Abstract

The capacity of the human cerebral cortex to track fast temporal changes in auditory stimuli is related to the development of language in children and to deficits in speech perception in the elderly. Although maturation of temporal processing in children and its deterioration in the elderly has been investigated previously, little is known about naturally occurring changes in auditory temporal processing between these limits. The present study examined age-related (19–45 years) changes in 3 electrophysiological measures of auditory processing: 1) the late transient auditory evoked potentials to tone onset, 2) the auditory steady-state response (ASSR) to a 40-Hz frequency–modulated tone, and 3) the envelope following response (EFR) to sweeps of amplitude-modulated white noise from 10 to 100 Hz. With increasing age, the latency of the auditory P1–N1 complex decreased, the oscillatory (ASSR) response became larger and more stable, and the resonant peak of the EFR increased from 38 Hz at 19 years to 46 Hz at 45 years. Source analysis localized these changes to the auditory regions of the temporal lobe. These results indicate persistent adaptation of cortical auditory processes into middle adulthood. We speculate that experience-driven myelination and/or refinement of inhibitory circuits may underlie these changes.

Introduction

Auditory perception involves the decoding of both temporal and spectral information. Fast and accurate processing of temporal features may be particularly important for recognizing complex communicative sounds across many species, including speech in humans (Rosen 1992; Ahissar and others 2001). Although both spectral and temporal cues are important in phoneme recognition (Xu and others 2005), words can be discriminated on the basis of the temporal variations in a sound envelope even when spectral information is severely reduced (Van Tasell and others 1987; Shannon and others 1995; Souza and Turner 1996). Speech recognition in continuous and interrupted noise, an ability that continues to mature into adolescence (Johnson 2000), has been linked to improvements in auditory temporal resolution (Stuart 2005). Difficulties in speech discrimination (Ali and Jerger 1992; Souza and Turner 1998; Souza 2000) and word deafness (Phillips and Farmer 1990; Otsuki and others 1998) have both been associated with deficits in the temporal processing of sound variations within the 1- to 100-ms range, the temporal range critical to phoneme discrimination. Older adults whose speech discrimination is disproportionately poor relative to their hearing loss have also shown deficits in temporal processing (Ali and Jerger 1992). Research on the development of auditory temporal resolution capacities of the brain and how they change over the lifespan can, therefore, contribute to our understanding of the mechanisms underlying language development and plasticity.

The high temporal resolution of electroencephalography (EEG) makes it ideally suited to noninvasive assessment of auditory processing speed. Traditionally, research in human subjects has focused on the latency of transient auditory evoked potentials (AEPs) as an indicator of processing speed, but an increasing number of studies have used auditory steady-state responses (ASSR) and envelope following responses (EFRs) to assess temporal auditory processing (Ross and others 2002; Picton and others 2003; Dimitrijevic and others 2004; Purcell and others 2004; Yamasaki and others 2005). Age-related research into auditory perception typically addresses the maturation of evoked responses during childhood and adolescence or their decline in elderly populations. The assumption that auditory responses are stable across middle adulthood has remained largely unexamined, with only one study (McArthur and Bishop 2002) reporting changes in the transient AEP in subjects ranging in age from 10 to 50 years. The present study employed transient, steady-state, and EFR methods to investigate changes in auditory processing across middle adulthood.

The transient AEP is a sequence of positive and negative waves, named by their polarity and their latency or position within the sequence. Measured at the vertex electrode, the late transient AEP in adults contain P1, N1, and P2 waves, with typical peak latencies of 50, 100, and 170 ms, followed by a more variable N2 wave. Source analyses have localized the primary generators of the P1 and N1 vertex peaks to the superior temporal plane, which includes the primary auditory cortex and adjacent belt regions of secondary auditory cortex (Scherg and Von Cramon 1986; Ponton and others 2002). Although the P2 source has also been localized to regions on the superior temporal plane (Hari and others 1987; Rif and others 1991), it may not have primary generators in auditory cortex (Knight and others 1988) but rather may reflect auditory output of the mesencephalic reticular activating system (Rif and others 1991; Ponton and others 2000; Crowley and Colrain 2004) or the overlapping activity in both temporal and frontal cortices (Picton and others 1999). In addition to these vertex-maximal peaks, the transient AEP also displays a series of peaks maximally recorded at temporal electrode sites, referred to as the T-complex. These include the Ta, Tb, and TP200 waves, with typical peak latencies of 100, 150, and 200 ms (Tonnquist-Uhlen and others 2003). Dipole source modeling has localized the generators of these T-complex peaks to the parabelt areas of secondary auditory cortex on the lateral surface of the temporal lobe (Ponton and others 2002).

Previous research examining developmental changes in the latencies of the late transient AEP peaks has identified characteristic periods based on the age at which the AEP peaks first emerge, their rates of change, and the ages at which adult-like morphology is reached (Shibasaki and Miyazaki 1992; Sharma and others 1997; Ponton and others 2000, 2002; Ceponiene and others 2002). In contrast to the P2, which matures by around 5 years of age (Ponton and others 2000), McArthur and Bishop (2002) found a slow decrease in P1 latency through to middle adulthood and a decrease in N1 latency up until the mid-20s. This suggests that there may be continued development into adulthood of physiological mechanisms that influence both P1 and N1 latencies.

The ASSR and EFR are oscillations in the EEG that are synchronized to temporal fluctuations in the stimulus envelope. These responses can be evoked by click trains, amplitude-modulated white noise or tones, or frequency-modulated tones. Depth recordings from primary auditory cortex in marmoset monkeys have revealed similar neuronal responses for amplitude- and frequency-modulated stimuli (Liang and others 2002). The ASSR and EFR have been elicited across a wide range of click and modulation rates. Early research was limited to measuring the ASSR at fixed modulation rates, but the ability of the brain to follow temporal changes can be more efficiently measured with auditory sweeps that are continuously modulated across a range of rates (John and Picton 2000; Purcell and others 2004). The resultant EFR is a measure of the instantaneous amplitude and phase of the brain response as the modulation rate changes. We shall use the terms ASSR and EFR to refer to brain responses that follow a fixed or varying modulation rate, respectively. The 40-Hz ASSR, which is sustained for the entire stimulus envelope and is phase synchronized to the temporal periodicity of the stimulus, differs from the transient gamma-band response, which occurs shortly after the onset of an auditory stimulus and lasts only a few cycles (Ross and others 2002). The transient gamma-band response was not investigated in the present study.

