The Time Course of the Amplitude and Latency in the Auditory Late ...

J Am Acad Audiol 20:239–250 (2009)

The Time Course of the Amplitude and Latency in the Auditory Late Response Evoked by Repeated Tone Bursts DOI: 10.3766/jaaa.20.4.4 Fawen Zhang* James Eliassen{ Jill Anderson* Peter Scheifele* David Brown*

Abstract Background: This study provides a detailed description of the time course of amplitude and latency in the auditory late response (ALR) elicited by repeated tone bursts. Research Design: Tone bursts (50 and 80 dB SPL) were presented via insert earphones in trains of ten with interstimulus intervals (ISIs) of 0.7 and 2 msec and an intertrain interval of 15 sec. Averages were derived independently for each tone burst within the train across the total number of train presentations. Study Sample: Participants were 14 normal-hearing young adults. Data Collection and Analysis: Data were analyzed in terms of the amplitudes and latencies of the N1 and P2 waves of the ALR as well as the N1-P2 amplitude. Results: The N1-P2 amplitude was a more stable measure than the amplitude of individual N1 and P2 peaks. The N1-P2 amplitude was maximal for the first tone burst and decreased in a nonmonotonic pattern for the remainder of the tone bursts within a stimulus train. The amplitude decrement was dependent on stimulus intensity and ISI. The latencies of N1 and P2 were maximal for the first tone burst and reduced approximately 20% for the rest of the stimuli in a train. The time course of N1 and P2 latencies was not dependent on stimulus intensity and ISI. Conclusions: The reduction of latency in the time course of the ALR might be related to the fact that neurons with shorter latencies had faster recovery speed from adaptation and/or refractoriness than those with longer latencies. This finding is meaningful in the context of future research to restore normal adaptation in abnormal hearing populations such as cochlear implant patients. Key Words: Adaptation, auditory cortex, auditory evoked response, electrophysiology, human, refractoriness, repetitive stimuli, response recovery Abbreviations: AEP 5 auditory evoked potential; ALR 5 auditory long latency response; CI 5 cochlear implant; EEG 5 electroencephalogram; EOG 5 electrooculography; ISI 5 interstimulus interval; ITI 5 intertrain interval

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he motivation of the present study was twofold. First, we hoped to establish normative data for the auditory late response (ALR) that could be compared with the data collected in future studies from

patients with cochlear implants (CIs). Second, we wanted to understand the neuronal mechanisms underlying the ALR to repeated stimuli, which have been controversial in the literature.

*Department of Communication Sciences and Disorders, University of Cincinnati, OH; {Center for Imaging Research, University of Cincinnati, OH Fawen Zhang, Department of Communication Sciences and Disorders, University of Cincinnati, Cincinnati, OH; Phone: 513-558-8513; Fax: 513558-850; E-mail: [email protected] This project was supported by the University Research Council of the University of Cincinnati.

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Journal of the American Academy of Audiology/Volume 20, Number 4, 2009

The adaptation and refractory features of neural responses evoked by electric stimulation via a CI are different from normal, and this situation becomes more severe due to deafness-related neural degeneration (Loquet et al, 2004; Zhang et al, 2007). Because the abnormal adaptation and refractory features can limit the temporal information of sound encoding, researchers have tried to mimic the features of normal neural responses to improve CI performance, but only in responses of individual neurons (Brown et al, 1990; Zhou et al, 1995; Chatterjee, 1999; Loquet et al, 2004). However, perhaps it is more logical to produce normal adaptation and refractory features using gross auditory evoked potential, since speech perception relies more on synchronized activities of groups of neurons instead of individual neurons. This would be especially true in the cortical responses, because these responses are more related to speech perception than gross responses from lower levels of the auditory system. The ALR is the auditory evoked response that is generated from the auditory cortex including the superior temporal plane and lateral superior temporal gyrus (Knight et al, 1988). It has been found that the ALR can be used to predict speech perception performance in both normal-hearing listeners and CI users (Hoppe et al, 2001; Kelly et al, 2005; Agung et al, 2006; Guiraud et al, 2007). Neural adaptation has been largely described in individual neurons, presenting as an exponential reduction of the responsiveness to constant stimuli or repeated stimuli (Smith, 1977; Smith and Brachman, 1982; Westerman and Smith, 1984; Boettcher et al, 1990; Chimento and Schreiner, 1991; Javel, 1996; Loquet et al, 2004; Zhang et al, 2007). Adaptation reflects the loss of novelty associated with an updating of a neuronal template of a stimulus following repeated exposure (Ritter et al, 1968). Normal adaptation may be useful for sound detection. For instance, adaptation reduces the response of the auditory system to constant stimuli so that the system becomes more sensitive to changes or fluctuations of signals that are common in speech, music, and other sounds (Micheyl et al, 1995; Kuriki et al, 2006). Refractoriness mainly refers to the phenomenon wherein a neuron can only respond to a stimulus after a certain period of refractory time following a response to the preceding stimulus. The neuron cannot respond to a stimulus at all during the absolute refractory period and can only respond to a stimulus with higher intensity during the relative refractory period. Therefore, in the relative refractory period, there is a lower firing probability or elevated threshold in individual neurons and amplitude reduction in auditory evoked potentials. Refractoriness limits the temporal coding ability of neurons because it results in a neuron firing rate limit. The time needed for recovering from

