Medicine, Faculty of
Audiology and Speech Sciences, School of
DSpace
UBCV
Tai, Charlotte
Master of Science - MSc
University of British Columbia
Purpose: Hidden hearing loss, also known as cochlear synaptopathy, describes auditory deficits in individuals with clinically normal hearing sensitivity in the conventional frequency range (250–8000 Hz) which include difficulty understanding speech in background noise. Individuals with central auditory processing disorder report similar complaints. We aimed to investigate the utility of a central auditory processing test battery, the Multiple Auditory Processing Assessment (MAPA), as a potential diagnostic tool for hidden hearing loss. An additional objective was exploration of the link between noise exposure and hidden hearing loss. Previous studies on mice have found a reduction in hair cell synaptic connections subsequent to noise exposure. Design: Three groups of participants completed the MAPA: a control group (n = 24), a musician group (n = 20), and a symptomatic group (n = 11). The control group had young individuals with normal hearing (≤ 25 dB HL thresholds at 250–8000 Hz) and no significant history of noise exposure, the musician group had young students with normal hearing who were considered to be at high risk for noise exposure due to their studies, and the symptomatic group had individuals with normal hearing who reported difficulties in challenging listening environments. Lifetime noise exposure was estimated using the Noise Exposure Structured Interview (NESI), and Bayesian logistic regression was used to model the relationship between the outcomes of MAPA subtests and noise exposure history. Results: The symptomatic group exhibited poorer performance on the MAPA compared to the control and musician groups. A right-ear advantage for all groups was apparent on several measures. The musician group had higher estimated lifetime noise exposure compared to the other groups. With the exception of the dichotic advantage score, NESI scores did not predict performance on the MAPA. Conclusion: Consistent with their reported difficulties, the symptomatic group performed more poorly on the MAPA. Despite their higher estimated lifetime noise exposure, no noise-induced central auditory processing deficits emerged in the musician group. Future studies should include older musicians with more lifetime noise exposure, and adjust for age effects by using a longitudinal study design or by age-matching across participant groups.
MULTIPLE AUDITORY PROCESSING ASSESSMENT AS A TOOL TO QUANTIFY HIDDEN HEARING LOSS by Charlotte Tai B.Mus., The University of British Columbia, 2019 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) August 2021 © Charlotte Tai, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Multiple Auditory Processing Assessment as a tool to quantify hidden hearing loss submitted by Charlotte Tai 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, Audiology and Speech Sciences, UBC Supervisor Dr. Valter Ciocca, Professor, Audiology and Speech Sciences, UBC Supervisory Committee Member Dr. Anthony Herdman, Associate Professor, Audiology and Speech Sciences, UBC Supervisory Committee Member iii Abstract Purpose: Hidden hearing loss, also known as cochlear synaptopathy, describes auditory deficits in individuals with clinically normal hearing sensitivity in the conventional frequency range (250–8000 Hz) which include difficulty understanding speech in background noise. Individuals with central auditory processing disorder report similar complaints. We aimed to investigate the utility of a central auditory processing test battery, the Multiple Auditory Processing Assessment (MAPA), as a potential diagnostic tool for hidden hearing loss. An additional objective was exploration of the link between noise exposure and hidden hearing loss. Previous studies on mice have found a reduction in hair cell synaptic connections subsequent to noise exposure. Design: Three groups of participants completed the MAPA: a control group (n = 24), a musician group (n = 20), and a symptomatic group (n = 11). The control group had young individuals with normal hearing (≤ 25 dB HL thresholds at 250–8000 Hz) and no significant history of noise exposure, the musician group had young students with normal hearing who were considered to be at high risk for noise exposure due to their studies, and the symptomatic group had individuals with normal hearing who reported difficulties in challenging listening environments. Lifetime noise exposure was estimated using the Noise Exposure Structured Interview (NESI), and Bayesian logistic regression was used to model the relationship between the outcomes of MAPA subtests and noise exposure history. Results: The symptomatic group exhibited poorer performance on the MAPA compared to the control and musician groups. A right-ear advantage for all groups was apparent on several measures. The musician group had higher estimated lifetime noise exposure compared to the iv other groups. With the exception of the dichotic advantage score, NESI scores did not predict performance on the MAPA. Conclusion: Consistent with their reported difficulties, the symptomatic group performed more poorly on the MAPA. Despite their higher estimated lifetime noise exposure, no noise-induced central auditory processing deficits emerged in the musician group. Future studies should include older musicians with more lifetime noise exposure, and adjust for age effects by using a longitudinal study design or by age-matching across participant groups. v Lay Summary Difficulty understanding speech in background noise despite normal hearing sensitivity is called “hidden hearing loss” because the standard hearing test cannot detect it. In noise-exposed mice, researchers have found damaged connections between inner ear cells and the nerve that sends information about sounds to the brain. We investigated the link between noise exposure and hidden hearing loss by testing three groups of people (control, high-risk/musician, and symptomatic). The symptomatic group had lower scores compared to the other groups on speech-in-background-noise tests and on tasks where different words were delivered to each ear. The musician group had higher noise exposure history but did not perform worse than the control group on the speech understanding tasks. Future studies should examine older musicians, as it is possible the musicians we studied had not been exposed to enough noise for it to affect their performance on tests of speech understanding in challenging situations. vi Preface This thesis is part of a larger research project on hidden hearing loss at the University of British Columbia Middle Ear Lab, conceived of and coordinated by Dr. Navid Shahnaz. The test battery and protocol were devised by Dr. Shahnaz. The project received ethics approval by the University of British Columbia Clinical Research Ethics Board under the title of “Hidden Hearing Loss - HHL” (certificate number H16-02052). Participant recruitment and data collection for the project were conducted by Ainsley Ma, Stéphanie Monette, Charlene Chang, and Natalie Tran, with the assistance of Renee Fong and Adele Kwok. UBC Audiology students (Elizabeth Dunlop, Haley Gynane, Mitchell Harris, Shandryn Kozin, Danielle Lafleur, Kelsey Miller, Darren Roussel, Saruka Santhiran, Natalie Tran, Jaclyn Wiebe) also contributed to data collection as part of a research methods class. I conducted the data analysis with the guidance and mentorship of Dr. Valter Ciocca. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................ xi List of Figures .............................................................................................................................. xii List of Abbreviations ................................................................................................................. xvi Acknowledgements ................................................................................................................... xvii Chapter 1 : Introduction .............................................................................................................. 1 1.1 Animal studies ................................................................................................................ 2 1.2 Behavioural, physiological, and histological findings in humans .................................. 4 1.2.1 Medial olivocochlear reflex .................................................................................... 4 1.2.2 Auditory brainstem response .................................................................................. 5 1.2.3 Other behavioural and electrophysiological tests ................................................... 6 1.2.4 Histological findings ............................................................................................. 10 1.3 Hidden hearing loss and central auditory processing disorder ..................................... 10 1.4 Musicians and central auditory processing ................................................................... 14 1.5 Tests of central auditory processing ............................................................................. 16 viii 1.5.1 The Multiple Auditory Processing Assessment .................................................... 16 1.6 Purpose of study ............................................................................................................ 17 Chapter 2 : Methods ................................................................................................................... 19 2.1 Participants .................................................................................................................... 19 2.1.2 High-risk (musician) group ................................................................................... 19 2.1.3 Control group ........................................................................................................ 20 2.1.4 Symptomatic group ............................................................................................... 20 2.1.5 Test battery............................................................................................................ 22 2.1.5.1 Case history ....................................................................................................... 22 2.1.5.2 Audiometry ........................................................................................................... 22 2.1.5.3 Multiple Auditory Processing Assessment ....................................................... 22 2.1.5.4 Noise Exposure Structured Interview ............................................................... 27 2.1.5.5 Other tests ......................................................................................................... 27 2.2 Data analysis ................................................................................................................. 27 Chapter 3 : Results...................................................................................................................... 30 3.1 Multiple Auditory Processing Assessment ................................................................... 30 3.1.1 Monaural Selective Auditory Attention Test ........................................................ 30 3.1.1.1 Non-competing condition ..................................................................................... 30 3.1.1.2 Competing condition ............................................................................................. 30 ix 3.1.2 Speech in Noise for Children and Adults ............................................................. 32 3.1.3 Pitch pattern test .................................................................................................... 33 3.1.4 Dichotic digits test ................................................................................................ 34 3.1.4.1 Dichotic digits ................................................................................................... 35 3.1.4.2 Diotic digits ....................................................................................................... 36 3.1.5 Competing Sentences ............................................................................................ 38 3.2 Noise Exposure Structured Interview ........................................................................... 40 3.3 Predictors of MAPA scores .......................................................................................... 41 3.3.1 Monaural Selective Auditory Attention Test, left ear ........................................... 41 3.3.2 Competing Sentences, left ear............................................................................... 44 3.3.3 Diotic digits ........................................................................................................... 47 3.3.4 Dichotic advantage................................................................................................ 52 Chapter 4 : Discussion ................................................................................................................ 56 4.1 Speech-in-noise ............................................................................................................. 56 4.2 Right-ear advantage ...................................................................................................... 56 4.3 Dichotic and diotic digits .............................................................................................. 58 4.4 Attention and memory .................................................................................................. 60 4.5 Age ................................................................................................................................ 61 4.6 Noise exposure history .................................................................................................. 61 4.7 Study limitations ........................................................................................................... 63 4.8 Future directions ........................................................................................................... 64 x References .................................................................................................................................... 67 Appendices ................................................................................................................................... 80 Appendix A Raw data............................................................................................................ 80 A.1 Raw data for the control group ............................................................................. 80 A.2 Raw data for the musician group .......................................................................... 83 A.3 Raw data for the symptomatic group .................................................................... 85 Appendix B Statistical models .............................................................................................. 86 B.1 Sample Bayesian multilevel logistic regression with random intercepts for participants, group and ear as fixed effects ........................................................................... 86 B.2 Bayesian logistic regression with group and NESI as predictors ......................... 88 B.3 Sample Bayesian logistic regression with a MAPA score as the outcome variable, and group and NESI as predictors ........................................................................................ 90 xi List of Tables Table 2.1 Symptomatic participants with thresholds > 25 dB HL when tested at the UBC Middle Ear Lab. ......................................................................................................................................... 21 Table 3.1 Summary of scores on the pitch pattern test by group. ................................................. 34 xii List of Figures Figure 2.1 Illustration of the dichotic digits test paradigm. Numbers are listed below the ear to which they are presented, and numbers of the same colour denote simultaneous presentation. .. 25 Figure 2.2 Illustration of the diotic digits test paradigm. Numbers are listed below the ear to which they are presented, and numbers of the same colour denote simultaneous presentation. .. 26 Figure 3.1 Plot of individual scores on the MSAAT competing condition by group and ear. The red dashed line denotes the maximum score................................................................................. 31 Figure 3.2 Plot of individual scores on the SINCA by group and ear. The red dashed line denotes the maximum score. ...................................................................................................................... 32 Figure 3.3 Plot of individual scores on the dichotic digits test by group. The red dashed line denotes the maximum score. ......................................................................................................... 35 Figure 3.5 Plot of individual scores on the diotic digits test by group. The red dashed line denotes the maximum score. ...................................................................................................................... 36 Figure 3.6 Plot of individual scores on the Competing Sentences by group and ear. The red dashed line denotes the maximum score. ...................................................................................... 38 Figure 3.7 Plot of individual log(NESI) scores by group. The red dashed line denotes the mean score. ............................................................................................................................................. 40 Figure 3.8 Plot of individual scores on the MSAAT competing condition by group for the left ear only. The red dashed line denotes the maximum score. ............................................................... 