It also likely

indicates that the ability of short-term choir training to

accelerate sensory processing speed is not consistent

across all types of auditory stimuli. Rather than a global

effect on attention across stimuli, choir training may

first modify the latency of N1 selectively in response to

speech sounds as presented in the syllable-in-noise task

as opposed to pure tones and white noise presented in

the oddball task. Speech perception involves top-down

processing (for review, see [101]), whereas perception

of pure tones, sounds that do not typically occur in the

natural environment, may not benefit as much from top-

down filling. In line with this, Shahin et al., [95]

observed enhancements of N1 and P2 to musical tones

as compared to pure tones in professional musicians.

Speech stimuli, as used in this study, are arguably more

similar to musical stimuli than are pure tones, given

their probability of occurrence in daily life. It is likely

that the attention-related reductions in N1 latency

attributed to music training were present in the SIN, but

not the oddball, task because training improved only

top-down modulation of sounds relevant to the natural

environment, such as speech, and not to computer-

generated stimuli typically unheard outside of a

laboratory.

Together, enhancements of N1 in the Choir group

across tasks demonstrate the ability of a short-term

music program to improve the early neural encoding of

both speech and tones. The observed overall effect of

music training on N1 is in accordance with

experimental [69] and cross-sectional work comparing

musicians to non-musicians, citing enhanced N1 during

passive tone listening [95] and active tone listening

[94]. After habituation in a passive task, musicians as

compared to non-musicians showed enhanced N1 when

presented with a brief active task, demonstrating rapid

plasticity [102]. Yet, others report no N1 differences

between musicians and non-musicians in response to

pure and piano tones, noise [103] or harmonics [104],

or report reduced amplitudes in musicians [105].

Discrepancies may be due to differences in EEG task

stimuli and design. For example, both [104, 105] used

an oddball-like paradigm. It may be that N1

enhancement in musicians observed in the context of

an attention-related task may produce less consistent

results, and that more research is needed to elucidate

these differences. For example, N1 response decreases

with increased predictability of a stimulus [44, 45] (i.e:

with high repetition in an oddball paradigm).

Differences in N1 may not be consistently detectable

across task designs due to the saturation of the neural

response, yet more investigation is needed.

Alternatively, as proposed by [103] discrepancies

between studies may reflect differences in dipole

estimation methods. Here, our results most closely

followed Zendel et al., 2019, whose study and EEG

task design more closely follow ours.

Change in N1 could be indicative of more synchronized

discharge patterns in N1 generator neuron populations

of Heschl’s gyrus or regions of the superior temporal

gyrus. This is supported by evidence that N1 responses

to speech in noise are predicted by neural phase locking,

as measured by inter-trial phase coherence [77].

Specifically, neural synchrony is positively correlated

with the earlier latencies and larger amplitudes of N1

that are observed when background noise is decreased

[77]. The shorter latency observed in the active

condition may additionally indicate faster conduction

time in these neurons [106].

Contributions of top-down and bottom-up processing

Using multiple EEG tasks, we aimed to address the

question regarding role of top-down versus bottom-up

processing in music training-related benefits to auditory

processing in general and speech perception specifically.

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Studies recruiting life-long musicians have provided

evidence primarily for top-down attention modulation to

improve speech processing abilities [70, 71]. In this

study, however, we provide evidence largely towards a

model of improved bottom-up processes. We notably did

not observe differences between groups in later

components of the oddball task (e.g: P3a or P3b) or in

the later attention-related positivity of the syllable-in-

noise task, suggesting that choir-training conferred a

general advantage to encoding acoustic features, but did

not modulate general attentional processes. This is in

line with N1 findings from the syllable-in-noise task,

where differences between groups were not affected by

noise level. This suggests that changes observed were

again due to general enhanced processing of the target

sound, rather than suppression of attention away from a

distracting noise. Importantly, however, it should be

noted that, although N1 is an early component thought to

reflect basic encoding, it can still be impacted by top-

down processes, namely attention, as seen in differences

in amplitude and latency when comparing active to

passive paradigms [75]. Here, we observed that choir

training enhanced the relationship between attention and

sensory processing in the syllable-in-noise task, as seen

in decreased latencies in the active condition only. This

suggests that choir training, while mainly impacting

bottom-up processes, may have had some impact on

attention-related processing of speech stimuli. This

effect was stimulus-specific, as no latency effects were

observed for N1, or any other component, during the

oddball task that involved pure tones as opposed to

speech sounds. This may reflect a more near-transfer

effect of choir training, which involves speech and not

pure tones, as compared to instrumental training. It may

additionally suggest simply that choir may selectively

improve top-down processing of stimuli that more

regularly occur in the environment; pure tones, as

compared to speech stimuli, are highly unusual outside

of a laboratory setting as they are built from an isolated

frequency. Due to their prevalence in the natural

environment, speech sounds also involve and benefit

more from top-down processing (review: [101])

than do pure tones. Therefore, we overall provide

evidence towards improved neural encoding with

some attentional modulation, suggesting that short

term choir training and long-term instrumental

training may produce benefits through different, or

proportionally different, mechanisms. As noted by Patel

[107], the proposed mechanisms may not be mutually

exclusive.

Speech perception involves top-down processing (for

review, see [101]), whereas perception of pure tones,

sounds that do not typically occur in the natural

environment, may not benefit as much from top-down

filling.

