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Investigating Influences of Medial Olivocochlear Efferent System on Central Auditory Processing and Listening in Noise: A Behavioral and Event-Related Potential Study

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This electrophysiological study investigated the role of the medial olivocochlear (MOC) efferents in listening in noise. Both ears of eleven normal-hearing adult participants were tested. The physiological tests consisted of transient-evoked otoacoustic emission (TEOAE) inhibition and the measurement of cortical event-related potentials (ERPs). The mismatch negativity (MMN) and P300 responses were obtained in passive and active listening tasks, respectively. Behavioral responses for the word recognition in noise test were also analyzed. Consistent with previous findings, the TEOAE data showed significant inhibition in the presence of contralateral acoustic stimulation. However, performance in the word recognition in noise test was comparable for the two conditions (i.e., without contralateral stimulation and with contralateral stimulation). Peak latencies and peak amplitudes of MMN and P300 did not show changes with contralateral stimulation. Behavioral performance was also maintained in the P300 task. Together, the results show that the peripheral auditory efferent effects captured via otoacoustic emission (OAE) inhibition might not necessarily be reflected in measures of central cortical processing and behavioral performance. As the MOC effects may not play a role in all listening situations in adults, the functional significance of the cochlear effects of the medial olivocochlear efferents and the optimal conditions conducive to corresponding effects in behavioral and cortical responses remain to be elucidated.
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brain
sciences
Article
Investigating Influences of Medial Olivocochlear
Eerent System on Central Auditory Processing and
Listening in Noise: A Behavioral and Event-Related
Potential Study
Aparna Rao 1, *, Tess K. Koerner 2, Brandon Madsen 2and Yang Zhang 3,*
1Department of Speech and Hearing Science, Arizona State University, Tempe, AZ 85287, USA
2VA RR & D National Center for Rehabilitative Auditory Research, Portland, OR 97239, USA;
Tess.koerner@va.gov (T.K.K.); brandon.madsen@va.gov (B.M.)
3Department of Speech-Language-Hearing Sciences & Center for Neurobehavioral Development,
University of Minnesota, Minneapolis, MN 55455, USA
*Correspondence: Aparna.Rao.1@asu.edu (A.R.); zhanglab@umn.edu (Y.Z.);
Tel.: +1-480-727-2761 (A.R.); +1-612-624-7818 (Y.Z.)
Received: 28 May 2020; Accepted: 30 June 2020; Published: 4 July 2020
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Abstract:
This electrophysiological study investigated the role of the medial olivocochlear (MOC)
eerents in listening in noise. Both ears of eleven normal-hearing adult participants were tested.
The physiological tests consisted of transient-evoked otoacoustic emission (TEOAE) inhibition and the
measurement of cortical event-related potentials (ERPs). The mismatch negativity (MMN) and P300
responses were obtained in passive and active listening tasks, respectively. Behavioral responses for
the word recognition in noise test were also analyzed. Consistent with previous findings, the TEOAE
data showed significant inhibition in the presence of contralateral acoustic stimulation. However,
performance in the word recognition in noise test was comparable for the two conditions (i.e., without
contralateral stimulation and with contralateral stimulation). Peak latencies and peak amplitudes
of MMN and P300 did not show changes with contralateral stimulation. Behavioral performance
was also maintained in the P300 task. Together, the results show that the peripheral auditory eerent
eects captured via otoacoustic emission (OAE) inhibition might not necessarily be reflected in
measures of central cortical processing and behavioral performance. As the MOC eects may not
play a role in all listening situations in adults, the functional significance of the cochlear eects of
the medial olivocochlear eerents and the optimal conditions conducive to corresponding eects in
behavioral and cortical responses remain to be elucidated.
Keywords:
event-related potential (ERP); medial olivocochlear (MOC) eerents; otoacoustic emissions
inhibition; contralateral acoustic stimulation (CAS); MMN; P300
1. Introduction
Human auditory perception depends on elaborate neural coding of acoustic properties of the
target sounds and involves bidirectional interactions of the aerent and eerent systems along the
auditory pathway. The neural coding of auditory stimulation begins in the aerent system of the
cochlear inner hair cells on the basilar membrane, which is regulated by the active mechanical behavior
of outer hair cells under the control of a complex eerent innervation system originating from the
medial olivary complex in the brainstem. There has been considerable discussion regarding the role of
the medial olivocochlear (MOC) eerents in auditory perception [
1
,
2
]. A major function of the MOC
eerents is thought to be the enhancement of transient signals in noise. However, it remains unclear
Brain Sci. 2020,10, 428; doi:10.3390/brainsci10070428 www.mdpi.com/journal/brainsci
Brain Sci. 2020,10, 428 2 of 17
how the activation of the MOC system by ipsilateral and/or contralateral stimulation contributes to
the enhanced central processing of the auditory target either autonomously or via interactions with
attentional control. The present electrophysiological study was designed to address this question by
selecting listening conditions known to be conducive to MOC activation and investigating how it
aects cortical-level responses in passive and attentive listening tasks.
Descending eerent neurons from the MOC system synapse on cochlear outer hair cells (OHCs),
the source of the cochlear amplifier, and influence their function. The MOC eerents may be activated
by the corticofugal system, which extends from the cortex toward subcortical nuclei [
3
], or by acoustic
stimulation of the ear [
4
]. The MOC eerents exert control in the cochlea by inhibiting OHC activity,
thereby reducing basilar membrane vibration. This inhibition occurs in quiet [
5
7
] and in the presence
of background noise, leading to reductions in neural fiber responses to noise and enhancing neural
responses to transient signals [
8
10
]. This is referred to as the “antimasking eect” or “unmasking
eect” [
6
,
11
]. The eerent “antimasking” eect is thought to have important implications for the
detection of tones and tonal complexes in noise [
12
14
]. Other important impacts of the MOC system
arise from its role in the process of learning [
15
18
] and its interactions with higher cognitive functions
such as attention [
17
,
19
]. MOC inhibition may also have a protective eect in the cochlea by reducing
the impact of acoustic trauma [20,21].
The beneficial link between MOC inhibition and speech perception remains controversial [
15
,
22
28
].
One complication here is that behavioral results and higher cognitive processing such as speech
perception are influenced by several factors [
15
,
22
]. For instance, Mishra and Lutman [
25
] suggest
that although the “unmasking” eect may be documented for speech stimuli, it may not be related to
MOC eects measured under artificial conditions. In particular, the MOC system may not be used in
natural listening conditions. Age may also play a role, with young children being more dependent
on the eerent system for listening in noise compared with older children (>9 years) and adults [
29
].
The MOC-mediated mechanisms in central auditory processing may be recruited only in some specific
conditions that remain to be investigated [25].
