Medicine, Faculty of
Audiology and Speech Sciences, School of
DSpace
UBCV
Ma, Ainsley
Master of Science - MSc
University of British Columbia
Purpose: Studies in rodents, and more recently humans, suggest that noise exposure can cause permanent damage to synaptic ribbons between cochlear inner hair cells and auditory nerve synapses. Because this damage occurs before it is apparent in outer hair cells, it remains undetectable on traditional clinical assessments. Individuals may present with difficulty listening to speech in noise, tinnitus, hyperacusis, or a combination. This neuropathy, known as cochlear synaptopathy, or “hidden hearing loss”, is relatively uninvestigated, and there is currently no known test protocol to detect it. This study aims to identify the diagnostic potential of thresholds in quiet and noise on noise-induced cochlear synaptopathy. Methods: 42 young adults (mean age = 20 years) were recruited. Twenty music students with normal hearing (≤ 25 dB, HL 250-8000 Hz) and 22 normal-hearing controls with limited noise exposure were tested. Lifetime noise exposure, hearing thresholds 250-16 000 Hz in quiet, and TEN test thresholds at 1000 Hz and 4000 Hz were recorded. Bayesian inference was used to analyze differences between thresholds by group, and interactions between noise exposure and thresholds in quiet and in noise. Results: Threshold differences between groups were found at 1000 Hz TEN. There was a trend for a negative association between thresholds in quiet and thresholds in noise in most multiple regression models; however, unexpected results were found with two outcomes involving lifetime noise exposure as an additional predictor. Though trends were observed, none of them were overtly conclusive. Conclusion: The present study found inconclusive evidence for differences between thresholds in quiet or in noise between early-career musicians and non-musicians. It is possible that the cohort tested did not yield conclusive results due to the nature of noise exposure, or other inherent characteristics of the group such as age and clinical symptoms. Further research is required to develop a reliable behavioural test battery sensitive to cochlear synaptopathy in humans.
BEYOND CONVENTIONAL AUDIOGRAMS: THRESHOLDS IN QUIET AND NOISE AND THEIR DIAGNOSTIC POTENTIAL FOR NOISE-INDUCED COCHLEAR SYNAPTOPATHY by Ainsley Ma B.A., The University of British Columbia, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Audiology and Speech Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) September 2021 © Ainsley Ma, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: Beyond Conventional Audiograms: thresholds in quiet and noise and their diagnostic potential for noise-induced cochlear synaptopathy submitted by Ainsley Ma in partial fulfillment of the requirements for the degree of Master of Science in Audiology and Speech Sciences Examining Committee: Dr. Navid Shahnaz, Associate Professor and Clinical Audiologist, School of Audiology and Speech Sciences, UBC Supervisor Dr. Valter Ciocca, Professor, School of Audiology and Speech Sciences, UBC Supervisory Committee Member Sasha Brown, Audiology Instructor and Clinical Audiologist, School of Audiology and Speech Sciences, UBC Supervisory Committee Member iii Abstract Purpose: Studies in rodents, and more recently humans, suggest that noise exposure can cause permanent damage to synaptic ribbons between cochlear inner hair cells and auditory nerve synapses. Because this damage occurs before it is apparent in outer hair cells, it remains undetectable on traditional clinical assessments. Individuals may present with difficulty listening to speech in noise, tinnitus, hyperacusis, or a combination. This neuropathy, known as cochlear synaptopathy, or “hidden hearing loss”, is relatively uninvestigated, and there is currently no known test protocol to detect it. This study aims to identify the diagnostic potential of thresholds in quiet and noise on noise-induced cochlear synaptopathy. Methods: 42 young adults (mean age = 20 years) were recruited. Twenty music students with normal hearing (≤ 25 dB, HL 250-8000 Hz) and 22 normal-hearing controls with limited noise exposure were tested. Lifetime noise exposure, hearing thresholds 250-16 000 Hz in quiet, and TEN test thresholds at 1000 Hz and 4000 Hz were recorded. Bayesian inference was used to analyze differences between thresholds by group, and interactions between noise exposure and thresholds in quiet and in noise. Results: Threshold differences between groups were found at 1000 Hz TEN. There was a trend for a negative association between thresholds in quiet and thresholds in noise in most multiple regression models; however, unexpected results were found with two outcomes involving lifetime noise exposure as an additional predictor. Though trends were observed, none of them were overtly conclusive. Conclusion: The present study found inconclusive evidence for differences between thresholds in quiet or in noise between early-career musicians and non-musicians. It is possible that the cohort tested did not yield conclusive results due to the nature of noise exposure, or other iv inherent characteristics of the group such as age and clinical symptoms. Further research is required to develop a reliable behavioural test battery sensitive to cochlear synaptopathy in humans. v Lay Summary Noise-induced hearing loss is a common phenomenon, but recent studies show that noise exposure can cause consequential damage to the auditory system without affecting thresholds on standard clinical audiograms. Animal studies suggest that it is the auditory nerve synapses that respond to loud signals that are more susceptible to this damage; however, human studies are limited and inconclusive. Research is necessary to develop a reliable test battery for differential diagnosis. This study investigates the diagnostic potential of audiometric thresholds, both in quiet at a wider frequency range than is currently used clinically, and in noise in the conventional frequency range. Developing a clinical test is not only important for the diagnosis of hidden hearing loss, but also for treatment and prevention. vi Preface This project is part of a larger ongoing research project on Hidden Hearing Loss under the direction of Dr. Navid Shahnaz in the Middle Ear Lab at the University of British Columbia. The project was approved by the University of British Columbia Research Ethics Board under the title “Hidden Hearing Loss – HHL” with certificate number #H16-02052. This research was conducted as partial fulfillment of requirements for the Master of Science degree in Audiology at the University of British Columbia, as a thesis project. The study design was conceived by Dr. Navid Shahnaz, and protocol implementation was a collaboration between him and me. Data collection was conducted by myself, Charlene Chang, Stéphanie Monette, and Natalie Tran. The full study protocol consisted of three separate testing sessions, of which I administered the first. This included screening tests, as well as audiometry, Thresholds in Noise test, and a CAPD assessment. I also occasionally administered tests that were part of the second session, which included the triple digits in noise test, the temporal modulation transfer function test, the hearing-in-noise test, and the random gap detection test. Audiology students from the University of British Columbia’s class of 2020 (Elizabeth Dunlop, Haley Gynane, Mitchel Harris, Shandryn Kozin, Danielle Lafleur, Kelsey Miller, Stéphanie Monette, Darren Roussel, Saruka Santhiran, Natalie Tran, Jaclyn Wiebe) also conducted the first two sessions of data collection for four musicians and three non-musician participants as part of a Research Methods class. Pilot testing for the first session was carried out by myself and my classmates as part of a Research Methods class for the University of British Columbia’s Audiology class of 2019. vii Table of Contents Abstract .......................................................................................................................................... iii Lay Summary .................................................................................................................................. v Preface ............................................................................................................................................ vi Table of Contents ......................................................................................................................... vii List of Figures .................................................................................................................................. x List of Abbreviations ...................................................................................................................... xi Acknowledgements ..................................................................................................................... xiii Dedication ..................................................................................................................................... xiv Chapter 1: Introduction .................................................................................................................... 1 1.1 Overview of Cochlear Synaptopathy ................................................................................. 1 1.2 Cochlear Synaptopathy in Humans ................................................................................... 3 1.3 Behavioural Assessments and Rationale ........................................................................... 5 1.4 Purpose of the current study .............................................................................................. 9 Chapter 2: Methods ....................................................................................................................... 12 2.1 Participants ...................................................................................................................... 12 2.1.1 Inclusion Criteria ...................................................................................................... 12 2.2 Procedure ......................................................................................................................... 13 2.2.1 Overview .................................................................................................................. 13 2.2.2 Thresholds in quiet ................................................................................................... 16 2.2.3 The TEN Test ........................................................................................................... 17 2.2.4 Noise Exposure Structured Interview (NESI) .......................................................... 18 viii 2.3 Data Analysis ................................................................................................................... 19 Chapter 3: Results .......................................................................................................................... 22 3.1 Group Comparisons of Thresholds .................................................................................. 22 3.2 Predicting Performance in Noise given Thresholds in Quiet and Group as Predictors ... 26 3.2.1. Thresholds in noise at 1000Hz vs thresholds in quiet at 1000Hz ............................ 27 3.2.2. Thresholds in noise at 1000Hz vs PTA ................................................................... 28 3.2.3. Thresholds in noise at 4000Hz vs thresholds in quiet at 4000Hz ............................ 30 3.2.4. Thresholds in noise at 4000Hz vs extended PTA .................................................... 32 3.2.5 Thresholds in noise at 1000Hz vs extended PTA ..................................................... 33 3.3 Predicting Performance in Noise given NESI scores and Group as Predictors ............... 35 3.3.1 Thresholds in noise at 1000Hz vs NESI scores ........................................................ 36 3.3.2 Thresholds in noise at 4000Hz vs NESI scores ........................................................ 38 3.3.3 Extended PTA vs NESI scores ................................................................................. 40 3.3.4 Thresholds in quiet at 16 000Hz vs NESI scores ..................................................... 42 Chapter 4: Discussion .................................................................................................................... 45 4.1 Interpretations of Results ................................................................................................. 45 4.2 Comparisons with Previous Findings .............................................................................. 47 4.3 Study Limitations ............................................................................................................ 50 4.4 Directions for further research ......................................................................................... 53 Chapter 5: Conclusion ................................................................................................................... 55 References ..................................................................................................................................... 56 Appendices .................................................................................................................................... 64 Appendix A – Supporting Documents for Methods .............................................................. 64 ix A1. Full list of assessments in larger HHL study .............................................................. 64 A2. NESI Vocal effort to dB A conversion chart from Guest et al. 2018 ......................... 