42 Figure 3.9 Predicted regression line for the control group, plotted for NESI scores versus left-ear competing MSAAT scores. ........................................................................................................... 42 Figure 3.10 Predicted regression line for the musician group, plotted for NESI scores versus left-ear competing MSAAT scores. ..................................................................................................... 43 xiii Figure 3.11 Predicted regression line for the symptomatic group, plotted for NESI scores versus left-ear competing MSAAT scores. .............................................................................................. 43 Figure 3.12 Plot of the NESI versus MSAAT (left-ear competing condition) slope difference between the musician and symptomatic groups. The blue dashed line denotes the slope of the musician group, the orange dashed line denotes the slope of the symptomatic group, and the black line denotes the difference in slope between the two groups. The red dashed line denotes a slope of 0. ...................................................................................................................................... 44 Figure 3.13 Plot of individual scores on the Competing Sentences by group for the left ear only. The red dashed line denotes the maximum score. ........................................................................ 45 Figure 3.14 Predicted regression line for the control group, plotted for NESI scores versus left-ear Competing Sentences scores. .................................................................................................. 45 Figure 3.15 Predicted regression line for the musician group, plotted for NESI scores versus left-ear Competing Sentences scores. .................................................................................................. 46 Figure 3.16 Predicted regression line for the symptomatic group, plotted for NESI scores versus left-ear Competing Sentences scores. ........................................................................................... 46 Figure 3.17 Plot of the NESI versus Competing Sentences (left-ear) slope difference between the control and symptomatic groups. The blue dashed line denotes the slope of the control group, the orange dashed line denotes the slope of the symptomatic group, and the black line denotes the difference in slope between the two groups. The red dashed line denotes a slope of 0. .............. 47 Figure 3.18 Predicted regression line for the control group, plotted for NESI scores versus diotic digits scores. .................................................................................................................................. 48 Figure 3.19 Predicted regression line for the musician group, plotted for NESI scores versus diotic digits scores......................................................................................................................... 48 xiv Figure 3.20 Predicted regression line for the symptomatic group, plotted for NESI scores versus diotic digits scores......................................................................................................................... 49 Figure 3.21 Plot of the NESI versus diotic digits slope difference between the control and symptomatic groups. The blue dashed line denotes the slope of the control group, the orange dashed line denotes the slope of the symptomatic group, and the black line denotes the difference in slope between the two groups. The red dashed line denotes a slope of 0. ................................ 50 Figure 3.22 Plot of the NESI versus diotic digits slope difference between the control and musician groups. The blue dashed line denotes the slope of the control group, the orange dashed line denotes the slope of the musician group, and the black line denotes the difference in slope between the two groups. The red dashed line denotes a slope of 0. ............................................. 51 Figure 3.23 Plot of the NESI versus diotic digits slope difference between the musician and symptomatic groups. The blue dashed line denotes the slope of the musician group, the orange dashed line denotes the slope of the symptomatic group, and the black line denotes the difference in slope between the two groups. The red dashed line denotes a slope of 0. ................................ 52 Figure 3.24 Plot of individual dichotic advantage scores by group. The red dashed line denotes a difference of zero in scores between the dichotic and diotic conditions. ..................................... 53 Figure 3.25 Predicted regression line for the control group, plotted for NESI scores versus dichotic advantage scores. ............................................................................................................ 53 Figure 3.26 Predicted regression line for the musician group, plotted for NESI scores versus dichotic advantage scores. ............................................................................................................ 54 Figure 3.27 Predicted regression line for the symptomatic group, plotted for NESI scores versus dichotic advantage scores. ............................................................................................................ 54 xv Figure 3.28 Plot of the NESI versus dichotic advantage slope difference between the musician and symptomatic groups. The blue dashed line denotes the slope of the musician group, the orange dashed line denotes the slope of the symptomatic group, and the black line denotes the difference in slope between the two groups. The red dashed line denotes a slope of 0. .............. 55 xvi List of Abbreviations ABR Auditory brainstem response AP Action potential CAPD Central auditory processing disorder DPOAEs Distortion product otoacoustic emissions HINT Hearing–in–noise test HL Hearing level MAPA Multiple Auditory Processing Assessment MSAAT Monaural Selective Auditory Attention Test NESI Noise Exposure Structured Interview QuickSIN Quick speech–in–noise test RETSPL Reference equivalent threshold sound pressure level SNR Signal-to-noise ratio SP Summating potential SPL Sound pressure level TBI Traumatic brain injury xvii Acknowledgements I am grateful to Dr. Navid Shahnaz, my thesis supervisor, for his encouragement of my work and for his guidance throughout the process. Thank you to my supervisory committee members, Dr. Valter Ciocca and Dr. Anthony Herdman, for their time and for their support. Special thanks to Dr. Ciocca for his generosity and patience in assisting me with the data analysis. I owe thanks to the many individuals who recruited participants and collected data for this thesis: Dr. Robert Pritchard for his help in recruitment of music students, and the UBC Middle Ear Lab and the UBC SASS Class of 2020 for their work in collecting data. To others involved in the project, especially Ainsley Ma, Charlene Chang, and Stéphanie Monette: it has been a pleasure learning from and collaborating with you. Thank you to the faculty, staff, and my fellow students at UBC SASS, who have been a great resource and support throughout my graduate studies. This research was funded by the SAC Research Scholarship from Speech-Language and Audiology Canada. 1 Chapter 1: Introduction Hidden hearing loss is a phenomenon that has been described as early as the 1940s. In humans, it involves auditory deficits in the absence of pure-tone audiogram threshold elevations in the conventional frequency range (250–8000 Hz). Areas of these deficits include suprathreshold discrimination tasks and neural temporal coding (Plack et al., 2014). Damage to low spontaneous rate (low-SR) auditory nerve fibres has been a particular focus of hidden hearing loss research, as low-SR fibres have high thresholds and are particularly important for encoding suprathreshold sounds (Bharadwaj et al., 2015). In contrast, high spontaneous rate (high-SR) fibres respond to sounds close to the threshold. Together, nerve fibres with differing spontaneous rates work to convey information about stimuli across a wide dynamic range. Lopez-Poveda and Barrios (2013) have proposed a model of “stochastic under sampling” in the case of a damaged auditory system with fewer functional neurons. This sampling affects auditory performance in background noise more than auditory performance in quiet, hence why speech-in-noise deficits may occur before changes in hearing sensitivity. A commonly experienced perceptual difficulty is trouble understanding speech in noise; in a large UK survey, 26% of adults reported “great difficulty” hearing speech in noise while only 16% had audiometric thresholds outside of the normal range (defined as thresholds of 25 dB HL or better at 500–4000 Hz), hence a type of hearing loss that is “hidden” by the pure-tone audiogram (Davis, 1989). Animal studies have found histological evidence of synaptic loss (in the absence of hair cell damage) after noise exposure (Kujawa & Liberman, 2009). Human 2 studies have focused on behavioural measures to quantify auditory deficits, as well as physiological measures such as auditory evoked potentials and otoacoustic emissions. Other auditory symptoms such as tinnitus and hyperacusis have also been associated with hidden hearing loss. One proposed mechanism behind these symptoms is an increase in neural gain to compensate for reduced output from a damaged cochlea (Plack et al., 2014). Individuals with hearing loss have damage in their cochlea as indicated by elevated pure-tone thresholds, while the search for damage in people with clinically normal hearing who suffer from tinnitus and/or hyperacusis as well as suprathreshold auditory deficits continues. These suprathreshold deficits have been associated with factors such as noise exposure (e.g. Kujala et al., 2004), old age (e.g. Pichora-Fuller & Souza, 2003), and traumatic brain injury (e.g. Fausti et al., 2009). Hidden hearing loss studies in animals and humans have examined: audiometry at conventional frequencies (250–8000 Hz), audiometry at extended high frequencies (above 8 kHz), tone detection in noise, speech perception in noise, acoustic stapedial reflex growth, electrocochleography, otoacoustic emissions, ipsilateral and contralateral otoacoustic emission suppression, auditory brainstem response (ABR), auditory steady-state response (ASSR), frequency-following response, envelope-following response, interaural time difference detection, self-report measures, and questionnaires (Barbee et al., 2018; Bramhall et al., 2019; Lokwani & Prabhu, 2021; Wang et al., 2021). 1.1 Animal studies The seminal study by Kujawa and Liberman (2009) was conducted on 12 mice, six of which were exposed to noise. Subsequent to noise exposure, the mice’s audiometric thresholds became elevated (as measured by ABR) and their distortion product otoacoustic emissions (DPOAE) amplitudes decreased. Within two weeks, both audiometric thresholds and DPOAEs 3 recovered to pre-exposure levels. However, the noise-exposed mice continued to exhibit a reduced wave I amplitude in the ABR in response to suprathreshold stimuli, and synaptic ribbon loss was observed histologically. DPOAEs are primarily a measure of outer hair cell function and are not sensitive to inner hair cells or afferent nerve fibres damage. For example, chinchillas (n = 4) who were administered the ototoxic drug carboplatin showed significant inner hair cell loss but no significant depression of the cochlear microphonic potential or DPOAEs (El-Badry & McFadden, 2006). In fact, some of the ears exhibited enhancement of these measures, with the proposed mechanism being a loss of efferent feedback to the outer hair cells due to reduced input from the afferent inner hair cells. Another study that found permanently reduced ABR wave I amplitude in noise-exposed mice (n = 10) who exhibited temporary audiometric threshold shifts also found poorer hearing in noise (measured behaviourally using a priming paradigm) (Lobarinas et al., 2017). Post-noise-exposure reduced ABR wave I amplitude has been observed in guinea pigs as well (Furman et al., 2013). This study exposed nine guinea pigs to noise and compared them to 14 control guinea pigs, finding that noise exposure resulted in selective loss of low spontaneous rate auditory nerve fibres. Parthasarathy and Kujawa (2018) examined 58 mice over the course of 16 to 128 weeks of age (the typical lifespan of laboratory mice is 104–129 weeks). The environment for this longitudinal study was controlled to ensure the mice were never exposed to high levels of sound, thus removing the potential confound of noise exposure. The authors observed gradual synaptic loss with age that occurred before, and to a greater degree than, losses in inner and outer hair 4 cells. Also observed with increasing age was a decrease in ABR wave I amplitude to suprathreshold stimuli. The finding of a post-noise-exposure reduction in the amplitude of ABR wave I in response to suprathreshold stimuli has been replicated in several animal studies. This depression occurred despite recovery of audiometric thresholds and DPOAEs. Histologically, synaptic ribbon loss and loss of low spontaneous rate auditory nerve fibres has been observed following noise exposure. 1.2 Behavioural, physiological, and histological findings in humans 1.2.1 Medial olivocochlear reflex The medial olivocochlear reflex is part of an efferent system that involves projections from the superior olivary complex to the outer hair cells and which serves to modulate input from the cochlea to the auditory nerve fibres. In a study by Bhatt (2017) that included 26 young adults with normal audiometric thresholds (≤ 15 dB HL) at conventional frequencies (250 to 8000 Hz), higher noise exposure background was associated with a stronger medial olivocochlear reflex. This result was unexpected, and the researchers hypothesized that the stronger reflex may be a “top-down” temporary protective mechanism from noise and that this temporary excitotoxicity may potentially become permanent over time with continued noise exposure. Findings from Yeend et al. (2017), who examined 122 participants, were that those with lower medial olivocochlear reflex strength generally had the lower self-reported listening ability and that there was an association between a stronger medial olivocochlear reflex and better performance on temporal processing tone-detection-in-noise tasks. Most (71%) of the 5 participants in this study had normal hearing thresholds (≤ 20 dB HL) from 250 to 6000 Hz, and the proportion that did not have thresholds in this range had “near-normal” hearing sensitivity. The medial olivocochlear efferent system has been found to be stronger in individuals with musical training. Brashears et al. (2003) found stronger medial olivocochlear suppression in response to binaural stimulation in musicians (n = 29) compared to a control group (n = 28) that was matched for age and gender. Bidelman et al. (2017) found similar results, for both ipsilateral and contralateral suppression in musicians (n = 12) compared to non-musicians (n = 8). 1.2.2 Auditory brainstem response Some studies in humans have attempted to replicate the results found in noise-exposed animals. Using the Noise Exposure Questionnaire, Stamper and Johnson (2015) estimated the lifetime noise exposure of 30 participants who had normal (≤ 20 dB HL) audiometric thresholds in the conventional frequencies. They found an association between higher estimated noise exposure and lower ABR wave I amplitude to suprathreshold stimuli, and no correlation between reported noise exposure and DPOAE measures. Prendergast, Guest, et al. (2017) examined 123 young adults (aged 18–36) with varying histories of noise exposure and levels of musical experience and found no significant relation between lifetime noise exposure and amplitude of ABR wave I or of the frequency-following response. All of the participants had normal audiograms as defined by hearing thresholds of < 25 dB HL from 500 to 8000 Hz. Another study which examined four groups of young (aged 19–35) participants with normal (≤ 20 dB HL) audiometric thresholds in the conventional frequencies found that the groups with noise exposure history had reduced ABR wave I amplitude compared to the “less-noise” groups, even after accounting for differences in gender and DPOAEs (Bramhall et al., 2017). There were no systematic between-group differences for conventional and extended high-frequency audiometric 6 thresholds or DPOAE input/output functions. The four groups that the researchers divided the participants into were: veterans with a “significant history” of noise exposure (n = 16), veterans with “less noise exposure” (n = 13), non-veterans with “less noise exposure” (n = 23), and non-veterans with a history of firearm use (n = 23). A possible confounding factor is that the noise exposure of the participants was primarily related to firearms, so the results obtained could potentially be related to, specifically, impulse noise (as has been found in mice in Cho et al., 2013). Bramhall et al. (2015) studied 57 adults spanning a large age range (19–90 years), finding that reduced ABR wave I amplitude correlated with older age and decreased speech-in-noise task performance, with greater effect for individuals with worse pure-tone thresholds. ABR wave I amplitude was not correlated with speech recognition in quiet. Not all participants in this study had normal hearing thresholds; the inclusion criterion used by the authors was a pure-tone average at 500, 1000, 2000, and 4000 Hz of 45 dB HL or better. Schaette and McAlpine (2011) found that 15 participants with chronic tinnitus had a reduced ABR wave I amplitude to suprathreshold stimuli compared to measures from 18 control participants. All participants tested were female (in order to remove the confound of gender differences in the magnitude of auditory evoked potentials) and had normal hearing thresholds (≤ 20 dB HL) from 125 to 8000 Hz. 1.2.3 Other behavioural and electrophysiological tests Liberman et al. (2016) compared two groups of young people, one considered to be at high risk of noise exposure (n = 22) and the other considered to be at low risk (n = 12). Both groups had normal (< 20 dB HL) audiometric thresholds at conventional frequencies. The authors found that overall, the high-risk group had elevated audiometric thresholds in the extended high frequencies (above 8 kHz), self-reported greater difficulty hearing in noise, self- 7 reported greater annoyance by bothersome everyday sounds, higher summating potential/action potential (SP/AP) ratio, and poorer word-recognition performance in challenging conditions (one with ipsilaterally-presented noise and another with time-compressed, reverberant speech). No significant between-group differences in DPOAEs were found. Of note, this study did not use a validated noise exposure measure. Group membership was decided based on participants’ responses on a case history questionnaire, and only two of the questions on the form related directly to noise exposure. Kumar et al. (2012) compared the performance of a noise-exposed group (train drivers, n = 28) and a non-noise-exposed, age-matched control group (n = 90) on behavioural measures: temporal processing (gap detection in noise, amplitude modulation detection, duration pattern test) and speech recognition in multi-talker babble. All participants had normal hearing thresholds at conventional frequencies. They found overall poorer results for the noise-exposed group on the tasks tested. However, some subtests did not show a significant between-group difference, and the groups were not audiometrically matched, nor was the length of noise exposure (in years) accounted for. Another behavioural study by Prendergast, Millman, et al. (2017) examined 138 young adults (many of the same participants from the Prendergast, Guest, et al. (2017) electrophysiological study). No significant correlation was found between the history of noise exposure and performance on tasks tested (frequency and intensity difference limens, amplitude modulation detection, and the digit triplet test). The authors did find an association between increased noise exposure and worse audiometric thresholds at 16 kHz, but only for the female participants. Yeend et al. (2017) chose to study an age group (age 30–57) deemed to be “not-too-young” and “not-too-old.” They wanted to capture participants who had been exposed to noise over many years without introducing the potential audiometric and cognitive confounds 8 associated with old age. The authors found no correlation between participants’ history of noise exposure and their performance on processing tasks. Kujala et al. (2004) tested noise-exposed individuals on auditory discrimination tasks in quiet and in noise. The participants completed these tasks while playing a distracting video game. The noise-exposed group (n = 10) performed worse on the tasks compared to the control group (n = 10), and they also had a diminished mismatch negativity response (a neurological response to the detection of a deviant stimulus) on the left side of their scalp. No criteria for normal peripheral hearing sensitivity were specified for this study, but pure-tone thresholds for 125 to 8000 Hz were measured, and the authors noted no significant difference in hearing thresholds between the two groups. As part of the UBC Middle Ear Lab Hidden Hearing Loss project, Chang (2020) examined data from 18 music students and 19 control participants. She found that the music students had generally higher lifetime noise exposure (as estimated using the Noise Exposure Structured Interview [NESI]) but that the between-group difference was not significant. Tympanic membrane electrocochleography was measured in response to suprathreshold click stimuli. There were no between-group differences for electrocochleography measures (SP and AP amplitudes; SP/AP amplitude and area ratios) or association of these measures with the amount of noise exposure. Data from Monette (2020) for the same project found no between-group differences on the HINT or the random gap detection test for 20 music students and 22 control participants but did find higher estimated lifetime noise exposure (again measured using the NESI) for the music student group. Presbycusis is a term referring to hearing loss due to age, and one of the hypothetical causes of presbycusis proposed by Schuknecht (1993) was “neural presbycusis” resulting from 9 loss of spiral ganglion cells. As stated by Humes et al. (2012) in their review of behavioural measures of central presbycusis, it is difficult to disentangle factors associated with declining performance on processing tasks. Confounds include audiometric hearing loss and age-related cognitive decline. Overall, the review found negative effects of age reported in most studies that examined speech perception in noise, perception of degraded speech, and binaural perception. Significant negative effects of age were reported in studies that used tests (e.g. gap detection) involving nonspeech stimuli. Dubno et al. (1984) designed their study to isolate age effects from hearing sensitivity, dividing 72 participants into four groups of equal size: young (< 44 years old) with normal hearing, young with mild sensorineural hearing loss, older (> 65 years old) with normal hearing, and older with mild sensorineural hearing loss. Normal hearing was defined as thresholds < 15 dB HL from 250 to 4000 Hz, and thresholds < 20 dB HL at 6000 Hz. The individuals with mild hearing loss all had spondee recognition thresholds < 35 dB HL. All participants had excellent (> 90%) word recognition scores in quiet, and no history of noise exposure or otologic disease. The authors found that, independent of hearing sensitivity, the older groups showed worse performance, but only for tasks involving competing signals (i.e. background noise). Physiological measures in humans have yielded mixed results. Some studies have found differences in ABR for noise-exposed participants compared to control participants, while others have not. The medial olivocochlear reflex has been found to be stronger in people with musical training; a stronger medial olivocochlear reflex has also been associated with better performance on auditory processing tasks. Regarding behavioural measures in humans, some studies have found correlations between increased noise exposure and decreased performance on processing tasks, while others have not. Making generalizations is challenging due to the variety of tasks 10 and measures used; there are also confounds such as participant age and audiometric thresholds. A recent systematic review by Lokwani and Prabhu (2021) examined 14 studies of behavioural measures of cochlear synaptopathy in humans. They concluded that from the current literature, the measures with the most utility are extended high-frequency audiometry and speech and tone detection in noise. The use of amplitude modulation detection should be further investigated due to the few published studies so far showing mixed results. Currently, there is little evidence for the use of other behavioural measures in the studies included in this review, such as localization and cognitive tests. 1.2.4 Histological findings Makary et al. (2011) examined spiral ganglion cell counts in 100 human temporal bones from 100 individuals (aged 0 to 100 years). The specimens selected for the study had a normal population of outer and inner hair cells, and “no evidence of inner ear abnormality except that normally seen in aged ears.” The authors found a decrease in spiral ganglion cells associated with increasing age, at an average of approximately 1000 fewer cells per decade of life. Similar results were found in five temporal bones of adults (aged 54–89, with no history of otologic disease) examined by Viana et al. (2015), who also observed a decrease in peripheral axons associated with increasing age. Histological findings in humans have been limited; however, the available evidence suggests that reductions in cell populations are associated with older age. 1.3 Hidden hearing loss and central auditory processing disorder An alternative hypothesis regarding the origin of hidden hearing loss in humans proposes central auditory system dysfunction rather than cochlear synaptopathy (Musiek et al., 2018). Hidden hearing loss and central auditory processing disorder (CAPD) are both characterized by 11 “listening problems despite normal audiometry” (Hind et al., 2011). Many of the areas in which people who have central auditory processing disorder (CAPD) struggle overlap with difficulties reported by individuals considered to have hidden hearing loss: understanding speech in noise and/or with reverberation, localization, understanding rapid speech, and interpreting prosody (American Academy of Audiology, 2010). CAPD is defined as “deficits in the neural processing of auditory information in the central auditory nervous system, not due to higher-order language or cognition” (American Speech-Language and Hearing Association, n.d.). There are many potential causes of CAPD, such as degenerative neurological disease, neurotoxicity, and “diffuse central nervous system dysfunction” in the absence of identifiable lesions; aging and peripheral hearing loss can also affect central auditory processing (American Academy of Audiology, 2010). It is seen in both children and adults, and due to different diagnostic criteria, CAPD prevalence figures vary widely. Musiek et al. (2018) asserted that, based on the speech-in-noise difficulty, CAPD is “the most likely source” of hidden hearing loss. However, teasing apart the auditory modality from more broad areas of cognition, attention, language, and memory continues to be a challenge for researchers. Kamerer et al. (2019) suggested that cognitive measures should be included in investigations of peripheral synaptopathy in humans, as tests used in hidden hearing loss batteries may be influenced by cognition. They tested 32 adults aged 20–74 with hearing sensitivity ranging from within normal limits to moderate hearing loss (no thresholds worse than 65 dB HL from 250 to 8000 Hz). Their most notable finding was that memory capacity accounted for almost all of the variance in scores on the frequency modulation detection task. In the previously mentioned study by Yeend et al. (2017), the authors found a positive correlation 12 between performance for the speech-in-noise measures and performance on working memory and attention tasks. No published study thus far has used a CAPD test battery as a diagnostic for hidden hearing loss. Traumatic brain injury (TBI) can cause damage to bone, hemorrhage, edema, inflammation, and disconnection of axons (Taber et al., 2006), which has been proposed to cause central auditory system deficits. Many studies examining post-TBI auditory deficits have investigated veterans, particularly those who were exposed to service-related blasts. After conducting a retrospective chart review of 240 veterans who had suffered mild TBI, Oleksiak et al. (2012) found that 37 of the veterans had attended audiology visits, at which the most common complaint was tinnitus (reported by 73.7% of the participants). “Subclinical auditory dysfunction” (abnormal results on other tests [otoacoustic emissions, immittance, and/or central auditory processing] despite a normal audiogram) was found in 24.3% of the participants, and 16.2% were identified to have CAPD. Gallun et al. (2012) studied 36 blast-exposed veterans and found that deficits displayed on behavioural tasks appeared to be relatively specific. There was a much higher rate of abnormal performance on the Staggered Spondaic Words test than on the dichotic digits test, despite their both being dichotic tasks with short (one- or two-syllable) stimuli. Overall, the group of veterans had poorer results on temporal resolution, binaural processing, and speech-in-noise measures compared to a control group (n = 29) that was matched by age, gender, and audiometric configuration. This study included participants with hearing loss (up to 50 dB HL pure-tone average, 500, 1000, and 2000 Hz). Potential confounds were that the veterans all had major non-head injuries resulting from the blasts, and that many of them were taking a variety of medications. Although the two participant groups were matched by audiometric configuration, the mean pure-tone thresholds of the control group were better than 13 those of the blast-exposed group. The blast-exposed veterans (n = 99) studied by Saunders et al. (2015) also showed an overall pattern of specific rather than global central deficits. Since the participants were part of a rehabilitation randomized control trial, there was no control group, and no baseline measures were possible as this study was retrospective in nature. Any potential participants who had a history of language or learning difficulties were excluded, as well as anyone who did not meet the criteria for “normal hearing” as defined by the researchers (thresholds of < 40 dB HL at 500, 1000, 2000, 3000, and 4000 Hz in both ears; thresholds < 30 dB HL at three or more of those frequencies). The authors found that the majority of participants exhibited deficits on central auditory processing measures (speech-in-noise, binaural processing, temporal resolution, speech segregation) in conjunction with normal findings on measures of working memory, cognition, and attention. Brattico et al., (2005) looked at the mismatch negativity response in 10 men who had been exposed to continuous (not impulsive, as was the case for the blast-exposed veterans) noise for at least five years, finding that the responses tended to be slower compared to those of an audiometrically matched control group (n = 10). The noise-exposed group also had a different pattern of hemispheric lateralization in the brain in response to the speech, and a smaller amplitude at two electrode sites. Central auditory processing disorder and hidden hearing loss present with similar symptoms of difficulty understanding speech in challenging situations. There are many potential causes of CAPD. When examining individuals with suspected CAPD, it is often difficult to separate general abilities such as cognition from auditory-specific skills. Some researchers have proposed a potential association between better auditory processing and better working memory 14 and attention, while others suggest central auditory processing skills are largely independent from general abilities. 1.4 Musicians and central auditory processing Some of these areas of central auditory processing have been studied in musicians; several studies have hypothesized that musical training enhances aspects of higher-level auditory abilities. Rammsayer and Altenmüller (2006) found enhanced performance on temporal processing tasks for those with musical training (n = 36) compared to a non-musical control group of equal size. Interestingly, the advantage they found was only apparent for the temporal tasks that involved “online” processing, and not for the task that required reference to memory. Overall, participants in the Yeend et al. (2017) study who had received musical training performed better than the non-musical participants on a temporal fine structure task and an amplitude modulation detection task but did not show an advantage for the speech-in-noise measures. Monette (2020) found comparable performance overall between a control group (n = 18) and a musician group (n = 19) on a gap detection task, with two musicians having a gap detection threshold of 2 milliseconds, and all other participants having a gap detection threshold of 4 milliseconds. Parbery-Clark, Skoe, Lam, et al. (2009) found that musicians (n = 16) performed better than non-musicians (n = 15) on two speech-in-noise tests (the quick speech–in–noise test [QuickSIN] and the hearing–in–noise test [HINT]). The between-group difference for HINT performance was small and only applicable for the test condition in which the signal and noise were not spatially separated. Performance on the QuickSIN was correlated with performance on a working memory task (on which the musicians also scored higher). A parallel study found ABRs of greater amplitude and shorter onset timing in musicians (n = 16) compared to non- 15 musicians (n = 15) in a background noise condition, as well as that ABRs of greater amplitude correlated with higher HINT scores (Parbery-Clark, Skoe, & Kraus, 2009). However, a more recent study by Zhang et al. (2019), which examined a similar sample size and participants of a similar age to the Parbery-Clark et al. studies, found no significant difference between musicians’ (n = 17) and non-musicians’ (n = 17) QuickSIN scores. Zhang et al. (2019) also examined the binaural masking level difference, finding that musicians performed better than non-musicians in the more difficult condition (where signal and noise are in phase with each other). Monette (2020) used a modified HINT that included four different conditions which varied in noise location (front or side) and in whether interaural time difference and interaural level difference cues were present. Participants performed worse in the noise-front condition than in the other conditions, consistent with findings in previous research that spatial separation of speech and noise aids in perception of the target speech. There was no performance difference between the control group (n = 22) and the musician group (n = 20). Trained musicians may have an advantage on central auditory processing tasks such as temporal and speech-in-noise measures, and this advantage may be task-dependent. However, findings must be interpreted with caution, as studies investigating the effects of musical training are plagued with methodological challenges. In a recent systematic review, McKay (2021) discussed researchers’ lack of acknowledgement of potential biases (e.g. expectation of a positive outcome from experimenters and/or participants) and the difficulty of controlling participant variables. Parsing out whether advantages or benefits seen are truly caused by musical training (rather than a result of innate ability) also continues to be a challenge. The systematic review examined 13 studies on speech perception in hearing-impaired listeners, and 16 concluded that there was no evidence to support a causal effect of musical training on speech perception in such listeners. 