The Auditory Brainstem Response to Complex Sounds

Physiological Parameters and Stimuli of ABRs

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Several studies have emphasized the importance of studying evoked auditory brain responses (ABRs) using a variety of stimuli and physiological parameters. Stimuli and physiological parameters include kainate (Le Prell et al.,200), induced noise (Le Prell & Brungart, 2016), verbal stimuli (Sanfins & Colela-Sanrtos,2015), and speech-in-noise sounds (Anderson &Kraus, 2010). These stimuli and physiologically evoking elements may result in a variety of ABRs. Dimitrijevic et al. (2017) observed that auditory evoked responses and temporal resolution were evaluated in relation to the AM stimuli used to elicit the responses, implying the significance of (amplitude modulated )AM responses for measuring temporal processing. Auditory nerve peripheral processing functions are also useful in determining hearing loss based on ABRs (Henri et al.,2019). BinKhamis et al. (2019) demonstrated that speech detection, speech recognition, and speech-in-noise detection all produced significantly higher speech ABRs. While ABRs are considered essential predictors of speech-in-noise detection, the efficacy of speech-ABRs in clinical applications as speech detectors is significant.

Causes of Hearing Loss

Several factors result in hearing loss based on ABRs. First, hearing loss is attributed by Bramhall et al. (2015) to spiral ganglion cell degeneration caused by noise exposure and aging. In the case of ganglion loss, the amplitude wave I of the ABR system can be related to the number of surviving ganglion neurons. Schoof and Stuart (2016) discovered significant hearing loss in adults, implying that enveloped auditory response degradation due to inner ear loss is possible (Branham et al.,2019). In addition, hearing loss caused by noise induction and metabolism has also been demonstrated in auditory nerves (Salvi et al.,2017). Not only does noise and metabolism have an impact on hearing, however; cochlear synaptic loss can also result in hair cell loss, resulting in low perceived speech responses and hearing (Bramhall et al.,2015). These factors can, however, have an interplay in causing hearing loss.

Inner Hair Loss Mechanisms and Reduced Hearing

Inner hair loss can be attributed to hearing loss caused by metabolism noise exposure and cochlear synaptic loss. These cases of hair loss are the result of chronic noise exposure in mechanisms that degrade the neural cells of the auditory systems (Branham et al.,2019; Salvi et al.,2017). Furthermore, significant loss of high threshold fibers can cause hearing problems in noisy environments, implying a role for cochlear inner hair cells in ABR and auditory perception. Recent research in both mice and guinea pigs has shown that excessive exposure can result in irreversible degeneration of AN fibers, resulting in reversible exposure thresholds (Lin et al., 2011). Although the loss of AN’s peripheral terminals and hair cell synapses occurs quickly (within a few hours of exposure), loss of AN occurs slowly (within a few days), it was surprising to learn that cochlear neural degeneration could be a primary cause of hair loss rather than a secondary effect (Branham et al.,2019). According to the hypothesis that roughened AN fiber have a selective loss for the low-SR and heavy-three safety groups (Melcher et al., 1996; Mehraei et al., 2016), Kujawa and Liberman (2009) highlight the idea that approximately 40% of AN cochlear fiber can be permanently destroyed without a permanent threshold increase in a brain response that reflects the summed activity of the AN fiber in a brain response that reflects the summed activity of the AN fiber in their initial waves could be expanded.

ABRs Threshold

The ABR and audiogram thresholds are considered sensitive and reliable indicators of human listeners (Le Prell et al.,2016). Noise exposure, according to Le Prell et al. (2016), causes neural degeneration of the peripheral auditory system without impairing auditory thresholds. BinKhamis et al. (2018), on the other hand, found background noise and stimulus duration to be factored in long peak latencies with decreased amplitude, implying auditory threshold impairment. Furthermore, supra-threshold tests for determining the effects of noise on the human inner ear are still necessary (Le Prell et al.,2016). Elberling and Don (1987) previously demonstrated that ABR threshold activity was affected by averaged background noise and the amplitude function. As a result, there is a conflict of interest concerning the effects of ABRs on auditory thresholds in the presence of background noise.