In adults, the magnitude of the ASSR and EFR typically peaks at modulation rates of about 40 Hz (Galambos and others 1981; Picton and others 2003). Source analyses suggest that both brain stem and cortical sources in the superior temporal plane contribute to the 40-Hz peak (Herdman and others 2002). In addition to the response enhancement seen around 40 Hz in the EFR and ASSR, several other lines of evidence indicate that the temporal resolution of the auditory cortex reaches an important threshold around 40 Hz (25-ms cycle). In speech, as voice onset time increases from 20 to 40 ms, perception shifts from a voiced to an unvoiced stop consonant (e.g., /b/ to /p/). Similarly, transient onset AEPs to individual clicks are attenuated at an interclick interval of about 20–25 ms, suggesting that constraints in temporal resolution may influence phoneme discrimination (Steinschneider and others 1999). Studies of temporal and rate-encoding properties of neurons in the monkey auditory cortex have also indicated that these neurons are capable of stimulus-synchronized discharges up to an interclick interval of about 20 ms (50 Hz), after which temporal periodicity must be translated into a rate code (Lu and others 2001; Liang and others 2002). Although it is not clear exactly how the human 40-Hz response is generated—superimposition of transient cortical responses, synchronization of ongoing EEG gamma rhythms, or reverberating thalamocortical circuits (reviewed in Picton and others 2003)—the auditory cortex is clearly essential.

The maturation of the ASSR and EFR across childhood has received little attention. Aoyagi and others (1994) demonstrated an increasing detectability of the 40-Hz ASSR with increasing age in sleeping children from 6 months to 15 years. Most recently, Rojas and others (2006) reported an age-related increase in the magnetic 40-Hz ASSR to click trains with a 25-ms interclick interval in subjects aged 5–52 years. Although both linear and exponential regressions were significant, this relationship appeared to asymptote during early adulthood, as indicated by a larger proportion of variance accounted for by the exponential regression in the left hemisphere. A recent study of the EFR in adults revealed a decrease in the frequency of the peak response from a mean of 41 Hz in younger adults (18–43 years) to 37 Hz in elderly adults (60–78 years) (Purcell and others 2004). Changes in the EFR as a function of age across early-to-middle adulthood have not been investigated.

In order to assess changes in the brain response to auditory stimulation during middle adulthood, we examined the relationship between age and 1) the latencies and amplitudes of the late-transient AEP peaks to the onset of a 1000-Hz pure tone, 2) the amplitude and variability of the steady-state response to 40-Hz frequency modulation of this 1000-Hz tone, and 3) the peak frequency of the EFR to white noise that was amplitude modulated in ascending and descending sweeps at modulation rates between 10 and 100 Hz. Dense-array, 128-channel EEG recordings of the transient and steady-state responses enabled the analysis of both scalp and source waveforms. By considering these multiple measures of the brain's electrical response to auditory stimulation within the same subjects and session, we hoped to shed light on the nature of age-related changes in the adult brain's physiological response to sound. Such patterns may provide clues to possible underlying mechanisms to be investigated in future research.

Materials and Methods

Participants

Twenty-three paid volunteers (19–45 years, M = 29 years; 12 males) participated after providing fully informed consent. Gender was equally represented across this age range. The Research Ethics Board of the Montreal Neurological Institute and Hospital approved the study. All participants were right handed as assessed by questionnaire (Crovitz and Zener 1962) and reported no history of neurological or psychiatric disorders. Audiometric testing ensured that all had normal-hearing acuity from 500 to 4000 Hz, with thresholds ≤25 dB hearing level tested in 5 dB increments at frequencies of 500, 1000, 2000, 3000, and 4000 Hz.

Stimuli

Two auditory experiments were conducted: the transient/steady-state auditory event-related potential (TSS) experiment, and the EFR experiment. Stimuli for both experiments were presented binaurally through insert earphones (Etymotic 3A) at 65 dB sound pressure level (SPL) for the TSS stimuli, and at 55 dB SPL for the EFR stimuli. Calibration was done using a 2 cc coupler and a Larson-Davis Model 824 Sound Level Meter.

Two stimuli, FM1 and FM2, were used for the TSS experiment. Both stimuli consisted of a 1000-ms tone with a rise and fall of 12.5 ms. Each stimulus began as a pure 1000-Hz tone, which was frequency modulated at a rate of 40 Hz to a depth of 25% (from 875 to 1175 Hz) beginning 100 ms after stimulus onset. Modulation was of opposite phase for the FM1 and FM2 stimuli. This allowed the disentanglement of steady-state and transient responses (Ross and others 2004). The FM1 and FM2 stimuli were presented in random order at a stimulus onset asynchrony of 2000 (±100) ms; a total of 400 stimuli were presented over a period of 14 min.

Stimuli for the EFR experiment were generated by the MASTER research system (John and Picton 2000) and consisted of continuous sweeps of white noise, amplitude modulated at a depth of 100%. Modulation rate changed gradually from 10 to 100 Hz across 15.36 s and then back down from 100 to 10 Hz across 15.36 s, for a total sweep of 30.72 s. Twenty such sweeps were presented to the subject, in 2 consecutive sequences of 10 sweeps each lasting 5.12 min for a total of 11 min.

Data Acquisition

All data were collected in a single session (which included other EEG measurements and lasted about 1.5 h), with the order of the TSS and EFR experiments counterbalanced across subjects. To maintain arousal during these auditory experiments, all subjects watched a silent video of their choice while listening to the experimental stimuli.

Two separate electrode montages were applied during set up to accommodate experiment-specific recording and analysis systems within the same session. For the TSS experiment, EEG was recorded using a 128-channel Geodesic Sensor Net (Tucker 1993) and Net Station, version 3.0.2. Scalp-electrode impedances were between 20 and 60 kOhms (Ferree and others 2001). All channels were referenced to Cz during acquisition. EEG was recorded using a 0.1- to 200-Hz band-pass filter (3 dB attenuation), amplified at a gain of 1000, sampled at a rate of 500 samples per second, and digitized with a 16-bit A/D converter.

For the EFR experiment, a bipolar EEG recording was acquired from silver-plated Grass electrodes applied at Fz and Oz (reference) using a Grass P55 battery-powered amplifier. Scalp-electrode impedances were below 10 kOhms. The EEG was amplified at a gain of 10 000, band-pass filtered from 1 to 300 Hz, sampled at a rate of 1000 Hz, and digitized with a 16-bit A/D converter.

TSS Data Processing

Two sets of analyses were performed on the TSS experiment data, one examining the late transient AEP peaks and the other the 40-Hz ASSR. All analyses were conducted in the time domain. Processing of the 128-channel EEG data prior to source analysis was conducted using Net Station, version 3.0.2. Statistical analyses of the AEP and ASSR scalp waveforms were restricted to the vertex electrode (Cz), where the auditory response is maximally recorded on the scalp. To separate the contribution of left and right auditory cortex activity to the scalp-recorded AEPs and ASSR, spatiotemporal source analyses using BESA, version 5.1, were performed using the full 128-channel data set, and statistical analyses were conducted on the resultant source waveforms.

