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Evidence of degraded representation of speech in noise, in the aging midbrain
and cortex
Alessandro Presacco,
1,2
Jonathan Z. Simon,
2,3,4,5
and Samira Anderson
1,2
1
Department of Hearing and Speech Sciences, University of Maryland, College Park, Maryland;
2
Neuroscience and Cognitive
Science Program, University of Maryland, College Park, Maryland;
3
Department of Electrical and Computer Engineering,
University of Maryland, College Park, Maryland;
4
Department of Biology, University of Maryland, College Park, Maryland;
and
5
Institute for Systems Research, University of Maryland, College Park, Maryland
Submitted 16 May 2016; accepted in final form 12 August 2016
Presacco A, Simon JZ, Anderson S. Evidence of degraded rep-
resentation of speech in noise, in the aging midbrain and cortex. J
Neurophysiol 116: 2346 –2355, 2016. First published August 17,
2016; doi:10.1152/jn.00372.2016.—Humans have a remarkable abil-
ity to track and understand speech in unfavorable conditions, such as
in background noise, but speech understanding in noise does deteri-
orate with age. Results from several studies have shown that in
younger adults, low-frequency auditory cortical activity reliably syn-
chronizes to the speech envelope, even when the background noise is
considerably louder than the speech signal. However, cortical speech
processing may be limited by age-related decreases in the precision of
neural synchronization in the midbrain. To understand better the
neural mechanisms contributing to impaired speech perception in
older adults, we investigated how aging affects midbrain and cortical
encoding of speech when presented in quiet and in the presence of a
single-competing talker. Our results suggest that central auditory
temporal processing deficits in older adults manifest in both the
midbrain and in the cortex. Specifically, midbrain frequency follow-
ing responses to a speech syllable are more degraded in noise in older
adults than in younger adults. This suggests a failure of the midbrain
auditory mechanisms needed to compensate for the presence of a
competing talker. Similarly, in cortical responses, older adults
show larger reductions than younger adults in their ability to
encode the speech envelope when a competing talker is added.
Interestingly, older adults showed an exaggerated cortical repre-
sentation of speech in both quiet and noise conditions, suggesting
a possible imbalance between inhibitory and excitatory processes,
or diminished network connectivity that may impair their ability to
encode speech efficiently.
aging; electrophysiology; midbrain; cortex; hearing
NEW & NOTEWORTHY
We investigate the underlying neurophysiology of age-
related auditory temporal processing deficits in normal-
hearing listeners using natural speech (in noise). Two
neurophysiological techniques are used—magnetoen-
cephalography and EEG—to investigate two different
brain areas— cortex and midbrain—within each subject.
Older adults have more exaggerated cortical speech rep-
resentations than younger adults in both quiet and noise.
Midbrain speech representations depend more critically on
noise level and synchronize more weakly in older adults
than younger.
THE ABILITY TO TRACK AND UNDERSTAND speech in the presence of
interfering speakers is one of the most complex communication
challenges experienced by humans. In a complex auditory
scene, both humans and animals show an innate ability to
detect and recognize individual auditory objects, an important
component in the process of stream segregation. The ability to
transform the noise-corrupted acoustic signal into a neural
representation suitable for speech recognition may occur in the
auditory cortex via adaptive neural encoding (Ding et al. 2014;
Ding and Simon 2012, 2013). Specifically, low-frequency
auditory cortical activity recorded with magnetoencephalogra-
phy (MEG) reliably synchronizes to the slow temporal modu-
lations of speech, even when the energy of the background
noise is considerably higher than the speech signal and even
when the background noise is also speech (Ding and Simon
2012). However, the accuracy of cortical speech processing
may also be affected by the precision of neural synchronization
in the auditory midbrain, as seen in studies that compare
cortical responses with those using the frequency following
response (FFR), believed to arise primarily from the midbrain
(Chandrasekaran and Kraus 2010). For example, noise has a
greater impact on the robustness of cortical speech processing
in children (with learning impairments) who have delayed peak
latencies in FFRs to a speech syllable (King et al. 2002). In
normal-hearing young adults, earlier peak latencies in the FFR
are associated with larger N1 amplitudes in cortical responses
to speech in noise, and larger N1 amplitudes are associated
with a better ability to recognize sentences in noise (Parbery-
Clark et al. 2011). Furthermore, Bidelman et al. (2014) dem-
onstrated that age-related temporal speech-processing deficits
arising from the midbrain may be compensated by a stronger
cortical response. Recent work from Chambers et al. (2016)
showed that a combination of profound cochlear denervation
and desynchronization can result in absence of wave I in the
brain stem but not in the cortex, suggesting compensatory
central gain increases that help restore the representation of the
auditory object in auditory cortex. Whereas these studies ex-
amined age- and hearing loss-related changes in midbrain and
cortical encoding of vowels and tones presented in quiet, the
comparison between midbrain and cortical encoding of speech
syllables and sentences presented in competing single-talker
speech has not yet been investigated in either younger or older
adults.
Such auditory temporal processing deficits are of great
relevance, since communication difficulties for older adults
have a significant social impact, with strong correlations seen
Address for reprint requests and other correspondence: A. Presacco, Dept. of
Otolaryngology, Univ. of California, Irvine. Medical Sciences D, Room 404,
Irvine, CA, 92697 (e-mail: presacca@uci.edu).
J Neurophysiol 116: 2346 –2355, 2016.
First published August 17, 2016; doi:10.1152/jn.00372.2016.
2346 0022-3077/16 Copyright © 2016 the American Physiological Society www.jn.org
between hearing loss and depression (Carabellese et al. 1993;
Herbst and Humphrey 1980; Kay et al. 1964; Laforge et al.
1992) and cognitive impairment (Gates et al. 1996; Lin et al.
2013; Uhlmann et al. 1989). Although audibility is an impor-
tant factor in the older adult’s ability to understand speech
(Humes and Christopherson 1991; Humes and Roberts 1990),
the use of hearing aids often does not improve speech under-
standing in noise, perhaps because increased audibility cannot
restore temporal precision degraded by aging. Several electro-
physiological studies in humans and animals support the hy-
pothesis that degraded auditory temporal processing may play
a role in explaining speech-in-noise problems experienced by
older adults (Alain et al. 2014; Anderson et al. 2012; Clinard
and Tremblay 2013; Lister et al. 2011; Parthasarathy and
Bartlett 2011; Presacco et al. 2015; Ross et al. 2010; Soros
et al. 2009).