64 A3. Noise Exposure Units to dB Conversion .................................................................... 65 Appendix B – Descriptive Statistics ...................................................................................... 66 B1. Group comparison plots of thresholds ........................................................................ 66 B2. Welch’s t-test results with P value and 95% confidence interval ............................... 68 B3. Predicting performance in noise given thresholds in quiet and group as predictors – better ear results ................................................................................................................. 68 B4. Predicting performance in noise and extended high frequency thresholds given NESI scores and group as predictors – worse ear results ............................................................ 72 Appendix C – Statistical Analyses ........................................................................................ 76 C1. Group comparisons - Bayesian t-test with group as fixed effect ................................ 76 C2. Multilevel regression model with Student t likelihood, TEN performance as the outcome variable, and group and thresholds in quiet as predictor variables ..................... 76 C3. Multilevel regression model with Student t likelihood, TEN performance/extended high frequency thresholds as the outcome variable, and group and NESI scores as predictor variables ............................................................................................................. 77 x List of Figures Figure 3.1 Data points for thresholds in quiet at 1000 Hz by group………………………….......23 Figure 3.2 Data points for thresholds in noise at 1000 Hz by group………………………….......24 Figure 3.3 Regression analysis for thresholds at 1000 Hz in quiet vs noise………………….......27 Figure 3.4 Difference in slope by group at 1000 Hz thresholds in quiet vs noise……………......28 Figure 3.5 Regression analysis for PTA thresholds in quiet vs 1000 Hz TEN…….....………......29 Figure 3.6 Difference in slope by group (PTA vs 1000 Hz TEN)……………………………......30 Figure 3.7 Regression analysis for thresholds at 4000 Hz in quiet vs noise…………….......…....31 Figure 3.8 Difference in slope by group at 4000 Hz thresholds in quiet vs noise......…......…......31 Figure 3.9 Regression analysis for extended PTA thresholds in quiet vs 4000 Hz TEN…….......32 Figure 3.10 Difference in slope by group (extended PTA vs 4000 Hz TEN)…………………....33 Figure 3.11 Regression analysis for extended PTA thresholds in quiet vs 1000 Hz TEN…….....34 Figure 3.12 Difference in slope by group (extended PTA vs 1000 Hz TEN)…………………....35 Figure 3.13 Regression analysis for NESI scores vs 1000 Hz TEN……………………….……..37 Figure 3.14 Difference in slope by group (NESI scores vs 1000 Hz TEN)……………………....38 Figure 3.15 Regression analysis for NESI scores vs 4000 Hz TEN………………….…………..39 Figure 3.16 Difference in slope by group (NESI scores vs 4000 Hz TEN)……………………....40 Figure 3.17 Regression analysis for NESI scores vs extended PTA thresholds in quiet………....41 Figure 3.18 Difference in slope by group (NESI scores vs extended PTA)………….……….….42 Figure 3.19 Regression analysis for NESI scores vs 16 000 Hz thresholds in quiet………….….43 Figure 3.20 Difference in slope by group (NESI scores vs 16 000 Hz)……………………….…44 xi List of Abbreviations ABG – air-bone gap ABR – auditory brainstem response ANF – auditory nerve fiber ARLT – acoustic reflex latency test BBN – broadband noise CAPD – central auditory processing disorder CDR – cochlear dead region CS – cochlear synaptopathy DPOAE – distortion product otoacoustic emissions HHL – hidden hearing loss HINT – Hearing In Noise Test HPDI – highest posterior density interval MAPA – Multiple Auditory Processing Assessment MCL – most comfortable loudness MCMC – Markov Chain Monte Carlo MLD – Masking Level Difference NEQ – Noise Exposure Questionnaire NESI – Noise Exposure Structured Interview OAE – otoacoustic emissions OM – otitis media PTA – pure-tone average RETSPL – reference equivalent threshold sound pressure level xii RGDT – Random Gap Detection Test SIN – speech in noise SPL – sound pressure level TEN – Threshold Equalizing Noise TEOAE – transient evoked otoacoustic emissions TINR – threshold in noise residual TMTF – temporal modulation transfer function UCL – uncomfortable loudness WAI – wideband acoustic immittance xiii Acknowledgements I would like to offer my deepest gratitude to my supervisor Dr. Navid Shahnaz for all of his tutelage over the years and for helping me every step of the way on my journey in the field of Audiology. Special thanks to Dr. Valter Ciocca for his help with the data analysis and generosity of his time. I also owe thanks to Charlene Chang, Stéphanie Monette, and Natalie Tran for the collaboration of testing for this project, and Charlotte Tai for her help in creating the database. Thank you to my thesis committee, Dr. Navid Shahnaz, Dr. Valter Ciocca, and Sasha Brown for their support, and Dr. Robert Pritchard from the UBC School of Music for help in the recruitment of music students. xiv Dedication All glory to God. 1 Chapter 1: Introduction 1.1 Overview of Cochlear Synaptopathy Noise exposure is a long-known and well-documented cause of hearing loss. Without hearing protection, exposure to loud sounds over time will gradually result in damage to the outer hair cells of the cochlea, translating to a hearing loss that can be seen at the conventional frequency range of 250-8000 Hz, which is routinely assessed clinically (Katz 2015). More recently, literature has shown that noise exposure can cause damage to the synaptic ribbons between inner hair cells and auditory nerve fibers and thus create deficits in hearing. However, the resulting neural damage does not show up as elevated thresholds on an audiogram at conventional frequencies (250-8000 Hz) until it is more akin to traditional sensorineural hearing loss (Lobarinas, Salvi, & Ding, 2013). This synaptic degeneration is thought to be more sensitive in the neural pathways that respond to loud signals. It can manifest as decreased performance in a speech in noise testing paradigm, even if the individual presents with normal hearing thresholds, thus garnering the name “hidden hearing loss” (HHL) (Schaette & McAlpine, 2011). However, there is currently no clinical test battery to diagnose HHL, and as such, there is currently no treatment for it (Ridley et al., 2018). Traditionally, hearing sensitivity is not tested beyond 8000 Hz clinically, but current literature suggests that hearing sensitivity can be compromised in the extended high frequencies due to noise exposure before it shows up as a loss at the conventional frequencies (Lopes et. al, 2009; Kumar et. al, 2017). In a recent study by Motlagh et al. (2019), hearing loss in the extended high-frequency range showed a correlation with self-reported difficulties with a speech 2 in noise test, even in “normally hearing” younger adults who did not have any loss in the conventional frequencies. Another name by which this condition is referred to is “cochlear synaptopathy” (CS), because of the synaptic ribbon damage that has been observed, while the sensory hair cells in the cochlea appear to be left intact. This damage results in functional cochlear neural synapse degeneration, mainly between the inner hair cells and afferent auditory nerve fibers. It is because of the intact outer hair cells that normal audiometric thresholds are preserved (Schaette & McAlpine, 2011). Animal studies have shown that noise-induced cochlear synaptopathy can be visible in as early as 24 hours after exposure and are permanent, even if thresholds recover (Kujawa & Liberman, 2009). This phenomenon was first researched in animal studies. Kujawa and Liberman (2009) not only found temporary threshold shifts but also found permanent damage to synapses and auditory nerve fiber deterioration in mice with noise exposure. They found that when these mice were exposed to two hours of high-intensity high-frequency noise, Wave I of the auditory brainstem response (ABR), which signifies auditory nerve function, was reduced at supra-threshold levels, as compared to their control group of mice. Otoacoustic emissions (OAEs), particularly distortion product otoacoustic emissions (DPOAEs) were also reduced 24 hours after exposure. Both of these measures indicate immediate neuropathy, as the researchers noted that up to half of the synapses between the inner hair cells and auditory nerve fibers were permanently lost, as compared to the loss of less than 5% in their control counterparts. Both inner and outer hair cells were preserved though, even two years post-exposure. Lobarinas et al. (2013) found that up to 80% of inner hair cells in chinchillas could be damaged without seeing 3 changes in conventional behavioural thresholds. Further, Fernandez et al. (2015) found that CS was exacerbated in aging mice that had been exposed to noise, even though their thresholds recovered, as compared to their non-exposed counterparts. Another factor that can cause loss of synaptic ribbons is blast exposure. Niwa et al. (2016) used laser-induced shockwave to create blast overpressure in rats and found synaptic ribbon loss and reduced ABR Wave I in the absence of threshold elevation. This suggests that a single incidence of a high enough intensity stimulus can also have permanent effects on cochlear neural synapses. Tinnitus and hyperacusis have also been flagged as possible indicators for cochlear synaptopathy. Schaette and McAlpine (2011) suggested that the perception of tinnitus is the result of the human brain creating central neural gain as it attempts to compensate for a loss of synapses. They conducted electrophysiological measures and found reduced Wave I amplitudes for those with tinnitus and normal audiograms. A central neural gain can also result in hyperacusis, a condition in which there is decreased tolerance of loud sounds. Tinnitus, hyperacusis, and difficulty understanding speech in the presence of background noise make up the most common symptoms reported in individuals with CS. 1.2 Cochlear Synaptopathy in Humans While this phenomenon is starting to garner research interest, there has been some conflicting data in human studies, as well as poor test-retest reliability. Some studies have reported differences in ABRs for noise-exposed individuals with normal audiograms (Valderrama et al. 2018; Bramhall et al. 2017; Stamper & Johnson 2015) but others have not 4 noted any differences (Prendergast et al. 2017; Kluk et al. 2016). For example, ABR Wave I amplitude was found to be highly variable in humans, both between and within individuals (Plack et al., 2016). This is thought to be due to differences in head shapes and neural generators in humans which decrease the validity of ABR as a diagnostic method for CS (Prendergast et al., 2018). Another study compared the literature on animal studies to results of human behavioural measurements and found no significant correlation (Prendergast et al., 2017). The authors posited that the lack of correlation may be due to the nature of noise exposure, as noise exposure given to rodents in a lab setting is much more systematic than humans’ daily noise exposure. As such, they theorized that CS may be difficult to diagnose on its own because noise exposure that is substantial enough to cause damage to human cochlear neural synapses would also be significant enough to cause damage to hair cells (Prendergast et al., 2017). It is also difficult to differentiate the effects of CS from presbycusis (age-related hearing loss). Presbycusis is a normal facet of aging, but its onset and early signs are similar to CS, even if there is no history of noise exposure. These two conditions may also be comorbid in many older individuals. Post-mortem studies of humans have shown that the cochlea can undergo significant natural degeneration that occurs with increasing age before hair cells are affected (Viana et al. 2015; Makary et al. 2011). They found that synaptic ribbon loss greatly increased as a function of age, even without sensory hair cell loss. As such, it can be hard to distinguish between natural age-related neural degeneration and that of noise-induced origin. One of the major complaints of individuals with CS is difficulty understanding speech in the presence of background noise, yet having little to no trouble in quiet. This speech in noise deficit can be explained by the differences in auditory nerve fibers (ANFs) and firing rates. There 5 are two types of ANFs that connect hair cells to higher up the auditory pathway: Type I, which synapse with inner hair cells, and Type II, which synapse with outer hair cells (Fuchs et al. 2003). Type I ANFs can be further categorized based on their spontaneous firing rates. High spontaneous rate fibers have low firing thresholds and thus respond to low-level stimuli, such as signals in quiet. Low spontaneous rate fibers have high thresholds and respond to higher intensity stimuli. The entire human dynamic range is encoded in these two types of fibers (Katz 2015). Additionally, there is evidence that the low spontaneous rate fibers are disproportionately affected by noise exposure (Furman et al. 2013). Researchers have suggested that glutamate exotoxicity is responsible for the vulnerability of these low spontaneous rate fibers (Liberman & Kujawa 2017). Glutamate is a neurotransmitter that, when released in excess, can be toxic to neurons. Low spontaneous rate fibers are more vulnerable to exotoxicity because they have a lower capacity of glutamate reuptake, as compared to high spontaneous rate fibers (Furness et al. 2003). When damage occurs to the low spontaneous rate fibers, hearing thresholds appear unchanged because the high spontaneous rate fibers are still able to respond to low-intensity sounds. 