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refractoriness is progressively longer for neurons at higher levels along the auditory system (Shore, 1995; Fitzpatrick et al, 1999). The effects of adaptation and refractoriness have been reported in various auditory evoked potentials (AEPs) in the form of reduced amplitude to repeated stimuli or a masked response to the second stimulus in a pair-stimulus paradigm (Soucek and Mason, 1992; Thornton and Slaven, 1993; Burkard et al, 1996; Ohashi et al, 2005). For the cortically recorded auditory late response (ALR) in humans, adaptation and refractory features are usually measured as the reduction of N1-P2 peak-to-peak amplitude during the stimulus presentation. Numerous studies have reported adaptation of the ALR by using stimulus trains with various numbers of successive stimuli and interstimulus intervals (ISIs) (Butler, 1968; Fruhstorfer et al, 1970; Fruhstorfer, 1971; Megela and Teyler, 1979; Prosser et al, 1981; Bourbon et al, 1987; Barry et al, 1992; Budd et al, 1998; Rosburg et al, 2004). Authors typically have found that the amplitude reduction can be approximately 50% over the first several stimuli before it reaches an asymptotic amplitude, which is lower for shorter ISIs than longer ISIs (Ritter et al, 1968; Bourbon et al, 1987). ALR amplitude does not change significantly for ISIs longer than 10 sec (Ritter et al, 1968). There has been a long-standing controversy over the mechanisms underlying the amplitude reduction of ALR within trains. This controversy concerns whether the reduction is an effect of true adaptation, refractoriness, or both (Megela and Teyler, 1979; Barry et al, 1992; Budd et al, 1998). Most researchers have used adaptation, or habituation, simply to describe a response reduction during presentation of repeated stimuli. However, this term should be used with caution to avoid confusion when it comes to interpreting results relative to the underlying neuronal mechanisms. One distinct feature of refractoriness is that amplitude reductions should stabilize immediately after repetition of a stimulus. Adaptation, however, could result in a more progressive amplitude decrement manifested as an exponential decay during stimulus repetition (Thompson and Spencer, 1966). Results from previous studies of the ALR to stimulus trains are mixed. Some studies have shown that N1 amplitude reduction followed a negative exponential function of the stimulus order and reached an asymptote by the third or fourth stimulus in a train, supporting the adaptation theory (Fruhstorfer et al, 1970; Fruhstorfer, 1971). Other researchers found that the ALR reduction became stabilized by the second stimulus, suggesting the existence of refractoriness (Bourbon et al, 1987; Budd et al, 1998). Moreover, some researchers reported that the ALR stimulated by a train failed to demonstrate dishabituation, an increase

Auditory Late Response to Repeated Stimuli/Zhang et al

in the response amplitude compared to previous habituated/adapted responses following a change stimulus in the train. Because dishabituation is one of the important criteria of adaptation, those previous studies suggested that adaptation was not the only explanation for the ALR amplitude reduction during the stimulus train (Ritter et al, 1968; Barry et al, 1992; Budd et al, 1998). While most researchers have reported the amplitude time course, the data on latency time course of the ALR evoked by repeated stimuli are relatively rare. Prosser et al (1981) reported that the N1 and P2 peak latencies were stable and independent of the stimulus order. But they used an intertrain interval of 5 sec, which was too short for the N1 and P2 to fully recover. Some studies briefly reported that there was an obvious latency decrease accompanying the amplitude reduction during the stimulus presentation, but the effect of stimulus parameters on the latency time course was not investigated (Butler, 1968; Ritter et al, 1968; Budd et al, 1998). In contrast to most previous studies, the effects of both stimulus intensity and ISI on the time courses of ALR amplitude and latency were examined in this study, using tone burst trains with an intertrain interval long enough to minimize the influence of across-train adaptation. Results of this study on normal hearing participants will be compared with those from cochlear implant users in our future research. It is hoped that this study will provide the basis for efforts to produce more natural cortical adaptation and refractory features in CI users by manipulating stimulus parameters to ultimately improve their speech understanding. The specific aims of this paper were (1) To characterize the time course of ALR amplitude and latency to repeated tone bursts in normal hearing listeners, and (2) To determine the stimulus parameter effects (stimulus intensity at 50 and 80 dB SPL; interstimulus interval at 0.7 and 2 sec) on ALR time course. METHOD Participants Participants were 14 normal-hearing, healthy, righthanded young adults (13 females and 1 male), ranging in age from 20 to 30 years. None of the participants were on medication or had any previous psychiatric or neurological illnesses. All participants had normal pure-tone sensitivity (20 dB HL or better) at octave frequencies from 250 to 4000 Hz. Each participant had a type ‘‘A’’ tympanogram, and their acoustic reflex thresholds were between 80 and 110 dB HL at 500, 1000, and 2000 Hz. After giving written consent, each participant wore a 40-channel quick-cap and was

