Neural Correlates of Directional Hearing Following Noise-Induced Hearing Loss in The

Neural Correlates of Directional Hearing following Noise-induced Hearing Loss in the

Inferior Colliculus of Dutch-Belted Rabbits

A dissertation presented to

the faculty of

the College of Arts and Sciences of Ohio University

In partial fulfillment

of the requirements for the degree

Doctor of Philosophy

Hariprakash Haragopal

August 2020

This dissertation titled

Neural Correlates of Directional Hearing following Noise-induced Hearing Loss in the

Inferior Colliculus of Dutch-Belted Rabbits

HARIPRAKASH HARAGOPAL

has been approved for

the Department of the Biological Sciences

and the College of Arts and Sciences by

Mitchell L. Day

Assistant Professor of the Department of the Biological Sciences

Florenz Plassmann

Dean, College of Arts and Sciences 3

Abstract

HARAGOPAL, HARIPRAKASH, Ph.D., August 2020, Neuroscience Graduate Program

Neural Correlates of Directional Hearing following Noise-induced Hearing Loss in the

Inferior Colliculus of Dutch-Belted Rabbits

Director of Dissertation: Mitchell L. Day

Sound localization is the ability to pinpoint sound source direction in three dimensions using auditory cues. Sound localization in the horizontal plane involves two binaural cues (involving two ears), namely, difference in the time of arrival of sounds and difference in the level of sounds between the ears, known as interaural time difference

(ITD) and interaural level difference (ILD), respectively. Electrical recordings of neural activity, mostly in awake Dutch-Belted rabbits, have shown that neurons in the auditory nervous system, especially the inferior colliculus, which is an obligatory area along the auditory pathway, use these binaural cues to encode sound source direction (that is, directional information) in their firing rates (that is, the number of times they fire an action potential in a second). However, not much is known about neural encoding of directional information following hearing loss. Studies on human subjects have revealed a deterioration of sound localization ability pointing to a potential degradation of neural encoding of directional information as well. Here, we probe this by measuring directional information in neural firing rates from the inferior colliculi of awake, Dutch-Belted rabbits with severe noise-induced hearing loss. To induce hearing loss, rabbits were overexposed to loud noise. Our overexposure resulted in widespread damage to sensory hair cells within the hearing organ embedded within the ears, called the cochlea, and 4 created a ~50-dB elevation in hearing thresholds (that is, minimum responsive sound levels). Neural measurements showed that neural firing rates, on average, contained less directional information with hearing loss than normal hearing, even when sounds were sufficiently loud to evoke responses from neurons. This was because many sound-driven neurons conveyed directional information via monaural (involving one ear) sound level cues. Remaining sound-driven neurons were purely ILD-sensitive and exhibited a complete lack of ITD sensitivity. Computational modeling suggested that the lack of ITD sensitivity could not be simply explained by damage to the cochlea, implying that changes in central auditory areas may underlie this deficit.

Dedication

To my parents,

To Katie,

And to my mentor, Dr. Mitchell Day

Acknowledgments

I would like to thank my committee members, Dr. Scott Hooper for suggesting edits to the document (except Chapter 3, which was already published at the time of drafting of this document) which has improved, considerably, both the readability of the document and my ability to revise textual content, Dr. Alexander Neiman for his critical review of the computational methods and suggestions that have increased the scope of interpretability of results (Chapter 5) and Dr. Li Xu for suggesting that we analyze peripheral effects of noise-induced hearing loss which has greatly improved our understanding of hearing loss in the Dutch-belted strain of rabbits (Chapter 3), and has paved the way for making educated simulation of hearing loss (Chapter 5).

I would like to thank Dr. Mitchell Day, my doctoral advisor and committee chair, who not only chiseled me into a better experimentalist in the lab, but also expertly guided me in writing, while stressing the value of practicing thoroughness and checking the appropriateness of, within the bounds of daily calendar time, data acquisition, data analysis, result presentation and interpretation, modeling procedures, and for showing me how to execute systematic time-bound planning that spreads across years.

COVID-19-related pandemic has not been easy on any of us, and to graduate during times like this, a lot of additional pains have to be taken when a student is devoid of the usual luxury of an in-person meeting with the advisor. I am grateful to Dr. Day for coming up with an effective communication strategy and a clear timeline for the summer, built around thoroughness of advising, and painstaking edits, which has supported my progress into completing the dissertation. 7

This project was supported by funds from NIDCD-NIH. I had really able hands helping me through the project. I would like to thank Dr. Soichi Tanda for extracting the cochleae from rabbit temporal bones, and Dr. Mark Berryman for providing confocal images of the cochleae. Thanks to the undergraduates: Holly Johnson (for counting hair cells in the cochleae), and Gareth Whaley, Noelle Stroud, Timothy Wohl, Lukas Palmer and Kinzie Bailey for acquiring neural data. I would like to thank the lab technicians, Dr.

Ryan Dorkoski and Austin Pollard, for assisting in surgeries, in addition to acquiring neural data. I am grateful to Ohio University for facilitating confocal facility for imaging cochleae, and for providing rabbit housing.

None of this would have been possible without the incredible help and support from my parents and Katie Knies.

Table of Contents

Page

Abstract ...... 3 Dedication ...... 5 Acknowledgments...... 6 List of Tables ...... 12 List of Figures ...... 13 List of Abbreviations...... 15 Chapter 1: Introduction ...... 17 Sound Localization: Physical, Psychophysical and Behavioral Aspects ...... 17 Neural Mechanisms Underlying Sound Localization: Circuit to Function ...... 20 Noise-induced Hearing Loss: Prevalence and Psychophysical Effects ...... 24 Noise-induced Hearing Loss: Peripheral and Central Auditory Effects ...... 25 Noise-induced Hearing Loss: Implications for Psychophysical and Neural Sound Localization and Leading Hypothesis of the Present Study ...... 28 Chapter 2: General Methods ...... 30 Choice of Animal Model ...... 30 Assessments of Hearing Sensitivity I: Auditory Brainstem Response ...... 31 Assessments of Hearing Sensitivity II: Distortion Product Otoacoustic Emissions ... 34 Noise Waveform for Overexposure ...... 37 Cochlear Histology ...... 38 Chronic Electrode Design ...... 40 Rabbit Preparation for Hearing Assessments and Neural Recordings ...... 42 Generation of Virtual Acoustic Space for Closed-Field Stimulus Presentation ...... 47 Chapter 3: Results I: Paired Measurements of Cochlear Function and Hair Cell Count in Dutch-Belted Rabbits with Noise-Induced Hearing Loss ...... 49 Abstract ...... 49 Introduction ...... 50 Methods...... 52 Animals ...... 52 ABR and DPOAE Measurement ...... 52 Noise Overexposure ...... 56 9

Immunofluorescence ...... 57 Cochlear Hair Cell Counts ...... 58 Statistical Analysis ...... 59 Results ...... 60 Functional Assessments of Cochleae in Unexposed Rabbits ...... 60 Functional Assessments of Cochleae in Noise Overexposed Rabbits ...... 66 Cochlear Hair Cell Counts in Unexposed and Noise Overexposed Rabbits ...... 72 Comparison of Cochlear Functional Measures to Hair Cell Counts...... 77 Discussion ...... 80 Data from Unexposed Rabbits: Comparison to Other Strains and Species ...... 80 Threshold Shifts following Noise Overexposure: Comparison to Other Strains and Species ...... 81 OHC Susceptibility and Critical Threshold Shifts ...... 84 Sex Difference in Susceptibility to Noise Overexposure ...... 85 Comparison of ABR and DPOAE Thresholds...... 86 Chapter 4: Results II: Specific Loss of Neural Sensitivity to Interaural Time Differences following Noise-induced Hearing Loss ...... 88 Abstract ...... 88 Introduction ...... 89 Materials and Methods ...... 91 Experimental Procedures ...... 91 Stimulus Presentation and Neural Data Acquisition ...... 92 Auditory Brainstem Response Measurement...... 93 Noise Overexposure ...... 94 Stimuli ...... 94 Data Analysis ...... 97 Experimental Design ...... 100 Statistical Analyses ...... 101 Results ...... 102 Hearing Loss following Noise Overexposure ...... 102 Frequency Tuning following Noise Overexposure ...... 105 Spontaneous Firing Rates and Diotic Noise Thresholds following Noise Overexposure ...... 109 Directional Sensitivity following Noise Overexposure ...... 111 10

Binaural vs. Monaural Sensitivity...... 115 Binaural Cues underlying Directional Sensitivity ...... 119 ITD Sensitivity following Noise Overexposure ...... 122 Spike-timing Precision following Noise Overexposure ...... 127 Discussion ...... 133 Comparison to Previous Studies ...... 139 Potential Mechanisms for Loss of ITD Sensitivity ...... 140 Chapter 5: Computational Modeling of Potential Mechanisms Underlying Specific Loss of ITD Sensitivity with Severe Noise-Induced Hearing Loss ...... 145 Abstract ...... 145 Introduction ...... 146 Computational Methods ...... 148 AN Model ...... 148 Ventral Cochlear Nucleus Model...... 150 Modeling Synaptic Noise ...... 153 Stimuli ...... 162 Low-frequency Temporal Fine Structure (TFS) Sensitivity of AN Model Responses ...... 163 Shuffled Autocorrelograms ...... 164 Binaural Model of ILD Sensitivity ...... 166 Results ...... 166 AN Model Response Characterization: Rate-Level Function and TFS Sensitivity ...... 166 ITD Sensitivity Present for the AN Model in the HL Condition ...... 170 Spike-timing Precision of VCN in the Absence of Synaptic Noise ...... 174 Synaptic Noise Reduces VCN Model Spike-timing Precision ...... 177 Discussion ...... 181 Limitations of the AN Model Implementation ...... 184 Limitations of the ILD Model ...... 185 Limitations of SAC-based ITD Extraction from the AN Model ...... 186 Limitations of the VCN Model Implementation ...... 186 Limitations in Choosing Synaptic Noise Parameters ...... 188 Physiological Implications of the Computational Results for Improving ITD Sensitivity ...... 190 11

Chapter 6: General Discussion...... 191 References ...... 202

List of Tables

Page

Table 1. Within-ear, Pairwise-frequency Correlation of ABR Thresholds ...... 61 Table 2. Within-ear, Pairwise-frequency Correlation of DPOAE Thresholds ...... 62 Table 3. Auditory Nerve Model Parameters ...... 150 Table 4. Ventral Cochlear Nucleus Model Parameters at 22°C...... 152 Table 5. Synaptic Noise Parameters ...... 161

List of Figures

Page

Figure 1. Example Rabbit ABRs and DPOAEs ...... 54 Figure 2. ABR and DPOAE Thresholds in Unexposed Rabbits...... 63 Figure 3. Peak Amplitudes and Latencies of Click-evoked ABR Waveforms in Unexposed Rabbits ...... 65 Figure 4. ABR Threshold Shifts following 60-min Noise Overexposure...... 68 Figure 5. ABR and DPOAE Threshold Shifts following 90-min Noise Overexposure ... 69 Figure 6. Comparisons of Within-frequency Threshold Shifts ...... 72 Figure 7. Histopathology of Noise-induced Inner Ear Damage ...... 73 Figure 8. Cochlear Hair Cell Survival in Unexposed Cochleae ...... 74 Figure 9. Cochlear Hair Cell Survival in Noise-overexposed Cochleae ...... 75 Figure 10. Relationship between IHC and OHC Survival following Noise Overexposure ...... 77 Figure 11. Relationship between Threshold shifts and Cochlear Hair Cell Survival ...... 79 Figure 12. Hearing Loss following Noise Overexposure ...... 103 Figure 13. Frequency Tuning following Noise Overexposure ...... 107 Figure 14. Spontaneous Firing Rates and Diotic Noise Thresholds following Noise Trauma ...... 110 Figure 15. Directional Sensitivity following Noise Overexposure ...... 113 Figure 16. Binaural vs. Monaural Sensitivity ...... 118 Figure 17. Binaural Cues underlying Directional Sensitivity ...... 121 Figure 18. ITD Sensitivity following Noise Overexposure...... 124 Figure 19. No Relationship between Spike-timing Precision and ITD Sensitivity...... 129 Figure 20. Spike-timing Precision following Noise Overexposure...... 133 Figure 21. Schematic of the AN-VCN Noisy Synapse Model ...... 159 Figure 22. Rate-level Functions and Systems-identification of TFS Sensitivity ...... 168 Figure 23. ILD Information in AN Responses is Unaffected by Hearing Loss ...... 170 Figure 24. ITD Present in AN Responses in Hearing Loss is Reduced by Wider Coincidence Window ...... 173 Figure 25. Influence of Synaptic and Channel Parameters for a Noise-Free Synapse on Time-locking in VCN Responses ...... 176 14

Figure 26. Influence of Synaptic and Channel Parameters for a Noisy Synapse on Time- locking in VCN Responses ...... 180 Figure 27. Dependence of ILD sensitivity on CF in IC neurons of normal-hearing rabbits ...... 195

List of Abbreviations

'ABR': Auditory Brainstem Responses

'CF': Characteristic Frequency

'Contra': Contralateral

'DCN': Dorsal Cochlear Nucleus

'DNLL': Dorsal Nucleus of Lateral Lemniscus

'DPOAE': Distortion Product Otoacoustic Emission

'DTF': Directional Transfer Function

'EPSC': Excitatory Post-Synaptic Current

'EPSG': Excitatory Post-Synaptic Conductance

'eVRC': evoked Vesicle Release Count

'FRA': Frequency Response Area

'HRTF': Head-related Transfer Function

'HL': Hearing Loss

'IC': Inferior Colliculus

'ICC': Central Nucleus of the Inferior Colliculus

'IHC': Inner Hair Cell

'ILD': Interaural Level Difference

'Ipsi': Ipsilateral

'ISI': Inter-Spike Interval

'ITD': Interaural Time Difference

'KHT': High-threshold K+ channel 16

'KLT': Low-threshold K+ channel

'LNTB': Lateral Nucleus of the Trapezoid body

'LSO': Lateral Superior Olive

'mESPC': miniature Excitatory Postsynaptic Current

'mESPG': miniature Excitatory Postsynaptic Conductance

'MI': Mutual Information

'MInorm': normalized Mutual Information

'MNTB': Medial Nucleus of the Trapezoid Body

'MSO': Medial Superior Olive

'NH': Normal Hearing

'OHC': Outer Hair Cell

're: ': relative to

'SAC': Shuffled Autocorrelogram

'SNHL': Sensorineural Hearing loss

'SOC': Superior Olivary Complex

'SPL': Sound Pressure Level

'sVRC': spontaneous Vesicle Release Count

'TFS': Temporal Fine Structure

'UFE': Upper Frequency Edge in the FRA

'VAS': Virtual Acoustic Space

'VCN': Ventral Cochlear Nucleus 17

Chapter 1: Introduction

Sound Localization: Physical, Psychophysical and Behavioral Aspects

Mammals, birds, reptiles, and other animals display the ability to localize sounds in 3-d space using only auditory cues. Sound localization is important for locating conspecific calls, evading predators, detecting the direction of sounds made by prey, and specifically, directing the line of sight to the appropriate direction when the target cannot immediately be seen. Animals use three auditory cues to localize sounds: interaural time difference, interaural level difference, and spectral cues. For a sound source in the horizontal plane, sound may reach the closer ear a few hundred microseconds earlier and a few decibels greater than the farther ear, creating an interaural time difference (ITD) and an interaural level difference (ILD), which are binaural (involving two ears) cues.

The brain produces a coherent representation of the sound (i.e., we hear a single sound) by using these cues to localize the sound source. The angle that the sound source location

(in the horizontal plane) makes with a ray pointing straight outward from the front of the head is called the azimuth. The duplex theory for azimuth determination states that ITDs dominate at low frequencies since the diffraction of low-frequency sound waves around the head produce small ILDs, whereas ILDs are more pronounced for high frequencies due to acoustic shadowing (through reflection of incident sound waves) by the head

(Rayleigh, 1907). However, when sound sources are very close to the head, ILDs are large even for low-frequency sounds (Kim et al., 2010). At both high- and low- frequencies, head size largely determines the range of ITDs for a sound source at a fixed distance from the head, also called the ethological range. Therefore, animals with small 18 head sizes (maximum ITD < 200 µs) tend to rely more on ILDs, while animals with large head sizes (maximum ITD > 400µs) rely more on ITDs for localizing sounds in the horizontal plane (Heffner et al., 2016). Spectral cues, which are monaural (involving one ear), are produced by the pinna acting like an acoustic filter and are most pronounced in the ear closest to the sound source (Musicant et al., 1984). Sound pressure waveforms can be represented as a sum of component sinusoids of different frequencies, with each component sinusoid having its own magnitude and phase. The complex 3-d geometry of the pinna interacts with sound pressure waves and filters out some of frequencies creating spectral notches, especially in the high-frequency range. These spectral notches vary with the elevation (sound source angle with respect to the head in the vertical plane) of the sound source (Grothe et al., 2010).

Psychophysical studies show that humans use ITDs and ILDs to localize sound with an acuity of 1° (Mills, 1958) and are sensitive to changes in ITD of ~10 µs (Klumpp et al., 1956) and changes in ILD of ~1 dB (Mills, 1960). ITDs for low-frequency sounds are extracted from their fine structure (fast, fluctuating instantaneous amplitude) and for high-frequency sounds from their ongoing envelopes (slow, time-varying aggregate energy amplitude) (McFadden et al., 1976) or onset and offset (Joris et al., 2019).

Humans mainly rely on ITDs to lateralize sounds with low frequencies and, to a large extent, on ILDs for high frequencies (Bernstein et al., 1985; Wightman et al., 1992).

Sensitivity to envelope ITDs is mainly influenced by the shape of the envelope, and therefore, reliance on this cue is greatly improved by the choice of envelope shape

(Bernstein et al., 2002; Dietz et al., 2016; Klein-Hennig et al., 2011). 19

Directional discrimination ability in the horizontal plane is not the same across species. For instance, rabbits have a behavioral just-noticeable difference of azimuth of

22° (Gandy et al., 1995) and a behavioral just-noticeable difference of ITD of about 40 µs

(Ebert et al., 2008). Their behavioral sensitivity to ILDs is not known. Chinchillas have a behavioral localization acuity similar to that of rabbits, and exhibit sensitivity to both

ITDs and ILDs (Heffner et al., 1994). Like rabbits and chinchillas, ferrets have a nearly

15-degree minimum discriminable angle (Rooney, 1994), minimum discriminable ITD of nearly 40 µs, and minimum discriminable ILD of less than 2 dB (Keating et al., 2013).

Monkeys are very similar to humans and have a minimum discriminable angle of 4°

(Brown et al., 1982). Differences in head size and hearing range only partially account for these differences in localization acuity across species (Heffner et al., 2016), and behavioral task complexity has little influence on localization acuity (Carney et al.,

2011). Visual acuity appears to be a more reliable predictor of how well the animal uses localization cues: those with a wide visual streak on the retina (broad field of vision), like cattle (~30° localization acuity), are more likely to display poor sound localization ability, even when ITD and ILD cues are available to them (Heffner et al., 1992).

Conversely, primates have a narrow field of vision (foveal) and display superior sound localization acuity. Surprisingly, neural sensitivity to ITDs and ILDs are similar across species in the brain’s auditory areas (Batra et al., 1997; Brand et al., 2002; Jones et al.,

2015; Tollin et al., 2005; Yin et al., 1990). Poor visual acuity may potentially lead to suboptimal use of azimuth information available in the auditory areas. 20

Neural Mechanisms Underlying Sound Localization: Circuit to Function

Neural sensitivity to ITDs and ILDs in the mammalian system relies on highly conserved mechanisms and involves neurons in the superior olivary nuclei of the brainstem, the first station of binaural processing in the auditory pathway (Grothe et al.,

2010). In the inner ear (cochlea), sound-driven vibrations of hair bundles of mechanosensory cells, or hair cells, activate the hair cell-auditory nerve terminal synapses, triggering a train of action potentials in the auditory nerve fibers projecting to the cochlear nucleus in the brainstem. This projection conveys acoustic information via a combination of average spike rate (rate coding) and spike timing (temporal coding). The place-frequency map in the cochlea (cochleotopy) is retained throughout the auditory pathway. Auditory nerve fibers originating basally and apically have higher and lower characteristic frequencies (CFs), respectively, which correspond to the mechanical frequency tuning of those regions in the cochlea (Narayan et al., 1998). Auditory neurons are constrained by the phase-locking (cycle-by-cycle variation in timing of spikes) limit of the hair cell receptor potential to fine structure, which is approximately 3 kHz (Russell et al., 1978). Low-frequency-tuned neurons phase-lock to fine structure, whereas high- frequency-tuned neurons time-lock to envelope features.

Cochlear nucleus neurons receive auditory nerve (AN) inputs from the ipsilateral ear (the ear on the same side as the cochlear nucleus). Convergence of AN inputs onto a single cochlear nucleus neuron leads to reduced jitter in the output spike timing of cochlear nucleus neurons and enhances temporal coding (Louage et al., 2006), likely through monaural coincidence detection (Joris et al., 1994b). The outputs of spherical 21 bushy cells of the ventral cochlear nuclei directly excite both ipsilateral and contralateral nuclei of the medial superior olive and lateral superior olive (MSO and LSO, respectively). Outputs of globular bushy cells reach medial and lateral nuclei of the trapezoid body (MNTB and LNTB, respectively), which inhibit (through temporally precise, glycinergic synapses) neurons in MSO and LSO (Schreiner et al., 2005). ITDs in the fine structure are represented in the binaural responses of low-CF (< ~2 kHz) MSO neurons (Yin et al., 1990). “Peak-type” sensitivity to ITDs in the fine structure is derived from sub-millisecond coincidence detection of contralateral excitatory and ipsilateral excitatory inputs (“EE” sensitivity). The Jeffress model postulated compensatory axonal delay lines to produce maximum input coincidence at the neuron’s preferred ITD (ITD at which the neuron fires maximally) (Jeffress, 1948). Although evidence supports this mechanism in barn owls (Carr et al., 1990), it has not been supported by data in mammals, where preferred ITDs tend to vary with CF (Brand et al., 2002; McAlpine et al., 1996; McAlpine et al., 2001) and often are beyond the ethological range (Fitzpatrick et al., 2002). Alternative sources for internal delay include delay arising from slight mismatches in the CFs of ipsi- and contralateral inputs onto MSO neurons (Joris et al.,

2006c), effective delays induced by fast, temporally precise inhibition (received from the trapezoid body; role much debated) (Brand et al., 2002; Pecka et al., 2008; Roberts et al.,

2013), or more recently, delay arising from the complex interaction of synaptic inputs and ion channel dynamics (Franken et al., 2015). ILDs and ITDs (of the envelopes) are represented in the binaural responses of high-CF (> ~2 kHz) LSO neurons (Joris et al.,

1995). ILDs were thought to be encoded via “IE” sensitivity: ipsilateral excitation and 22 contralateral inhibition would interact to produce firing rates that varied as a function of

ILD; different level differences produce different combinations of inhibition and excitation. Since the inhibition is fast and precise (Tollin et al., 2005), IE sensitivity underscores “trough-type” sensitivity to ITDs (Joris et al., 1995); maximum coincidence of ipsilateral and contralateral inputs would lead to firing rate suppression. Like MSO neurons (Franken et al., 2015), LSO neurons exhibit fast, sub-millisecond membrane potential fluctuations (short time constants) and encode ILDs through coincidence detection (Franken et al., 2018). ILDs are thought to generate small time delays between left and right inputs because spike latencies vary as a function of stimulus intensity. Time differences between ipsilateral excitation and contralateral inhibition may be detected by

LSO neurons with the same coincidence detection mechanism as for ITDs (Ashida et al.,

2017).

The central nucleus of the inferior colliculus, or ICC (auditory midbrain), is an obligatory auditory station that receives convergent sensory inputs from all other brainstem auditory areas, including LSO, MSO, DCN and the dorsal nucleus of the lateral lemniscus (DNLL). MSO neurons send excitatory projections directly to the ipsilateral

ICC, while LSO neurons send excitatory projections to the contralateral ICC and inhibitory projections to the ipsilateral ICC directly and indirectly through the DNLL, which sends inhibitory projections to the ICC (Schreiner et al., 2005). The ICC has a frequency map. CFs of ICC neurons increase in the dorsolateral-to-ventromedial direction. Frequency-specific layers are arranged in well-defined fibrodendritic laminae

(Oliver et al., 1984). The ICC contains two prominent cell types: disc-shaped and stellate 23 neurons. Disc-shaped cells primarily synapse with other disc-shaped and stellate cells within the same layer. Stellate cells additionally make synapses with neurons across different layers (cross-frequency analyzer) (Schreiner et al., 2005). The neurons show a strong preference for contralateral stimuli (Moore et al., 1984). For broadband stimuli,

ICC neurons exhibit strong azimuth sensitivity and are sensitive to both ITDs and ILDs.

Their azimuth tuning curves (i.e., firing rate as a function of azimuth) have heterogeneous shapes, with a bias towards broad contralateral preference (that is, firing rates increase in the ipsilateral-to-contralateral direction) (Day et al., 2013; Delgutte et al., 1999). ITD sensitivity in the ICC is attributed to inputs from MSO for low-CF neurons (Loftus et al.,

2004) and convergent inputs from MSO and LSO for high-CF neurons, leading to ITD coding properties not observed in the MSO or LSO. For instance, the same ICC neuron can display either trough-type or peak-type ITD sensitivity depending on the modulation rate of the envelope (Wang et al., 2014). ILD sensitivity is partially driven by inputs from

LSO (Chase et al., 2005). Unlike brainstem nuclei, ICC neurons show weaker temporal coding, similar to AN fibers (Joris et al., 2006b). They also transmit information about

ITD and ILD mainly in their average firing rates (Chase et al., 2005; Chase et al., 2008).

The overall sensitivity to azimuth in an ICC neuron occurs through a complex interaction of ITD and ILD (Palmer et al., 2007). ICC neurons have been shown to be sensitive to

ITDs of fine-structure (if low-CF (~<2 kHz)), envelope (if high-CF) (Devore et al.,

2010), or both (CFs around 1kHz) (Joris, 2003). Since ICC neurons are sensitive to interaural correlation (Coffey et al., 2006; Joris, 2003; Mc Laughlin et al., 2007; Mc

Laughlin et al., 2008), their ITD tuning can be modeled as a simple cross-correlation of 24 left and right inputs (Hancock et al., 2004). ICC neurons use slower (millisecond-scale) temporal integration for ILD sensitivity (Brown et al., 2016).

Noise-induced Hearing Loss: Prevalence and Psychophysical Effects

More than 30 million people in the US are affected by some degree of hearing loss, often due to chronic exposure to loud noise in the work environment, such as mining, military and various industrial activities involving heavy motorized equipment

(Donoghue, 2004; Hong, 2005; Le et al., 2017; Rabinowitz, 2000; Saunders et al., 2009).

Typically, noise-induced hearing loss depends on the noise level, duration and frequency of exposures (Bohne et al., 1982).