Effect of training on P3-like component

In our analysis on the P3-like component during the

active syllable-in-noise task, we investigated whether

we could replicate findings observed by [69]. In [69],

the music group showed greater amplitude of this

peak, and this result was interpreted as an index of

increased voluntary attention allocation similar to a

P3b response. Here, we observed enhanced amplitude

in the control group in the P3-like component during

the active condition of the syllable-in-noise task.

However, this difference was driven by a single

participant in the control group and thus does not

reflect true differences between groups. Discrepancies

between our findings and those of [69] may simply be

due to task design, as noted previously [69]. Observed

a positivity peaking from 200-1000 ms in both the

passive and the active tasks, whereas in this study we

were only able to reliably measure a similar

component in the active task and in a much smaller

time window (~250-450ms). This may again indicate

that the stimuli used by [69] required more effort to

process and thus was more sensitive to training-

related effects.

Absence of behavioral change

Despite observed changes on early auditory encoding,

we report no effect of training on behavioral measures

of speech-in-noise perception. Groups did not differ in

pre- to post-training improvements of sentence-in-noise

tasks during or outside EEG recording. This is in

contrast to experimental evidence demonstrating

benefits in behavioral speech-in-noise abilities after 10

weeks of choir training [68] and 6 months of piano

training [69], both in older adults. However, with the

same group of participants, [108] did not observe

behavioral differences in an in-scanner task of hearing

in noise. Differences between observed behavioral

speech-in-noise improvements and the results of this

study may reflect differences in tasks [68] used the

QuickSIN [109, 110], which consists of sentences

embedded in 4-talker babble. Comparison of QuickSIN

and BKB-SIN, as used in this study, show greater

differences between groups of differing hearing

abilities in QuickSIN as compared to BKB-SIN, a

difference associated with increased contextual cues

present in the BKB-SIN that lead to better recognition

in individuals with greater hearing loss [111]. It is

possible that the BKB-SIN was not sensitive enough to

pick up on potential differences resulting from a short-

term training program. In [69], stimuli consisted of 150

different monosyllabic words were presented over a 4-

talker babble. In contrast, the stimuli presented during

EEG in this study consisted of a single repeated

syllable presented in a 2-talker babble. It is possible

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that the addition of two more babble speakers, thereby

increasing the difficulty, may have impacted accuracy

during this task between groups, especially as [69]

found differences only during the most difficult

condition of the task (0dB SNR), and participants in

the present study performed at ceiling. Differences

in results between [69, 108], in which the same

participants were assessed, were attributed to

differences in the speech-in-noise task. The task

completed during [69] EEG session had lower signal-

to-noise ratios, as compared to the task presented in

[69, 108], single words were presented in noise without

context, whereas [108] presented sentences in noise, for

which participants could use contextual cues. Here,

both our behavioral speech-in-noise task (BKB-SIN)

and results are more similar to that of [108], indicating

that in measurement choice could explain the absence

of behavioral change, and that a more difficult task

may produce different results.

We also observed no behavioral change between

groups on the music-in-noise task. This task is

intended to measure auditory segregation ability in the

context of musical excerpts. Musicians outperformed

non-musicians in the original study of the task, and

years of music (minimum of 2 years) training

predicted task performance [112]. However, no

studies to our knowledge have examined the effects of

short-term music training on the MINT. Here, we

show that 12 weeks of choir training for older adults

with no prior music training may not be sufficient to

provide an advantage in hearing musical excerpts in

noise.

Well-being

Through qualitative assessment, participants who

participated in choir reported more perceived social

benefit, while participants in the passive listening

group reported more perceived emotional benefit.

Group music production has been found to produce

feelings of social cohesion and group belonging [113,

114], while music listening may help individuals

regulate emotions [115]. While individuals in the

passive listening group did participate in online group

discussions about the playlists, qualitative results here

demonstrate that singing together was a more effective

way to gain a sense of social well-being. However, no

observed differences were found between groups in

quantitative measures of well-being. In a recent

waitlist-control study, 6 months of choir singing was

shown to reduce loneliness and improve interest in life

in older adults [116]. It may be that twelve weeks of

group singing is not sufficient time to alter feelings of

loneliness and well-being outside of the immediate

choir context, as was measured in this study.

Limitations

A limitation of the present study is small sample size

due to high rates of attrition before and during the

intervention period. While robust statistical methods

were utilized to ensure appropriate capture of training

effects, statistical methodology cannot replace overall

power gained from high Ns.

Additionally, a possible limitation in this study is the

degree to which we were able to match the groups on

programmatic aspects related to the intervention,

specifically the nature and setting of social engagement.

In the passive-listening control group, participants

responded to prompts and collectively discussed playlists

on an online platform and were encouraged to attend

specific in-person concerts with the research team and

other participants. Thus, social engagement between

participants was encouraged and facilitated. However,

this type of engagement differed from the social activity

experienced by participants in the choir group, where

participants worked together towards the common goal of

a cohesive musical sound. This difference may have

contributed to the observed qualitative well-being or

auditory processing findings. Additionally, while we

believe that matching of auditory-based interventions was

a reasonable method of control, we do acknowledge that

differences in social setting and differential

enhancements in social functioning could have benefitted

cognitive abilities and subsequently impacted auditory

processing.