comfortably seated in a double-walled sound treated booth. Participants were instructed to avoid eye movements and body movements as much as possible. During the experiment, participants were required to read a self-chosen interesting magazine to avoid sleep and to ignore the acoustic stimuli, as attention has effects on the amplitude of the ALR (Barry et al, 1992). Participants were given several minutes of break for body position change and for keeping alert once every 30 minutes. Participants were financially compensated for their time. The research protocol was approved by the institutional research board of the University of Cincinnati. Stimuli Tone bursts at 1000 Hz with a duration of 60 msec (10 msec rise/fall time) were used as stimuli. All stimuli were generated digitally and presented using STIM2 software (Compumedics Neuroscan, Inc., El Paso, TX). The tone bursts were presented through insert earphones (Etymotics, ER-3A) to both ears of each participant. Tone bursts were presented in trains of 10 tone bursts each, with an intertrain interval (ITI) of 15 sec. The interstimulus interval (ISI) was 0.7 or 2 sec and the intensity was 50 or 80 dB SPL. The four test conditions (two intensities and two ISIs) for each participant were randomized in order. A total of 30 trains was presented in each test condition. Electroencephalographic Recording The electroencephalogram (EEG) activity was recorded using a 40-channel system (NuAmps, Compumedics Neuroscan, Inc., El Paso, TX). Electrode placement was determined with the International 10–20 system, using an Electro-Cap placed on a participant’s head, with linked ears as the reference. SCAN software (version 4.3, Compumedics Neuroscan, Inc., El Paso, TX) was used to record the continuous EEG activity. Electro-ocular activity (EOG) was recorded with a bipolar electrode montage placed 1 cm lateral to the outer canthus of each eye (horizontal EOG), together with a bipolar EOG montage placed 1 cm above and below the left eye (vertical EOG). Electrode impedances were maintained at less than 5 kV. Continuous EEG recordings were collected from the participants. The total recording time was approximately 2 hr for each participant for the aforementioned four test conditions as well as some repeated conditions for test-retest reliability. Data Analysis The EEG recordings were digitally filtered (0.3– 30 Hz with a slope of 12/dB octave) and then placed

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Figure 1. One example of the ALR for the condition of 80 dB SPL and ISI at 0.7 sec at all electrode sites recorded from one subject. The trial number for averaging is 60. The averaged ALR using responses to all stimuli (thin) and the response to the first tone burst in a train (thick line) by averaging across stimulus trains for this subject were superimposed.

into epochs of 100 msec of pre-stimulus time and 500 msec of post-stimulus time. Average activity in the pre-stimulus period of 100 msec was used as the baseline activity and was subtracted from poststimulus activity as a correction factor. Epochs were rejected from the analysis if the EOG levels exceeded 6100 mV. Finally, the EEG recordings for each condition were averaged across stimulus trains. Averaging was separately performed 10 times to obtain 10 averaged responses, one for each of the tone bursts within the stimulus train. If the ALR could not be identified due to excessive noise, the repeated recordings and the primary recordings for those conditions were combined before averaging to obtain a better ALR response. If no N1-P2 complex was detectable, the recording was excluded from future data analysis. For each recording, the N1 and P2 were identified as the maximum negative peak and maximum positive peak in a latency range of 70–130 msec and 150– 250 msec, respectively (Fruhstorfer et al, 1970; Budd et al, 1998; Crowley and Colrain, 2004). For each averaged response to a given tone burst, the measures used were amplitudes of N1, P2, and N1-P2 as well as the latencies of N1 and P2. To compare data across conditions and participants, the normalized amplitude or latency was derived by dividing the response amplitude or latency to a given tone (numbers 1–10 of a train) by the amplitude or latency to the first tone of a stimulus train.