Auditory event-related potentials (ERPs) provide an avenue for investigating the neurophysiological
underpinnings of behavioral effects and attentional processing, which influence the perception of complex
signals in noise. ERPs are non-invasive recordings of electrical voltages created due to synchronous
post-synaptic cortical activity time-locked to an auditory stimulus. ERPs reflect specific sensory and/or
cognitive processes [
30
]. Specific ERPs that may be used to study the perception of signals in noise are the
N1, P2, mismatch negativity (MMN) and P300 responses [
31
40
]. Of particular interest to the current
study are the MMN and P300 components. MMN occurs as a negative wave in the frontal region of
the brain and is elicited when there is a change in a repeated auditory signal. Typically, an oddball
paradigm is used with “standard” stimuli occurring repeatedly and “deviant” or target stimuli
randomly interspersed. The MMN occurs even when subjects are not attending to stimuli and thus
represents an automatic change-detection process. The MMN is sensitive to small stimulus changes
and seems to correlate with behavioral discrimination thresholds in normal subjects as well as in
clinical populations [
41
]. By contrast, the P300 is a positive waveform, which peaks around 300 ms and
is enhanced when subjects attend to and detect deviants. The P300 is associated with the cognitive
processes of stimulus evaluation and categorization. The amplitude of P300 is proportional to the
amount of attentional resources required for stimulus processing [
42
], with latency reflecting stimulus
evaluation [43].
In this exploratory study, we used cortical potentials to study the consequences of MOC activation.
MOC eerent eects in humans can be measured noninvasively using spontaneous otoacoustic
emissions (OAEs) [
44
] or evoked OAEs [
19
,
25
,
45
48
], which are by-products of the cochlear amplifier
mechanism. The MOC activity measured in one ear may be influenced by incoming sound at either
ear, due to ipsilateral, contralateral and bilateral brainstem circuits. The most widely used procedure is
to compare the amplitudes of click-evoked OAEs with and without contralateral broadband noise,
also called “contralateral acoustic stimulation” (CAS), to document the reduction in the OAE amplitude
Brain Sci. 2020,10, 428 3 of 17
when CAS is introduced. This contralateral inhibition of OAEs or the “MOC reflex” [
49
] has been
used as a measure of MOC inhibition strength. For the present study, the transient-evoked otoacoustic
emissions (TEOAEs) were recorded with and without CAS to document the MOC reflex in participants.
This is also the most frequently used protocol for measuring the eects of the MOC reflex [
11
17
].
The ERP tasks for our study were designed with and without CAS to parallel the OAE paradigm.
ERPs were obtained in an active P300 experimental task and a passive MMN task. Signals were
embedded in ipsilateral noise; contralateral noise was used to elicit a greater MOC eect. All the
stimulus levels and characteristics were chosen to optimize the recruitment of MOC eerent eects.
Our study was built upon two recent speculations: (1) individuals may not be using the MOC
unmasking eect for listening in all speech-in-noise situations, and (2) the cochlear eects of MOC
function may not be reflected in tasks that require higher-level processes such as attention. We explored
the links between MOC function and listening in noise by obtaining combined electrophysiological and
behavioral measurements while stimulating the MOC system. If target recognition is facilitated by the
eerent unmasking eect, the modulation of the MMN would be expected in the non-attentive listening
condition. In the active listening condition, however, the unmasking eect would not necessarily be
reflected in the P300 for attention-related processing. Traditional word recognition in noise testing was
also completed for the same participants for comparison.
2. Materials and Methods
2.1. Participants and Qualification Testing
Data obtained from eleven individuals (10 females, mean age of all participants =22.3 years,
SD =1.8 years, range =19–26 years) were analyzed. The data obtained from two participants in
addition to the eleven could not be included for analysis due to the noisy measurements obtained on
one or more physiological tests (OAEs and ERPs). A negative history of speech, language or cognitive
diculties was established through case history interview. The Human Subjects’ Protection Program at
the University of Minnesota approved the study (ethical research approval code: 1308M41281), and all
the participants provided informed consent. The participants were scheduled for two 2-hr sessions
and were paid for their time. The first session consisted of a comprehensive audiologic examination,
including otoscopy, pure-tone air and bone conduction testing, speech reception threshold testing,
and word recognition testing using the Central Institute for the Deaf (CID) W-22 word lists. Participants
were tested behaviorally on nonsense word recognition. Otoacoustic emissions were also recorded
during the first session. The second session consisted of ERP recordings in both passive and active
conditions to record the MMN and P300 ERPs, respectively.
All the participants were right-handed as determined by the Edinburgh Handedness Inventory [
50
].
The participants had hearing within normal limits as revealed by audiometric thresholds at octave
frequencies ranging from 250 to 8000 Hz in both ears. The average thresholds and standard deviations
at the dierent octave frequencies in the two ears are presented in Table 1.
Table 1.
Average audiometric thresholds (in dB Hearing Level (HL)) and standard deviations (within
parentheses) at dierent octave frequencies in the two ears for participants in the study.
Ear 250 Hz 500 Hz 1000 Hz 2000 Hz 4000 Hz 8000 Hz
Right 5 (4.9) 4.2 (3.9) 2.3 (4.7) 2.3 (7.5) 3.8 (7.2) 2.6 (7.8)
Left 5 (8.3) 4.6 (5.7) 3.5 (4.3) 2.3 (7.9) 1.9 (6.1) 2.3 (8.5)
The speech reception thresholds (SRTs) measured using spondees and presented live-voice [
51
]
were consistent with pure-tone averages bilaterally. The word recognition scores (WRSs) obtained with
the recorded W-22 lists [
52
] were excellent (average WRS for right ear =95.27%, SD =3.44%; average
WRS for left ear =95.27%, SD =2.93%) at 30 dB Sensation Level (SL) with reference to the SRT (average
right ear =1.82 dB Hearing Level (HL), SD =2.52 dB; average left ear =2.72 dB HL, SD =2.61 dB).
Brain Sci. 2020,10, 428 4 of 17
The SRT and WRS results were used as qualification criteria to ensure good test reliability and adequate
word recognition abilities in quiet, respectively.
The CAS used to elicit the MOC reflex was broadband and was presented from a portable screening
audiometer (Beltone 120, Beltone Electronics Company, Glenview, IL, USA) through an Etymotic
Research ER-3A (Etymotic Research Inc, Elk Grove Village, IL, USA) insert headphone. The portable
audiometer was calibrated to ANSI-ASA standard S3.6-2010 [
53
]. The spectrum from 250 to 5000 Hz
was within 5 dB of the level at 1000 Hz. The thresholds for the broadband noise (BBN) stimulus were
found using the modified Hughson-Westlake procedure. The broadband noise was presented at 30 dB
SLwith reference to the behavioral threshold for all conditions using CAS. For all participants, the BBN
was between 50 and 55 dBA SPL when measured using a sound level meter. Middle-ear acoustic
reflexes were not obtained to the dierent stimuli used in this experiment due to time and equipment
constraints. While this level is below the average middle-ear reflex threshold for young adults [
54
],
it has been used in similar studies [29].
2.2. OAEs
Click-evoked OAEs were recorded using the Biologic Scout OAE system (2009) (Natus Medical
Incorporated, Mundelein, IL, USA). Clicks were presented at 70 dB peSPL at a presentation rate of
11.72 per second. The responses to 2000 clicks were averaged. The clicks were presented in two
conditions in each ear: (1) in quiet and (2) in the presence of CAS (broadband noise) presented at 30 dB
SL with reference to the BBN behavioral threshold. The recordings for each listening condition were
repeated three times per ear, and the results were averaged. The participants were asked to relax in
order to obtain good measurements. The filter range was 800 to 6000 Hz. The responses between
3.5 ms and 16.6 ms were analyzed. The total amplitudes of the click-evoked OAEs were calculated by
summing the energy in the fast Fourier transform spectrum between 800 and 4500 Hz. The amplitudes
of the click-evoked OAEs were compared between ears and conditions.