CONCLUSIONS

In older adults, age-related declines in speech-in-noise

abilities may significantly disrupt daily communication

and overall well-being. Underlying such declines are

hypothesized reductions in neural conduction speeds

and population synchrony of neurons in the auditory

cortex. Auditory training programs have shown to

improve speech-in-noise abilities (for review, see

[117]), but are frequently expensive, time-consuming,

and require high consistency and motivation. Singing is

a low-cost activity that is often fun and engaging, and

thus may be easier to implement and maintain across a

variety of situations. Here, we observed that 12 weeks

of choir singing produces enhancements in early sound

encoding, as seen in earlier latencies and larger

amplitudes of the N1 response, in a group of older

adults with mild subjective hearing loss. Enhanced N1

response may reflect more synchronized firing and

accelerated conduction velocity in regions of the

auditory cortex that are involved in processing of

speech and music. Thus, using a randomized-control

design, we provide experimental evidence for the

efficacy of a low-cost, non-invasive method to improve

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neural processing of speech, specifically early sound

encoding, in individuals who are particularly vulnerable

to declines in such abilities due to age. Additionally, we

demonstrate that group singing, through its socially

engaging nature, may improve certain indices of well-

being. Importantly, the use of an active control

demonstrates that advantages conferred to the choir

group were related specifically to group music

production, rather than passive music listening. Our

findings diverge from previous investigations in that

behavioral improvements in speech-in-noise abilities

were not observed, likely due to differences in

measurement method. Future work utilizing a variety of

hearing-in-noise tasks in a larger sample could provide

clarification.

MATERIALS AND METHODS

Participants

Participants between the ages of 50-65 were recruited

from local community centers in the Los Angeles area,

and from the Healthy Minds Research Volunteer

Registry, a database of potential participants interested

in studies at the University of Southern California

related to aging and the brain. Participants were pre-

screened based on inclusion and exclusion criteria.

Participant inclusion criteria were: 1) native English

speaker with experience of subjective hearing loss; 2)

normal cognitive function, as measured by the Montreal

Cognitive Assessment (score ≥ 23). Subjective hearing

loss was assessed by verbally asking participants if they

noticed problems with their hearing, or if they struggled

to hear in noisy environments. Participant exclusion

criteria were: 1) use of prescribed hearing aids; 2)

severe hearing loss (thresholds of 50db for all recorded

frequencies; see Figure 4); 3) current diagnosis of

neurological or psychiatric disorders; 4) formal music

training, where participant currently plays a musical

instrument or has had more than 5 years of formal

music training in their life, excluding music classes as

part of typical education curriculum.

Study design was a pre-post randomized control trial.

Participants took part in two testing sessions: the Pretest

session took place up to one month prior to intervention

and the Posttest took place up to one month after 12

weeks of intervention. After all participants had

completed the Pretest session, participants were

randomized by an independent statistic consultant into

two groups (Control and Choir), stratified by gender

and age (<57, ≥57). During Pretest and Posttest,

participants completed behavioral assessments of socio-

emotional well-being, speech-in-noise perception,

music in noise perception and two auditory tasks with

simultaneous EEG recording.

Figure 4. Pure tone thresholds for participants in choir and control groups at pre-test.

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Seventy-six participants were recruited to participate in

the study. Five participants dropped out prior to pre-

screening assessment. After pre-screening, 11

participants were excluded, leaving 60 participants who

completed the Pretest session. After randomization, 17

participants withdrew from the study due to personal

circumstances, change in schedule, or relocation. 2

participants were removed for insufficient completion

of the intervention (missed more than 3 choir rehearsals

or 3 weeks of music listening). This resulted in forty-

one participants completing Pretest and Posttest

(Control group N = 23, Choir group N = 18).

Demographics of participants within each group are

summarized in Table 1.

Interventions

Choir-singing group

The choir-singing group (Choir group hereafter)

participated in 2-hour weekly group choir singing

sessions for 12 consecutive weeks. Participants were

given at-home vocal training and music theory exercises

to complete outside of class for an estimated 1 hour per

week. The choir was directed by a doctoral student from

the Department of Choral and Sacred Music at USC

Thornton School of Music and accompanied by a

pianist. Four singers from Thornton School of Music

sang with each voice part of the choir, as “section

leaders”. Participants learned a variety of songs across

genres and performed them at the end of the 12-week

period as a small concert. The performance included

folk (i.e: “Sally Gardens”), musical theater (i.e: “Food

Glorious Food” from Oliver!), holiday (i.e: “Carol of

the Bells”), renaissance (i.e: “El Grillo), Baroque (i.e;

“Bist du Bei Mir”, by J.S Bach), and traditional choral

music (i.e: “Life’s Joy” by Schubert, and “Laudate

Dominum”). Participants in the choir were given an

additional $15 per rehearsal attended to cover parking

and transportation expenses.

Passive-listening group

The passive-listening group (Control group hereafter)

received twelve weekly 3-hour musical playlists that

they were asked to listen to throughout the week.

Playlists were curated by a doctoral student in the

Thornton School of Music to reflect a variety of musical

genres that would be enjoyable to participants in this

age group. Participants were given the choice to listen

to the playlists on a provided MP3 player, or on a

personal device through Spotify. Reminders to listen

each week were administered via text. Participants

interacted with other participants on a private online

platform to discuss the previous week’s playlist.

Additionally, participants were given opportunities to

attend free weekly live concerts and musical events as a

group. Attendance at live events was not required for

participation in the study, but on average different

combinations of 4-5 participants attended each week.

Stimuli

Behavioral tasks

Cognitive abilities were assessed for pre-screening

purposes using the Montreal Cognitive Assessment

(MoCA) [118], which includes measures of memory,

language, attention, visuospatial skills, calculation, and

orientation and is intended to detect mild cognitive

impairment. Audiometric thresholds were obtained

bilaterally at octave intervals 0.5-8 kHz using a Maico

MA 790 audiometer in a sound-attenuated booth.