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In summary, the independent variables were test condition (80 dB/2 sec, 80 dB/0.7 sec, 50 dB/2 sec, and 50 dB/0.7 sec) and tone burst order. The dependent variables were the normalized amplitude and latency of the ALR. Data were submitted to repeated ANOVA for statistical analysis using SPSS software (version 15) to examine the effects of the independent variables on the ALR amplitude and latency in a stimulus train. To test within-session reliability, the results from two repeated recordings were submitted to Pearson’s correlation analysis to calculate the correlation coefficient. RESULTS

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ll participants finished the four test conditions and one to four additional repeated conditions. Only two recordings out of 56 primary recordings (14 participants 3 4 conditions) were excluded due to unidentifiable peaks. In order to evaluate the time course of the ALR stimulated by a tone burst train, the response to each stimulus was averaged across trains. The amplitudes of N1, P2, and N1-P2 were maximal for the first tone burst and reduced for the later stimuli in a train. Figure 1 shows examples of the averaged waveform for all stimuli in a train (thin line) and the waveform for the first stimulus (thick line), for multiple electrode positions from one typical participant. These waveforms demonstrate the typical morphology and


to later tone bursts. Listed in Table 1 are the means and standard deviations of the amplitudes of N1, P2, and N1-P2 as well as the latencies of N1 and P2 for first, second, third, seventh, and tenth tone bursts under each test condition across all participants. The measure of N1-P2 amplitude displayed less variability than the amplitudes of N1 and P2. Thus we restricted the later analysis to N1-P2 amplitude for evaluating the amplitude time course to repeated stimuli. N1-P2 Amplitude as a Function of Stimulus Order

Figure 2. One example of the ALR to different stimuli (first, second, third, seventh, and tenth) in a train at Cz for the condition of 80 dB SPL and ISI at 0.7 sec from one participant. The trial number for averaging is 60. The response to the first tone burst is the largest and that for the second tone burst is the smallest in a series of responses to individual tone bursts in the train. Note that the latency for the response to the first tone burst is longer than that for later tone bursts.

scalp topography for the ALR. The ALR amplitude for the first stimulus was much larger than the averaged response for all stimuli, because averaging washes out the effect of adaptation and/or refractoriness on the ALR. Furthermore, the ALR amplitude is the largest for electrode Cz and becomes progressively smaller for electrodes distant from Cz. Thus we restricted the later analysis to measures from Cz. Figure 2 shows ALR waveforms for the first, second, third, seventh, and tenth tone bursts within a train at Cz from one typical participant. The amplitude was the largest for the first tone burst and tended to be the smallest for the second tone burst in a train. The amplitude partially recovered for later tone bursts. The latencies for N1 and P2 were the longest for the response to the first tone burst compared to responses

Figure 3 shows the averaged normalized amplitude of N1-P2 as a function of the stimulus order, at electrode Cz under four test conditions. Normalized amplitude was derived by dividing the amplitude for a given tone burst by the amplitude for the first tone burst. Within the train, N1-P2 amplitude quickly decreased after the first stimulus, showing a reduction up to approximately 60% depending on the stimulus intensity and ISI. The time course of the ALR across stimulus order had a complex pattern. Specifically, the response amplitude to the first tone burst of the train was the largest. Responses to second or third tone bursts in the train were the smallest, followed by partial recovery for the third, fourth, and fifth tone bursts before the response amplitude reduced again for the remainder of the tone bursts. This resulted in a nonmonotonic function characterized by three components: fast decrement, partial recovery, and steadystate portion. The fast decrement was most prominent for the 80 dB SPL/0.7 sec condition, whereas the partial recovery component was more prominent for the 80 dB SPL/2 sec condition and the 50 dB SPL/ 0.7 sec condition. Two-way repeated ANOVA was used to investigate the effect of tone burst order and test condition on the normalized N1-P2 amplitude at Cz. There was a significant main effect of test condition (F(3,9) 5 73.66, P , 0.01), tone burst order (F(9,3) 5 12.40, P 5 0.03), and the interaction of both factors (F(27,297) 5 3.83, P , 0.01). Due to the significant interaction, one way repeated ANOVA was conducted to determine the effect of tone burst order on the normalized N1-P2 amplitude for each condition. For the 80 dB/2 sec condition, the effect of tone burst order was significant (F(9,5) 5 10.71, P 5 0.01). Bonferroni post hoc tests showed that the normalized N1-P2 amplitude for the second tone burst (M 5 0.60, SD 5 0.17) and the seventh tone burst (M 5 0.63, SD 5 0.25) were significantly smaller than that for the first tone burst. For the 80 dB/0.7 sec condition, the effect of tone burst order was significant (F(9,5) 5 157.28, P , 0.01). Compared to the response amplitude to the first tone burst, responses for all later tone bursts were significantly smaller. For the 50 dB/2 sec condition, the

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Table 1. Means and Standard Deviations for Amplitudes of N1, P2, and N1-P2 as Well as Latencies of N1 and P2 for Different Tone Bursts (first, second, third, seventh, and tenth) for Each Test Condition Test Condition

N1 Amplitude

P2 Amplitude

N1-P2

N1 Latency

P2 Latency

80 dB/2 sec 1

212.38 (5.07)

11.03 (8.86)

23.41 (8.37)

112.36 (15.54)

220.79 (37.59)