2.3. Listening Conditions for Word Recognition Testing and Event-Related Potentials
Four listening conditions were used during word recognition testing and the ERP experiments:
(1) the stimuli were presented to the right ear with ipsilateral noise at a +10 dB signal-to-noise ratio
(SNR) without CAS; (2) the same stimuli were presented to the right ear with the addition of CAS;
(3) the stimuli were presented to the left ear with ipsilateral noise at a +10 dB SNR, without CAS;
and (4) the stimuli were presented to the left ear with CAS. Therefore, Conditions 2 and 4 were similar
to Conditions 1 and 3, respectively, but with CAS. Stimuli were presented to the right ear in Conditions
1 and 2 and to the left ear in Conditions 3 and 4. The conditions were randomized throughout the
experiment for each participant. The ipsilateral and contralateral noises used in this study were
uncorrelated, to prevent the eects of central masking and to reduce masking level dierence. A quiet
condition was not used, as the premise of the testing was the perception of signals in noise with and
without CAS.
2.4. Word Recognition in Noise Test
The Nonsense Syllable Test (NST) [
55
] was used to evaluate monosyllabic nonsense word
recognition. This test was chosen to eliminate the eects of semantic content on word recognition.
Participants listened to words using TDH 39P headphones and were instructed to repeat back what
they heard from the compact-disc recording. Two research assistants knowledgeable in phonetics
scored the phoneme responses and averaged the results for a final score. A dierent list of 25 words
was used for each listening condition. Words were presented to the test ear at 30 dB SL with reference
to the speech reception threshold (SRT) a +10 dB SNR through an Aurical diagnostic audiometer
(GN Otometrics, Schaumburg, IL, USA). Speech-shaped noise was presented continuously through
the second channel of the audiometer to the same ear at a +10 dB SNR. As with the other tests in this
protocol, CAS was presented continuously at 30 dB SL with reference to the BBN behavioral threshold.
Brain Sci. 2020,10, 428 5 of 17
2.5. ERPs
Participants were seated in a comfortable chair in an acoustically and electrically treated booth
(IAC systems, Bronx, NY, USA). An oddball paradigm included a pure-tone contrast with frequently
repeating 2000 Hz standard stimuli and less frequently occurring 2016 Hz deviant stimuli. The signals
were mixed with a white-noise masker presented ipsilaterally at a +10 dB SNR. The signals were
presented at 50 dB SL relative to the behavioral detection threshold at 2000 Hz. The signal intensity as
measured using a sound level meter was 58 to 62 dB SPL. The deviant of 2016 Hz was chosen based
on behavioral pilot experiments in which participants scored 80% accuracy. The pure tone stimuli
and masker were created using Sony Sound Forge 10 (version 10, Sony Creative Software, Middleton,
WI, USA). The pure tone stimuli were 100 ms in duration and included a 10 ms rise time and 10 ms
fall time. The presentation probability was 70% for standards and 30% for deviants. Five blocks of
48 trials were presented with 170 standards and 70 deviants per listening condition. The interstimulus
interval between two stimuli was randomized within the range of 900~1100 ms. The stimulus order
was randomized within the following two constraints: the deviants must have always been separated
by at least two standards, and the first stimulus presented in each block must have been a standard.
To measure eerent eects, contralateral broadband noise was presented at 30 dB SL with reference to
the BBN behavioral threshold (50 to 55 dB SPL A) using the same portable audiometer used for the
OAE and word recognition test protocol.
The same oddball paradigm was used in passive MMN and active P300 conditions. The session
always began with the passive MMN blocks to avoid drawing participants’ attention to deviants.
In this portion of the experiment, participants were asked to watch a movie of their choice that was
played silently with subtitles. In the active P300 task, the participants were asked to press a button
to identify the deviant stimulus. Participants were given a short practice block in quiet before being
presented with the active listening conditions. Behavioral accuracy and response times were recorded
from the participant’s button press responses to calculate the sensitivity index d’ and Criterion [56].
EEG data were collected using the Advanced Neuro Technology (ANT) electroencephalography
system and a 64-channel Waveguard cap (ANT Neuro, Enschede, Netherlands) in the Zhang Lab
at the University of Minnesota. The impedance of the electrodes was below 15 k
. The data were
band-pass filtered between 0.016 Hz and 200 Hz and digitized using a sampling frequency of 512 Hz.
ERP responses were analyzed o-line using ANT’s Advanced Source Analysis software (version 4.6,
ANT Neuro, Enschede, Netherlands). The responses were band-pass filtered o-line between 0.2 Hz
and 40 Hz. Trials with potentials exceeding
±
50
µ
V were then rejected. The analysis window was from
100 ms to +600 ms relative to the stimulus onset.
Waveforms were referenced to linked mastoids and analyzed using the peak measures from
individual electrodes for amplitude and latency values. The latency ranges used for analysis were
120–350 ms and 200–600 ms for MMN and P300 ERPs, respectively. MMN analysis was conducted
for data obtained from frontal, mid-frontal, central and mid-central electrodes. The electrode sites for
analysis were chosen based on scalp maps that showed intense activation in these regions. Similar
methods for electrode grouping were used in previous ERP studies [
57
60
]. The frontal electrodes
included F3, F5, F7, FC3, FC5, FT7 and the corresponding electrodes on the right hemisphere. The central
electrodes included T7, TP7, C3, C5, CP3, CP5 and the corresponding electrodes on the right hemisphere.
The midline frontal electrodes included F1, Fz, F2, FC1, FCz and FC2. The midline central electrodes
included C1, Cz, C2, CP1, CPz and CP2. P300 analysis was conducted for central, mid-central,
parietal and mid-parietal sites. The parietal electrodes included P3, P5, P7, PO3, PO5, PO7 and the
corresponding right-hemisphere electrodes. The midline parietal electrodes included P1, Pz, P2 and
POz. All the ERP measures from the 11 subjects were subjected to repeated-measures ANOVA with
the ear and condition as independent variables.
Brain Sci. 2020,10, 428 6 of 17
3. Results
3.1. OAE Data
Click-evoked OAEs were present at acceptable SNRs in both ears for all the conditions, and the
probe stability was always close to 100%. The average amplitudes of the click-evoked OAEs were
compared for conditions with and without contralateral masking noise. The mean overall amplitudes
for the right and left ears are shown in Figure 1. The amplitude data from the two ears were
analyzed using repeated-measures ANOVA with the ear and condition as independent variables.
There was a significant ear eect (F(1,10) =6.68; p=0.03,
ηp2
=0.40). A main eect of condition
was also seen (F(1,10) =6.87; p=0.03,
ηp2
=0.41), with lower amplitudes in the presence of CAS.
The interaction between the ear and noise was not significant (F(1,10) =0.02; p=0.91, ηp2=0.002).