Importance of ABRs in Speech and Hearing Studies

ABRs are dependable and effective tools for evaluating brainstem speech sound coding (Sanfins & Colella-Santos, 2016) ABRs have also been shown to be useful in the differential diagnosis of diseases. According to Le Prell (2018), ABRs are effective tools for detecting and treating cochlear synaptopathy. Anderson and Kraus (2010) show that because ABRs are similar to evoking responses, they can provide objective indices of certain acoustic elements important for hearing. Furthermore, cABRs and ABRs have been shown to be capable of providing sufficient information about the nature of hearing and assisting in the development of the most effective disease treatment strategies (Anderson & Kraus, 2010). According to BinKhamis et al. (2019), ABR has the potential to be used in clinical audiology as a measure of speech-in-noise performance, sound discrimination, and speech sounds, with speech-ABR supplementing traditional ABRs. ABRs were used by Sanfins and Colella-Santos (2016) to assess the integrity of auditory pathways, allowing them to identify fundamental neural abnormalities. Nonetheless, the nonverbal stimuli elicited by the tractional ABR are attributed to ABRs (Anderson & Kraus, 2010). Clinical application of ABR has established the model’s effectiveness and speed, allowing it to be used as a biomarker for a variety of conditions such as learning disabilities and auditory processing defects (Sanfins and Colella-Santos, 2016). As a result, both systems use speech-ABR. As a result, the verbal stimuli paradigm can be used to better understand auditory responses, speech-in-noise hearing, and the underlying causes of impairment.

Science of Complex Hearing

Numerous studies have been carried out in order to identify the neurobiological mechanisms underlying complex sound processing (Stapells, 1997; Anderson and Kraus, 2011). complex auditory brainstem stimuli (cABR) can be used in addition to a noise signal to record noise, noise signals, speech sounds, and music (Anderson and Kraus, 2011). Several studies have found that the cABR measure, which is elicited by tonal bursts or clicks, provides a more accurate assessment of time and frequency representation than the traditional ABR measure ( Anderson & Kraus, 2010; Chandraskaran & Kraus, 2010). Picton (2011) discovered, however, that the cABR is unaffected by attention, remaining a pre-attentive AEP. Martin et al. (2008) linked complex sound processing to physiological measures of neural detection, claiming that these neural systems determined how well subjects integrated and perceived sound. Lister et al. (2011) discovered that complex sound perception necessitates similar physiological neural encoding patterns. These measures, however, can be attributed to the ABRs evaluation of the auditory system.

Mechanism of ABRs in Speech Sound Evaluation

The ABRs system is used to determine and process complex sound systems in a variety of ways (Encina-Llamas & Epp, 2021). In addition to ABR systems, cABR has been developed to supplement them (Anderson & Kraus, 2011). Peak latency differences are regarded as critical tools in cABR, which is experience-independent, and response changes to cABR are frequently attributed to brief auditory exposure and training (Anderson & Kraus, 2019; Song, Nicol & Kraus, 2011). In contrast, Chandraskaran and Kraus (2010) establish a link between cABR cognitive processes such as music and language, allowing for the evaluation of cognitive functions. Anderson et al. (2010), however, associates ABR with poor hearing-in-noise performance. It should be noted that the cABR’s efficacy demonstrates its utility in studying a variety of evoked responses associated with auditory function and language comprehension impairment in affected individuals.

Temporal Coding and ABRs Measurement

Temporal coding remains significant in auditory perception, allowing for the measurement of ABRs and projection of the auditory perception effects of inner hair loss Liberman & Kujawa, 2017). According to Bharadwaj et al. (2014), cochlear filtering causes each ANF to be driven by low-frequency sound energy bands, allowing for the logical separation of temporal information coding. However, cochlear neuropathy is caused by a reduction in the total number of ANFs conveying sound resulting in a decrease in the temporal coding of suprathreshold counts (Liberman & Kujawa, 2017). El-Brady & McFadden (2009) discovered that individuals with AN have impaired synchronous firing of neurons at the onset of several stimuli. Neuropathy and fiber loss was attributed to the rapid death of ANF cells and synapses, resulting in an increased threshold sensitivity.