Musical experience was measured at pre-test only using

the Goldsmiths’ Musical Sophistication Index [119],

which measures musical experience as a function of six

facets: active engagement, perceptual abilities, musical

training, singing abilities, emotions, and general

sophistication. Socio-emotional well-being was assessed

using Ryff’s Psychological Well-Being Scale [120, 121],

which includes 42 self-report items that measures six

aspects of wellbeing: autonomy, environmental mastery,

personal growth, positive relations with others, purpose

in life, and self-acceptance. Loneliness was measured at

post-test only, with the Dejong Giervald Loneliness

Scale [122], consisting of 11 self-report items asking

participants about current feelings of social and

emotional loneliness. At post-test, participants were

additionally asked to respond in writing to the open-

ended prompt: “Do you feel that the music intervention

has had any impact on your social life or feelings of

connection with other people?”.

Hearing-in-noise abilities were assessed with the Music-

In-Noise Task (MINT) [112] and the Bench, Kowal, and

Bamford Sentences test (BKB-SIN) [123]. In the MINT,

participants were presented with a musical excerpt

embedded within musical noise, followed by a matching

or non-matching repetition of the target excerpt in silence

and are asked to determine whether the two presented

sounds matched. This portion of the task is divided into

Rhythm or Pitch matching conditions. In a third

condition of the task (Prediction), participants were first

presented with the target stimulus in silence before being

asked to determine if the following excerpt within noise

was a match. Accuracy and response times were

recorded. Participants completed this task using

headphones in a sound attenuated room. In the BKB-SIN,

speech-in-noise abilities were assessed by asking

participants to repeat simple sentences embedded in four-

talker babble at increasing noise levels. The BKB-SIN

uses Bench, Kowal, and Bamford Sentences [124], which

are short stimuli written at a first-grade reading level rich

with syntactic and contextual cues. A verbal cue

(“ready”) is presented before each sentence. Background

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Table 1. Gender, age, and MoCA scores for choir and

control groups.

Total

Choir

Control

Gender

# Females

Age

Mean

58.29

58.22

58.39

4.19

4.35

4.10

MoCA

Total Score

26.32

26.48

26.11

2.13

2.06

2.25

babble is presented at 21, 18, 15, 12, 9, 6, 3, 0, -3, and -6

dB SNR. Six lists containing ten sentences each were

presented through a single loudspeaker in a sound

attenuated room at 60 dBA. Each sentence contains three

or four key words that are scored as correct or incorrect.

An experimenter recorded responses, and a total score

and a SNR-50 (23.5 – total score) were calculated.

EEG tasks

Participants completed two tasks during EEG recording:

an auditory oddball, and a syllable-in-noise task. The

syllable-in-noise (SIN) task consisted of an active and a

passive condition. In the active condition, participants

pressed a button when they were able to hear a target

syllable within background babble. In the passive

condition, participants watched a muted nature

documentary while passively listening to the stimuli.

Stimuli consisted of the syllable /da/ presented at 65 dB

SPL within a two-talker babble at one of four SNR

conditions (silent (no background noise), 0dB, 5dB, and

10dB). Each target stimulus was presented for 170 ms

with an inter-stimulus interval jittered at 1000, 1200, or

1400 ms, for a total trial length of 1370 ms. Each SNR

condition was presented in a block of 150 stimuli for

both the active and the passive condition. Accuracy and

response time during the active condition were

recorded. Auditory stimuli for both tasks were presented

binaurally with ER-3 insert earphones (Etymotic

Research). In the oddball task, 400 trials were presented

with a 1000msec Intertrial Interval; stimuli consisted of

280 standard pure tones (500 Hz), 60 oddball target

tones (1000 Hz), and 60 white noise distracter stimuli,

each presented for 60ms. Stimuli were presented at 76

dB SPL. Participants were instructed to press a button

only for the oddball stimulus. Accuracy and response

times were recorded.

Procedure

Recruitment and induction protocols were approved

by the University of Southern California Institutional

Review Board. Informed consent was obtained in writing

from participants, and participants could end participation

at any time. Participants received monetary compensation

for assessment visits ($20 per hour). All participants were

tested individually at the Brain and Creativity Institute at

the University of Southern California.

EEG recording and averaging

Electrophysiological data was collected from 32 channels

of a 64-channel BrainVision actiCAP Standard-2 system.

Electrodes were labeled according to the standard

International 10-20 system [125]. Participants were

seated in a comfortable chair in a dark, sound-attenuated

and electrically-shielded room. Impedances were kept

below 10 kΩ. Data were sampled at 500 Hz.

EEG data processing was conducted with EEGLab

[126] and ERPLAB [127]. Data were resampled offline

to 250 Hz sampling rate, and bandpass filtered with

cut-offs at .5 Hz and 50 Hz. Channels with excessive

noise were removed and then manually interpolated.

The data were visually inspected for artifacts, and

segments with excessive noise were removed. Ocular

movements were identified and removed using

independent components analysis. Data were then

bandpass filtered at 1-20 Hz. Epochs were average

referenced (excluding EOG and other removed

channels) and baseline corrected (-200 to 0 ms prior to

each note). Epochs with a signal change exceeding +/-

150 microvolt at any EEG electrode were artifact-

rejected and not included in the averages. For the

Active and Passive syllable-in-noise tasks, EEG data

were divided into epochs starting 200ms before and

ending 800 ms after the onset of each stimulus. A

repeated measures ANOVA was conducted, with SNR

Condition and Time as within-subject factors, and

Group as the between-subjects factor for the Passive

and Active tasks separately to assess differences in

number of trials accepted. No differences in accepted

trials were observed in the Passive syllable-in-noise

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task (ps > 0.05). An effect of time was observed in the

Active syllable-in-noise task, (F(1, 32) = 5.96, p <

0.05), where more trials were accepted at post-test than

at pre-test across conditions and groups. No other

differences were observed (see Table 2).