For the AEP analyses, the continuous EEG was first filtered with a 20-Hz low-pass filter (2-Hz roll-off, −40 dB attenuation) and then segmented into 2 responses (FM1 and FM2) with a 1500-ms stimulus-locked epoch beginning 100 ms before stimulus onset. All trials contaminated by noise, eye movements, blinks, or other artifacts, as identified by Net Station's moving-average algorithm and voltage thresholds, were eliminated. Individual bad channels were replaced using spherical spline interpolation. Trials were then averaged separately for FM1 and FM2 stimuli, each channel was rereferenced to an average reference, and the measurements were made relative to the 100-ms prestimulus baseline. To isolate the transient AEP peaks, the 20-Hz low-pass–filtered FM1 and FM2 averages were further averaged together; this canceled the out-of-phase 40-Hz steady-state responses, leaving the transient and sustained components.

The scalp-recorded transient evoked potentials recorded at Cz provided 3 peak measurements. P1 was identified as the maximum positive peak between 30 and 90 ms, N1 the maximum negative peak between 70 and 140 ms, and P2 the maximum positive peak between 130 and 210 ms. Because the N2 peak was not reliably identified in 8 of the subjects, it was not further evaluated.

Spatiotemporal source analysis (BESA, version 5.1) was conducted on the 129-channel average-referenced, grand-average AEP data using a 4-shell (brain, cerebrospinal fluid, skull, and scalp) ellipsoidal head model. After applying a temporary 2-Hz high-pass filter (to remove some frontal slow waves, likely related to eye movements), 2 symmetric regional sources were fit to the time window of the vertex N1. The resulting sources localized on the medial superior temporal plane, in the vicinity of the auditory cortex. The filter was then removed, and 2 additional dipoles were fit to capture residual eye artifact. Next, the symmetry constraint was removed, and the 2 temporal regional sources were refit during the same time window. The right source moved 4.5 mm anterior relative to the left source. Finally, the orientation of the tangential vector was adjusted to capture the vertex N1 peak, and the radial vector was oriented to a TP200 peak occurring at 216 ms in the grand-average data. The third vector, oriented in the anterior–posterior plane showed an early positive wave at about 60 ms in the grand-average data, but this was small and not reliably recorded across subjects. Individual source waveforms, obtained for left and right auditory cortices for each subject based on the sources derived from the grand-average data, were then measured. The tangential vectors provided peak measurements corresponding to the Cz recording; radial vectors provided the Tb and TP200 peaks (cf., Ponton and others 2002), measured between the latencies of 110–190 and 180–260 ms, respectively.

In preparation for the 40-Hz ASSR analyses, the continuous EEG was first filtered with a 30- to 50-Hz band-pass filter, followed by segmentation, artifact rejection, bad channel replacement, averaging, average referencing, and baseline correction, as described for the AEP analyses above. The difference between the 30- to 50-Hz band-pass–filtered FM1 and FM2 average waveforms was then computed in order to isolate further the ASSR activity: (FM1 − FM2)/2. Although an easier way to distinguish the ASSR from the AEP would be simply to band-pass filter at the frequency of the ASSR, our more complicated method of subtracting responses to stimuli modulated in opposite phase removes distortion of the onset of the ASSR by the evoked (transient) gamma-band response to tone and modulation onsets (Ross and others 2004). Following this subtraction, the root-mean-square (RMS) amplitude was then calculated in 50-ms bins across the 1500-ms epoch. The mean RMS amplitude of the ten 50-ms windows from 400 to 900 ms, the standard deviation (SD) of this mean, and the coefficient of variation (CV = SD/mean) were computed for each subject. The SD and CV variability measures were used to assess the stability of the 40-Hz brain response to auditory stimulus modulation across this 500-ms window.

As with the 20-Hz lowpass AEP data, the 129-channel ASSR data were further analyzed with spatiotemporal source analysis (BESA 5.1) using a 4-shell (brain, cerebrospinal fluid, skull, and scalp) ellipsoidal head model. To maximize the signal-to-noise ratio for source modeling, the difference data from 300 to 1000 ms were further band-pass filtered between 39 and 41 Hz and collapsed to yield a single-cycle evoked potential waveform of 25 ms. These data were then fit using regional sources in the brain stem and cortices (cf., Herdman and others 2002). A single, regional source was fit first across the 25-ms epoch. Two symmetric regional sources were then added and iteratively fit with the initial single source, resulting in a final model with one brain stem regional source and 2 symmetric temporal regional sources. In addition to the empirical model fitting of the brain stem source, its inclusion can be further justified for the following reasons: 1) the brain stem is clearly active during the ASSR, its recurrent responses across the duration of the ASSR overlapping (in time-lagged fashion) with the subsequent cortical responses measured at the scalp (as opposed to the late-transient onset AEP, where there is no temporal superposition with the earlier occurring transient brain stem activation), 2) this brain stem activity generates electrical fields that are recorded at the scalp (unlike magnetic fields from the brain stem which cannot be detected at the scalp and would not obtain in source modeling of magnetoencephalographic (MEG) ASSR scalp data), and 3) previous research supports the inclusion of a brain stem contribution to the scalp-recorded electrical 40-Hz ASSR (Herdman and others 2002). This grand-average source model was then applied to the collapsed single-cycle data of each subject. The RMS power of the 3 vectors for each regional source was computed to yield the power of source activity at each time point across this 25-ms cycle. The peak RMS amplitude of the source waveforms for this 25-ms cycle provided a measure of the strength of the ASSR at each source location.

EFR Data Processing

Processing of the EFR bipolar data was carried out using in-house laboratory software (Purcell and others 2004). A digitally implemented Fourier analyzer measured the amplitude and phase of the EEG activity that was at the same instantaneous frequency as the sweeping modulation frequency, which served as the reference signal for the analyzer. The analyzer multiplied the incoming EEG signal by the sine and cosine of the modulation frequency and filtered the products to remove the higher harmonics. Periods of recording that were more than 1.5 SDs from the mean amplitude (likely those contaminated by muscle and movement artifacts) were eliminated prior to analysis. The up and down parts of the sweep were collapsed together and the data smoothed to remove local peaks related to noise. Three measures from the EFR of each subject were extracted: 1) the frequency of the peak amplitude of response between 30 and 50 Hz, 2) the amplitude at this peak, and 3) the apparent latency (or group delay) of the response, measured as the slope of the phase versus modulation–frequency relationship over the frequency range 30–50 Hz.