To investigate further the neural mechanisms underlying
age-related deficits in speech-in-noise understanding, this cur-
rent study evaluated the effects of aging on temporal synchro-
nization of speech in the presence of a competing talker in both
cortex and midbrain. To de-emphasize the effects of audibility,
only clinically normal-hearing listeners were included in both
the younger and older age groups. We posit several hypotheses.
First, in responses arising from midbrain, we hypothesize that
younger adults encode speech with greater neural fidelity,
reflected by higher amplitude responses and higher stimulus-
to-response and quiet-to-noise correlations than older adults
when the signal is presented in quiet and in noise. This
hypothesis was driven by the results of the above-mentioned
studies, showing more robust and less jittered responses in
quiet in younger adults (Anderson et al. 2012; Clinard and
Tremblay 2013; Mamo et al. 2016; Presacco et al. 2015) and an
age-related effect of noise (Parthasarathy et al. 2010). In
contrast, for cortical responses, we hypothesize that older
adults will show an over-representation of the response both in
quiet and noise. This hypothesis is driven by evidence showing
age-related increases in amplitude (Alain et al. 2014; Soros et
al. 2009) and in latency (Tremblay et al. 2003) of the main
peaks of auditory cortical responses. Finally, we hypothesize
that within an individual, better speech-in-noise understanding
(at the behavioral level) correlates with greater fidelity of
neural encoding of speech, regardless of age.
MATERIALS AND METHODS
The experimental protocol and all procedures were reviewed and
approved by the Institutional Review Board of the University of
Maryland. Participants gave written, informed consent, according to
principles set forth by the University of Maryland’s Institutional
Review Board, and were paid for their time.
Participants
Participants comprised 17 younger adults (18 –27 yr, means ⫾SD
22.23 ⫾2.27, 3 men) and 15 older adults (61–73 yr, means ⫾SD
65.06 ⫾3.30, 5 men), recruited from the Maryland; Washington,
D.C.; and Virginia areas. All participants had clinically normal hear-
ing (Fig. 1), defined as follows: 1) air conduction thresholds ⱕ25 dB
hearing level from 125 to 4,000 Hz bilaterally and 2) no interaural
asymmetry (⬎15 dB hearing-level difference at no more than 2
adjacent frequencies). Participants had a normal intelligence quotient
[scores ⱖ85 on the Wechsler Abbreviated Scale of Intelligence (Zhu
and Garcia 1999)] and were not significantly different on intelligence
quotient [F
(1,30)
⫽0.660, P⫽0.423] and sex (Fisher’s exact, P⬎
0.05). Because of the established effects of musicianship on subcor-
tical auditory processing (Bidelman and Krishnan 2010; Parbery-
Clark et al. 2012), professional musicians were excluded. In addition,
the older adults were screened for dementia on the Montreal Cognitive
Assessment (Nasreddine et al. 2005). All participants spoke English
as their first language, and none of them were tonal language speakers.
Speech Intelligibility
The Quick Speech-in-Noise test (QuickSIN) (Killion et al. 2004)
was used to quantify the ability to understand speech presented in
noise composed of four-talker babble.
EEG: Stimuli and Recording
A 170-ms/da/(Anderson et al. 2012) was synthesized at a 20-kHz
sampling rate with a Klatt-based synthesizer (Klatt 1980). The stim-
Fig. 1. Audiogram (mean ⫾1 SE) of the grand averages of both
ears of younger (gray) and older (black) adults. All participants
have clinically normal hearing. HL, hearing level.
2347AGING EFFECTS OF NEURAL PROCESSING OF SPEECH IN NOISE
J Neurophysiol •doi:10.1152/jn.00372.2016 •www.jn.org
ulus was presented at an 80 peak-dB sound-pressure level diotically
with alternating polarities at a rate of 4 Hz through electromagneti-
cally shielded insert earphones (ER·1; Etymotic Research, Elk Grove
Village, IL) via Xonar Essence One (ASUS, Taipei, Taiwan) using
Presentation software (Neurobehavioral Systems, Berkeley, CA). A
single-competing female talker narrating A Christmas Carol by
Charles Dickens was used as the background noise. FFRs were
recorded in quiet and in noise [signal-to-noise ratios (SNRs): ⫹3, 0,
⫺3, and ⫺6 dB] at a sampling frequency of 16,384 Hz using the
ActiABR-200 acquisition system (BioSemi B.V., Amsterdam, Neth-
erlands) with a standard vertical montage of five electrodes (Cz active,
2 forehead ground common mode sense/driven right leg electrodes,
earlobe references) and with an online 100- to 3,000-Hz bandpass
filter. During the recording session (⬃1 h), participants sat in a
recliner and watched a silent, captioned movie of their choice to
facilitate a relaxed yet wakeful state. Artifact-free sweeps (2,300)
were recorded for each condition from each participant.
Data analysis. Data recorded with BioSemi B.V. were analyzed in
MATLAB (version R2011b; MathWorks, Natick, MA) after being
converted into MATLAB format with the function pop_biosig from
EEGLab (Scott Makeig, Swartz Center for Computational Neurosci-
ence, University of California, San Diego, CA) (Delorme and Makeig
2004). Sweeps with amplitude in the ⫾30-
V range were retained and
averaged in real time and then processed offline. The time window for
each sweep was ⫺47 to 189 ms, referenced to the stimulus onset.
Responses were digitally bandpass filtered offline from 70 to 2,000 Hz
using a fourth-order Butterworth filter to minimize the effects of
cortical low-frequency oscillations (Galbraith et al. 2000; Smith et al.
1975). A final average response was created by averaging the sweeps
of both polarities to minimize the influence of cochlear microphonic
and stimulus artifact on the response and simultaneously maximize
the envelope response (Aiken and Picton 2008; Campbell et al. 2012;
Gorga et al. 1985). Root-mean-square (RMS) values were calculated
for the transition (18 – 68 ms) and steady-state (68 –170 ms) regions.