1.3 Behavioural Assessments and Rationale Currently, the only definitive way to diagnose and quantify the loss of ANFs in humans is post-mortem analysis, and there is not yet a reliable clinical test to detect CS. Although ABR itself appeared to be a promising diagnostic tool for animal models, this was not the case for humans. However, even though it may not be a suitable diagnostic tool for CS, Ridley et al. (2018) found that the neural pathway of ABR wave I may still yield some clues. Because high noise levels are processed in the same afferent fibers and neural pathway as ABR wave I, it is thought that other tests that tap into this pathway should corroborate the results of ABR wave I. 6 The Threshold Equalizing Noise (TEN) test makes use of high noise levels was found to be a good predictor of HHL in humans when paired with other tests, like ABR wave I (Ridley et al., 2018). The TEN test was originally developed by Moore et al., (2000) as a quick and simple means of delineating cochlear dead regions. A cochlear dead region (CDR) is defined as a section of the cochlea that no longer has any functioning inner hair cells or neurons (Moore 2001). Characteristics that could point towards a CDR include severe to profound hearing loss, steeply sloping loss, and hearing thresholds above 70 dB HL (Moore 2010). This is why the test is traditionally conducted at a noise level of 70 dB – an intensity that is high enough to activate the low spontaneous rate high threshold auditory nerve fibers. The TEN noise is spectrally shaped such that masked pure tone thresholds across all frequencies from 250-10000 Hz are equal for normal-hearing listeners (Moore 2000). With this noise in place, it allows thresholds to be accurately estimated in a suspected CDR, where otherwise a false threshold could be picked up due to a phenomenon called “off-frequency listening”. Off-frequency listening occurs when the intensity of a signal is sufficiently high, such that neighbouring regions of the cochlea “hear” it (Moore et al., 2000). These neighbouring regions can detect the high-intensity signal because it vibrates more of the basilar membrane, where there are functioning inner hair cells. The off-frequency vibrations will then show up on an audiogram as a threshold; however, it is likely not the true threshold, hence the need for the TEN test. Although the TEN test is traditionally utilized to detect the presence of cochlear dead regions, it has been employed in this research to find thresholds in noise. 7 Ridley et al. (2018) reported a promising use for the TEN test in quantifying CS, the first study to their knowledge that has investigated the TEN test as a diagnostic tool for CS. Ridley et al. (2018) theorized that because a high level of noise is processed in the same afferent fibers and neural pathway as ABR wave 1, results from this test should corroborate those of ABR Wave 1, which was a good indicator of cochlear synaptopathy in animal models (Kujawa & Liberman 2009; Fernandez et al. 2015). They measured thresholds in quiet and thresholds in noise using the TEN test, at 1000 Hz and 4000 Hz and paired this with other physiological tests to model predictions of the presence of CS. They calculated a “threshold in noise residual” (TINR), defined as the residual of the correlation between TEN scores and thresholds in quiet, visualized with a best-fit line, and obtained the difference between the measured TEN score and the predicted score from the regression line. Their regression analyses were performed on the assumption that the TINR took out the variance in TEN test performance that would be correlated to the variance of thresholds in quiet, and what is leftover represents CS. They concluded that the presence of CS could be predicted based on the TINR, as any correlation to thresholds in quiet has already been accounted for. Noise exposure is likely a factor behind CS; however, estimating noise exposure can be difficult, as it can vary so much over one’s lifetime. It is also impossible to conduct human studies in which noise exposure is experimentally controlled. Noise exposure history from human participants is often collected through self-report questionnaires or interviews. One such questionnaire is the Noise Exposure Questionnaire (NEQ), developed by Johnson et al. (2017). The NEQ can give a good glimpse into an individual’s noise exposure annually; however, it is only limited to noise exposure over the past year (i.e., does not tap into variations in noise exposure over a lifetime). It also does not account for any hearing protection. The Noise 8 Exposure Structured Interview (NESI), developed by Guest et al. (2018), gathers information about an individual’s noise exposure through different activities over different periods of life, by duration, intensity, and whether or not hearing protection was worn. Individuals are asked to match the level of their noisy activities with a vocal effort chart, and vocal effort is then converted to sound pressure level. With this information, lifetime noise exposure is estimated in units of noise exposure, which is equivalent to one year of work at 90 dB A. The method of using vocal effort as a measure of noise levels has been shown to have good reliability. In a study by Ferguson et al. (2019), vocal effort was verified against noise levels measured using a dosimeter in different workplaces. They found that 91% of the 168 young adults tested reported noise levels within 6 dB and 56% reported noise levels within 3 dB of actual measurements. Based on this evidence, we can be confident that the NESI provides a reasonable assessment of noise exposure, given an accurate recollection of noisy events. Perhaps it is because of the difficulty in precisely quantifying noise exposure that this phenomenon of CS is difficult to pinpoint. It is also difficult to distinguish between noise-induced and age-related hearing damage when individuals are at an age where both could play a role in hearing health. To adjust for the effects of presbycusis, research with young adults must be conducted. A recent study by Liberman et al. (2016) observed differences in hearing between 22 “high-risk” college-aged individuals, most of which were music students, and 12 of their “normal” hearing peers. Their results revealed significantly elevated thresholds in the extended high-frequency range (10000-16000 Hz) in the high-risk students; this presentation is similar to the early stages of noise-induced hearing loss. They also found the high-risk students performed significantly poorer on speech in noise tests as compared to the low-risk students, and attributed this to their noise exposure history. 9 The most recent study examined the effects of noise exposure on behavioural, electrophysiological, and self-reported measures of hearing health in young adult early career musicians versus non-musicians with normal audiometric thresholds (Couth et al., 2020). Just as Liberman et al. (2016) targeted musicians as an at-risk cohort for HHL, Couth et al. (2020) postulated early career musicians could have differences in behavioural and electrophysiological measures as compared to non-musician adults of the same age. They tested 76 music students within one year of completing a degree in performance-based music studies and 47 non-musicians and found significant differences in ABR measures, as well as self-reported tinnitus, hyperacusis, and difficulty listening in noise, in their musician population. They also found a correlation between OAE levels and extended high-frequency thresholds, where individuals with the most noise exposure presented with reduced outer hair cell function as compared to those with the least noise exposure. These findings indicate that close monitoring of extended high-frequency thresholds, together with OAEs, can be a useful early measure of noise-induced hearing damage when thresholds are in the clinically normal range (Couth et al., 2020). Even though frequencies beyond 8000 Hz are not traditionally tested in the clinic, it may be useful to routinely assess the extended high-frequency range in populations that are at risk of noise-induced cochlear synaptopathy. 1.4 Purpose of the current study Research by Mealings et al. (2020) revealed the CS population feels there is a lack of treatment options, is typically told that their hearing is “normal” and is not offered any follow-up, which lead to feelings of frustration. Clinicians similarly felt lost and that they did not have adequate training or resources to help these clients. The existing options offered to clients included: referral for CAPD testing, counselling and/or communication strategies, and offering 10 hearing aid or assistive listening device trials. Most clients reported not being offered any treatment at all. While some participants thought of their “normal” results as positive, meaning there was no diagnosable cause for needing a hearing aid, many clients reported feeling dissatisfied because their real-life perceptions were not being validated clinically. As this also meant a lack of treatment options, they felt disappointed that their difficulties could not be solved. This research highlights the need for development of a reliable test battery sensitive to CS so that effective treatment options can be established to help this population. An extensive review of the literature yields limited or conflicting results and a lack of replication, especially in human populations. The current study is part of a larger ongoing study whose aim is to develop a reliable test battery to better understand and diagnose CS. This larger study employs a variety of behavioural and electrophysiological tests in listeners at risk of noise-induced CS. In the first phase of this larger study, audiology students at the University of British Columbia’s School of Audiology and Speech Sciences did a pilot study comparing the performance of a group of student musicians as the high-risk noise exposure group to a control (low-risk noise exposure) group in a variety of behavioural tests (Bernard et al. 2019). The pilot study’s test battery consisted of tympanometry and otoacoustic emissions (DPOAEs and TEOAEs), Bekesy audiometry (pure tone air conduction thresholds 250-16000 Hz), the Noise Exposure Questionnaire (NEQ), QuickSIN, Masking Level Difference (MLD), the Threshold Equalizing Noise (TEN) test, and finally the Multiple Auditory Processing Assessment (MAPA). The rationale for the selection of these tests is included in the Methods section. This pilot study did not find any significant differences in any of the measures between the two groups. Limitations of this study included the small sample size (n=6) and the type of noise exposure 11 questionnaire used, which was only limited to noise exposure within the past year rather than lifetime noise exposure. The current study was built off the test battery selected for the pilot study, with a few amendments. The amendments included replacing the QuickSIN with the Hearing In Noise Test (HINT) (Nilsson et al., 1994), and conducting the Noise Exposure Structured Interview (NESI) as the questionnaire, as it taps into lifetime noise exposure. The two main areas of focus in the current thesis are to explore whether thresholds in quiet and thresholds in noise are different between students with normal hearing and no significant history of noise exposure and a group of music students who are regularly being exposed to noise during regular musical practice. As Liberman et al. (2016) found elevated thresholds in quiet in the extended frequency range of their college-aged musicians; this study attempts to replicate that finding in the same population. As well, Ridley et al. (2018) reported the TEN test to be a potentially useful measure of predicting the presence of HHL based on thresholds in noise; this study attempts to further validate that proposition. 12 Chapter 2: Methods 2.1 Participants Participants consisted of 42 adults, aged 18-28 (mean age 20 years). They were a mix of males and females (females: n=29; males: n=13) with various ethnicities and were placed in either of two categories: the high-risk noise exposure group, or the low-risk noise exposure group. The high-risk noise exposure group consisted of 20 music students recruited from the University of British Columbia’s School of Music. This group will be referred to as “Musicians” hereafter. They played a variety of instruments including woodwinds (n=5), brass (n=6), strings (n=1), keyboards (n=4), and voice (n=4), and self-reported significant exposure to loud sounds as a result of musical practice, rehearsals, and concerts. The low-risk noise exposure group consisted of 22 non-musician young adults recruited from online postings and through the UBC School of Audiology and Speech Sciences. This group will be referred to as “Controls” hereafter. Four Musicians self-reported tinnitus, while none of the Controls did. All participants provided informed consent. They were recruited through online postings, as well as circulation of study details by faculty members in both the University of British Columbia’s School of Audiology and Speech Sciences and the School of Music. 