1.5 Tests of central auditory processing Central auditory processing should be assessed using a test battery approach, as auditory processing involves many distinct skills, and the goal of assessment is to identify and quantify deficits in any area (Baran, 2014). To that end, an appropriate test battery should “describe [an] individual’s functional difficulties related to auditory dysfunction”; the subtests included should have good sensitivity, specificity, and efficiency, as well as clear pass/fail criteria (Canadian Interorganizational Steering Group for Speech-Language Pathology and Audiology, 2012). CAPD tests may be behavioural, electroacoustic (e.g. acoustic reflex thresholds), or electrophysiological (e.g. middle latency response) (Schochat et al., 2014). For a diagnosis of CAPD, the American Speech-Language and Hearing Association (2005) has specified the criterion of scoring two standard deviations or more below the mean on at least two different behavioural central auditory tests. 1.5.1 The Multiple Auditory Processing Assessment The Multiple Auditory Processing Assessment (MAPA) is a test battery for central auditory processing. The most recent version, the MAPA-2, includes six subtests in three areas: 1) monaural (monaural selective auditory attention test, speech-in-noise test for children and adults), 2) temporal (tap test, pitch pattern test), and 3) binaural (dichotic digits, competing sentences). There are also two supplemental tests in the temporal domain: duration pattern test and gap detection test. According to the American Speech-Language and Hearing Association (1996), CAPD should be assessed using tests in five areas: temporal processes, localization and lateralization, 17 low-redundancy monaural speech, dichotic stimuli, and binaural interaction. The MAPA was chosen as the measure of central auditory function in the present study because it covers at least three of these areas, while another CAPD battery, the SCAN, only covers two (Domitz & Schow, 2000). As well, based on its poor test-retest reliability, the SCAN is not an adequate battery to be used in isolation to evaluate central auditory processing (Lampe, 2011). The MAPA also has higher sensitivity than the SCAN, with increasing sensitivity if multiple subtests are performed (Domitz & Schow, 2000). Schow et al. (2020) conducted a study on the validity of the MAPA-2, calculating a sensitivity of 89% and a specificity of 82% based on 643 children, 18 of which had been “independently formally identified” as having auditory processing disorder. The authors also noted that the assessment scores showed an acceptable amount of spread and avoided a ceiling effect. 1.6 Purpose of study This study intends to build upon previous research in the areas of 1) noise-induced hidden hearing loss, and 2) musician enhancement of auditory abilities. It investigates the use of the Multiple Auditory Processing Assessment as a potential diagnostic tool to quantify hidden hearing loss, as well as whether reported lifetime noise exposure history is correlated with performance on central auditory processing tasks. As previous research has found evidence of noise-exposed individuals having depressed temporal processing skills and diminished ability to perform in situations with competing signals, it is expected that a negative correlation will be found between amount of noise exposure and scores on the MAPA. However, the strength of this correlation for the group of participants considered to be at high risk of noise exposure (music students) may be offset by the effects of musical training. Studies such as Rodrigues et al. (2014) have found that classical musicians are 18 likely regularly exposed to excessive noise. Although the music students have a history of noise exposure and the control group participants do not, they also have a history of and are continuing to receive musical training. It is hypothesized that any deficits in central auditory processing in this group that is at high risk for noise exposure would be offset by musical training effects. Due to the young age of the participants, the noise-induced deficits may not be pronounced enough to show a between-group difference, as with the data analyzed by Chang (2020) and Monette (2020). Similar to the findings of Prendergast, Guest, et al. (2017), the cumulative noise exposure time of these young participants may not be enough to depress central auditory processing abilities at the time of assessment. The results of this study contribute to a normative adult data pool for the MAPA-2. Previous research on the MAPA has focused on children (Domitz & Schow, 2000; Idaho State University, n.d.; Schow et al., 2020), and its utility as a measure of central auditory processing skills can be further expanded with the collection of data in adults. 19 Chapter 2: Methods 2.1 Participants Participants for the study comprised three groups: a control group, a group considered to be at high risk for noise exposure (music students), and a symptomatic group. Members of the first two groups were students aged 18–28 from the University of British Columbia who were compensated with an honorarium for their time. Participants in the symptomatic group were recruited primarily through a local clinic, and also through word-of-mouth. All participants provided informed consent. The study procedures received ethics approval as part of a project in the UBC Middle Ear Lab. Both the control group and high-risk group participants satisfied these inclusion criteria: 1. Normal hearing as defined by pure-tone thresholds of 25 dB HL or better at 250–8000 Hz. These are the frequencies used in the conventional pure-tone audiometric assessment. 2. No active middle ear involvement, to remove a possible confound. 3. No asymmetry in hearing thresholds of more than 15 dB HL at each frequency (250–8000 Hz) between the left and the right ears, since the Multiple Auditory Processing Assessment includes dichotic stimuli. 4. Fluency in English, as the instructions and verbal stimuli in the Multiple Auditory Processing Assessment, are in English. 2.1.2 High-risk (musician) group The high-risk group (n = 20) consisted of music students from the University of British Columbia School of Music, recruited through faculty advertisement. They had a variety of musical backgrounds (instruments played, years of experience, amount of time spent in noisy 20 musical environments). The primary instruments of the musicians were: voice (n = 6), trombone (n = 3), piano (n = 3), oboe (n = 2), trumpet (n = 2), horn (n = 1), flute (n = 1), clarinet (n = 1), and viola (n = 1). Of the 20 musicians, none reported significant hearing difficulties. Seventeen of the musicians denied experiencing tinnitus, and three reported bilateral tinnitus. 2.1.3 Control group The control group (n = 24) comprised students from the University of British Columbia, recruited through an advertisement on the UBC research website. Additional inclusion criteria for this group was used to help remove possible confounds: 1. No asymmetry in hearing thresholds of more than 15 dB HL at each frequency (9–16 kHz) between the left and the right ears. 2. No history of loud noise exposure. 3. No history of ear surgery or ear injury. 4. No recent ear infections. 5. No history of head trauma or concussions. 6. No history of tobacco use. 7. No personal listening device use at more than 60% of the maximum volume. 2.1.4 Symptomatic group The symptomatic group was made up of individuals (n = 11) who reported difficulties in challenging listening environments. These participants ranged in age from 22–55. At the time of referral, all of the participants in this group had thresholds of 25 dB HL or better from 250–8000 Hz. When tested as part of this project at the UBC Middle Ear Lab, five ears from this group had thresholds that fell outside of this range, which were likely due to differences in equipment and calibration between the Middle Ear Lab and the local clinic where most of the participants were 21 initially tested. Table 2.1 lists the thresholds obtained at the Middle Ear Lab that were greater than 25 dB HL at the conventional audiometric frequencies (250–8000 Hz). Participant and ear Thresholds > 25 dB HL when tested at the UBC Middle Ear Lab Participant 1, left ear 27 dB HL at 500 and 750 Hz Participant 2, right ear 38 dB HL at 4000 Hz, 30 dB HL at 6000 Hz Participant 2, left ear 26 dB HL at 500 Hz, 31 dB HL at 4000 Hz, 31 dB HL at 6000 Hz, 30 dB HL at 8000 Hz Participant 3, right ear 39 dB HL at 8000 Hz Participant 3, left ear 33 dB HL at 8000 Hz Table 2.1 Symptomatic participants with thresholds > 25 dB HL when tested at the UBC Middle Ear Lab. Both the Middle Ear Lab and the clinic used the automated Bekesy method of obtaining thresholds and the Otometrics Madsen Astera2 audiometer; however, the calibration of each audiometer is individualized and may have contributed to obtaining different dB HL thresholds. Another equipment difference was the model of headphone used. Both locations used circumaural Sennheiser headphones, but the Middle Ear Lab used the HDA 200 model and the clinic used the HDA 300 model. These differences are not considered to be a problem for the inclusion criteria or an impediment to data analysis, as all threshold data was converted from dB 22 HL to dB SPL with audiometer-specific reference equivalent threshold sound pressure level (RETSPL) conversions. 2.1.5 Test battery 2.1.5.1 Case history All participants filled out a case history form which included demographic information, questions regarding the inclusion criteria, and a query about situations in which they had difficulty hearing (if any). 2.1.5.2 Audiometry The automatic and adaptive Bekesy method (with pulsed tones in 1 dB steps) was used to obtain pure-tone thresholds at 250 Hz, 500 Hz, 750 Hz, 1000 Hz, 1500 Hz, 2000 Hz, 3000 Hz, 4000 Hz, 6000 Hz, 8000 Hz, 9000 Hz, 10000 Hz, 11200 Hz, 12500 Hz, 14000 Hz, and 16000 Hz. Audiometry was conducted in a sound-treated booth using Sennheiser HDA 200 headphones connected to a Otometrics Astera2 audiometer. Thresholds for each frequency were converted from dB HL to dB SPL with RETSPLs that were obtained using: a Larson Davis PRM902 preamplifier, a Larson Davis AEC101 artificial ear, a Larson David one-half inch microphone Model 2559, a flat plate attachment, and a 3.2 lb (1.5 kg) weight (Chang, 2020). 2.1.5.3 Multiple Auditory Processing Assessment The most recent technical report from the American Speech-Language and Hearing Association emphasizes the need for central auditory processing test batteries to be individualized based on a client’s referring complaint(s), and for test results to be viewed holistically with other relevant information such as case history (American Speech-Language and Hearing Association, 2005). As participants in the present study did not necessarily present with complaints, a pre-packaged test battery, the MAPA, was chosen. Also mentioned by the 23 American Speech-Language and Hearing Association are other important characteristics of a test battery, which include but are not limited to: good reliability and validity with high sensitivity, specificity, and efficiency; measures that examine different central processes; and inclusion of both verbal and non-verbal stimuli. The MAPA fulfills these criteria, and as mentioned previously, has higher sensitivity than another commonly administered CAPD battery, the SCAN (Domitz & Schow, 2000). Normative data for children aged 7–14, which were based on testing 748 American children, are described in the MAPA-2 manual (Idaho State University, n.d.). To facilitate comparison with age-based norms, tables are provided in the MAPA-2 manual for conversion of raw subtest scores to scaled and standard scores (Idaho State University, n.d.). The subtest scaled scores are based on a population distribution having a mean and median of 10 and a standard deviation of 3, and the standard scores are based on a population distribution having a mean and median of 100 and a standard deviation of 3. The sum of scaled scores from each domain (monaural, temporal, binaural) can be converted into a standard domain score; standard domain scores can then be added together to obtain an overall standard score. Domain standard scores can only be interpreted if all subtests within the domain were completed, and the overall standard score can only be interpreted if all subtests in the MAPA were completed. All participants underwent the Multiple Auditory Processing Assessment. Participants were seated in a sound-treated booth, and the stimuli were delivered through the same headphones and audiometer used to collect hearing thresholds. Biological calibration was conducted for both audiometer channels before each administration of the MAPA. Testing was 24 conducted at each participant’s most comfortable level, or 55 dB HL if the participant’s most comfortable level was below 55 dB HL. Details of the MAPA-2 subtests are below: 1. Monaural: a. The Monaural Selective Auditory Attention Test (MSAAT) examines monaural separation and closure. It includes two conditions: “non-competing” (monaural word recognition in quiet) and “competing” (monaural word recognition in high-interest noise, which consists of speech from the same talker, at a signal-to-noise ratio of 0 dB SNR). b. The Speech in Noise for Children and Adults (SINCA) test measures monaural word recognition in noise (four-speaker babble) at decreasing signal-to-noise ratios (from +16 dB SNR to 0 dB SNR). 2. Temporal: a. The Tap Test assesses temporal resolution, and consists of binaural presentation of a series of taps, after which the participant must indicate the number of taps heard. b. The Pitch Pattern Test is a measure of pattern recognition that comprises a series of four tones, presented binaurally, that may be high or low in pitch. After each series the participant must identify the pattern of pitches (e.g. high-low-high-high) heard. 3. Binaural: a. The Dichotic Digits task tests binaural integration, attention and auditory memory. It involves dichotic presentation of three numbers in series to one ear and a simultaneous series of three numbers to the other ear. The participant must 25 attempt to repeat the numbers heard. Refer to Figure 2.1 for an illustration of the test paradigm. Figure 2.1 Illustration of the dichotic digits test paradigm. Numbers are listed below the ear to which they are presented, and numbers of the same colour denote simultaneous presentation. i. The Diotic Digits test is included as a supplement to the Dichotic Digits. Similar to the Dichotic Digits, it also involves presentation of six numbers; however, in the diotic condition, both sets of numbers are RIGHT EAR LEFT EAR 5 2 3 6 8 1 26 delivered to both ears. Refer to Figure 2.2 for an illustration of the test paradigm. Figure 2.2 Illustration of the diotic digits test paradigm. Numbers are listed below the ear to which they are presented, and numbers of the same colour denote simultaneous presentation. b. The Competing Sentences test examines binaural separation and binaural integration. It is also dichotic, with the stimuli consisting of two different sentences (one in each ear). The participant must attempt to repeat both sentences heard. For this study, the tap test was not administered. The decision to exclude the tap test from the battery was made on the basis of results from the lab’s pilot study which found that the tap test may have a ceiling effect in adults. The two supplemental tests on the MAPA-2 (duration pattern test and gap detection test) were also omitted as there were other temporal measures RIGHT EAR LEFT EAR 6 8 1 6 8 1 5 2 3 5 2 3 27 included in the hidden hearing loss project test battery (temporal modulation transfer function and random gap detection test). 2.1.5.4 Noise Exposure Structured Interview The noise exposure history measure used was the NESI, a tool that was designed to be comprehensive, clear, and flexible to represent each individual’s lifetime noise exposure (Guest et al., 2018). It estimates the duration of each type of noise exposure (recreational, occupational, and firearm) and the sound level for each exposure event. The NESI takes into account the use of hearing protection devices and has clear administration and scoring instructions, allowing it to be used in a consistent manner. Scoring an individual’s NESI responses results in a total of “noise exposure units” which may be used for comparison across participants. 2.1.5.5 Other tests In addition to completing the MAPA-2 test battery, participants also underwent otoscopic examination and provided data for measures which included: Tinnitus Handicap Inventory, NESI, modified HINT (see Section 1.4 for details), temporal modulation transfer function, triple digits in noise, random gap detection test, loudness scaling, threshold equalizing noise test, wideband tympanometry, acoustic reflexes, DPOAEs, contralateral suppression of otoacoustic emissions, and electrocochleography (Chang, 2020). 