Noise-induced hearing loss in humans impairs high-frequency hearing while leaving low-frequency hearing relatively unaffected (Erickson et al., 1980; Wheeler,

1950). It can induce chronic and persistent tinnitus (perception of ringing without actual sound) (König et al., 2006; Mulders et al., 2009) and hyperacusis (audible sounds feel very loud) (Auerbach et al., 2014). High-frequency hearing loss has debilitating consequences for speech-in-noise detection (Smoorenburg, 1992), where high-frequency speech components (like sibilants) are swamped by enhanced low-frequency modulations of background noise (Füllgrabe et al., 2003). Moreover, temporal processing alterations lead to deficits in pitch, gap and speech perception (Fitzgibbons et al., 1987; Hopkins et al., 2010; Moore, 2008; Strelcyk et al., 2009). Some of these perceptual deficits, like deterioration of speech intelligibility in noise, are substantial even for mild hearing loss

(Festen et al., 1990; Liberman et al., 2016; Zurek et al., 1987). Therefore, extensive 25 research has been conducted on different animal models to understand peripheral and central processing alterations following noise overexposure.

Noise-induced Hearing Loss: Peripheral and Central Auditory Effects

Noise overexposure damages the sensory epithelium and the innervating AN in the cochlea, but leaves the eardrum and middle ear bones intact, producing sensorineural hearing loss and elevating the threshold of audibility.

At low sound levels near the threshold of normal hearing, AN fibers are sensitive to a very narrow band of sound frequencies (Kiang et al., 1976). AN frequency selectivity is thought to arise from a cochlear amplifier dependent on outer hair cells (OHCs)

(Liberman et al., 1984a; Liberman et al., 2002). OHCs, through their electromotility

(Liberman et al., 2002), are thought to boost basilar membrane oscillations in a cycle-by- cycle manner at low sound levels (Hudspeth, 2014), producing narrow-band frequency selectivity in the region. However, this form of active boosting has been recently debated regarding OHCs in basal regions (Vavakou et al., 2019) because 1) they have an in vivo phase-locking limit of ~3 kHz, and 2) there is a lack of direct physical evidence of active boosting (van der Heijden et al., 2015). At higher sound levels, OHCs are compressive: they tend to dampen basilar membrane oscillations (van der Heijden et al., 2015). Noise overexposure damages OHCs. Damage to OHCs results in both elevation (or in extreme cases, an absence) of the frequency tuning curve tip and broadening of frequency tuning of AN fibers at high sound levels (Liberman et al., 1978). Inner hair cells (IHCs) are responsible for transduction and transmission of acoustic information to the AN through

IHC-AN-fiber synapses. 26

Susceptibility to noise-induced damage involves different vulnerable structures in the cochlea. For mild-to-moderate levels of noise overexposure (100 dB SPL or lower), there is a reduction in the number of viable synapses between IHCs and AN-fiber terminals, known as cochlear synaptopathy (Kujawa et al., 2009). High concentration of synaptic glutamate released by IHCs under intense stimulation (excitotoxicity) damages nerve terminals (Puel et al., 1998). When stimulation level is high enough, stereocilia of

IHCs and OHCs show various hallmarks of mechanical damage, such as the absence of a

V-shaped pattern, bends, and tears that render them non-motile (Borg et al., 1995). Noise overexposure experiments in rabbits suggest that stereocilia of IHCs are more prone to damage than those of OHCs (Engström, 1983). However, OHCs are less likely to survive noise exposure than IHCs, as shown in rabbits (Borg et al., 1995), guinea pigs (Fredelius et al., 1987), monkeys and chinchillas (Stebbins et al., 1979). Cell death may occur from accumulating reactive oxygen species leading to apoptosis (Kurabi et al., 2017).

Supporting cells are less susceptible to damage from intense noise (Hu et al., 2006).

Spectral resolution is compromised following hearing loss. The cochlea no longer operates like a series of overlapping narrow-band channels but operates more like a lowpass filter with basal-ward spread of sensitivity to low frequencies, consistent with the loss of OHCs. AN fibers in the basal region of noise-exposed cochleae phase-lock to the fine structure of low-frequency components of sound (Henry et al., 2016) and also display exaggerated sensitivity to envelopes (Zhong et al., 2014) at CFs lower than those found in unexposed cochleae (Kale et al., 2010). Consistent with widening of frequency selectivity to lower frequencies, basal AN fibers display high temporal precision of 27 envelope coding in their responses (that is, enhanced time-locking to envelopes) (Henry et al., 2014). Enhanced time-locking to fluctuating background sounds, including envelope coding in low-CF AN fibers and fine-structure coding of low frequencies in high-CF AN fibers, may alter temporal coding of speech frequencies in noise.

Input deprivation causes an increase in central gain (that is, an enhanced neural response to suprathreshold sound relative to AN fiber responses). Central gain is compensatory in that even with selective loss of IHC-AN synapses through pharmacological means, there is no dramatic increase in pure tone thresholds of neurons of the inferior colliculus (Chambers et al., 2016) or auditory cortex (Asokan et al., 2018).

Central gain changes are seen to some extent in the cochlear nucleus, but only for certain cell types (Cai et al., 2009; Manzoor et al., 2012). Central gain does not appear to affect temporal coding properties in the cochlear nucleus (Cai et al., 2009) and only restores rate coding (Chambers et al., 2016). Increased disinhibition and possible left-right asymmetries in hearing loss (Kontorinis et al., 2014) cause central hyperexcitability and elevated spontaneous rates, leading to tinnitus (Mulders et al., 2011) and hyperacusis

(Auerbach et al., 2014). Hyperexcitability in response to input deprivation may be a result of weakening of inhibitory synapses and increased excitatory synaptic strength (Le Prell,

2012; Vale et al., 2002) or increased corticofugal activity from layer 5 of auditory cortex

(Asokan et al., 2018). Thus, noise overexposure affects human perception of sounds likely through complex neural adaptation to sensory deprivation. Sound localization ability and its underlying neural mechanisms are also likely not immune to these alterations. 28

Noise-induced Hearing Loss: Implications for Psychophysical and Neural Sound

Localization and Leading Hypothesis of the Present Study

Humans with compromised hearing have difficulties with accurately pinpointing sound sources. In one study (Lorenzi et al., 1999a; Lorenzi et al., 1999b), listeners with either bilaterally symmetric, high-frequency hearing loss or normal hearing, were presented with a train of broadband clicks filtered to contain high-frequency or low- frequency components to test sound localization in both frequency ranges. Listeners with hearing loss showed poorer sound localization ability for sounds with high frequencies, partly due to some frequencies being inaudible. Only a subset of listeners that also had some low-frequency hearing loss made errors in localizing low-frequency sounds.

Further, localization errors increased when the filtered clicks were played in the presence of a masking white noise. However, the study did not look at how the two sound localization cues were affected. Later studies showed that ITD sensitivity was reduced in the hearing-impaired (Lacher-Fougere et al., 2005), where elevated ITD detection thresholds of 50-200 µs were found (normal-hearing ITD thresholds were < 50 µs) (Dai et al., 2018). Elevation of ITD thresholds resulted in poor performance in a spatial attention task, suggesting a temporal processing issue (Dai et al., 2018).

Neural mechanisms underlying azimuth sensitivity following noise overexposure are not yet understood, nor is the effect of noise-induced hearing loss on the neural coding of

ITDs and ILDs known. One study reported altered azimuth tuning curves in the IC of the

C57 strain of mice with bilateral hearing loss, but the hearing loss was age-related

(McFadden et al., 1994b). However, since acoustic trauma affects inhibition and causes 29 alterations in temporal coding, both of which are crucial to neural sensitivity to azimuth cues, the key hypothesis of the present study is that azimuth sensitivity in ICC neurons is reduced following noise-induced hearing loss owing to reduced neural sensitivity to both

ITDs and ILDs. To test this hypothesis, Dutch-belted rabbits were used. This strain of rabbit is widely used in sound localization research and highly amenable to awake, head- fixed neural recordings. The experimental paradigm involved exposing Dutch-belted rabbits to intense noise under anesthesia to induce severe hearing loss, assessing their hearing levels, and surgically implanting chronic electrodes into the ICC to measure neural responses to changes in azimuth, ILDs and ITDs of sounds. Neural responses from normal-hearing rabbits served as controls. Our experimental strategy also included collecting both pre- and postexposure neural data from a subset of rabbits. A subset of rabbits that were used to obtain normal-hearing data for this study were also used in a previous normal-hearing study in the lab (Dorkoski et al., 2020) and were not overexposed. An additional hypothesis was that cochlear damage from noise overexposure in this strain of rabbit shares similarities with other strains of rabbit and other animal species, thereby expanding the scope of the results in rabbits to other species and potentially to humans with severe hearing loss. To test this additional hypothesis, results from paired measurements of structural and functional alterations in overexposed cochleae were compared against the literature.

Chapter 2: General Methods

This chapter introduces various techniques used in this dissertation study. These techniques have been developed over several years in the auditory neuroscience field, culminating into a body of research that has expanded the understanding of the auditory nervous system. This chapter distributes the topic into 7 broad divisions: choice of the animal model; assessments of hearing sensitivity; noise waveform for overexposure; cochlear histology; chronic electrode design; rabbit preparation for hearing assessments, noise trauma and neural recording; and generation of virtual acoustic space for closed- field stimulus conditions.

Choice of Animal Model

Rabbits have a hearing range that overlaps with human audiograms (Heffner et al., 1980), including low and high frequencies. They have binaural sensitivity to interaural phase correlations, similar to humans (Bernstein et al., 1998; Early et al.,

2001), have been studied extensively following noise overexposure (Borg et al., 1995), and show a behavioral localization acuity of ~20° (Gandy et al., 1995). The Dutch-belted strain of rabbits has a behavioral ITD discrimination threshold of ~40 µs (Ebert et al.,

2008). It is, however, unknown if rabbits can detect elevation or differentiate front/back locations as well as humans, but they can detect sound source direction (Kuwada et al.,

2015). This strain of rabbits has been used to study subcortical neural mechanisms of sound localization in various auditory areas like the IC (Day et al., 2013; Kuwada et al.,

1989; Kuwada et al., 2011), DNLL (Kuwada et al., 2006), SOC (Batra et al., 1997), medial geniculate nucleus of the thalamus (Stanford et al., 1992) and the auditory cortex 31

(Fitzpatrick et al., 1999) in awake conditions. Anesthesia can alter neural coding of ITDs in the rabbit (Kuwada et al., 1989), and would not reflect physiological processes in the awake condition. Also, Dutch-belted rabbits are amenable to lengthy recording sessions under restraint.

Assessments of Hearing Sensitivity I: Auditory Brainstem Response

The two methods in this study for assessing hearing levels in rabbits are auditory brainstem response (ABR) measurements, and distortion product otoacoustic emission

(DPOAE) measurements.

ABRs can be identified as aggregate electrical responses in the auditory pathway evoked by transient sounds, such as short tone pips or clicks, and recordable using surface electrodes or subdermal needle electrodes. ABR recording typically involves 3 subdermal needles, one on the vertex (top point of the head), one on the mastoid (side of the head, close to the ear) and one on the back (grounding). The typical recordings are voltage differences, measured vertex to mastoid, and subtracting the common ground from the difference voltage. The electrical response waveforms are tiny fluctuations in the difference voltage in time (amplitude ~ few µV). Peaks in the fluctuations, separated by approximately 0.8 ms (Eggermont, 2019), have been traced to synchronized activity in specific generators in the auditory pathways, namely the auditory nerve and various sources residing in the brainstem. These sources, identified by selective lesions in the cat

(Melcher et al., 1996),represent the approximate order of transmission of information along the auditory pathway. The ABR waveform typically consists of Waves I through V, defined by the peaks, and may contain additional peaks. Waves I–V correspond to 32 activities of the auditory nerve, cochlear nucleus, olivary complex, lateral lemniscus, and

IC, respectively. Additional waves indicate auditory thalamic and cortical activity. The delineation of different waveform components of the ABR into these generators in the time-domain is not strict. If the decay time constant in the preceding generator is greater than the time of transmission and rise of activity in the succeeding source, then the preceding generator could contribute to the succeeding peaks. This is particularly observed at high sound levels. For example, before sound-evoked superior olivary nucleus activity dies down, lateral lemniscus activity could increase. Therefore, ABRs can have fused waves, which exhibit, for instance, double-peaks, and in the above case, a double peak riding on a fused Wave III and Wave IV. Moreover, although lesioning a nucleus in the auditory pathway reduces activity in successive nuclei, it does not fully eliminate activity from the preceding nucleus (Melcher et al., 1996).

Collecting many ABRs to the same stimulus and averaging the response reduces measurement noise. These averaged ABRs have a minimum sound level at which they appear unambiguously, marking the hearing threshold for that sound. For sound levels above the threshold, average ABR waveforms increase in amplitude and have sharper definition. In hearing loss, ABR thresholds are elevated and the observed shifts are a measure of hearing loss. An indication of permanent hearing loss is a persistent threshold shift. ABR waveforms are also informative about conduction delays (interpeak delays) and level of synchrony (peak amplitudes), both of which change with hearing loss (Don et al., 1994). First-wave peak latency (time-to-peak relative to stimulus onset) and amplitude are indicative of the health of IHC-AN synapses and they point to clinical 33 potential for the use of ABRs in detecting cochlear synaptopathy (damage to IHC-AN synapses) (Mehraei et al., 2016). AN-fibers are categorized into three groups based on spontaneous rates (SR): low-SR (which also have high thresholds), medium-SR, and high-SR (which also have low thresholds). Low-SR fibers (<18 spikes/s) encode information only at high sound levels, while medium- and high-SR fibers (>18 spikes/s) encode at low sound levels, but saturate in firing rate at moderate sound levels

(Liberman, 1978). Together, the AN-fiber types confer a large dynamic range for rate- coding of sound intensity. Low-SR fibers are most susceptible to noise-induced damage and their selective neuropathy can be detected by reduced ABR Wave I peak amplitude, whereas ABR thresholds are unaltered (Furman et al., 2013). This implies that ABR thresholds are largely determined by the activity of high-SR fibers. Ratios of peak amplitudes (e.g., Wave V-to-Wave I) can reflect gain (central responses relative to auditory nerve activity) in the auditory system. Abnormalities in peripheral hearing can result in altered peak ratios, such as an enhanced Wave V/I peak ratio in tinnitus

(Schaette et al., 2011). Animal studies have shown that ABR threshold shifts for mild-to- moderate (20–40 dB) hearing loss can be correlated with peripheral damage to stereocilia on the inner and outer hair cells, and hair cell loss (Borg et al., 1995; Chen et al., 2014;

Harding et al., 2002). Tone-evoked ABR threshold shifts can quantify cochlear transduction issues at specific frequency locations along the cochlea (Henry et al., 2011).

Tone-evoked ABR thresholds correlate well with AN fiber thresholds for fibers with a

CF near the tone frequency (Henry et al., 2013). Since thresholds of AN fibers are particularly sensitive to the health of OHCs and less so for IHCs (Liberman et al., 1984a), 34

ABR threshold shifts reflect loss of OHCs. Other ABR-based metrics like the slope of the function of Wave-V/I peak ratio vs. sound level can help determine the extent of hair cell loss from synaptopathy in mixed pathologies (Verhulst et al., 2016).

Assessments of Hearing Sensitivity II: Distortion Product Otoacoustic Emissions

The existence of otoacoustic emissions—tiny sounds emitted by cochlear activity—was first proposed as a theoretical consequence of cochlear amplification at low sound levels (Gold, 1948). Although the cochlea can spontaneously generate these emissions, they were first recorded with a brief pure tone stimulation (Kemp, 1978).

When stimulated by two or more tones, the cochlea emits sounds that contain novel frequency components, called distortion products that are thought to reflect the non-linear cochlear amplification process (Dong et al., 2013; Kemp, 1979; Shera, 2004b). Such novel sounds emitted by the cochlea are termed distortion product otoacoustic emissions

(DPOAEs). With two-tone stimulation of primary and secondary tones of frequencies f1 and f2, respectively, with f1 < f2, the largest (in amplitude) of the distortion products emitted by the cochlea is the cubic distortion product, 2f1-f2 component (Lasky et al.,

1994).

There are two cochlear sources for the 2f1-f2 distortion product, namely the generative and reflective sources, which interfere to produce the resultant amplitude in cochlear emissions (Whitehead et al., 1992). Basilar membrane vibrations, corresponding to f1 and f2 frequencies, produce distortion products or novel tones, including the component at 2f1-f2, through a mechanism involving cochlear non-linear amplification process (described below). The amplitude of distortion products depends on the f2/f1 35 ratio. A ratio of 1.22 produces the largest amplitudes of the distortion product, whereas deviations above or below this ratio result in reduced amplitudes (Abdala, 1996). The traveling wave envelope peaks at a characteristic location along the length of the cochlea dependent on the stimulus frequency, and exhibits a sharp apical cutoff and shallow basal slope (Olson et al., 2012). Increasing the sound level of the preferred frequency decreases the basal slope, widening the traveling wave envelope basal ward. For a fixed f2/f1 ratio, as the sound level at f1 (the lower of the two stimulating frequencies) increases above the sound level at f2, f1-related BM vibrations grow in the basal direction and overlap the apical aspect of f2-related BM vibrations. When the f2/f1 ratio and sound levels of the two tones, L1 and L2, are chosen such that there is maximal overlap of vibrational modes related to f1 and f2, the location of maximal overlap, where f1 and f2 interact non-linearly, serves as the generation site for the 2f1-f2 DPOAE. For a f2/f1 ratio of 1.22, maximum

DPOAE amplitudes are obtained for L1 = L2+10 dB (Abdala, 1996; Whitehead et al.,

1995). Although the exact mechanism of transmission of DPOAEs to the stapes is unclear

(Ren, 2004), these waveforms travel back through the middle ear bone and tympanum and can be measured in the ear canal by a sensitive, low-noise probe microphone. The cochlear location corresponding to the generation site is closer to the f2 location (Kim et al., 1980). Therefore, the f2 cochlear location has most influence on DPOAE amplitudes when sound levels are high (>50 dB). High sound levels reduce the growth of DPOAE amplitude due to compressive action by OHCs. The f1 and f2-related BM vibrations are thought to be amplified by OHCs close to the f2 location, generating the 2f1-f2 component, which travels to and is amplified at its preferred cochlear location (2f1-f2 36 location) (Kim et al., 1980). The amplified distortion product travels backward to the stapes (Shera et al., 1999). The reflection component dominates at low sound levels due to additional amplification at the 2f1-f2 location, since energy originating purely from the generation site at these frequencies is very small. However, because the generation site determines the existence of the distortion product, DPOAE amplitudes ultimately reflect

OHC health in a region close to the f2 location.

Since DPOAE amplitudes increase with input sound level and depend on OHC activity at the f2 location (Harding et al., 2002), DPOAE amplitude and threshold are good indicators of OHC health in the f2 region. Because DPOAE amplitudes are reported at a fixed sound level, they do not compare directly to ABR thresholds; therefore,

DPOAE thresholds are also often reported (Liberman et al., 2002). One way to quantify a

DPOAE threshold is to estimate the L2 (or L1) level at which the DPOAE amplitude crosses a criterion amplitude. The choice of criterion amplitude depends on the noise floor of the probe microphone. A low criterion amplitude (such as 0 dB SPL or -5 dB

SPL) may be chosen if the noise floor is low and has the advantage of providing sensitive measurements of DPOAE thresholds. Threshold can also refer to the sound level at which

DPOAEs exceed a criterion signal-to-noise ratio. This has the advantage of DPOAE amplitudes not being overestimated due to the presence of noise (Whitehead et al., 1993); however, the noise floor may vary across frequency depending on the acoustic assembly

(this point is discussed later in this chapter). Regardless of the choice of threshold metric,

DPOAE threshold shifts correlate with loss of OHC function (Fernandez et al., 2020). To compute DPOAE amplitudes, acoustic waveforms measured at the ear canal are averaged 37 over a large number of repetitions of the two-tone stimulus (to reduce noise), then are

Fourier transformed (decomposing sound pressure from the time domain into the frequency domain) to produce the magnitude spectrum. In the magnitude spectrum, several distortion products along with f1 and f2 tones appear as peaks. Peak amplitude corresponding to the 2f1-f2 DPOAE is measured. The noise floor is typically computed from the average of amplitudes of 4 frequencies immediately flanking either sides of 2f1- f2 frequency in the magnitude spectrum (Liberman et al., 2002).

Noise Waveform for Overexposure

Two free-field speakers, one pointed at each ear, were used to create bilateral hearing loss. This concurrent noise presentation design was adopted to ensure that 1) noise waveforms could be independently varied, and 2) waveforms could be symmetrically presented to each ear to create bilateral hearing loss with the aim of reducing asymmetric hearing loss. As sound localization circuits are sensitive to the direction of a sound source, using a single speaker placed directly in front of the head may bias hearing loss towards a fixed ILD and ITD of 0. However, with two concurrent sound sources, noise waveforms can be designed such that they are uncorrelated between the speakers. Uncorrelated waveforms produce fluctuating instantaneous ILD and ITD values in time (Day et al., 2012) and avoid bias toward a fixed ILD and ITD. Waveforms were decorrelated in this study using pseudorandom number generators with independent noise seeds for each speaker. The noise waveforms were octave-band, geometrically centered at 750 Hz (530 Hz to 1061 Hz). Noise overexposure at frequencies in the middle of the hearing range induces high-frequency hearing loss in the rabbit (Borg et al., 1995, 38 their Fig. 13). Therefore, low-frequency hearing may not be affected if the noise spectrum was centered at a high frequency. Preserved low-frequency hearing may allow

ITD sensitivity in central sound localization circuits. The noise spectrum was therefore chosen to compromise both low- and high-frequency hearing.

Cochlear Histology

To count hair cells, cochleae were extracted from rabbits, postmortem. The cochlea is embedded in a snail-shaped bony spiral shell called the labyrinth and is difficult to remove from its encasement without damaging it. However, the bony shell can be softened by EDTA, which chelates and removes calcium, such that very little bone remains and the Organ of Corti can be dissected from the remaining cochlea under a dissection microscope. The Organ of Corti consists of a highly systematic arrangement of epithelial cells, including the sensory hair cells, throughout the extent of the cochlea, with

3 rows of OHCs and 1 row of IHCs (Pickles, 2013). The supporting pillar cells are wedged between IHCs and OHCs. On the modiolar (towards the central axis of the cochlea) side of the IHCs, AN-fiber tracts lead into a cluster of nerve cell bodies making up the spiral ganglion. We used an immunostaining technique to visualize hair cells. The technique relies on using cell-specific marker proteins that differentiate cell types. Hair cells contain the protein Myo-7A, a myosin variant found exclusively in these cells within the cochlea (Hasson et al., 1997), which maintains the structure and mechanical function of stereocilia (Gibson et al., 1995). The immunostaining technique involves fluorescently tagging these proteins. Briefly, a primary antibody against the target binds to the target. A secondary antibody attached to a fluorescent tag molecule binds the 39 primary antibody. This way, the fluorescent tag reports the presence (or absence) of the target protein. In the case of dead hair cells due to noise trauma, Myo-7A is no longer available and immunostaining reveals holes. Supporting epithelial cells are more resilient to reactive oxygen species than hair cells (Sha et al., 2001) and their damage indicates severe mechanical trauma (Hu, 2012). Support cells are stained for actin using phalloidin

(which binds actin) attached to a fluorescent tag. Since hair cells have actin, they also stain for actin.

We measured noise-induced ABR and DPOAE threshold shifts for specific tone frequencies. To compare to threshold shifts, hair cells were counted in cochlear regions specific to those tested frequencies. For this, the Organ of Corti was cut into well-defined pieces sequentially from base to apex and whole mounts (containing the entire tissue) of the pieces were immunostained and visualized. This way, different pieces would correspond to different tonotopic regions of the cochlea. A Greenwood (1990) function was used to map frequency to place along the cochlea and has been determined for various animal species . The use of this function helps pinpoint the cochlear position of interest; however, one problem persists: the defining of a region around that location in which hair cells should be counted. Two observations helped resolve this problem. 1)

Regardless of the location, there were ~15 hair cells/ percent cochlear distance (where cochlear distance is the length of the entire cochlea). 2) Staining can reveal either survival (presence of staining) or death (absence of staining) of hair cells and may be modeled as a Bernoulli process. If N is the total possible number of hair cells in a region, p is the proportion of surviving hair cells, and 1-p is the proportion of dead hair cells, 40

푝∗(1−푝) then the standard error of p is √ . For a given N, a p of 0.5 gives the maximum 푁 standard error. For a criterion maximum standard error of 8%, N is 37.5. A region containing twice that number of possible hair cells (75, or 5% of cochlear distance) would yield the same maximum standard error if half of the tissue were damaged. So, a region of ±2.5% cochlear distance centered at each frequency place (or ~0.4-oct total bandwidth for rabbit cochleae) was chosen to estimate percent survival with a standard error of no more than 8%.

Chronic Electrode Design

Neural recordings were carried out extracellularly using tetrodes, which are constructed from 4 recording microelectrodes. Each microelectrode was a thin, insulated platinum-iridium wire of ~20-µm diameter, which matches the somatic diameter of an IC neuron (Ito et al., 2009)). Microelectrodes were twisted about one another and heat- bonded to keep wires together. A tetrode provides 4 slightly different snapshots of electrical activity at the tetrode tip location in the brain. Raw voltage traces contain information about local field potentials (LFPs; slow-varying component) on which ride action potentials (fast-varying component). To extract action potentials, the traces were high-pass filtered to remove the LFP, then low-pass filtered to remove high-frequency noise. The filtered voltage traces appear as small-amplitude noise punctuated by sharp positive or negative spikes (extracellular action potentials). These spikes are not necessarily responses from a single neuron (single unit) and may be contaminated by activity from other neurons (multiunit). Therefore, spikes from a single unit can only be isolated if they differ in some property from those of the other units, e.g., in peak height, 41 slope, or waveform shape. Recording from one electrode does not provide as many features that define a single unit as does recording from 4 electrodes. For instance, each electrode on a tetrode is at a specific distance from the spike generator and may measure different peak heights from the same neuron. Thus, tetrodes offer improved feature-based clustering of single units from multiunit activity (Gray et al., 1995; Harris et al., 2000).

Multitetrode drives contain multiple tetrodes, each of which can be independently advanced or retracted (Dorkoski et al., 2020). Tetrodes are encased within their middle portion by polyimide tubes. The polyimide-encased tetrodes go through stainless steel guidetubes that project downward from the drive body. The guidetubes are spaced such that inter-tetrode distances in the drive are ~0.3 mm. One end of the tetrode is a single shank of 4 twisted wires, which is advanced into the brain. This end has an exposed portion (that is, the part that comes outside of polyimide) which is stiffened by application of cyanoacrylate adhesive, thereby preventing the tetrode from bending when advanced into the brain (Jog et al., 2002). The other exposed end of the tetrode is splayed into its 4 component wires, each of which is pinned to a recording channel on the electrode interface board (EIB), which is mounted over the drive body. The EIB provides voltage readouts through a connection port wired to the acquisition electronics for amplification, computer display, and storage for offline analysis of the recordings. For advancement or retraction of tetrodes, the drive body contains sleeves for movable tetrode shuttles that are attached to the polyimide-encased tetrode shank. The shuttles can be moved up and down using screws. Whole turns of the shuttle screws move the tetrodes up or down (clockwise or anticlockwise, respectively) by 250 µm. Tetrodes were 42 electroplated to reduce impedance by increasing tip surface area (Ferguson et al., 2009).