Brain Sci. 2020, 10, x FOR PEER REVIEW 6 of 18
compared for conditions with and without contralateral masking noise. The mean overall amplitudes
for the right and left ears are shown in Figure 1. The amplitude data from the two ears were analyzed
using repeated-measures ANOVA with the ear and condition as independent variables. There was a
significant ear effect (F(1,10) = 6.68; p = 0.03, ηp2 = 0.40). A main effect of condition was also seen (F(1,10)
= 6.87; p = 0.03, ηp2 = 0.41), with lower amplitudes in the presence of CAS. The interaction between the
ear and noise was not significant (F(1,10) = 0.02; p = 0.91, ηp2 = 0.002).
Figure 1. Mean overall amplitudes of click-evoked otoacoustic emissions (OAEs) for the two
conditions: (1) without contralateral acoustic stimulation (CAS) and (2) with CAS in the two ears.
Error bars indicate standard errors.
3.2. WRS Data
The word recognition scores for the NST calculated for the conditions with and without CAS are
presented in Table 2. The main effects (ear: F(1,10) = 0.17; p = 0.67, ηp2 = 0.02; condition: F(1,10) = 1.79;
p = 0.21, ηp2 = 0.15) and interaction (ear × interaction: F(1,10) = 0.25; p = 0.63, ηp2 = 0.02) were non-
significant.
Table 2. Descriptive statistics of word recognition scores without and with contralateral acoustic
stimulation (CAS) in the two ears. SD = Standard Deviation.
Ear Condition Word Recognition Score in Percentage (SD)
Right Without CAS 78.3 (3.6)
With CAS 76.8 (3.7)
Left Without CAS 77.4 (5.2)
With CAS 77 (3.3)
3.3. MMN Data for Passive Listening Condition
Grand average waveforms of the MMNs obtained for the two conditions are presented in Figure
2. A repeated-measures ANOVA was performed after selecting electrodes from the frontal, mid-
frontal, central and mid-central locations of the scalp, where the MMN was robust. The latencies and
amplitudes were subjected to separate repeated-measures ANOVAs with condition and electrode
location as factors. Descriptive statistics of the MMN are presented in Table 3, and the repeated-
measures ANOVA results, in Table 4. A significant main effect of ear was seen for the MMN latencies,
with peak latencies longer for the left ear than for the right ear. The other main effects and interactions
were non-significant.
Table 3. Descriptive statistics of peak amplitudes and peak latencies of mismatch negativity (MMN)
in the two conditions in the two ears. Please see text for sites grouped for analysis. CAS = Contralateral
Acoustic Stimulation, µV = micro Volts, ms = milliseconds, SD = Standard Deviation.
Ear Condition
MMN
Amplitude
(µV) (SD)
Latency
(ms) (SD)
Right Without CAS 1.22 (0.47) 227.3 (23.5)
-2
0
2
4
6
Right Ear Left Ear
Level in dB SPL
Without Contralateral Acoustic Stimulation
With Contralateral Acoustic Stimulation
Figure 1.
Mean overall amplitudes of click-evoked otoacoustic emissions (OAEs) for the two conditions:
(1) without contralateral acoustic stimulation (CAS) and (2) with CAS in the two ears. Error bars
indicate standard errors.
3.2. WRS Data
The word recognition scores for the NST calculated for the conditions with and without CAS
are presented in Table 2. The main eects (ear: F(1,10) =0.17; p=0.67,
ηp2
=0.02; condition:
F(1,10) =1.79; p=0.21,
ηp2
=0.15) and interaction (ear
×
interaction: F(1,10) =0.25; p=0.63,
ηp2
=0.02)
were non-significant.
Table 2.
Descriptive statistics of word recognition scores without and with contralateral acoustic
stimulation (CAS) in the two ears. SD =Standard Deviation.
Ear Condition Word Recognition Score in Percentage (SD)
Right Without CAS 78.3 (3.6)
With CAS 76.8 (3.7)
Left Without CAS 77.4 (5.2)
With CAS 77 (3.3)
3.3. MMN Data for Passive Listening Condition
Grand average waveforms of the MMNs obtained for the two conditions are presented in Figure 2.
A repeated-measures ANOVA was performed after selecting electrodes from the frontal, mid-frontal,
central and mid-central locations of the scalp, where the MMN was robust. The latencies and amplitudes
were subjected to separate repeated-measures ANOVAs with condition and electrode location as factors.
Descriptive statistics of the MMN are presented in Table 3, and the repeated-measures ANOVA results,
in Table 4. A significant main eect of ear was seen for the MMN latencies, with peak latencies longer
for the left ear than for the right ear. The other main eects and interactions were non-significant.
Brain Sci. 2020,10, 428 7 of 17
Brain Sci. 2020, 10, x FOR PEER REVIEW 7 of 18
With CAS
1.15 (0.34)
Left
Without CAS
1.21 (0.33)
With CAS
1.1 (0.52)
Table 4. Repeated-measures ANOVA results for MMN amplitude and latency. (* stands for p < 0.05).
Main Effects and
Interactions
MMN Peak
Amplitude
MMN Peak Latency
Ear
F(1,10) = 0.145
p = 0.71
ηp2 = 0.014
F(1,10) = 6.71
p = 0.03 *
ηp2 = 0.40
Condition
F(1,10) = 1.25
p = 0.29
ηp2 = 0.11
F(1,10) = 2.11
p = 0.17
ηp2 = 0.175
Ear × Condition
F(1,10) = 0.058
p = 0.81
ηp2 = 0.006
F(1,10) = 1.4
p = 0.26
ηp2 = 0.263
Figure 2. Grand average waveforms for event-related potential (ERP) responses to standard and
deviant stimuli in the passive (MMN) condition, and the difference between the two (deviant
standard). Waveforms were averaged across frontal, mid-frontal, central and mid-central sites. Panel
(A): left ear, no contralateral acoustic stimulation (CAS). Panel (B): left ear, with CAS. Panel (C): right
ear, no CAS. Panel (D): right ear, with CAS.
3.4. P300 Data for Active Listening Condition
Grand average waveforms of the P300s obtained for the two conditions are presented in Figure
3. Descriptive statistics are presented in Table 5. A repeated-measures ANOVA was performed after
selecting the central, mid-central, parietal and mid-parietal scalp locations where the P300 was most
prominent, and the results are shown in Table 6. Once again, the condition without contralateral noise
was compared with the condition with contralateral noise. The Peak P300 latencies and peak
amplitudes did not show significant differences between the two conditions.
Table 5. Descriptive statistics of peak amplitudes and peak latencies of MMN and P300 in the two
conditions in the two ears. Please see text for sites grouped for analysis. CAS = Contralateral Acoustic
Stimulation, µV = micro Volts, ms = milliseconds, SD = Standard Deviation.
Ear
Condition
MMN
P300
Amplitude
Latency
Amplitude
Latency
Figure 2.
Grand average waveforms for event-related potential (ERP) responses to standard and
deviant stimuli in the passive (MMN) condition, and the dierence between the two (deviant–standard).