2.2 Data analysis The statistical analysis used a Bayesian approach to estimate the differences in performance on various MAPA tests between the control, musician, and symptomatic groups. Logistic regression models were used to estimate the relationship between the outcomes of MAPA components and noise exposure history. The Bayesian approach takes prior knowledge and reallocates credibility for a given explanation by incorporating new data. This approach is 28 appropriate for data involving small sample sizes and large variance, as it can employ models with likelihoods that naturally accommodate outliers, such as the “Student t” distribution (Lange et al., 1989). Bayesian analysis is unaffected by sampling intentions and allows for the addition of new data as they are acquired (Kruschke, 2013). Bayesian parameter estimation takes into account prior knowledge, and by allocating a low probability mass to unrealistically high or low parameter values, allows for a more conservative approach to analysis of small data sets. Logistic regression models with varying effects (also known as “Generalized Linear Mixed Effects” models) were used with MAPA test results as the outcomes, and group (control, musician, symptomatic) and either ear or condition (diotic versus dichotic) as predictors. Participants, group, and ear/condition were modeled as varying intercepts within these models, using adaptive priors estimated from the data. The models used a binomial likelihood that is appropriate for the analysis of binary data (i.e. the “correct/incorrect” dichotomy of each score in the MAPA test battery). Using a binomial likelihood, the parameter p estimated the log-odds of the outcome that consisted of a correct or wrong response for each trial. Partial pooling was used to improve the estimates through the phenomenon of “shrinkage,” based on which estimates for cells that had more extreme values (that is, very high or low proportion of correct responses) were adjusted by the model to be closer to the means of the parameters included in the model. Shrinkage typically results in more accurate estimates of the unknown population values (e.g. McElreath, 2020). The priors for the group, ear/condition, and participant varying effects were normally distributed with a mean of 0 and a standard deviation that was estimated from the data as hyperpriors. Hyperpriors for standard deviation parameters had an exponential distribution with rate parameter equal to 1. The posterior was estimated using the Stan statistical language for the 29 R software. Stan uses a Hamiltonian Monte Carlo sampling procedure (Carpenter et al., 2017). Four chains of 6000 samples each (4000 for warmup) were generated for each model. Traceplots were visually inspected to ensure the sampling process converged for all of the models. Rhat4, another convergence diagnostic, was equal to 1 for all parameters of all models, as expected when the sampling process converges. The posterior was extracted, and estimates were calculated in log-odds and on the scale of the outcome variable (proportions of correct responses) for the various predictors (group, ear/condition, and group by ear/condition interactions). The 89% Highest Posterior Density Intervals were calculated, and are denoted in the results with square brackets. For each measure analyzed, the numbers of effective samples for the group or group by ear conditions and for the sigma (standard deviation) parameters were over 1000. 30 Chapter 3: Results 3.1 Multiple Auditory Processing Assessment Analysis was performed on subtests of the MAPA by group (control, musician, symptomatic) and by ear (left, right). 3.1.1 Monaural Selective Auditory Attention Test 3.1.1.1 Non-competing condition The majority of participants obtained the maximum possible score (all five items per ear correct) for the non-competing condition (monaural word recognition in quiet). Only five ears (one from the control group, two from the musician group, and two from the symptomatic group) out of the 109 total ears tested had a score below the maximum possible, and all five of those ears scored four out of five items correct. Due to the relative uniformity of scores on this portion of the MSAAT, statistical analysis was not completed. 3.1.1.2 Competing condition The competing condition (monaural word recognition in noise at 0 dB SNR) consisted of 25 items per ear. Figure 3.1 shows individual scores plotted by group and ear. 31 Figure 3.1 Plot of individual scores on the MSAAT competing condition by group and ear. The red dashed line denotes the maximum score. As illustrated in the plot, most of the group and ear comparisons yielded overlap. Only two comparisons showed differences in the mean of the scores. Between the musicians and the symptomatic individuals, the marginal posterior of the difference of the mean in the scores was 6.18% [0.36%, 11.82%] in proportion correct (i.e. the musicians scored higher). The effect size of the difference was 1.32 [0.99, 1.63] and the probability of this increase in odds was 96%. Between the control participants and the symptomatic participants, the marginal posterior of the difference of the mean in the scores for only the right ear was 7.95% [1.11%, 14.95%] in 32 proportion correct (i.e. the controls scored higher). The effect size of the difference was 1.42 [1.02, 1.83] and the probability of this increase in odds was 97%. 3.1.2 Speech in Noise for Children and Adults The SINCA, another monaural word recognition in noise measure (at SNRs varying from +16 to 0) consisted of 20 items per ear. Figure 3.2 shows individual scores plotted by group and ear. Figure 3.2 Plot of individual scores on the SINCA by group and ear. The red dashed line denotes the maximum score. Two group comparisons showed a difference in score means. Between the control and the symptomatic groups, the marginal posterior of the difference of the mean in the scores was 4.64% [0%, 0.1%] in proportion correct (i.e. the controls scored higher). Despite this uncertainty interval being narrow and very close to 0, the effect size of the difference was 1.22 [0.98, 1.46] and the probability of this increase in odds was 94%. When considering the left ear only, the 33 marginal posterior of the difference of the mean in scores was 5.99% [0.46%, 11.73%] in proportion correct between the controls and the musicians (i.e. the controls scored higher). The effect size of the difference was 1.29 [0.98, 1.56] and the probability of this increase in odds was 95%. Strong ear effects were indicated by the model. Among all the participants, the marginal posterior of the difference of the mean in scores was -7.64% [-11.26%, -4.17%] in proportion correct between the left and the right ears (i.e. the right ears scored higher). The effect size of the difference was 0.73 [0.62, 0.84]; a decrease in odds of the difference between left and right ears with a high degree of certainty. When analyzed by group, both the musician and the symptomatic groups also showed a negative marginal posterior of the difference of the mean in scores between the left and the right ears (i.e. the right ears scored higher) and a decrease in odds from left to right ears with a high degree of certainty. Between the left and right musicians’ ears, the marginal posterior of the difference of the mean in scores was -10.53% [-16.21%, -5.24%] in proportion correct and the effect size was 0.65 [0.5, 0.79]. Between the left and right symptomatics’ ears, the marginal posterior of the difference of the mean in scores was -8.92% [-16.38%, -1.63%] in proportion correct and the effect size was 0.7 [0.49, 0.92]. The control group by itself did not show an ear advantage. 3.1.3 Pitch pattern test The pitch pattern test consisted of 20 binaural presentations of four tones each. Each tone was either high or low in pitch, and participants were instructed to repeat the pitch pattern of each set of tones. A response was counted as correct only if the participant accurately named the 34 entire four-tone string. That is, there were no partial points awarded, and the test was scored out of 20. Table 3.1 summarizes participants’ scores on the pitch pattern test. A large number of participants had perfect or near-perfect scores on this measure. Due to this ceiling effect and the overall small sample size, further analysis was not conducted on the pitch pattern test results. These high scores are consistent with the normative data collected by the MAPA battery developers, particularly the difficulty score that was computed based on the 748 children who completed the test battery. The difficulty scores were calculated based on the proportion of correct answers, and the pitch pattern test had the second-highest score (i.e. was the second easiest) after the tap test. 3.1.4 Dichotic digits test The “dichotic digits” portion of the MAPA battery comprised a dichotic digits test and a supplemental diotic digits test. Administering both allowed for individual comparisons between Number of participants who scored 20/20 Non-perfect scores Controls (n = 24) 16 14, 15, 16, 18 (n = 2), 19 (n = 3) Musicians (n = 20) 18 18, 19 Symptomatics (n = 11) 5 5, 12, 13 (n = 2), 15, 17 Table 3.1 Summary of scores on the pitch pattern test by group. 35 the dichotic and diotic scores for each participant and group comparisons of the dichotic versus diotic conditions. 3.1.4.1 Dichotic digits The dichotic digits test was made up of 20 sets of six numbers each. For scoring, each digit was marked as correct or incorrect, for a total score out of 120 (20 sets of six numbers per set). Figure 3.3 shows individual scores plotted by the groups. Figure 3.3 Plot of individual scores on the dichotic digits test by group. The red dashed line denotes the maximum score. Two group comparisons showed a likely difference in score means. Between the controls and symptomatics, the marginal posterior of the difference of the mean in the scores was 5.53% [-1.25%, 12.59%] in proportion correct (i.e. controls scored higher). The effect size of the difference was 1.57 [0.79, 2.25] and the probability of this increase in odds was 94%. Between the musicians and symptomatics, the marginal posterior of the difference of the mean in the 36 scores was 5.07% [-1.98%, 12.12%] in proportion correct (i.e. musicians scored higher). The effect size of the difference was 1.52 [0.77, 2.21] and the probability of this increase in odds was 88%. Overall, it is more likely than not that both the control group and the musician group performed better than the symptomatic group on this measure, but there was a large amount of uncertainty in the estimate of the differences between groups. 3.1.4.2 Diotic digits The supplemental diotic digits test also included 20 sets of six numbers each. As with the dichotic test, each digit was marked as correct or incorrect, for a total score out of 120. Figure 3.4 shows individual scores plotted by the groups. Figure 3.4 Plot of individual scores on the diotic digits test by group. The red dashed line denotes the maximum score. There was a high probability that musicians performed better than the other groups on this task. Between the controls and musicians, the marginal posterior of the difference of the 37 mean in the scores was -9.43% [-14.16%, -4.45%] in proportion correct (i.e. musicians scored higher). The effect size of the difference was 0.61 [0.46, 0.78] and the probability of this decrease in odds was 99.8%. Between the musician and symptomatic groups, the marginal posterior of the difference of the mean in the scores was 15.32% [8.9%, 21.86%] in proportion correct (i.e. musicians scored higher). The effect size of the difference was 2.21 [1.51, 2.86] with a 100% probability of the increase in odds. The model also suggested a difference between the control and symptomatic groups, albeit one that was smaller and less certain: the marginal posterior of the difference of the mean in the scores was 5.89% [-0.8%, 12.66%] in proportion 38 correct (i.e. controls scored higher). The effect size of the difference was 1.33 [0.92, 1.71] and the probability of this increase in odds was 92%. 3.1.5 Competing Sentences Competing Sentences was a dichotic test, with two sentences (one in each ear) presented simultaneously for a total of 30 sentences. Figure 3.5 shows individual scores plotted by group and ear. Figure 3.5 Plot of individual scores on the Competing Sentences by group and ear. The red dashed line denotes the maximum score. Group comparisons for Competing Sentences scores revealed some differences. Between the control and symptomatic groups, the marginal posterior of the difference of the mean in the scores was 13.59% [-0.5%, 27.9%] in proportion correct (i.e. controls scored higher). The effect size of the difference was 2 [0.81, 3.14] and the probability of this increase in odds was 94%. Between the control and symptomatic groups, for the right ear only, the marginal posterior of the 39 difference of the mean in the scores was 17.39% [4.05%, 29.12%] in proportion correct (i.e. controls scored higher) and the effect size of the difference was 2.97 [1.07, 4.71] with a 99% probability of this increase in odds. Between the musician and symptomatic groups, the marginal posterior of the difference of the mean in the scores was 17.69% [3.33%, 31.94%] in proportion correct (i.e. musicians scored higher), and the effect size of the difference was 2.51 [0.95, 3.94] with a 98% probability of this increase in odds. Between the musician and symptomatic groups, for the left ear only, the marginal posterior of the difference of the mean in the scores was 15.75% [-0.85%, 31.55%] in proportion correct (i.e. musicians scored higher). The effect size of the difference was 2.13 [0.76, 3.36] with a 93% probability of this increase in odds. For the participants overall and within each group, the model indicated strong ear effects with a high degree of certainty. Between left and right ears overall, the marginal posterior of the difference of the mean in the scores was -23.26% [-27.16%, -19.93%] in proportion correct (i.e. right ears scored higher) and the effect size of the difference was 0.32 [0.28, 0.37]. For the control group, the marginal posterior of the difference of the mean in the scores between the two ears was -29.61% [-35.65%, -23.97%] in proportion correct (i.e. right ears scored higher) and the effect size of the difference was 0.22 [0.17, 0.26]. Between the musicians’ left and right ears, the marginal posterior of the difference of the mean in the scores was -19.5% [-24.73%, -13.63%] in proportion correct (i.e. right ears scored higher) and the effect size of the difference was 0.33 [0.25, 0.41]. The marginal posterior of the difference of the mean in the scores between ears for 40 the symptomatic group was -17.78% [-25.04%, -10.82%] in proportion correct (i.e. right ears scored higher) and the effect size was 0.47 [0.33, 0.61]. 3.2 Noise Exposure Structured Interview As NESI scores (in noise exposure units) were highly variable, they were converted to log scores to aid in data display. Figure 3.6 shows individual log(NESI) scores plotted by the groups. Figure 3.6 Plot of individual log(NESI) scores by group. The red dashed line denotes the mean score. The estimated difference in the mean scores of the musician group and the control group was 1.63 [0.68, 2.53] (i.e. musicians had higher NESI scores), and the average effect size was 1.22 [0.43, 1.94]. There was a 95% probability of at least a small effect size. The model was uncertain about a difference in the size of the standard deviations between the two groups; the mean difference of the standard deviations was -0.39 [-1.05, 0.29]. 41 The model was uncertain about a difference in the mean scores of the symptomatic group and the control group; the estimated difference was 0.54 [-0.73, 1.65]. A difference in the size of the standard deviations between these two groups was also uncertain; the mean difference of the standard deviations was 0.31 [-0.77, 1.37]. At 0.32 [-0.32, 1.09], the effect size was small and uncertain. The estimated difference in the mean scores of the musician group and the symptomatic group was 1.09 [-0.11, 2.15] (i.e. musicians likely had higher NESI scores), and the average effect size was 0.74 [-0.07, 1.52]. There was a 95% probability of at least a small effect size. The model was uncertain about a difference in the size of the standard deviations between the two groups; the mean difference of the standard deviations was -0.7 [-1.84, 0.3]. 3.3 Predictors of MAPA scores Logistic regression models were run to determine whether scores on the Noise Exposure Structured Interview and membership in the control, musician, or symptomatic group predicted performance on subtests of the MAPA. The subtests chosen for analysis were selected to be representative of the variety of central processes tested in the MAPA, including monaural speech-in-noise, binaural separation, and binaural integration. The specific conditions were chosen due to larger variability in scores (e.g. the score distribution for MSAAT competing, left ear was more spread compared to the score distribution for MSAAT competing, right ear). 3.3.1 Monaural Selective Auditory Attention Test, left ear Figure 3.7 shows individual MSAAT competing scores plotted by the groups for the left ear only. Figure 3.8, Figure 3.9, and Figure 3.10 show predicted regression lines for scores on the NESI versus scores on the MSAAT competing condition, left ear. 42 Figure 3.7 Plot of individual scores on the MSAAT competing condition by group for the left ear only. The red dashed line denotes the maximum score. Figure 3.8 Predicted regression line for the control group, plotted for NESI scores versus left-ear competing MSAAT scores. 43 Figure 3.9 Predicted regression line for the musician group, plotted for NESI scores versus left-ear competing MSAAT scores. Figure 3.10 Predicted regression line for the symptomatic group, plotted for NESI scores versus left-ear competing MSAAT scores. 