Correlation (Pearson’s linear correlation) between the envelope re-
sponse in quiet and noise was calculated for each subject to estimate
the extent to which noise affects the FFR. Pearson’s linear correlation
was also used to quantify the stimulus-to-response correlation in the
steady-state region, during which the response more reliably follows
the stimulus. For this analysis, the envelope of the analytic signal of
the stimulus was extracted and then band-pass filtered using the same
filter as for the response. Average spectral amplitudes over 20 Hz bins
were also calculated from each response using a fast Fourier transform
(FFT) with zero padding and 1 Hz interpolated frequency resolution
over the transition and steady-state regions for the fundamental
frequency (F
0
) and the first two harmonics. An additional analysis
signal was created by subtracting and then averaging the sweeps of the
two polarities to enhance the temporal fine structure (TFS) (Aiken and
Picton 2008). One younger adult was removed from the TFS analysis
because of corruption by stimulus artifact. Average spectral ampli-
tudes over 20 Hz bins were calculated for the TFS from each response
using a FFT with zero padding and 1 Hz interpolated frequency
resolution over the transition and steady-state regions for the frequen-
cies of 400 and 700 Hz, which represent the two main peaks of interest
from the two time regions (Anderson et al. 2012).
MEG: Stimuli and Recording
Participants were asked to attend to one of two stories (foreground)
presented diotically while ignoring the other one. The stimuli for the
foreground consist of segments from the book, The Legend of Sleepy
Hollow by Washington Irving, whereas the stimuli for the background
were the same as were used in the EEG experiment. The foreground
was spoken by a male talker, whereas the background story was
spoken by a female talker. Additional stimuli using a background
narration in an unfamiliar language were also presented, but the
responses to those stimuli are not analyzed here. Each speech mixture
was constructed, as described by Ding and Simon (2012), by digitally
mixing two speech segments into a single channel with a duration of
1 min. Five different conditions were recorded: quiet and ⫹3, 0, ⫺3,
and ⫺6 dB SNR. Four different segments from the same foreground
story were used to minimize the possibility that the clarity of the
stories could affect the performance of the subjects. The same seg-
ment was played for quiet and ⫺6 dB. To maximize the level of
attention of the subject on the foreground segment, participants were
asked beforehand to count the number of times a specific word or
name was mentioned in the story. The sounds, ⬃70 dB sound-
pressure level when presented with a solo speaker, were delivered to
the participants’ ears with 50 ⍀sound tubing (E-A-RTONE 3A;
Etymotic Research), attached to E-A-RLINK foam plugs inserted into
the ear canal. The entire acoustic delivery system was equalized to
give an approximately flat transfer function from 40 to 4,000 Hz,
thereby encompassing the range of the delivered stimuli. Neuromag-
netic signals were recorded using a 157-sensor whole-head MEG
system (Kanazawa Institute of Technology, Nonoichi Ishikawa, Ja-
pan) in a magnetically shielded room, as described in Ding and Simon
(2012).
Data analysis. Three reference channels were used to measure and
cancel the environmental magnetic field by using time shift-principal
component analysis (de Cheveigné and Simon 2007). MEG data were
analyzed offline using MATLAB. The 157 raw MEG data channel
responses were first filtered between 2 and 8 Hz, with an order 700
windowed (Hamming) linear-phase finite impulse response filter, then
decomposed using nspatial filters into nsignal components (where
nⱕ157) using the denoising source separation (DSS) algorithm (de
Cheveigné and Simon 2008; Särelä and Valpola 2005). The first six
DSS component filters were then used for the analysis. The filtering
range of 2– 8 Hz was chosen based on previous results showing the
absence of intertrial coherence above 8 Hz (Ding and Simon 2013)
and the importance of the integrity of the modulation spectrum above
1 Hz to understand spoken language (Greenberg and Takayuki 2004).
The signal components used for analysis were then re-extracted from
the raw data for each trial, spatially filtered using the six DSS filters
just constructed, band-pass filtered between 1 and 8 Hz (Ding and
Simon 2012) with a second-order Butterworth filter, and averaged
over trials. Reconstruction of the envelope was performed using a
linear reconstruction matrix estimated via the Boosting algorithm
(David et al. 2007; Ding et al. 2014; Ding and Simon 2013). Success
of the reconstruction is measured by the linear correlation between the
reconstructed and actual speech envelope. The reconstructed envelope
was obtained from the unmixed speech of the single speaker to which
the participant was instructed to attend, not from the acoustic stimulus
mixture. The envelope was computed as the 1- to 8-Hz band pass-
filtered magnitude of the analytic signal. Data were analyzed using
three different time windows for this reconstruction model: 500, 350,
and 150 ms. The choice to narrow the integration window down to
150 ms is based on previous results, showing that the ability to track
the speech envelope substantially worsens as the window decreases
down to 100 ms (Ding and Simon 2013). These values refer to the
time shift imposed on our data with respect to the onset of the speech
and to the corresponding integration window of our reconstruction
matrix. Specifically, if processing time for younger and older adults
is the same, then their performance should follow the same pattern
as the integration window changes. Conversely, if older adults
require more time to process the information because of the
possible presence of temporal processing deficits, then the narrow-
ing of the integration window should negatively affect their per-
formance more than for younger adults. The noise floor was
calculated by using the neural response recorded from each con-
dition to reconstruct the speech envelope of a different stimulus
than was used during this response.
2348 AGING EFFECTS OF NEURAL PROCESSING OF SPEECH IN NOISE
J Neurophysiol •doi:10.1152/jn.00372.2016 •www.jn.org
Statistical Analyses
All statistical analyses were conducted in SPSS version 21.0 (IBM,
Armonk, NY). Fisher’s z transformation was applied to all of the
correlation values calculated for the midbrain and cortical analysis
before any statistical analysis. Split-plot ANOVAs were used to test
for age-group ⫻condition interactions for the RMS values of the FFR
response in the time domain, for the stimulus-to-response correlations
of the FFR, and for correlation values calculated for the cortical data.