2.1.1 Inclusion Criteria A case history questionnaire was used to screen participants. To meet inclusion criteria for the high-risk noise exposure group, participants must have had no history of the following: ear infections, major or minor concussions, family history of hearing loss from a young age, dizziness or vertigo, ear injuries, or surgery, and any fever requiring hospitalization. These 13 criteria were included as it has been shown that bouts of OM can result in an extended high-frequency loss (Margolis et al. 2000; Cordeiro et al. 2018). Inclusion criteria for the low-risk noise exposure group were more strict. These individuals must have met all the inclusion criteria for the high-risk noise exposure group, as well as have had no history of tinnitus or hyperacusis and loud noise exposure through recreation, vocation, or military. One aspect of screening for noise exposure included meeting criteria for volume and duration of streaming media on personal listening devices such as cell phones, tablets, iPods, and laptops. The World Health Organization (2015) recommends a maximum of 60% volume for a maximum duration of 60 minutes before taking a break from listening to personal listening devices. This is the criterion used as well in this research. Once invited to participate, all participants underwent an audiometric screening as part of their first session to further ensure inclusion criteria: 1. normal pure tone thresholds in both ears at conventional frequencies (defined as 25 dB HL or under at 250-8000 Hz); 2. no interaural asymmetry (under 15 dB HL difference in thresholds); 3. no conductive loss as measured by an air-bone gap (ABG) ≤ 10 dB at 250-4000 Hz; 4. normal middle ear function as determined through Wideband Acoustic Immittance (WAI) testing; 5. normal outer hair cell function as determined through OAE testing; 6. have native competency in English, as some tests required linguistic proficiency. 2.2 Procedure 2.2.1 Overview The entire larger study’s test battery consisted of a comprehensive set of behavioural, electroacoustic, and electrophysiological assessments. The full list of tests can be found in 14 Appendix A. The study was split into three testing sessions, lasting about one to two hours each. The first session consisted of screening and a comprehensive behavioural test battery including some speech comprehension tests. The second session included the rest of the behavioural and speech comprehension tests: Hearing in Noise Test (HINT), Random Gap Detection Test (RGDT), Temporal Modulation Transfer Function (TMTF), and triple digits in noise. The remaining session consisted of the NESI, electrocochleography, and contralateral suppression of OAEs. The current thesis focuses on the measurements collected during the first testing session, specifically on the results of the thresholds in quiet as obtained through Bekesy audiometry, and the results of the TEN test. Details of the testing methodology for the entirety of the first session are provided below. These tests were included because of their utility for potentially diagnosing CS, based on the described symptoms (see Chapter 1, Section 1). It has also been theorized that CS is a subtype of central auditory processing disorder (CAPD) since it presents as difficulty with speech in noise and temporal processing deficits despite the presence of normal conventional audiometric thresholds (Musiek et al. 2018). If this is the case, then the pathology may originate in the central nervous system (Hind et al. 2011). Because CS has been proposed to be a sub-type of CAPD, MAPA was added to the test battery as well (Musiek 2018). MAPA testing has been recognized in the literature as an appropriate multiple-test CAPD battery (Domitz and Schow, 2000). In a similar vein, MLD was also performed because it can be used to detect the presence of a brainstem disorder, so any differences in performance between groups on MLD could be classified as such (Olsen 1976). 15 In the first session, all participants first underwent a hearing screening. Firstly, a Welch Allyn otoscope was used to perform otoscopy to determine the presence of occluding cerumen or other abnormalities in the ear canal. Then, middle ear assessment consisting of multi-frequency tympanometry, acoustic reflexes with 500 Hz and broadband noise (BBN) stimulus, acoustic reflex latency test (ARLT), wideband acoustic immittance measures, as well as OAEs (DPOAE and TEOAE) were conducted with an Interacoustics Titan (software suite version 3.4.1) connected to a Windows computer running Interacoustics Otoaccess database software version V.121, and a GSI TympStar Pro tympanometer (version 1.2 Beta). Eligibility for this portion of screening included the presence of acoustic reflexes with both 500 Hz and BBN stimuli and present otoacoustic emissions (at least 3/5 TEOAEs, and 4000 Hz DPOAE and TEOAE), and no presence of fluid or tympanic membrane perforation. Audiometry was then conducted – see below for further details. The rest of the first session was performed with Sennheiser HDA200 circumaural transducers. The order of tests conducted was as follows: measuring most comfortable loudness (MCL) and uncomfortable loudness (UCL) levels, Loudness Scaling, MLD, TEN test, and finally MAPA. MCL, UCL, loudness scaling, MLD, and the TEN test used built-in materials in the OTOsuite software, and the MAPA stimulus was played through the audiometer from the MAPA-2 CD with the accompanying scoring package. All behavioural testing was completed using appropriately calibrated equipment (ANSI S3.6, 2004) in a sound booth that met minimum ambient noise level requirements (ISO 8253-1:2010). Calibration of the Titan was done daily, or before each participant, if more than a day elapsed between participants. Calibration of the audiometer and transducers was performed twice throughout the study. All thresholds were recorded in reference equivalent threshold sound 16 pressure levels (RETSPL). A RETSPL is defined as the sound pressure level (SPL) developed by an earphone in a standardized coupler, at a specified frequency, and for a stated force of application of the earphone to the head. SPL levels in all test frequencies were recorded using a Larson Davis System 824 sound level meter. A Larson Davis PRM902 preamplifier, AEC101 artificial ear, and one-half inch microphone Model 2559, as well as a flat plate attachment and a 3.2 lb (1.5 kg) weight, were all used as the calibration set up. Conversion values were the average of the values obtained at both calibrations and then applied to the dial dB HL readings obtained during audiometry as correction factors. Hearing level, frequency accuracy, and distortion were tested using the same equipment to verify the accuracy of the pure tone frequencies and distortion of stimulus output. 2.2.2 Thresholds in quiet Thresholds in the extended high-frequency range (9000-16000 Hz) were obtained in this study in an attempt to replicate the results of Liberman et al. (2016), who found higher thresholds for a high-risk noise exposure group than for a control group in this frequency range. These thresholds in quiet, compared against the thresholds obtained in noise, were the primary focus of the current project. Thresholds were obtained as part of the screening portion of the first session. Provided middle ear function was normal, Bekesy audiometry (250-16000 Hz) with an Otometrics Madsen Astera audiometer, a computer running OTOsuite (GN Otometrics OTOsuite 4.80.00), Sennheiser HDA200 circumaural transducers, and bone conduction (500-4000 Hz) were performed in the sound booth. Air conduction thresholds were obtained via the automatic Bekesy module in OTOsuite with a fixed frequency approach in 1 dB steps and pulsed tones. Tested frequencies were 250 Hz, 500 Hz, 1000 Hz, 2000 Hz, 3000 Hz, 4000 Hz, 6000 Hz, 8000 17 Hz, 9000 Hz, 10000 Hz, 11200 Hz, 12500 Hz, 14000 Hz, and 16000 Hz. The system automatically took three samples at each frequency and averaged them to create an overall threshold value. Bone conduction was also conducted manually from 250-4000 Hz, as eligibility criteria required all audiometric thresholds to be within normal limits in the conventional frequencies, with no conductive losses or interaural asymmetries 15 dB or greater at any frequency. This concluded the screening portion of the first session. Provided the participant passed all the screening criteria, the TEN test and the rest of the tests of the first session were then conducted. 2.2.3 The TEN Test The TEN test is currently clinically employed to detect cochlear dead regions (Moore 2000), but it was utilized to find thresholds in noise in this study. It is not possible to do a direct comparison of thresholds in noise with thresholds in quiet in the extended high-frequency range because an effective masking noise does not yet exist for specific frequencies in the extended high-frequency range. According to the TEN HL test guidelines, possible frequencies for the test tone are 500 Hz, 750 Hz, 1000 Hz, 1500 Hz, 2000 Hz, 3000 HZ, and 4000 Hz (Moore 2010). Therefore, thresholds in noise were measured in the conventional frequency range, specifically at 1000 Hz and 4000 Hz, just as Ridley et al. (2018) did. The TEN test was performed using a module in the OTOsuite software, at 1000 and 4000 Hz in each ear individually, as it is an ipsilateral test. TEN noise was white noise calibrated for effective masking from 500-4000 Hz. It was set at 70 dB HL for the test, though it was initially presented at a lower level, around 40-50 dB, and gradually increased to allow the participant to acclimate to the noise. None of the participants reported the noise to be intolerably loud. Once 18 the noise reached 70 dB, it was left on continuously for the duration of the threshold seek. The stimulus tone was pulsed and presented at 80 dB initially, then a threshold was found manually, using bracketing of 2 dB steps twice ascending. The participants were instructed to ignore the noise and just listen for a series of two or three quick beeps in a row, and to push the response button when they heard the beeps. TEN scores were recorded as the difference between the measured threshold in noise and the TEN noise level set at 70 dB. This was also the method employed by Ridley et al. (2018) in their study attempting to quantitatively define HHL. 2.2.4 Noise Exposure Structured Interview (NESI) The NESI was employed to estimate the lifetime noise exposure history for each participant. Developed by Guest et al. (2018), it relies on self-report to measure the level (SPL) of exposure to noise based on activity and time periods in one’s life. The standardized interview script was followed and participants were instructed to report all their “noisy” (above ~80 dBA) activities, both recreational and occupational, in terms of vocal effort. Vocal effort was specified to be a scale of how much exertion is required for the participant if to communicate with someone four feet away, with normal hearing and visual aids such as gestures and facial expressions available. This ranged from “talk normally” to “shout in listener’s ear” (Guest et al. 2018). The chart used to convert vocal effort to environmental sound pressure level is included in Appendix A. Self-report of duration, frequency, and level of the noise, as well as attenuation from hearing protection (if any), were obtained. These factors were then used to calculate noise exposure units, which were the reported measure for this study. Formulas for calculating noise exposure units can be found in Appendix A; Chang (2020) and Monette (2020) give full detailed explanation of the calculations. 19 2.3 Data Analysis The data analysis was performed using a Bayesian approach. Bayesian statistics develops a model based on specified parameters with corresponding prior distributions, and on the given data. The outcome of a Bayesian model is a posterior distribution that describes the uncertainty in the model’s estimates as probabilities of parameter values (Kruschke and Liddell 2018). Bayesian inference was selected as it is valid for datasets with small sample size, and continuous variables like thresholds (Lee and Song 2004). Thresholds were converted from dB HL to dB SPL using correction factors specific to the audiometer used. Threshold measurements were standardized prior to the analyses. To analyze differences between groups (Controls, Musicians) in terms of thresholds in quiet (1000 Hz, 4000 Hz, traditional PTA consisting of the average of 500 Hz, 1000 Hz, and 2000 Hz thresholds, and extended PTA consisting of the average of 10 000 Hz, 12 500 Hz, and 14 000 Hz thresholds) and in noise (TEN 1000 Hz and TEN 4000 Hz), Bayesian models which are similar to robust t-tests in classical statistics were used. These models employed a Student t likelihood function with 3 degrees of freedom and included group as a fixed effect. A Student t likelihood was selected instead of the more commonly used Normal likelihood because the outcome of the comparison is less likely to be affected by extreme data points. Welch’s Two-Sample t-tests were also conducted as a comparison. To investigate the correlations between measures of hearing in noise and in quiet, multiple regression models with TEN scores as the outcomes, and group and thresholds in quiet as predictors were applied to the data. Additionally, multiple regression models with group and NESI scores as predictors and either TEN scores or thresholds in quiet as the outcomes were 20 applied to the data to investigate correlations among noise exposure history and hearing thresholds in noise and select measures of hearing in quiet. Interactions between group and predictors were also included in these models. Specifically, these models included: 1. TEN at 1000 Hz as the outcome and group (Controls, Musicians) and 1000 Hz thresholds in quiet as predictors; 2. TEN at 1000 Hz as the outcome and group and traditional PTA as predictors; 3. TEN at 4000 Hz as the outcome and group and 4000 Hz thresholds in quiet as predictors; 4. TEN at 4000 Hz as the outcome and group and extended PTA as predictors; 5. TEN at 1000 Hz as the outcome and group and extended PTA as the predictors; 6. TEN at 1000 Hz as the outcome and group and NESI as predictors; 7. TEN at 4000 Hz as the outcome and group and NESI as the predictors; 8. Extended PTA as the outcome and group and NESI as the predictors; and 9. 