2 3 7 10 80 dB/0.7 sec 1 2 3 7 10 50 dB/2 sec 1 2 3 7 10 50 dB/0.7 sec 1 2 3 7 10

26.65 27.56 28.47 28.02

(3.64) (3.29) (2.69) (5.37)

212.02 (5.04) 23.85 25.40 23.67 24.25

(2.73) (2.36) (1.77) (2.67)

27.88 (2.24) 26.78 26.51 24.96 25.88

(1.30) (2.57) (1.67) (2.21)

29.03 (4.56) 25.33 23.55 24.42 24.57

(2.76) (2.39) (2.37) (2.35)

6.57 8.22 5.19 7.09

11.16 (6.48) 2.92 3.22 4.28 4.08

(2.99) (2.76) (2.40) (1.83)

3.18 (3.81) 2.01 3.07 4.03 3.36

(2.45) (2.30) (3.06) (2.62)

5.48 (4.33) 0.37 1.91 1.65 1.79

effect of tone burst order was not significant. For the 50 dB/0.7 sec condition, the effect of tone burst order was significant (F(9,4) 5 31.27, P , 0.01). Responses for all later tone bursts except the fourth tone burst (M 5 0.62, SD 5 0.37) were significantly smaller than that for the first tone burst. In summary, the effect of tone burst order was seen in all conditions except the 50 dB/

Figure 3. The mean normalized N1-P2 amplitude as a function of tone burst order at Cz. Normalized amplitude was derived by dividing the amplitude for a given tone burst by that for the first tone burst. The error bar indicates one standard deviation.

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(3.22) (3.44) (3.84) (3.23)

(2.57) (2.56) (2.93) (2.26)

13.22 15.78 13.66 15.11

(4.15) (4.82) (4.89) (5.16)

23.18 (8.41) 6.77 8.62 7.95 8.33

(2.90) (3.03) (2.05) (2.07)

11.07 (4.30) 8.82 9.57 8.99 9.24

(2.79) (1.87) (3.07) (2.39)

14.50 (6.35) 5.70 5.46 6.07 6.35

(1.85) (2.04) (3.46) (2.56)

96.79 98.93 98.36 97.21

(9.53) (9.34) (9.90) (8.86)

178.36 174.43 175.71 180.29

(20.22) (15.24) (13.96) (19.90)

111.29 (18.71)

218.29 (46.54)

101.14 96.00 101.29 94.93

168.50 166.57 177.00 178.29

(19.74) (12.55) (10.93) (17.39)

(19.14) (31.27) (34.49) (25.43)

126.62 (22.49)

238.54 (27.21)

105.23 102.46 106.31 107.85

177.62 190.38 181.46 182.08

(7.66) (14.21) (12.93) (13.57)

(27.69) (28.28) (23.38) (22.42)

124.23 (23.72)

234.85 (51.95)

105.15 101.69 104.77 99.54

198.00 168.08 184.23 177.00

(15.65) (11.63) (15.60) (8.81)

(41.33) (26.47) (47.04) (26.95)

2 sec condition. In the 80 dB/2 sec and 50 dB/0.7 sec conditions, partial recovery of the N1-P2 amplitude reached a level such that no significant differences were observed when the amplitude measures for the fourth through the sixth bursts were compared to that for the first burst. Figure 4 shows the correlation between the mean of the normalized N1-P2 amplitude for the last four tone bursts (steady-state portion) and the absolute amplitude for the first tone burst. A significant linear correlation was found, with Pearson correlation coefficient of 0.6 (P , 0.01). This indicates that the N1-P2

Figure 4. Correlation between the mean normalized N1-P2 amplitude for the last four tone bursts in a train and the N1-P2 amplitude for the first tone burst.