The Greenhouse-Geisser test was used when the Mauchly’s sphericity
test was violated. Paired t-tests were used for within-subject group
analysis for the correlation values and amplitudes for the cortical data,
whereas one-way ANOVAs were used to analyze the RMS amplitude
values of the FFR, stimulus-to-noise correlation of the FFR, FFT of
the FFR, quiet-noise correlations, and the correlation values for the
cortical data. The nonparametric Mann-Whitney U-test was used in
place of the one-way ANOVA when Levene’s test for Equality of
Variances was violated. Two-tailed Spearman’s rank correlation (
)
was used to evaluate the relationships among speech-in-noise scores,
midbrain, cortical parameters, and pure-tone average. The false dis-
covery rate procedure (Benjamini and Hochberg 1995) was applied to
control for multiple comparisons where appropriate.
RESULTS
Speech Intelligibility (QuickSIN)
Younger adults (means ⫾SD ⫽⫺0.57 ⫾1.13 dB SNR
loss) scored significantly better [F
(1,30)
⫽10.613, P⫽0.003]
than older adults (means ⫾SD ⫽0.8 ⫾1.25 dB SNR loss) on
the QuickSIN test, suggesting that older adults’ performance in
noise may decline compared with younger adults, even when
audiometric thresholds are clinically normal.
Midbrain (EEG)
Amplitude analysis. Figure 2 shows the grand average of
FFRs of the stimulus envelope of younger and older adults in
quiet and in one of the four noise conditions tested (⫺6 dB).
Figure 3Adisplays the RMS values for each condition tested in
younger and older adults in the transition and steady-state
regions. In both regions, the RMS values of the responses in
noise of younger and older adults are significantly higher than
the RMS calculated for the noise floor (all, P⬍0.007).
TRANSITION REGION. A one-way ANOVA showed that
younger adults have significantly higher RMS values in quiet
[F
(1,30)
⫽4.255, P⫽0.048]. When all of the noise conditions
were collapsed together, one-way ANOVA showed significant
differences between younger and older adults [F
(1,126)
⫽5.150,
P⫽0.025; Fig. 3B]. The follow-up results of paired t-tests
suggest that noise significantly decreases response amplitude in
both younger and older adults in all of the noise conditions
tested (all, P⬍0.01). Repeated-measures ANOVA showed a
condition ⫻age interaction between quiet and noise at ⫺3dB
[F
(1,30)
⫽6.264, P⫽0.018] and ⫺6dB[F
(1,30)
⫽6.696, P⫽
0.015] but not at the other conditions tested [F
(1,30)
⫽1.125,
P⫽0.297 and F
(1,30)
⫽0.333, P⫽0.568 for ⫹3 and 0 dB,
respectively]. Repeated-measures ANOVA showed significant
differences across noise conditions in younger [F
(3,48)
⫽
13.384, P⬍0.001] but not in older [F
(3,48)
⫽0.885, P⫽
0.457] adults (Fig. 3A).
STEADY-STATE REGION. A one-way ANOVA showed that
younger adults have significantly higher RMS values than
older adults in quiet [F
(1,30)
⫽6.877, P⫽0.014]. The fol-
Fig. 2. Grand average (n⫽17 for younger and n⫽15 for
older adults) of the response to the stimulus envelope for
younger (left) and older [right; quiet ⫽dark lines; noise (⫺6
dB) ⫽light lines] adults. Statistical analyses carried out on
individual subjects show that in both the transition and
steady-state regions, noise resulted in a significant decrease
(P⬍0.01 and ⬍0.05 for the transition and steady-state
region, respectively) in the amplitude response for both
younger and older adults at all of the conditions tested. Higher
RMS values were also found in younger adults in both
regions (P⬍0.05).
Fig. 3. RMS values ⫾1 SE of the envelope for the
conditions (Q ⫽Quiet, ⫹3⫽⫹3 dB, 0 ⫽0 dB, ⫺3⫽
⫺3 dB, and ⫺6⫽⫺6 dB) tested in younger (gray bars)
and older (black bars) adults. A: average RMS for each
single condition. B: average RMS collapsed across all
noise conditions tested. Younger adults had significantly
higher RMS values in quiet in both the transition and the
steady-state regions. An RMS ⫻group-interaction effect
was noted in the transition at ⫺3 and ⫺6 dB but not in the
steady-state region. Repeated-measures ANOVA, applied
to the 4 noise conditions, shows significant differences in
younger adults in both the transition and steady-state
regions but not in older adults. Noise minimally affects
older adults, likely because their response in quiet is
already degraded. *P⬍0.05, ***P⬍0.001.
2349AGING EFFECTS OF NEURAL PROCESSING OF SPEECH IN NOISE
J Neurophysiol •doi:10.1152/jn.00372.2016 •www.jn.org
low-up results of paired t-tests suggest that noise significantly
decreases response amplitude in both younger and older adults in
all of the noise conditions tested (all, P⬍0.05). Repeated-
measures ANOVA showed no condition ⫻age interaction be-
tween quiet and noise at any of the conditions tested [F
(1,30)
⫽
0.072, P⫽0.791; F
(1,30)
⫽0.000, P⫽0.986; F
(1,30)
⫽2.574,
P⫽0.119; and F
(1,30)
⫽3.197, P⫽0.084 for ⫹3; 0; ⫺3; and ⫺6
dB, respectively]. Repeated-measures ANOVA showed signifi-
cant differences across noise conditions in younger [F
(3,48)
⫽
19.847, P⬍0.001] but not in older [F
(3,48)
⫽0.874, P⫽0.462]
adults (Fig. 3A). When all of the noise conditions were collapsed
together, a follow-up one-way ANOVA showed significant dif-
ferences between younger and older adults [F
(1,126)
⫽27.364,
P⬍0.001; Fig. 3B].