16 000 Hz thresholds in quiet as the outcome and group and NESI as the predictors. All statistical analyses were completed using the R statistical programming language (R Core Team, 2017) with the “rethinking” package (McElreath, 2020). This package uses the Stan platform for performing Markov Chain Monte Carlo (MCMC) sampling to obtain the posterior (Carpenter et al., 2017). For the two-group comparisons, posteriors were calculated by using 2000 iterations after warm-up, and 1 chain. The number of effective samples (n_eff) exceeded 1,200 for all models, and Rhat did not exceed 1.01 for any parameter. To check that the models had converged, traceplots of the MCMC chains were also inspected. For the multiple regressions, the number of samples was 8000 after warm-up; Rhat numbers did not exceed 1.01; the number of effective samples (n_eff) exceeded 4200 for all parameters. For regression models, a Student t likelihood with 3 degrees of freedom was selected. Model parameters were not highly correlated (-0.6, 0.25) for any of the models; therefore, multi-collinearity was not a concern. Priors for slope 21 parameters of all the models were normally distributed with mean=0 and standard deviation=0.35. Continuous predictors and outcomes were standardized for these analyses. 22 Chapter 3: Results Results for two-group comparisons, multiple regression models involving group and thresholds in quiet as predictors, and multiple regression models involving group and NESI scores are all reported in their own separate sections. 3.1 Group Comparisons of Thresholds Thresholds were compared between the two groups (Controls, Musicians) at 1000 Hz in quiet, 4000 Hz in quiet, PTA (average of 500, 1000, and 2000 Hz) in quiet, and extended PTA (average of 10 000, 12 500, and 14 000 Hz) in quiet. Thresholds in noise (1000Hz, 4000Hz TEN) were also compared between groups. All results involving group comparisons of thresholds in quiet and noise, and thresholds in noise as outcomes with thresholds in quiet as a predictor are reported for the worse ear. The worse ear was defined by higher audiometric thresholds averaged across both the traditional and extended pure-tone averages. It can be expected that any trends appearing that are related to HHL will show up as more pronounced in the worse ear, thus the worse ear was selected for displaying results. Analyses were also done for the better ear, but results were very similar; thus, only the worse ear results have been included unless specified. There was one analysis for which better ear results are reported instead, where NESI scores were a predictor (see Section 3.3); this is due to unexpected results which will be highlighted and discussed. All other results for the better ear can be found in Appendix B. First, thresholds in quiet, recorded in RETSPL, and thresholds in noise, as measured by the TEN test, were compared between the two groups. Thresholds were converted to z scores to provide comparability of thresholds, to make it easier for the MCMC sampling procedures used to carry out the data analysis to converge, and for ease of interpreting outcomes. Bayesian t-tests 23 were conducted to examine any differences between the groups in seven outcomes: 1000, 4000, and 16 000 Hz in quiet; PTA and Extended PTA; 1000Hz TEN, and 4000Hz TEN. Sample plots of thresholds in quiet and thresholds in noise at 1000 Hz by group is included here (Figure 3.1 and Figure 3.2) to show the typical variability in the data. Plots for all group comparisons at all frequencies in quiet and in noise can be found in Appendix B. At first glance, the data for both groups appear similar; hence Bayesian analyses were performed to further investigate the differences between the groups. Figure 3.1: This plot shows the individual data points for thresholds in quiet at 1000 Hz in RETSPL for Controls (red) and Musicians (blue). The dashed red line indicates the overall mean threshold. 24 For each of the seven outcomes, the difference between the means of the marginal posteriors for the two groups (mean difference) and the 89% highest posterior density interval (HPDI) were calculated. The mean difference was calculated as the mean threshold of the Musicians subtracted from the mean threshold of the Controls; thus, a positive value indicates a larger mean for the Controls. For thresholds in quiet, a larger mean is the result of higher absolute thresholds (i.e. worse auditory sensitivity), but for thresholds in noise, a larger mean results from a higher TEN score (i.e. better performance on the TEN test). The effect size for each outcome was also calculated. Effect size is a measure of the size of the difference between the means of the two groups relative to the variability in the sample, and was calculated as the mean difference divided by the square root of the mean-variance; 89% HPDI intervals for the effect size were also obtained. Bayesian t-tests show inconclusive results of the comparison between the two groups at all of the thresholds in quiet, including PTA and extended PTA, and in noise, except for 1000Hz Figure 3.2: This plot shows the individual data points for thresholds in noise (1000 Hz TEN) for Controls (red) and Musicians (blue). The dashed red line indicates the overall mean thresholds. 25 TEN. Results reported here are for the worse ear; better ear results can be found in Appendix B. The results were deemed inconclusive due to large uncertainty intervals, most of which have an uncertain sign as well. When an uncertainty interval has probability mass in both the negative and positive realms, the model is not sure of the sign, and the larger the range is, the less certain the model is. Both the mean difference and effect size for 1000Hz TEN show positive values with positive uncertainty intervals. These data can be found in Table 3.1, with uncertainty intervals shown within square brackets. Outcome Mean difference Effect size 1000 Hz 0.20 [-0.19, 0.57] 0.32 [-0.30, 0.91] 4000 Hz -0.12 [-0.63, 0.33] -0.15 [-0.79, 0.44] 16 000 Hz 0.17 [-0.22, 0.62] 0.26 [-0.37, 0.86] PTA 0.25 [-0.08, 0.53] 0.50 [-0.08, 1.14] Extended PTA 0.18 [-0.17, 0.57] 0.29 [-0.33, 0.85] 1000Hz TEN 0.65 [0.22, 1.04] 0.94 [0.30, 1.59] 4000Hz TEN 0.26 [-0.33, 0.76] 0.31 [-0.40, 0.90] Table 3.1: This table reports the mean of the marginal posterior of the difference of the means of the two groups, and the effect size of the difference between groups for each outcome. The 89% Highest Posterior Density intervals are shown within square brackets. For comparison purposes, a Welch’s t-test was also conducted for each outcome. All Welch’s t-tests resulted in p values of 0.08 or higher, supporting the same conclusion as their Bayesian counterparts, except for 1000Hz TEN. The Welch’s t-test for 1000Hz TEN showed inconclusive results (p value = 0.09). Appendix B shows the full results of the Welch’s t-tests. 26 3.2 Predicting Performance in Noise given Thresholds in Quiet and Group as Predictors Multiple regression models with TEN as the outcome and group membership and thresholds in quiet as predictors were applied to the data. To visualize the relationships, thresholds in noise were plotted against thresholds in quiet for each group to yield a scatterplot with a regression line. Initial models consisted of: 1. TEN 1000Hz as the outcome and group and 1000 Hz thresholds in quiet as the predictors; 2. TEN 1000Hz as the outcome and group and traditional PTA as the predictors; 3. TEN 4000Hz as the outcome and group and 4000 Hz thresholds in quiet as the predictors; and 4. TEN 4000Hz as the outcome and group and extended PTA as the predictors. TEN scores were calculated as the difference between the level of the TEN noise (70 dB HL) and the measured threshold in noise; thus a higher absolute value of TEN score refers to a larger deviation between the TEN noise and the measured threshold. A positive TEN score refers to a lower threshold relative to the 70 dB TEN noise, whereas a negative TEN score refers to a higher threshold relative to TEN noise. A positive score thus indicates better performance on the TEN test, i.e. that the participant was better able to hear the stimulus tone below the level of the masking noise. A negative correlation between thresholds in quiet and TEN scores, therefore, suggests that lower thresholds in quiet correlate with better performance in noise. A larger slope indicates a stronger correlation. In each of the cases below, all regression lines showed a negative correlation between thresholds in quiet (single frequency and PTA/extended PTA) and their threshold in noise counterparts (1000Hz TEN or 4000Hz TEN). To examine whether the groups differed in the relationship between thresholds in quiet and noise, the marginal posterior distribution of the difference between slopes of the regression lines was then calculated for each of the outcomes. 27 3.2.1. Thresholds in noise at 1000Hz vs thresholds in quiet at 1000Hz Figure 3.3 presents a scatterplot and a regression line for each group, with thresholds in noise plotted against thresholds in quiet. The grey shaded area displays the 89% HDPI around the mean regression line. Both groups showed a negative correlation between thresholds in quiet and in noise at 1000 Hz, meaning the lower the thresholds in quiet, the lower the thresholds in noise. In this case, though not a large difference, there was a slightly stronger negative correlation for Controls. The marginal posterior distribution of the difference between the slopes of the regression lines for Controls and that of Musicians was then calculated to examine how the groups differed in the association between performance in noise and in quiet. The slope difference between groups is displayed in Figure 3.4, which shows the distribution of probabilities that the slope for Controls is larger than that of Musicians. The dashed orange and blue lines refer to the distribution of the slope for Controls and Musicians respectively. The solid black line refers to the distribution of probabilities that the slope for Controls is larger than that of Musicians. The percentage of the area above zero that falls underneath the black curve is the probability that the Figure 3.3: Regression lines show a negative correlation between thresholds in quiet at 1000 Hz and at TEN 1000 Hz for both groups (Musicians =-0.25, Controls =-0.42). 28 slope is larger for Controls, and the percentage of the black curve’s area that falls below zero is the probability that the slope is larger for Musicians. This model estimated a probability of 87% that the slope was steeper for Controls than for Musicians in the worse ear. The model was quite certain of a difference in regression slopes, and thus performance, of the two groups (Figure 3.4). In the better ear, however, the difference in slope is 9%. This is the equivalent of saying the probability that the slope for Musicians is larger than that of Controls is 91%, which indicates the model has about the same confidence that the difference goes the opposite way at the two ears (see Appendix B). 3.2.2. Thresholds in noise at 1000Hz vs PTA PTA was chosen as an additional predictor to tap into a larger range across the conventional frequency range, as the previous analysis only compared thresholds in quiet and Figure 3.4: Difference in slopes between Controls (orange) and Musicians (blue) with outcome TEN 1000 Hz and predictor 1000 Hz thresholds in quiet in the worse ear. The solid black line displays the probability distribution of the marginal posterior of the difference in slope for Controls and Musicians. 29 noise at one specific frequency (1000 Hz). The analysis of PTA as an additional predictor for the outcome of TEN 1000 Hz yielded very similar results to the previous analysis in that both groups showed a negative correlation between thresholds in quiet and noise (-0.33 and -0.13 for Controls and Musicians respectively), as shown in Figure 3.5. The marginal posterior distribution of the difference between the groups also appears similar (Figure 3.6). This model estimated a probability of 86% that the slope was steeper for Controls than for Musicians in the worse ear. In the better ear, the difference in slope is 8%. Again, this is the same trend seen in the previous analysis with 1000 Hz thresholds in quiet as the additional predictor. Figure 3.5: Regression lines show a negative correlation between PTA and TEN 1000 Hz for both groups (Controls =-0.33; Musicians =-0.13). 30 3.2.3. Thresholds in noise at 4000Hz vs thresholds in quiet at 4000Hz Similar to the previous analyses with 1000 Hz TEN as the outcome, both groups showed a negative correlation between thresholds in quiet and noise (Controls = -0.40; Musicians -0.41). This trend was not as strong as seen with the 1000 Hz TEN outcome; there was a 47% probability that there was a difference in slopes between the two groups (Figure 3.7). For this outcome, there was essentially no difference between the worse and the better ear; worse ear results are reported. Figure 3.6: Difference in slopes between Controls (orange) and Musicians (blue) with outcome TEN 1000 Hz and predictor PTA in the worse ear. The solid black line displays the probability distribution of the marginal posterior of the difference in slope for Controls and Musicians. 31 Figure 3.7: Regression lines show a negative correlation between thresholds in quiet at 4000 Hz and TEN 4000 Hz for both groups (Controls =-0.40; Musicians =-0.41). Figure 3.8: Difference in slopes between Controls (orange) and Musicians (blue) with outcome TEN 4000 Hz and predictor 4000 Hz thresholds in quiet in the worse ear. The solid black line refers to the distribution of probabilities that the slope for Controls is larger than that of Musicians (47%). 