Reducing impedance improves the signal-to-noise ratio. The post-coating tetrode tip diameter was ~50µm (Jog et al., 2002).

Rabbit Preparation for Hearing Assessments and Neural Recordings

All rabbit procedures were approved by the Ohio University Institutional Animal

Care and Use Committee (IACUC). Dutch-belted rabbits (purpose-bred; that is, bred specifically for use in experiments) were obtained from Covance at 4 to 5 months of age.

Rabbits were quarantined for a week upon arrival and before handling. Following this, the males and females were housed separately in respective rooms and in separate cages.

Rabbits were kept on a 12-hr day-night cycle. They were on a diet that would keep their weights near 2 kg. All surgical procedures involved keeping rabbits under surgical planes of anesthesia (areflexia; here, confirmed by an absence of reflex to a firm toe-pinch), while their vitals (heart rate, blood-oxygen levels and temperature) and areflexia were monitored every 15 minutes. Body temperature was maintained by a feedback heating pad (38°–39°C). For headbar implantation surgery, rabbits were anesthetized with an initial dose of xylazine (6 mg/kg), acepromazine (1 mg/kg), and ketamine (44 mg/kg) and given booster doses of ketamine (15 mg/kg) during surgery, if animals showed a reflex to toe-pinch. For all other anesthetized procedures, namely, creating earmolds, tetrode drive implantation, noise overexposure and hearing loss assessments, rabbits were administered xylazine (6 mg/kg) and ketamine (35 mg/kg), and kept under isoflurane anesthesia

(provided by a gas mask delivering a mixture of the anesthetic and oxygen; 1-2%).

Rabbits undergoing invasive head surgeries (headbar implantation and tetrode drive 43 implantation or replacement) were given post-operative doses of dexamethasone (to prevent brain swelling; 0.3 mg/kg) and 4 doses of buprenorphine (to alleviate pain; 0.025 mg/kg; one right after the surgery and 3 others spaced in 12-hr intervals).

Microdrives are designed for chronic recording, that is, recording for a long time using the same drive. Therefore, rabbits underwent surgery under anesthesia to affix a headbar directly on the skull to help secure the drive for chronic recording and provide the angle for electrode penetration into the IC. The back portion of the headbar had a slot through which the skull could be accessed to target the drive to either the left IC or right

IC. The headbar also had its front portion projecting out, which could be used to head-fix rabbits in awake neural recordings. At least 2 weeks after headbar implantation surgery and after allowing for recovery, rabbits underwent drive implantation. The portion of the skull within the headbar slot was drilled until dura mater covering the occipital cortex was exposed (craniotomy). Then, the dura was cut, and the drive implanted and cemented to the headbar with dental acrylic. This cementing procedure secured the drive firmly in place. The tetrodes were immediately extended to their full extent, and only retracted during the neural recording sessions in ½-turn steps. Advancing tetrodes step-by-step into the brain would result in guidetubes being plugged by tissue debris, especially, during long-term recordings (2–3 weeks), causing tetrodes to get stuck and precluding sampling of neurons in the deeper (ventral) tonotopic areas of the ICC. Such issues are avoided by retracting tetrodes. Tetrode drive replacement surgeries were performed to record from the same rabbit many times. 44

Sounds were delivered to the acoustically sealed ear canal (closed field) through earmold inserts fitted with a sound transfer tube and a microphone probe tube.

Immediately before the start of hearing assessments or neural recordings, the acoustic assembly was calibrated by presenting a frequency sweep stimulus and sampling sounds in the ear canal with the probe tube connected to a microphone on the other end. Sound waveforms at the ear canal may be transformed (altered with respect to the original waveform) by the speaker or the sound transfer path through the earmold. Calibration maps out the relationship between the voltage waveform at the speaker and the actual sound waveform measured in the ear canal and helps correct for any spectral transformation introduced by the acoustic setup. For neural recordings, earmolds had one sound transfer tube, whereas for hearing assessments, they were augmented to have an extra sound transfer tube for DPOAE measurement, where two tones were presented each through separate speakers, to prevent distortion arising from the acoustic assembly. For

DPOAE measurements, the noise floor at different 2f1-f2 frequencies depended on both the quality of the acoustic seal (e.g., a tighter seal produced lower noise floors) and the microphone noise floor.

All rabbit recordings and noise overexposure were carried out inside a double- walled, sound-attenuating chamber on a vibration-isolating table and monitored via closed-circuit video throughout the recording session. For hearing assessments, rabbits were kept on lower isoflurane anesthesia (1% in oxygen) since higher levels can reduce hearing sensitivity (Cederholm et al., 2012; Ruebhausen et al., 2012). DPOAEs were measured before ABRs since measuring ABRs immediately before DPOAEs increases 45

DPOAE thresholds (Mhatre et al., 2010). For DPOAEs, two tones were delivered at the ear canal and the probe microphone measured acoustic pressure waveforms. ABRs were measured in response to tones and clicks using subdermal needles placed at the vertex, mastoid, and back. For overexposure, sounds presented from each free-field speaker were adjusted in the absence of the rabbit until the desired sound level was measured at the approximate location of the center of the rabbit’s head, midway between the two speakers. Rabbits were then overexposed for durations of more than 1 hr. Rabbit overexposure studies have shown that it takes about 2 weeks for postexposure thresholds to stabilize (Borg et al., 1995). Therefore, we did not assess postexposure thresholds until

2 weeks after overexposure. Additionally, animals were overexposed to noise only during daytime as noise susceptibility can be influenced by circadian effects (Meltser et al.,

2014).

For awake neural recordings, rabbits were loosely wrapped in a diaper pad and snugly secured within a chair by means of Velcro straps, then head-fixed by sliding the protruding front portion of the headbar into the head-holder and securing with screws.

Rabbits were fitted with earmold inserts and the acoustic setup including the inserts were calibrated with a sweeping chirp stimulus. A sweeping chirp stimulus is a frequency- modulated sound that has a flat magnitude spectrum and helps quickly sample system response to multiple frequencies. After calibration, neural responses to sounds were measured with a tetrode drive whose EIB was connected to intermediate amplifiers and displayed on the computer after suitable digital processing for online analysis. Stimuli were short-duration sounds followed by quiet and repeated over many trials. The 46 recordings usually lasted 2-2.5 hr. Online analysis involved filtering raw signals (filter passband: 1–10 kHz) to identify neural responses to ongoing stimuli, thresholding action potentials to get peak heights, manually sorting peak heights that form a cluster separated from multiunit background responses, and analyzing spike times in the sorted cluster in relation to sound start and stop (also called peristimulus spike times). Sound-evoked responses were displayed on-screen as a running average of neural firing rates (ratio of spike counts during stimulus-on to the duration of stimulus-on period) against stimulus parameters. Online analysis enabled heuristic judgements about spike cluster quality and making decisions regarding whether to continue with the recording, such as when the real-time clustering revealed an absence of sound-evoked responses or the cluster weakened or disappeared. Offline analysis followed a similar procedure but gave better control over spike-sorting quality. Cluster separation of single-unit from multiunit activity was deemed sufficient when there was a visible separation and when a criterion based on the interspike interval (ISI) distribution of spike times within the cluster was achieved. Due to the refractory period of a single neuron, sub-ms ISIs indicate contamination of a cluster by spikes from additional neurons. We set the tolerance of sub- ms ISIs to 2% of total number of ISIs. Then, peri-stimulus spike times were used to obtain spike counts for each trial stimulus.

For rabbits to acclimatize to long recording sessions, they were first habituated over a period of a week and a half to the recording setup, which involved them getting used to sitting in the chair first, then being head-fixed for increasing durations, and finally having sounds played through the earmolds while being head-fixed. Habituation started at 47 least two weeks after headbar implantation and two weeks prior to the initial tetrode drive implantation and stopped only when the rabbit sat quietly throughout the 2-hr duration.

During recording sessions, even the most well-behaved rabbits sometimes moved.

Animal movement resulted in high-frequency artifactual spikes in the neural recording.

Offline processing of neural recordings included identifying and discarding trials that had potential movement contamination (spike counts outside a criterion range were excluded, assuming spike counts followed a Poisson distribution with mean equal to the average spike count such that there would be a low probability of excluding non-artifactual trials).

Generation of Virtual Acoustic Space for Closed-Field Stimulus Presentation

This study investigated the effect of hearing loss on azimuth sensitivity in IC neurons. IC neural responses were measured while varying sound stimulus azimuths.

Since stimuli were presented closed-field (via earmolds) and not free-field (using free- field speakers), it was not possible to present sound sources from real physical locations.

However, it was possible to simulate direction using filters called head-related transfer functions (HRTFs) (Wightman et al., 1989a; Wightman et al., 1989b). HRTFs capture the head- and pinna-related transformations of the sound spectrum for sources presented at various spatial locations. For each spatial location, this produces a pair of HRTFs, one for each ear. Therefore, filtering sounds with HRTFs in closed field produces the appropriate

ITDs and ILDs for each azimuth (if spatial locations are restricted to a fixed elevation of

0°) and spectral notches corresponding to that elevation. Perception of sound source location with free-field speakers is well-replicated in listeners subject to HRTF-filtered sounds, giving validity to the use of HRTF-filtered sounds (Wightman et al., 1989a). 48

HRTFs used in the current study were previously measured from a cadaver rabbit

(Day et al., 2012) and used in a previous study from the lab (Dorkoski et al., 2020). The pinnae were kept in an erect position and the probe tube microphone kept close to the tympanic membrane. The distance of the sound source was fixed at 1 m since distances less than 1 m could have influenced ILDs and ITDs in the rabbit. Kim et al. (2010) reported that ILDs increase and ITDs decrease with decreasing distance. Moreover,

HRTFs were computed for azimuths in front of the rabbit but not the back. Neurons of the IC of rabbits are sensitive to azimuths both in the front and the back (Examples of

300°-range neural tuning in the rabbit IC in Kuwada et al.,(2011)). HRTFs were transformed to directional transfer functions (DTFs) by removing the large, non- directional ear canal resonance component. DTFs were then further simplified to their first principal component using principal component analysis, which retained frequency- specific ITDs and ILDs specific to each azimuth while removing idiosyncratic spectral features (Day et al., 2012). When DTFs no longer contain spectral features such as those associated with filtering by the pinna, front and back HRTFs would be similar for a head modeled as an ideal sphere; neural tuning to azimuths in virtual acoustic space (VAS) for the back would be identical to that in VAS for the front, as ITDs and ILDs depend only on the head size. DTFs offer a way to independently manipulate ITDs or ILDs to produce sounds in which only ILD or only ITD varies with azimuth. To produce ILD-only and

ITD-only sounds, the magnitude and phase spectra of the DTFs, respectively, were set to zero while the other spectra was allowed to vary with azimuth (Day et al., 2012). Such sound manipulations help tease apart neural sensitivity to ILDs and ITDs. 49

Chapter 3: Results I: Paired Measurements of Cochlear Function and Hair Cell

Count in Dutch-Belted Rabbits with Noise-Induced Hearing Loss

Abstract1

The effects of noise-induced hearing loss have yet to be studied for the Dutch- belted strain of rabbit, which is the only strain that has been used in studies of the central auditory system. We measured auditory brainstem responses (ABRs), 2f1−f2 distortion product otoacoustic emissions (DPOAEs), and counts of cochlear inner and outer hair cells (IHCs and OHCs, respectively) from confocal images of Myo7a-stained cochlear whole-mounts in unexposed and noise-overexposed, Dutch-belted, male and female rabbits in order to characterize cochlear function and structure under normal-hearing and hearing-loss conditions. Using an octave-band noise exposure centered at 750 Hz presented under isoflurane anesthesia, we found that a sound level of 133 dB SPL for 60 min was minimally sufficient to produce permanent ABR threshold shifts. Overexposure durations of 60 and 90 min caused median click-evoked ABR threshold shifts of 10 and

50 dB, respectively. Susceptibility to overexposure was highly variable across ears, but less variable across test frequencies within the same ear. ABR and DPOAE threshold shifts were smaller, on average, and more variable in male than female ears. Similarly, post-exposure survival of OHCs was higher, on average, and more variable in male than female ears. We paired post-exposure ABR and DPOAE threshold shift data with hair

1 This chapter was previously published as: Haragopal, H., Dorkoski, R., Johnson, H.M., Berryman, M.A., Tanda, S., Day, M.L. 2020. Paired measurements of cochlear function and hair cell count in Dutch-belted rabbits with noise-induced hearing loss. Hear Res 385, 107845.

50 cell count data measured in the same ear at the same frequency and cochlear frequency location. ABR and DPOAE threshold shifts exhibited critical values of 46 and 18 dB, respectively, below which the majority of OHCs and IHCs survived and above which

OHCs were wiped out while IHC survival was variable. Our data may be of use to researchers who wish to use Dutch-belted rabbits as a model for the effects of noise- induced hearing loss on the central auditory system.

Introduction

The effects of noise overexposure on cochlear structure and function have been studied in many mammalian animal models, including but not limited to mice (Wang et al., 2002), rats (Chen et al., 2003), chinchillas (Hamernik et al., 1989), guinea pigs (Cody et al., 1983), macaques (Stebbins et al., 1979), and rabbits. Previous studies on rabbits were conducted on the New Zealand (Fahey et al., 2008; Franklin et al., 1991; Luebke et al., 2015) and chinchilla strains (Borg et al., 1983a; Borg et al., 1983b; Borg et al., 1989), but none on the Dutch-belted strain, which is the only strain of rabbit that has been and continues to be used in studies of the central auditory system (Blanks et al., 2007;

Buechel et al., 2018; Carney et al., 2014; Chung et al., 2019; Day et al., 2016; Kim et al.,

2015). Borg et al. (1995) previously reported rabbit strain differences in susceptibility to noise overexposure, with higher auditory brainstem response (ABR) threshold shifts resulting from the same overexposure in the New Zealand white strain as compared to the chinchilla strain. This indicates that overexposure parameters developed for one strain of rabbit do not necessarily produce the same degree of hearing loss in other strains. In the present study, we determined the appropriate noise overexposure parameters to produce 51 permanent ABR threshold shifts in Dutch-belted rabbits. We first report data from unexposed, male and female rabbits, including analyses of ABR thresholds, peak amplitudes and latencies, and thresholds of 2f1−f2 distortion product otoacoustic emissions (DPOAEs). We then report the effect of noise overexposure on both ABR and

DPOAE thresholds and survival of cochlear inner and outer hair cells (IHCs and OHCs, respectively). Finally, we paired within-ear threshold shift and hair cell count data to assess the relationship between the two measures in which hair cell survival could be predicted from threshold shifts.

Rabbits are valued by hearing scientists for various reasons. They are a mammalian model with an audible range that extends to relatively low frequencies

(Heffner et al., 1980), comparable to humans (Poulsen et al., 2000). Importantly, they can discriminate interaural time differences at low frequencies (Ebert et al., 2008), which is the binaural cue that dominates sound source directional perception in humans

(Wightman et al., 1992). Their behavior is docile, which makes them useful for awake, head-fixed single-unit recordings in the central auditory system where motion may disrupt data collection. In particular, the Dutch-belted strain is a smaller strain that is easy to handle and has been previously used in numerous studies of the central auditory system, as listed above. Our data may therefore be of use to researchers who wish to use

Dutch-belted rabbits as a model for the effects of noise-induced hearing loss on the central auditory system.

Methods

Animals

A total of 27 purpose-bred, Dutch-belted rabbits were used in the study (7 males and 20 females; Covance). Of these, 24 were young adults between 4.5 and 8 months old, and three were adults aged 11, 19, and 29 months. All but three underwent noise overexposure. Rabbits were housed singly or in pairs in the Animal Care Facility at Ohio

University in a 12-hr light/12-hr dark cycle with lights on beginning at 06:00. All animal procedures were approved by the Ohio University Institutional Animal Care and Use

Committee.

ABR and DPOAE Measurement

Rabbits were initially anesthetized with xylazine (6 mg/kg, s.c.) and ketamine (35 mg/kg, i.m.), then maintained with 1% isoflurane mixed with oxygen presented through a gas mask for the duration of the procedure. Glycopyrrolate (0.01 mg/kg, s.c.) was administered to prevent mucus blockage of the airways. Body temperature was monitored continuously and maintained with a microwaveable heating pad, or for the noise overexposure procedure, a feedback-regulated, electric blanket. All procedures took place within a walk-in, double-walled, sound-attenuated chamber (ETS-Lindgren Acoustic

Systems). ABRs and DPOAEs were measured from one ear in a single procedure, then in the other ear two days later. For a given ear, DPOAEs were always measured before

ABRs (Mhatre et al., 2010). Postexposure measurements were made two weeks after the noise overexposure, by which time thresholds had likely settled to permanent levels

(Borg et al., 1995). 53

Stimulus presentation and data acquisition were controlled by Eaton-Peabody

Laboratories Cochlear Function Test Suite (CFTS) software running on a National

Instruments (NI) chassis (PXIe-1082). Sound stimuli were created digitally, converted to analog signals by a 24-bit digital-to-analog converter at a sampling rate of 50 kHz (NI

PXI-4461), and amplified (Tucker-Davis Technologies SA1) to drive an insert earphone

(Etymotic ER-2). The sound tubes of two earphones (driven separately) and a probe tube microphone were embedded in a custom ear mold made from impression material

(Reprosil), positioning the ends of all three tubes at the entrance of the ear canal. Sound was therefore presented in closed field. The probe tube microphone (Etymotic ER-7C) was used to both calibrate sound stimuli and measure DPOAEs. Measured acoustic signals were converted to digital signals at a sampling rate of 100 kHz (NI PXI-4461).

ABRs were measured with platinum, subdermal needle electrodes (Grass Instruments) positioned at the vertex, mastoid (of the ear presented with sound), and back (common).

Signals were amplified and filtered from 0.3 to 3 kHz (Grass P55), then converted to digital signals by a 16-bit analog-to-digital converter at a sampling rate of 25 kHz (NI

PXIe-6341).

ABRs were measured in response to monaural, unipolar clicks (100 µs) or tone pips (5-ms duration, 0.5-ms sin2 on/off ramps) at frequencies of 0.5 to 16 kHz in octave steps at 30 repetitions/s. The ABR at each sound level was averaged over 512 stimulus repetitions. For clicks and tone-pip frequencies at and above 2 kHz, stimulus polarity was alternated between successive repetitions to prevent contamination of the signal by the cochlear microphonic at high sound levels. Stimulus polarity was not alternated for 0.5- 54 or 1-kHz tones so that the phase-locked auditory nerve signal in Wave I was not eliminated. Stimuli were presented at sound levels from 10 to 75 dB SPL in 5-dB steps before noise overexposure and from 20 to 100 dB SPL after overexposure. ABR threshold was defined as the lowest sound level at which the average ABR first appeared, determined by visual inspection (Fig. 1A and B).

Figure 1

Example Rabbit ABRs and DPOAEs

(A) Average click-evoked ABRs measured at the sound levels indicated on the left. Asterisk indicates threshold. (B) Average tone-evoked ABRs measured at 1 kHz, same as in A. Stimulus polarity was not alternated between repetitions at this frequency, and the cochlear microphonic (with 1-ms period) can be seen contaminating the signal at highest sound levels. Data in A and B from the same ear. (C) Magnitude spectrum of time- averaged acoustic waveform measured at the entrance of the ear canal during DPOAE measurement. Amplitude at primary (f1) and secondary (f2 = 4 kHz) tone frequencies and 2f1−f2 DPOAE indicated. (D) 2f1−f2 DPOAE amplitude (blue line) vs. secondary tone level (L2) for the same ear and same f1 and f2 as in C. DPOAE threshold indicated by asterisk, where DPOAE amplitude crosses the criterion value (dashed line). Noise floor (red line).

For off-line measurement of waveform peak amplitudes and latencies, average

ABRs were additionally digitally filtered between 0.3 and 4 kHz with a 4-pole 55

Butterworth filter. The first peak (Wave I) was defined as the maximum within 1.5–2.5 ms after stimulus onset. The following peak (Wave II) was defined as the next maximum after the trough following Wave I. Wave III was not prominent in most ABRs. Wave IV was defined as the maximum within 4.2–5 ms following stimulus onset. Wave V was defined as the next maximum after the trough following Wave IV. Peaks identified by this algorithm were inspected manually for accuracy. All data analyses were carried out in MATLAB (Mathworks).

DPOAEs were measured in response to two concurrent, 50-ms tones of frequencies f1 and f2 and sound levels L1 and L2, respectively. Tones were presented from separate drivers to prevent the production of distortion by the acoustic system. Tones were presented with fixed frequency ratio of f2/f1 = 1.2 for f2 frequencies of 2 to 16 kHz in octave steps. Each tone-frequency pair was presented at a fixed difference of levels L1

− L2 = 10 dB for L2 levels of 10 to 75 dB SPL in 5-dB steps. L2 was restricted to a maximum of 75 dB SPL to avoid distortion from non-physiological sources (Kujawa et al., 2009). Acoustic responses were averaged over 1,024 stimulus repetitions for each combination of f2 and L2, then the magnitude spectrum of the Fourier transform of the average acoustic response was computed. The amplitude of the 2f1 – f2 DPOAE and surrounding noise floor were extracted from the magnitude spectrum (Fig. 1C). For each f2, DPOAE amplitude was plotted vs. L2 (Fig. 1D); threshold was defined as the interpolated L2 value at which the amplitude crossed the criterion value of 0 dB SPL.

DPOAEs were not measured for an f2 of 0.5 or 1 kHz because the noise floor was higher 56 than the criterion amplitude; noise floor was below the criterion amplitude for all other frequencies.

Threshold shift following noise overexposure was computed as the difference of pre- and postexposure thresholds. For postexposure thresholds that exceeded the maximum sound level used (ABRs: 90–100 dB SPL depending on frequency and calibration; DPOAEs: 75 dB SPL), the minimum possible threshold shift was defined as the difference of pre-exposure threshold and maximum sound level presented. Data that are minimum possible threshold shifts are clearly labeled in figures.

Noise Overexposure

Rabbits were anesthetized prior to noise overexposure, as described in Section

2.2, and monitored by video throughout the procedure. Noise waveforms were created in

CFTS software and digitally filtered in an octave band geometrically centered at 750 Hz

(530–1,061 Hz). Noise was converted to an analog signal at a sampling rate of 50 kHz

(NI PXI-4461), amplified (Samson Servo 200), and produced by a pair of drivers

(Selenium D250-X) coupled to exponential-shaped horns (Selenium HL14-25), one directed perpendicularly at each ear. The locations of the speakers relative to the rabbit were reproduced for all rabbits. Noise waveforms presented from left and right speakers were independent, producing binaurally uncorrelated sound. This presentation method was used because some rabbits were utilized in a different study related to sound localization. Sound level was calibrated for each speaker by dangling the end of the probe tube microphone (using the −20 dB gain setting) at the approximate location of the center of the rabbit’s head in the absence of the rabbit. Sound level when both speakers were 57 presenting noise was 3 dB greater than when one speaker was presenting noise, which was expected from the sum of two independent noise waveforms. Noise overexposure level reported in the Results section is the sound level when both speakers were presenting sound. Several noise levels were tested between 122 and 135 dB SPL as detailed in Results.

Immunofluorescence

Following postexposure ABR and DPOAE measurements, rabbits were euthanized with pentobarbital (100 mg/kg, i.v.), then transcardially perfused with saline followed by 4% formaldehyde in phosphate buffered saline (PBS). Temporal bones containing the cochleae were immediately dissected. The oval and round windows were punctured, then the cochleae were gently perfused with 4% formaldehyde in PBS and post-fixed for 2 hrs at room temperature. Temporal bone fragments were decalcified in

0.25 M EDTA (pH 8.0) for several days, then cochleae were dissected in PBS. As previously described (Salles et al., 2014), the organ of Corti was microdissected from the rest of the cochlea and cut into pieces. Pieces were permeabilized for 30 min at room temperature in 0.5% Triton X-100 in PBS, then rinsed in PBS before blocking overnight in 4% BSA in PBS at 4°C. Pieces were then incubated for 15 hrs at 37°C in primary antibody against MYO7A (mouse anti-Myo7A antibody, DSHB, 1:100 dilution in 4%

BSA/PBS), washed in PBS, then incubated for 1 hr at room temperature in secondary antibody (goat anti-mouse IgG conjugated with Alexa-488; Invitrogen Molecular Probes) and phalloidin (conjugated with Alexa-546; Invitrogen Molecular Probes). After washing, pieces were mounted in Prolong Gold (Invitrogen Molecular Probes). Cochlear 58 pieces were then imaged with a confocal microscope (Nikon A1R) using a 10× objective lens. Cochlear hair cells were identified in maximum intensity projections of confocal z- stacks as cells positive for MYO7A, using phalloidin counter-staining as a marker for

IHC and OHC stereocilia bundles (Hasson et al., 1995; Hasson et al., 1997).

Cochlear Hair Cell Counts

The mapping between tone frequency and location along the cochlea with greatest sensitivity to that frequency was assumed to be the Greenwood function (Greenwood,

1990) modified for the upper frequency limit of rabbits (50 kHz) (Heffner et al., 1980): f

= 400(102.1x − 0.85), where f is the tone frequency in Hz and x is the fractional distance from apex. Confocal images of cochlear pieces were assembled from base to apex, then distance along the cochlea was measured along the cuticular plates of inner hair cells using Nikon NIS Elements software. Counts of both hair cells and empty “holes” where hair cells were missing were made within an area spanning 5% of the total cochlear distance centered at the cochlear places associated with each of the six tone frequencies used for ABR measurements. Counts were only made if at least half of the area about the cochlear frequency place was undisrupted by tissue folding or sectioning. Percent survival was computed as the number of MYO7A-positive hair cells divided by the sum of the number of hair cells and holes. In tissue pieces where it was difficult to count the number of holes (e.g., Fig. 7C and D), the sum of the number of hair cells and holes was assumed to be equal to the average of that across rabbit ears at the same frequency place where hair cells and holes were countable.

Statistical Analysis

To assess potential dependence of tone-evoked ABR and DPOAE thresholds of unexposed rabbits on tone-pip frequency and sex, ABR and DPOAE thresholds were each fit with a linear mixed-effects model with frequency, sex, and their interaction as fixed effects and individual ear as a random effect. Models were fit in MATLAB software via the function “fitlme” using restricted maximum likelihood estimation.

Statistical significance of each fixed effect was determined using a F-test with a significance level of 0.05. If a fixed-effect term was significant, the model was re-fit using regular maximum likelihood estimation, then post hoc comparisons were made using F-tests, correcting for multiple comparisons using the Benjamini-Hochberg procedure.