CORRELATION ANALYSIS. To analyze the robustness of the
response in noise, we linearly correlated (Pearson correlation)
the average response obtained in quiet with that recorded in
noise for both the transition and steady-state regions for
each subject. Repeated-measures ANOVA showed no sig-
nificant noise condition ⫻age interaction in either the
transition [F
(3,90)
⫽1.129, P⫽0.342] or the steady-state
[F
(3,90)
⫽1.015, P⫽0.390] region. When all of the noise
conditions were grouped together, a follow-up Mann-Whit-
ney U-test showed significantly higher Fisher-transformed r
values in younger adults in the steady-state [U(128) ⫽
1,272, Z⫽⫺3.667, P⬍0.001] but not in the transition
[U(128) ⫽1,675, Z⫽⫺1.743, P⫽0.081] region.
STIMULUS-TO-RESPONSE CORRELATION. Repeated-measures
ANOVA showed a significant noise condition ⫻age inter-
action between quiet and noise at all of the noise conditions
tested [F
(1,30)
⫽5.915, P⫽0.021; F
(1,30)
⫽4.302, P⫽
0.047; F
(1,30)
⫽5.786, P⫽0.023; and F
(1,30)
⫽8.318, P⫽
0.007 for ⫹3; 0; ⫺3; and ⫺6 dB, respectively]. A one-way
ANOVA showed that the younger adults’ correlation values
were significantly higher than those of older adults in all of
the noise conditions tested [F
(1,30)
⫽7.768, P⫽0.009;
F
(1,30)
⫽5.535, P⫽0.025; F
(1,30)
⫽5.166, P⫽0.030; and
F
(1,30)
⫽8.838, P⫽0.006 for ⫹3; 0; ⫺3; and ⫺6 dB,
respectively] but not in quiet [U(62) ⫽114, Z⫽⫺0.510,
P⫽0.628].
Frequency analysis (envelope). TRANSITION REGION. A one-
way ANOVA showed no significant amplitude differences in
the transition region at F
0
and at the first two harmonics in all
of the conditions tested (all, P⬎adjusted threshold), except
the second harmonic at ⫹3dB[F
(1,30)
⫽9.046, P⫽0.005].
Repeated-measures ANOVA showed significant differences
across noise conditions only in younger adults and only at the
second harmonic [F
(3,48)
⫽6.141, P⫽0.007].
STEADY-STATE REGION. A one-way ANOVA showed that
younger adults’ F
0
amplitude is significantly higher than that of
older adults in all of the noise conditions tested [F
(1,30)
⫽
9.287, P⫽0.005; F
(1,30)
⫽9.598, P⫽0.004; F
(1,30)
⫽6.518,
P⫽0.016; and F
(1,30)
⫽8.901, P⫽0.006 for ⫹3; 0; ⫺3; and
⫺6 dB, respectively] but not in quiet [F
(1,30)
⫽3.390, P⫽
0.076]. No significant differences were found at the second
harmonic (all, P⬎0.05), whereas the amplitude of the third
harmonic is significantly higher in younger adults than in older
adults in all of the noise conditions tested [F
(1,30)
⫽12.744,
P⫽0.001; F
(1,30)
⫽9.259, P⫽0.005; F
(1,30)
⫽4.318, P⫽
0.046; and F
(1,30)
⫽7.517, P⫽0.010 for ⫹3; 0; ⫺3; and ⫺6
dB, respectively] and in quiet [F
(1,30)
⫽26.771, P⬍0.001].
Repeated-measures ANOVA showed significant amplitude dif-
ferences across noise conditions only in younger adults at the
F
0
[F
(3,48)
⫽3.987, P⫽0.013] and at the first [F
(3,48)
⫽3.065,
P⫽0.037] and second [F
(3,48)
⫽8.421, P⬍0.001] harmonics.
Frequency analysis (TFS). TRANSITION REGION. A one-way
ANOVA showed no significant group differences in amplitude
at 400 or 700 Hz at any of the conditions tested (all, P⬎0.05).
Repeated-measures ANOVA showed significant differences
across noise conditions only in younger adults and only at 400
Hz [F
(3,45)
⫽4.406, P⫽0.008] but not for 700 Hz in the
younger adults or for either frequency in the older adults (all,
P⬎0.05).
STEADY-STATE REGION. A one-way ANOVA showed that the
400-Hz amplitude in younger was significantly higher than that
of older adults but only in quiet [F
(1,29)
⫽12.908, P⫽0.001]
and at 0 dB SNR [F
(1,29)
⫽9.654, P⫽0.004]. No significant
differences were found at 700 Hz at any SNR (all, P⬎0.05)
or in quiet (P⬎0.05, Mann-Whitney test). Repeated-measures
ANOVA showed significant amplitude differences across noise
conditions in the younger adults at 400 Hz [F
(3,45)
⫽4.802,
P⫽0.006] but not for 700 Hz in the younger adults or for
either frequency in the older adults (all, P⬎0.05).
Cortex (MEG)
Reconstruction of the attended speech envelope. The ability
to reconstruct the low-frequency speech envelope from cortical
activity is a measure of the fidelity of the neural representation
of that speech envelope (Ding and Simon 2012). Figure 4A
shows an example of reconstruction of the speech envelope of
the foreground in noise (⫺6 dB) from a representative younger
and older adult. Figure 4Bplots the rvalues for each partici-
pant, at each tested condition, in ascending order. Figure 5A
displays the grand average ⫾SE of the reconstruction accuracy
for younger and older adults for all of the conditions tested.
One-way ANOVA showed significantly higher correlation val-
ues in older adults compared with younger adults in quiet
[F
(1,30)
⫽14.923, P⫽0.001] and in all of the noise conditions
tested [Fig. 5A;F
(1,30)
⫽13.315, P⫽0.001; F
(1,30)
⫽13.374,
P⫽0.001; F
(1,30)
⫽15.331, P⬍0.001; and F
(1,30)
⫽6.195,
P⫽0.019 for ⫹3; 0; ⫺3; and ⫺6 dB, respectively]. All of the
reconstruction values were significantly higher than the noise
floor (all, P⬍0.01). Since the difference between older and
younger adults is minimized at ⫺6 dB, this condition was used
to analyze the effect of the integration window on the fidelity
of the reconstruction of the speech envelope. Results from a
split-plot ANOVA, applied to the three integration windows
used (Fig. 5B) for the analysis, revealed a reconstruction
window ⫻age-group interaction in quiet [F
(2,60)
⫽9.332, P⫽
0.004] but not in noise [F
(2,60)
⫽0.105, P⫽0.802]. Repeated-
measures ANOVA, applied to 500, 350, and 150 ms integra-
tion windows, shows significant differences in older adults in
both quiet [F
(2,32)
⫽14.954, P⫽0.001] and noise [F
(2,32)
⫽
5.048, P⫽0.037] but not in younger adults [F
(2,32)
⫽4.213,
P⫽0.048 and F
(2,32)
⫽1.195, P⫽0.302 in quiet and noise,
respectively]. A follow-up paired t-test of the foreground
reconstructed in quiet and noise at 500 vs. 350 ms and 500 vs.