44 The slopes of the regression lines are close to 0 and similar for all groups, indicating that NESI scores are not a strong predictor of MSAAT (left-ear competing condition) scores for any of the groups. Figure 3.11 plots the slope difference in proportion units between the two most different groups, the musician and the symptomatic groups. Between these two groups, the marginal posterior of the difference of the mean in the slopes was -0.37% [-0.34%, 2.5%] (i.e. the slope was larger for the symptomatic group) and the probability of this difference was 41%. Figure 3.11 Plot of the NESI versus MSAAT (left-ear competing condition) slope difference between the musician and symptomatic groups. The blue dashed line denotes the slope of the musician group, the orange dashed line denotes the slope of the symptomatic group, and the black line denotes the difference in slope between the two groups. The red dashed line denotes a slope of 0. 3.3.2 Competing Sentences, left ear Figure 3.12 shows individual Competing Sentences scores plotted by group for the left ear only. Figure 3.13, Figure 3.14, and Figure 3.15 show predicted regression lines for scores on the NESI versus scores on the Competing Sentences, left ear. 45 Figure 3.12 Plot of individual scores on the Competing Sentences by group for the left ear only. The red dashed line denotes the maximum score. Figure 3.13 Predicted regression line for the control group, plotted for NESI scores versus left-ear Competing Sentences scores. 46 Figure 3.14 Predicted regression line for the musician group, plotted for NESI scores versus left-ear Competing Sentences scores. Figure 3.15 Predicted regression line for the symptomatic group, plotted for NESI scores versus left-ear Competing Sentences scores. 47 The slopes of the regression lines are close to 0 and similar for all groups, indicating that NESI scores are not a strong predictor of Competing Sentences left-ear scores for any of the groups. Figure 3.16 plots the slope difference in proportion units between the two most different groups, the control and the symptomatic groups. Between these two groups, the marginal posterior of the difference of the mean in the slopes was 0.83% [-1.59%, 3.13%] (i.e. the slope was larger for the control group) and the probability of this difference was 71%. Figure 3.16 Plot of the NESI versus Competing Sentences (left-ear) slope difference between the control and symptomatic groups. The blue dashed line denotes the slope of the control group, the orange dashed line denotes the slope of the symptomatic group, and the black line denotes the difference in slope between the two groups. The red dashed line denotes a slope of 0. 3.3.3 Diotic digits Figure 3.4 shows individual diotic digits scores plotted by group. Figure 3.17, Figure 3.18, and Figure 3.19 show predicted regression lines for scores on the NESI versus diotic digits scores. 48 Figure 3.17 Predicted regression line for the control group, plotted for NESI scores versus diotic digits scores. Figure 3.18 Predicted regression line for the musician group, plotted for NESI scores versus diotic digits scores. 49 Figure 3.19 Predicted regression line for the symptomatic group, plotted for NESI scores versus diotic digits scores. The slopes of the regression lines for all groups are close to 0, indicating that NESI scores are not a strong predictor of diotic digits scores for any of the groups. However, the slope for the control group is negative, while the slope for the symptomatic group is positive. Visual inspection of Figure 3.19, the plot for the symptomatic group, reveals that the individual who scored the highest out of the symptomatic individuals on the diotic digits test also reported the highest NESI score of the symptomatic group. Figure 3.20 plots the slope difference in proportion units between the control group and the symptomatic group. Between these two groups, the marginal posterior of the difference of the mean in the slopes was -4.67% [-5.93%, -3.36%] (i.e. the slope was larger for the symptomatic group) with a probability of 100%. The single extreme data point in the symptomatic group 50 likely had a disproportionately large influence on the slope for the group, particularly given the small number of total data points. Figure 3.20 Plot of the NESI versus diotic digits slope difference between the control and symptomatic groups. The blue dashed line denotes the slope of the control group, the orange dashed line denotes the slope of the symptomatic group, and the black line denotes the difference in slope between the two groups. The red dashed line denotes a slope of 0. Figure 3.21 plots the slope difference in proportion units between the control group and the musician group. Between these two groups, the marginal posterior of the difference of the mean in slopes was 50.68% [49.53%, 51.93%] (i.e. the slope was larger for the control group) with a probability of 99.9%. 51 Figure 3.21 Plot of the NESI versus diotic digits slope difference between the control and musician groups. The blue dashed line denotes the slope of the control group, the orange dashed line denotes the slope of the musician group, and the black line denotes the difference in slope between the two groups. The red dashed line denotes a slope of 0. Figure 3.22 plots the slope difference in proportion units between the musician group and the symptomatic group. Between these two groups, the marginal posterior of the difference of the mean in the slopes was -1.72% [-3.1%, -0.15%] (i.e. the slope was larger for the symptomatic group) with a probability of 96%. 52 Figure 3.22 Plot of the NESI versus diotic digits slope difference between the musician and symptomatic groups. The blue dashed line denotes the slope of the musician group, the orange dashed line denotes the slope of the symptomatic group, and the black line denotes the difference in slope between the two groups. The red dashed line denotes a slope of 0. 3.3.4 Dichotic advantage The dichotic advantage score is calculated by subtracting an individual’s score on the diotic digits task from their score on the dichotic digits task. It is a comparison between the dichotic condition and the diotic condition. A positive dichotic advantage score indicates better performance in the dichotic condition, a negative dichotic advantage score indicates better performance in the diotic condition, and a dichotic advantage score of zero indicates no difference in performance between the two conditions. Figure 3.23 shows individual dichotic advantage scores plotted by the groups. Figure 3.24, Figure 3.25, and Figure 3.26 show predicted regression lines for scores on the NESI versus dichotic advantage scores. 53 Figure 3.23 Plot of individual dichotic advantage scores by group. The red dashed line denotes a difference of zero in scores between the dichotic and diotic conditions. Figure 3.24 Predicted regression line for the control group, plotted for NESI scores versus dichotic advantage scores. 54 Figure 3.25 Predicted regression line for the musician group, plotted for NESI scores versus dichotic advantage scores. Figure 3.26 Predicted regression line for the symptomatic group, plotted for NESI scores versus dichotic advantage scores. 55 All of the regression lines have negative slopes and are similar between the groups, suggesting that a higher NESI score (i.e. more reported noise exposure) is associated with a smaller dichotic advantage. Figure 3.27 plots the slope difference in proportion units between the two most different groups, the musician and the symptomatic groups. Between these two groups, the marginal posterior of the difference of the mean in the slopes was 4.17% [-16.27%, 26.68%] (i.e. the slope was larger for the musician group) and the probability of this difference was 64%. Figure 3.27 Plot of the NESI versus dichotic advantage slope difference between the musician and symptomatic groups. The blue dashed line denotes the slope of the musician group, the orange dashed line denotes the slope of the symptomatic group, and the black line denotes the difference in slope between the two groups. The red dashed line denotes a slope of 0. 56 Chapter 4: Discussion The purpose of this study was to investigate the hypothesis of central auditory system dysfunction as an origin of hidden hearing loss in humans. Central auditory processing disorder and hidden hearing loss manifest similarly in a difficulty understanding speech in challenging conditions. The MAPA test battery was administered to control, musician, and symptomatic groups to quantify performance on central auditory processing tasks and explore whether the amount of reported lifetime noise exposure is associated with performance on these tasks. 4.1 Speech-in-noise Overall, for the two monaural speech-in-noise components of the MAPA, the control and musician groups outperformed the symptomatic group. Although the symptomatic individuals had hearing thresholds within the normal range at the conventional frequencies, even minimal hearing loss can have an effect on speech perception in noise. Consistent with the review by Lokwani and Prabhu (2021), the present study’s results show that speech-in-noise testing has utility in identifying individuals with hidden hearing loss. 4.2 Right-ear advantage Results of the present study revealed better performance in the right ear among all participant groups for the SINCA and the Competing Sentences tasks. This is consistent with the phenomenon of the right-ear advantage for auditory processing of verbal stimuli, which has been documented since the 1900s and studied extensively since. Doreen Kimura (1961) found that for a dichotic task, a damaged left temporal lobe affected overall performance, and if a lobectomy was done the performance of the ear contralateral to the lobectomy was affected. The explanation proposed states that the crossed auditory pathways are more dominant than the uncrossed pathways and that the left temporal lobe is more important than the right for 57 processing verbal material. More recent investigations have provided additional evidence for this theory. Della Penna et al. (2007) found through magnetoencephalography that dichotic speech stimuli had an inhibitory effect on ipsilateral auditory pathways and that the inhibition of the left-ear-to-right-auditory-cortex pathway was stronger than the inhibition of the right-ear-to-left-auditory-cortex pathway. Despite the high proportion of individuals with left-hemisphere language lateralization, there are factors that may predispose individuals to an atypical lateralization; these factors include left-handedness (Mazoyer et al., 2014) and musical training (Villar-Rodríguez et al., 2020). Potential right/left asymmetry at the non-cortical level has also been explored. In a large number of normal-hearing neonates, Sininger et al. (1998) found that ABR wave V amplitude in response to stimuli close to threshold was larger in the right ear than in the left. More recently, a study by Sininger and Cone-Wesson (2004) on transient- and click-evoked otoacoustic emissions in infants proposed that innate asymmetry at the cochlear level may facilitate cortical hemispheric specialization. Pirilä et al. (1992) found that in three large groups of children, younger adults, and older adults, the left ear tended to have worse audiometric thresholds at 4000 Hz, even after accounting for the influence of firearm noise exposure (which may affect the left ear more due to the positioning of the firearm). They suggested that the left ear may be more susceptible to noise damage based on data from the younger adults (15 to 50 years of age) showing more asymmetry than the older adults (50 years of age and above), with the premise that poorer hearing thresholds in the younger group could be attributed primarily to noise exposure and poorer hearing thresholds in the older group could be influenced by presbycusis. The MAPA normative data does not include information on differences in scores between the left and right ears, but other central auditory processing tests such as the SCAN do. 58 Normative data for the SCAN shows a diminishing right-ear advantage as age increases. The advantage becomes negligible beyond 11 years of age, and an unusually large right-ear advantage given a person’s age is considered a flag for central auditory dysfunction (Keith, 2000). Other studies have also found age-related decreases (from preschool up to 12 years of age) in the magnitude of ear advantage on tasks with verbal stimuli (e.g. Moncrieff, 2011). On the interpretation of maturational changes in the right-ear advantage, Porter and Berlin (1975) cautioned that different levels of language processing may be lateralized differently and/or develop at different rates. In the absence of published data on adults for the MAPA, the magnitude of right-ear advantage observed on the SINCA and the Competing Sentences cannot be interpreted to be potentially indicative of a noise-induced left-ear deficit or central auditory dysfunction. However, it is interesting to note that on the SINCA, both the musician and symptomatic groups showed a right-ear advantage, while the control group by itself did not. Previous research on the right-ear advantage has focused on verbal dichotic stimulation; the observation of a right-ear advantage for a monaural speech-in-noise task is a novel finding. This imbalance between ears for a monaural task should be further investigated, and may have connections to the overall left-hemisphere dominance of language and/or to the non-cortical asymmetries that have been observed in humans of various ages. 4.3 Dichotic and diotic digits On this digit recall task, the diotic condition is considered to be more difficult than the dichotic condition due to the presence of ipsilateral informational masking (Cameron et al., 2016). For both the dichotic and diotic conditions, the control and musician groups outperformed the symptomatic group. Additionally, on the diotic digits, the musician group outperformed the control group. This finding of musicians performing better than control subjects on a challenging 59 auditory task is consistent with some previous findings on superior processing in musical individuals. Particularly relevant is the study by Zhang et al. (2019), in which musicians outperformed controls in the more difficult condition (signal and noise in phase with each other) of a binaural masking level difference task. Cameron et al. (2016) developed the “Dichotic Digits Difference Test” which was designed to tease apart effects of cognitive ability and the true dichotic listening ability for CAPD diagnosis in the pediatric population. The premise of the test is that the diotic condition is the same type of task (utilizing the same cognition, attention, and memory resources) as the dichotic condition, with the only difference being whether the same stimuli are presented to both ears or not. A follow-up study on a larger sample found that little of the variance in dichotic scores could be attributed to true dichotic ability; rather, that most of the variance could be accounted for by simultaneous segregation and short-term memory skills (Cameron & Dillon, 2020). Musical training has been associated with improved performance on non-musical cognitive abilities, including working memory, verbal memory, and processing speed (for a review, see Benz et al., 2016). Thus, one potential explanation for musicians’ superior performance on the diotic digits task is increased general cognitive capacity rather than an advantage specific to the auditory modality. Another complicating factor is the difficulty of gathering evidence for a causal, rather than correlational, association between musical training and better auditory or non-auditory skills. Musicians may have a performance advantage in difficult listening conditions; quantifying this advantage is difficult as it may be task-dependent. The cause of this potential advantage is also challenging to isolate, as general (non-auditory) abilities influence performance on dichotic tests. 60 4.4 Attention and memory In measuring central auditory processing abilities, it is difficult to disentangle the influence of more general functions such as attention and memory from auditory-specific effects. One feature of the MAPA battery is that both of the dichotic tests included (dichotic digits and Competing Sentences) are to be administered in a free recall condition. A free recall condition is a test of binaural integration (repeating stimuli from both ears) while a forced recall condition is a test of binaural separation (repeating stimuli from the left or the right ear only, as directed). It has been argued that performance on a free recall condition may be more influenced by attention and working memory, while performance on a directed recall condition may be more of a reflection of auditory-specific ability (e.g. Jerger et al., 1994). Memory has also been studied in conjunction with the right-ear advantage. Penner et al. (2009) found that with greater memory load (more auditorily presented letters to recall), participants’ right-ear advantage became more robust, but only up to a point (i.e. a ceiling was reached). This may explain the present study’s finding of a stronger ear effect on the Competing Sentences task compared to the SINCA, as presenting two sentences dichotically increases memory load compared to presenting one word monaurally. Non-auditory abilities, especially memory and attention, may influence individuals’ performance on auditory processing tasks. The MAPA dichotic digits task does not distinguish between left and right ears in its scoring; however, it would be interesting to assess the presence and magnitude of an ear effect on a digit task and draw comparisons with the ear effects seen for the Competing Sentences and the SINCA. 61 4.5 Age The potential influence of participants’ age must be considered in the interpretation of group differences on auditory processing tasks. In the present study, individuals in the symptomatic group were aged 22–55, while those in the control and musician groups were aged 18–28. The older average age of the symptomatic group may be a factor in their poorer performance relative to the control and musician groups. Previous research has found a decline in auditory processing ability associated with older age, independent of hearing sensitivity (e.g. Dubno et al., 1984); however, this research has generally focused on people who are 65 years of age or older. 