For the Oddball task, data was epoched from -200ms

to +1000ms relative to the onset of each stimulus. For

the Oddball task, separate repeated measures

ANOVAs were calculated to assess if time or group

impacted the number of accepted trials in each

condition (Oddball, Standard, and Distractor). No

effect of group or time on the number of accepted

trials was observed in the Oddball (p > 0.05), Standard

(p > 0.05), or Distractor conditions (p > 0.05) (see

Table 2).

Mean amplitude and peak latency for ERPs were

calculated automatically in time-windows centered on

the peak of the retrospective component of the grand

average waveform. Latencies were analyzed at a

single electrode chosen from existing literature

[57, 60] and verified based on location of peak

activity observed in topographic headplots. Time-

windows and electrodes for peak measurements for

each component of the Oddball and the syllable-in-

noise task are summarized in Tables 3–5. In addition

to examining well-studied ERP components (P1, N1,

P2, P3), we investigated the effects of choir training

on a frontally-distributed, P3-like positive peak

occurring at 200-1000ms during the syllable-in-noise

task as described by Zendel et al., [69]. This peak was

interpreted as a marker of attention orienting, given its

temporal overlap with the P3 [69].

Statistical analysis

All statistical analyses were performed using R statistics

[128]. Difference scores were calculated for all

behavioral and EEG measures (Posttest - Pretest) and

used as the primary outcome of interest. Much of the

data presented as not normally distributed or

homoscedastic, thus robust estimators were used, with

R functions from [129] and the WRS2 package [130].

Pairwise comparisons were conducted using a robust

bootstrap-t method (R function linconbt from functions

in [129]). This method computes sample trimmed

means (20%) and Yuen’s estimate of squared standard

errors, before generating bootstrap samples to estimate

the distribution. For tasks that included multiple

conditions, a robust bootstrap-trimmed-mean method

was used (R functions bwtrim and bwwtrim from

WRS2). 20% trimming was used in all tests as it is a

compromise between the mean and median. These

robust methods perform well under non-normal

conditions and small sample sizes [129]. Effect sizes

were computed (R function ES.summary) for all

significant main effects and interactions using QS, a

heteroscedastic, non-parametric measure based on

medians. An alpha level of 0.05 was used for all tests.

Behavioral analysis

Separate robust bootstrap-t tests were conducted for

each behavioral task, with Group as the between-groups

factor and difference score as the dependent variable.

For the MINT, task condition was included as a within-

groups factor (Prediction, Melody, and Rhythm). For

Ryff’s and the Goldsmith MSI, each subcategory was

assessed separately. DeJong’s scale was assessed at

post-test only, and scores on the emotional and social

subcategory were assessed separately. For the open-

ended well-being prompt (“Do you think that the music

intervention has had any impact on your social life or

feelings of connection with other people?”) responses

were transcribed and sorted into one of three categories

: 1) social impact, 2) emotional impact, or 3) no impact

and proportion of responses in each category were

assessed by Group. These categories were aimed to

parallel the “social” and “emotional” aspect of

loneliness measured in the DeJong scale [122]. For the

EEG syllable-in-noise task, SNR condition was

included as a within-groups factor (silent, 0dB, 10dB,

5dB). Accuracy and reaction time during the EEG

syllable-in-noise task were only recorded during the

Active listening condition. For the EEG Oddball task,

group differences in accuracy and reaction time were

compared separately.

EEG analysis

Separate bootstrap-trimmed-means tests were conducted

for each EEG task, for each component of interest for

amplitude and latency difference scores. When

appropriate, laterality was included as a factor in both

EEG tasks due to the known right-lateralized processing

of musical pitches [131], the mediating effect of pitch

perception on speech-in-noise abilities [68, 132], and

influence of musical training on right- lateralized

temporal structures [133, 134]. For the syllable-in-noise

task, SNR Condition (Silent, 10dB, 5dB, 0dB), Laterality

(amplitude only), and Frontality (amplitude only; frontal

vs central electrodes) were included as within-subjects

factors, and Group was included as a between-subjects

factor. The Active and Passive listening conditions of the

syllable-in-noise task were analyzed separately. For the

Oddball task, components were assessed separately for

each trial type (Oddball, Standard, and Distractor).

Laterality (amplitude only; left, middle and right) or

Frontality (amplitude only; frontal, central, parietal) was

included as a within-subjects factor, and group was

included as a between-subjects factor.

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Table 2. Trials in EEG tasks.