Numerous studies have been conducted to better understand ABRs and their effect on assessing auditory integrity (Lofti et al.,2019; BinKhamis et al.,2018). BinKhamis et al. (2018) observe that when background noise is introduced, speech-ABRs exhibit lower peak amplitudes and longer latencies than quiet speech-ABRs. Additionally, another study used speech-ABR systems to track the spectral and temporal characteristics of the consonant-vowel stimulus, allowing for a more robust understanding of speech perception (Lofti et al.,2019). BinKhamis et al.(2019) noted, however, that using ABR to assess background noise was more effective than using a quiet background. In their study, Salvi et al. (2017) found out that Chinchillas with large IHC lesions have surprisingly normal thresholds, indicating a calm detection of sounds with only a few IHCs. Henry et al. (2011), however, established that Chinchilla’s noise exposure caused changes in all ABR properties. The latter findings conflict with Salvi et al.(2017) in that hearing difficulty caused by carboplatin-treated Chinchilla is unlikely to be caused by the loss of a specific class of auditory neurons, as opposed to noise-related hidden hearing losses. Following noise exposure, we noticed a significant increase in ABR levels. Furthermore, the ABR wave I latency was reduced to a lesser extent at the same SL and SPL. Finally, with the same SPL, the amplitude of ABR wave I decreased. Similar to Henry et al.’s (2011) findings, El-Badry and McFadden determined a possible SP amplifier after the loss of IHC in carbo-platinum chinchillas. Nonetheless, the speech-ABR approach was deemed to be the most appropriate for elucidating behavioral responses to sound systems. This system is based on current speech stimulus elicitation, which can generate a variety of ABRs.

Chinchilla Model Use in ABRs Studies

Chinchilla animal models have been used to study auditory function and the effects of hearing loss due to their resemblance to humans (Trevino et al.,2019). In their study, Bramhall et al. (2015) discovered that Chinchilla models resembled human hearing. These animal models do, in fact, have more sensitive hearing and a frequency range compared to humans. Another study, carried out by Henry et al. (2011), discovered that Chinchillas and humans have similar hearing frequencies, as opposed to mice and rats, which have higher frequencies. Systemic carboplatin injector chinchillas, a commonly used platinum-based chemotherapeutic agent, is a system model for determining whether selective IHC and ANF injuries can take AN into account. Several studies have shown that carboplatin quickly destroys Chinchillas IHCs and ANFs in the cochlea while leaving OHCs intact and functional (El-Badry & McFadden, 2009). Henry et al. (2019) used Chinchilla models to study auditory nerve encoding deficits and found significant findings regarding peripheral neural coding defects. Taking into account the characteristics of continuous and impulse noise exposure, Chinchillas have been used on numerous occasions to investigate the anatomic, physiological, and pathological effects that result in temporary or permanent threshold changes (Trevino et al.,2019). Based on knowledge gained from noise exposure studies about the mechanisms of action of various drugs, chinchillas were also used in pre-clinical drug studies for the prevention and rescue of noise-induced hearing losses. It is also simple to induce noise hearing loss in Chinchillas and compare it to normal hearing, as demonstrated by BinKhamis et al. (2019). Carbo-platinum lesions in the IHC Chinchilla population demonstrated that selective loss of IHCs compromises noise hearing in conjunction with the psychophysical tonic of noise detection tasks in speech-in-noise synaptopathy studies. Furthermore, cochlear synaptopathy used Chinchilla models to understand its mechanisms of responses to aging and noise exposure, according to. A similar study using ANL lesions of Chinchilla modes injected with carboplatin found that decreased response amplitude in injected models.

Although studies have used ABRs to evaluate and diagnose hearing defects and to provide an alternative treatment for specific individuals, little is known about whether ABR responses can serve as indicators of selective inner hair cell loss, which remains unknown.