amplitude for the steady-state portion was inversely related to the amplitude of the response to the first tone burst, indicating more amplitude decrement from the greater initial N1-P2 amplitude. To determine the effect of test condition on the steady-state portion of the response (the averaged normalized N1-P2 amplitude for the last four tone bursts), a one way repeated ANOVA was conducted. The main effect of test condition was significant (F(3,9) 5 21.72, P , 0.01). Bonferroni tests showed that the steady-state portion was significantly greater for the 80 dB/2 sec condition (M 5 0.63, SD 5 0.25) than for the 80 dB/0.7 sec condition (M 5 0.38, SD 5 0.18, P , 0.01). The steady-state portion of the response for the 50 dB/2 sec condition (M 5 0.87, SD 5 0.36) was significantly greater than that for the 50 dB/0.7 sec condition (M 5 0.49, SD 5 0.26, P , 0.01) and the 80 dB/2 sec condition (P 5 0.02). There was no significant difference between the 80 dB/0.7 sec and the 50 dB/0.7 sec conditions. In summary, the steadystate portion was smaller for the higher intensity and longer ISI conditions, indicating greater amplitude decrements for the higher intensity or shorter ISI conditions. Latency as a Function of Stimulus Order Figure 5 shows the averaged normalized latencies of N1 (top panel) and P2 (bottom panel) as a function of stimulus order. Normalized latency was derived by dividing the latency for a given tone burst by the response latency for the first tone burst. The latencies of N1 and P2 were maximal for the first tone burst and reduced for the second response by approximately 20%, after which the latency stabilized for the remainder of the tone bursts within a train. A two-way ANOVA was conducted to determine the effects of test condition (80 dB/2 sec, 80 dB/0.7 sec, 50 dB/2 sec, and 50 dB/0.7 sec) and tone burst order on the normalized latencies of N1 and P2. The main effect of tone burst number was significant for both N1 (F(9,99) 5 13.68, P , 0.01) and P2 (F(9,99) 5 13.31, P , 0.01). There was no significant effect of test condition or the interaction between test condition and tone burst order. Whereas the latency for each tone burst was significantly shorter than that for the first tone burst, there were no significant differences in latency among the later bursts (numbers two through ten). Reliability Measures Figure 6 shows the amplitude data for (N1, P2, and N1-P2, top panel) and latency data (N1 and P2, bottom panel) from two repeated measures. The Pearson correlation coefficients were 0.89 (P , 0.01) and 0.84

Figure 5. The mean normalized latency for N1 (top) and P2 (bottom) as a function of tone burst order at Cz. Normalized latency was derived by dividing the latency for a given tone burst by that for the first tone burst. The error bar indicates one standard deviation.

(P , 0.01), respectively, indicating that the ALR recording was reliable at different times during the experiment. DISCUSSION

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he primary goal of this study was to investigate the time course of ALR amplitude and latency evoked by repeated tone bursts. This time course cannot be seen in clinical ALR recording. The clinical ALR is the averaged response to a series of stimuli, overlooking the fast change of the response amplitude and latency to repeated stimuli. The ALR amplitude for the first stimulus could be much larger than the averaged response for all stimuli in a train, given the ISI used in this study (Fig. 1). In this study, long-term adaptation across stimulus train might not have been obvious because the intertrain interval was 15 sec. Previous studies have reported that ALR amplitude increased as the stimulus rate decreased until the ISI was approximately 10 sec (Ritter et al, 1968). Therefore, we only considered short-term or within-train time course in this study.

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Effects of Stimulus Intensity and ISI on the ALR This study has shown significant effects of stimulus intensity and ISI on ALR amplitude time course (Fig. 3). ALR decrement elicited by a tone burst train with shorter ISIs was greater than for longer ISIs. When the ISI was 2 sec, higher intensity sound produced more decrement than lower intensity sound. The normalized N1-P2 amplitude in the steady-state response was negatively related to the initial amplitude for the first tone burst, indicating more decrement occurred for greater initial amplitude. Ohman and Lader (1972) reported similar results, proposing that a ‘‘law of initial values’’ might play a role in the auditory evoked potential reduction to repeated stimuli. The latency time course was different from the amplitude time course. The latency was the longest for the response to the first stimulus and it was reduced by approximately 20% for later responses, after which the latency stabilized (Fig. 4). The stimulus intensity and ISI appeared to have no effect on the latency time course. There were several studies that briefly reported the latency change of the ALR stimulated by repeated stimuli without investigating the effects of stimulus parameters on the latency time course (Butler, 1968; Megela and Teyler, 1979; Budd et al, 1998). Mechanisms underlying ALR Time Course

Figure 6. Amplitude data (top) and latency data (bottom) from two repeated recordings during the same test session. Amplitude data include those for N1, P2, and N1-P2. Latency data include those for N1 and P2. The correlation coefficient (R square) is labeled for each plot.

The ALR is the largest from the electrode close to the vertex and becomes progressively smaller for the locations farther away from Cz (Fig. 2). The steadiest measure was N1-P2 amplitude, consistent with previous reports (Prosser et al, 1981). The amplitude of N1P2 reduced quickly (up to approximately 60%) after the first stimulus, comparable to the findings in previous studies (Bourbon et al, 1987; Barry et al, 1992). Attenuation of P2 displayed larger variability (not shown). This might be due to the vigilance factor of the participants (Prosser et al, 1981; Barry et al, 1992). Although the participants were instructed to ignore stimuli and allowed to have breaks during the experiment for keeping alert, their vigilance might have fluctuated during the test time.

