Minimum Detectable Differences in Electrocochleography Measurements: Bayesian-Based Predictions

Human Olivocochlear Effects: A Statistical Detection Approach Applied to the Cochlear Microphonic Evoked by Swept Tones

The human medial olivocochlear (MOC) reflex was assessed by observing the effects of contralateral acoustic stimulation (CAS) on the cochlear microphonic (CM) across a range of probe frequencies. A frequency-swept probe tone (125-4757 Hz, 90 dB SPL) was presented in two directions (up sweep and down sweep) to normal-hearing young adults. This study assessed MOC effects on the CM in individual participants using a statistical approach that calculated minimum detectable changes in magnitude and phase based on CM signal-to-noise ratio (SNR). Significant increases in CM magnitude, typically 1-2 dB in size, were observed for most participants from 354 to 1414 Hz, where the size and consistency of these effects depended on participant, probe frequency, sweep direction, and SNR. CAS-related phase lags were also observed, consistent with CM-based MOC studies in laboratory animals. Observed effects on CM magnitude and phase were in the opposite directions of reported effects on otoacoustic emissions (OAEs). OAEs are sensitive to changes in the motility of outer hair cells located near the peak region of the traveling wave, while the effects of CAS on the CM likely originate from MOC-related changes in the conductance of outer hair cells located in the basal tail of the traveling wave. Thus, MOC effects on the CM are complementary to those observed for OAEs.

Effects of contralateral noise on envelope-following responses, auditory-nerve compound action potentials, and otoacoustic emissions measured simultaneously

This study assessed whether the effects of contralateral acoustic stimulation (CAS) are consistent with eliciting the medial olivocochlear (MOC) reflex for measurements sensitive to outer hair cell (otoacoustic emissions, OAEs), auditory-nerve (AN; compound action potential, CAP), and brainstem/cortical (envelope-following response, EFR) function. The effects of CAS were evaluated for simultaneous measurement of OAEs, CAPs, and EFRs in participants with normal hearing. Clicks were presented at 40 or 98 Hz in three ipsilateral noise conditions (no noise, 45 dB SPL, and 55 dB SPL). For the no noise condition, CAS suppressed or enhanced EFR amplitudes for 40- and 98-Hz clicks, respectively, while CAS had no significant effect on CAP amplitudes. A follow-up experiment using slower rates (4.4-22.2 Hz) assessed whether this insignificant CAS effect on CAPs was from ipsilateral MOC stimulation or AN adaptation; however, CAS effects remained insignificant despite favorable signal-to-noise ratios. CAS-related enhancements of EFR and CAP amplitudes in ipsilateral noise were not observed, contrary to the anti-masking effect of the MOC reflex. EFR and OAE suppression from CAS were not significantly correlated. Thus, the effects of CAS on EFRs may not be solely mediated by the MOC reflex and may be partially mediated by higher auditory centers.

A Time-Saving Alternative to "Peak-Picking" Algorithms: A Gaussian Mixture Model Feature Extraction Technique for the Neurodiagnostic Auditory Brainstem Response

OBJECTIVES

The accurate and efficient analysis of neurodiagnostic auditory brainstem responses (ABR) plays a critical role in assessing auditory pathway function in human and animal research and in clinical diagnosis. Traditional analysis of the neurodiagnostic ABR analysis involves visual inspection of the waveform and manually marking peaks and troughs. Visual inspection is a tedious and time-consuming task, especially in research where there may be hundreds or thousands of waveforms to analyze. "Peak-picking" algorithms have made this task faster; however, they are prone to the same errors as visual inspection. A Gaussian mixture model-based feature extraction technique (GMM-FET) is a descriptive model of ABR morphology and an alternative to peak-picking algorithms. The GMM-FET is capable of modeling multiple waves and accounting for wave interactions, compared with other template-matching approaches that fit single waves.

DESIGN

The present study is a secondary analysis applying the GMM-FET to 321 ABRs from adult humans from 2 datasets using different stimuli and recording parameters. Goodness-of-fit of the GMM-FET to waves I and V and surrounding waves, that is, the summating potential and waves IV and VI, was assessed, and latency and amplitude estimations by the GMM-FET were compared with estimations from visual inspection.

RESULTS

The GMM-FET had a similar success rate to visual inspection in extracting peak latency and amplitude, and there was low RMS error and high intraclass correlation between the model and response waveform. Mean peak latency differences between the GMM-FET and visual inspection were small, suggesting the two methods chose the same peak in the majority of waveforms. The GMM-FET estimated wave I amplitudes within 0.12 µV of visual inspection, but estimated larger wave V amplitudes than visual inspection.

CONCLUSIONS

The results suggest the GMM-FET is an appropriate method for extracting peak latencies and amplitudes for neurodiagnostic analysis of ABR waves I and V.

Estimation of Cochlear Frequency Selectivity Using a Convolution Model of Forward-Masked Compound Action Potentials

PURPOSE

Frequency selectivity is a fundamental property of the peripheral auditory system; however, the invasiveness of auditory nerve (AN) experiments limits its study in the human ear. Compound action potentials (CAPs) associated with forward masking have been suggested as an alternative to assess cochlear frequency selectivity. Previous methods relied on an empirical comparison of AN and CAP tuning curves in animal models, arguably not taking full advantage of the information contained in forward-masked CAP waveforms.

METHODS

To improve the estimation of cochlear frequency selectivity based on the CAP, we introduce a convolution model to fit forward-masked CAP waveforms. The model generates masking patterns that, when convolved with a unitary response, can predict the masking of the CAP waveform induced by Gaussian noise maskers. Model parameters, including those characterizing frequency selectivity, are fine-tuned by minimizing waveform prediction errors across numerous masking conditions, yielding robust estimates.

RESULTS

The method was applied to click-evoked CAPs at the round window of anesthetized chinchillas using notched-noise maskers with various notch widths and attenuations. The estimated quality factor Q10 as a function of center frequency is shown to closely match the average quality factor obtained from AN fiber tuning curves, without the need for an empirical correction factor.

CONCLUSION

This study establishes a moderately invasive method for estimating cochlear frequency selectivity with potential applicability to other animal species or humans. Beyond the estimation of frequency selectivity, the proposed model proved to be remarkably accurate in fitting forward-masked CAP responses and could be extended to study more complex aspects of cochlear signal processing (e.g., compressive nonlinearities).