Data Evaluation and Statistical Analyses

Data screening led to the exclusion of one participant from all TSS statistical analyses due to technical difficulties during the 128-channel recording. Two additional subjects were excluded from the AEP analyses due to excessive eye artifact (leaving too few trials to provide reliable data). The eye artifact did not significantly affect the 30- to 50-Hz filtered data, so these 2 participants were retained for the ASSR analyses. One subject was eliminated from statistical analysis of the ASSR source measures due to an unacceptably high residual variance (RV) of fit for this subject's data to the grand-average source model (34% RV vs. 2–16% RV for the other subjects). Finally, for each set of analyses on the remaining subjects, box plots were examined to identify extreme univariate outliers, and Cook's distance and leverage diagnostics were conducted to identify extreme multivariate outliers with disproportionate influence on subsequent regression equations (SPSS, version 12). This led to the elimination of one additional subject from analysis of the AEP scalp and source measures and one subject from the EFR analyses. In neither case did elimination of the outlier affect the patterns of significance of the correlations. The outlier elimination ensured more representative estimates of the group means and of the slopes of the regressions with age. Statistical analyses were thus conducted on a total of 19 subjects for the AEP scalp and source measures, 22 subjects for the ASSR scalp measures, 21 subjects for the ASSR source measures, and 22 subjects for the bipolar EFR measures.

In order to test the hypothesis that measurements changed with age, regressions were performed using both linear and exponential models. The exponential model was included to test for exponential growth trends commonly observed in maturational data (Eggermont 1988; Ponton and others 2000). For each measure, possible gender differences were also examined with independent-samples t-tests. Repeated-measures analysis of variance (ANOVA) and paired-samples t-tests were conducted on source measures to test for the differences in brain response across source regions, including laterality differences. Finally, for source measures that correlated significantly with age, t-test contrasts were conducted on the correlation coefficients to test for the differences across brain regions in the relationship of brain response to age.

Results

Transient AEPs: Scalp Waveforms

The 129-channel topographic plot of the average-referenced, grand-average AEP is shown in Figure 1. In the expanded waveform at Cz, the typical auditory P1-N1–P2-N2 complex is clearly visible, followed by a sustained potential for the duration of the tone. Descriptive statistics for the individual P1, N1, and P2 latencies and amplitudes for the 19 retained subjects are provided in Table 1.

Figure 1.

Average-referenced, grand-average 129-channel AEP from −100 to 1400 ms, 20-Hz low-pass filtered. Inset shows expanded waveform at Cz. Broken vertical lines indicate time of stimulus onset and offset.

Figure 1.

Average-referenced, grand-average 129-channel AEP from −100 to 1400 ms, 20-Hz low-pass filtered. Inset shows expanded waveform at Cz. Broken vertical lines indicate time of stimulus onset and offset.

Table 1

Mean and SD of P1, N1, and P2 latencies and amplitudes at Cz and the corresponding correlation coefficients (r) and significance values (P) for linear and exponential curve fits with age (N = 19)

 Mean (SD) rlin Plin rexp Pexp 
Latency (ms)      
    P1 58 (8) −0.627 0.004 −0.637 0.003 
    N1 105 (5) −0.550 0.015 −0.559 0.013 
    P2 165 (11) −0.214 0.379 −0.210 0.390 
Amplitude (μV)      
    P1 0.76 (0.50) −0.210 0.390   
    N1 −2.26 (1.26) −0.281 0.243   
    P2 1.81 (1.06) 0.239 0.323   
 Mean (SD) rlin Plin rexp Pexp 
Latency (ms)      
    P1 58 (8) −0.627 0.004 −0.637 0.003 
    N1 105 (5) −0.550 0.015 −0.559 0.013 
    P2 165 (11) −0.214 0.379 −0.210 0.390 
Amplitude (μV)      
    P1 0.76 (0.50) −0.210 0.390   
    N1 −2.26 (1.26) −0.281 0.243   
    P2 1.81 (1.06) 0.239 0.323   

T-tests revealed no significant gender differences in the AEP peak latencies or amplitudes. Linear and exponential curve-fit analyses were conducted to determine the best-fit functions for the relationship between age and each AEP latency and amplitude at Cz (Table 1). Only the P1 and N1 latencies showed a significant relationship with age (Fig. 2), decreasing at a linear rate of 0.72 and 0.38 ms per year, respectively.

Figure 2.

Scatterplots of P1 and N1 latencies at Cz as a function of age, with superimposed linear and exponential curve fits. Filled symbols indicate females.

Figure 2.

Scatterplots of P1 and N1 latencies at Cz as a function of age, with superimposed linear and exponential curve fits. Filled symbols indicate females.

Transient AEPs: Source Waveforms

The AEP source model accounted for 96% of the variability in grand-average scalp data from 0 to 300 ms poststimulus, the window of the late AEP onset transients. The locations of the sources and the resulting grand-average source waveforms for each of the vector orientations are shown in Figure 3.

Figure 3.

Transient onset AEP source model and waveforms: (a) locations for left (blue; Talairach: −34.9, −22.9, 14.4) and right (red; Talairach: 31.8, −14.2, 9.8) regional sources; eye dipoles (black) captured residual blink artifact and (b) grand-average waveforms for left (blue) and right (red) regional source vector orientations.

Figure 3.

Transient onset AEP source model and waveforms: (a) locations for left (blue; Talairach: −34.9, −22.9, 14.4) and right (red; Talairach: 31.8, −14.2, 9.8) regional sources; eye dipoles (black) captured residual blink artifact and (b) grand-average waveforms for left (blue) and right (red) regional source vector orientations.

The grand-average source model was applied to individual subject topographies, accounting for 74–95% of the variability in the scalp data of the individual subjects (mean = 89%). The tangential P1, N1, and P2, and radial Tb and TP200 peak latencies and amplitudes (Table 2) were identified within windows surrounding the peaks of the grand-average source waveforms. No significant gender differences obtained for any of the AEP source measures. T-tests for laterality differences revealed that the tangential N1 peak was both shorter in latency, t18 = −2.24, P = 0.038, and larger in amplitude, t18 = −2.77, P = 0.013, on the right than left. Latencies of the tangential P1 and N1 of the right, but not left, temporal source also decreased significantly with increasing age (Table 2 and Fig. 4). Direct t-test comparisons of the left versus right linear correlation coefficients with age were not statistically significant; however, there was a trend toward a stronger correlation with age on the right than left for P1 latency, t16 = −1.72, P = 0.10. The magnitude of the radial Tb also decreased significantly with age on the right only (Table 2, slope = 0.92 nAm per year). A direct t-test contrast revealed that the correlation of Tb magnitude with age on the right was significantly larger than the nonsignificant correlation on the left, t16 = 2.23, P = 0.04.

Figure 4.

Scatterplots of the latencies for left and right temporal tangential vector source peaks, P1 and N1, as a function of age, with superimposed linear and exponential curve fits. Filled symbols indicate females.

Figure 4.

Scatterplots of the latencies for left and right temporal tangential vector source peaks, P1 and N1, as a function of age, with superimposed linear and exponential curve fits. Filled symbols indicate females.