Pre-test

mean (SD)

Post-test

mean (SD)

Choir

Control

Choir

Control

Syllable-in-noise Active

Silent

123.53 (31.01)

132.68 (19.29)

119.26 (37.64)

112.79 (32.71)

10 dB

121.87 (33.01)

132.26 (22.22)

114.13 (44.14)

111.58 (34.99)

5 dB

130.33 (29.75)

135.37 (16.77)

119.47 (36.54)

111.63 (40.04)

0 dB

123.00 (34.86)

134.00 (19.23)

116.80 (39.49)

115.89 (36.52)

Syllable-in-noise Passive

Silent

147.11 (4.09)

148.33 (33.93)

148.67 (3.01)

148.16 (2.48)

10 dB

146.22 (6.34)

144.78 (25.28)

149.33 (1.85)

147.78 (4.28)

5 dB

146.61 (3.18)

139.94 (14.90)

149.00 (1.61)

147.28 (9.61)

0 dB

147.83 (2.50)

141.83 (12.19)

147.33 (8.35)

148.22 (3.57)

Oddball

Standard

274.89 (6.64)

263.2 (36.92)

276.28 (5.97)

269.35 (20.47)

Oddball

55.00 (7.11)

51.75 (11.27)

54.11 (5.94)

53.25 (8.28)

Distractor

56.89 (1.94)

54.35 (6.47)

57.11 (1.45)

54.75 (4.52)

Table 3. Syllable-in-noise active task.

Time

Component

Condition

Electrodes

Window

Pre

Silent

F3, FZ,F4

C3, Cz*, C4

35 70

10db

50 80

5db

65 110

0db

60 105

Post

Silent

F3, FZ,F4

C3, Cz*, C4

45 70

10db

50 85

5db

55 95

0db

65 100

Pre

Silent

F3, FZ,F4

C3, Cz*, C4

90 125

10db

115 170

5db

125 190

0db

130 200

Post

Silent

F3, FZ,F4

C3, Cz*, C4

85 130

10db

105 175

5db

125 175

0db

155 205

Pre

Silent

F3, FZ,F4

C3, Cz*, C4

155 200

Post

Silent

F3, FZ,F4

C3, Cz*, C4

160-245

Pre

P3-like component

Silent

F3, FZ,F4

C3, Cz*, C4

275 400

10db

270 430

5db

280 440

0db

295 480

Post

P3-like component

Silent

F3, FZ,F4

C3, Cz*, C4

275 400

10db

280 410

5db

275 430

0db

305 445

*Electrode from which latency was calculated.

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Table 4. Syllable-in-noise passive task.

Time

Component

Condition

Electrodes

Window

Pre

Silent

F3, FZ,F4

C3, Cz*, C4

40 75

10db

50 100

5db

55 105

0db

55 110

Post

Silent

F3, FZ,F4

C3, Cz*, C4

40 70

10db

55 95

5db

55 105

0db

65 115

Pre

Silent

F3, FZ,F4

C3, Cz*, C4

90 130

10db

130 195

5db

145 200

0db

144 215

Post

Silent

F3, FZ,F4

C3, Cz*, C4

90 130

10db

125 185

5db

145 200

0db

155 200

Pre

Silent

F3, FZ,F4

C3, Cz*, C4

160 230

Post

Silent

F3, FZ,F4

C3, Cz*, C4

165 230

*Electrode from which latency was calculated.

Table 5. Oddball task.

Time

Component

Condition

Electrodes

Window

Pre

Oddball,

Standard,

Distractor

F3, FZ*,F4

C3, Cz, C4

65 115

Post

70 110

Pre

Oddball

Cz*

145 250

Standard

135 185

Distractor

190 265

Post

Oddball

Cz*

125 155

Standard

115 145

Distractor

115 145

Pre

Oddball

P3, Pz*, P4

300 625

Post

Oddball

P3, Pz*, P4

315 610

Pre

P3a

Distractor

Fz*

345 395

Post

P3a

Distractor

Fz*

320 390

Pre

P3b

Distractor

Cz*

450 660

Post

*Electrode from which latency was calculated.

 
www.aging-us.com  9485  AGING 
AUTHOR CONTRIBUTIONS 
 
Project  was  conceptualized  by  AH  and  SH.  AH 
acquired  funding.  SH  and  AW  curated  data  and 
performed project administration. SH performed formal 
analysis,  and  RW  provided  critical  revisions.  SH  and 
AH  drafted  manuscript,  with  revisions  from  RW  and 
AW.  All  authors  approved  the  final  version  of  the 
manuscript for submission. 
 
ACKNOWLEDGMENTS 
 
We  thank  Andrew  Schultz  and  Barry  Tan,  our 
wonderful choir director and accompanist, and our choir 
group section  leaders,  Shelby  Stroud, Hee-Seong Lee, 
Alex  Belohlavek,  and  Joshua  Tan  for  their 
contributions.  We  also  thank  Chrysa  Kovach  for  her 
work  curating  playlists,  and  Amita  Padiyar  and  the 
Brain  and  Music  Lab  Research  Assistants  for  their 
assistance in data collection. 
 
CONFLICTS OF INTEREST 
 
The authors have no conflicts of interest to declare. 
 
FUNDING 
 
This project was funded by a grant from the Southern 
California  Clinical  and  Translational  Science  Institute 
awarded to A. Habibi. 
 
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SUPPLEMENTARY MATERIALS

Supplementary Tables

Supplementary Table 1. Means and standard deviations for behavioral tasks by group and time.