150 ms showed that the reconstruction accuracy of younger
adults is not significantly affected by the integration windows
in noise [t(16) ⫽0.366, P⫽0.719 and t(16) ⫽1.162, P⫽
0.262 for 500 vs. 350 ms and 500 vs. 150 ms, respectively],
whereas in quiet, the 500-ms integration window had signifi-
2350 AGING EFFECTS OF NEURAL PROCESSING OF SPEECH IN NOISE
J Neurophysiol •doi:10.1152/jn.00372.2016 •www.jn.org
cantly lower values than 350 ms but not than 150 ms [t(16) ⫽
⫺3.722, P⫽0.002 and t(16) ⫽0.973, P⫽0.345 for 500 vs.
350 ms and 500 vs. 150 ms, respectively]. Conversely, older
adults’ ability to track the speech envelope of the foreground is
significantly reduced at 350 and 150 ms in both quiet [t(14) ⫽
⫺0.248, P⫽0.807 and t(14) ⫽3.779, P⫽0.002 for 500 vs.
350 ms and 500 vs. 150 ms, respectively] and noise [t(14) ⫽
2.064, P⫽0.058 and t(14) ⫽2.512, P⫽0.0248 for 500 vs.
350 ms and 500 vs. 150 ms, respectively].
Reconstruction of the unattended speech envelope. Repeated-
measures ANOVA showed a significant correlation ⫻age
interaction across the four noise conditions tested [F
(3,90)
⫽2.909,
P⫽0.039]. A one-way ANOVA showed significantly higher
reconstruction accuracy in older adults at all of the noise condi-
tions tested except ⫹3dB[F
(1,30)
⫽3.487, P⫽0.072; F
(1,30)
⫽
4.99, P⫽0.033; F
(1,30)
⫽7.523, P⫽0.01; and F
(1,30)
⫽19.251,
P⬍0.001 for ⫹3; 0; ⫺3; and ⫺6 dB, respectively]. All of the
reconstruction values were significantly higher than the noise floor
(all, P⬍0.01).
Relationships among Behavioral, Midbrain, and Cortical
Data
Two-tailed Spearman’s rank correlation coefficient was used
to study the correlations among the following measurements:
speech-in-noise score, cortical decoding accuracy in quiet and
in noise with an integration window of 500 ms, and the
quiet-to-noise correlation value in the steady-state region of
midbrain responses. No significant correlations were found in
either younger or older adults in any of the relationships tested.
DISCUSSION
The results of this study provide support for most, but not
all, of the initial hypotheses. Behavioral data showed that older
adults do have poorer speech understanding in noise than
younger adults, despite their normal, audiometric hearing
thresholds. In midbrain, noise suppresses the response in
younger adults to a greater extent than in older adults, whereas
the fidelity of the reconstruction of speech in cortex is higher
Fig. 4. A: example of the reconstruction of the speech
envelope of the foreground for a representative
younger (top) and older (bottom) adult in noise (⫺6
dB SNR). The waveforms have been standardized for
visualization purposes. B: scatter plots of rvalues for
each participant at each condition tested, plotted in
ascending order.
Fig. 5. Reconstruction accuracy ⫾1 SE of the speech envelope
of the foreground for younger and older adults. A: results in
quiet and in all of the noise conditions tested. The black
horizontal line shows the noise floor. Older adults’ reconstruc-
tion accuracy is significantly higher in quiet (P⫽0.001).
However, as a completing talker is added to the task, the
differences between the 2 age groups are reduced. B: recon-
struction accuracy in quiet and at ⫺6 dB for the 3 integration
windows tested: 500, 350, and 150 ms. Significant differences
across the 3 integration windows were found only in older
adults in both quiet (P⫽0.001) and noise (P⬍0.05). *P⬍
0.05, **P⬍0.01, ***P⬍0.001.
2351AGING EFFECTS OF NEURAL PROCESSING OF SPEECH IN NOISE
J Neurophysiol •doi:10.1152/jn.00372.2016 •www.jn.org
in older than in younger adults. Differently from what was
initially hypothesized, no significant associations were found
between behavioral and electrophysiological data and between
midbrain and cortex.
Midbrain (EEG)
Amplitude response. The greater amplitude decrease in noise
in younger adults compared with older adults was unexpected.
However, an RMS ⫻age-group interaction was only signifi-
cant in the transition region and may be explained by reduced
audibility in the high frequencies in older adults, given that the
transition region is characterized by the presence of a high-
frequency burst. These results are consistent with an earlier
study that suggested that older adults’ high-frequency hearing
loss might disrupt their ability to encode the high-frequency
components of a syllable (Presacco et al. 2015).
Not surprisingly, in younger adults, the loss of amplitude
between quiet and noise conditions was also larger in the
steady-state region, although no significant RMS ⫻age inter-
action was observed. The lack of significant differences ob-
served in the steady-state region is consistent with results
reported by Parthasarathy et al. (2010), where amplitude mod-
ulation following responses differed in younger and older rats
only under specific SNR conditions. Specifically, they ob-
served that at the highest SNR, there were no significant
differences at any of the modulation frequencies tested, but
with a 10-dB loss of SNR, the amplitude modulation following
responses of younger rats tended to decrease substantially,
whereas older rats’ responses showed negligible changes. This
is consistent with results showing significant differences across
noise conditions only in younger adults. Additionally, previous
studies have shown that hearing loss may lead to an exagger-
ated representation of the envelope in midbrain (Anderson et
al. 2013; Henry et al. 2014). Despite having clinically normal,
audiometric thresholds up to 4 kHz, most of our older adults
have a mild, sensorineural hearing loss at higher frequencies (6
and 8 kHz). This mild hearing loss might have potentially
contributed to generating an amplitude response big enough to
reduce the RMS ⫻age interaction in the steady-state region.