Waveforms were averaged across frontal, mid-frontal, central and mid-central sites. Panel (
A
): left ear,
no contralateral acoustic stimulation (CAS). Panel (B): left ear, with CAS. Panel (C): right ear, no CAS.
Panel (D): right ear, with CAS.
Table 3.
Descriptive statistics of peak amplitudes and peak latencies of mismatch negativity (MMN) in
the two conditions in the two ears. Please see text for sites grouped for analysis. CAS =Contralateral
Acoustic Stimulation, µV=micro Volts, ms =milliseconds, SD =Standard Deviation.
Ear Condition
MMN
Amplitude
(µV) (SD)
Latency
(ms) (SD)
Right Without CAS 1.22 (0.47) 227.3 (23.5)
With CAS 1.15 (0.34) 219.65 (18.63)
Left Without CAS 1.21 (0.33) 247.72 (20.93)
With CAS 1.1 (0.52) 228.95 (18.84)
Table 4. Repeated-measures ANOVA results for MMN amplitude and latency. (* stands for p<0.05).
Main Eects and Interactions MMN Peak Amplitude MMN Peak Latency
Ear
F(1,10) =0.145
p=0.71
ηp2=0.014
F(1,10) =6.71
p=0.03 *
ηp2=0.40
Condition
F(1,10) =1.25
p=0.29
ηp2=0.11
F(1,10) =2.11
p=0.17
ηp2=0.175
Ear ×Condition
F(1,10) =0.058
p=0.81
ηp2=0.006
F(1,10) =1.4
p=0.26
ηp2=0.263
3.4. P300 Data for Active Listening Condition
Grand average waveforms of the P300s obtained for the two conditions are presented in Figure 3.
Descriptive statistics are presented in Table 5. A repeated-measures ANOVA was performed after
selecting the central, mid-central, parietal and mid-parietal scalp locations where the P300 was most
prominent, and the results are shown in Table 6. Once again, the condition without contralateral
Brain Sci. 2020,10, 428 8 of 17
noise was compared with the condition with contralateral noise. The Peak P300 latencies and peak
amplitudes did not show significant dierences between the two conditions.
Brain Sci. 2020, 10, x FOR PEER REVIEW 8 of 18
(µV) (SD)
(ms) (SD)
(µV) (SD)
(ms) (SD)
Right
Without CAS
−1.22 (0.47)
227.3 (23.5)
2.09 (0.99)
420.5 (54.97)
With CAS
−1.15 (0.34)
219.65 (18.63)
1.8 (0.95)
417.03 (64.5)
Left
Without CAS
−1.21 (0.33)
247.72 (20.93)
1.99 (0.98)
409.52 (49.83)
With CAS
−1.1 (0.52)
228.95 (18.84)
1.97 (0.98)
444.12 (50.45)
Table 6. Results of repeated-measures ANOVA for P300 peak amplitude and peak latency.
Main effects and
Interactions
P300 Peak Amplitude
P300 Peak Latency
Ear
F(1,10) = 0.022
p = 0.88
ηp2 = 0.002
F(1,10) = 0.72
p = 0.416
ηp2 = 0.067
Condition
F(1,10) = 1.20
p = 0.298
ηp2 = 0.107
F(1,10) = 0.85
p = 0.378
ηp2 = 0.078
Ear × Condition
F(1,10) = 0.533
p = 0.482
ηp2 = 0.051
F(1,10) = 1.65
p = 0.228
ηp2 = 0.142
Figure 3. Grand average waveforms for ERP responses to standard and deviant stimuli in the active
discrimination (P300) condition and the difference between the two (deviantstandard). Waveforms
were averaged across central, mid-central, parietal and mid-parietal sites. Panel (A): left ear, no
contralateral acoustic stimulation (CAS). Panel (B): left ear, with CAS. Panel (C): right ear, no CAS.
Panel (D): right ear, with CAS.
The criterion and d were measured from the hits and false alarms obtained in the active
discrimination (P300) task. The reaction times from the two conditions (with and without CAS) were
also subjected to statistical analysis. Descriptive statistics for the d’, criterion and reaction times are
shown in Table 7. None of the measures showed significant differences (Table 8).
Table 7. Descriptive statistics of d (sensitivity), criterion and reaction times obtained in the active-
discrimination condition (P300 task) without and with contralateral acoustic stimulation (CAS) in the
two ears. SD = Standard Deviation.
Ear
Condition
d’ (SD)
Criterion (SD)
Reaction Times (SD)
Right
Without CAS
2.07 (1.12)
−0.69 (0.38)
556.01 (115.66)
Figure 3.
Grand average waveforms for ERP responses to standard and deviant stimuli in the active
discrimination (P300) condition and the dierence between the two (deviant–standard). Waveforms were
averaged across central, mid-central, parietal and mid-parietal sites. Panel (
A
): left ear, no contralateral
acoustic stimulation (CAS). Panel (
B
): left ear, with CAS. Panel (
C
): right ear, no CAS. Panel (
D
):
right ear, with CAS.
Table 5.
Descriptive statistics of peak amplitudes and peak latencies of MMN and P300 in the two
conditions in the two ears. Please see text for sites grouped for analysis. CAS =Contralateral Acoustic
Stimulation, µV=micro Volts, ms =milliseconds, SD =Standard Deviation.
Ear Condition
MMN P300
Amplitude
(µV) (SD)
Latency
(ms) (SD)
Amplitude
(µV) (SD)
Latency
(ms) (SD)
Right Without CAS 1.22 (0.47) 227.3 (23.5) 2.09 (0.99) 420.5 (54.97)
With CAS 1.15 (0.34) 219.65 (18.63) 1.8 (0.95) 417.03 (64.5)
Left Without CAS 1.21 (0.33) 247.72 (20.93) 1.99 (0.98) 409.52 (49.83)
With CAS 1.1 (0.52) 228.95 (18.84) 1.97 (0.98) 444.12 (50.45)
Table 6. Results of repeated-measures ANOVA for P300 peak amplitude and peak latency.
Main Eects and Interactions P300 Peak Amplitude P300 Peak Latency
Ear
F(1,10) =0.022
p=0.88
ηp2=0.002
F(1,10) =0.72
p=0.416
ηp2=0.067
Condition
F(1,10) =1.20
p=0.298
ηp2=0.107
F(1,10) =0.85
p=0.378
ηp2=0.078
Ear ×Condition
F(1,10) =0.533
p=0.482
ηp2=0.051
F(1,10) =1.65
p=0.228
ηp2=0.142
The criterion and d’ were measured from the hits and false alarms obtained in the active
discrimination (P300) task. The reaction times from the two conditions (with and without CAS) were
Brain Sci. 2020,10, 428 9 of 17
also subjected to statistical analysis. Descriptive statistics for the d’, criterion and reaction times are
shown in Table 7. None of the measures showed significant dierences (Table 8).
Table 7.
Descriptive statistics of d’ (sensitivity), criterion and reaction times obtained in the
active-discrimination condition (P300 task) without and with contralateral acoustic stimulation (CAS)
in the two ears. SD =Standard Deviation.