4.6 Noise exposure history The musician group had overall higher reported lifetime noise exposure compared to the control and symptomatic groups. Despite this, the musician group did not exhibit deficits in central auditory processing compared to the other groups. This is in contrast to the findings of Liberman et al. (2016), who found poorer word recognition in challenging conditions for the “high-risk of noise exposure” group. However, the participants in the Liberman et al. (2016) high-risk group self-reported listening difficulties, while the high-risk group in the current study did not. One potential explanation for this is that the musicians we studied had not accumulated enough lifetime noise damage for deficits to be evident at the time of assessment. For all but one of the measures analyzed, NESI scores were not revealed to be strong predictors of central auditory processing task performance. Although NESI was not shown in the model to be a predictor of the diotic digits test, the predicted regression line slopes differed between the control, musician, and symptomatic groups. The slope for the symptomatic group was positive, illustrating an apparent association between a higher NESI score (i.e. more reported 62 noise exposure) and better performance on the diotic digits test. This was likely due to one participant’s data, which can be seen plotted in the top right corner of Figure 3.19. This participant had the highest NESI score as well as the highest diotic digits score in their group. This individual’s scores caused a widening of the distribution of data within their group, and despite the conservative analysis model, disproportionately affected the model’s estimate of the regression line. Analyses of predicted regression line slope differences between the groups revealed that for the diotic digits test, there was a very high probability that the slope for the symptomatic group was larger than the slopes of the control and musician groups. This is consistent with the visually apparent difference as explained above. For the diotic digits test, there was a very high probability that the control group slope was larger than the musician group slope. One potential explanation for the weaker association between reported noise exposure and diotic digits performance in the musician group is that their musical training and/or innate abilities (refer to section 4.3) were such that as a group, they were able to overcome the general trend of higher reported noise exposure. However, this is purely speculative, especially given the small sample size. Predicted regression lines for the dichotic advantage score (the difference in items correct between the dichotic and diotic conditions) were similar for all three participant groups. Negative slopes indicated that higher NESI scores (i.e. more reported noise exposure) predicted a smaller dichotic advantage. This association suggests that greater lifetime noise exposure may reduce individuals’ ability to integrate dichotic stimuli, which could have implications for localization skills and spatial release of masking in noisy situations. 63 Overall, the musician group had higher reported lifetime noise exposure and comparable performance to the control group on the auditory processing tasks. The analysis revealed a weaker association between reported noise exposure and diotic digits performance in the musicians compared to the other two groups, potentially explained by a more robust innate or trained auditory system in the musicians. More research in the area of dichotic stimuli integration is necessary, as current findings on the subject are contradictory or inconclusive (for a review, see Lokwani and Prabhu (2021). The thesis by Monette (2020) which was part of UBC Middle Ear Lab Hidden Hearing Loss Project did not find an association between participants’ amount of reported lifetime noise exposure and their ability to use localization cues for a speech-in-noise task. 4.7 Study limitations One limitation of the present study was the small number of participants (24 in the control group, 20 in the musician group, and 11 in the symptomatic group). Although there are well-cited studies with participant pools of comparable or smaller size (e.g. Liberman et al. (2016) included 22 “high risk of noise exposure” participants and 12 “low risk of noise exposure” participants), having fewer participants restricts the ability to make generalizations. There are also several data points missing in each group in the present study due to logistical issues at the time of testing. Particularly in the symptomatic group, scores on the MAPA measures and for the NESI spanned a large range. Despite the Bayesian approach to analysis, this variability made it difficult to characterize trends in the data given the small sample size. Regarding the musician group showing a trend of higher reported lifetime noise exposure (compared to the other two groups) without exhibiting marked deficits on central auditory processing tasks, the young age of the music students may be a factor. The type of musical 64 situations they are in regularly may also have had an influence; although the individuals in the musician group represented a variety of backgrounds and instruments, it is important to note that six of the 20 musicians were primarily vocalists, who may be exposed to less noise in training and performance environments. Due to the self-selective nature of the symptomatic individuals recruited for this study, the study groups were not age-matched. As the symptomatic group was, on average, older than the control and musician groups, there is a possibility that any decreased performance noted in that group may be at least partially associated with older age. Truly objective quantification of lifetime noise exposure is not possible in a human study, and ethical concerns preclude experimental noise exposure studies in humans. Although the NESI is a comprehensive lifetime noise exposure estimation tool, it is still sensitive to individual participants’ biases of memory, perceived vocal effort, and interpretation of the interview questions. Given that the NESI is a fairly new measure, continued evaluation of it using dosimetry in different situations would improve its utility as a noise exposure estimation tool, with the caveat that its inherent flaws should be considered. 4.8 Future directions Further research on all areas of central auditory processing in adults would contribute to understanding the mechanism and effects of hidden hearing loss. The MAPA battery is a promising tool for use in adults, given the lack of ceiling effects for most of its components; a continued collection of data in adults would aid in building a normative data pool. The use of temporal measures of auditory processing should be investigated, given that the present study did not include analysis on any temporal task, and that previous studies’ results have been inconclusive. Also, future studies could include non-auditory cognitive measures of attention and 65 memory as past findings have been contradictory; Kamerer et al. (2019) and Yeend et al. (2017) found associations between working memory and central auditory processing, while Saunders et al. (2015) did not. The right-ear advantage could be further examined using physiological measures. Previous research on the right-ear advantage has focused on dichotic stimuli; the present study found a right-ear advantage for a monaural speech-in-noise task. Measures such as suppression of contralateral otoacoustic emissions, contralateral reflex growth, and encephalography could be used. To explore potential age effects, future investigations on noise-induced auditory processing deficits could employ a longitudinal, rather than cross-sectional, study design. An alternative option would be the use of age-matched groups. Concerning the amount of lifetime noise exposure necessary to show processing deficits, researchers could study individuals with higher lifetime noise exposure, for example, professional symphony musicians rather than music students. The present study included a control group, a high-risk group, and a symptomatic group. To examine the connection between noise exposure and auditory processing skills, a future study could examine participants on a continuum rather than divided into groups. The high-risk group in the present study had overall more lifetime noise exposure than the other two groups, but participants in the high-risk group did not generally report symptoms associated with hidden hearing loss (with the exception of three individuals who reported bilateral tinnitus). The symptomatic group presented with symptoms associated with hidden hearing loss (notably difficulty with speech in background noise) but the statistical model was uncertain whether they had higher reported lifetime noise exposure compared to the control group. To continue investigating the connection between noise exposure and hidden hearing loss, future studies 66 should examine individuals with significant noise exposure history (compared to age-matched control participants) and self-reported auditory deficits. 67 References American Academy of Audiology. (2010). Diagnosis, Management, and Treatment of Children and Adults with Central Auditory Processing Disorder. American Speech-Language and Hearing Association. (1996). Grand rounds. Central auditory processing: Current status of research and implications for clinical practice. American Journal of Audiology, 5(2), 41–54. CINAHL Complete. American Speech-Language and Hearing Association. (2005). (Central) Auditory Processing Disorders. https://web.archive.org/web/20200106023606/https://www.asha.org/policy/TR2005-00043/ American Speech-Language and Hearing Association. (n.d.). Central Auditory Processing Disorder. Baran, J. A. (2014). Test battery principles and considerations. In F. E. Musiek & G. D. Chermak (Eds.), Handbook of central auditory processing disorder: Volume 1, Auditory neuroscience and diagnosis (Vol. 1, pp. 344–378). Plural Publishing. Barbee, C. M., James, J. A., Park, J. H., Smith, E. M., Johnson, C. E., Clifton, S., & Danhauer, J. L. (2018). Effectiveness of Auditory Measures for Detecting Hidden Hearing Loss and/or Cochlear Synaptopathy: A Systematic Review. Seminars in Hearing, 39(2), 172–209. https://doi.org/10.1055/s-0038-1641743 Benz, S., Sellaro, R., Hommel, B., & Colzato, L. S. (2016). Music Makes the World Go Round: The Impact of Musical Training on Non-musical Cognitive Functions—A Review. Frontiers in Psychology, 6, 2023. https://doi.org/10.3389/fpsyg.2015.02023 68 Bharadwaj, H. M., Masud, S., Mehraei, G., Verhulst, S., & Shinn-Cunningham, B. G. (2015). Individual differences reveal correlates of hidden hearing deficits. The Journal of Neuroscience, 35(5), 2161–2172. https://doi.org/10.1523/JNEUROSCI.3915-14.2015 Bhatt, I. (2017). Increased medial olivocochlear reflex strength in normal-hearing, noise-exposed humans. PloS One, 12(9), e0184036–e0184036. https://doi.org/10.1371/journal.pone.0184036 Bidelman, G. M., Schneider, A. D., Heitzmann, V. R., & Bhagat, S. P. (2017). Musicianship enhances ipsilateral and contralateral efferent gain control to the cochlea. Hearing Research, 344, 275–283. https://doi.org/10.1016/j.heares.2016.12.001 Bramhall, N., Beach, E. F., Epp, B., Le Prell, C. G., Lopez-Poveda, E. A., Plack, C. J., Schaette, R., Verhulst, S., & Canlon, B. (2019). The search for noise-induced cochlear synaptopathy in humans: Mission impossible? Hearing Research, 377, 88–103. https://doi.org/10.1016/j.heares.2019.02.016 Bramhall, N., Konrad-Martin, D., McMillan, G. P., & Griest, S. E. (2017). Auditory Brainstem Response Altered in Humans With Noise Exposure Despite Normal Outer Hair Cell Function. Ear and Hearing, 38(1), e1–e12. https://doi.org/10.1097/AUD.0000000000000370 Bramhall, N., Ong, B., Ko, J., & Parker, M. (2015). Speech Perception Ability in Noise is Correlated with Auditory Brainstem Response Wave I Amplitude. Journal of the American Academy of Audiology, 26(5), 509–517. https://doi.org/10.3766/jaaa.14100 Brashears, S. M., Morlet, T. G., Berlin, C. I., & Hood, L. J. (2003). Olivocochlear Efferent Suppression in Classical Musicians. Journal of the American Academy of Audiology, 14(6), 314–324. https://doi.org/10.1055/s-0040-1715747 69 Brattico, E., Kujala, T., Tervaniemi, M., Alku, P., Ambrosi, L., & Monitillo, V. (2005). Long-term exposure to occupational noise alters the cortical organization of sound processing. Clinical Neurophysiology, 116(1), 190–203. https://doi.org/10.1016/j.clinph.2004.07.030 Cameron, S., & Dillon, H. (2020). Are “Dichotic” Deficits Uniquely Dichotic? Investigating Dichotic Performance with the Dichotic Digits Difference Test (DDdT) in a Large Clinical Population of Children Referred for an Auditory Processing Assessment. Journal of the American Academy of Audiology, 31(3), 233–242. https://doi.org/10.3766/jaaa.19037 Cameron, S., Glyde, H., Dillon, H., Whitfield, J., & Seymour, J. (2016). The Dichotic Digits difference Test (DDdT): Development, Normative Data, and Test–Retest Reliability Studies Part 1. Journal of the American Academy of Audiology, 27(6), 458–469. https://doi.org/10.3766/jaaa.15084 Canadian Interorganizational Steering Group for Speech-Language Pathology and Audiology. (2012). Canadian Guidelines on Auditory Processing Disorder in Children and Adults: Assessment and Intervention. Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., Riddell, A., Columbia Univ., N. Y., NY (United States), & Harvard Univ., C., MA (United States). (2017). Stan: A Probabilistic Programming Language. Journal of Statistical Software, 76(1), 1–32. https://doi.org/10.18637/jss.v076.i01 Chang, S. L. (2020). Electrocochleography as a diagnostic tool for noise-induced cochlear synaptopathy [University of British Columbia]. https://open.library.ubc.ca/collections/ubctheses/24/items/1.0390317 70 Cho, S.-I., Gao, S. S., Xia, A., Wang, R., Salles, F. T., Raphael, P. D., Abaya, H., Wachtel, J., Baek, J., Jacobs, D., Rasband, M. N., & Oghalai, J. S. (2013). Mechanisms of hearing loss after blast injury to the ear. PloS One, 8(7), e67618–e67618. https://doi.org/10.1371/journal.pone.0067618 Davis, A. C. (1989). The Prevalence of Hearing Impairment and Reported Hearing Disability among Adults in Great Britain. International Journal of Epidemiology, 18(4), 911–917. https://doi.org/10.1093/ije/18.4.911 Della Penna, S., Brancucci, A., Babiloni, C., Franciotti, R., Pizzella, V., Rossi, D., Torquati, K., Rossini, P. M., & Romani, G. L. (2007). Lateralization of Dichotic Speech Stimuli is Based on Specific Auditory Pathway Interactions: Neuromagnetic Evidence. Cerebral Cortex, 17(10), 2303–2311. https://doi.org/10.1093/cercor/bhl139 Domitz, D. M., & Schow, R. L. (2000). A New CAPD Battery—Multiple Auditory Processing Assessment: Factor Analysis and Comparisons with SCAN. American Journal of Audiology, 9(2), 101–111. https://doi.org/10.1044/1059-0889(2000/012) Dubno, J. R., Dirks, D. D., & Morgan, D. E. (1984). Effects of age and mild hearing loss on speech recognition in noise. The Journal of the Acoustical Society of America, 76(1), 87–96. https://doi.org/10.1121/1.391011 El-Badry, M. M., & McFadden, S. L. (2006). Electrophysiological correlates of progressive sensorineural pathology in carboplatin-treated chinchillas. Brain Research, 1134(1), 122–130. https://doi.org/10.1016/j.brainres.2006.11.078 Fausti, S. A., Wilmington, D. J., Gallun, F. J., Myers, P. J., & Henry, J. A. (2009). Auditory and vestibular dysfunction associated with blast-related traumatic brain injury. Journal of 71 Rehabilitation Research & Development, 46(6), 797+. Gale OneFile: Health and Medicine. Furman, A. C., Kujawa, S. G., & Liberman, M. C. (2013). Noise-induced cochlear neuropathy is selective for fibers with low spontaneous rates. Journal of Neurophysiology, 110(3), 577–586. https://doi.org/10.1152/jn.00164.2013 Gallun, F. J., Diedesch, A. C., Kubli, L. R., Walden, T. C., Folmer, R. L., Lewis, M. S., McDermott, D. J., Fausti, S. A., & Leek, M. R. (2012). Performance on tests of central auditory processing by individuals exposed to high-intensity blasts. Journal of Rehabilitation Research and Development, 49(7), 1005–1025. https://doi.org/10.1682/JRRD.2012.03.0038 Guest, H., Dewey, R. S., Plack, C. J., Couth, S., Prendergast, G., Bakay, W., & Hall, D. A. (2018). The Noise Exposure Structured Interview (NESI): An Instrument for the Comprehensive Estimation of Lifetime Noise Exposure. Trends in Hearing, 22, 2331216518803213. https://doi.org/10.1177/2331216518803213 Hind, S. E., Haines-Bazrafshan, R., Benton, C. L., Brassington, W., Towle, B., & Moore, D. R. (2011). Prevalence of clinical referrals having hearing thresholds within normal limits. International Journal of Audiology, 50(10), 708–716. https://doi.org/10.3109/14992027.2011.582049 Humes, L. E., Dubno, J. R., Gordon-Salant, S., Lister, J. J., Cacace, A. T., Cruickshanks, K. J., Gates, G. A., Wilson, R. H., & Wingfield, A. (2012). Central Presbycusis: A Review and Evaluation of the Evidence. Journal of the American Academy of Audiology, 23(8), 635–666. https://doi.org/10.3766/jaaa.23.8.5 72 Idaho State University. (n.d.). Multiple Auditory Processing Assessment. https://www.isu.edu/audiology/research-in-practice/multiple-auditory-processing-assessment/ Jerger, J., Chmiel, R., Allen, J., & Wilson, A. (1994). Effects of Age and Gender on Dichotic Sentence Identification. Ear and Hearing, 15(4), 274–286. https://doi.org/10.1097/00003446-199408000-00002 Kamerer, A. M., AuBuchon, A., Fultz, S. E., Kopun, J. G., Neely, S. T., & Rasetshwane, D. M. (2019). The Role of Cognition in Common Measures of Peripheral Synaptopathy and Hidden Hearing Loss. American Journal of Audiology, 28(4), 843–856. https://doi.org/10.1044/2019_AJA-19-0063 Keith, R. W. (2000). Development and standardization of SCAN-C test for auditory processing disorders in children. Journal of the American Academy of Audiology, 11(8), 438–445. CINAHL Complete. Kimura, D. (1961). Some effects of temporal-lobe damage on auditory perception. Canadian Journal of Psychology/Revue Canadienne de Psychologie, 15(3), 156–165. APA PsycArticles. https://doi.org/10.1037/h0083218 Kruschke, J. K. (2013). Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General, 142(2), 573–603. APA PsycArticles. https://doi.org/10.1037/a0029146 Kujala, T., Shtyrov, Y., Winkler, I., Saher, M., Tervaniemi, M., Sallinen, M., Teder-Salejarvi, W., Alho, K., Reinikainen, K., & Naatanen, R. (2004). Long-term exposure to noise impairs cortical sound processing and attention control. Psychophysiology, 41(6), 875–881. https://doi.org/10.1111/j.1469-8986.2004.00244.x 73 Kujawa, S. G., & Liberman, M. C. (2009). Adding Insult to Injury: Cochlear Nerve Degeneration after “Temporary” Noise-Induced Hearing Loss. The Journal of Neuroscience, 29(45), 14077–14085. https://doi.org/10.1523/jneurosci.2845-09.2009 Kumar, U. A., Ameenudin, S., & Sangamanatha, A. V. (2012). Temporal and speech processing skills in normal hearing individuals exposed to occupational noise. Noise & Health, 14(58), 100–105. https://doi.org/10.4103/1463-1741.97252 Lampe, B. (2011). Are currently available pre-packaged behavioural test batteries (SCAN and MAPA) effective for use in the assessment and or diagnosis of Auditory Processing Disorder (APD) in children assuming the American Speech-Language Hearing Association (ASHA) definition of APD? Lange, K. L., Roderick J. A. Little, & Jeremy M. G. Taylor. (1989). Robust Statistical Modeling Using the t Distribution. Journal of the American Statistical Association, 84(408), 881–896. JSTOR. https://doi.org/10.2307/2290063 Liberman, M. C., Epstein, M. J., Cleveland, S. S., Wang, H., & Maison, S. F. (2016). Toward a Differential Diagnosis of Hidden Hearing Loss in Humans. PloS One, 11(9), e0162726–e0162726. https://doi.org/10.1371/journal.pone.0162726 Lobarinas, E., Spankovich, C., & Le Prell, C. G. (2017). Evidence of “hidden hearing loss” following noise exposures that produce robust TTS and ABR wave-I amplitude reductions. Hearing Research, 349, 155–163. https://doi.org/10.1016/j.heares.2016.12.009 Lokwani, P., & Prabhu, P. (2021). Efficacy of behavioral audiological tests in identifying cochlear synaptopathy: A systematic review. European Archives of Oto-Rhino-Laryngology. https://doi.org/10.1007/s00405-021-06927-x 74 Lopez-Poveda, E. A., & Barrios, P. (2013). Perception of stochastically undersampled sound waveforms: A model of auditory deafferentation. Frontiers in Neuroscience, 7(Journal Article), 124–124. https://doi.org/10.3389/fnins.2013.00124 Makary, C. A., Shin, J., Kujawa, S. G., Liberman, M. C., & Merchant, S. N. (2011). Age-related primary cochlear neuronal degeneration in human temporal bones. Journal of the Association for Research in Otolaryngology : JARO, 12(6), 711–717. PubMed. https://doi.org/10.1007/s10162-011-0283-2 Mazoyer, B., Zago, L., Jobard, G., Crivello, F., Joliot, M., Perchey, G., Mellet, E., Petit, L., & Tzourio-Mazoyer, N. (2014). Gaussian mixture modeling of hemispheric lateralization for language in a large sample of healthy individuals balanced for handedness. PloS One, 9(6), e101165–e101165. https://doi.org/10.1371/journal.pone.0101165 McElreath, R. (2020). Statistical rethinking: A Bayesian course with examples in R and Stan (Second). Chapman & Hall/CRC. McKay, C. M. (2021). No Evidence That Music Training Benefits Speech Perception in Hearing-Impaired Listeners: A Systematic Review. Trends in Hearing, 25, 2331216520985678. https://doi.org/10.1177/2331216520985678 Moncrieff, D. W. (2011). Dichotic listening in children: Age-related changes in direction and magnitude of ear advantage. Dichotic Listening Anniversary Special Issue, 76(2), 316–322. https://doi.org/10.1016/j.bandc.2011.03.013 Monette, S. (2020). Using the hearing-in-noise test as a screening tool for cochlear synaptopathy in student musicians [University of British Columbia]. https://open.library.ubc.ca/collections/ubctheses/24/items/1.0395199 75 Musiek, F. E., Chermak, G. D., Bamiou, D.-E., & Shinn, J. (2018). CAPD: The Most Common ‘Hidden Hearing Loss’: Central auditory processing disorder—And not cochlear synaptopathy—Is the most likely source of difficulty understanding speech in noise (despite normal audiograms). ASHA Leader, 23(3), 6–9. https://doi.org/10.1044/leader.FMP.23032018.6 Oleksiak, M., Smith, B. M., St Andre, J. R., Caughlan, C. M., & Steiner, M. (2012). Audiological issues and hearing loss among Veterans with mild traumatic brain injury. Journal of Rehabilitation Research and Development, 49(7), 995–1004. https://doi.org/10.1682/JRRD.2011.01.0001 Parbery-Clark, A., Skoe, E., & Kraus, N. (2009). Musical Experience Limits the Degradative Effects of Background Noise on the Neural Processing of Sound. The Journal of Neuroscience, 29(45), 14100–14107. https://doi.org/10.1523/jneurosci.3256-09.2009 Parbery-Clark, A., Skoe, E., Lam, C., & Kraus, N. (2009). Musician Enhancement for Speech-In-Noise. Ear and Hearing, 30(6), 653–661. https://doi.org/10.1097/aud.0b013e3181b412e9 Parthasarathy, A., & Kujawa, S. G. (2018). Synaptopathy in the Aging Cochlea: Characterizing Early-Neural Deficits in Auditory Temporal Envelope Processing. The Journal of Neuroscience, 38(32), 7108. https://doi.org/10.1523/JNEUROSCI.3240-17.2018 Penner, I.-K., Schläfli, K., Opwis, K., & Hugdahl, K. (2009). The role of working memory in dichotic-listening studies of auditory laterality. Journal of Clinical and Experimental Neuropsychology, 31(8), 959–966. MEDLINE with Full Text. https://doi.org/10.1080/13803390902766895 76 Pichora-Fuller, M. K., & Souza, P. E. (2003). Effects of aging on auditory processing of speech. International Journal of Audiology, 42 Suppl 2, 2S11-6. MEDLINE with Full Text. Pirilä, T., Jounio-Ervasti, K., & Surri, M. (1992). Left-Right Asymmetries in Hearing Threshold Levels in Three Age Groups of a Random Population. Audiology, 31(3), 150–161. https://doi.org/10.3109/00206099209072910 Plack, C. J., Barker, D., & Prendergast, G. (2014). Perceptual Consequences of “Hidden” Hearing Loss. Trends in Hearing, 18(Journal Article), 233121651455062. https://doi.org/10.1177/2331216514550621 Porter, R. J., & Berlin, C. I. (1975). On interpreting developmental changes in the dichotic right-ear advantage. Brain and Language, 2, 186–200. https://doi.org/10.1016/S0093-934X(75)80063-0 Prendergast, G., Guest, H., Munro, K. J., Kluk, K., Léger, A., Hall, D. A., Heinz, M. G., & Plack, C. J. (2017). Effects of noise exposure on young adults with normal audiograms I: Electrophysiology. Hearing Research, 344, 68–81. https://doi.org/10.1016/j.heares.2016.10.028 Prendergast, G., Millman, R. E., Guest, H., Munro, K. J., Kluk, K., Dewey, R. S., Hall, D. A., Heinz, M. G., & Plack, C. J. (2017). Effects of noise exposure on young adults with normal audiograms II: Behavioral measures. Hearing Research, 356, 74–86. https://doi.org/10.1016/j.heares.2017.10.007 Rammsayer, T., & Altenmüller, E. (2006). Temporal Information Processing in Musicians and Nonmusicians. Music Perception: An Interdisciplinary Journal, 24(1), 37–48. JSTOR. https://doi.org/10.1525/mp.2006.24.1.37 77 Rodrigues, M., Freitas, M., Neves, M., & Silva, M. (2014). Evaluation of the noise exposure of symphonic orchestra musicians. Noise and Health, 16(68), 40. Gale OneFile: Health and Medicine. Saunders, G. H., Frederick, M. T., Arnold, M., Silverman, S., Chisolm, T. H., & Myers, P. (2015). Auditory difficulties in blast-exposed Veterans with clinically normal hearing. Journal of Rehabilitation Research and Development, 52(3), 343–360. https://doi.org/10.1682/JRRD.2014.11.0275 Schaette, R., & McAlpine, D. (2011). Tinnitus with a normal audiogram: Physiological evidence for hidden hearing loss and computational model. The Journal of Neuroscience, 31(38), 13452–13457. https://doi.org/10.1523/JNEUROSCI.2156-11.2011 Schochat, E., Rabelo, C. M., & Musiek, F. E. (2014). Electroacoustic and electrophysiological auditory measures. In F. E. Musiek & G. D. Chermak (Eds.), Handbook of central auditory processing disorder: Volume 1, Auditory neuroscience and diagnosis (Vol. 1, pp. 550–577). Plural Publishing. Schow, R. L., Whitaker, M. M., Seikel, J. A., Brockett, J. E., & Domitz Vieira, D. M. (2020). Validity of the Multiple Auditory Processing Assessment–2: A Test of Auditory Processing Disorder. Language, Speech & Hearing Services in Schools, 51(4), 993–14. https://doi.org/10.1044/2020_LSHSS-20-00001 Schuknecht, H. F. (1993). Pathology of the ear (2nd ed.). Lea & Febiger. Sininger, Y. S., & Cone-Wesson, B. (2004). Asymmetric Cochlear Processing Mimics Hemispheric Specialization. Science, 305(5690), 1581. Agricultural & Environmental Science Collection; Earth, Atmospheric & Aquatic Science Collection. 78 Sininger, Y. S., Cone-Wesson, B., & Abdala, C. (1998). Gender distinctions and lateral asymmetry in the low-level auditory brainstem response of the human neonate. Hearing Research, 126(1), 58–66. https://doi.org/10.1016/S0378-5955(98)00152-X Stamper, G. C., & Johnson, T. A. (2015). Auditory Function in Normal-Hearing, Noise-Exposed Human Ears. Ear and Hearing, 36(2), 172–184. https://doi.org/10.1097/aud.0000000000000107 Taber, K. H., Warden, D. L., & Hurley, R. A. (2006). Blast-Related Traumatic Brain Injury: What Is Known? The Journal of Neuropsychiatry and Clinical Neurosciences, 18(2), 141–145. https://doi.org/10.1176/jnp.2006.18.2.141 Viana, L. M., O’Malley, J. T., Burgess, B. J., Jones, D. D., Oliveira, C. A. C. P., Santos, F., Merchant, S. N., Liberman, L. D., & Liberman, M. C. (2015). Cochlear neuropathy in human presbycusis: Confocal analysis of hidden hearing loss in post-mortem tissue. Hearing Research, 327, 78–88. https://doi.org/10.1016/j.heares.2015.04.014 Villar-Rodríguez, E., Palomar-García, M.-Á., Hernández, M., Adrián-Ventura, J., Olcina-Sempere, G., Parcet, M.-A., & Ávila, C. (2020). Left-handed musicians show a higher probability of atypical cerebral dominance for language. Human Brain Mapping, 41(8), 2048–2058. https://doi.org/10.1002/hbm.24929 Wang, Q., Yang, L., Qian, M., Hong, Y., Wang, X., Huang, Z., & Wu, H. (2021). Acute Recreational Noise-Induced Cochlear Synaptic Dysfunction in Humans With Normal Hearing: A Prospective Cohort Study. Frontiers in Neuroscience, 15, 325. https://doi.org/10.3389/fnins.2021.659011 79 Yeend, I., Beach, E. F., Sharma, M., & Dillon, H. (2017). The effects of noise exposure and musical training on suprathreshold auditory processing and speech perception in noise. Hearing Research, 353, 224–236. https://doi.org/10.1016/j.heares.2017.07.006 Zhang, F., Roland, C., Rasul, D., Cahn, S., Liang, C., & Valencia, G. (2019). Comparing musicians and non-musicians in signal-in-noise perception. International Journal of Audiology, 58(11), 717–723. https://doi.org/10.1080/14992027.2019.1623424 80 Appendices Appendix A Raw data A.1 Raw data for the control group Participant Ear (0 = right, 1 = left) MSAAT non-competing MSAAT competing SINCA Pitch pattern Dichotic digits Diotic digits CS NESI (units of noise exposure) 1 0 5 14 11 20 107 93 29 0.035 1 5 14 11 18 2 0 5 15 12 20 77 75 22 1.514 1 5 17 10 2 3 0 5 16 14 20 107 93 26 0.017 1 5 17 12 22 4 0 5 16 13 20 117 92 14 0.219 1 5 18 11 11 5 0 5 17 12 20 113 82 30 0.144 1 5 16 14 27 6 0 5 20 – 20 116 – 28 0.058 1 5 20 – 27 7 0 5 14 14 20 104 – 13 – 1 5 11 9 6 8 0 5 16 11 20 107 90 30 0.215 1 5 19 10 20 81 Participant Ear (0 = right, 1 = left) MSAAT non-competing MSAAT competing SINCA Pitch pattern Dichotic digits Diotic digits CS NESI (units of noise exposure) 9 0 5 20 14 20 103 75 23 1.333 1 5 17 14 9 10 0 5 13 11 20 105 104 19 0.559 1 5 15 12 23 11 0 5 20 14 14 100 90 22 2.075 1 4 15 13 8 12 0 5 18 14 18 109 63 27 4.438 1 5 17 14 19 13 0 5 15 12 16 91 73 26 7.151 1 5 18 14 26 14 0 5 16 10 18 111 85 24 0.04 1 5 13 19 16 15 0 5 14 14 20 108 75 26 0.052 1 5 17 12 13 16 0 5 14 12 20 105 87 24 0.101 1 5 16 11 9 17 0 5 13 13 20 97 83 23 3.223 1 5 13 9 8 18 0 5 13 13 20 81 79 24 0.821 1 5 12 10 8 19 0 5 16 13 20 105 94 29 0.069 1 5 20 12 21 82 Participant Ear (0 = right, 1 = left) MSAAT non-competing MSAAT competing SINCA Pitch pattern Dichotic digits Diotic digits CS NESI (units of noise exposure) 20 0 5 20 14 19 100 92 30 6.616 1 5 21 10 22 21 0 5 21 – 19 94 79 25 1.02 1 5 9 – 26 22 0 5 15 15 20 116 72 28 1.1 1 5 16 12 27 23 0 5 17 16 15 91 77 25 2.83 1 5 18 15 26 24 0 5 20 13 19 94 92 24 0.86 1 5 17 15 8 83 A.2 Raw data for the musician group Participant Ear (0 = right, 1 = left) MSAAT non-competing MSAAT competing SINCA Pitch pattern Dichotic digits Diotic digits CS NESI (units of noise exposure) 25 0 4 13 13 20 111 95 17 14.022 1 5 12 9 18 26 0 5 19 10 20 111 111 29 14.5 1 5 14 9 22 27 0 5 19 13 20 98 113 30 138.025 1 5 21 15 28 28 0 4 13 14 20 83 78 13 11.997 1 5 13 6 12 29 0 5 15 14 19 100 93 24 320.607 1 5 16 8 14 30 0 5 17 11 20 101 97 28 0.344 1 5 18 12 20 31 0 5 22 15 20 116 86 30 0.489 1 5 17 14 28 32 0 5 20 14 18 91 98 26 20.17 1 5 19 12 22 33 0 5 16 15 20 105 72 24 2.872 1 5 18 11 17 34 0 5 15 13 20 108 101 27 2.208 1 5 18 13 24 35 0 5 14 10 20 95 84 22 5.835 1 5 14 8 16 84 Participant Ear (0 = right, 1 = left) MSAAT non-competing MSAAT competing SINCA Pitch pattern Dichotic digits Diotic digits CS NESI (units of noise exposure) 36 0 5 13 13 20 93 84 23 2.742 1 5 18 9 19 37 0 5 19 12 20 102 95 27 7.049 1 5 20 7 20 38 0 5 15 15 20 114 105 28 0.601 1 5 16 11 25 39 0 5 15 12 20 99 79 23 4.602 1 5 17 14 16 40 0 5 15 13 20 105 103 26 2.566 1 5 19 13 14 41 0 5 15 14 20 102 95 27 4.822 1 5 20 13 23 42 0 5 19 14 20 106 104 28 1.483 1 5 16 9 23 43 0 5 17 15 20 114 113 28 1.121 1 5 19 14 24 44 0 5 12 14 20 99 82 24 3.826 1 5 16 13 10 85 A.3 Raw data for the symptomatic group Participant Ear (0 = right, 1 = left) MSAAT non-competing MSAAT competing SINCA Pitch pattern Dichotic digits Diotic digits CS NESI (units of noise exposure) 45 0 5 17 13 20 110 82 27 0.086 1 5 12 10 22 46 0 5 16 13 13 76 69 24 0.191 1 5 19 10 17 47 0 5 18 17 20 118 108 29 1560.721 1 5 19 15 22 48 0 5 16 10 15 69 78 11 2.636 1 5 12 10 14 49 0 5 14 12 20 120 85 22 – 1 5 15 12 23 50 0 4 11 10 13 76 68 3 73.91 1 5 12 11 0 51 0 5 7 11 12 65 61 9 0 1 5 10 7 5 52 0 5 14 14 20 104 72 28 0.148 1 5 18 13 23 53 0 5 16 14 17 99 73 25 1.955 1 5 19 10 13 54 0 4 12 12 5 80 68 9 0.948 1 5 17 10 9 55 0 5 18 13 20 105 80 29 2.745 1 5 20 10 25 86 Appendix B Statistical models B.1 Sample Bayesian multilevel logistic regression with random intercepts for participants, group and ear as fixed effects m.msaat_ncpp <- ulam( alist( msaat ~ dbinom(25 , p ), logit(p) <- z[lid]*sigma_l + bGE[group,ear], real['3,2']:bGE ~ normal(0,sigma), # fixed priors for group-by-ear sigma ~ dexp(1), # now group by ear is varying too z[lid] ~ dnorm (0, 1), # adaptive prior for listener sigma_l ~ dhalfnorm (0,1), gq> vector[lid]:l <<- z*sigma_l ) , data=dat, chains = 4, cores = 4, warmup = 4000, iter = 6000, control = list(adapt_delta=0.95, max_treedepth = 15 ), log_lik = TRUE ) precis(m.msaat_ncpp, 3, digits = 2, pars = c("bGE[1,1]", "bGE[1,2]", "bGE[2,1]", "bGE[2,2]", "bGE[3,1]","bGE[3,2]","sigma_l", "sigma")) 87 ## mean sd 5.5% 94.5% n_eff Rhat4 ## bGE[1,1] 0.5756230 0.1040862 0.40977457 0.7425879 5492.300 1.0002590 ## bGE[1,2] 0.6263596 0.1059181 0.45583299 0.7982690 5358.823 1.0000270 ## bGE[2,1] 0.7394740 0.1189381 0.55275112 0.9257805 5649.521 1.0003600 ## bGE[2,2] 0.5801547 0.1149183 0.39580631 0.7647127 5200.883 1.0006377 ## bGE[3,1] 0.4991597 0.1515132 0.25812956 0.7425244 5881.329 0.9997154 ## bGE[3,2] 0.2908061 0.1503638 0.05273952 0.5311202 5855.506 1.0003059 ## sigma_l 0.3060983 0.0623411 0.21012874 0.4088193 3437.846 1.0000074 ## sigma 0.6774736 0.2441519 0.39868971 1.0994034 6187.221 1.0005016 88 B.2 Bayesian logistic regression with group and NESI as predictors ml.NESI <- ulam( alist( nesi ~ dstudent(2, mu , sigma ), mu <- a[gid], a[gid] ~ dnorm( 0 , sigma_ml), # group as a varying effect sigma <- sig[gid], # estimate sd separately for each group sig[gid] ~ dexp(1), sigma_ml ~ dexp(1 ) # hyperprior for group sd ) , data=dm_l, chains = 1, cores = 1, warmup = 4000, iter = 10000, control = list(adapt_delta=0.95, max_treedepth = 15 ), log_lik = TRUE) 89 precis(ml.NESI, depth=2) ## mean sd 5.5% 94.5% n_eff Rhat4 ## a[1] -0.45669038 0.4194011 -1.1496481 0.1848881 4955.087 0.9998458 ## a[2] 1.17514173 0.3602257 0.5855704 1.7362833 3540.776 1.0001073 ## a[3] 0.08208775 0.6058223 -0.8559629 1.0498878 4002.862 1.0001241 ## sig[1] 1.54764606 0.3206599 1.0991753 2.1157547 4989.482 1.0008111 ## sig[2] 1.15747498 0.2878094 0.7580756 1.6756697 4202.989 0.9999648 ## sig[3] 1.85712437 0.6326687 1.0339769 2.9844781 5074.916 0.9998346 ## sigma_ml 1.03963276 0.5406280 0.4297692 2.0020064 3702.189 0.9998389 90 B.3 Sample Bayesian logistic regression with a MAPA score as the outcome variable, and group and NESI as predictors m.d_ngs <- ulam( alist( diotic ~ dbinom(120 , p ), logit(p) <- group[gid] + bG[gid] * nesi, group[gid] ~ dstudent( 2, 0, 0.5), # fixed prior for group bG[gid] ~ dstudent(2, 0, 0.3) ) , data=dm_l, chains = 1, cores = 4, warmup = 4000, iter = 6000, control = list(adapt_delta=0.95, max_treedepth = 15 ), log_lik = TRUE ) precis(m.d_ngs, depth=2) 91 ## mean sd 5.5% 94.5% n_eff Rhat4 ## group[1] 0.78097306 0.04606612 0.70903183 0.85576092 1855.555 0.9997091 ## group[2] 1.25861364 0.06776237 1.14956463 1.36526253 1465.614 1.0022601 ## group[3] 0.51958469 0.06655452 0.41595162 0.62955118 2226.649 0.9995057 ## bG[1] -0.09085053 0.02425603 -0.12958411 -0.05354926 2027.081 0.9995154 ## bG[2] 0.02712504 0.03004531 -0.02008239 0.07595275 1405.141 1.0010804 ## bG[3] 0.09616409 0.02233505 0.05902356 0.13115628 2176.168 1.0007997
Thesis/Dissertation
eng
Audiology and Speech Sciences
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Shahnaz, Navid
Multiple Auditory Processing Assessment as a tool to quantify hidden hearing loss
Text