32 3.2.4. Thresholds in noise at 4000Hz vs extended PTA Even though 4000 Hz is technically not considered part of the extended high-frequency range, it was chosen as the comparison for the extended high-frequency PTA, as there was no TEN test available beyond 4000 Hz. Both groups once again showed a negative correlation between thresholds in quiet and in noise (Controls = -0.30; Musicians = -0.22), as shown in Figure 3.9. For this outcome, like the last analysis with TEN 4000 Hz as the outcome, there was essentially no difference between the ears. The marginal posterior distribution of the difference between the groups shows a 36% probability that there was a difference in slopes between the two groups in the worse ear (Figure 3.10). Again, there was no difference between the worse and better ear, so worse ear results are reported. Figure 3.9: Regression lines show a negative correlation between extended PTA and TEN 4000 Hz for both groups (Controls =-0.30; Musicians =-0.22). 33 3.2.5 Thresholds in noise at 1000Hz vs extended PTA While it was not overwhelmingly conclusive, there was a trend for a stronger difference in slopes between controls and musicians with TEN 1000 Hz as the outcome, especially seen at TEN 1000 Hz vs PTA. In light of this, an additional analysis was run with TEN 1000 Hz as the outcome and group and extended PTA as predictors, since loss in the extended high frequencies has been noted in the literature as a symptom of HHL (Liberman et al. 2016). In all cases where TEN 1000 Hz was the outcome, the trend was slightly stronger for Controls. This stronger trend indicates better thresholds in quiet are associated with better performance in noise. For Musicians, this relationship between thresholds in quiet and noise was not as clear. Figure 3.10: Difference in slopes between Controls (orange) and Musicians (blue) with outcome TEN 4000 Hz and predictor extended PTA in the worse ear. The solid black line refers to the distribution of probabilities that the slope for Controls is larger than that of Musicians (36%). 34 The findings of this additional analysis were in line with that of the previous TEN 1000 Hz analyses. There was a negative correlation between extended PTA and thresholds in noise for both groups (-0.12 for musicians and -0.27 for controls). The marginal posterior distribution of the difference between the slope of both regression lines was calculated and there was found to be a 76% probability that the slope for controls is larger than the slope for musicians. Figure 3.11: Regression lines show a negative correlation between extended high frequency average thresholds and TEN 1000 Hz for both groups (Musicians =-0.12, Controls =-0.27). 35 3.3 Predicting Performance in Noise given NESI scores and Group as Predictors The above results thus far do not paint a picture of a simple causal model of noise exposure affecting thresholds. It is reasonable to expect that group membership causes noise exposure, based on the fact that musical practice results in significant noise exposure history. To analyze data with a statistical model that is consistent with the simple causal model, NESI scores were included as a predictor as well as group membership in the following regression analyses. Given the wide range of NESI scores, the log of these scores was used to facilitate the convergence of the MCMC sampling procedure, and to make it easier to display the data. Thresholds in noise were analyzed against the log of the NESI scores for each group. The models yielded a scatterplot with a regression line, with plots displaying the posterior distribution. In Figure 3.12: Difference in slopes between Controls (orange) and Musicians (blue) with outcome TEN 1000 Hz and additional predictor extended PTA. The solid black line refers to the distribution of probabilities that the slope for Controls is larger than that of Musicians (76%). 76% of the area under the black curve falls above zero. 36 these models, outcomes consisted of TEN 1000 Hz, TEN 4000 Hz, extended high-frequency PTA, and thresholds in quiet at 16000 Hz. Group and the log of the NESI scores served as predictors for all outcomes. Outcomes for thresholds in quiet focussed on the extended high frequencies because this frequency range is where differences in thresholds have been noted in people with HHL symptoms. As cochlear synaptopathy is not a variable we can measure directly, we rely on analysis of HHL symptoms, such as difficulty listening in noise and elevated extended high-frequency thresholds, as a means of determining the presence of cochlear synaptopathy. Again, analyses were done for both ears; however, the analyses on the better ear produced unexpected results, so these will be presented. Results of worse ear analyses will be discussed, but full results can be found in the Appendix. The lower the NESI score, the lower the amount of lifetime noise exposure an individual has had. In these models, a negative correlation refers to lower NESI scores being associated with higher TEN scores (i.e. better performance on the TEN test), whereas a positive correlation refers to both of these variables having high scores. 3.3.1 Thresholds in noise at 1000Hz vs NESI scores The first analysis employed NESI scores and group membership as predictors, and TEN at 1000 Hz as the outcome. When predicting thresholds in noise in this condition, the Controls were found to have a weak negative correlation (-0.1) with a relatively large 89 % uncertainty interval [-0.26, 0.035]. The Musicians, on the other hand, did not show such a trend. There was a positive correlation (0.18) with uncertainty interval also positive [0.01, 0.38]. This is surprising, as it suggests there is a relationship between higher noise exposure and better performance on the 37 TEN test. The uncertainty interval is large though, and although mostly positive, does come very close to 0, meaning there is a possibility of no relationship between the two. This is shown in Figure 3.13. The marginal posterior distribution of the difference between the slopes of the regression lines for Controls and that of Musicians was then calculated and the probability that the distribution of slopes between the two groups is positive is 97% (Figure 3.14). The model is fairly certain that there is a difference between thresholds in noise of the two groups based on NESI scores. Figure 3.13: Regression lines show opposite trends for both groups. There is a negative correlation (-0.1) between NESI scores and TEN 1000 Hz thresholds for the Controls but a positive correlation (0.18) for the Musicians. 38 3.3.2 Thresholds in noise at 4000Hz vs NESI scores A similar trend is seen with the outcome of TEN 4000 Hz – a weakly negative slope for the Controls and a positive slope for the Musicians. The slope for the Controls was -0.05 with 89% HDPI [-0.22, 0.11] and the slope for the Musicians was 0.16 with 89% HDPI [-0.01, 0.33], as shown in Figure 3.3.2.1. Though the trend is weaker than seen for the outcome of 1000 Hz TEN, there is still a clear difference between the slopes for the two groups, as shown in Figure 3.15. Again, the uncertainty intervals for both groups are large, and the sign is uncertain. Figure 3.14: Difference in slopes between Controls (orange) and Musicians (blue) with outcome TEN 1000 Hz and additional predictor NESI. The solid black line refers to the distribution of probabilities that the slope for Controls is larger than that of Musicians (97%). 97% of the area under the black curve falls above zero. 39 The marginal posterior distribution of the difference between the slopes of the regression lines for Controls and that of Musicians was then calculated. The probability that the distribution of slopes between the two groups is positive was predicted to be 92% (Figure 3.16). Once again, the model is fairly certain that there is a difference between thresholds in noise of the two groups based on NESI scores. Figure 3.15: Regression lines show opposite trends for both groups. There is a negative correlation (-0.05) between NESI scores and TEN 1000 Hz thresholds for the Controls but a positive correlation (0.16) for the Musicians. 40 3.3.3 Extended PTA vs NESI scores The next two outcomes do not show the same trend as the two previous NESI analyses. Weak negative slopes were exhibited for both groups, but especially for Controls (-0.04). The slope of the regression line for Musicians was -0.15. This weak negative correlation was accompanied by a large 89% HDPI with uncertain sign (Controls [-0.19, 0.10], Musicians [-0.37, 0.05]). This is shown in Figure 3.17. Figure 3.16: Difference in slopes between Controls (orange) and Musicians (blue) with outcome TEN 4000 Hz and additional predictor NESI. The solid black line refers to the distribution of probabilities that the slope for Controls is larger than that of Musicians (92%). 92% of the area under the black curve falls above zero. 41 The marginal posterior distribution of the difference between the slopes of the regression lines for Controls and that of Musicians was then calculated and the probability that the distribution of slopes between the two groups is positive is 25% (Figure 3.18). The model is fairly uncertain that there is a difference between extended high-frequency thresholds of the two groups based on NESI scores. Figure 3.17: Regression lines show a negative correlation between NESI scores and extended high frequency average thresholds for both groups (Controls = -0.04, Musicians = -0.15). 42 3.3.4 Thresholds in quiet at 16 000Hz vs NESI scores Weak negative slopes were exhibited for both groups, but especially for Musicians (-0.01). The slope of the regression line for Controls was -0.18. This weak negative correlation was accompanied by a large 89% HDPI with uncertain sign (Controls [-0.39, 0.04], Musicians [-0.16, 0.14]). This is shown in Figure 3.18. Figure 3.18: Difference in slopes between Controls (orange) and Musicians (blue) with outcome extended PTA and additional predictor NESI. The solid black line refers to the distribution of probabilities that the slope for Controls is larger than that of Musicians (25%). 25% of the area under the black curve falls above zero. 43 The marginal posterior distribution of the difference between the slopes of the regression lines for Controls and that of Musicians was then calculated and the probability that the distribution of slopes between the two groups is positive is 14% (Figure 3.20). The model is fairly uncertain that there is a difference between thresholds in noise of the two groups based on NESI scores. Figure 3.19: Regression lines show a negative correlation between NESI scores and 16 000 Hz thresholds in quiet for both groups (Controls = -0.01, Musicians = -0.18). 44 All of the analyses involving NESI presented above are results for the better ear. Overall, for the worse ear, there are no strong associations shown for any of the analyses using TEN and EHF thresholds as the outcomes and NESI as an additional predictor. Previous analyses involving thresholds in quiet as the additional predictor show the opposite trend, where associations are shown to be in the worse ear instead of the better ear. All previous analyses involving thresholds in quiet also only show negative associations – there were no instances of positive associations between predictors and outcomes. Figure 3.20: Difference in slopes between Controls (orange) and Musicians (blue) with outcome 16 000 Hz thresholds in quiet and additional predictor NESI. The solid black line refers to the distribution of probabilities that the slope for Controls is larger than that of Musicians (14%). 14% of the area under the black curve falls above zero. 45 Chapter 4: Discussion The purpose of the current study was to investigate differences between thresholds in noise and quiet in two groups of young adults that differ by noise exposure history, to examine the efficacy of behavioural assessments for detecting the presence of CS. A discussion of our findings compared with previous research, and its interpretations, as well as limitations and future directions will be presented below. 