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The amplitude decrement of the ALR stimulated by repeated tone burst observed in this study is likely attributed to neural adaptation. This is because our results of amplitude time course met at least some of the adaptation criteria such as: (1) response decrement to repeated stimulation, and (2) direct relation between stimulus repetition rates and response decrement (Thompson and Spencer, 1966). The adaptation in the synapses between the hair cells and the auditory nerve fibers due to presynaptic neurotransmitter depletion as well as the postsynaptic receptor-channel dynamics constitute the initial source of neural adaptation in the auditory system (Smith, 1977; Meddis, 1988; Chimento and Schreiner, 1991). The auditory nerve fiber in deafened ears without functional hair cell synapses exhibit adaptation when presented with electrical stimuli, suggesting that the auditory nerve fiber membrane property provides another source of adaptation in the auditory periphery (Litvak et al, 2001; Zhang et al, 2007). Compared to peripheral stages, the central neurons display additional and possibly greater local adaptation (Loquet et al, 2004; Meyer et al, 2007). Because adaptation of evoked responses recorded along the auditory pathway in animals appears to be more pronounced as the response generator is at more central levels, the adaptation of the ALR observed in the current study might reflect a combination of the


adaptation along the auditory system from the cochlea to the auditory cortex and its associated areas (Simons et al, 1966; Fruhstorfer et al, 1970). However, neural adaptation is not the only source contributing to the amplitude reduction of the ALR in this study. Some researchers have found that the characteristics of the N1 time course to repeated stimuli meet some of the criteria for adaptation but fail to meet other criteria such as dishabituation (Butler, 1968; Megela and Teyler, 1979; Barry et al, 1992). The nonmonotonic time course of the ALR in this study cannot be explained by neural adaptation alone, because adaptation should have resulted in an exponential decay during repeated stimuli (Thompson and Spencer, 1966). The neural refractoriness might play an important role in the amplitude reduction of the ALR to repeated stimuli, especially for the response to the second stimulus. The term of refractoriness derives from studies of single neurons where there is a recovery cycle following a spike firing before the neurons respond normally. In studies using a stimulus-pair paradigm in single-unit studies and AEP studies, the response to the second stimulus versus the interval between the two stimuli had shown an exponential curve with two time constants. It has been accepted that the fast time constant is mainly influenced by refractoriness (Gaumond et al, 1982; Zhou et al, 1995; Parham et al, 1996; Parham et al, 1998; Ohashi et al, 2005). We speculate that the ALR amplitude reduction with the ISIs used in this study was likely to have been influenced by refractoriness. This speculation is supported by the latency data to be discussed. The effect of neural refractoriness on auditory evoked potentials is not necessarily related temporally to the refractory period of individual neurons, which ranges from less than one millisecond to hundreds of milliseconds depending on the level of the neurons in the auditory system (Gaumond et al, 1982; Parham et al, 1996; Fitzpatrick et al, 1999; Miller et al, 2001). However, both types of refractoriness are related, as AERs are the summated activities of groups of neurons and multiple synapses. It is possible that the ALR stimulated by rapidly presented stimuli is affected by refractoriness of ALR generators. The partial recovery in the time course of the ALR might be attributable to the recovery of a subgroup of neurons from the refractoriness and/or adaptation. After being activated by a stimulus, the neurons require time to return to their normal resting state before they are capable of responding maximally to the next signal. Previous studies found that cortical neurons with shorter latencies typically exhibit faster recovery from forward-masking and higher repetition rate than neurons with longer latencies (Schreiner et al, 1997; Parham et al, 1998; Kilgard and Merzenich, 1999). It is possible that the partial recovery in the

ALR time course is the result of a subgroup of recovered neurons with shorter latencies. Despite several studies in which a partial recovery of ALR amplitude for the third stimulus in a train has been observed, most studies found that the amplitude reduction was complete by the second stimulus and no recovery was noted (Prosser et al, 1981; Bourbon et al, 1987; Barry et al, 1992; Budd et al, 1998). The reason for such results might be that shorter ISIs and higher intensities were used in those studies. As shown in the current study, partial recovery was only observed in conditions with longer ISI or lower stimulus intensity when the ISI was short. The latency reduction by the second tone burst is surprising. One might speculate that the latency should be prolonged because the neuron spike initiation time and spike propagation velocity are delayed during the relative refractory period following the preceding response, as suggested by previous studies with single-unit response or AEPs at lower level of auditory system (Harkins et al, 1979; Gaumond et al, 1982; Soucek and Mason, 1992; Parham et al, 1996). Because the ALR reflects multiple synaptic activities, different mechanisms from those for single neuron response might play a role in the latency reduction. Indeed, researchers have found increasing ISIs resulted in an increase of N1 latency (Hari et al, 1982; Alcaini et al, 1994). Alcaini et al (1994) reported that, when the ISI increases from 1 sec to 2 min, the N1 latency increases from 95 msec to 134 msec. One explanation of the latency reduction of N1 and P2 by the second tone burst is the existence of P300, which might have been caused by subjects’ anticipation of the first stimulus during ITIs. Bourbon et al (1987) found that the latency for the first stimulus in a train was longest in a stimulus train when the participants were required to close their eyes and await the start of each train. When the participants were asked to self-initiate the stimulus train when they were ready, the latency differences in the responses between the first stimulus and later stimuli were greatly reduced, indicating the contribution of anticipation of the stimulus train to the latency reduction of the ALR. However, a latency reduction of approximately 10 msec for later stimuli in a train could still be seen under self-initiated conditions. The influence of participants’ anticipation might not be substantial in the current study, as all participants read a magazine during the experiment. If the P300 existed in those few cases, it was not as great as the preceding P2, and thus the identification of P2 was not changed. Mechanisms other than participants’ anticipation might have contributed to this latency reduction in this study. The second reason for the latency reduction of the ALR was the attention status of participants. Previous studies