Ear Condition d’ (SD) Criterion (SD) Reaction Times (SD)
Right Without CAS 2.07 (1.12) 0.69 (0.38) 556.01 (115.66)
With CAS 2.09 (1.18) 0.76 (0.34) 567.29 (134.59)
Left Without CAS 2.59 (1.34) 0.66 (0.5) 543.18 (121.04)
With CAS 2.64 (1.42) 0.67 (0.43) 551.19 (110.43)
Table 8.
Results of repeated-measures ANOVA for d’ (sensitivity), criterion and reaction times obtained
in the active listening condition (P300 task). (* stands for p<0.05).
Main Eects and Interactions d’ Criterion Reaction Time
Ear
F(1,10) =7.133
p=0.02 *
ηp2=0.41
F(1,10) =0.702
p=0.42
ηp2=0.06
F(1,10) =1.24
p=0.292
ηp2=0.11
Condition
F(1,10) =0.62
p=0.80
ηp2=0.006
F(1,10) =1.81
p=0.208
ηp2=0.153
F(1,10) =0.420
p=0.53
ηp2=0.04
Ear ×Condition
F(1,10) =0.121
p=0.735
ηp2=0.012
F(1,10) =0.708
p=0.420
ηp2=0.066
F(1,10) =0.015
p=0.91
ηp2=0.001
4. Discussion
This exploratory study attempted to address an important gap in our understanding of the function
of the auditory MOC eerent system in specific listening conditions by assessing how contralateral
noise aected behavioral and physiological measures. The eects of eerent suppression on OAE
amplitude and word recognition in noise were measured along with ERPs in passive and active tasks.
For one physiological measure, namely the otoacoustic emissions elicited by clicks presented in quiet,
contralateral noise decreased the level. For the other physiological and behavioral measures, stimuli
were presented in ipsilateral noise with and without contralateral noise, and no statistically significant
eect of contralateral noise was seen in any condition.
4.1. OAE Inhibition
In this study, click-evoked OAEs were analyzed in the 1000 Hz to 4000 Hz frequency range,
where maximum suppression is seen in humans [
61
64
]. Animal studies also reveal that mid-to-high
frequencies are strongly aected by the activation of the MOC system [
11
]. The overall amplitudes
of the click-evoked OAEs were reduced in the presence of CAS, which is consistent with previous
findings, exhibiting an inhibitory eect at the cochlear level [
4
,
45
]. The dierences in OAE levels were
seen as an ear eect, with the right ear showing higher levels compared with the left. The ear eect may
be attributed to the asymmetry between the two ears [
65
], which could be related to handedness [
66
].
In our study, all the participants were right-handed.
4.2. Word Recognition Testing
In the present study, the nonsense-syllable recognition scores in noise from the nonsense syllable
test were unchanged in the presence of CAS. This contrasts with previous findings obtained using
similar methodology but with words that carried semantic content. Studies of speech perception have
used monosyllables in noise in the past [
23
,
24
]. When nonsense syllables are used, listeners have to
Brain Sci. 2020,10, 428 10 of 17
rely solely on acoustic features due to the lack of linguistic context. These results suggest that the
antimasking eect alone is insucient to lead to improvement in word recognition scores in adults.
In a 2012 study by de Boer et al. [
22
], phoneme discrimination was uncorrelated with OAE inhibition.
Similarly, in another study [
15
], once subjects had been suciently trained in phoneme discrimination,
the correlation between OAE inhibition and phoneme discrimination was lost, suggesting a stronger
role for attention and/or other central mechanisms. The fact that several other studies have failed
to show a relationship between word recognition scores and OAE inhibition may be attributed to
dierences in the methods used to test MOC eects on word recognition [
27
]. Modeling of eerent
eects [
67
] shows that optimal speech perception is achieved when the amount of eerent activity is
proportional to the level of noise, with the amount of unmasking dependent on both the signal level
and noise level. It has also been shown that individuals with stronger MOC eerent responses are more
responsive to changes in the SNR [
68
]. Therefore, it seems that a specific combination of signal level
and noise level is required to achieve maximal improvement. Additionally, improvements may be age
related [
24
], with young participants showing greater reliance on eerent function in noise. Those who
have received a vestibular neurectomy show nearly normal performance, which points to the role of
possible compensatory mechanisms in word recognition [
26
,
66
]. In summary, it is improbable that the
MOC eect on the cochlea induced using CAS alone at a single SNR would appreciably aect speech
intelligibility in ipsilateral noise at the group level.
4.3. ERPs
Although significant OAE inhibition was seen due to contralateral acoustic stimulation, the MMN
(passive task) showed no statistically significant dierence between the conditions with and without
CAS. Obligatory changes that facilitate signal processing in noise can be measured in the cochlea
and the cochlear nerve (in terms of changes to cochlear gain and the enhanced coding of signals
in noise, respectively), but these may not be reflected at the cortical level in the MMN response.
Alternatively, perhaps the changes are reflected at the cortical level but are suciently subtle that
the MMN was not sensitive enough to capture them—at least when using these particular sound
levels. Interestingly, an ear eect was noted for the MMN peak latency, with the latencies for the
stimuli presented to the left ear longer than the latencies for the stimuli presented to the right ear.
This latency dierence suggests longer processing times for stimuli in the left ear. Ear dierences have
been reported in MMN amplitudes in response to monaural stimulation, especially in patients with
various cortical lesions [
69
]. Interestingly, the left ear was also where the average d’ wsa highest in the
P300 behavioral task.
The P300, which is reflective of conscious perception, was not shown by the ANOVA results to be
significantly aected by the presence or absence of CAS. Behavioral performance was also essentially
identical at the group level regardless of whether CAS was present. Once again, although a peripheral
facilitatory mechanism is likely to be operating, its eects were not observed at the cortical level in
the presence of attentional processing. As with MMN, the possibility remains that the P300 was
simply not sensitive enough to capture the subtle changes transmitted from the cochlear mechanism.
Interestingly, the ANOVA of the behavioral results showed a significant ear eect, with d’ being higher
when stimuli were presented to the left ear rather than the right. It is possible that the participants
were expending greater eort when stimuli were being presented to the left ear, with consequent gains
in behavioral outcomes.
Evidence from others [
22
,
29
] points to a dynamic relationship between MOC function and
central mechanisms during tasks in which individuals are attending to signals in noise. Behavioral
output is modulated by interactions between the MOC system and central mechanisms that are
attention- and experience-dependent. Indeed, long-term training was found to preserve performance
in a signal-in-noise sound localization task in cats with MOC lesions. The authors attributed this to the
development of alternate listening strategies, which were able to minimize the functional consequences
of the auditory lesions [
70
]. Therefore, central mechanisms may compensate for significant changes
Brain Sci. 2020,10, 428 11 of 17
in peripheral function in the processing of complex signals involving redundancy (e.g., speech) or
during highly routine tasks (e.g., the psychophysical testing of intensity or frequency discrimination).
Alternately, the unmasking provided by the MOC system may not be used at all [
16
,
25
]. It has been
shown that the eerent system may have a greater role to play during auditory development in early
childhood, when the central mechanisms for listening in noise are still immature [
29
]. The ERP data
from the present adult study also support the hypothesis that passive and active central mechanisms
may not reflect the facilitatory changes recorded at the periphery in laboratory conditions.