4.1 Interpretations of Results It was initially predicted that thresholds in noise, especially higher frequency thresholds (i.e. above 4000Hz), would be compromised for the Musicians, given their relatively higher level of lifetime noise exposure. This was not seen to be the case; in fact, it was the 1000Hz thresholds in noise that resulted in a positive mean difference (i.e. better performance on the TEN test for Controls). It was also predicted that Musicians would show elevated thresholds in quiet in the extended high-frequency range. Both of these predictions were made on the basis of previous findings (Liberman et al., 2016), with the assumption that there is a negative association between thresholds in quiet and in noise, and that this correlation is influenced by noise exposure. Neither of these predictions was fulfilled in this study, though there were hints of thresholds in quiet and noise correlating as expected. In all cases, there was a negative correlation between the thresholds in quiet and TEN test performance, meaning lower thresholds in quiet predicted better TEN test performance, as anticipated. While the negative correlation was very slight in some cases, and accompanied by a large uncertainty interval with uncertain sign in most cases, it is still worthy of note, as this trend may become stronger if a larger sample size is used. This negative correlation between thresholds in quiet and in noise makes sense, as we would assume 46 that performance in noise would decrease with elevated thresholds in quiet, due to saturation and cumulative damage of low spontaneous rate ANFs. In some of the analyses, performance between ears differed in that a trend was seen in one ear but not the other. It is difficult to say exactly why there is such a difference between the ears, but perhaps it is due to the small sample size. Small sample sizes have a greater risk of unusual trends surfacing by chance. For example, the analyses involving thresholds in quiet as the additional predictor show stronger associations in the worse ear instead of the better ear, whereas analyses involving NESI as the additional predictor show unexpected associations in the better ear. As discussed earlier, we would expect any associations that show up to be stronger in the worse ear, due to the assumption that CS symptoms would be more pronounced in the worse ear. This does not appear to be the case in the NESI analyses. Further, not only is there a relationship in the unexpected ear (i.e. the better ear) in all NESI analyses, but the relationship that is shown is the opposite of what we would expect. All the analyses involving NESI in the worse ear show a negative association or none at all, which is not unlike the other analyses with thresholds in quiet as the additional predictor. In these models, a negative correlation refers to lower NESI scores being associated with higher TEN scores (i.e. better performance on the TEN test), whereas a positive correlation refers to both of these variables having high scores. The negative correlation is anticipated, as higher NESI scores are indicative of more lifetime noise exposure, which is expected to correlate with decreased performance in noise. In the better ear, however, the opposite was found, and the positive correlation between NESI scores and TEN scores implies more noise exposure leads to better performance in noise. It is not clear why it appears that this deficit exists in one ear and not the other. Outliers in the sample could be to blame for unusual results. Even though Bayesian inference with a Student 47 likelihood was applied, the output of the model must be taken with caution. Again, it may be by chance due to the small sample size or there could be an underlying factor that has not yet been considered – further research will be needed to investigate this. The most recent systematic review on the efficacy of behavioural tests in CS diagnostics concluded that both extended high-frequency audiometry and speech recognition and tone detection-in-noise assessments are recommended in the test battery (Lokwani & Prabhu, 2021). Diagnoses should be made with caution though, as thresholds in this range are often poorer even in normal populations, and the pairing of behavioural tests with electrophysiological measures are also recommended for accurate diagnosis (Lokwani & Prabhu, 2021). 4.2 Comparisons with Previous Findings The current study found no major differences between thresholds in quiet and in noise in the high-risk noise exposure group or the low-risk noise exposure group at any frequency with any outcome, except for a group difference at 1000Hz TEN. The results of thresholds in quiet analyses are consistent with the pilot study, but are in contrast to previously published results that did show a difference between these two groups. The main study of comparison found that the high-risk noise exposure group had elevated thresholds in the extended high-frequency range as compared to their control counterparts (Liberman et al., 2016). This difference could be due to the differences in the methodology of Liberman et al.’s (2016) study. Compared to the current study, their participants had a larger age range of 18-41 years of age and were subjected to no further inclusion criteria apart from normal conventional thresholds and no history of ear or neurologic problems, including OM. As well, the high-risk noise exposure group, mostly comprised of musicians, self-reported more hearing and localization difficulties in noisy 48 environments than the low-risk group, and more behaviours consistent with hyperacusis (Liberman et al., 2016). Another key factor could be that Liberman et al. (2016) assigned their participants to the high-risk vs low-risk groups following the results of noise exposure questionnaires, whereas our participants were recruited based on their membership in a specific group. Because noise exposure is a continuum, delineating categories is an arbitrary decision. These factors could contribute to more variability in their participant pool, which may influence the amount of noise exposure and/or the status of their hearing systems beyond what was screened. Liberman et al. (2016) also employed a smaller sample size than our study (n=34) and used a classical statistical approach in their data analysis. As the chance of a trend appearing by chance increases with a small sample size in classical statistics, this could also be the source of the difference between their results and ours. Lastly, thresholds are an estimate. While Liberman et al. (2016) did not specify their method or dB step size for obtaining audiometric thresholds, the current study used 1 dB steps in RETSPL using the automatic Bekesy module with a fixed frequency option and three-sample averages. These factors allow for precise and accurate threshold estimation, as it also removes any tester bias that may arise from manual threshold estimation. Couth et al. (2020) also suggest that monitoring of extended high frequencies may be a good measure of CS; however, they reported that their musicians and non-musicians had similar levels of lifetime noise exposure history, as their non-musician cohort reported relatively high levels of recreational noise exposure. In fact, the musicians with low noise exposure had the highest proportion of self-reported hearing in noise problems as compared to musicians with high noise exposure and non-musicians. Overall though, their musicians reported worse hearing 49 in noise abilities than their non-musicians. This is in direct contrast to the noise exposure history collected in our study, which showed that the Musicians had more lifetime noise exposure than the Controls. From their results, Couth et al. (2020) postulated that young adults in general are less susceptible to CS than older adults with aging hearing systems. Further, there could be other factors that contribute to compromised extended high-frequency thresholds. For example, Otitis Media (OM) has also been shown to have a detrimental effect on thresholds, particularly in the extended high-frequency range. Margolis et al. (2000) reported that children who have recovered from chronic OM have significantly worse hearing in the extended high-frequency range as compared to children without a history of OM. This appears to be independent of any middle ear involvement, suggesting a cochlear origin. Recent research has shown that even one instance of OM can have negative effects on extended high-frequency thresholds (Cordeiro et al. 2018). Patients from the otolaryngology emergency room admitted because of acute unilateral OM were found to have significantly elevated hearing thresholds in that ear after their infection, as compared to their non-diseased contralateral ear. A large percentage of these patients also reported tinnitus during the acute phase of OM, with some having persistent tinnitus beyond the acute phase. The mean threshold in the extended high-frequency range was found to be significantly elevated in the ears that had had OM, especially if there was also tinnitus involved (Cordeiro et al. 2018). Since Liberman et al. (2016) did not have stringent inclusion criteria regarding middle ear issues, it is a possibility that OM could have been a contributing factor to the elevated thresholds seen in their study. In terms of thresholds in noise, Ridley et al. (2018) used the TEN test results as the outcome variable with behavioural and physiological measures as explanatory variables, much 50 like we used thresholds in noise as outcomes in the current study. Their TINR was generated to quantify CS, and thus a higher TINR indicated worse performance in noise, and therefore suggested the presence of CS. However, they found that noise exposure was not correlated with TINR, which again is in contrast to our findings, in which we found a positive correlation between noise exposure and performance on the TEN test. This discrepancy could be due to differences in collection of noise exposure data, as Ridley et al. (2018) employed the NEQ, whereas we used the NESI. While there appeared to be a trend of thresholds in noise and thresholds in quiet having a negative correlation in the current study, there was a high degree of uncertainty in the estimated relationships between outcomes and predictors used in the current regression models. There are a variety of factors that could have contributed to this uncertainty, which will be discussed below. 4.3 Study Limitations Although larger than the pilot study (n=12), this study still suffers from a relatively small sample size, with 22 Controls and 20 Musicians. Bayesian statistics, as opposed to traditional statistics, is fairly well equipped to analyze small sample sizes. Though trends were emerging, it could be that the sample size was not large enough to obtain more accurate estimates. The small sample size could also be the explanation for the differences between ears, where there appeared to be an unexplainable difference. This study could be replicated in the future with larger sample sizes, although it would be difficult to know exactly how many participants would suffice. That being said, Liberman et al. (2016) recruited 22 music students and 12 control participants and found a significant difference between extended high-frequency thresholds in their cohorts. While the data presented by Liberman et al. (2016) show a significant difference between group 51 thresholds above 8000Hz, it is apparent that their high-risk noise exposure group did not actually exhibit any true hearing loss in these frequencies; all of their thresholds above 8000Hz remained better than 20 dB HL, which is considered normal hearing. Further, the high-risk noise exposure group, though they were indeed at higher risk of noise exposure than the low-risk noise exposure group, could have had the inherent flaw of not having enough noise exposure history. It has been described in the literature that participants have more difficulty with speech recognition in noise given more noise exposure history (Liberman et al., 2016; Plack et al., 2014). In light of this, when selecting the recruitment criteria for the high-risk cohort, we assumed that the link between noise exposure and HHL symptoms, whether currently present or expected in the near future, was a given; this was verified with a measure (the NESI) that was able to estimate group differences in estimated noise exposure. Our cohort of musicians, though they did self-report a history of noise exposure, did not actually exhibit symptoms of CS. They did not report difficulty understanding speech in any situations. It could be, however, that our music student participants simply have a lack of lifetime noise exposure. Our participants were all relatively young (mean age=20), so they may not have enough cumulative noise exposure over their lifetimes to account for any perceivable neural damage. Although the music students did exhibit significantly greater lifetime noise exposure as recorded on the NESI (Monette, 2020), it is not clear how much noise exposure is enough to see a difference in extended high-frequency thresholds and/or neural damage. Monette (2020) analyzed NESI scores from the same participants in this study and found 94.5% probability that the effect size for the difference in NESI scores between groups was between 1 and 2.6, a large to very large effect size. Of course, there is the caveat that as participant age increases, so does the incidence of presbycusis. 52 There is also a risk of misreporting noise exposure history when collected via self-report measures, such as the NESI. Since the NESI asks participants to reflect on their entire lifespan’s worth of noise exposure, there is always the risk of participants misremembering or accidentally omitting noise exposure events during the interview. Even if participants accurately remember all instances of noise exposure in their lifetime, there may be discrepancies between the noise level they report and the actual noise level they experienced. When verifying vocal effort as a scale for reporting noise exposure, Ferguson et al. (2019) found that 56% of participants reported noise levels within 3 dB of actual measured noise levels. Since the dB scale is logarithmic, a difference in 3 dB is equal to doubling of the acoustic energy. An in-ear dosimeter would be a more accurate way to directly measure noise exposure. Since it would be impossible to log every single instance of noise exposure in an individual with an in-ear dosimeter, future research could employ a combination of self-report and actual noise measurement of the physical environmental noise. As well, in recruiting music students, while it was noted which instrument(s) they play, the type of instrument was not an inclusion/exclusion criterion. This was also the case in the Liberman et al. (2016) study after which the current study was modeled. As such, some instruments could result in more or less noise exposure, thus potentially skewing the overall Musicians’ noise exposure. For example, it is reasonable to assume that musicians who perform more regularly in orchestras will accrue more noise exposure than those who perform by themselves, such as vocalists, of which this study included four. It could be useful to have stricter instrument inclusion criteria in future studies, to ensure a more comparable noise exposure history within the group. 