Pre-test

mean (SD)

Post-test

mean (SD)

Choir

Control

Choir

Control

BKB-SIN

Total

24.38 (1.12)

24.54 (1.24)

25.03 (1.04)

24.83 (1.24)

Goldsmith MSI

Engagement

37.27 (9.03)

37.47 (11.70)

Perceptual

44.00 (8.96)

46.53 (8.29)

Training

21.47 (9.66)

18.47 (10.86)

Singing

25.13 (8.48)

23.33 (10.22)

Emotions

29.80 (6.66)

32.27 (5.44)

General

69.93 (21.21)

64.73 (19.34)

MINT

Rhythm Accuracy

0.61 (0.17)

0.61 (0.13)

0.66 (0.14)

0.59 (0.14)

Pitch Accuracy

0.66 (0.15)

0.62 (0.13)

0.64 (0.15)

0.61 (0.17)

Prediction Accuracy

0.73 (0.13)

0.69 (0.14)

0.74 (0.13)

0.65 (0.15)

Rhythm RT

4.01 (2.13)

4.11 (1.35)

3.86 (1.77)

3.94 (1.57)

Pitch RT

4.08 (1.52)

4.90 (2.06)

4.56 (3.12)

4.13 (2.09)

Prediction RT

2.47 (0.96)

2.66 (0.64)

2.74 (0.93)

3.01 (1.86)

Ryff’s

Autonomy

38.07 (7.48)

38.86 (5.73)

39.21 (6.99)

38.50 (6.12)

Environmental Mastery

36.44 (7.84)

38.04 (6.37)

37.00 (8.44)

37.65 (7.51)

Personal Growth

40.13 (5.78)

43.78 (5.20)

40.38 (5.82)

44.52 (4.95)

Positive Relations

36.67 (7.58)

39.83 (7.67)

36.60 (7.56)

40.35 (7.02)

Purpose

39.00 (5.41)

40.55 (5.75)

37.29 (7.52)

41.05 (6.07)

Self-Acceptance

36.88 (6.18)

35.50 (6.12)

36.81 (8.23)

36.05 (6.69)

Dejong’s

Social Loneliness

3.20 (1.78)

2.21 (2.04)

Emotional Loneliness

2.40 (1.80)

2.36 (2.24)

EEG syllable-in-noise

Silent Accuracy

0.94 (0.12)

0.96 (0.06)

0.94 (0.09)

0.91 (0.17)

10 dB accuracy

0.93 (0.14)

0.96 (0.09)

0.97 (0.03)

0.89 (0.18)

5 dB Accuracy

0.94 (0.08)

0.96 (0.08)

0.97 (0.05)

0.88 (0.21)

0dB Accuracy

0.97 (0.03)

0.98 (0.02)

0.94 (0.13)

0.91 (0.16)

Silent RT

0.24 (0.06)

0.25 (0.08)

0.28 (0.08)

0.25 (0.08)

10 dB RT

0.29 (0.08)

0.29 (0.07)

0.29 (0.11)

0.28 (0.08)

5 dB RT

0.30 (0.08)

0.30 (0.07)

0.31 (0.09)

0.30 (0.08)

0dB RT

0.30 (0.10)

0.32 (0.07)

0.34 (0.09)

0.33 (0.08)

EEG Oddball

Accuracy

0.95 (0.11)

0.93 (0.12)

0.93 (0.10)

0.96 (0.06)

0.47 (0.10)

0.46 (0.10)

0.48 (0.11)

0.44 (0.08)

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Supplementary Table 2. Means and standard deviations of amplitudes for EEG tasks by group and time.

Pre-test

Post-test

Choir

Control

Choir

Control

Mean

amplitude (SD)

Mean

amplitude (SD)

Mean

amplitude (SD)

Mean

amplitude (SD)

Syllable-in-noise, active

P1 Silent

0.21 (0.61)

0.37 (0.72)

0.46 (0.67)

0.20 (0.85)

P1 10dB

0.27 (0.57)

0.08 (0.51)

0.24 (0.45)

0.28 (0.55)

P1 5dB

0.23 (0.46)

0.05 (0.42)

0.19 (0.49)

0.31 (0.58)

P1 0dB

-0.03 (0.48)

0.01 (0.46)

-0.07 (0.64)

0.00 (0.41)

N1 Silent

-0.69 (1.18)

-0.94 (1.58)

-1.03 (0.98)

-0.90 (1.57)

N1 10dB

-0.57 (0.76)

-0.54 (1.13)

-0.23 (0.73)

-0.39 (0.92)

N1 5dB

-0.38 (0.72)

-0.69 (0.82)

-0.72 (1.01)

-0.55 (0.89)

N1 0dB

-0.78 (0.77)

-0.62 (0.86)

-0.66 (0.76)

-0.54 (0.97)

P2 Silent

1.65 (1.09)

1.28 (1.02)

1.68 (1.08)

1.53 (1.30)

P3-like Silent

1.15 (1.12)

1.31 (1.20)

1.00 (1.31)

1.70 (1.07)

P3-like 10 dB

0.69 (0.75)

1.29 (0.96)

0.78 (1.21)

1.38 (0.82)

P3-like 5 dB

0.85 (1.24)

1.18 (0.87)

0.87 (1.14)

1.25 (1.08)

P3-like 0 dB

0.81 (0.86)

1.08 (0.96)

0.72 (0.95)

1.25 (1.09)

Syllable-in-noise, passive

P1 Silent

0.52 (0.59)

0.49 (0.65)

0.50 (0.63)

0.44 (0.59)

P1 10dB

0.50 (0.33)

0.42 (0.42)

0.50 (0.42)

0.57 (0.46)

P1 5dB

0.32 (0.32)

0.35 (0.26)

0.37 (0.41)

0.52 (0.42)

P1 0dB

0.31 (0.27)

0.42 (0.30)

0.30 (0.34)

0.37 (0.53)

N1 Silent

-0.93 (0.82)

-1.39 (0.84)

-1.18 (0.80)

-1.34 (0.68)

N1 10dB

-0.28 (0.52)

-0.61 (0.42)

-0.48 (0.52)

-0.56 (0.45)