4.4. Methodological Challenges and Lessons
All the physiological measures for our study were obtained with stimuli ideal for
eliciting a particular type of response while maintaining the general characteristics of the protocol.
For example, transient tone bursts were embedded in white noise to elicit ERPs, and clicks were used
to elicit OAEs to record OAE inhibition. Attentional focus was dependent on the task. In the OAE
task, the participants were asked to be quiet and relax as fully as possible. In the passive MMN task,
the participants were watching a movie; during the active P300 task, the participants’ focus was on
the ear being tested. This variation in methodology was inevitable due to the range of physiological
indices used. A frequency discrimination task was used to elicit ERPs, as it is an important skill for
speech perception in noise [
71
]. Additionally, the frequency discrimination thresholds of the second
formant frequency are reportedly aected by the lesioning of the MOC system in cats [
65
]. However,
we need to acknowledge the possibility that the stimuli and presentation levels chosen, including for
the contralateral noise, may be unnatural and work against the original hypotheses. ERP measurements
require a considerable amount of the participant’s time, and we had four conditions even without
multiple signal levels. Given the time constraint, we chose the most frequently reported SNR in the
literature that showed enhancement with syllables or speech. For each condition in our study, only one
level for the stimulus and the ipsilateral noise was chosen, which may not have been the optimal levels
for demonstrating the associations between MOC eects and central auditory processing [
72
]. This is
particularly important given the fact that the ipsilateral noise would also be evoking the MOC reflex
in the presence of considerable within- and across-subject variabilities upon contralateral acoustic
stimulation [73].
Another possible limitation is the methodology used to measure OAE inhibition [
74
]. Stimulus-
frequency OAEs (SFOAEs) have been proposed to be superior to click-evoked OAEs in eliciting OAE
efferent inhibition, given that the signal levels required to elicit click-evoked or tone-evoked OAEs may
themselves cause MOC activation. SFOAEs may be elicited with continuous tones at lower stimulus
intensities than click-evoked OAEs. However, commercial systems that measure SFOAEs are not currently
available. The subclinical activation of the middle-ear muscle reflex could contaminate MOC reflex
eects. We present the caveat that due to the signal levels used in this study, although unlikely,
it is possible that the middle-ear reflex may have contaminated the results obtained from couple of
participants with low middle-ear reflex activation thresholds [75,76].
As the measures of OAE, word recognition in noise, MMN and P300 were obtained in dierent test
sessions with dierent sets of stimuli and listening conditions, involving considerable amounts of test
time for each of our participants, it is methodologically challenging to test many dierent signal levels
to find out what may be sensitive to the interactions between MOC activity and higher-level cortical
processing when listening in noise. Given that the MOC eerent system works both ipsilaterally
and contralaterally with much of the innervation being ipsilateral, we cannot rule out the possibility
that the ipsilateral MOC could have been fully activated in our behavioral experiment, providing
enhanced listening-in-noise recognition. Presumably, this should apply whether or not contralateral
noise stimulation was applied. Likewise, if the ipsilateral MOC has achieved full activation in the EEG
experiments for delivering the hypothesized anti-masking benefits, there is no reason to expect any
additional benefit from adding contralateral noise. Therefore, our conditions may represent a degree of
MOC activation (less activation with ipsilateral noise only vs. more activation with added contralateral
Brain Sci. 2020,10, 428 12 of 17
noise stimulation) as opposed to conditions with vs. without eerent activation. In experiments with
human subjects, it is methodologically challenging to implement protocols for introducing a condition
with no MOC activation for comparison to verify the contributions of MOC to listening in noise [2].
Changes have been reported in ERPs in the presence of ipsilateral noise compared with a quiet
condition [
33
,
34
,
38
,
39
,
77
80
]. Generally, amplitudes are reduced and latencies are prolonged for the
MMN and P300 when stimuli are presented in ipsilateral noise, which may be activating the eerent
system. As we did not include a quiet condition for comparison, we were unable to capture this eect.
Future studies may need to consider simultaneous experimental protocols that can measure
attentional modulation eects in the periphery as well as at the cortical level in the same test
session [
81
]. Nevertheless, the exploratory results reported here could provide a cautionary note to
avoid simplistic expectations or interpretation with regard to cortical and medial eerent systems
in auditory perception [
25
]. For instance, one should not erroneously assume that due to the MOC
“antimasking” eect, presenting contralateral noise at a single predetermined “optimal” SNR would
automatically lead to enhanced ipsilateral speech-in-noise performance. The relationship of MOC
eerent activity with higher-level auditory and speech processing is highly dependent on the task
characteristics, including the SNRs [72].
4.5. Clinical Relevance and Future Directions
In many clinical populations exhibiting diculties with speech perception in noise (for example,
individuals with learning disabilities and children with auditory processing disorders), inadequate
functioning of the MOC system has been documented [
65
,
82
,
83
]. Based on the results of this study, it can
be hypothesized that inecient MOC function is just one piece of the puzzle in these individuals who
have not been able to develop alternative listening strategies, as normal individuals do, to compensate
for deficiencies in MOC function.
Interestingly, increased MOC activity has been documented in musicians [
84
]. Greater OAE
inhibition has been found in musicians compared with age- and gender-matched normal subjects.
Evidence suggests that musicians have superior behavioral speech-in-noise processing skills and
brainstem coding [
85
,
86
]. An interesting hypothesis is that the enhanced auditory experience in
musicians leads to a cumulative strengthening of these mechanisms.
Future studies should be directed toward understanding the role of the MOC system and its
interactions with attention, learning and speech perception. Furthermore, information is required about its
role during the developmental period [
29
] and in clinical populations [
87
,
88
]. This will inform the optimal
procedures to use for the testing and rehabilitation of those who exhibit speech-in-noise difficulties.
5. Conclusions
In summary, this auditory perception study implemented different behavioral and neurophysiological
protocols that involved varying degrees of MOC activation. OAE inhibition was seen in the presence of
contralateral acoustic stimulation. The recognition of nonsense syllables did not appear to change with
the degree of MOC stimulation. Similarly, cortical ERPs as assessed by the MMN and P300 responses
in passive and active listening conditions did not reflect the facilitatory effects seen in the contralateral
inhibition of OAE at the cochlear level. Our findings are consistent with the view that individuals do not
necessarily make use of the available MOC induced unmasking mechanisms for higher-level auditory
processing and speech perception in noise, which are highly subject to influence from age-dependent
attentional, cognitive and experiential factors [
25
]. The dissociation patterns demonstrate the limitations
of the materials and methods implemented in the present study, which underlines the need for further
studies to address the complexity and challenge with suitable protocols to reveal the possible associations
and interactions between cortical and MOC efferent mechanisms in auditory perception.
Brain Sci. 2020,10, 428 13 of 17
Author Contributions:
A.R. and Y.Z. conceived and designed the experiments; A.R., T.K.K. and B.M. performed
the experiments and collected the data; A.R., T.K.K. and Y.Z. analyzed the data; A.R. and Y.Z. wrote the paper;
all the authors revised the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding:
This study was made possible through a grant-in-aid grant from the University of Minnesota to the first
author (A.R.). Zhang was additionally supported by the University of Minnesota’s Grand Challenges Exploratory
Research Project award.