Table 2

Mean and SD of tangential and radial source peak latencies and amplitudes and the corresponding correlation coefficients (r) and significance values (P) for linear and exponential curve fits with age (N = 19)

 Mean (SD) rlin Plin rexp Pexp 
Latency (ms)      
    P1 right 58 (9) −0.667 0.002 −0.694 0.001 
    P1 left 60 (10) −0.354 0.137 −0.332 0.165 
    N1 right 103 (6) −0.597 0.007 −0.615 0.005 
    N1 left 107 (6) −0.354 0.138 −0.354 0.137 
    P2 right 168 (14) −0.187 0.445 −0.190 0.434 
    P2 left 163 (12) −0.277 0.251 −0.277 0.252 
    Tb right 147 (9) −0.187 0.445 −0.187 0.442 
    Tb left 147 (11) 0.077 0.757 0.071 0.774 
    TP200 right 221 (12) 0.164 0.500 0.152 0.534 
    TP200 left 219 (19) 0.164 0.500 0.164 0.499 
Amplitude (nAm)      
    P1 right 9.4 (5.9) −0.071 0.763   
    P1 left 10.3 (6.8) −0.170 0.486   
    N1 right −26.8 (12.1) 0.077 0.749   
    N1 left −19.8 (13.7) −0.346 0.146   
    P2 right 17.7 (13.6) 0.435 0.063   
    P2 left 14.5 (12.6) 0.293 0.223   
    Tb right −11.5 (8.4) 0.684 0.001   
    Tb left −12.3 (7.1) 0.237 0.330   
    TP200 right 19.2 (8.6) −0.272 0.260   
    TP200 left 17.2 (7.1) −0.303 0.208
 
  
 Mean (SD) rlin Plin rexp Pexp 
Latency (ms)      
    P1 right 58 (9) −0.667 0.002 −0.694 0.001 
    P1 left 60 (10) −0.354 0.137 −0.332 0.165 
    N1 right 103 (6) −0.597 0.007 −0.615 0.005 
    N1 left 107 (6) −0.354 0.138 −0.354 0.137 
    P2 right 168 (14) −0.187 0.445 −0.190 0.434 
    P2 left 163 (12) −0.277 0.251 −0.277 0.252 
    Tb right 147 (9) −0.187 0.445 −0.187 0.442 
    Tb left 147 (11) 0.077 0.757 0.071 0.774 
    TP200 right 221 (12) 0.164 0.500 0.152 0.534 
    TP200 left 219 (19) 0.164 0.500 0.164 0.499 
Amplitude (nAm)      
    P1 right 9.4 (5.9) −0.071 0.763   
    P1 left 10.3 (6.8) −0.170 0.486   
    N1 right −26.8 (12.1) 0.077 0.749   
    N1 left −19.8 (13.7) −0.346 0.146   
    P2 right 17.7 (13.6) 0.435 0.063   
    P2 left 14.5 (12.6) 0.293 0.223   
    Tb right −11.5 (8.4) 0.684 0.001   
    Tb left −12.3 (7.1) 0.237 0.330   
    TP200 right 19.2 (8.6) −0.272 0.260   
    TP200 left 17.2 (7.1) −0.303 0.208
 
  

ASSR: Scalp Waveforms

The 30- to 50-Hz band-pass–filtered, average-referenced, grand-average FM1, FM2, and amplitude-corrected difference waveforms ([FM1 − FM2]/2) at Cz for the 22 retained subjects are displayed in Figure 5a,b. The RMS amplitude within 50-ms bins across the 1500-ms epoch is plotted for the grand-average data in Figure 5c. The mean RMS amplitude of the ten 50-ms windows from 400 to 900 ms, the SD of this mean, and the CV (CV = SD/mean) were computed for each subject. The grand mean and SD for these measures are presented in Table 3. No significant gender differences were found for the mean amplitude, SD, or CV of the ASSR. Curve-fit analyses revealed a small, but significant, linear increase in the amplitude of the 40-Hz ASSR as a function of age (Fig. 6, left). This was accompanied by a decrease in ASSR variability with age, as indicated by a significant decaying exponential curve in the CV computed across the ten 50-ms windows of the steady-state response (Fig. 6, right). The CV was not correlated with RMS amplitude from 1300 to 1500 ms (r = −0.13, P = 0.57), which suggests that this change in variability cannot be attributed to overall noise level in the recording.

Figure 5.

Average-referenced, grand-average 40-Hz ASSR at Cz (30–50 Hz band-pass filtered): (a) superposed response to FM1 and FM2 stimuli, (b) Difference between FM1 and FM2 responses divided by 2 to extract the ASSR, and (c) RMS amplitude of ASSR in 50-ms bins. Data from individual subjects within the time window indicated by the boxed area was subjected to subsequent statistical analyses.

Figure 5.

Average-referenced, grand-average 40-Hz ASSR at Cz (30–50 Hz band-pass filtered): (a) superposed response to FM1 and FM2 stimuli, (b) Difference between FM1 and FM2 responses divided by 2 to extract the ASSR, and (c) RMS amplitude of ASSR in 50-ms bins. Data from individual subjects within the time window indicated by the boxed area was subjected to subsequent statistical analyses.

Figure 6.

Scatterplots of the mean RMS amplitude (left) of the 40-Hz steady-state response from 400 to 900 ms and of its CV (right) as a function of age, with superimposed linear and exponential curves. Filled symbols indicate females.

Figure 6.

Scatterplots of the mean RMS amplitude (left) of the 40-Hz steady-state response from 400 to 900 ms and of its CV (right) as a function of age, with superimposed linear and exponential curves. Filled symbols indicate females.

Table 3

Group mean and SD of the individual mean, SD, and CV of the RMS 40-Hz ASSR magnitude computed within ten 50-ms windows from 400 to 900 ms, as measured at Cz (N = 22) and the group mean and SD peak RMS amplitude of the collapsed single-cycle 40-Hz ASSR for each regional source (N = 21)

 Mean (SD) rlin Plin rexp Pexp 
Scalp amplitude at Cz (μV)      
    Mean 0.13 (0.04) 0.438 0.041 0.382 0.079 
    SD 0.03 (0.01) −0.095 0.671 −0.126 0.580 
    CV 0.23 (0.10) −0.411 0.057 −0.445 0.038 
Source amplitude (nAm)      
    Right temporal 1.31 (0.62) 0.224 0.330 0.207 0.365 
    Left temporal 1.28 (0.63) 0.609 0.003 0.605 0.004 
    Brain stem 1.42 (0.66) 0.170 0.457 0.164 0.476 
 Mean (SD) rlin Plin rexp Pexp 
Scalp amplitude at Cz (μV)      
    Mean 0.13 (0.04) 0.438 0.041 0.382 0.079 
    SD 0.03 (0.01) −0.095 0.671 −0.126 0.580 
    CV 0.23 (0.10) −0.411 0.057 −0.445 0.038 
Source amplitude (nAm)      
    Right temporal 1.31 (0.62) 0.224 0.330 0.207 0.365 
    Left temporal 1.28 (0.63) 0.609 0.003 0.605 0.004 
    Brain stem 1.42 (0.66) 0.170 0.457 0.164 0.476 

Also presented are the corresponding correlation coefficients (r) and significance values (P) for linear and exponential curve fits with age.