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reported that P2 latency was longer when listeners were inattentive (Ritter et al, 1968; Fruhstorfer et al, 1970). Other researchers found no significant effect of attention on the latency of N1 and P2 (Picton and Hillyard, 1974). In the current study, the subjects were instructed not to pay attention to the auditory stimuli, and we did not compare ALR results under attentive and inattentive conditions. However, the attentive state of participants was not measured and cannot be excluded as a factor causing the latency reduction. An alternative explanation for the latency reduction lies in the differential effect of neural refractoriness on generators with different latencies. Previous studies reported that N1 and P2 are generated from at least two cortical generators for each that are spatially and temporally distinct (Fruhstorfer and Bergstro¨m 1969; Fruhstorfer et al, 1970; Fruhstorfer, 1971; Alcaini et al, 1994). The posterior component has a shorter latency and a shorter refractory period of 1–3 sec, whereas the anterior component shows longer latency and a much longer recovery period (Hari et al, 1982). Some evidence has shown that two N1 components can be distinguished on the basis of their distinct refractory properties (Hari et al, 1982; Lu et al, 1992). The component from the frontal cortex can be elicited by stimuli at any rates, and a later component from the auditory cortex can be elicited by stimuli at much slower rates (Na¨a¨ta¨nen and Picton, 1987; Alcaini et al, 1994). Therefore, we speculate that the reduction of N1 and P2 latencies might be related to faster recovery of neurons with shorter latencies when neurons with longer latencies are still influenced by refractoriness. This hypothesis can be tested by investigating the refractory features of different subcomponents of the ALR in future studies. This project tested within-session reliability and found acceptable correlation coefficients of 0.89 for amplitude data and 0.84 for latency data. The latency plot displays larger variability for the data points in a latency range of 150–250 msec, where P2 was identified. Nevertheless, together with previous studies reporting high correlation of ALR data from two different sessions with several weeks’ interval, our study indicates that the ALR can be reliably measured (Kinoshita et al, 1996). Implication and Future Study The current study demonstrated evidence of adaptation and refractoriness mechanisms in the ALR to repeated stimuli, as measured by the nonmonotonic amplitude decrement and stabilized latency reduction after the first stimulus in a train. Results also demonstrate that a systematic manipulation of stimulus parameters such as intensity and ISI can produce a certain level of ALR adaptation. The current data

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should be compared with that from cochlear implant users so that the differences between CI users and normal hearing listeners can be characterized. Ultimately, reducing these differences by manipulating stimulus parameters will be explored to improve speech understanding of CI users. Further studies will use a forward-masking paradigm with more interstimulus intervals to provide more information about the adaptation and refractory features of subcomponents of the ALR. Such research will be valuable in revealing the physiological mechanisms involved in how the auditory cortex responds to repeated stimuli and further improving the sound perception by restoring nature adaptation and refractory features in patients such as CI users. SUMMARY

T

he time course of ALR amplitude and latency was determined in 14 normal hearing participants. The following conclusions were reached: 1. Significant effects of stimulus intensity and interstimulus interval were found for normalized N1-P2 amplitude, with more amplitude decrement in the steady-state portion of the response for higher intensity level and shorter interstimulus intervals. This feature is consistent with adaptation. Thus, results of this study indicate that ALR amplitude decrement is at least partially due to adaptation that might be accumulated along the auditory system. 2. The time course of N1-P2 showed that N1-P2 amplitude was maximal for the first stimulus and decreases maximally for the second stimulus in a train by about 60%. A partial recovery was be seen for longer ISIs and lower intensity when the ISI was short. 3. The time course of N1 and P2 latencies showed that the latency was greatly reduced for the second tone burst compared to the first tone burst, after which the reduction of latency was stabilized for the rest of the tone bursts in a train. This observation suggests that the neural generators of the ALR recover differentially, possibly with faster recovery speed for neurons that have shorter latency responses.

Acknowledgments. We thank all participants in this research. We also thank Dr. Flint Boettcher and Dr. Laura Kretschmer for sharing their precious insights upon reading an early version of this manuscript and Ms. Theresa Hommock for her input to the data analysis.

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