Similarly, the amplitudes and latencies of the stereotypical peaks (or “waves”) in the average click-evoked ABR waveform of unexposed rabbits were each fit with a linear mixed-effects model with wave number, sex, and their interaction as fixed effects and individual ear as a random effect. ABR and DPOAE threshold shifts following noise overexposure were fit with a linear mixed-effects model with only frequency as a fixed effect and individual ear as a random effect. Models of threshold shifts were fit separately for male and female data because the variances of shifts were clearly different between the sexes.

Results

Functional Assessments of Cochleae in Unexposed Rabbits

We measured ABRs and DPOAEs from 27 rabbits of both sexes prior to noise overexposure. There was a small but significant sex difference of click-evoked ABR thresholds, with male thresholds on average 4 dB lower than female thresholds (t-test, p =

0.006, N = 14 male and 39 female ears) (Fig. 2A). Thresholds of tone-evoked ABRs were strongly dependent on tone-pip frequency, but not sex, with lowest thresholds between 4 and 16 kHz (F-test, frequency: p = 4×10−92, sex: p = 0.11, N = 14 male and 39 female ears). There was a small but significant interaction between frequency and sex due to a significant sex difference of thresholds only at 16 kHz (F-test, interaction: p = 0.048, male vs. female at 16 kHz: p = 0.002). Thresholds of DPOAEs were also strongly dependent on frequency with lowest thresholds between 4 and 16 kHz, but there was no significant effect of sex or frequency-sex interaction (F-test, frequency: p = 1×10−21, sex: p = 0.80, interaction: p = 0.24, N = 14 male and 16 female ears) (Fig. 2B). For both ABR and DPOAE thresholds, we fit a separate linear mixed-effects model with age as an additional fixed effect to test for potential variance in the data due to the inclusion of a minority of older rabbits. There was no significant effect of age on either ABR (F-test, p

= 0.19) or DPOAE thresholds (p = 0.07).

In order to assess whether individual ears tended to have higher-than-average or lower-than-average thresholds across frequencies, we performed a within-ear, cross- frequency correlation analysis of ABR and DPOAE thresholds. Tables 1 and 2 show the correlation coefficients between thresholds measured in the same ear at two frequencies, 61 for each possible pair of tone-pip frequencies. Most frequency pairs had significant, positive correlation, for both ABRs and DPOAEs. This means that ears with lower-than- average thresholds at one frequency tended to have lower-than-average thresholds at other frequencies, and vice versa for ears with higher-than-average thresholds. Further,

ABR data had higher correlation coefficients for pairs closer in frequency; the same was not evident in DPOAE data. For example, the correlation coefficient between within-ear

ABR thresholds at 0.5 and 1 kHz was 0.68 whereas that between thresholds at 0.5 and 16 kHz was 0.30. This means that ears with lower-than-average ABR thresholds at one frequency had a stronger tendency to have lower-than-average thresholds at nearby frequencies rather than distant frequencies.

Table 1

Within-ear, Pairwise-frequency Correlation of ABR Thresholds

kHz 0.5 1 2 4 8 16

0.5 ─ 0.68 0.62 0.46 0.31 0.30

1 0.68 ─ 0.57 0.27 0.25 0.23

2 0.62 0.57 ─ 0.59 0.51 0.35

4 0.46 0.27 0.59 ─ 0.71 0.46

8 0.31 0.25 0.51 0.71 ─ 0.61

16 0.30 0.23 0.35 0.46 0.61 ─

Pearson’s correlation coefficients (N = 53 ears). Bold values indicate significant correlations (p<0.05). 62

Table 2

Within-ear, Pairwise-frequency Correlation of DPOAE Thresholds

kHz 2 4 8 16

2 ─ 0.54 0.01 0.40

4 0.54 ─ 0.24 0.40

8 0.01 0.24 ─ 0.41

16 0.40 0.40 0.41 ─

Same as in Table 1 (N=30). Bold values indicate significant correlations (p<0.05).

In order to assess whether individual rabbits tended to have higher-than-average or lower-than-average thresholds between left and right ears, we performed an additional pairwise correlation analysis. We computed correlation coefficients from z-scored data in order to control for the frequency-dependence of ABR and DPOAE thresholds: for threshold values at each frequency, mean threshold was subtracted from the threshold values, then values were divided by their standard deviation. Figure 2C and D show scatterplots of z-scored left-ear vs. right-ear ABR and DPOAE thresholds, respectively, pooled across frequency. Both ABR and DPOAE z-scored thresholds were correlated between left and right ears. This means that there was a tendency for the threshold in one ear of a rabbit to be more similar to the threshold of its opposite ear than other rabbits’ ears. In other words, both ABRs and DPOAEs were dependent between left and right ears even at the same frequency. We performed a similar correlation analysis between z- 63 scored thresholds of ABRs and DPOAEs measured at the same ear in response to the same tone-pip frequency. Z-scored ABR and DPOAE thresholds were weakly correlated

(Fig. 2E). This indicates there was only a weak tendency for a lower-than-average ABR threshold to have a lower-than-average DPOAE threshold at the same frequency, and vice versa for higher-than-average thresholds.

Figure 2

ABR and DPOAE Thresholds in Unexposed Rabbits

(A) Click- and tone-evoked ABR thresholds (mean ± SD) (red line: males, N = 14 ears; blue line: females, N = 39 ears). For comparison: rabbit ABR thresholds from Borg and Engström (1983b) (brown dashed line), and rabbit behavioral thresholds from Borg and Engström (1983b) (magenta dashed line) and Heffner and Masterton (1980) (green dashed line). (B) DPOAE thresholds (mean ± SD) (red line: males, N = 14 ears; blue line: females, N = 16 ears). (C) Within-frequency, z-scored right- vs. left-ear ABR thresholds (N = 182). Unity line (black line). Pearson’s correlation coefficient and p-value indicated 64 in upper left corner. (D) Within-frequency, z-scored right- vs. left-ear DPOAE thresholds (N = 60), same as in C. (E) Within-ear-and-frequency, z-scored DPOAE vs. ABR thresholds (N = 120). Regression line (tan line).

The ABR waveform contains a sequence of peaks, each of which originates from successive levels of the auditory pathway (Fig. 3A, Waves I–V). We measured the peak amplitudes and latencies of Waves I, II, IV and V from the average ABR in response to

70-dB SPL clicks (Wave III did not have a prominent peak). Wave I is of particular interest because its amplitude has been shown to be correlated with number of IHC ribbon synapses and may be used to infer synaptopathy following overexposures that cause temporary threshold shifts (Kujawa et al., 2009). Peak amplitudes varied substantially across ears and were best-fit by lognormal distributions. Log peak amplitude had a strong effect of wave number, but not sex, and significant interaction (F-test, wave number: p = 7×10−28, sex: p = 0.87, interaction: p = 0.002, N = 14 male and 39 female ears) (Fig. 3B). Post hoc tests revealed that the interaction of wave number and sex was complex. There were no significant sex differences within any wave—sex differences arose across wave number. However, log amplitude of Wave V was less than that of

Waves I and II, regardless of sex. Log amplitude of Wave I was mildly correlated with that of Waves II, IV and V in the same ear, meaning there was a weak tendency for an ear with a large-amplitude Wave I to also have large amplitudes for the other waves, and vice versa for small-amplitude waves (r [p] = 0.35 [0.01], 0.45 [7×10−4], and 0.41 [0.002] for

II/I, IV/I and V/I, respectively). Log amplitude was also correlated between left and right ears for all waves, indicating there was a tendency for peak amplitude in one ear of a rabbit to be more similar to that of its opposite ear than that of other rabbits’ ears (r [p] = 65

0.68 [2×10−4], 0.59 [0.002], 0.47 [0.016], and 0.43 [0.03] for Waves I, II, IV and V, respectively). There was no significant correlation between the log amplitude of Wave I at 70 dB SPL and click-evoked ABR threshold in the same ear (r = −0.13, p = 0.37).

Figure 3

Peak Amplitudes and Latencies of Click-evoked ABR Waveforms in Unexposed Rabbits

(A) Example average ABR waveforms from 4 rabbit ears in response to 70-dB SPL clicks. Roman numerals indicate wave number. (B) Peak amplitudes and (C) peak latencies (mean ± SD) of different wave numbers for male (red, N = 14) and female (blue, N = 39) ears. (D) Peak amplitude vs. peak latency for each wave number (N = 53 ears). Pearson’s correlation coefficient and p-value indicated above each plot.

Peak latency had a significant effect of sex, but no significant interaction between sex and wave number (the increase in latency with wave number is expected and trivial)

(F-test, sex: p = 0.006, interaction: p = 0.11) (Fig. 3C). Female latencies lagged male latencies by 0.17 ms on average across wave number. The variability of peak latency was 66 relatively small but increased with wave number, consistent with the accumulation of spike-timing variability through a sequence of synapses along the auditory pathway.

Furthermore, the standard deviation of peak latencies was, on average, twice as large in females (0.18 ms) as in males (0.09 ms). Peak latencies were similar between left and right ears for all wave numbers, with correlation coefficients between 0.78 and 0.84 and regression line slopes between 0.995 and 1.05. For Waves II and V there was a tendency of higher amplitudes to have shorter latencies (Fig. 3D); the significance of this relationship is unclear. However, the same correlation was not seen in data from Waves I or IV.

Finally, we again fit peak amplitude and latency data with linear mixed-effects models that included age as a fixed effect. There was no significant effect of age on either log peak amplitude (F-test, p = 0.96) or peak latency (p = 0.95).

Functional Assessments of Cochleae in Noise Overexposed Rabbits

In order to find noise overexposure parameters that would create permanent hearing loss but not profound deafness, we initially measured pilot pre- and postexposure

ABR thresholds in female rabbits with different combinations of exposure level and duration. For a 15-min exposure duration, exposure levels of 122, 125, 129 and 133 dB

SPL caused no threshold shift in response to clicks, while 134 and 135 dB SPL shifted thresholds above the limit of our acoustic system (~100 dB SPL). We next fixed exposure level at 133 dB SPL and varied exposure duration: median click-evoked threshold shifts were 0, 10 and 52.5 dB for durations of 30, 60 and 90 min, respectively. 67

In our first set of experiments, we exposed five female rabbits to noise at 133 dB

SPL for 60 min. All of these rabbits were between 5 and 8 months old at the time of exposure. We measured pre- and postexposure thresholds for ABRs, but not DPOAEs.

Postexposure click-evoked ABR thresholds (58 ± 10 dB SPL, mean ± SD) were highly variable compared to pre-exposure thresholds (44 ± 3 dB SPL), leading to a range of click-evoked threshold shifts from 0 to 30 dB with median 10 dB (Fig. 4A and B).

Median tone-evoked threshold shifts were similar across frequency at around 18 dB.

However, variability of postexposure tone-evoked thresholds was greater between 4 and

16 kHz, which led to large variability in threshold shifts in this frequency range. Data points in Figure 4B connected by same-colored lines indicate thresholds measured from the same ear. By following lines of the same color in the upper frequency range (2–16 kHz), one can see that only two ears had thresholds shifts that were clearly consistent across frequency. Nonetheless, variability of individual-ear susceptibility was greater than variability of threshold shifts across frequency within the same ear; 62% of the total variance in threshold shifts between 2 and 16 kHz could be explained by differences in mean threshold shift across individual ears. Altogether, while thresholds shifts were similar, on average, across frequencies, they were less variable across ears for frequencies associated with apical cochlear locations near our exposure band (0.53–1.1 kHz), and more variable for frequencies associated with cochlear locations basal to the exposure band.

Figure 4

ABR Threshold Shifts following 60-min Noise Overexposure

(A) Click- and tone-evoked ABR thresholds (mean ± SD) before (black solid line) and two weeks after (gray dashed line) noise overexposure (N = 10 ears). (B) Click- and tone-evoked ABR threshold shifts. Red dashes indicate medians. Data from same ear connected by same-colored line. Gray shaded regions in A and B indicate exposure band. DPOAE thresholds were not measured for this group.

In our second set of experiments, we exposed five female and five male rabbits to noise at 133 dB SPL for 90 min. All of these rabbits were between 5 and 8 months old at the time of exposure, except for two females aged 12 and 29 months. We measured pre- and postexposure thresholds for both ABRs and DPOAEs. In practice, thresholds of postexposure ABRs could not be identified for 0.5- and 1-kHz tone pips because the cochlear microphonic overwhelmed the signal at sound levels lower than that at which the ABR appeared (see Section 2.2). Since ABR thresholds at 0.5 and 1 kHz in unexposed rabbits are around 50 dB SPL and the cochlear microphonic appears at about

70 dB SPL at these frequencies, the threshold shifts at 0.5 and 1 kHz were therefore greater than 20 dB. Figure 5 shows ABR and DPOAE threshold shifts in response to 69 clicks and tone pips at the upper frequencies (2–16 kHz), separated by sex. Median click- evoked ABR threshold shift in females was 55 dB—much greater than that following a

60-min overexposure (10 dB; Fig. 5B and 4B; females only). For some stimuli, postexposure ABR or DPOAE thresholds were above the limit of our acoustic system; such cases are labeled by solid squares in Figure 5 indicating minimum possible threshold shifts.

Figure 5

ABR and DPOAE Threshold Shifts following 90-min Noise Overexposure

(A,B) Click-evoked and tone-evoked ABR threshold shifts for males and females, respectively (N = 10 ears each). (C,D) DPOAE threshold shifts for males (N = 10 ears) 70 and females (N = 8 ears), respectively. In all panels: measured threshold shifts (open circles), minimum possible threshold shifts (solid squares), medians (red dashes). Data points from same ear connected by same-colored line.

The most striking characteristic of our data was the difference in threshold shifts between males and females following the same 90-min overexposure, for both ABRs and

DPOAEs. Whereas median click-evoked ABR threshold shifts were similar between males and females (50 and 55 dB, respectively), the range of click-evoked threshold shifts for males spanned substantially lower than that for females: 5–65 dB for males and

45–60 dB for females. The same expansion to lower threshold shifts in males was also observed for tone-evoked ABR threshold shifts and DPOAE threshold shifts. Lines of the same color in Figure 5 link data points from the same ear; those male ears that had smaller threshold shifts (either ABR or DPOAE) at one tone frequency had smaller threshold shifts at the other frequencies. Furthermore, the three male ears with smallest click-evoked threshold shifts came from three different rabbits; the other ears of these three rabbits had higher threshold shifts. Therefore, some male ears were less susceptible to noise overexposure at all frequencies (2–16 kHz), and an individual male rabbit may have one highly susceptible ear and one less susceptible ear. The large variability of susceptibility in males was not due to differences in age at the time of overexposure because male rabbits were homogeneous in age whereas female rabbits included two individuals exposed at older ages.

There was a weak effect of frequency on tone-evoked ABR threshold shifts of males (F-test, p = 0.004) due to a statistically significant difference in shifts between 2 and 4 kHz (F-test, p = 0.0007), but no effect of frequency on that of females (F-test, p = 71

0.06). Although variability of tone-evoked ABR threshold shifts in females was less than that in males, it was still relatively large (SD = 12 dB over 2–16 kHz) and similar to that for females following a 60-min overexposure (SD = 13 dB over 2–16 kHz). The variability of tone-evoked ABR threshold shifts was dominated by variability across ears rather than variability across frequency within the same ear; 83% of the total variance in threshold shifts could be explained by differences in mean threshold shifts across ears.

For DPOAEs, threshold shifts systematically increased with frequency for females (F- test, p = 6×10−7), but not males.

In Figure 6A and B, we plot threshold shifts in response to the same tone-pip frequency for left vs. right ears for ABRs and DPOAEs, respectively. Left- and right-ear threshold shifts were correlated for female rabbits for both ABRs and DPOAEs (ABR: r

= 0.69, p = 8×10−4, N = 20; DPOAE: r = 0.79, p = 3×10−4, N = 16). In Figure 5D, we showed that DPOAE threshold shifts in female ears systematically increased with tone frequency. This raises the possibility that the correlation of left and right female DPOAE threshold shifts may be simply due to dependence on frequency. Sample sizes at each frequency were too small to accurately estimate z-scores, so we could not rule out this possibility. Male rabbits, on the other hand, often had highly asymmetric threshold shifts between ears, for both ABRs and DPOAEs. Three of the five male rabbits had an asymmetry of ABR threshold shifts of 15 dB or more for at least one tone frequency. In

Figure 6C, we plot ABR threshold shifts vs. DPOAE threshold shifts measured in the same ear in response to the same tone-pip frequency. ABR and DPOAE threshold shifts 72 were correlated (r = 0.75, p = 7×10−14, N = 72). Most data points fell below the unity line, indicating ABR thresholds shifted by a greater amount than DPOAE thresholds.

Figure 6

Comparisons of Within-frequency Threshold Shifts

(A) Within-frequency ABR threshold shifts of right vs. left ears for males (red circles and crosses, N = 20) and females (blue circles and crosses, N = 20). (B) Same as in A for within-frequency DPOAE threshold shifts (males: N = 20, females: N = 16). (C) Within- ear, within-frequency threshold shifts of DPOAEs vs. ABRs (males: N = 40, females: N = 32). In all panels: data for which both threshold shifts in pair are measured threshold shifts (circles), data for which either one or both of threshold shifts in pair are minimum possible threshold shifts (crosses), unity line (black line).

Cochlear Hair Cell Counts in Unexposed and Noise Overexposed Rabbits

In order to assess how noise overexposure affected the structural integrity of the cochlea, we counted the number of cochlear hair cells in nine rabbits, including three males and three females following overexposure, and two females and one male unexposed to noise (controls). Hair cell counts were restricted to the tonotopic regions of the cochlea associated with threshold activity of each of the six tone frequencies used in

ABR measurements (0.5 to 16 kHz in octave steps). Figure 7A shows a cochlear micrograph from an unexposed rabbit; one row of inner hair cells and three rows of outer 73 hair cells are clearly visible. The rest of the panels in Figure 7 show increasing levels of damage following noise overexposure from left to right: a partial loss of OHCs with completely intact IHCs (B), complete wipe-out of OHCs with mostly intact IHCs (C), and complete wipe-out of both IHCs and OHCs (D). Figure 8 shows the percent survival of hair cells vs. cochlear frequency place for the six unexposed, control ears. At least

92% of IHCs and 78% of OHCs survived across all frequency locations. The two ears with lowest OHC counts at 1 and 2 kHz were from the same female rabbit.

Figure 7

Histopathology of Noise-induced Inner Ear Damage

Cochlear whole-mount sections immunolabeled for Myo7A (green) and actin (red). Scale bar: 100 μm. (A) Unexposed control. (B–D): Noise overexposed. White solid triangles point to intact OHCs, yellow solid triangles to intact IHCs, red solid triangles to support cells, dashed white triangles to holes left by missing OHCs, and dashed yellow triangle to missing IHCs.

Figure 8

Cochlear Hair Cell Survival in Unexposed Cochleae

Percent survival of OHCs (blue circles) and IHCs (magenta circles) within each cochlear frequency place. Medians (red dashes).

Figure 9 shows the percent survival of IHCs and OHCs following a noise overexposure of 133 dB SPL for 90 min, separated by sex. OHC and IHC survival in exposed ears was clearly lower than in unexposed ears, although not consistently across ears. OHC survival was dramatically affected by overexposure: only one ear (male) was unaffected by the exposure. OHC survival in female ears was mostly below 30% and largely consistent across cochlear frequency place (Fig. 9A). On the other hand, OHC survival in male ears across cochlear frequency locations was complex: two ears had less than 20% survival across frequencies (OHC wipe-out), two ears had survival mostly above 80% across frequencies, one ear had OHC wipe-out at and above 2 kHz, and the last ear had OHC wipe-out at and below 8 kHz (Fig. 9B). There were no significant differences in OHC survival between the three OHC rows when compared at the same cochlear frequency places (Wilcoxon signed-rank test, p = 0.95, 0.85 and 0.41 for OHC row 1 vs. 2, 1 vs. 3, and 2 vs. 3, respectively). The overall effect of overexposure on 75

OHCs was similar to that of ABR and DPOAE threshold shifts (Fig. 5) in that female ears were strongly and consistently susceptible while damage to male ears was highly variable. The effect of overexposure on IHC survival was less than that for OHCs and did not display the same obvious sex differences. Unlike OHCs, IHC survival was variable across ears and across cochlear frequency locations within an ear for both males and females (Fig. 9C and D).

Figure 9

Cochlear Hair Cell Survival in Noise-overexposed Cochleae

(A,B) Percent OHC survival at each cochlear frequency place for females and males, respectively (N = 6 ears each), following 90-min noise overexposure. (C,D) Same as in A 76 and B, but for percent IHC survival. In all panels: data from same ear connected by same- colored line, medians (red dashes), overexposure band (gray shaded area).

In Figure 10, we plot a 2D histogram of percent IHC and OHC survival following noise overexposure occurring at the same cochlear frequency place within the same ear.

Data are contained within the upper and left sides of the plot with a complete absence of points in the lower right corner, meaning IHC wipe-out never co-occurred with majority

OHC survival at the same place in the cochlea. Data points in the upper right corner represent cochlear locations where most IHCs and OHCs survived following overexposure. If overexposure affected IHCs and OHCs to the same degree, we would expect the remaining points to fall along the diagonal line. Instead, data indicate an order to the severity of hair cell loss: cochlear locations either had 1) varying amounts of OHC loss with IHCs remaining largely intact (upper-right to upper-left corner), or 2) OHC wipe-out with varying amounts of IHC loss (upper-left to lower-left corner). These data indicate that rabbit OHCs are more susceptible to noise damage than IHCs.

Figure 10

Relationship between IHC and OHC Survival following Noise Overexposure

Two-dimensional histogram of the percent survival of IHCs and OHCs in the same ear at the same cochlear frequency location following 90-min noise overexposure (N = 72).

Comparison of Cochlear Functional Measures to Hair Cell Counts

The six rabbits whose cochlear hair cell counts were described above (133 dB

SPL, 90-min exposure) were a subset of the rabbits used for initial noise parameter titration experiments on postexposure ABR and DPOAE measurements. Therefore, we had paired measurements with which to observe potential relationships between ABR or

DPOAE threshold shifts and hair cell counts. Figure 11 shows tone-evoked ABR or

DPOAE threshold shifts vs. percent OHC or IHC survival. ABR or DPOAE data in response to a particular tone frequency was paired with hair cell count data from the same ear and at the cochlear frequency place matching that of the tone. For both ABRs and

DPOAEs, OHC survival data were characterized by a critical threshold shift, below or above which most OHCs alternatively survived or died, respectively. We computed the critical threshold shifts that classified OHC data points into either greater or lesser than

50% survival with least error: threshold shifts of 46 dB for ABR and 18 dB for DPOAE

(dashed lines in Fig. 11). This pattern was evident for data from male ears (red symbols) 78 but not female ears (blue symbols) because male ears had threshold shift values that straddled the critical threshold shifts. However, one data point each for ABRs and

DPOAEs from female ears was separated from the rest of the data and occurred near the critical threshold shifts. This suggests that the critical threshold shifts predicting OHC survival may not depend on sex. Included in the figures are data points indicating minimum possible threshold shifts (crosses); nearly all these data were associated with cochlear locations with OHC survival of 20% or less. Although there were fewer data points below the critical threshold shifts, there was a trend of OHC survival decreasing gradually with increasing ABR threshold shift (Fig. 11A). On the other hand, DPOAE threshold shift was not useful in predicting gradations of OHC survival below the critical threshold shift (Fig. 11B).

Figure 11

Relationship between Threshold shifts and Cochlear Hair Cell Survival

(A,B) Percent survival of OHCs vs. threshold shift for ABRs or DPOAEs (N = 48 each), respectively, following 90-min noise overexposure. Data were matched between tone frequency and cochlear frequency place, measured in the same ear. (C,D) Percent survival of IHCs vs. ABR or DPOAE threshold shifts, respectively, same as in A and B. In all panels: male data (red circles and crosses), female data (blue circles and crosses), measured threshold shifts (circles), minimum possible threshold shifts (crosses), critical threshold shifts (gray dashed lines).

IHC survival data were also characterized by critical threshold shifts for both

ABRs and DPOAEs, below which there was greater than 90% survival, and above which there was a large range of percent survival spanning 5% to 100%. The transition at the critical threshold shifts was less pronounced than that for OHCs but corresponded well with the critical threshold shift values computed for OHCs (Fig. 11C and D). Altogether, 80

ABR or DPOAE threshold shifts below their critical values strongly predicted corresponding cochlear locations with majority survival of OHCs and IHCs, whereas threshold shifts above their critical values strongly predicted corresponding cochlear locations with majority death of OHCs and variable survival of IHCs.

Discussion

Data from Unexposed Rabbits: Comparison to Other Strains and Species

Click-evoked ABR thresholds in our Dutch-belted rabbits (42 dB SPL) were higher than those reported for New Zealand white rabbits (35 dB p.e. SPL) (Stieve et al.,

2006), rats (30 dB SPL) (Popelar et al., 2003), and mice (34 dB SPL) (Zhou et al., 2006), but similar to gerbils (40─45 dB SPL) (Burkard et al., 1989) and chinchillas (43 dB SPL)

(Janning et al., 1998). The dependence of tone-evoked ABR thresholds on frequency was similar to that of the chinchilla strain of rabbits (Borg et al., 1983b) in that higher frequencies had lower thresholds (Fig. 2A). However, ABR thresholds at 0.5 kHz were higher in the chinchilla strain than in our Dutch-belted rabbits, which suggests that the

Dutch-belted strain may have more sensitive low-frequency hearing. Behavioral thresholds of the chinchilla strain of rabbits mimicked a shifted version of their ABR thresholds (Borg et al., 1983b), but behavioral thresholds of the New Zealand strain of rabbits had most sensitive thresholds down to 1 kHz (Heffner et al., 1980), which suggests that their low-frequency hearing may be more sensitive than that of the Dutch- belted strain (Fig. 2A).

We found that thresholds were correlated between tone frequencies within the same ear, which implies that some ears are, overall, more or less sensitive to sound than 81 other ears. Further, thresholds were mildly correlated between left and right ears.

Together, these results suggest that some individual rabbits, and not simply individual ears, are more or less sensitive to sound than other rabbits.

We repeatedly observed Waves I, II, IV and V in click-evoked ABR waveforms of our Dutch-belted rabbits, with Wave III most often obscured, whereas Stieve et al.

(2006) most often observed Waves I–IV in New Zealand rabbits, with Wave V often unobservable. In both strains of rabbit, peak amplitude decreased with wave number—a pattern similar to mice (Zhou et al., 2006), cats (Melcher et al., 1996), guinea pigs (Wada et al., 1983), and chinchillas (Henry et al., 2011), but in contrast to humans, in which the amplitude of Wave V is larger than that of Wave I (Verhulst et al., 2016). In contrast to our finding that the variability of peak latency increased with wave number in Dutch- belted rabbits, Stieve et al. (2006) found constant, very low variability of peak latency across wave number in New Zealand rabbits, potentially due to their use of a higher-level click to evoke the ABR.

Threshold Shifts following Noise Overexposure: Comparison to Other Strains and

Species

Following acoustic overexposure, greatest permanent threshold shifts tend to occur at frequencies within and above the spectrum of the insulting sound, with corresponding damage to the cochlea at frequency places within and basal to the spectrum (Borg et al., 1995; Harding et al., 2007). Our aim was to use a noise that would cause widespread damage across the cochlea, including both basal and apical regions.