ASSR: Source Waveforms

The 129-channel, average-referenced, grand-average, collapsed single-cycle data used for source modeling and the resultant regional source locations are shown in Figure 7. The model accounted for 99% of the variance in these grand-average data. This model was applied to the individual data of the 21 retained subjects, accounting for 84–98% of the variability in the scalp data of the individual subjects (mean = 92%). The peak RMS amplitude of the source waveforms was computed for statistical analysis. Descriptive statistics are provided in Table 3.

Figure 7.

Left: Average-referenced, grand-average 129-channel ASSR, 39–41 Hz band-pass filtered, and collapsed into a single 25-ms cycle for source analysis. Right: Locations of the left temporal (Talairach: −33, −11.4, 12.1), right temporal (Talairach: 33, −11.4, 12.1), and brain stem (Talairach: −2.9, −26.1, −7.3) regional sources.

Figure 7.

Left: Average-referenced, grand-average 129-channel ASSR, 39–41 Hz band-pass filtered, and collapsed into a single 25-ms cycle for source analysis. Right: Locations of the left temporal (Talairach: −33, −11.4, 12.1), right temporal (Talairach: 33, −11.4, 12.1), and brain stem (Talairach: −2.9, −26.1, −7.3) regional sources.

No significant gender differences in the maximum RMS amplitude of the ASSR obtained for any source region. A repeated-measures ANOVA on source location revealed no differences in maximum RMS amplitude of the ASSR across sources (F < 1). Curve-fit analyses with age (Table 3), on the other hand, revealed significant linear and exponential increases in the maximum RMS amplitude of the 40-Hz ASSR as a function of age for the left temporal source (Fig. 8). The correlations with age in the right temporal and brain stem sources were not significant. T-tests directly comparing the correlation coefficients indicated a significantly stronger correlation of ASSR RMS amplitude with age for the left temporal versus brain stem source, t18 = 2.23, P = 0.04, and a trend toward a stronger correlation with age for the left versus right temporal source, t18 = 1.97, P = 0.06. There was no difference between the right temporal and brain stem correlations with age (P = 0.85).

Figure 8.

Scatterplot of the maximum RMS amplitude of the 40-Hz steady-state response as a function of age for the left temporal source. Linear and exponential curve fits are superimposed. Filled symbols indicate females.

Figure 8.

Scatterplot of the maximum RMS amplitude of the 40-Hz steady-state response as a function of age for the left temporal source. Linear and exponential curve fits are superimposed. Filled symbols indicate females.

Given the potential of a deep source, such as the brain stem source, to draw some power away from the more superficial cortical sources, we further evaluated these effects with just the 2 cortical sources in the model. This 2-source model accounted for 98% of the variance in the grand-average scalp data. The magnitude of these 2 cortical sources increased by approximately 20% with the brain stem source excluded. Importantly, the asymmetry in the correlation of the cortical sources with age persisted, yielding significant linear and exponential increases in the 40-Hz ASSR for the left temporal source, rlin = 0.610, P = 0.003; rexp = 0.574, P = 0.006, but not the right, rlin = 0.391, P = 0.079; rexp = 0.389, P = 0.081.

Envelope Following Response

The vector average and amplitude average of the EFR to 100% amplitude-modulated white noise from 10 to 100 Hz are plotted in Figure 9. Each data point represents the grand-average amplitudes (thin line) at the instantaneous modulation frequency. The vector average data (thick line) show smaller values due to phase variance between subjects, the phases of the different subjects being very similar at frequencies above 30 Hz but not below. The peak frequency between 25 and 55 Hz and the amplitude at that peak frequency for each of the 22 retained subjects were identified. The mean peak frequency per subject ranged from 32 to 52 Hz (M = 41 Hz, SD = 4.70), with peak amplitudes ranging from 0.21 to 0.53 μV (M = 0.39 μV, SD = 0.08). No gender differences obtained for either peak frequency or peak amplitude. The frequency of the peak response increased with age, as shown by significant linear and exponential curve fits (Fig. 10). In contrast, neither was the amplitude at the peak frequency significantly related to age (mean value of 0.4 μV, P > 0.06 for both linear and exponential curve fits) nor was there a change in apparent latency as a function of age (mean value of 32 ms, P > 0.50 for both linear and exponential curve fits).

Figure 9.

Grand vector average (thick line) and amplitude average (thin line) of the EFR.

Figure 9.

Grand vector average (thick line) and amplitude average (thin line) of the EFR.

Figure 10.

Scatterplot of the EFR peak frequency as a function of age, with superimposed linear and exponential curves. Filled symbols indicate females.

Figure 10.

Scatterplot of the EFR peak frequency as a function of age, with superimposed linear and exponential curves. Filled symbols indicate females.

Discussion

The present study found significant changes in the neural response to auditory stimulation in adults from 19 to 45 years of age. First, the latency of the auditory P1–N1 complex decreased with age. Second, the oscillatory steady-state response to a 40-Hz frequency–modulated tone was both larger and less variable with increasing age. Finally, the frequency peak of the EFR increased with age. These changes cannot be explained on the basis of changes in the peripheral auditory system. Although our subjects were screened for normal hearing, they might have shown a small increase in threshold (about 5–10 dB) over the age range that we studied (Brant and Fozard 1990; Pearson and others 1995). But this would have increased response latency and decreased response amplitude—the opposite of what we found. We propose that our results are consistent with persistent adaptation of cortical and/or corticothalamic mechanisms in the auditory system during middle adulthood.

Transient AEPs

Among the vertex AEPs, both P1 and N1 latency were negatively correlated with age across middle adulthood, with no age-related change in P2 latency. This fits well with previous research on AEP maturation during childhood and adolescence that found no change in P2 latency after 5 years of age but a gradual shortening in the latencies of the P1 and N1 into late adolescence (Ponton and others 2000). Whereas Ponton and others found that P1 and N1 latencies reached asymptote by about 16 years of age, we found there to be a slow, but persistent, decrease well into adulthood that could be equally well fit with either linear or exponential curves. Eggermont, Ponton and others (Ponton and Eggermont 2001; Ponton and others 2002; Eggermont and Ponton 2003) argued that the P1 actually reaches maturity much earlier than the N1 but that the superposed emergence of the N1 causes an apparent decrease in both the magnitude and latency of the P1 peak. Given that our results yielded a slope for P1 latency with age that was about twice that for N1 latency, it is unlikely that the decrease in P1 latency here was driven solely by superposition of the N1. To explore this question further, we examined the relationship between age and P1 latency after partialing out both N1 latency and amplitude; age still predicted a significant 16% of variance in P1 latency. In contrast, after partialing out both P1 latency and amplitude, there remained no significant relationship between age and N1 latency. McArthur and Bishop (2002) observed a similar significant negative correlation between P1 latency and age from 11 to 50 years, whereas the reduction in N1 latency with age was found to asymptote in the early 20s. Thus, in contrast to P1 and N1 maturation patterns in adolescence, these findings suggest that neural mechanisms related to slow reductions in P1 latency and, perhaps to a lesser extent N1 latency, continue to act during early-to-middle adulthood. Into later adulthood, a different pattern has been observed. In comparing an older (53–82 years) with a younger adult group (20–40 years), Pekkonen and others (1995) found no difference in latency for the P1m, but a delayed ipsilateral N1m in the older group, tentatively attributed to possible deterioration of callossal connections between left and right auditory cortices or greater vulnerability during aging of the sparser ipsilateral ascending fibers.

