The Derived-Band Envelope Following Response and its Sensitivity to Sensorineural Hearing Deficits

Abstract

<jats:title>Abstract</jats:title><jats:p>The envelope following response (EFR) has been proposed as a non-invasive marker of synaptopathy in animal models. However, its amplitude is affected by the spread of basilar-membrane excitation and other coexisting sensorineural hearing deficits. This study aims to (i) improve frequency specificity of the EFR by introducing a derived-band EFR (DBEFR) technique and (ii) investigate the effect of lifetime noise exposure, age and outer-hair-cell (OHC) damage on DBEFR magnitudes. Additionally, we adopt a modelling approach to validate the frequency-specificity of the DBEFR and test how different aspects of sensorineural hearing loss affect peripheral generators. The combined analysis of simulations and experimental data proposes that the DBEFRs extracted from the [2-6]-kHz frequency band is a sensitive and frequency-specific measure of synaptopathy in humans. Individual variability in DBEFR magnitudes among listeners with normal audiograms was explained by their self-reported amount of experienced lifetime noise-exposure and corresponded to amplitude variability predicted by synaptopathy. Older listeners consistently had reduced DBEFR magnitudes in comparison to young normal-hearing listeners, in correspondence to how age-induced synaptopathy affects EFRs and compromises temporal envelope encoding. Lastly, OHC damage was also seen to affect the DBEFR magnitude, hence this marker should be combined with a sensitive marker of OHC-damage to offer a differential diagnosis of synaptopathy in listeners with impaired audiograms.</jats:p>

The variability in potential biomarkers for cochlear synaptopathy after recreational noise exposure

“…A study by the same lab could not find noise exposure as a predictor of EFR-strength (Prendergast et al, 2019). In contrast, animal studies (Möhrle et al, 2016; Parthasarathy and Kujawa, 2018; Shaheen et al, 2015) and those that used computational modelling of the auditory periphery or human data have shown the possible value of EFRs in diagnosing CS (Bharadwaj et al, 2015; Garrett & Verhulst, 2019; Keshishzadeh, Garrett, Vasilkov, et al, 2020; Paul et al, 2017; Vasilkov et al, 2021).…”

Envelope reconstruction of speech and music highlights unique tracking of speech at low frequencies

“…We focused particularly on reconstructions using the 2-16 Hz, 4-Hz, and 8-64 Hz models which produced the best reconstructions on average (Figure 6c). We then subtracted the spectra from null spectra generated by randomizing the phases in the reconstructions for each subject and averaging these randomized reconstructions (see Zhu et al, 2013;Keshishzadeh et al, 2020) (Figure 6d). From these adjusted spectral values we identified the peaks occurring at the tempo of the music (see Methods) as well as 2 -4x the tempo, since the peak energy may occur at multiples of the expected musical beat frequency of the music based on the acoustics (Ding et al, 2017) or neural activity following subcortical processing (Zuk et al, 2018).…”

“…Then a null distribution of power spectral densities was created by shuffling the phases in each of the reconstructions, averaging the randomized reconstructions, and computing the power spectral density. This technique is based on methods to quantify magnitudes of peaks in frequency-following responses (Zhu et al, 2013;Keshishzadeh et al, 2020), where randomizing the phases of each signal and then averaging captures the spectra associated with the noise floor. 100 null spectra were computed for each model.…”

Towards personalized auditory models: predicting individual sensorineural-hearing-loss profiles from recorded human auditory physiology

“…cochlear synaptopathy (CS), (Kujawa and Liberman, 2009; Furman et al, 2013; Sergeyenko et al, 2013; Shaheen et al, 2015). However, applying the same AEP markers for CS diagnosis in humans has yielded mixed success, since AEP amplitudes can be affected by (i) other coexisting SNHL aspects such as outer-hair-cell (OHC) damage (Don and Eggermont, 1978; Gorga et al, 1985; Herdman and Stapells, 2003; Verhulst et al, 2016; Chen et al, 2008; Garrett and Verhulst, 2019; Keshishzadeh et al, 2020) and (ii) subject-specific factors such as age, gender, and head-size (Trune et al, 1988; Mitchell et al, 1989; Hickox et al, 2017). Moreover, the sensitivity of AEPs to different degrees of OHC-loss and CS is unclear, and a direct quantification of AN fiber damage through histopathology is impossible in live humans (Bharadwaj et al, 2014).…”

“…However, similar to the ABR wave-V, EFR generators have latencies associated with IC processing (Purcell et al, 2004), thus differences in central auditory processing may reflect on the EFR magnitude to mask individual synaptopathy differences (Chambers et al, 2016; Möhrle et al, 2016; Parthasarathy et al, 2019a,b). To address these issues, relative EFR and ABR metrics were proposed in several studies to cancel out subject-specific factors and isolate the CS component of SNHL in listeners with coexisting OHC-loss: ABR wave-I amplitude growth as a function of stimulus intensity (Furman et al, 2013), ABR wave-I -V latency difference (Coats and Martin, 1977; Elberling and Parbo, 1987; Watson, 1996), the wave-V and I amplitude ratio (Gu et al, 2012; Schaette and McAlpine, 2011; Hickox and Liberman, 2014), EFR amplitude slope as a function of modulation depth (Bharadwaj and Shinn-Cunningham, 2014; Guest et al, 2018), the derived-band EFR (Keshishzadeh et al, 2020), or the combined use of the ABR wave-V and EFR (Vasilkov and Verhulst, 2019). While these relative metrics are promising, it is not known how OHC-loss and CS differentially impact AEPs.…”