Therefore, we centered our octave-band noise at 750 Hz (0.53–1.1 kHz), which 82 corresponds to a cochlear frequency place near the apex in rabbits. We found that a sound level of 133 dB SPL for a duration of at least 60 min was sufficient to produce permanent threshold shifts. ABR threshold shifts and IHC and OHC survival were all similar across frequency, on average, indicating widespread damage of the cochlea was achieved; however, variability across ears was substantial. Our overexposure protocol may therefore be of use to researchers who wish to measure central auditory function in

Dutch-belted rabbits following damage to both basal and apical regions of the cochlea.

In previous extensive studies by Borg et al. (1995) on the chinchilla strain of rabbits, a noise of only 125 dB SPL and 15 min duration was sufficient to produce permanent ABR threshold shifts. Their octave-band noise exposure was centered at 2.8 kHz (2–4 kHz), which partially overlaps with the range of most sensitive hearing in rabbits, while our exposure was centered in a less sensitive range. The difference in normal-hearing ABR thresholds between the two center frequencies of exposure is approximately 15 dB (Fig. 2A). Borg et al. (1995) also found that anesthesia increased threshold shifts for the same exposure and decreased the “critical” sound level sufficient to produce threshold shifts. Their critical sound level of 125 dB SPL was measured in response to awake overexposures and is likely higher than that if measured in response to anesthetized exposures, as in our study. Together, differences in exposure spectrum and arousal state may account for the difference in critical sound level sufficient to produce permanent threshold shifts. On the other hand, Borg et al. (1995) also found that chinchilla and albino strains of rabbits differed in ABR threshold shifts by as much as 10 dB following the same overexposure. The higher critical sound level found in our Dutch- 83 belted rabbits may therefore also be due in part to differences in strain. Of particular note is our finding that, at a duration of 15 min, permanent ABR threshold shifts went from zero at 133 dB SPL to extremely high at 134 dB SPL, whereas Borg et al. observed in the chinchilla strain for the same exposure duration a graded increase in threshold shifts with incremental increases in sound level over a 15-dB range. Again, a potential confound between the two studies may be the arousal state of the rabbits during overexposure.

Anesthesia is known to suppress the middle-ear muscle reflex (Borg et al., 1975; Borg et al., 1983c).

Noise overexposure levels lower than that used in our study were sufficient to produce permanent threshold shifts in other species, including mice (110 dB SPL)

(Erway et al., 1996), chinchillas (115 dB SPL) (Henry et al., 2011; Strominger et al.,

1995), cats (111 dB SPL) (Ngan et al., 2001), and guinea pigs (114 dB SPL) (McFadden et al., 2005). However, in all of these studies the spectrum of the exposure band was positioned within the most sensitive hearing range, whereas in our study the exposure band was below the most sensitive range, as described above. In contrast to our study, macaques required a greater noise overexposure of 140 dB SPL to produce permanent threshold shifts using a 50-Hz-wide band centered at 2 kHz (Valero et al., 2017).

ABR threshold shifts following our noise overexposure were highly variable across ears—dramatically so for males but also substantially for females. This variability was quantitatively similar to that shown previously for the chinchilla strain of rabbits following noise overexposure (Borg et al., 1995). Similar variability in susceptibility to noise overexposure has also been reported in guinea pigs (Cody et al., 1983). Differences 84 in individual susceptibility to overexposure may be partially due to genetic factors involved in regulating reactive oxygen species or maintaining the structural integrity of the organ of Corti (Konings et al., 2009). Such genetic factors would explain variability across individuals but not variability between ears of the same individual.

OHC Susceptibility and Critical Threshold Shifts

We found that OHC survival was more susceptible to noise overexposure than

IHC survival, consistent with data from many species, including humans (Borg et al.,

1995; Chen et al., 2003; Harding et al., 2004; Rask-Andersen et al., 2000; Stebbins et al.,

1979). The dominant factor that makes OHCs more susceptible to overexposure than

IHCs is unknown, but may be due to a difference in Ca2+ homeostasis (Fettiplace et al.,

2018). On the other hand, widespread IHC loss with only moderate OHC loss has been reported in the chinchilla strain of rabbits in response to 15 min of noise overexposure at

134 dB SPL (Borg et al., 1995).

It is well-known that complete OHC loss is linked with highly elevated thresholds

(Borg et al., 1983b; Dallos et al., 1978). Our data indicated critical ABR and DPOAE threshold shifts above which OHCs transitioned from majority survival to near complete wipe-out (Fig. 11A and B). The existence of a sharp, critical threshold shift for OHC loss is not common in the literature. In chinchillas and rats, no sharp, critical threshold shift was observed for ABRs or DPOAEs, but for some frequencies the relationship between

OHC loss and threshold shift was linear (Chen et al., 2003; Hamernik et al., 2000). In our data, threshold shifts below the critical threshold shift were not correlated with OHC survival. Shifts in ABR or DPOAE thresholds without loss of OHCs were likely due to 85 stereocilia damage. Stereocilia of IHCs in the chinchilla strain of rabbit have been shown to become damaged without loss of OHCs up to an ABR threshold shift of 40 dB (Borg et al., 1995). In our histological preparations of overexposed cochleae, we observed some hair cells with faint Myo-7A staining and counted them as surviving hair cells. It is unclear whether or not such faint staining was due to dysfunction of the hair cells.

Above the critical threshold shift, IHC survival was variable and could not be predicted from threshold shifts (Fig. 11C and D). A previous study in chinchillas reported that behavioral thresholds were insensitive to specific loss of IHCs exceeding 80%

(Lobarinas et al., 2013). This suggests that thresholds measured in our data were unrelated to IHC survival.

Sex Difference in Susceptibility to Noise Overexposure

We found that some male ears were less susceptible to noise overexposure than female ears (Fig. 5). Previous studies have also reported sex differences in susceptibility to overexposure, but not always in the same direction. In the chinchilla strain of rabbits, male ears were more susceptible to overexposure than female ears (Borg et al., 1995), while in chinchillas, sex differences in susceptibility were flipped between the low- and high-frequency hearing ranges (McFadden et al., 1999). Sex differences in susceptibility have also been found in some strains of mice (Milon et al., 2018), but not others (Erway et al., 1996; Kujawa et al., 2006). In our data, it is unlikely that the sex difference in susceptibility was due to differences that would affect both ears, like body mass or head size, because threshold shifts were asymmetric between the left and right ears of three of the five male rabbits (Fig. 6A and B). Loudspeakers and rabbit head placement were in 86 the same position during noise overexposure procedures for all rabbits, therefore the possibility of off-center head placement between the speakers was unlikely; furthermore, repeated off-center placement would not be expected to affect males and not females. We did not inspect ear canals for obstruction before noise overexposures; however, such obstruction would have affected measurements of pre-exposure thresholds.

Comparison of ABR and DPOAE Thresholds

ABR and DPOAE thresholds are thought to be predominately influenced by OHC function even though the two signals arise from different sources of generation (Borg et al., 1983b; Hamernik et al., 2000; Ohlms et al., 1991; Probst et al., 1991). Consistent with this, unexposed ears in our data set had quantitatively similar frequency-dependent ABR and DPOAE thresholds, on average (Fig. 2A and B). On the other hand, we found only weak correlation between ABR and DPOAE thresholds matched for ear and frequency

(Fig. 2E). Similar weak correlation between ABR and DPOAE thresholds has been reported in macaques (Lasky et al., 1999). One possibility for such weak correlation may be that potentially strongly correlated measures were swamped by measurement error.

We did not implement a test/retest protocol to estimate measurement error; however, a previous estimate of error in measurement of ABR thresholds in the chinchilla strain of rabbits had a SD of 5 dB (Borg et al., 1995), which is equal to the across-ear SD of ABR thresholds in our study. Another possible explanation for weak correlation between ABR and DPOAE thresholds may be due to the sources of generation of DPOAEs; one source arises from the same location along the basilar membrane as that of the ABR, but the 87 other source arises from a more apical location (reviewed in Shaffer et al., 2003; Shera,

2004a).

We found that post-exposure ABR threshold shifts were greater and more variable than DPOAE threshold shifts (Fig. 6C). This was not due to a ceiling effect of the range of sound levels tested because there were more data that were minimum possible threshold shifts for ABRs than for DPOAEs (Fig. 5). We also found that ABR threshold shifts were correlated with DPOAE threshold shifts. Similar correlation between the two post-exposure shifts has been reported in gerbils (Mills, 2003).

Our results indicate that ABR and DPOAE threshold shifts were equivalently useful in predicting majority OHC survival or OHC wipe-out (Fig. 11A and B).

Similarly, they could both be used to predict majority survival of IHCs vs. potential loss of IHCs (Fig. 11C and D). Knowledge of both threshold shifts did not increase predictability of survival for either OHCs or IHCs—with respect to predicting OHC or

IHC survival, the two measures were redundant.

Chapter 4: Results II: Specific Loss of Neural Sensitivity to Interaural Time

Differences following Noise-induced Hearing Loss

Abstract2

Sensorineural hearing loss (SNHL) causes an overall deficit in binaural hearing, including the abilities to localize sound sources, discriminate interaural time and level differences (ITDs and ILDs, respectively), and utilize binaural cues to aid signal detection and comprehension in noisy environments. Very few studies have examined the effect of SNHL on binaural coding in the central auditory system, and those that have, have mainly focused on age-related hearing loss. We induced hearing loss in male and female Dutch-belted rabbits via noise overexposure and compared unanesthetized, single- unit responses of their inferior colliculi (HL neurons) to those of unexposed rabbits.

Sound-level thresholds of HL neurons to diotic noise were elevated by 75 dB, on average.

Sensitivity of firing rates of HL neurons to the azimuth of a broadband noise stimulus was reduced, on average, but was confounded by differences in sound level with respect to detection threshold between groups. We independently manipulated ITD and ILD in virtual acoustic space and found directional sensitivity in binaurally sensitive HL neurons was entirely due to ILD sensitivity and no different than that for unexposed rabbits.

However, ITD sensitivity was completely absent in binaurally sensitive HL neurons for both sources in virtual acoustic space and sources with ITDs extending to ±3 ms. HL neurons also had weaker spike-timing precision and slightly increased spontaneous rates.

Overall, ILD sensitivity was uncompromised whereas ITD sensitivity was completely

2 Most parts of this chapter are taken verbatim from a manuscript currently under review 89 lost, implying a specific inability to utilize information in the timing or correlation of acoustic waveforms between the two ears following severe SNHL.

Introduction

Sensorineural hearing loss (SNHL) involves damage to the cochlea, auditory nerve, or central auditory areas and is the most common form of permanent hearing loss.

It is often acquired by cumulative overexposure to loud sounds; one study estimated that nearly one in four adults in the U.S. had SNHL due to noise overexposure (Carroll et al.,

2017). Its most obvious effect is reduced sensitivity to sound, e.g., individuals must use hearing aids to boost sound to a detectable level. However, even with clearly audible sound, individuals with SNHL are often impaired in various aspects of binaural hearing.

For example, sound localization accuracy and precision are degraded in quiet and even more dramatically in the presence of background noise (Lorenzi et al., 1999a). The association between SNHL and the ability to discriminate either of the two main binaural cues, interaural time and level differences (ITDs and ILDs, respectively), is mixed. Some have reported small increases in ILD discrimination thresholds in individuals with SNHL

(Gabriel et al., 1992; Smith-Olinde et al., 1998), while others have not (Hausler et al.,

1983; Spencer et al., 2016a). All reported increases in ITD discrimination thresholds with

SNHL for at least some stimuli, but effects varied both in magnitude and across stimuli, potentially due to individual differences in hearing loss (Best et al., 2019; Gabriel et al.,

1992; Hausler et al., 1983; Lacher-Fougere et al., 2005; Smith-Olinde et al., 1998;

Smoski et al., 1986; Spencer et al., 2016a). Lastly, individuals with SNHL have an 90 impaired ability to filter out distracting sound sources based on spatial location while attempting to comprehend a target source (Dai et al., 2018; Marrone et al., 2008).

Despite the above evidence of a perceptual impairment of binaural hearing with

SNHL, there have been surprisingly very few studies of the effect of SNHL on binaural coding in the central auditory system; the few that exist, focused on age-related hearing loss (Costa et al., 2016; Laumen et al., 2016; McFadden et al., 1994a; McFadden et al.,

1994b). Laumen et al. (2016) studied evoked potentials at the skin surface, and the Costa et al. (2016) and McFadden and Willott (1994a; 1994b) studied rats and mice, respectively, which do not have good hearing at low frequencies where ITD sensitivity dominates. Therefore, there is a gap in knowledge as to how SNHL, and specifically acoustic trauma, affects the encoding of ITD and ILD by individual neurons. The aim of the present study was to fill this gap. We accordingly formulated two main research questions. The first was whether sensitivity of neural firing rates to sound source direction in the horizontal plane (azimuth) decreases following noise-induced hearing loss, similar to the deficit in sound localization ability observed in individuals with SNHL

(Lorenzi et al., 1999a). The second was whether neural sensitivity to ITD or ILD alone was affected by noise-induced hearing loss.

In order to address the above research questions, we measured single-unit responses from the inferior colliculus (IC) of unanesthetized rabbits before and after a noise exposure that caused permanent hearing loss. The IC is an ideal target because it is the brain area where projections of the primary binaural nuclei in the auditory brainstem converge. Furthermore, rabbits are an appropriate species because they hear over a range 91 that spans ITD- and ILD-dominated frequencies (Heffner et al., 1980). We used an overexposure protocol that we recently showed produced widespread damage of outer hair cells and variable damage of inner hair cells across the extent of the cochlea

(Haragopal et al., 2020). We then measured azimuth, ITD and ILD tuning curves of IC neurons in rabbits with and without hearing loss and compared between the two groups.

Materials and Methods

Data were collected from three female and two male Dutch-belted rabbits

(Envigo) over a period from 5 to 32 months of age. All experimental procedures were approved by the Ohio University Institutional Animal Care and Use Committee.

Experimental Procedures

Detailed surgical and experimental procedures are found in Dorkoski et al.

(2020). Briefly, rabbits underwent an original surgery to implant a headbar for head fixation during awake neural recording sessions; a subsequent surgery to both drill a hole into the skull dorsal to the IC and implant a tetrode microdrive (Neuralynx 9 Drive); and additional surgeries as needed to replace the microdrive and aim for different regions within the IC (including both sides of the brain). Anesthesia for the first surgery was induced with 6 mg/kg s.c. xylazine, 44 mg/kg i.m. ketamine, and 1 mg/kg s.c. acepromazine and maintained with 15 mg/kg i.m. ketamine as needed; and for subsequent surgeries was induced with 6 mg/kg s.c. xylazine and 35 mg/kg i.m. ketamine and maintained via mask with 1.5–2% isoflurane mixed with O2 (1 L/min). Atropine (0.25 mg/kg s.c.) was administered to reduce mucosal secretions and prevent tracheal blockage.

Post-operatively, rabbits were administered 0.3 mg/kg s.c. dexamethasone to reduce 92 potential brain swelling and 0.025 mg/kg s.c. buprenorphine every 12 hrs for at least 36 hrs for analgesia.

Each microdrive was loaded with four independently drivable tetrodes (0.1–0.5

MΩ impedance). Data were collected during daily, 2.5-hr, awake recording sessions within a double-walled, sound-attenuated chamber (ETS-Lindgren Acoustic Systems).

Rabbits were loosely secured in a custom-made “chair”, head-fixed via headbar, fitted with ear molds for sound presentation (Reprosil, Dentsply), and monitored by video throughout the session. Before the first session, rabbits were gradually acclimated to the set-up over 1.5 weeks.

Stimulus Presentation and Neural Data Acquisition

Detailed methods for presentation of acoustic stimuli and acquisition of neural data are in Dorkoski et al. (2020). Acoustic stimuli were created digitally at 50-kHz sampling rate separately for each ear, converted to analog signals, and amplified to drive insert earphones (Etymotic ER-2) whose sound tubes were embedded in ear molds. The ear molds sealed in the ears to create a closed field. The in-ear acoustic assembly was calibrated (Etymotic ER-7C) for level and phase at the beginning of every recording session using a chirp stimulus.

Electrical potentials measured with the tetrode microdrive were digitized at 25- kHz sampling rate, filtered between 0.4 Hz and 11.25 kHz, then stored on disk. Spike sorting for reported data was performed offline (Plexon Offline Sorter). Sorting was based on identifying single-unit clusters of data points within a 2D or 3D scatterplot, where the axes were action potential peak amplitude simultaneously measured from two 93 or three electrodes within a tetrode, respectively. Details of spike sorting may be found in

Dorkoski et al. (2020). Spike data were considered single-unit if they 1) formed a complete cluster separate from others, and 2) had less than 2% submillisecond interspike intervals. Only single-unit data are reported.

Auditory Brainstem Response Measurement

Auditory brainstem responses (ABRs) were measured both to quantify hearing loss in rabbits overexposed to noise and to ensure that control, unexposed rabbits had normal hearing. Detailed methods for ABR measurement may be found in Haragopal et al. (2020). Briefly, ABRs were measured under isoflurane anesthesia with platinum, subdermal needle electrodes (Grass Instruments) positioned at the vertex, mastoid (of the ear presented with sound), and back (common). ABRs were measured in response to monaural, unipolar clicks (100 µs) and tone pips (5-ms duration, 0.5-ms sin2 on/off ramps) at frequencies of 0.5 to 16 kHz in octave steps at 30 repetitions/s. Stimuli were presented at multiple sound levels in 5-dB steps. The ABR at each sound level was averaged over 512 stimulus repetitions. For clicks and tone pips with frequencies at and above 2 kHz, stimulus polarity was alternated between successive repetitions to prevent contamination of the signal by the cochlear microphonic at high sound levels. Stimulus polarity was not alternated for 0.5- or 1-kHz tones so that the phase-locked auditory nerve signal in Wave I was not eliminated. ABR threshold was defined as the lowest sound level at which the average ABR first appeared, determined by visual inspection. 94

ABRs were measured prior to neural recordings. For rabbits that underwent noise overexposure, ABRs were re-measured two weeks post-exposure, by which time it is likely that the thresholds had settled to permanent levels (Borg et al., 1995).

Noise Overexposure

Detailed methods for noise overexposure are in Haragopal et al. (2020). Rabbits were exposed to octave-band noise geometrically centered at 750 Hz (530–1061 Hz) for

75 or 90 min under isoflurane anesthesia. Noise waveforms were presented over two loudspeakers pointed at the left and right ears, respectively. Waveforms from left and right speakers were independent, producing binaurally uncorrelated sound. This ensured that overexposure was not restricted to a fixed ITD or ILD since both cues fluctuate moment-to-moment for binaurally uncorrelated sound. The sound levels of noise from the two speakers were equal and combined to 133 dB SPL when presented simultaneously, as measured at the end of a probe tube microphone (Etymotic ER-7C) dangled at the approximate location of the center of the rabbit’s head in the absence of the rabbit.

Stimuli

For each neuron the following were measured: 1) the frequency-and-level response area (FRA); 2) the sound level tuning curve in response to diotic, broadband

(0.1–16 kHz) noise; 3) azimuth tuning curves in response to broadband noise under four different binaural cue conditions; 4) ITD tuning curves in response to broadband and lowpass (0.1–1.5 kHz) noises; and 5) responses to monaural, broadband noise in the left and right ears. For each measurement, the order of stimulus parameters was varied randomly across 15 repetitions, except the FRA and monaural noise (2 and 50 repetitions, 95 respectively). Stimuli were either tone pips (100 ms on, 200 ms off, 5-ms on/off sin2 ramps) or frozen noise bursts (300 ms on, 300 ms off, 5-ms on/off sin2 ramps).

The number of stimulus repetitions (usually 15) was chosen to exceed a criterion of accuracy of firing rate estimation. Under the assumption that the distribution of spike counts in response to a repeated stimulus approximately follows a Poisson distribution

(Dorkoski et al., 2020), at least 13 stimulus repetitions are necessary to estimate a mean firing rate of 100 spikes/s over a 300-ms window with a 95% confidence interval of ±10 spikes/s. The same repetitions would estimate mean firing rates of 50 and 20 spikes/s with 95% confidence intervals of ±7 and ±4 spikes/s, respectively.

For FRA measurement, tone pips were presented to the contralateral ear at frequencies from 0.1 to 16 kHz in ¼-oct steps and at sound levels from 5 to 70 dB SPL

(unexposed, “normal-hearing” rabbits [NH]) or 35 to 100 dB SPL (overexposed,

“hearing-loss” rabbits [HL]) in 5-dB steps. For sound level tuning curves, noise bursts were presented at 5 to 70 dB SPL or 35 to 100 dB SPL for NH and HL rabbits, respectively, in 5-dB steps. For ITD tuning curves, ITD was varied over −1,000 to +1,000

μs (ipsilateral- to contralateral-leading, respectively) or ±800 μs in 50-μs steps. An additional ITD tuning curve in response to broadband noise was measured over ±3 ms in

150-μs steps.

Acoustic waveforms presented over earphones were made directional by filtering with left and right directional transfer functions (DTFs) specific to each azimuth within the front horizontal plane (Day et al., 2012; Dorkoski et al., 2020). For all azimuth tuning curves, stimuli were presented at azimuths between −90° and +90° (ipsilateral and 96 contralateral to the recording site, respectively) in 15° steps where 0° was directly in front of the rabbit. Noise bursts were alternatively filtered with 1) normal DTFs (congruent binaural cues; “ITD+ILD”), 2 and 3) two different DTFs augmented to manipulate ITD and ILD independently (incongruent binaural cues; “ITD-only” and “ILD-only”), and 4) normal DTFs with sound only in the contralateral ear (monaural; “contra-only”). “ITD- only” DTFs had left and right magnitude spectra fixed to one, which fixed ILD to that at

0° while allowing ITD to vary with azimuth. “ILD-only” DTFs had left and right phase spectra set to zero, which fixed ITD to that at 0° while allowing ILD to vary with azimuth.

Azimuth and ITD tuning curves (except the ITD tuning curve over ±3-ms delays) were measured both at an absolute sound level of 70 dB SPL (same between individuals) and a “near-threshold” sound level (different between individuals). The absolute sound level was chosen as the highest level for which NH rabbits would listen to repeated stimuli without indication of agitation. For NH rabbits, the near-threshold level was chosen to be near or equal to the average of the click-evoked ABR thresholds between the two ears of each individual rabbit (in practice, 40 to 52.5 dB SPL). Click-evoked

ABR thresholds in HL rabbits were usually above the limit of our acoustic system (see

Results), therefore the same method could not be used to choose the near-threshold level.

For HL rabbits, near-threshold level was chosen as either 92.5 or 97.5 dB SPL, which was the highest sound level at which our acoustic system could present noise stimuli without clipping waveforms, and was equal to median postexposure threshold in our previous study using the same overexposure (Haragopal et al., 2020). Monaural noise 97 bursts and ITD tuning curves over ±3-ms delays were only presented at near-threshold level.

Data Analysis

Stimulus trials that contained artifacts due to infrequent rabbit movements were omitted from analysis. Stimulus-evoked and spontaneous spike counts were made in the stimulus window and last 100 ms of the 300-ms silent period of each trial, respectively.

The FRA (average firing rate as a function of both log frequency and level) was upsampled by a factor of two then smoothed with a 2D filter made of a Gaussian frequency window (SD = 1/16 oct) and triangular level window (Palmer et al., 2013). For

NH rabbits, CF was determined by visual observation as the frequency that elicited an increase or decrease of firing rate from spontaneous rate at the lowest sound level. For single-units with complex FRAs, CF was determined as that of the multiunit FRA at the same tetrode site. For single-units at ventral recording sites where the low-frequency

FRA tail was visible, but the high-frequency tip exceeded the range of tone frequencies presented, the CF was assigned a value of 20 kHz.

Multiunit FRAs were recorded at regularly-spaced depths along each tetrode track and displayed a tonotopic progression of “V”-shapes with CF systematically decreasing in the ventral-to-dorsal direction (e.g., left panel of Fig. 13A), consistent with the central nucleus of the IC (ICC; Aitkin et al., 1972; Hind et al., 1963; Merzenich et al., 1974). For

NH rabbits, only single-units recorded within this region are reported. For HL rabbits, all single-units are reported because these rabbits lacked a clear tonotopic region (see

Results). Furthermore, CF was not determined for single-units from HL rabbits. 98

The threshold of the level tuning curve in response to diotic, broadband noise was defined as the lowest sound level at which the firing rate deviated from spontaneous rate at a significance level of 0.01 (Wald test) at that level and at least 5 dB above.

For azimuth and ITD tuning curves, an estimate of the mutual information (MI;

Cover et al., 2006) between spike count, n, and stimulus azimuth or ITD, θ, was

̂ 푃(푛|휃) computed: 푀퐼(푛; 휃) = ∑푛 ∑휃 푃(푛|휃)푃(휃) log2 푃(푛) . The conditional spike count distribution, P(n|θ), was assumed to be a Poisson distribution (Dorkoski et al., 2020) with

휆(휃) as the mean spike count at azimuth or ITD θ as follows,

휆푛(휃)exp⁡[−휆(휃)] 푝(푛|휃) = 푛!

The marginal distribution, P(θ), was a uniform distribution of the M stimulus azimuths or

1 ITDs used to measure the tuning curve, 푃(휃) = . The other marginal distribution, P(n), 푀 was computed as 푃(푛) = ∑휃 푃(푛|휃)푃(휃). A bootstrap procedure was used to correct for bias in the MI estimate (Chase et al., 2005). Spike count data were resampled with replacement, then MI was computed on the resampled data (MIboot). The bias was estimated as 퐵푖푎푠 = 푀퐼̅̅̅̅푏표표푡 − 푀퐼̂ , where 푀퐼̅̅̅̅푏표표푡 was the average over 500 bootstrap- resampled data sets. Debiased MI was 푀퐼 = 푀퐼̂ − 퐵푖푎푠. Finally, normalized MI (MInorm) was computed by dividing MI by the entropy of the stimulus azimuth or ITD, log2 푀, which is the largest value MI may take. Bootstrapped estimates of variance or confidence interval in MI were not computed because the bias correction takes care of the low sample size and is accurate even for limited sampling. SD of MIs computed by the bootstrap technique are very low. A previous study shows that MIs for the same azimuth 99 tuning stimuli as in the present study, have SDs of 0.08 bits for 10 trials in the IC neurons, where SDs reduce with increased trial presentations (Day et al., 2016); here, we use 15 repetitions, so SDs for our MIs must be even lower than those for 10 trials.

Moreover, the bootstrapped estimate of the sampling bias also reduces to ~ 0.02 bits (Day et al., 2016), which implies that the bias correction procedure is highly accurate, as mentioned earlier, even for limited number of trials.