N1 5dB

-0.33 (0.45)

-0.59 (0.44)

-0.50 (0.51)

-0.70 (0.54)

N1 0dB

-0.19 (0.46)

-0.47 (0.50)

-0.49 (0.50)

-0.58 (0.44)

P2 Silent

1.13 (0.83)

1.15 (0.82)

1.13 (0.78)

1.46 (0.87)

Oddball

N1 Oddball

-1.25 (1.46)

-2.32 (1.56)

-0.87 (1.55)

-2.04 (1.35)

N1 Standard

-0.99 (0.97)

-1.81 (1.33)

-0.95 (1.16)

-1.55 (1.22)

N1 Distractor

-1.34 (1.27)

-1.92 (2.03)

-0.77 (1.24)

-1.95 (1.58)

P2 Oddball

1.38 (1.77)

0.81 (1.27)

0.92 (2.42)

1.12 (1.47)

P2 Standard

1.70 (0.92)

1.59 (0.93)

1.48 (1.01)

1.82 (0.97)

P2 Distractor

1.62 (1.48)

1.55 (1.43)

1.21 (1.79)

1.50 (1.40)

P3a Distractor

1.66 (1.57)

1.37 (2.15)

1.79 (1.93)

1.73 (2.06)

P3b Oddball

0.29 (0.71)

0.03 (1.18)

0.25 (0.92)

0.04 (1.06)

P3b Standard

0.22 (0.37)

-0.05 (0.56)

0.28 (0.52)

0.00 (0.52)

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Supplementary Table 3. Means and standard deviations of latencies for EEG tasks by group and time.

Pre-test

Post-test

Choir

Control

Choir

Control

Mean latency

(SD)

Mean latency

(SD)

Mean latency

(SD)

Mean latency

(SD)

Syllable-in-noise, active

P1 Silent

62.82 (11.29)

62.00 (13.20)

60.47 (9.37)

62.60 (12.40)

P1 10dB

67.06 (14.53)

70.00 (15.44)

67.53 (13.26)

66.40 (15.10)

P1 5dB

91.06 (13.31)

87.80 (12.81)

73.65 (18.50)

72.40 (18.76)

P1 0dB

78.82 (15.67)

77.60 (16.69)

79.29 (17.51)

89.60 (21.06)

N1 Silent

109.65 (12.33)

108.20 (11.20)

105.41 (10.19)

108.60 (11.12)

N1 10dB

136.94 (14.39)

142.60 (17.76)

143.29 (27.50)

150.80 (22.67)

N1 5dB

159.76 (17.23)

147.40 (19.04)

148.47 (17.37)

150.00 (19.23)

N1 0dB

182.12 (19.80)

176.40 (19.68)

168.47 (15.55)

179.00 (19.11)

P2 Silent

191.53 (18.86)

195.60 (23.36)

195.29 (18.83)

197.80 (23.91)

P3-like Silent

341.18 (45.12)

307.78 (37.51)

325.65 (40.33)

319.11 (31.96)

P3-like 10 dB

355.76 (54.62)

332.89 (42.39)

345.65 (49.30)

346.89 (40.74)

P3-like 5 dB

367.06 (53.00)

350.22 (43.41)

366.12 (51.03)

356.22 (49.70)

P3-like 0 dB

372.00 (61.04)

370.22 (43.09)

368.00 (51.13)

364.44 (38.90)

Syllable-in-noise,

passive

P1 Silent

58.89 (10.70)

56.63 (10.61)

57.33 (10.08)

55.37 (12.46)

P1 10dB

76.00 (13.72)

72.84 (12.90)

77.33 (11.15)

72.63 (14.44)

P1 5dB

81.56 (16.01)

77.05 (14.47)

80.89 (15.97)

85.68 (14.13)

P1 0dB

85.33 (16.35)

84.42 (13.39)

94.89 (14.64)

84.00 (16.97)

N1 Silent

110.89 (7.36)

109.89 (10.01)

109.11 (9.39)

109.05 (10.31)

N1 10dB

157.56 (19.12)

162.11 (20.59)

161.78 (14.21)

158.53 (20.62)

N1 5dB

180.67 (16.54)

177.26 (16.71)

175.33 (18.21)

173.05 (17.07)

N1 0dB

177.56 (18.36)

174.53 (18.39)

184.00 (14.06)

182.11 (10.94)

P2 Silent

192.22 (18.94)

195.58 (20.91)

195.33 (19.32)

201.26 (19.28)

Oddball

N1 Oddball

88.89 (9.76)

92.17 (8.54)

87.33 (10.01)

89.8 (10.26)

N1 Standard

89.33 (7.76)

92.67 (7.04)

88.67 (8.92)

91.40 (8.24)

N1 Distractor

89.56 (11.16)

96.17 (12.11)

88.67 (13.11)

96.60 (12.26)

P2 Oddball

170.22 (32.27)

172.33 (33.21)

163.56 (31.25)

176.80 (34.86)

P2 Standard

192.67 (29.43)

194.00 (31.16)

197.56 (32.03)

205.80 (25.84)

P2 Distractor

197.33 (30.62)

205.00 (28.73)

197.56 (34.30)

213.60 (22.57)

P3a Distractor

317.78 (17.79)

317.33 (18.64)

324.22 (20.00)

325.60 (18.33)

P3b Oddball

578.00 (87.15)

605.33 (103.97)

567.11 (84.78)

567.40 (100.28)

P3b Standard

625.56 (92.21)

637.50 (83.95)

602.22 (97.75)

648.00 (72.94)