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55 70 85 100
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
chirp, amplitudes
amplitude in µV
level in dB peSPL
V−N19 (data) N19−P30 (data) V−N19 (model) N19−P30 (model)
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0 5 10 15 20 25 30 35 40
chirp, latencies
latency in ms
level in dB peSPL
V (data) N19 (data) P30 (data) V (model) N19 (model) P30 (model)
Figure 5.6: Amplitudes (left panel) and latencies (right panel) of the chirp-evoked poten-tials shown in Fig.5.5. Closed symbols correspond to measured data, while model predictions are indicated by open symbols. Parameters as in Fig.5.4.
These results are compatible with results from the literature: for example, Galambos et al. (1981) used clicks with rates in the range from 10 to 55 Hz (in steps of 5 Hz). They found the amplitude maximum always in the 35 to 45-Hz range, with the mean data showing a peak at 40 Hz. A study ofAzzena et al.(1995) used clicks with rates of 7.9, 20, 30, 40, 50 and 60 Hz to evoke steady-state responses (SSR). They found the highest amplitude between 30 and 50 Hz. Their mean data revealed a peak at 40 Hz. As in the present study, Azzena et al. (1995) investigated the phase component as a function of the click rate. Their results were very similar as those described in the present study. They tried to predict their SSR data for click rates between 30 and 60 Hz by superimposing MLRs to “single” clicks at suitable time intervals and found a similar mismatch between the simulated and the measured data.
Section 5.5 Discussion 95
4
mean over subjects
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40
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260
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521
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1 µV
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click at different rates, channel IZ
model output
Figure 5.7: Left panel: auditory evoked potentials averaged across all five subjects (chan-nel IZ, bandpass filtered with cut-off frequencies of 2 and 1500 Hz). The potentials were elicited by a 100-µs click at a constant level of 100 dB peSPL. Stimulus presentation rate varied between 4 and 521 Hz as indicated along the ordinate. The gray area indicates ±3 standard errors. The right panel shows the corresponding model output.
MLRs were not very surprising, at least at the higher stimulation levels, since the UR was the result of a deconvolution of click data at a high level. But the click evoked responses are known to reflect activity from more basal, high-frequency regions of the cochlea (e.g., Neely et al., 1988). In contrast, the chirp stimulus is assumed to produce synchronous discharges along the length of the human cochlear partition. The additional contributions mainly from
96 Modeling MLR Chapter 5
101 102
0 0.1 0.2 0.3 0.4 0.5
amplitude, channel IZ
amplitude in µV
click rate in Hz data
model
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phase, channel IZ
phase in cycles
click rate in Hz data
model
Figure 5.8: Amplitude (left panel) and phase (right panel) of the potentials shown in Fig. 5.7, plotted against the click rate. Stimulation parameters as in Fig. 5.7. Amplitude and phase of the FFT bin corresponding to the click rate were measured. Open and closed symbols correspond to model output and measured data, respectively.
low-frequency fibers lead to increased evoked potentials (Dauet al.,2000;Bellet al.,2002a,b;
Rupp et al., 2002; Wegner and Dau, 2002). Thus the chirp MLR may represent a better choice than the click stimulus to obtain the UR. In additional simulations (not shown), it was tested whether such a UR would improve the predictions and could account for both click and chirp data. However, in this case, results were better for the chirp but the simu-lated click response amplitudes were clearly too large. Thus, the use of the chirp-based UR leads to a similar mismatch between the predicted amplitudes of click and chirp potentials.
Dau(2003) used a chirp with a flat temporal envelope (and not a flat magnitude spectrum as in the present study) and found a good agreement between model predictions and data.
Due to the flat waveform, the chirp inDau(2003) has much more low-frequency energy than the flat-spectrum chirp used in the present study. Therefore, his results may not be directly comparable to the results presented here. However, it is possible that the ABR model would have the same difficulties if the flat-spectrum chirp was used. Thus, there might be a problem with the specific assumptions made in the preprocessing model that is responsible for the deviations – and not a problem with the principal modeling approach described here. The latter argument might be supported by the modeling results of Rupp et al. (2002). They also used chirp stimuli with a click-like spectrum to record middle-latency auditory evoked
Section 5.5 Discussion 97
fields (MAEF). They used the Auditory Image Model (AIM;Pattersonet al.,1992,1995) to calculate the neural activity pattern (NAP). The BM stage of the model consists of a one-dimensional transmission-line filterbank. They derived different URs independently for the different stimuli they used (a click, two different chirps and their time-reversed waveforms) and compared them with each other. They found the UR to be very similar in all conditions.
Thus, it appears that the AIM model predicts the delay line characteristic of the BM in a more realistic way than does the model of Heinz et al. (2001) which is based on (level dependent) gammatone filters.
Nonetheless, the MLR model presented here accounts for some of the main effects of the MLR as a function of the click rate. The predicted response amplitude shows a clear maximum around 32 and 40 Hz, which is very similar as in the data. However, the model underestimates the amplitude for the click at the rate of 40 Hz. This result is, in princi-ple, comparable with the results of the Azzena et al. (1995) study. They investigated the mechanisms underlying the generation of the 40-Hz steady-state response (SSR) by record-ing click-evoked responses with different click rates. They tried to predict each response by superimposing MLRs at suitable time intervals. They concluded that (i) a model based on linear addition of transient MLRs is not able to adequately predict their results, and that (ii) other mechanisms related to the recovery cycle of the activated system play an impor-tant role in the response generation. Such recovery cycles might be assumed to be included within the MLR model presented here, because the AN model ofHeinzet al.(2001) considers adaptation effect at least at a peripheral level. However, these are apparently not sufficient to account for the response behaviour in the measured data.
The main question of the present study was to investigate to what extent middle-latency responses can be described quantitatively using a simple linear system’s “black box” ap-proach with only very few assumptions. One assumption was that the “driving” neural excitation function, the instantaneous discharge rate functions, are approximately given by the functions known at the level of the auditory nerve. All nonlinearity within the entire modeling approach was considered to be reflected within the auditory periphery (i.e., effects of stimulation level and peripheral adaptation). Some of the key observations in the data could indeed be described well by the model, such as the general amplitude behavior at different repetition rates and, in particular, the right latency values for the click and the chirp. Many details, however, which are of particular interest such as the well known strong
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response at and around the repetition rate of 40 Hz, cannot be fully explained. The origin of this strong response can therefore most likely not be explained by linear superposition of single MLR responses but need additional processes not considered in the model. Nonethe-less, overall the present model might serve as a very useful tool since it can be applied to any stimulus configuration of interest. Also, the model may be applied to any form of simulated cochlear hearing loss in order to understand the effects of hearing impairment on evoked potential generation.