53 4.4 Directions for further research In the current study, the TEN test was conducted at 1000 Hz and 4000 Hz, with 4000 Hz being used as the frequency for comparison of high-frequency thresholds. The question remains of whether 4000 Hz is a high enough frequency to be comparable to the extended high frequencies, as clinically 4000 Hz is considered on the low end of the high-frequency spectrum. A frequency of 4000 Hz was selected as the high-frequency comparison because the TEN test is not currently calibrated for any higher frequencies. The development of an effective masking noise for the extended high-frequency range will be required to run the TEN test at a higher frequency. This will allow a direct comparison of thresholds in quiet vs noise, at the same frequency across the extended high-frequency range, much like the direct comparison that can be made between thresholds in quiet and in noise at 1000 Hz in this study. Another direction that can be taken is to include stricter inclusion criteria for the high-risk noise exposure group, and recruit only individuals who are symptomatic for CS or who have higher levels of noise exposure than our current musician cohort. The current approach extrapolates CS from noise exposure and could be missing a few crucial steps in between, such as amount or type of noise exposure. By shifting the focus to CS symptoms, such as difficulty listening in noise, rather than using noise exposure history only as the main high-risk criterion, some of these questions of the specific characteristics of noise exposure that will lead to CS can potentially be answered. If difficulty understanding speech in noise is to be an inclusion criterion for the high-risk noise exposure group, we would have to be careful to distinguish populations with central auditory processing disorder. This can be done though; in fact, MAPA was already included as part of the test battery of this study. The caveat is that CS has been proposed to be a sub-type of CAPD (Musiek 2018), so distinguishing CS from CAPD may be difficult. By 54 including participants based on an increased level of noise exposure, we would have to recruit a different cohort within the same age range as the current study, to ensure age is not a contributing factor to CS symptoms. Industry and military exposure are two possible fields to recruit from. The other factor to consider in shifting the target population is age. As it may be difficult to find the same age group of young musicians with a mean age of 20 that have difficulty listening in noise, the age of the high-risk noise exposure group could shift upwards, towards a more presbycusis-expected age range. 55 Chapter 5: Conclusion Although this study found largely inconclusive evidence of differences in thresholds in quiet and noise between low and high-risk noise exposure groups, it leaves opportunities for much more research into the early effects of noise exposure, detection using clinical practices, and cochlear synaptopathy as a whole. As more research surfaces, we can be hopeful that a reliable test battery can be developed to detect and thus treat hidden hearing loss. If nothing else, this may serve as a warning for young adults to protect their hearing and limit noise exposure where possible. Current Canadian workplace regulations for noise exposure levels and hearing protection were developed to only take into account traditional hearing loss (elevated thresholds on a conventional audiogram) (Arenas & Suter, 2014). They operate on the assumption that temporary threshold shifts due to noise exposure do not cause permanent damage to hair cells (Arenas & Suter 2014). Given the recent research developments, amendments to these regulations should be considered to further prevent noise-induced neural damage. There is a glimmer of hope in scientific advancements though, as there could be treatments on the horizon. Liberman et al. 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Supra-threshold auditory brainstem response amplitudes in humans: Test-retest reliability, electrode montage and noise exposure. Hearing research, 364, 38-47. Ridley, C. L., Kopun, J. G., Neely, S. T., Gorga, M. P., & Rasetshwane, D. M. (2018). Using thresholds in noise to identify hidden hearing loss in humans. Ear and hearing, 39( 5), 829-844. Schaette, R., & McAlpine, D. (2011). Tinnitus with a normal audiogram: physiological evidence for hidden hearing loss and computational model. Journal of Neuroscience, 31(38), 13452-13457. 63 Stamper, G. C., & Johnson, T. A. (2015). Auditory function in normal-hearing, noise-exposed human ears. Ear and hearing, 36(2), 172. Valderrama, J. T., Beach, E. F., Yeend, I., Sharma, M., Van Dun, B., & Dillon, H. (2018). Effects of lifetime noise exposure on the middle-age human auditory brainstem response, tinnitus and speech-in-noise intelligibility. Hearing Research, 365, 36-48. Viana, L. M., O'Malley, J. T., Burgess, B. J., Jones, D. D., Oliveira, C. A., Santos, F., ... & Liberman, M. C. (2015). Cochlear neuropathy in human presbycusis: Confocal analysis of hidden hearing loss in post-mortem tissue. Hearing research, 327, 78-88. World Health Organization. (2015). Hearing loss due to recreational exposure to loud sounds: a review. https://apps.who.int/iris/handle/10665/154589\ 64 Appendices Appendix A – Supporting Documents for Methods A1. Full list of assessments in larger HHL study Session 1: Consent form, Tinnitus Handicap Inventory (THI), otoscopy, wideband acoustic immittance testing consisting of multi-frequency tympanometry and otoacoustic emissions (DPOAEs and TEOAEs), acoustic reflexes (500 Hz and BBN), acoustic reflex latency test (ARLT at 500 Hz and BBN), Bekesy audiometry (pure tone air conduction thresholds 250-16000 Hz), bone conduction thresholds (250-4000 Hz), most comfortable loudness (MCL) and uncomfortable loudness (UCL) levels using the rainbow passage, Loudness Scaling at 4000 Hz, Masking Level Difference (MLD), the Threshold Equalizing Noise (TEN) Test, and the Multiple Auditory Processing Assessment (MAPA). Session 2: The Hearing In Noise Test (HINT), the Random Gap Detection Test (RGDT), the Temporal Modulation Transfer Function (TMTF), and triple digits in noise. Session 3: The Noise Exposure Structured Interview (NESI), electrocochleography (ECochG), and contralateral suppression of otoacoustic emissions. A2. NESI Vocal effort to dB A conversion chart from Guest et al. 2018 Vocal Effort Required Estimated Level Talk normally from 4 feet (1.2 m) ≤ 80 dB A Raise voice from 4 feet (1.2 m) 87 dB A Talk loudly from 4 feet (1.2 m) 90 dB A Talk very loudly from 4 feet (1.2 m) 93 dB A Shout from 4 feet (1.2 m) 99 dB A Shout from 2 feet (0.6 m) 105 dB A Shout in listener’s ear 110 dB A 65 A3. Noise Exposure Units to dB Conversion The number of noise exposure units was calculated using the following formulas: For exposure activities where no hearing protection was worn: U = ! × $ × % × &'()( × 10!"#$%$ For exposure activities where hearing protection was worn and reduced sound to ≤80 dBA: U = ! × $ × % × &'()( × (1 − 𝑃) × 10!"#$%$ For exposure activities where hearing protection was worn and did not reduce sound to ≤80 dBA: 𝑈 = 𝑌 × 𝑊 × 𝐷 × 𝐻2080 × 1𝑃 × 10*+,+-(.( + (1 − 𝑃) × 10*+-(.( 3 U is units of noise exposure, Y is the years of exposure, W is weeks per year of exposure, D is days per week of exposure, H is hours per day of exposure, P is proportion of time that hearing protection was worn (between 0 to 1), L is the sound level in dB A as estimated from vocal effort, A is the attenuation of hearing protection in dB, and 2080 (40 hours per week x 52 weeks in a year) corresponds to the number of hours in a working year (Chang, 2020; Monette, 2020). 66 Appendix B – Descriptive Statistics B1. Group comparison plots of thresholds Group comparison plots for 1000 Hz thresholds in quiet of the worse ear were shown in the main text. The following plots are group comparisons for the worse ear in all other outcomes (4000 Hz, PTA, extended PTA, 16 000 Hz, 1000 Hz TEN, and 4000 Hz TEN). 67 The following plots are group comparisons for each outcome in the better ear (1000 Hz, 4000 Hz, PTA, extended PTA, 16 000 Hz, 1000 Hz TEN, and 4000 Hz TEN) 68 B2. Welch’s t-test results with P value and 95% confidence interval P value > 0.05 = not significant. Outcome P value 95% Confidence Interval 1000 Hz 0.69 -5.03, -7.53 4000 Hz 0.99 -6.27, -6.29 PTA 0.60 -4.02, -6.84 Extended PTA 0.41 -5.08, -12.11 1000 Hz TEN 0.09 -0.08, 1.16 4000 Hz TEN 0.61 -0.49, 0.81 B3. Predicting performance in noise given thresholds in quiet and group as predictors – better ear results The table below shows the slope of the regression line for each group, with 89% HDPI in square brackets, and the marginal posterior distribution of the difference in slopes between groups. Regression lines with uncertainty intervals (grey shaded area) for each group, followed by marginal posterior distribution of the difference between regression slopes, are shown for each of the following outcomes: TEN 1000 Hz vs 1000 Hz quiet, TEN 1000 Hz vs PTA, TEN 4000 Hz vs 4000 Hz quiet, TEN 4000 Hz vs extended PTA, and TEN 1000 Hz vs extended PTA. All results below are for the better ear. 69 Outcome Slope of regression line (Controls) Slope of regression line (Musicians) Marginal posterior distribution of the difference in slope 1000 Hz vs TEN 1000 Hz -0.22 [-0.43, 0.02] -0.51 [-0.85, -0.16] 10% 4000 Hz vs TEN 4000 Hz -0.11 [-0.36, 0.13] -0.20 [-0.51, 0.09] 35% PTA vs TEN 1000 Hz -0.20 [-0.41, 0.01] -0.47 [-0.78, -0.16] 9% Extended PTA vs TEN 4000 Hz -0.13 [-0.35, 0.09] -0.31 [-0.65, 0.03] 20% Extended PTA vs TEN 1000 Hz -0.18 [-0.38, 0.03] -0.42 [-0.78, -0.06] 14% 70 71 72 B4. Predicting performance in noise and extended high frequency thresholds given NESI scores and group as predictors – worse ear results Regression lines with uncertainty intervals (grey shaded area) for each group, followed by marginal posterior distribution of the difference between regression slopes, are shown for each of the following outcomes: TEN 1000 Hz vs NESI, TEN 4000 Hz vs NESI, extended PTA vs NESI, and 16000 Hz vs NESI. The table shows the slope of the regression line for each group, with 89% HDPI in square brackets, and the marginal posterior distribution of the difference in slopes between groups. All results below are for the worse ear. Outcome Slope of regression line (Controls) Slope of regression line (Musicians) Marginal posterior distribution of the difference in slope TEN 1000 Hz vs NESI score -0.02 [-0.16, 0.14] 0.02 [-0.15, 0.20] 62% TEN 4000 Hz vs NESI score 0.08 [-0.08, 0.26] 0.01 [-0.20, 0.20] 34% Extended PTA vs NESI score -0.02 [-0.14, 0.11] -0.16 [-0.35, 0.02] 15% 16 000 Hz vs NESI score -0.03 [-0.18, 0.10] -0.18 [-0.37, 0.02] 17% 73 74 75 76 Appendix C – Statistical Analyses C1. Group comparisons - Bayesian t-test with group as fixed effect dlist <- list( q_1ks = q_1ks, group = d_worse$group_id) m.q_1k <- ulam( alist( q_1ks ~ dstudent(3, mu , sigma ), mu <- a[group], a[group] ~ dnorm( 0 , 2), sigma <- sig[group], sig[group] ~ dexp(1) # nu ~ dexp(1) ) , data=dlist, chains = 1, cores = 1, warmup = 2000, iter = 4000, control = list(adapt_delta=0.95, max_treedepth = 15 ) ) precis(m.q_1k, depth=2, digits = 2) ## mean sd 5.5% 94.5% n_eff Rhat4 ## a[1] 0.3358406 0.1408553 0.1099312 0.5561512 1539.892 0.9996129 ## a[2] 0.1351839 0.1947630 -0.1787917 0.4396160 2248.280 0.9995461 ## sig[1] 0.5621152 0.1450284 0.3643522 0.8251102 1918.313 1.0001040 ## sig[2] 0.7071427 0.1808759 0.4691657 1.0238456 2251.792 0.9995039 C2. Multilevel regression model with Student t likelihood, TEN performance as the outcome variable, and group and thresholds in quiet as predictor variables mten_1k.u <- ulam( alist( ten_1k ~ dstudent(3, mu , sigma ), mu <- a + bG*group + bT*q_1k + bGT*q_1k*group, a ~ dnorm(0, 1), c(bG, bT) ~ dnorm(0, 0.35), bGT ~ dnorm(0, 0.35), sigma ~ dexp (1) ), data=dlist, warmup = 2000, iter = 10000 precis( mten_1k.u, corr=TRUE) ## mean sd 5.5% 94.5% n_eff Rhat4 ## a 0.2088723 0.1588149 -0.05164330 0.4533892 4708.806 0.9998759 77 ## bT -0.4152080 0.1522832 -0.64699274 -0.1624840 4128.499 0.9998766 ## bG -0.5136752 0.2094689 -0.83987175 -0.1714458 4611.545 1.0000847 ## bGT 0.2281869 0.2015023 -0.09423098 0.5475625 4702.154 0.9999315 ## sigma 0.6416114 0.1030641 0.49480585 0.8191496 4762.821 1.0000800 C3. Multilevel regression model with Student t likelihood, TEN performance/extended high frequency thresholds as the outcome variable, and group and NESI scores as predictor variables dlist <- list( ten_1k = dw1$ten_1ks, nesi = dw1$logNESI, group = dw1$group_id) m2.ten1k <- ulam( alist( ten_1k ~ dstudent(3, mu , sigma ), mu <- a[group] + bG[group] * nesi, a[group] ~ dnorm(0, 0.5), bG[group] ~ dnorm(0, 0.3), sigma ~ dexp (1) ), data=dlist, warmup = 4000, iter = 8000 precis( m2.ten1k, depth=2) ## mean sd 5.5% 94.5% n_eff Rhat4 ## a[1] 0.15770115 0.17781566 -0.1290688 0.442318965 3108.950 0.9998982 ## a[2] -0.39317908 0.23485983 -0.7517297 -0.003507999 2592.696 1.0002966 ## bG[1] -0.02125108 0.09541994 -0.1719789 0.133699099 2970.878 1.0001046 ## bG[2] 0.01510503 0.10885804 -0.1697533 0.176369868 2526.538 1.0001112 ## sigma 0.68429578 0.11114521 0.5237797 0.874000676 2917.741 1.0000934
Thesis/Dissertation
eng
Audiology and Speech Sciences
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Shahnaz, Navid
Beyond conventional audiograms : thresholds in quiet and noise and their diagnostic potential for noise-induced cochlear synaptopathy
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