 

 

 

References

Anderson, S., & Kraus, N. (2010). Objective neural indices of speech-in-noise perception.Trends in amplification,14(2), 7383.

Anderson, S., & Kraus, N. (2011). Neural Encoding of Speech and Music: Implications for Hearing Speech in Noise.Seminars in Hearing,32(2), 129141. https://doi.org/10.1055/s-0031-1277234

BinKhamis, G., Elia Forte, A., Reichenbach, T., O’Driscoll, M., & Kluk, K. (2018). Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures.Trends in Hearing,23, 2331216519848297. https://doi.org/10.1177/2331216519848297

BinKhamis, G., Lger, A., Bell, S. L., Prendergast, G., O’Driscoll, M., & Kluk, K. (2019). Speech Auditory Brainstem Responses: Effects of Background, Stimulus Duration, Consonant-Vowel, and Number of Epochs.Ear and Hearing,40(3), 659670.

Bramhall, N. F., Niemczak, C. E., Kampel, S. D., Billings, C. J., & McMillan, G. P. (2020). Evoked Potentials Reveal Noise Exposure-Related Central Auditory Changes Despite Normal Audiograms.American JournalJournal of audiology,29(2), 152164. https://doi.org/10.1044/2019_AJA-19-00060

Bramhall, N., Ong, B., Ko, J., & Parker, M. (2015). Speech Perception Ability in Noise is Correlated with Auditory Brainstem Response Wave I Amplitude.Journal of the American Academy of Audiology,26(5), 509517. https://doi.org/10.3766/jaaa.14100

BinKhamis, G., Elia Forte, A., Reichenbach, T., O’Driscoll, M., & Kluk, K. (2019). Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures.Trends in Hearing,23, 2331216519848297. https://doi.org/10.1177/2331216519848297

Dimitrijevic, A., Alsamri, J., John, M. S., Purcell, D., George, S., & Zeng, F. G. (2016). Human Envelope Following Responses to Amplitude Modulation: Effects of Aging and Modulation Depth.Ear and Hearing,37(5), e322e335.

El-Badry, M. M., & McFadden, S. L. (2009). Evaluation of inner hair cell and nerve fiber loss as sufficient pathologies underlying auditory neuropathy.Hearing Research,255(1-2), 8490. https://doi.org/10.1016/j.heares.2009.06.003

Elberling, C., & Don, M. (1987). Threshold characteristics of the human auditory brain stem response.The Journal of the Acoustical Society of America,81(1), 115121.

Encina-Llamas, G., Dau, T. & Epp, B. On the use of envelope following responses to estimate peripheral level compression in the auditory system.Sci Rep11,6962 (2021). https://doi.org/10.1038/s41598-021-85850-x

Furman, A. C., Kujawa, S. G., & Liberman, M. C. (2013). Noise-induced cochlear neuropathy is selective for fibers with low spontaneous rates.Journal of Neurophysiology,110(3), 577586. https://doi.org/10.1152/jn.00164.2013

Henry, K. S., Kale, S., Scheidt, R. E., & Heinz, M. G. (2011). Auditory brainstem responses predict auditory nerve fiber thresholds and frequency selectivity in hearing-impaired chinchillas.Hearing Research,280(1-2), 236244. https://doi.org/10.1016/j.heares.2011.06.002

Henry, K. S., Sayles, M., Hickox, A. E., & Heinz, M. G. (2019). Divergent Auditory Nerve Encoding Deficits Between Two Common Etiologies of Sensorineural Hearing Loss.The JournalJournal of neuroscience: the official JournalJournal of the Society for Neuroscience,39(35), 68796887.

Kujawa, S. G., & Liberman, M. C. (2009). Adding insult to injury: cochlear nerve degeneration after “temporary” noise-induced hearing loss.The Journal of neuroscience: the Official Journal of the Society for Neuroscience,29(45), 1407714085. https://doi.org/10.

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