To quantify spike-timing precision, a normalized shuffled autocorrelogram (SAC) was computed for each neuron from its responses to 50 presentations of the same monaural, broadband noise waveform (Joris, 2003; Louage et al., 2004). The SAC was a histogram of all-order interspike intervals computed between spikes from all possible pairs of non-identical trials. Bin size (Δt) of the SAC was set at 200 µs, which was small enough to resolve the temporal structure of the SAC. SACs were normalized by dividing by 푁(푁 − 1)푟2Δ푡퐷, where N was the number of stimulus presentations, r was the average firing rate, and D was the stimulus duration. A SAC value of 1 at an interspike delay of zero (termed the "correlation index"; Joris et al., 2006a) indicates random spike timing across trials, while that above or below 1 indicates correlated or anti-correlated spike timing, respectively. Accuracy of the estimation of the SAC was limited by the total number of spikes. In order to set a criterion of accuracy, we implemented a statistical test similar to that in Joris et al. (2006a). For each data set, which consisted of spike trains for each of 50 trials, a resampled data set was created in which 1) the first spike latency of each trial was drawn randomly from a uniform distribution between stimulus onset and the original latency, and 2) the remaining spike times within the trial were created by a 100 random permutation of the interspike intervals within the same trial. The resampled data set therefore had the same number of spikes and same set of first-order interspike intervals as the original data set, but spike times were scrambled across trials. A correlation index was then computed on the resampled data. The resampling procedure was repeated 2000 times to create a distribution of correlation indices based on scrambled spike times. As our criterion for estimation accuracy, we only report correlation indices for which the upper bound of the 95% confidence interval of correlation indices computed on scrambled spike times was less than 2, meaning data were sufficient to detect a correlation index of at least 2 or greater at a significance level of 0.05. In practice, this criterion was usually met for firing rates of 10 sp/s or greater.

Experimental Design

Data from NH rabbits came from a total of 76 ICC neurons, including 59 neurons in three female rabbits (rabbit ID–number of neurons: R04–29, C01–28, D05–2) and 17

ICC neurons in two male rabbits (D06–2, E04–15). Of these, 10 neurons (13%) came from the left side of the brain. Data from HL rabbits came from a total of 73 IC neurons

(see Results regarding FRAs for a difference in assignment of IC for HL and ICC for

NH), including 22 neurons in two female rabbits (R04–8, D05–14) and 51 neurons in one male rabbit (D06). Of these, 8 neurons (11%) came from the left side of the brain. Data were recorded from all three of the HL rabbits prior to noise overexposure; 43% of NH data (33/76 neurons) were from rabbits that subsequently underwent noise overexposure.

Single-unit isolation was not always maintained for every stimulus listed above.

Furthermore, some stimuli were implemented later on during the period of data 101 collection. Therefore, sample sizes are not the same for every stimulus. No significant differences of the normalized MI of azimuth or ITD tuning curves were found based on sex or side of the brain (p > 0.05, Wilcoxon rank-sum test); therefore, data were pooled across these two variables.

Each tetrode microdrive was constructed to aim the tetrodes emerging from the drive tip to pass through the skull at a certain location on the skull surface. The location at which the tetrodes emerged from the drive tip could be varied for different microdrives. In a typical NH rabbit, several microdrives—each with tetrodes emerging from a different place on the drive tip—were implanted before the tonotopic region of

ICC was found. Subsequent microdrives were then constructed and implanted to aim tetrodes at or near the same region. In order to increase the chances of recording from

ICC neurons in HL rabbits, tetrodes were aimed at locations that, in neural recordings in the same rabbit prior to noise overexposure, yielded tetrode tracks that penetrated a clear, tonotopic region (e.g., Fig. 13A).

Statistical Analyses

All statistical tests were performed in MATLAB (Mathworks). Deviation of the firing rate from spontaneous rate was assessed with a Wald test at a significance level of

2 2 0.01. The Wald statistic was (푟 − 푟푠)/√푠 ⁄푚 + 푠푠 ⁄푚푠, where r and rs are the average firing rates over the stimulus duration and last 100 ms of the 300-ms interstimulus silent

2 2 period, respectively, s and ss are the sample variances of the rates over the same windows, and m and ms are the number of repetitions of the stimulus and silent periods, respectively. Stimuli used in azimuth or ITD tuning curve measurement were considered 102

“above threshold” if the firing rate at any azimuth or ITD deviated significantly from spontaneous rate, as assessed with multiple Wald tests at a significance level of 0.01 and corrected for multiple comparisons using the Benjamini-Hochberg procedure

(Wasserman, 2004).

Differences between azimuth tuning curves from the same neuron measured under different binaural-cue conditions were assessed with multiple Wald tests at a significance level of 0.01 and corrected for multiple comparisons using the Benjamini-Hochberg procedure. In this case the Wald statistic was

푟1(휃) − 푟2(휃) 푊휃 = 푠2(휃) 푠2(휃) √ 1 + 2 푚1 푚2 where 푟1(휃) and 푟2(휃) are the average firing rates at azimuth θ under binaural-cue

2 2 conditions 1 and 2, respectively, 푠1 (휃) and 푠2 (휃) are the sample variances of the same firing rates, and m1 and m2 are the number of repetitions. Effect size was defined as the sum of the absolute value of Wald statistics over all azimuths: 푊 = ∑휃|푊휃|.

Differences between the medians of values measured from NH vs. HL rabbits were assessed with a Wilcoxon rank-sum test at a significance level of 0.05.

Results

Hearing Loss following Noise Overexposure

We confirmed normal hearing in control NH rabbits (including those that later underwent noise overexposure) by measuring their ABR thresholds and comparing to those of other unexposed rabbits. The ABR is an evoked potential measured at the skin surface whose generators are known to be the auditory nerve and auditory brainstem and 103 midbrain (Melcher et al., 1996). In NH rabbits, mean click-evoked ABR threshold was 43 dB SPL and mean tone-evoked ABR thresholds were 48, 52.5, 45, 30.5, 28 and 38 dB

SPL for 0.5, 1, 2, 4, 8 and 16-kHz tones, respectively (Fig. 12). These values were highly consistent with those of a larger sample of Dutch-belted rabbits in a previous study

(Haragopal et al., 2020, their Fig. 2A: N = 53 ears, of which 8 ears were from rabbits in the present study). The across-ear SD of ABR thresholds (5 dB) was also the same as that in the previous study.

Figure 12

Hearing Loss following Noise Overexposure

Click-evoked and tone-evoked ABR thresholds for all rabbits in the study. Data below and above the dashed line were collected before and two weeks after noise overexposure, respectively. Lines connect data from the same rabbit. Different colored lines indicate different rabbits, indicated to the right of the plot. Responses of left and right ears indicated by circles and squares, respectively. Blue shaded rectangle indicates frequency passband of the noise overexposure.

104

We induced permanent hearing loss in three of the NH rabbits by overexposing them to high-intensity noise under anesthesia using the same exposure parameters as in a previous study (Haragopal et al., 2020). In that study, a 90-min overexposure led to median ABR threshold shifts of approximately 55 dB at all frequencies, but large variability across ears. A minority of ears had postexposure thresholds that were above the sound-level limit of the acoustic system, meaning only minimum possible threshold shifts could be estimated for those ears. In our HL rabbits, most postexposure thresholds were above the limit of our acoustic system (~100 dB SPL; filled symbols in Fig. 12). We attempted to decrease postexposure thresholds slightly by lowering the exposure duration for two rabbits (D05 and D06) from 90 min to 75 min, but interestingly, the postexposure thresholds of these rabbits were all above the limit of the acoustic system, whereas those of the rabbit that had a 90-min exposure (R04) were more often below the limit. That our attempt to slightly lower postexposure thresholds failed is not surprising given the known high variability of susceptibility to noise overexposure (Cody et al., 1983; Haragopal et al., 2020). Altogether, click-evoked threshold shifts were at least 50 dB and tone-evoked threshold shifts were at least 50, 55, 50 and 50 dB at frequencies of 2, 4, 8 and 16 kHz, respectively. Minimum possible post-exposure thresholds at 0.5 and 1-kHz frequencies were lower than the rest not because these thresholds were above the limit of the acoustic system, but because the cochlear microphonic began to dominate the signal at a sound level lower than the threshold. The actual threshold shifts at these frequencies were likely similar to those at higher frequencies since, based on a separate set of 60-min exposures in Haragopal et al. (2020) in which postexposure thresholds were measurable at all 105 frequencies, average threshold shifts were within about 10 dB of each other at all frequencies.

Haragopal et al. (2020) measured ABR threshold shifts and cochlear hair cell counts in the same ears and found a “critical” ABR threshold shift of 46 dB; tone-evoked

ABR threshold shifts above this value were always associated with frequency-matched regions of the cochlea that had less than 25% outer hair cell (OHC) survival and variable amounts of inner hair cell (IHC) survival. Since tone-evoked ABR threshold shifts in our

HL rabbits were at least 50 dB at frequencies between 2 and 16 kHz, we infer that the basal and middle regions of their cochleae had very few surviving OHCs and variable amounts of surviving IHCs. The same inference could not be made for the apical region of their cochleae since accurate threshold shifts at low frequencies were not measurable for the reasons listed above, but Haragopal et al. (2020), using the same overexposure, found that 75% of ears had less than half OHC survival in the apical region of the cochlea.

Frequency Tuning following Noise Overexposure

The left column of Figure 13A shows multiunit FRAs measured at different depths along a single tetrode track within a NH rabbit. Tetrode descent into the dorsal border of the ICC was indicated by a transition of the FRA from a diffuse pattern, to broad tuning, to a distinctive “V”-shape with frequency tuning tip that increased systematically with depth. At the ventral border, the frequency tuning tip surpassed the range of tone-pip frequencies leaving only the tail visible, then the FRA often returned to broad tuning followed by a diffuse pattern. The tetrode in Figure 13A (left) entered the 106

ICC at the level of a CF of 3 kHz and exited at a CF above 16 kHz, but other tetrodes traversed different spans of the cochleotopic axis, such as entering at 300 Hz and exiting at 4 kHz. This indicated that the orientations of tetrode tracks were oblique to the cochleotopic axis of the ICC.

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Figure 13.

Frequency Tuning following Noise Overexposure

(A) Multiunit FRAs recorded from two different tetrodes (tetrode IDs at top of each column) at 12–13 different depths (plots within each column). Depth relative to the ventral border of auditory-evoked activity is indicated to the right of each plot. Tetrodes were from the same rabbit before (left) and after (right) noise overexposure. In each FRA, the color associated with firing rate is normalized to highest (white) and lowest (black) rates. Before overexposure, tuning to frequency within the ICC clearly increases with depth. (B) Scatterplot of CF vs. upper-frequency edge of multi-unit FRAs measured at several depths along 5 different tetrodes in NH rabbits (N = 29 sites; several data points 108 overlap). Red line is fit line with correlation coefficient indicated in upper-left corner. (C) Histogram of the CFs of neurons from NH rabbits (N = 60). (D) Histogram of the CFs of neurons from HL rabbits predicted from the upper-frequency edge of the FRA measured from either the neuron or multi-unit data at the same site (N = 58). Prediction made using the fit line in (B). Neurons with no upper-frequency edges in their FRAs were unclassified (UC). (E) Example neurons from HL rabbits with FRAs with no tuning, or excitatory, inhibitory, or mixed excitatory and inhibitory regions (top to bottom). Neuron ID indicated in upper-left corner of each FRA. (F) Example neurons from NH rabbits demonstrating similar FRA subtypes to those in (E). CF additionally indicated in upper- right corner of each FRA.

The right column of Figure 13A shows multiunit FRAs from a tetrode track in the same rabbit following noise overexposure. Frequency tuning tips were completely absent in this tetrode track as well as all tracks postexposure, with thresholds to tone pips usually not lower than 70- or 80-dB SPL (note the higher range of sound levels). Since frequency tuning tips did not exist following noise overexposure, we could not directly measure CFs nor determine whether single-units were within the ICC or shell regions of the IC. For this reason, we from now on interchangeably use IC (for hearing loss and when relating hearing loss to normal hearing) or ICC (for normal hearing). Furthermore, since tetrode tracks were oblique to the cochleotopic axis, we could not infer CF from tetrode depth.

However, we noted that the upper-frequency edge of FRAs (UFE; highest frequency with a response approximately 75% of maximum firing rate: yellow color in FRAs) increased systematically with tetrode depth in HL rabbits similar to that in NH rabbits (Fig. 13A).

For multiunit FRAs measured in NH rabbits, log CF was linearly related to log UFE (Fig.

13B) by the equation log10 퐶퐹 = 1.2 log10 푈퐹퐸 − 0.24, where CF and UFE were in kHz. Under the assumption that UFEs remained the same before and after noise overexposure (e.g., Izquierdo et al., 2008), we measured the UFEs of all neurons from 109

HL rabbits from either the single-unit FRA or the multi-unit FRA at the same tetrode site, then predicted CF using the above equation. Predicted CFs of neurons from HL rabbits were all high (Fig. 13D) whereas directly measured CFs of neurons from NH rabbits spanned the cochleotopic axis (Fig. 13C).

Approximately half of neurons from HL rabbits had diffuse FRAs with no coherent features (Fig. 13E, top); approximately one-third had a single excitatory region at high frequencies and intensities (Fig. 13E, second from top); and the rest had either a single inhibitory region (Fig. 13E, second from bottom) or combinations of excitatory and inhibitory subregions (Fig. 13E, bottom). FRAs of neurons from NH rabbits were very heterogeneous and included those similar to the subtypes found in HL rabbits, meaning that, NH neurons also possessed FRAs that had only excitatory, or only inhibitory or dual subregions; their FRAs, however, showed a sharp tip (Fig. 13F).

Spontaneous Firing Rates and Diotic Noise Thresholds following Noise Overexposure

Spontaneous firing rates of neurons in NH and HL rabbits covered a wide, overlapping range whose distributions were best represented on a logarithmic axis (Fig.

14A). Median spontaneous firing rate was slightly greater for HL than for NH rabbits (p

= 0.028, Wilcoxon rank-sum test). This result was consistent with many previous reports of increased spontaneous firing rates of ICC neurons following acoustic trauma (e.g.,

Longenecker et al., 2011; Ma et al., 2006; Mulders et al., 2009), and is likely due to loss of high-threshold potassium channel conductances (Anderson et al., 2018), although the effect size varies widely between studies.

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Figure 14

Spontaneous Firing Rates and Diotic Noise Thresholds following Noise Trauma

(A) Histograms of spontaneous firing rates of neurons from NH (top; N = 61) and HL (bottom; N = 59) rabbits. Dashed lines indicate medians. Asterisk indicates p < 0.05 (Wilcoxon rank-sum test). (B) Histograms of sound-level thresholds to diotic, broadband noise for neurons from NH (top; N = 64) and HL (bottom; N = 61) rabbits. Dashed lines indicate medians computed under the assumption that neurons with no threshold had actual thresholds that were > 100 dB SPL. (C) Scatterplot of sound-level threshold vs. CF for neurons from NH rabbits. Gray line marks the lower envelope of the data and red line marks this same envelope following a hypothetical shift of 75 dB—the same shift in median threshold following overexposure as in (B). Dashed line indicates approximate sound-level limit of the acoustic system. The hypothetical distribution of CFs of neurons with measurable responses would be biased to high CFs following overexposure.

There was no overlap of distributions of sound-level thresholds to diotic, broadband noise between NH and HL rabbits (Fig. 14B). Of neurons from HL rabbits,

64% (39/61) had measurable thresholds—i.e., within the limit of our acoustic system

(≤ 100 dB SPL). Under the assumption that neurons with no measurable thresholds had actual thresholds greater than 100 dB SPL, median threshold was 75 dB greater for HL than for NH rabbits. Therefore, HL rabbits had substantial hearing loss. A greater 111 percentage of neurons in HL than in NH rabbits had firing rates in response to broadband noise at threshold sound level that were suppressed below, as opposed to facilitated above, spontaneous rate (HL: 38% [15/39], NH: 8% [5/61]—only neurons with measurable thresholds), suggesting either overrepresentation of the suppressive subtype of neurons or increased strength of inhibition following noise overexposure.

Thresholds of neurons from NH rabbits were lowest at high CFs (Fig. 14C), consistent with previous behavioral and physiological studies in rabbits (Day et al., 2016;

Heffner et al., 1980). We shifted the lower envelope of the scatterplot in Figure 14C by

75 dB—the same shift in median threshold shown in Figure 14B—to predict the hypothetical lower envelope of data following noise overexposure. The hypothetical envelope was cut off at low CFs by the sound-level limit of our acoustic system, suggesting a bias towards high CFs for neurons from HL rabbits, consistent with the predicted CFs in Figure 14D.

Directional Sensitivity following Noise Overexposure

For each neuron, we measured the azimuth tuning curve to broadband noise in the front horizontal plane then quantified the neuron’s directional sensitivity as the MI between firing rate and stimulus azimuth, normalized by the maximum possible MI

(MInorm). A MInorm of one indicates the azimuth of the stimulus can be exactly predicted by observing the firing rate, whereas a MInorm of zero indicates a flat azimuth tuning curve, which provides no information about azimuth. Figures 15A and B show azimuth tuning curves of two neurons from HL rabbits that had relatively strong directional sensitivity—one with a contralaterally maximal sigmoidal shape and the other with a 112 broad bell shape centered at 0°. Tuning curve shapes of neurons from HL rabbits were heterogeneous, including shapes similar to those in Figure 15A, shapes similar to those in

Figure 15B but centered at different azimuths, shapes that were a hybrid of those in

Figures 15A and B, and ipsilaterally maximal sigmoidal shapes (Fig. 15C). Such tuning curve heterogeneity was consistent with that of neurons from NH rabbits, both in the present and previous studies (Day et al., 2013; Kuwada et al., 2011). Many neurons from

HL rabbits had low (Fig. 15D) or no (Fig. 15E and F) directional sensitivity; the latter occurred due to the stimulus either evoking a response that had no directional sensitivity

(Fig. 15E), or simply, not being sufficient to create a deviation of the firing rate from spontaneous rate (Fig. 15F). (Note that the MInorm in Figure 15E was slightly negative, which could occur due to bias correction; in this case, bootstrapped estimate of the bias was larger than the MI computed from the original data.)

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Figure 15

Directional Sensitivity following Noise Overexposure

(A–F) Azimuth tuning curves of six different neurons from HL rabbits measured at near- threshold level (purple curves; mean ± SEM). Normalized mutual information (MInorm) of each curve indicated by bold purple number adjacent to curve. Positive azimuths indicate 114 sound source azimuths contralateral to the recording site. Dashed black lines indicate spontaneous rate. Unit ID indicated in upper-left corner of each plot. (G) Histograms of MInorm for azimuth tuning curves of neurons from NH (top; N = 56) and HL (bottom; N = 60) rabbits measured at absolute level. Stacked light and dark green bins represent tuning curves that were and were not significantly different than spontaneous rate, respectively (i.e., above and below threshold, respectively; Wald tests with correction for multiple comparisons, p < 0.01). Overall medians indicated by brown dashed lines. (H) Same as (G) for tuning curves measured at near-threshold level (top: NH, N = 42; bottom: HL, N = 60). Dotted blue lines indicate medians computed only for tuning curves that were above threshold. Asterisks indicate p < 0.05 (Wilcoxon rank-sum test). (I) Scatterplot of MInorm measured at near-threshold level vs. CF for neurons from NH rabbits. (J) Histograms of near-threshold sound level with respect to each neuron’s threshold to diotic, broadband noise for neurons from NH (top; N = 33) and HL (bottom; N = 33) rabbits. Data only included for neurons with azimuth tuning curves that were above threshold. “N/A” indicates neurons with no measurable thresholds. Black lines indicate medians computed under the assumption that sound levels associated with neurons with no thresholds were at the lowest end of the data set.

At an absolute sound level of 70 dB SPL, neurons from NH rabbits had a broad range of MInorm whereas those from HL rabbits had MInorm near zero (Fig. 15G; p =

1×10−10, Wilcoxon rank-sum test). The firing rates of most neurons from HL rabbits did not deviate from spontaneous rate at 70 dB SPL (dark green bins in Fig. 15G; similar to that in Fig. 15F). Therefore, the lack of directional sensitivity in neurons from HL rabbits could be attributed to the stimulus being below threshold. At “near-threshold” sound levels (that is, the sound level was “ at or near” the average of left and right click-evoked

ABR thresholds (See Methods)), the stimulus was “above neural response threshold”

(that is, neural responses significantly deviated from spontaneous rates) for most neurons from HL rabbits and the range of MInorm increased, but median MInorm was still less than

−5 that for neurons from NH rabbits (Fig. 15H; p = 9×10 ). The decrease in median MInorm for neurons from HL rabbits remained statistically significant when computed only on azimuth tuning curves that deviated from spontaneous rate (i.e., above-threshold stimuli; 115 blue dotted lines in Fig. 15H; p = 0.015, N = 41 and 38 for NH and HL, respectively), which indicates that decreased directional sensitivity of neurons from HL rabbits was not solely due to the stimulus being below threshold for some neurons.

Figure 15I shows that MInorm of neurons from NH rabbits could have relatively high values across the cochleotopic axis, indicating that decreased directional sensitivity of neurons from HL rabbits could not be explained by a potential sample bias towards high-CF neurons. While stimuli presented at near-threshold level were above response threshold for most neurons from HL rabbits, it is important to note that they were not at the same relative sound level for NH and HL rabbits. When “near-threshold” sound level was expressed relative to each neuron’s threshold to diotic (i.e., at 0°), broadband noise, median thresholds were 22.5 dB above and 2.5 dB below neural threshold for NH and HL rabbits, respectively (Fig. 15J). The proximity of sound levels to response threshold of

HL rabbits’ neurons could decrease their directional sensitivity.

Binaural vs. Monaural Sensitivity

As a sound source moves from ipsilateral to contralateral to one ear, the intensity in that ear is attenuated by the obstructing head. In rabbits, this attenuation is very pronounced above approximately 4 kHz (Day et al., 2012; Kim et al., 2010). Neurons that are exclusively sensitive to sound in one ear may therefore have azimuth tuning curves that deviate from spontaneous rate due to purely monaural, not binaural, sensitivity.

Responses to monaural sound level are not useful for determining sound source azimuth, which depends upon binaural cues. In order to disambiguate binaural from monaural sensitivity for each neuron, we additionally measured the azimuth tuning curve with 116 sound presented only to the contralateral ear (“contra-only”), then compared it to the binaural tuning curve (“ITD+ILD”). The “contra-only” condition and not “ipsi-only” condition was used because the input to the IC neurons is generally weighted towards the contralateral ear. Figures 16A and B show binaural azimuth tuning curves that do not significantly differ from their contra-only curves (p ≥ 0.01, Wald tests with correction for multiple comparisons), indicating monaural sensitivity, whereas Figures 16C and D show binaural azimuth tuning curves that do significantly differ from their contra-only curves

(p < 0.01), indicating binaural sensitivity. All data in Figures 16A–D are from HL rabbits. Most binaural interaction in neurons from HL rabbits was relatively mild, similar to that in Figure 16C. We quantified binaural interaction by the effect size of the statistical test of difference between binaural and monaural tuning curves (number to right of each panel). Figure 16D shows data from the neuron with greatest binaural interaction in HL rabbits.

Most monaurally sensitive neurons from NH or HL rabbits had low directional sensitivity (Fig. 16E), similar to that in Figure 16A; Figure 16B shows data from the monaurally sensitive neuron with greatest directional sensitivity. Neurons from HL rabbits were more often monaurally sensitive than those from NH rabbits (66% [25/38] vs. 17% [7/41] for HL and NH rabbits, respectively; only neurons with above-threshold azimuth tuning curves). The relatively greater proportion of monaurally sensitive neurons in HL rabbits may again potentially be explained by the proximity of near-threshold sound levels to neural thresholds for HL rabbits. Kuwada, Kim and colleagues (Kuwada et al., 2011) showed that azimuth tuning curves of IC neurons measured at 10 dB re: 117 neural threshold were similar between binaural and contralateral sound presentation; neurons that were monaurally sensitive at low relative levels were binaurally sensitive at high relative levels. When only azimuth tuning curves of binaurally sensitive neurons were considered, the difference in median MInorm between NH and HL rabbits was no longer significant (Fig. 16E; p = 0.88, Wilcoxon rank-sum test, N = 34 and 13 for NH and HL, respectively). The overall decrease in median MInorm for neurons from HL as compared to NH rabbits (Fig. 15H) can therefore be accounted for, first, by stimuli being below threshold for some neurons, and second, due to a greater proportion of monaural neurons, the latter of which may be due to stimuli being near neural threshold.

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Figure 16

Binaural vs. Monaural Sensitivity

(A–D) Azimuth tuning curves of four different neurons from HL rabbits measured at near-threshold level under binaural (ITD+ILD; blue solid lines) and monaural (contra- only; purple dotted lines) conditions. In the monaural condition, sound was only presented to the contralateral ear. Dashed black lines indicate spontaneous rate. MInorm of each ITD+ILD curve indicated by bold blue number adjacent to curve. Effect size of Wald tests for differences between ITD+ILD and contra-only curves are listed to the right of each panel. Asterisks indicate p < 0.01. Unit ID indicated in upper-left corner of each plot. (E) Histograms of MInorm for azimuth tuning curves (ITD+ILD) of neurons from NH (top; N = 41) and HL (bottom; N = 38) rabbits measured at near-threshold level. Only neurons with tuning curves significantly different than spontaneous rate were included 119

(i.e., above threshold; p < 0.01, Wald tests with correction for multiple comparisons). Stacked light and dark blue bins represent curves that were and were not significantly different than contra-only curves, respectively (i.e., binaural and monaural sensitivity, respectively; p < 0.01, Wald tests with correction for multiple comparisons). Dashed green lines indicate medians computed only for neurons with binaural sensitivity. “n.s.” indicates no statistical significance (p > 0.05, Wilcoxon rank-sum test).

There remains the possibility that azimuth tuning curves we deemed binaural may have been due to exclusive sensitivity to the ipsilateral ear since we only measured monaural responses to the stimuli presented in the contralateral ear. We tested whether the neurons classified as “binaural” also had significant “contra-only” responses. This is because if in the case that the neurons had no “contra-only” responses, then their azimuth tuning must arise from ipsilateral inputs.

Of the 13 neurons from HL rabbits whose azimuth tuning curves were significantly different with respect to both spontaneous rate and contra-only curves, 12 had contra-only curves that were also significantly different than spontaneous rate, directly indicating sensitivity to the contralateral ear and indirectly indicating sensitivity to the ipsilateral ear via binaural interaction. Therefore, the neurons were indeed binaural.

Binaural Cues underlying Directional Sensitivity

In order to determine if the directional sensitivity of a binaurally sensitive neuron was due to underlying sensitivity to ITD, ILD, or both, we additionally measured azimuth tuning curves using filters that preserved either the relationship of ITD and azimuth while fixing ILD to that at 0° (“ITD-only”) or the relationship of ILD and azimuth while fixing

ITD to that at 0° (“ILD-only”), then compared these curves to the normal ITD+ILD curve. Figure 17A shows data from a neuron from a NH rabbit whose ITD+ILD azimuth 120 tuning curve significantly differed (p < 0.01, Wald tests with correction for multiple comparisons) from the ILD-only but not ITD-only curve, indicating directional sensitivity due to underlying ITD sensitivity. Conversely, Figure 17B shows data from a neuron from a HL rabbit whose ITD+ILD azimuth tuning curve was significantly different than the ITD-only but not ILD-only curve, indicating directional sensitivity due to underlying ILD sensitivity. Figure 17C shows data from a neuron from a NH rabbit whose ITD+ILD azimuth tuning curve was significantly different than both ITD-only and

ILD-only curves, indicating directional sensitivity due to the interaction of ITD and ILD sensitivities.