Acknowledgments:
We would like to thank Robert D. Melara, Glenis R. Long, Sharon Miller and Luodi Yu for
their valuable comments for improving this manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
ANOVA Analysis of variance
BBN Broadband noise
CAS Contralateral acoustic stimulation
CID Central Institute for the Deaf
ER Etymotic research
ERP Event-related potentials
MOC Medial olivocochlear
MMN Mismatch negativity
NST Nonsense Syllable Test
P300 Positive peak at 300 ms
OHC Outer hair cell
OAE Otoacoustic emission
SL Sensation level
SPL Sound pressure level
SNR Signal-to-noise ratio
SRT Speech reception threshold
TEOAE Transient-evoked otoacoustic emissions
WRS Word recognition score
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2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
... Furthermore, in these papers, the procedures vary significantly. For example, the recent study by Rao et al. [40] showed no connection between P3 and OAE suppression when listening in noise. The present results might be more closely related to a study by Dragicevic et al. [17] who showed some correlations in the modulation of OAEs and ERPs, although these were of a different kind than in the present study. ...
... Even though we ensured good quality measurements with high SNRs, we did not find a difference between tasks (unlike in some previous experiments). This is in line with some other studies that failed to confirm earlier reported effects on the MOC reflex, for example, there are studies that have failed to show any connection between the MOC reflex and gender or laterality [46], adaptation to noise and central auditory processing [39,40], auditory processing disorders [47], tinnitus [48], or sickle cell disease [49]. Specifically, it has been suggested that previous work on auditory processing disorders probably did not fulfill appropriate SNR criteria in order to ensure reliability [47]. ...
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Understanding speech in background noise is difficult for many listeners with and without hearing impairment (HI). This study investigated the effects of HI on speech discrimination and recognition measures as well as speech-evoked cortical N1-P2 and MMN auditory event-related potentials (AERPs) in background noise. We aimed to determine which AERP components can predict the effects of HI on speech perception in noise across adult listeners with and without HI. The data were collected from 18 participants with hearing thresholds ranging from within normal limits to bilateral moderate-to-severe sensorineural hearing loss. Linear mixed effects models were employed to examine how hearing impairment, age, stimulus type, and SNR listening condition affected neural and behavioral responses and what AERP components were correlated with effects of HI on speech-in-noise perception across participants. Significant effects of age were found on the N1-P2 but not on MMN, and significant effects of HI were observed on the MMN and behavioral measures. The results suggest that neural responses reflecting later cognitive processing of stimulus discrimination may be more susceptible to the effects of HI on the processing of speech in noise than earlier components that signal the sensory encoding of acoustic stimulus features. Objective AERP responses were also potential neural predictors of speech perception in noise across participants with and without HI, which has implications for the use of AERPs as a potential clinical tool for assessing speech perception in noise. Full text for personal sharing available before December 6. 2018 at this web link: https://authors.elsevier.com/c/1XynD1M5IZOSKX
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Since sound perception takes place against a background with a certain amount of noise, both speech and non-speech processing involve extraction of target signals and suppression of background noise. Previous works on early processing of speech phonemes largely neglected how background noise is encoded and suppressed. This study aimed to fill in this gap. We adopted an oddball paradigm where speech (vowels) or non-speech stimuli (complex tones) were presented with or without a background of amplitude-modulated noise and analyzed cortical responses related to foreground stimulus processing, including mismatch negativity (MMN), N2b, and P300, as well as neural representations of the background noise, i.e. auditory steady-state response (ASSR). We found that speech deviants elicited later and weaker MMN, later N2b, and later P300 than non-speech ones, but N2b and P300 had similar strength, suggesting more complex processing of certain acoustic features in speech. Only for vowels, background noise enhanced N2b strength relative to silence, suggesting an attention-related speech-specific process to improve perception of foreground targets. In addition, noise suppression in speech contexts, quantified by ASSR amplitude reduction after stimulus onset, was lateralized towards the left hemisphere. The left-lateralized suppression following N2b was associated with the N2b enhancement in noise for speech, indicating that foreground processing may interact with background suppression, particularly during speech processing. Together, our findings indicate that the differences between perception of speech and non-speech sounds involve not only the processing of target information in the foreground but also the suppression of irrelevant aspects in the background.
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Objectives: The medial olivocochlear (MOC) efferent system can modify cochlear function to improve sound detection in noise, but its role in speech perception in noise is unclear. The purpose of this study was to determine the association between MOC efferent activity and performance on two speech-in-noise tasks at two signal-to-noise ratios (SNRs). It was hypothesized that efferent activity would be more strongly correlated with performance at the more challenging SNR, relative to performance at the less challenging SNR. Design: Sixteen adults aged 35 to 73 years participated. Subjects had pure-tone averages ≤25 dB HL and normal middle ear function. High-frequency pure-tone averages were computed across 3000 to 8000 Hz and ranged from 6.3 to 48.8 dB HL. Efferent activity was assessed using contralateral suppression of transient-evoked otoacoustic emissions (TEOAEs) measured in right ears, and MOC activation was achieved by presenting broadband noise to left ears. Contralateral suppression was expressed as the decibel change in TEOAE magnitude obtained with versus without the presence of the broadband noise. TEOAE responses were also examined for middle ear muscle reflex activation and synchronous spontaneous otoacoustic emissions (SSOAEs). Speech-in-noise perception was assessed using the closed-set coordinate response measure word recognition task and the open-set Institute of Electrical and Electronics Engineers sentence task. Speech and noise were presented to right ears at two SNRs. Performance on each task was scored as percent correct. Associations between contralateral suppression and speech-in-noise performance were quantified using partial rank correlational analyses, controlling for the variables age and high-frequency pure-tone average. Results: One subject was excluded due to probable middle ear muscle reflex activation. Subjects showed a wide range of contralateral suppression values, consistent with previous reports. Three subjects with SSOAEs had similar contralateral suppression results as subjects without SSOAEs. The magnitude of contralateral suppression was not significantly correlated with speech-in-noise performance on either task at a single SNR (p > 0.05), contrary to hypothesis. However, contralateral suppression was significantly correlated with the slope of the psychometric function, computed as the difference between performance levels at the two SNRs divided by 3 (decibel difference between the 2 SNRs) for the coordinate response measure task (partial rs = 0.59; p = 0.04) and for the Institute of Electrical and Electronics Engineers task (partial rs = 0.60; p = 0.03). Conclusions: In a group of primarily older adults with normal hearing or mild hearing loss, olivocochlear efferent activity assessed using contralateral suppression of TEOAEs was not associated with speech-in-noise performance at a single SNR. However, auditory efferent activity appears to be associated with the slope of the psychometric function for both a word and sentence recognition task in noise. Results suggest that individuals with stronger MOC efferent activity tend to be more responsive to changes in SNR, where small increases in SNR result in better speech-in-noise performance relative to individuals with weaker MOC efferent activity. Additionally, the results suggest that the slope of the psychometric function may be a more useful metric than performance at a single SNR when examining the relationship between speech recognition in noise and MOC efferent activity.