Interestingly, the frequency domain analysis shows significant
differences across noise conditions only in younger adults in
both the transition (second harmonic in the envelope and 400
Hz in the TFS) and in the steady-state regions (fundamental
and both harmonics in the envelope and 400 Hz in the TFS),
consistent with observations of Parthasarathy et al. (2010).
Robustness of the envelope to noise. The correlation analysis
supported the initial hypothesis that younger adults’ responses
should be more robust to noise than those of older adults.
Younger adults showed significantly higher correlations when
all of the noise conditions were collapsed. The higher robust-
ness of the envelope to noise in younger adults is also con-
firmed by the results of the stimulus-to-response correlation,
which shows that the ability of older adults’ responses to
follow the stimulus is significantly worse than that of younger
adults in noise. These differences between the two age groups
may be due to disruption of periodicity in the encoded speech
envelope, which has been suggested to cause a decrease in
word identification (Pichora-Fuller et al. 2007).
Cortex (MEG)
Reconstruction of the speech envelope. The results of the
reconstruction of the speech envelope show that older adults
had higher correlation values both in quiet and in noise. An
enhanced reconstruction in older adults, both in quiet and in
noise, is consistent with studies showing an exaggerated rep-
resentation of cortical responses in older adults, both with and
without hearing loss. Specifically, Alain et al. (2014), Lister et
al. (2011), and Soros et al. (2009) report abnormally higher
amplitude for the P1 and N1 peaks in normal-hearing older
adults compared with normal-hearing younger adults, in agree-
ment with results from previous studies that showed that aging
might alter inhibitory neural mechanisms in the cortex (de
Villers-Sidani et al. 2010; Hughes et al. 2010; Juarez-Salinas et
al. 2010; Overton and Recanzone 2016). The P1 and N1 peaks
reflect different auditory mechanisms. Specifically, P1, occur-
ring 50 ms after the stimulus onset, originates in Heschl’s
gyrus and can be modulated by stimulus rate, intensity, and
modulation depth (Ross et al. 2000). Conversely, N1, occurring
100 ms after stimulus onset, originates in the Planum tempo-
rale and has been shown to be modulated by attention (Oka-
moto et al. 2011). Therefore, this exaggerated response might
be a reflection of changes in the way that the acoustical stimuli
are processed and as a consequence, in the level of attention
required to process them. Interestingly, Chambers et al. (2016)
recently showed that recovery from profound cochlear dener-
vation in rats leads to cortical spike responses higher than the
baseline recorded before inducing auditory neuropathy; this
finding reinforces the possibility that auditory neuropathy
could play a critical role in the over-representation of an
auditory stimulus. It is also possible that peripheral hearing
loss contributes to problems in the speech-encoding process, as
several studies have shown that this cortical neural enhance-
ment is exacerbated by hearing loss (Alain et al. 2014; Trem-
blay et al. 2003). However, no significant correlation was
found (two-tailed Spearman’s rank correlation) between the
pure-tone average for the frequencies between 2 and 8 kHz and
cortical reconstruction (all, P⬎0.05). The above-mentioned,
exaggerated cortical response, which can take the form of both
better cortical reconstruction and higher peak amplitude (P1
and N1), is perhaps counterintuitive and in disagreement with
the concept of “stronger is better,” as observed in the midbrain.
However, if we assume that a decrease of inhibition leads to
larger neural currents, then we can hypothesize that this neural
enhancement is mainly the result of imbalance between excit-
atory and inhibitory mechanisms.
As higher cognitive processes affect the cortical representa-
tion of the speech signal, the higher reconstruction in older
adults may be related to an inefficient use of cognitive re-
sources and an associated decrease in cortical network connec-
tivity reported in older adults (Peelle et al. 2010). Decreased
cortical network connectivity would result in neighboring cor-
tical areas processing the same stimulus redundantly instead of
cooperatively. Such an overuse of neural resources would lead
to the over-representation observed here. Decreased connec-
tivity may translate to using significantly more energy to
accomplish a task that younger adults can complete with much
less effort. This explanation would be in agreement with
several studies showing that overuse of cognitive resources
2352 AGING EFFECTS OF NEURAL PROCESSING OF SPEECH IN NOISE
J Neurophysiol •doi:10.1152/jn.00372.2016 •www.jn.org
leads to poorer performance on a secondary task (Anderson
Gosselin and Gagné 2011; Tun et al. 2009; Ward et al. 2016).
Importantly, the addition of a competing talker caused a
substantial drop of decoding accuracy in older adults, who
required a much longer integration time than younger adults.
This finding is consistent with several psychoacoustic (Fitzgib-
bons and Gordon-Salant 2001; Gordon-Salant et al. 2006) and
electrophysiological (Alain et al. 2012; Lister et al. 2011)
studies, demonstrating that older adults’ responses are affected
to a greater degree than younger adults when temporal param-
eters are varied. Specifically, older adults required longer time
to process specific temporal acoustic cues, such as voice-onset
time, vowel duration, silence duration, and transition duration
(Gordon-Salant et al. 2008). The degradation of the cortical
response from quiet to noise observed in both age groups is
also consistent with previous results showing that the evoked
response seen in quiet is affected by the presence of noise
(Billings et al. 2015). Specifically, cortical response amplitude
decreases as SNR decreases in both younger and older adults,
consistent with the current findings showing a reduction in
reconstruction accuracy within each group. Interestingly, even
with an integration window as narrow as 150 ms, older adults
still show evidence of enhanced reconstruction of the speech
envelope. These results contribute to understanding the signif-
icant group differences observed in the reconstruction of the
background noise. Older adults’ difficulty in understanding
speech in noise may also partially arise from the reduced
ability to suppress unattended stimuli, as suggested by an
over-representation also present in the reconstruction of the
background talker. Note also that even when low, the rvalues
in this study are unlikely to be tied to noise-floor effects:
reconstruction values in both age groups are well above the
noise floor and are consistent with previously published data
(Ding and Simon 2012).