Spatiotemporal source analysis of the scalp AEP revealed asymmetries in the AEP responses and their relationship with age. The source waveform peak corresponding to the vertex N1 was significantly greater in magnitude and shorter in latency in the right compared with the left hemisphere. This right hemisphere dominance may reflect greater coherence among auditory regions on the right than left during the processing of simple tones, as recently demonstrated by Harle and others (2004). Interestingly, it was also only the right tangential source peak latencies corresponding to the P1 and N1 vertex peaks that correlated significantly with age. Although the linear correlation coefficients for the right and left sources were not found to be significantly different in t-test comparisons, there was a statistical trend toward a stronger correlation with age on the right than left for the latency of the P1 tangential source peak. In line with Karni's minimal level hypothesis (Karni 1996; Karni and Bertini 1997), these small age-related changes in auditory processing may be most evident in the right auditory cortex because of the simple tonal nature of the stimuli. Perhaps, more speech-based stimuli would reveal similar age-related changes in the left auditory cortex.

In contrast to the tangential P1 and N1 source peaks, radial peak latencies were not correlated with age. Previous research with children found that although the latency of the radial Tb was mature by the age of 6 years, the radial TP200 showed a gradual decrease in latency extending into late adolescence (Ponton and others 2002). Our results suggest that this gradual decrease in TP200 latency does not persist across adulthood. There was, however, a significant decrease in magnitude of the radial Tb peak with increasing age, a decrease that was previously reported to start at about 14 years of age (Ponton and others 2002). As with the latency changes in the tangential P1 and N1, this change in magnitude of the radial Tb was also right lateralized.

Eggermont, Ponton and others (Eggermont 1988; Ponton and others 2002) argue that shifts in AEP latency arise from changes in axonal myelination and the maturation of synaptic mechanisms, whereas changes in amplitude reflect the number of pyramidal cell synapses contributing to postsynaptic potentials. This suggests that different mechanisms may be influencing the latency and amplitude changes we have observed in tangential and radial peaks, respectively. Given their orientation, the tangential vectors capture activity arising primarily from the superior surface of the temporal lobe comprised of primary auditory cortex and belt regions of secondary auditory cortex, whereas the radial vectors reflect processing in secondary auditory cortex of the parabelt regions on the lateral surface of the temporal lobe. Following this, we speculate that our finding of age-related reduction in the latencies of the tangential P1 and N1, but not the Tb, reflects changes to primary auditory cortex and possibly belt, but not parabelt, regions of secondary auditory cortex. In contrast, the reduction in amplitude of the radial Tb peak may reveal continued modifications to parabelt regions.

Auditory Steady-State Response

In the present study, the strength of the steady-state response to a 40-Hz frequency–modulated tone also showed age-related changes. First, there was a significant increase in magnitude across middle adulthood. Although Rojas and others (2006) found evidence of an asymptote in the magnitude of the magnetic 40-Hz ASSR in early adulthood, the linear regression for their 5- to 52-year age range was also significant and together with our results may indicate a continued, though slower, increase in the 40-Hz ASSR into middle adulthood. Overall, previous studies have not found a difference in the strength of the 40-Hz response when comparing normal-hearing young and elderly adults (Johnson and others 1988; Picton and others 2003). Picton and others (2003) did report, however, a nonsignificant increase of 2.6 nV per year in the steady-state response to a 43-Hz modulated tone in unpublished data from 30 adults aged from 20 to 81 years. This is very similar to the vertex-measured increase of 2.8 nV per year in our subjects, which did reach statistical significance. The CV of the magnitude of the vertex-recorded ASSR also decreased with age. This effect must be interpreted cautiously as it might be susceptible to signal-to-noise confounds. The increase in magnitude (and possible decrease in variability) of the 40-Hz ASSR with age indicates continued refinement of the ability of the auditory cortex to synchronize its neuronal response to temporal information carried by auditory stimuli (Yamasaki and others 2005). Such resonant activity may play an important role in identifying and isolating a stimulus, such as speech, from background noise (Dimitrijevic and others 2004).

The source analysis of the 40-Hz ASSR revealed that the relationship between ASSR magnitude and age was significant only for the left temporal source, where the correlation was even stronger and more reliable than when measured at the scalp. This may have been due to at least 3 factors: 1) separation in source space of the left temporal from the right temporal and brain stem ASSR, the latter 2 of which did not correlate with age, 2) better capture of the tangentially oriented activity in auditory cortex in source space than in the scalp recordings, and 3) the inclusion, through computation of the RMS of the regional source vectors, of both tangential and radial temporal source components of the ASSR (Herdman and others 2002; Picton and others 2003). Although the mean strength of the ASSR was similar for all 3 regional sources, the correlation with age in the left auditory cortex suggests a systematic neural refinement of this region related to the temporal processing of sound. Potential neural mechanisms underlying these findings are discussed below.

Finally, it should be noted that although inclusion of the brain stem source increased the variance in the scalp data accounted for by the source model by only 1% (from 98 to 99%), it did not appear to be redundant, nor interfere, with the cortical sources in the left and right temporal lobes. Our source model yielded cortical ASSR source magnitudes ranging between 0.4 and 3.2 nAm across subjects (mean = 1.31 and 1.28 nAm for right and left sources, respectively), comparable with previous source estimates for the 40-Hz ASSR in studies employing EEG (Herdman and others 2002) and MEG (Ross and others 2000, 2002; Schoonhoven and others 2003). Exclusion of the brain stem source from the source model had a relatively small impact on these cortical source magnitudes and, most importantly, the asymmetry in the relationship with age persisted if we removed the brain stem source and evaluated just the cortical sources. These results and reasons presented earlier support the inclusion of the brain stem source in the model.