Many neurons in our sample from NH rabbits had directional sensitivity due to each of the three types of underlying binaural sensitivities demonstrated in Figures 17A–

C: exclusive ITD or ILD sensitivity, or the interaction of ITD and ILD sensitivities (Fig.

17D, left). In particular, 82% (28/34) of binaurally sensitive neurons from NH rabbits had directional sensitivity that was influenced by underlying ITD sensitivity. Contrary to this, no binaurally sensitive neurons from HL rabbits had directional sensitivity influenced by

ITD sensitivity; all were exclusively sensitive to ILD (Fig. 17D, right). These binaurally sensitive neurons were from two HL rabbits. In order to compare ILD sensitivity between neurons from NH and HL rabbits, we computed the MInorm of ILD-only tuning curves as a way to quantify ILD sensitivity within the physiological range. There was no significant difference of median MInorm of ILD-only curves between binaurally sensitive neurons of

NH and HL rabbits (p = 0.26, Wilcoxon rank-sum test). Altogether, the remaining 121 directional sensitivity of neurons from HL rabbits was exclusively due to ILD sensitivity, and there was no evidence of a change in ILD sensitivity following noise overexposure.

Figure 17

Binaural Cues underlying Directional Sensitivity

(A–C) Azimuth tuning curves of three different neurons measured at near-threshold level under congruent (blue thin lines; “ITD+ILD”) and two incongruent binaural cue conditions, where in the latter cases only either ITD (green dotted lines; “ITD-only”) or ILD (orange thick lines; “ILD-only”) varied with azimuth while the other cue was fixed to that at 0°. Dashed lines indicate spontaneous rate. Data in (A) and (C) were from NH rabbits with CF indicated in upper-right corner, while data in (B) was from a HL rabbit. Unit ID indicated in upper-left corner. Effect sizes of Wald tests for differences between ITD+ILD and each of ITD-only and ILD-only curves are listed to the right of each panel, color-matched to each incongruent-cue curve. Asterisks indicate p < 0.01. (D) Scatterplots of the effect size between congruent- and incongruent-cue curves of neurons from NH (left; N = 34) and HL (right; N = 13) rabbits measured at near-threshold level. Only neurons with ITD+ILD curves that significantly differed in both spontaneous rate and contra-only curves were included (i.e., above threshold and binaural). Statistically significant differences (p < 0.01, Wald tests with correction for multiple comparisons) between the ITD+ILD and each of the ITD-only and ILD-only curves indicated by symbol shape and color: both different (purple squares), only ILD-only different (green diamonds), only ITD-only different (orange circles), neither different (black triangles). Arrow indicates data point beyond the axis. (E) Histograms of MInorm for ILD-only 122 tuning curves of neurons from NH (N = 35) and HL (N = 12) rabbits measured at near- threshold level. Only neurons with ILD-only curves that were significantly different than both spontaneous rate and contra-only curves were included. Dashed lines indicate medians. “n.s.” indicates no statistical significance (p > 0.05, Wilcoxon rank-sum test).

ITD Sensitivity following Noise Overexposure

As a virtual sound source moves from −90° to +90° the ITD in the high-frequency limit shifts from −275 to +275 µs. In order to ensure that we did not miss potential ITD sensitivity outside of the physiological range of ITDs, we measured ITD tuning curves to broadband noise over ±1-ms delays. Figures 18A–C show ITD tuning curves of three different neurons from NH rabbits: the first showing a prominent suppression of firing rate as compared to that at large ITDs (“trough” type), characteristic of the temporal interaction of excitatory input from one ear and inhibitory input from the other; the second a prominent facilitation of firing rate as compared to that at large ITDs (“peak” type), characteristic of the temporal interaction of excitatory inputs from both ears; and the third an intermediate response type. Figure 18D shows the ITD tuning curve of a neuron from a HL rabbit that had no ITD sensitivity yet responded to the stimulus with a firing rate well above spontaneous rate. Similar to that for directional sensitivity, we quantified the ITD sensitivity of a neuron as the MInorm between firing rate and ITD.

At an absolute sound level of 70 dB SPL, neurons from NH rabbits had a broad range of MInorm whereas those of neurons from HL rabbits were approximately zero (Fig.

18E). This result again could be explained by most stimuli for HL rabbits being below threshold. However, the same pattern of results persisted for stimuli presented at near- threshold level even though stimuli for the majority of neurons from HL rabbits were 123 above threshold (Fig. 18F). MInorm values equal to or greater than that of the ITD tuning curve in Figure 18C (0.04) were found at all CFs in NH rabbits (Fig. 18G). Therefore, the lack of ITD sensitivity in neurons from HL rabbits could not be accounted for by a bias towards high-CF neurons.

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Figure 18

ITD Sensitivity following Noise Overexposure

(A–D) ITD tuning curves (mean ± SEM) of four different neurons measured at near- threshold level. Positive ITDs indicate contralateral-leading ITDs. Data in (A–C) from NH rabbits with CFs indicated in upper-right corners and data in (D) from HL rabbit. Unit IDs indicated in upper-left corners. Dashed lines indicate spontaneous rates. MInorm of ITD tuning curve indicated by bold number next to each curve. (E) Histograms of MInorm for ITD tuning curves of neurons from NH (top; N = 61) and HL (bottom; N = 59) 125 rabbits measured at absolute level. Stacked light and dark red bins represent tuning curves that were and were not significantly different than spontaneous rate, respectively (i.e., above and below threshold, respectively; Wald tests with correction for multiple comparisons, p < 0.01). Medians indicated by dashed lines. (F) Same as (E) for tuning curves measured at near-threshold level (top: NH, N = 50; bottom: HL, N = 66). (G) Scatterplot of MInorm vs. CF for neurons from NH rabbits measured at absolute (purple squares) and near-threshold (blue circles) levels.

To ensure that lack of ITD sensitivity was not simply a result of neurons from HL rabbits being monaurally sensitive, we measured firing rates to broadband noise presented separately to the ipsi- and contralateral ears at near-threshold level. We found

12 neurons in two different HL rabbits that had firing rates significantly different than spontaneous rate (p < 0.01, Wald test) for both ipsi- and contralateral sound presentation.

All had firing rates that were facilitated above spontaneous rate for sound presented to either ear. These 12 binaural neurons only partially overlapped with the previous 12 ILD- sensitive neurons that had evidence of binaural interaction in their azimuth tuning curves when compared between binaural and monaural conditions. Combining these together, 20 different neurons from HL rabbits had evidence of responsiveness to binaural input at near-threshold level, and all had MInorm of their ITD tuning curves approximately equal to zero.

There remains the possibility that stimulation of a neuron near its sound-level threshold may not be sufficient to produce an ITD-sensitive response, even though that stimulation may be sufficient to produce a response to separate ipsi- and contralateral stimulation. In our sample of neurons from NH rabbits, it was difficult to assess the existence of a potential dependence of ITD sensitivity on relative sound level because 1) most ITD tuning curves were measured at a sound level more than 10 dB above neural 126 threshold (Fig. 15J), and 2) thresholds measured from level tuning curves were not always an accurate measurement of overall sound-level threshold because the stimulus was fixed at zero ITD (diotic). The dependence of estimated sound level threshold on the

ITD of the stimulus is illustrated by the ITD tuning curve in Figure 18A: a level tuning curve measured with a stimulus fixed at 250 µs would yield a much higher threshold than a level tuning curve measured with a stimulus fixed at −500 µs. We found one neuron from a NH rabbit that avoided the above limitations: its ITD tuning curve was measured at −2.5 dB re: neural threshold (similar to that for neurons from HL rabbits; see Fig. 15J) and it had an ITD tuning peak at zero ITD, meaning its level tuning curve measured with a stimulus at zero ITD yielded an accurate estimate of sound-level threshold. The ITD tuning curve of this neuron, which was measured at 52.5 dB SPL, is shown in Figure

18B. Its corresponding level tuning curve (not shown), which was measured in response to the same stimulus fixed at zero ITD and in level steps of 5 dB, had firing rates equal to spontaneous rate at 50 dB SPL and 12 sp/s above spontaneous rate at 55 dB SPL, meaning the 52.5-dB-SPL stimulus used to measure the ITD tuning curve was either at or within a few decibels of threshold. Furthermore, its MInorm value (0.17) was one of the highest in the sample. Therefore, stimulation near the sound-level threshold of the neuron did not preclude it from being sensitive to the ITD of the stimulus.

Lastly, we measured additional ITD tuning curves at near-threshold level in response to broadband noise over ±3-ms delays (HL: N = 51) and lowpass noise (0.1–1.5 kHz) over ±1-ms delays (HL: N = 56) in order to specifically search for sensitivity to very large ITDs and to ITDs in the fine structure of sound waveforms, respectively. In all 127 neurons from HL rabbits, MInorm was approximately zero (data not shown). Overall, the complete lack of ITD sensitivity in neurons from HL rabbits could not be accounted for by subthreshold stimuli, monaural sensitivity, stimulation near level threshold, potential sample bias in CF, range of delays tested, or broadband vs. lowpass stimulus spectrum.

Spike-timing Precision following Noise Overexposure

ITD sensitivity arises in neurons of the superior olivary complex (SOC) via the detection of coincidence between precisely timed neural inputs originating from the ipsi- and contralateral ears. One way by which ITD sensitivity may be decreased is through a reduction in temporal precision of the inputs to the SOC. Since the ICC is one synapse away from the SOC, we tested whether the temporal precision of ipsi- and contralateral inputs to the SOC was reflected in the spike-timing precision of ICC neurons to ipsi- and contralateral sound presentation.

The top and bottom panels of Figure 19A show raster plots of spike trains from two different neurons from NH rabbits in response to 50 trials of the same broadband noise waveform presented to the contralateral ear. Spike trains of the neuron in the top panel clearly aligned vertically at various time points indicating high spike-timing precision and, therefore, locking of the neuron’s response to particular features in the ongoing noise waveform. Spike trains of the neuron in the bottom panel showed no apparent vertical alignment of spikes, indicating poor spike-timing precision. We quantified the degree of spike-timing precision by computing the SAC, which is a histogram of time intervals between spikes from different trials. For example, the middle panels of Figures 19D and C show the SACs of the spike trains in the top and bottom 128 panels of Figure 19A, respectively. The value of the SAC at zero delay, henceforth called the correlation index, indicates the number of spikes that occurred at the same time across trials, normalized so that a value of 1 indicates the number of coincidences that would occur by chance for random spike times at the same mean rate (no spike-timing precision) and values greater than 1 indicate coincidences more often than by chance

(some degree of spike-timing precision).

The SACs in Figure 19B for responses to ipsi- and contralateral noise both had relatively high correlation indices. The fact that this ICC neuron had spikes that were precisely timed to features within the noise waveform presented to the ipsi- or contralateral ear implies that its input from all areas upstream, including the ipsi- and contralateral inputs to the SOC, also encoded temporal features of the noise waveform in their spike times. The same neuron was also sensitive to ITD (rightmost panel of Fig.

19B).

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Figure 19

No Relationship between Spike-timing Precision and ITD Sensitivity

(A) Raster plots of spike times relative to stimulus onset in response to the same broadband noise waveform presented to the contralateral ear at near-threshold level. Top and bottom panels are data from two different neurons from NH rabbits, with unit ID indicated in upper-left corner. Each dot indicates one action potential. Each row is 1 of 50 trials. Thick and thin lines at top indicate times when the stimulus was on and off, respectively. Silent period extended past the axis limit to 600 ms. (B–D) Data from three different neurons from NH rabbits. Unit ID and CF indicated above panels in first column. Left and middle columns contain SACs of responses to broadband noise at near- threshold level presented to the ipsi- and contralateral ears, respectively. Correlation index indicated next to each correlogram. Right column contains ITD tuning curves measured at near-threshold level. MInorm indicated next to each curve. Firing rates to monaural presentation of the stimulus to the ipsi- and contralateral ears indicated by respective red and black triangles to the right of each plot. Dashed lines indicate spontaneous rate.

130

ITD sensitivity in ICC neurons is likely inherited from their inputs from the SOC, as there is no evidence of de novo ITD sensitivity in the ICC (McAlpine et al., 1998;

Wang et al., 2014). Furthermore, since ITD sensitivity arises from the coincidence of precisely timed inputs to the SOC, the existence of ITD-sensitive responses in an ICC neuron implies precisely timed inputs to the SOC. Since both temporal coding and ITD sensitivity in ICC neurons are inherited from upstream brain areas, we hypothesize that

ITD sensitivity in ICC neurons would correlate with correlation indices to ipsi- and contralateral noise, similar to the pattern of that in Figure 19B, and that this potential relationship could be exploited to identify degradation of spike timing as a mechanism for loss of ITD sensitivity in HL rabbits.

While data from some neurons were consistent with the pattern in Figure 19B, data of other neurons were not. Figure 19C shows data from a neuron from a NH rabbit that had poor spike-timing precision but strong ITD sensitivity. Firing rates to monaural presentation were well above spontaneous rate, meaning that the lack of spike-timing precision was not due to stimuli being below threshold. As mentioned above, the existence of ITD sensitivity in this neuron implies that the upstream inputs to the SOC were precisely timed, yet this precision was not reflected in the spiking output of the ICC neuron. As is demonstrated by these data, temporal precision in inputs upstream to a neuron does not necessarily imply precision in the neuron’s spiking output. Figure 19D shows data from another neuron from a NH rabbit that had high spike-timing precision but weak ITD sensitivity. As mentioned above, high spike-timing precision in the ICC implies temporal precision upstream, yet this precision did not correlate with ITD 131 sensitivity. The weak ITD sensitivity in this ICC neuron could have been inherited from its inputs from the SOC or was a result of the input/output function of the neuron on inputs with strong ITD sensitivity. As is demonstrated by these data, weak ITD sensitivity does not necessarily imply poor temporal precision in the inputs upstream to a neuron. Overall, we found no relationship between spike-timing precision and ITD sensitivity in ICC neurons of NH rabbits.

While we could not test degradation of spike timing as a potential mechanism for the loss of ITD sensitivity in HL rabbits, the autocorrelogram analysis did allow us to simply compare spike-timing precision between neurons from NH and HL rabbits.

Correlation indices for neurons from NH rabbits spanned a range of values corresponding to high or low spike-timing precision, whereas those of neurons from HL rabbits mostly took on values corresponding to low spike-timing precision (Fig. 20A; p = 3×10−4,

Wilcoxon rank-sum test). The correlation index of a neuron may potentially change based on the extent to which the neuron is driven by a stimulus. For example, if a stimulus weakly drives a neuron, a greater proportion of total spikes may occur spontaneously than when driven by a stronger stimulus; the former and latter cases could potentially lead to lower and higher correlation indices, respectively. Since stimuli at near-threshold level were at lower sound levels with respect to neural response threshold for neurons of HL rabbits (Fig. 15J), we considered the possibility that lower correlation indices in neurons of HL rabbits may be due to weaker stimuli. To quantify the extent to which a neuron was driven by the monaural noise stimulus, we computed the Wald statistic between driven rate and spontaneous rate (see Methods). The Wald statistic is similar to a 132 difference of firing rates, but additionally accounts for the fact that firing rate variability across trials increases with mean firing rate. The greater the Wald statistic deviates from zero in either direction, the greater the difference between driven and spontaneous rates.

Wald statistics for neurons from HL rabbits had relatively low values compared to those for neurons from NH rabbits (Fig. 20B), which is consistent with the discrepancy in the sound level re: neural threshold. However, neurons from NH rabbits that had Wald statistics that overlapped with those of neurons from HL rabbits had correlation indices that spanned the range from low to high spike-timing precision. Therefore, the low spike- timing precision (temporal coding) observed in neurons of HL rabbits could not be accounted for by neurons simply being driven less by the stimulus, and was consistent with reduced temporal coding in neurons from a previous study of IC receiving residual inputs following near-complete cochlear denervation in mice (Chambers et al., 2016).

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Figure 20

Spike-timing Precision following Noise Overexposure

(A) Histograms of correlation indices of responses to monaural broadband noise at near- threshold level for neurons from NH (top; N = 28 values from 15 neurons) and HL (bottom; N = 13 values from 11 neurons) rabbits. Data pooled across ipsi- and contralateral presentation. Values only included for responses that both significantly deviated from spontaneous rate (i.e., above threshold; p < 0.01, Wald test) and had sufficient total number of spikes to accurately estimate correlation index (see Methods). Dashed lines indicate medians. Asterisk indicates p < 0.05 (Wilcoxon rank-sum test). (B) Scatterplot of correlation index vs. Wald statistic computed on firing rate to monaural noise and spontaneous rate (see Methods). The Wald statistic is a difference of firing rates scaled by a factor that accounts for the increase in firing rate variability with mean firing rate. Data from NH and HL rabbits indicated by blue circles and red squares, respectively.

Discussion

We exposed rabbits to a high-intensity, octave-band noise stimulus centered at

750 Hz, which caused a shift in ABR thresholds of 50 dB or greater at frequencies from 2 to 16 kHz and caused median neural sound-level threshold to increase by 75 dB for IC neurons from HL as compared to NH rabbits. Directional sensitivity of IC neurons from

HL rabbits was reduced in comparison to that from NH rabbits and could be accounted 134 for by stimuli being subthreshold for some neurons and by HL rabbits having a greater proportion of monaurally sensitive neurons, which tended to have lower MInorm.

Remaining directional sensitivity in binaurally sensitive neurons of HL rabbits was not significantly different from that of NH rabbits, and was entirely due to underlying sensitivity to ILD. We found no evidence for a decrease in neural sensitivity to ILD in neurons from HL rabbits, but found a complete lack of neural sensitivity to ITD, which could not be accounted for by subthreshold stimuli, monaural sensitivity, stimulation near sound-level threshold, potential sample bias in CF, range of delays tested, or use of a broadband vs. lowpass stimulus. Lastly, in comparison to NH rabbits, we found neurons from HL rabbits to have weaker spike-timing precision, slightly increased spontaneous rates, and to have lost the low-level tips of their FRAs, which in NH rabbits define the tonotopic map in the ICC.

At two weeks following noise overexposure, ABR thresholds of all overexposed rabbits exceeded a “critical” shift at frequencies from 2 to 16 kHz, which according to our previous study (Haragopal et al., 2020), indicates that the basal and middle regions of their cochleae had very few surviving OHCs and variable loss of IHCs. Furthermore, it is likely that these rabbits had damage to the stereocilia of their IHCs and loss of synapses between IHCs and auditory nerve fibers because such damage is known to occur at exposure levels less than that which causes OHC loss (Borg et al., 1995; Kujawa et al.,

2009). Our noise overexposure therefore succeeded in producing widespread damage of the cochlea. In our previous study (Haragopal et al., 2020), the same noise overexposure caused a 50-dB increase in median click-evoked ABR threshold, but there was high 135 variability in threshold shifts across ears and rabbits. The 75-dB shift in neural sound- level thresholds from the three HL rabbits in the present study was therefore greater than that expected from average ABR threshold shifts of the previous study, and was likely due to the rabbits having higher-than-average susceptibility to overexposure.

The first of our two main research questions was whether the directional sensitivity of IC neurons decreases with noise-induced hearing loss; the idea being that such a neural deficit may be a correlate of the deficit in sound localization of human subjects with SNHL (Lorenzi et al., 1999a). We did find a decrease in directional sensitivity in neurons from HL rabbits, but the effect could be accounted for by two dependent variables: proportion of neurons for which the stimulus was subthreshold and proportion of monaurally sensitive neurons. The first of these dependent variables may seem trivial; however, if a stimulus is presented at a fixed sound level with respect to the perceptual threshold of detection (e.g., similar to amplification performed by a hearing aid), the proportion of neurons for which that stimulus is suprathreshold may differ between NH and HL listeners and may cause differences in localization ability. Our intention in choosing the “near-threshold” levels for NH and HL rabbits was to present sound at similar levels with respect to detection threshold. We assumed median pre- and postexposure ABR thresholds measured in our previous study (Haragopal et al., 2020) would be a sufficient means to select approximately equal sound levels with respect to detection threshold in NH and HL rabbits. However, our data proved this assumption to be incorrect. If we assume that detection threshold is equal to the lowest individual sound-level threshold across the neural sample, then detection thresholds would be 5- and 136

75-dB SPL for NH and HL rabbits, respectively (Fig. 14B). Accordingly, selected “near- threshold” levels were 35–47.5 dB and 17.5–22.5 dB re: detection threshold for NH and

HL rabbits, respectively. Since these relative sound levels did not overlap, our data is inconclusive as to whether the greater proportion of neurons for which the stimulus was subthreshold and also the greater proportion of monaurally sensitive neurons in HL rabbits were either due to hearing loss or due to a lower sound level with respect to detectability. Furthermore, a previous study reported azimuth tuning curves measured at

10 dB re: neural threshold to be similar between binaural and contralateral-only sound presentation (Kuwada et al., 2011), which suggests the greater proportion of monaurally sensitive neurons in HL rabbits may have been due to sound levels being near the thresholds of individual neurons (Fig. 15J).

The second of our two main research questions was whether coding of ITD or

ILD was affected by noise-induced hearing loss. Surprisingly, sensitivity to ILD remained unaltered at the population level whereas sensitivity to ITD was absent in neurons from HL rabbits. Our result regarding ILD sensitivity was tempered by a smaller sample size (N = 13) after omitting data from neurons that were either monaurally sensitive or for which the stimulus was subthreshold. Nonetheless, the difference in median MInorm between neurons from NH and HL rabbits was small (Fig. 17E), which suggests any potentially significant difference for larger sample sizes would also be small. Our finding that ILD sensitivity remained stable after noise overexposure is consistent with reports that impairment of ILD discrimination in individuals with SNHL is either absent or small at best (Gabriel et al., 1992; Hausler et al., 1983; Smith-Olinde et 137 al., 1998; Spencer et al., 2016a). Our finding that ITD sensitivity was completely lost after noise overexposure suggests that behavioral ITD discrimination would be near impossible, but previous psychophysical reports show that ITD discrimination is only impaired, not abolished (Best et al., 2019; Gabriel et al., 1992; Hausler et al., 1983;

Lacher-Fougere et al., 2005; Smith-Olinde et al., 1998; Smoski et al., 1986; Spencer et al., 2016a). However, some studies showed that individuals with the greatest hearing loss had extremely large ITD discrimination thresholds (Best et al., 2019; Lacher-Fougere et al., 2005), so the extreme loss of neural ITD sensitivity in the present study may potentially be due to the severity of the hearing loss.

In neurons from NH rabbits, many azimuth tuning curves were shaped by sensitivity to both ITD and ILD and fewer by ILD alone (Fig. 17D). Therefore, some of the neurons from HL rabbits likely had azimuth tuning curves that were shaped by both cues prior to overexposure, and after overexposure, only sensitivity to ILD remained, causing a change in shape of the azimuth tuning curve. On the one hand, downstream areas of the brain may depend upon consistency of the shapes of azimuth tuning curves of

IC neurons over time and therefore, a potential change in tuning curve shape with hearing loss may contribute to a deficit in sound localization. On the other hand, downstream areas may be adaptable to changes in tuning curve shapes over time and only depend upon the ability of neurons to transmit information about azimuth in their firing rates. In the latter case, noise-induced hearing loss would not affect the localization of high- frequency sounds, which primarily depend upon ILD cues (Dorkoski et al., 2020;

Macpherson et al., 2002; Strutt, 1907). Localization of low-frequency sounds primarily 138 depends upon ITD cues (ibid.). In a recent study, we showed that at a sound level of 70 dB SPL the firing rates of high-CF neurons contribute substantial information about the azimuth of low-frequency sound sources, and this information was based on sensitivity to

ITD (Dorkoski et al., 2020). Since our sample of neurons from HL rabbits was likely biased towards high CFs, our results suggest that with noise-induced hearing loss the contribution of information from high-CF neurons regarding the direction of low- frequency sounds is absent.

The main limitations of our study were the presentation of stimuli at different relative sound levels for NH and HL rabbits, which confounded the effect of hearing loss with that of level differences, and the sample bias of neurons from HL rabbits towards high CFs. These limitations arose both because the degree of hearing loss was more severe than expected and because we were limited by the output ceiling of our earphones.

The output ceiling is a technical limitation that can theoretically be surmounted, though testing with sound levels greater than 100 dB SPL daily for multiple months runs the risk of creating further damage to the cochleae. Furthermore, humans with SNHL have an abnormally steep relationship between stimulus intensity and perceived loudness, a phenomenon termed “loudness recruitment”. While our overexposed rabbits showed no signs of discomfort with sound levels near 100 dB SPL, more intense stimuli may be perceived by the rabbit as uncomfortably loud. In future studies, it may be advantageous to induce a lesser degree of hearing loss to see if results for severe hearing loss in the present study hold. Moderate hearing loss would also resolve the limitations of the present study by increasing the range of sound levels between neural threshold and 139 earphone output ceiling. However, control over the degree of hearing loss is not straightforward: susceptibility varies widely across individuals, and we previously found median ABR threshold shift to be only 10 dB for a shortened duration of 60 min at the same exposure level (Haragopal et al., 2020). One way to induce a more moderate hearing loss (e.g., a 30-dB threshold shift) may be to present noise at a slightly lower sound level for a much longer duration.

Comparison to Previous Studies

There is a paucity of published work dealing with the effect of SNHL on binaural coding in the brain. The few that are available, are on age-related, rather than noise- induced, hearing loss. Laumen et al. (2016) found the binaural interaction component of the ABR (difference between response to binaural and monaural presentation) to be smaller in aged gerbils, suggesting weakened binaural interaction in central auditory areas. Our finding of reduced number of ICC neurons with binaural sensitivity in HL rabbits is consistent with this, however it is confounded by the different relative sound levels used for NH and HL rabbits, as described above. Costa et al. (2016) found that azimuth tuning curves of superior colliculus neurons in aged rats were wider than in younger rats. Since directional sensitivity in the superior colliculus likely derives from

ILD sensitivity (Slee et al., 2013; Slee et al., 2014), their result suggests a decrease in

ILD sensitivity, unlike in the present study. Neural sound-level thresholds in their aged rats were only 8 dB greater than in younger rats, therefore the observed differences in the two studies could arise from what caused the hearing loss, viz age or exposure, and differences in the areas of brain under investigation.. 140

Most relevant are two studies by McFadden and Willott (1994a; 1994b) of binaural and spatial coding in IC neurons of C57 mice, a strain with early-onset hearing loss. They found only a small decrease in directional sensitivity of IC neurons from aged as compared to young mice. Interestingly, in a tone-in-noise detection paradigm, they found that the sound-level threshold for detection of a tone based on changes in firing rate was not different between cases where the masking noise was spatially co-located or separated from the tone. Normally a tone can be detected at a lesser threshold when the masker is spatially separated, a phenomenon known as “spatial release from masking”, which can be explained by neural sensitivity to interaural correlation (Lane et al., 2005).