Effect of Hearing Threshold Differences and Cognitive
Decline on Cortical Results
The possibility that the over-representation of the response
of older adults in quiet might be due to significant differences
in the hearing thresholds cannot be ruled out. In fact, even
though the older adults that we tested had clinically normal
hearing, all of their thresholds were significantly higher than
younger adults (P⬍0.05), a typical occurrence for the major-
ity of aging studies. Cochlear synaptopathy has also been
suggested to result from aging and to be a possible contributor
to difficulties in understanding speech-in-noise (Sergeyenko et
al. 2013). Furthermore, several studies have also shown the
existence of age-related cognitive declines (Anderson Gosselin
and Gagné 2011; Pichora-Fuller et al. 1995; Surprenant 2007;
Tun et al. 2009) that may play an important role in compro-
mising attentional resources believed to be critical for proper
representation of the auditory object (Shamma et al. 2011).
Relationships Among Behavioral, Midbrain,
and Cortical Data
The absence of correlations among behavioral and electro-
physiological measurements suggests the possibility that our
behavioral measurements might not completely account for the
presence of temporal processing deficits in the central auditory
system. Caution should be used when interpreting the results
due to important factors. 1) Behavioral data were collected
with four-talker babble as the background noise, whereas
cortical and subcortical data were recorded using a single-
competing talker. A single-competing talker may draw the
subjects’ attention away from the target to a greater extent than
would four-talker babble, given the fact that multiple talkers
generate speech without meaning (little informational mask-
ing) (Larsby et al. 2008; Tun et al. 2002). 2) Several studies
have also shown that the performance in a task varies depend-
ing on different features of the masker (i.e., spectral differ-
ences, SNR level, etc.) (Calandruccio et al. 2010; Larsby et al.
2008). The speech materials used for the electrophysiological
recording were not equated for spectral differences with the
speech material used for the speech-in-noise test.
No significant association was found between midbrain and
cortical results. Even though previous results showed relation-
ships between weak speech encoding in the midbrain (FFR)
and an over-representation of the cortical response (Bidelman
et al. 2014), a more recent animal study (Chambers et al. 2016)
suggests that the absence of auditory brain stem response wave
I does not necessarily lead to an absence of cortical spike
response, suggesting compensatory central gain increases that
could help restore the representation of the auditory object at
the cortical level. This finding may also explain the lack of
association between midbrain and cortex findings. It could also
be argued that the absence of correlation between midbrain and
cortex could be linked to the different stimuli used for the EEG
(speech syllable/da/) and MEG (1 min of speech) task. Addi-
tionally, midbrain and cortical responses were filtered in dif-
ferent frequency ranges to reflect the frequency differences in
the responses emerging from different parts of the auditory
systems. The use of different stimuli was necessitated by the
larger number of trials required to obtain clear responses from
midbrain. Whereas for the cortical analysis, three runs were
sufficient to obtain a clear response above the noise floor, in the
midbrain, a minimum of 2,000 runs was needed, making the
use of stimuli longer than 170 ms not feasible for this long
experiment. Finally, subjects were passively listening to the
auditory stimuli in the EEG experiment, whereas in the MEG,
subjects were actively engaged in listening to the target
speaker.
Concluding Remarks
The results of our studies add compelling evidence to the
notion that age-related temporal processing deficits are a key
factor in explaining speech comprehension problems experi-
enced by older adults, particularly in noisy environments.
Auditory midbrain responses revealed an age-related failure to
encode speech syllables in quiet, which reduces the ability to
cope with the presence of a background talker. Whereas
younger adults adapt to the presence of noise and changes in its
loudness, older adults’ midbrain responses seem to be less
affected by different SNRs, suggesting a failure to encode
properly both the target and the irrelevant speech. This result is
likely not due to the noise-floor effect, as all of the RMS values
calculated in noise were significantly higher than the RMS of
the prestimulus. Our study also reveals an over-representation
of the cortical response, consistent with previous studies (Alain
et al. 2014; Lister et al. 2011; Soros et al. 2009); this neural
enhancement is reduced with the addition of a competing
2353AGING EFFECTS OF NEURAL PROCESSING OF SPEECH IN NOISE
J Neurophysiol •doi:10.1152/jn.00372.2016 •www.jn.org
talker, suggesting that larger cortical responses are not bene-
ficial and might, in fact, represent a failure of the brain to
process speech properly. Critically, we were unable to find any
significant correlations between midbrain and cortex measures.
We believe this result brings additional support to recent
findings that suggest that cortical plasticity may partially re-
store temporal processing deficits at lower levels of the audi-
tory system (Chambers et al. 2016), although we cannot ex-
clude the possibility that a lack of correlation may reflect
differences in the stimuli used to elicit the midbrain and
cortical responses.
This apparent lack of relationship between midbrain and
cortex further highlights the relevance of this study, which is
the importance of investigating simultaneously different areas
of the auditory system to understand better the mechanisms
underlying age-related degradation of speech representation.
ACKNOWLEDGMENTS
The authors are grateful to Natalia Lapinskaya for excellent technical
support.
GRANTS
Funding for this study was provided by the University of Maryland College
Park (UMCP) Department of Hearing and Speech Sciences, UMCP AD-
VANCE Program for Inclusive Excellence (NSF HRD1008117), and National
Institute on Deafness and Other Communication Disorders (Grants
R01DC008342, R01DC014085, and T32DC-00046).
DISCLOSURES
The authors declare no competing financial interests.
AUTHOR CONTRIBUTIONS
A.P., J.Z.S., and S.A. conception and design of research; A.P. performed
experiments; A.P. analyzed data; A.P., J.Z.S., and S.A. interpreted results of
experiments; A.P. and S.A. prepared figures; A.P., J.Z.S., and S.A. drafted
manuscript; A.P., J.Z.S., and S.A. edited and revised manuscript; A.P., J.Z.S.,
and S.A. approved final version of manuscript.
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