Envelope Following Response

In the EFR experiment, in which white noise was amplitude modulated in continuous sweeps at a rate from 10 to 100 Hz, the peak response occurred at a modulation frequency of 41 Hz. This peak frequency is identical to a previous finding of 41 Hz in adults ranging in age from 18 to 43 years (Purcell and others 2004). Correlations with age observed in the present study revealed, however, that the frequency of this peak is not invariant across middle adulthood. A significant linear increase in peak frequency with age indicated that young adults respond maximally at a slower modulation rate than do middle-aged adults. Specifically, the regression equation yielded an increase of 0.32 Hz per year from a peak at 38-Hz modulation at 19 years to a peak at 46-Hz modulation at 45 years of age. This outcome indicates enhanced efficiency in the neuronal response to rapidly modulated sounds. Subcortical changes in myelination or in synaptic efficiency would have likely decreased the latency of the response. Because this did not occur, we suggest that the observed changes in the peak frequency indicate changes at the cortical level. Although this increase in peak frequency was best fit with a linear function between 19 and 45 years of age, previous research found that the frequency of this peak dropped to 37 Hz in a group of elderly adults between 60 and 78 years (Purcell and others 2004), suggesting a curvilinear function across a wider age range. As in our results, Purcell and others found that frequency, but not amplitude, of the peak response was related to age. Ali and Jerger (1992) found the maximum phase coherence in the transient midlatency response occurred at 30 Hz rather than 40 Hz in an elderly group whose speech understanding was worse than would be predicted by their audiometric hearing sensitivity loss. The 40-Hz ASSR was also degraded in this group, further suggesting a relationship between auditory temporal processing in the 30- to 50-Hz range and speech perception. Whether a similar relationship exists between speech perception and EFRs in the 30- to 50-Hz range in normal-hearing, middle-aged adults remains to be tested.

Potential Underlying Mechanisms

As described above, the pattern of results for the transient evoked potential latencies and for oscillatory responses to a frequency-modulated tone and amplitude-modulated white noise indicates persistent adaptation of neuronal mechanisms of temporal auditory processing across middle adulthood. Although our data cannot directly address the physiological bases of these changes, they are most consistent with mechanisms that are cortical in origin, and which we speculate may include continued myelination and/or refinement of corticocortical and corticothalamic projections and adaptation of cortical inhibitory circuits.

Sources for both the P1 and N1 transients and the 40-Hz steady-state response localized to auditory cortex on the supratemporal plane (with an additional brain stem source for the ASSR that did not correlate with age). These results are consistent with previous source analyses of the late AEP (Picton and others 1999; Ponton and others 2002) and the ASSR (Pantev and others 1996; Herdman and others 2002) and suggest that the mechanisms underlying the age-related effects we obtained are based primarily in the cerebral cortex. As noted earlier, the absence of an effect of age on the apparent latency of the EFR response further points to a cortical mechanism.

The late maturation of P1 and N1 morphology and their proposed upper laminar origin (Steinschneider and others 1994) fit well with research demonstrating continued maturation into late adolescence of white matter tracts of commissural (Giedd and others 1996; Snook and others 2005) and putative corticocortical (Paus and others 1999) projections to and from auditory cortex. Myelination and changes in white matter density have also been shown to continue into the fourth decade of life and even later (Benes and others 1994; Benes 1998; Bartzokis and others 2001; Good and others 2001), particularly in regions with extensive corticocortical association fibers. Such changes may contribute to faster and more synchronous neuronal transmission and would be consistent with the continued decrease in P1 and N1 latencies and the increase in magnitude and stability of the 40-Hz ASSR observed across middle adulthood in the present study.

γ-Aminobutyric acidergic inhibitory interneurons are widespread in the auditory cortex (Winer 1992; Prieto and others 1994a, 1994b) and mature relatively late in development (Gao and others 1999). Refinement in these inhibitory circuits during maturation may allow the emergence of the N1, the decrease in its latency across adolescence and into adulthood (Gilley and others 2005; Sharma and others 2005), and its progressively shorter refractory period (Paetau and others 1995; Gilley and others 2005). Inhibitory interneurons also play an important role in oscillatory synchronization (Jefferys and others 1996; Sturm and Konig 2001; Maex and De Schutter 2003; Hasenstaub and others 2005; Jedlicka and Backus 2006). The thalamocortical phase-locking loop model proposed by Ahissar and others (Ahissar and others 1997) illustrates how cortical oscillations can be entrained to the instantaneous frequency of an input stimulus via a negative feedback loop consisting of cortical oscillatory neurons, inhibitory interneurons, and thalamic gating. It would be interesting to explore whether such a model may apply to the encoding of temporal information by the auditory cortex, such as seen in the steady-state and EFRs.

Finally, myelination and intracortical inhibitory circuits have been implicated in experience-dependent plasticity (Gao and others 1999; Soto-Trevino and others 2001; Bao and others 2004). For example, Swindale (2003) argued that synchronization in the timing and speed of axonal conduction may depend on path length compensation, refined through learning and experience. An increasing number of studies indicate preserved capacity for plasticity in even basic auditory processes during adulthood, including changes in temporal processing properties. For example, after completing 15 daily training sessions, each consisting of 480 trials of frequency discrimination with 40-Hz modulated carrier tones, both the latency of the N1 and the phase of the 40-Hz response in adult nonmusicians were shortened, indicating modified temporal processing in auditory cortex (Bosnyak and others 2004).

In summary, we speculate that the more accelerated myelination and refinement of inhibitory circuits occurring during childhood and adolescence may continue at a slower pace into adulthood and contribute to the age-related changes observed in the present study. Faster conduction and neuronal synchrony are enhanced by increased myelination, loss of unused connections, and the refinement of inhibitory intracortical circuits, all of which have been shown to be relatively late maturing. This late maturation can extend plasticity, allowing the neuronal circuitry to be shaped by significant environmental input.

Conclusion and Future Directions

The present study revealed age-related changes from early-to-middle adulthood in the latencies of the auditory P1 and N1 transients, and in the ASSR and EFR, suggesting persistent plasticity of mechanisms important for the temporal resolution of sounds during adulthood. We did not, however, assess the relationship between these EEG measures and auditory perception or linguistic skills. Additional studies that relate the rate of change in a physiological response to changes in auditory abilities will be helpful. For example, do these age-related changes differ in subjects with hearing impairment or disorders of language and reading (Bishop and McArthur 2004)? It would also be of great interest to examine these auditory brain responses across a wider age range extending into late adulthood in order to determine when the changes we have noted reverse to give what is found in the elderly.

Finally, we have speculated that continued myelination and refinements to inhibitory circuits are possible mechanisms underlying the age effects on temporal auditory processing observed here. Continued research with humans and animal models would help determine the extent to which these, or other, processes may be implicated during adulthood and the degree to which experience can drive such changes.

This research was supported by the Santa Fe Institute Consortium and the Canadian Institutes of Health Research. We wish to thank Rhonda Amsel for statistical consultation, Candice Cartier for assistance in subject recruitment, and Andrée Hardy for data collection. We would also like to acknowledge the insightful comments provided by Bob Burkard and 2 anonymous reviewers of an earlier version of the manuscript. Conflict of Interest: None declared.

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