Their result implies a deficit in neural sensitivity to interaural correlation, consistent with the deficit in ITD sensitivity, as in our data.

Potential Mechanisms for Loss of ITD Sensitivity

Sensitivity of firing rates to ITD first arises in neurons of the medial and lateral superior olives (MSO and LSO, respectively) via the detection of coincident, excitatory synaptic inputs from the ipsi- and contralateral ventral cochlear nucleus (VCN), whose responses are precisely time-locked to the sound waveform (Franken et al., 2015; Joris et al., 1994a; Joris et al., 1994b; Plauska et al., 2016; van der Heijden et al., 2013). [The contralateral VCN input to the LSO is by way of the medial nucleus of the trapezoid body, which retains the spike-timing precision of the VCN. (Paolini et al., 2001)] ITD sensitivity in the ICC is thought to be inherited from synaptic inputs from these SOC nuclei. Degradation of ITD sensitivity in the ICC may therefore develop in response to any of the following: 1) a decrease in spike-timing precision of VCN neurons; 2) a 141 widening of the effective coincidence detection window of SOC neurons; or 3) a change in the input/output function of ICC neurons such that ITD sensitivity in the synaptic input from SOC is nullified in the output firing rate.

Previous studies in MSO were able to accurately predict ITD tuning from the comparison of spike trains in response to the stimulus presented separately to the ipsi- and contralateral ears (Goldberg et al., 1969; Yin et al., 1990). This prediction was accurate presumably because the temporal precision of action potentials of MSO neurons was faithful to the temporal precision of their synaptic inputs. To our knowledge, the same result has not been reported in the ICC. Yin and Chan (1990) implied the same test could not be performed in the ICC because “cells in the ICC generally do not respond to monaural stimulation of both ears…” (p. 476). We found that ICC neurons readily responded to stimulation of either ear. Their difficulty in obtaining monaural responses to stimuli in both ears may have been due to use of an anesthetized preparation.

Nonetheless, we found no relationship between spike-timing precision of responses to monaural broadband noise and ITD sensitivity in ICC neurons of NH rabbits. Therefore, we were unable to infer from our data potential changes in spike-timing precision of

VCN neurons from HL rabbits. While spike-timing precision was lower in ICC neurons of HL as compared to NH rabbits, the same could not necessarily be inferred for VCN neurons. Previous studies of AN fibers found no deficit in temporal precision following either acoustic trauma except in the presence of background noise (Henry et al., 2012;

Henry et al., 2014; Kale et al., 2010), or age-related hearing loss (Heeringa et al., 2020).

To ur knowledge, the effect of acoustic trauma on temporal precision of VCN neurons in 142 vivo has not been reported. VCN neurons in a strain of mice with early-onset hearing loss have weaker entrainment to a volley of excitatory synaptic potentials in vitro, but no increase in jitter (Wang et al., 2006). However, acoustic trauma is correlated with degeneration and hyperactivity of VCN neurons (Sekiya et al., 2012; Vogler et al., 2011).

In MSO, the temporal window for detection of coincident synaptic inputs from the ipsi- and contralateral VCN is regulated by several factors, including activation of low-voltage-activated K+ channels (Kv1) and hyperpolarization-activated cation current

(Ih), glycinergic inhibition from the medial and lateral nuclei of the trapezoid body, and activation of GABAB receptors (Khurana et al., 2011; Mathews et al., 2010; Roberts et al., 2013; Svirskis et al., 2002). As yet, no studies have examined the effect of acoustic trauma on these factors. In LSO, acoustic trauma in mice increases the decay time of excitatory postsynaptic currents evoked by shocks to VCN axons, but this effect reverses to normal after two months (Pilati et al., 2016). Kv1 currents also likely regulate the coincidence detection window in principal neurons of the LSO (Barnes-Davies et al.,

2004). Changes in ion channel distribution may occur following decreased auditory input.

For example, in a study of the avian cochlear nucleus, K+ channels in the axon initial segment redistributed subtypes from fast to slow kinetics following removal of the cochlea, leading to enhanced excitability (Kuba et al., 2015).

Delgutte and colleagues found ITD sensitivity in most IC neurons of deaf animals

(with induced or congenital deafness) when stimulated bilaterally with electric pulse trains via cochlear implants (Chung et al., 2016; Hancock et al., 2013; Smith et al., 2007).

That ITD sensitivity existed in these IC neurons is inconsistent with the third hypothetical 143 mechanism listed above, that following SNHL, it is the IC neurons’ functional impairment that adversely affects ITD sensitivity. Furthermore, their neurons were sensitive to even submillisecond changes in ITD suggesting the coincidence detection window was uncompromised. Unlike acoustic stimuli, electric pulses (delivered via cochlear implants) directly stimulate the auditory nerve; this may produce higher spike- timing precision in AN. Such artificially precise timing could potentially overcome possible increased spike-timing jitter of VCN neurons following SNHL. While we measured ITD sensitivity only with broadband noise, their results suggest ITD sensitivity may be retained with an acoustic stimulus that causes higher spike-timing precision, such as click trains, pure tones, or amplitude-modulated tones. However, it is important to note that their method of deafening (ototoxic drugs or hypotonic stress) only killed cochlear hair cells, whereas acoustic trauma additionally overstimulates the central auditory system.

A final important question regarding our results is: why would ITD sensitivity be eliminated but ILD sensitivity be spared? The answer may be related to the finding that

ILD sensitivity depends on binaural integration within a relatively large temporal window

(>3 ms; Brown et al., 2016), whereas ITD sensitivity depends on coincidence detection within a submillisecond window. Potential disruption of temporal precision at the level of the SOC (either in the inputs or the coincidence window) would therefore be expected to have a larger effect on ITD than ILD sensitivity. Interestingly, a recent study in LSO showed that principal neurons, the most numerous subtype, integrate excitatory and inhibitory inputs from the ipsi- and contralateral ears, respectively, over a time scale 144 similar to that of MSO neurons, whereas non-principal neurons integrate the same inputs over longer time scales (Franken et al., 2018). Disruption of temporal precision would therefore be expected to have a greater effect on principal than non-principal LSO neurons. Lastly, ILD sensitivity of ICC neurons is not only inherited from LSO neurons, but also arises due to interaction between many excitatory and inhibitory projections to the area (review: Pollak, 2012). Potential disruption to ILD sensitivity in the LSO caused by acoustic trauma may be masked by de novo ILD sensitivity of the ICC.

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Chapter 5: Computational Modeling of Potential Mechanisms Underlying Specific

Loss of ITD Sensitivity with Severe Noise-Induced Hearing Loss

Abstract

Temporal jitter, or reduced spike time correlations, in the inputs to SOC may weaken ITD sensitivity. Therefore, the purpose of this computational study was to identify potential sources of temporal jitter. For this, AN fiber responses were modeled using simulations of hair cell loss independently for IHCs and OHCs. The model behaved as expected under simulated hearing loss conditions, demonstrating increased low- frequency fine-structure sensitivity, elevated threshold shifts, and a steepening of the slope of the rate-level function for high-CF model fibers following simulated hearing loss. We asked if peripheral changes alone could explain our data. For this, model AN fibers were altered to simulate hearing loss and binaural models were constructed to extract ITD and ILDs from AN responses. From model responses to frozen broadband sounds that varied in azimuth or ITD alone, ITD and ILD sensitivity persisted even after simulated hearing loss at sound levels in the middle of the dynamic range. Model outputs also had strong temporal precision. We tested whether changes in synaptic conductance gain and number of AN inputs to the ventral cochlear nucleus (VCN) could account for loss of ITD sensitivity by feeding AN spike trains into a VCN model. The results suggested that spike-timing precision was preserved in HL with increased gain and decreased inputs. We additionally modeled noisy synaptic inputs onto VCN bushy cells.

Parameters of the VCN model included the amount of jitter in evoked vesicle release times, mean per-spike vesicle release count, rate of spontaneous vesicle release, and peak 146 conductance amplitude in response to a single vesicle. Of these parameters, increases in synaptic jitter increased temporal jitter of VCN spike times the most in response to frozen broadband noise. Decreasing the mean number of vesicles released per spike weakened spike timing precision. Low- and high- threshold potassium channel conductance manipulations also had an effect when incorporated with synaptic noise. Thus, our model points to multiple candidate mechanisms for increasing temporal jitter in the output spikes of the VCN.

Introduction

Sensorineural hearing loss can result in increased difficulty in localizing sound sources in humans (Lorenzi et al., 1999a). Sensitivity to the underlying localization cues,

ITDs and ILDs, have been reported to be variably affected with most reporting some reduction in ITD sensitivity (Dai et al., 2018; Smoski et al., 1986; Spencer et al., 2016b) and variable reduction in ILD sensitivity (Hausler et al., 1983; Spencer et al., 2016b).

However, neural mechanisms underlying these deficits are still not known. Therefore, we asked if neural sensitivity to ITDs and ILDs is reduced in the IC of the Dutch-belted rabbit with severe noise-induced hearing loss.

For this, we overexposed rabbits to loud noise (530–1061 Hz) and characterized peripheral damage in terms of hair cell counts. The degree of hearing loss was severe: rabbits had ABR thresholds at least 50 dB above normal and there was widespread damage to hair cells with less than 20% OHC survival and variable IHC survival, even in areas basal to the noise-band (Haragopal et al., 2020). We then measured directional sensitivity of neural responses from the IC to sounds presented in virtual acoustic space. 147

We found a specific loss of ITD sensitivity, whereas ILD sensitivity was largely retained

(Chapter 4).

In the auditory pathway, sharp spectral resolution provides narrowband information, such that low-CF neurons extract ITDs from the fine-structure and high-CF neurons extract ITDs from envelopes (Joris et al., 1995). Sensitivity to ITDs of broadband sounds has also been attributed to the narrow passband of the cochlea, with particular importance of interaural correlation for developing ITD sensitivity (Joris et al.,

2006b).

Therefore, we wondered if degradation of tonotopy (spectral resolution) following acoustic trauma explains loss of ITD sensitivity. However, AN fibers show little variation in temporal coding with hearing loss, except in the presence of noise where phase-locking to tones is degraded (and this is due to exaggerated time-locking to low frequency fluctuations in the noise) (Henry et al., 2012; Kale et al., 2010). If temporal coding is not lost at the level of the auditory nerve in hearing loss, how is ITD sensitivity lost centrally? To look for potential explanations, we turned to computational modeling.

Here, we employed a model of the auditory periphery that allowed independent manipulations of % functioning OHCs and IHCs (Bruce et al., 2018). We incorporated these model AN inputs into a model of type II bushy cells of the ventral cochlear nucleus

(VCN) by Rothman and Manis (2003).

Since SOC nuclei are the primary location of coincidence detection, and no de novo sensitivity to ITD arises in the IC (McAlpine et al., 1998; Wang et al., 2014), loss of

ITD sensitivity in the IC is likely due to dysfunction of ITD coding by coincidence 148 detectors in the SOC. In the SOC, ITD sensitivity may be influenced by responses of

VCN bushy cells (presynaptic), VCN-SOC synapses, and postsynaptic (SOC) membrane channel conductances. Therefore, the VCN model can be used to test potential mechanisms that may contribute to ITD loss: decreased spike-timing precision in the inputs to the coincidence detector.

Computational Methods

AN Model

The AN model of Bruce et al. (2018) (downloaded from https://www.ece.mcmaster.ca/~ibruce/zbcANmodel/zbcANmodel.htm) was implemented in MATLAB. It was originally fit to AN fiber data from cats. The AN model transforms the input sound pressure waveform into a spike train. Briefly, the model filters the sound pressure waveform (in Pa), sampled at 100 Hz, by a middle ear filter, then feeds the filtered waveform to an inner ear stage. The inner ear stage consists of filters that control level-dependent frequency tuning of the AN model. The filters incorporate one OHC- dependent and two IHC-dependent components. The OHC-dependent filter is scaled by a parameter cohc, which ranges from 0 to 1, 0 being no contribution (loss of OHCs) and 1 for intact OHCs. When cohc is 0, AN responses lose their sound-level-dependent frequency tuning and exhibit a broad tuning profile with elevated threshold. The 2 IHC- dependent components reflect activity from tall and short stereocilia on the IHCs. The filter corresponding to the contribution from tall stereocilia is scaled by cihc, which also ranges from 0 and 1, with 0 reflecting only a contribution from short stereocilia. This is because short stereocilia are more resilient to damage than tall stereocilia, but tall 149 stereocilia account for frequency tuning at low sound levels by interacting with OHCs

(Liberman et al., 1984b). Accordingly, when cihc is 1, the tall stereocilia filter accounts for AN model responses modulated by the output of the OHC-dependent filter. The IHC-

AN synapse stage accounts for adaptations at the onset and ongoing portions of the AN response. The IHC-synapse stage output generates an interspike interval (ISI) probability distribution as a function of the IHC output, which incorporates AN refractoriness. Spike times are finally obtained from ISIs randomly sampled from this distribution.

Here, we simulated hearing loss by setting cohc to 0 and cihc to 0.1; our rabbit data point to widespread loss of OHCs and variable IHC loss, but it is expected that for a shift of 40 dB or more stereocilia are extensively damaged in both types of hair cells. The fixed model parameters are tabulated below.

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Table 3

Auditory Nerve Model Parameters

Model parameters* Values Rationale

Spontaneous rate 60 Hz High-SR fibers are more likely to survive

trauma (Furman et al., 2013)

CF 0.6/6 kHz

Absolute refractory period 0.6 ms Within-range (Li et al., 1993)

Relative refractory period 0.7 ms Within-range (Ref same as above)

*Two other parameters: a fractional Gaussian noise (which relates to long-term spontaneous firing rate oscillations) and actual power implementation were set to 0 (not included) and 1 (included), respectively.

Ventral Cochlear Nucleus Model

The output of the AN model described above was input to the VCN model of

Rothman and Manis (2003) and implemented in MATLAB. The VCN model was a

Hodgkin-Huxley-type, single-compartment model with membrane conductances found in the bushy cells of the VCN (Rothman et al., 2003). Briefly, it included a voltage-gated, fast sodium conductance (gNa), a non-inactivating, high-threshold voltage-gated potassium conductance (gKHT), a slow-inactivating, low-threshold voltage-gated potassium conductance (gKLT), a hyperpolarization-activated cation current (Ih), a leak current, and an additional synaptic current (Isyn). The current-balance equation below was solved using the forward Euler method with a time step of 0.01 ms:

푑푉 퐶 ⁡ = ⁡ −퐼 −⁡퐼 − 퐼 − 퐼 − 퐼 − 퐼 , where 푚 푑푡 푁푎⁡ 퐾퐻푇 퐾퐿푇 퐻 푙푒푎푘 푠푦푛 151

3 퐼푁푎 = 푔̂푁푎푚 ℎ(푉 − 퐸푁푎),

2 퐼퐾퐻푇 = 푔̂퐾퐻푇(0.85푛 + 0.15푝)(푉 − 퐸퐾),

4 퐼퐾퐿푇 = 푔̂퐾퐿푇푤 푧(푉 − 퐸퐾),

퐼ℎ = 푔̂ℎ푟(푉 − 퐸ℎ), and

퐼푙푒푎푘 = 푔̂푙푒푎푘(푉 − 퐸푙푒푎푘), where 푔̂푥 and 퐸푥 are the maximum conductance and reversal potential, respectively, for conductance x. The other variables m, h, n, p, w, z, and r are the channel gating variables for various ion channels. They generally depend on voltage

푑푥 푥∞(푉)−푥(푡) by the equation, = , where 푥 is the gating variable and the functions 푥∞(V) 푑푡 휏푥(푉) and 휏푥(푉) are empirically derived and described in Rothman and Manis (2003). Model parameters and maximum conductances at 22°C are shown in table 4.

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Table 4

Ventral Cochlear Nucleus Model Parameters at 22°C

Parameter Value

Reversal potentials

ENa +55 mV

EK –70 mV

Eh –43 mV

Eleak –65 mV

Maximum conductances gNa 1000 nS gKHT 150 nS gKLT 200 nS gh 20 nS gleak 2 nS gE 8.6 nS

Number of AN inputs 2–4

Spiking threshold –20 mV

The synaptic current (Isyn) in the VCN model was 퐼푠푦푛 = 푔푠푦푛(푡)(푉 − 퐸푠푦푛), where

푔푠푦푛(푡) was the time-varying synaptic conductance and the reversal potential of the synaptic current, Esyn, was 0 mV. The synaptic conductance, 푔푠푦푛(푡), was set as the 153 convolution of the input AN spike train, s(t), and the miniature excitatory postsynaptic conductance waveform, 푔푚𝑖푛𝑖: 푔푠푦푛(푡) = (푔푚𝑖푛𝑖 ∗ 푠)(푡), where (*) denotes convolution.

The AN spike train, s(t), was a time sequence of zeros and ones, where a value of 1 indicated a spike time. The miniature excitatory postsynaptic conductance waveform

푡 푡 (푔푚𝑖푛𝑖) was an alpha function, 푔푚𝑖푛𝑖 = 푔푎푖푛 × 푔̂퐸( ⁄휏퐸)exp⁡[1 − ( ⁄휏퐸)], where 휏퐸 =

0.4⁡ms at 22°C. All maximum conductances and time constants in the model (including synaptic) were multiplied by 3.03 and 0.17, respectively, to scale membrane properties to

37°C. For multiple AN inputs, synaptic conductances were summed across inputs in the time domain to give the total time-varying conductance. Importantly, all AN model inputs to the VCN model were of the same CF (reviewed by Ryugo et al., 2003) and same high spontaneous rate, similar to globular bushy cells (Liberman, 1991). An additional scaling constant (푔푎푖푛:⁡0.4 − 1.3) was used in 푔푚𝑖푛𝑖 to adjust the maximum conductance of the synapse. Action potential times of the VCN model were extracted from the output voltage trace when the membrane potential crossed a threshold voltage

(Table 4).

Modeling Synaptic Noise

Increased jitter of spike timing following noise-induced hearing loss has not been directly observed in the VCN or SOC but has been observed in the IC (Chapter 4). A study on age-related hearing loss in VCN failed to find an increase in synaptic jitter

(Wang et al., 2006). However, this may be due to no loss of synapses in the VCN in age- related hearing loss (McGuire et al., 2015), whereas in acoustic trauma there is a reduction in AN inputs (Kim et al., 2004). Since convergence of inputs onto VCN is 154 pivotal to the enhancement of time-locking compared to the AN (Louage et al., 2006), reduction of inputs may increase output jitter. Output jitter may also increase due to increased synaptic jitter. Synaptic noise was included in the VCN model to introduce additional temporal jitter. This noise was introduced before the convolution operation described in the above section.

The model incorporated 3 key presynaptic sources of noise: stochasticity in the number of vesicles released per action potential, stochasticity in the timing of vesicles released per action potential, and spontaneous vesicle release. In the endbulb of Held

(Fig. 21A), which is the large synapse between an AN fiber and a VCN bushy cell, studies have shown that single action potentials can induce quantal glutamate release from 40 to 70 vesicles (Bellingham et al., 1998; Lin et al., 2011). There are ~ 4 endbulbs per VCN bushy cell (Fig. 21B). Counts of evoked release of vesicles in the endbulbs have shown that they are well approximated by a Poisson distribution due to a large reserve pool of vesicles (>1000) (Isaacson et al., 1995; Lin et al., 2011; Oleskevich et al., 2000).

Therefore, the statistics of action-potential-evoked vesicle release were modeled as a

Poisson distribution (Fig. 21C). The rationale for the synaptic noise implementation is that when an action potential reaches the axon terminal, it evokes release of vesicles containing glutamate that bind to post synaptic receptors (Fig. 21A), resulting in an excitatory postsynaptic conductance (EPSG) and post synaptic current (EPSC). The number of vesicles released may vary from spike to spike. Moreover, release times of the vesicles relative to each spike have some amount of jitter (that is, not all vesicles are released at the same time; Xie and Manis (2017)). Evoked neurotransmitter release at the 155 normal functioning endbulb synapses is classified as synchronous or asynchronous based on their release times relative to the EPSC peak. Synchronous vesicle release is thought to be locked to input spike times (with sub-ms jitter) and contribute to EPSC portion (1- ms region around the EPSC peak), while asynchronous vesicle release is thought to define the amplitude of the adjoining EPSC tail portion and can extend up to 10s of ms

(Xie et al., 2017). At the endbulb, asynchronous vesicle release is dominant only for high-frequency current pulse stimuli (>400 Hz), in vitro, and induced by presynaptic accumulation of Ca2+. Since our model AN inputs never had spike rates above 120 Hz

(Fig. 22A), we did not consider asynchronous vesicle release while modeling synaptic noise. The description below pertains to synchronous (evoked) and spontaneous vesicle release.

Each vesicle, upon release, induces a miniature excitatory postsynaptic conductance (mEPSGs) and current (mEPSCs), which is scaled by the conductance amplitude gain. This gain parameter may be interpreted as being related to the number of glutamate molecules released per vesicle. This interpretation of gain applies to the noise- free synaptic model in the previous section as well. Furthermore, there is an overall low rate of spontaneous EPSCs elicited by stochastic vesicle release independent of the occurrence of an action potential, mixed with evoked EPSCs (for multiple AN inputs, these EPSCs are a sum of all EPSCs across synapses).

For simulation of evoked vesicle release, vesicle release count for every input spike in the AN spike train was randomly sampled from a Poisson distribution with a fixed mean evoked vesicle release count (eVRC). Then, each vesicle in the count for that 156

AN spike was assigned a delay relative to spike time, where the delay was randomly sampled from a normal distribution with a mean of zero and a non-zero standard deviation, called the synaptic jitter, which spread the vesicle release times around the spike time.

Spike-evoked vesicle release count, 푁(푡) was drawn from a Poisson distribution such that,

훿(푡)푒푉푅퐶푁(푡)푒−푒푉푅퐶 푃(푁(푡)) = ⁡, where 푃(. ) is the Poisson probability of 푁(푡), 푁(푡)!

훿(푡) is the occurrence of spike at time t (1 if a spike occurred, 0 otherwise) and 푒푉푅퐶 is the mean vesicle release count evoked by a single spike.

∆푡(푛)⁡~⁡ℵ(0, 푠푦푛푎푝푡푖푐⁡푗푖푡푡푒푟2), ∀⁡푛 ∈ {1, . . 푁(푡)}, where ∆푡(푛) is the vesicle release time relative to the timing of the spike that evoked its release. Vesicle release time was normally distributed with 0 mean and standard deviation equal to synaptic jitter for the nth vesicle. Since vesicle release times were randomly assigned in the model, some of the times were likely to be repeated and therefore, were counted as simultaneously released vesicles. Large jitter values yielded greater temporal spread of the evoked vesicle release times relative to AN spike time.

Further, letting 푉𝑖푠푝푖푘푒(푡 + ∆푡(푛)) denote the count of vesicles released at time

∆푡(푛) with respect to the ith spike in the train gave,

𝑖 푉 푠푝푖푘푒(푡) = ⁡ ∑푣푒푠𝑖푐푙푒푠 훿(푡(푛))⁡⁡, where 훿(푡(푛)) is 1, if a vesicle is released at the time t for the nth vesicle, or 0, otherwise, vesicles range from 1 to N(i), the evoked vesicle count for the ith spike, and counts the number of repeated vesicle times (that is, count of simultaneously released vesicles). 157

푘 𝑖 푘 푉 푎푙푙푠푝푖푘푒푠(푡) = ∑푠푝𝑖푘푒푠 푉 푠푝푖푘푒(푡), where 푉 푎푙푙푠푝푖푘푒푠(푡) sums, across all spikes, the occurrences of vesicles with overlapping release times counted for a time

th point, 푡푣푒푠𝑖푐푙푒(푛), for the k spike train. Altogether, this procedure computed an evoked vesicle release train for each spike train (Fig. 21C; magenta-dotted plots) that was a result of counting the overlapping vesicle release times for vesicles from the same spike and for those across spikes.

To compute the time-varying total evoked vesicle release counts or 푉푒푣표푘푒푑(푡),

푉푘푎푙푙푠푝푖푘푒푠(푡) was computed for all k trains (here k ranged from 1–4, Fig. 21B and C), and summed across all spike trains.

Thus, the time sequence of vesicle release also accounted for simultaneous vesicle release and for multiple AN inputs, the time sequence of vesicle release was separately obtained for each AN input and summed across inputs (Fig. 21C).

To simulate spontaneous vesicle release, vesicle release count over the duration of the stimulus was sampled from a Poisson distribution, then individual vesicle release times were randomly selected from a uniform distribution within a time range encompassing the duration of the stimulus (0 to 1 sec). Thus, this was a Poisson point process. Herein,

(푠푉푅퐶)푁푠푝표푛푡푒−푠푉푅퐶 푃(푁푠푝표푛푡) = , where 푃(푁푠푝표푛푡) is the probability of 푁푠푝표푛푡! spontaneously released vesicle count,⁡푁푠푝표푛푡, for the 1-s stimulus duration, 푠푉푅퐶 is the mean spontaneous vesicle release count/rate. Then, the vesicles were uniformly distributed such that 푃(푡) = 1/푁푠푝표푛푡

Then, the spontaneous vesicle train, 푉푠푝표푛푡(푡) was computed as, 158

푉푠푝표푛푡(푡) = ⁡ ∑푣푒푠𝑖푐푙푒푠 훿(푡(푛)), where 훿(푡(푛)) is 1, if there is a vesicle released at the time t for the nth vesicle, or 0, otherwise, vesicles represent vesicle index ranging from

1 to 푁푠푝표푛푡. 푉푠푝표푛푡(푡) is the blue dotted plot in Figure 21C, which shows the time- varying spontaneous vesicle release counts.

To summarize, for each trial output spike train from VCN, up to 4 AN spike train inputs that were used to compute 4 vesicle release trains, and a Poisson point process to compute one spike-independent spontaneous vesicle release train.

Finally, 푉푒푣표푘푒푑(푡) was added to 푉푠푝표푛푡(푡) to yield the resultant vesicle release train,⁡푉푛푒푡(푡). 푉푛푒푡(푡) was convolved with 푔푚푖푛푖 to yield the conductance waveform. As in the noise-free synapse, 푔푚푖푛푖 is an alpha function which represents the conductance waveform for one vesicle, and its amplitude is scaled by the synaptic conductance gain or synaptic gain.

The synaptic noise had 4 parameters that could be independently varied: 1) the mean number of vesicles released per action potential, 2) the expected rate of spontaneous vesicle release, 3) synaptic conductance gain, and 4) synaptic jitter. To determine which parameter contributed the most to reduction in temporal spiking precision (that is, time-locking), each parameter was varied separately while the others were fixed (Table 5).

159

Figure 21

Schematic of the AN-VCN Noisy Synapse Model

(A) Schematic of an AN-VCN neuron synapse (that is, endbulb of Held) showing spike- evoked (vesicles with purple outline) release and spontaneous release (vesicles with blue outline) of vesicles. Vesicles contain glutamate (yellow dots within the vesicles). Upon release, glutamate molecules bind to post-synaptic receptors and induces a change in the postsynaptic membrane potential. (B) Input architecture for VCN showing 1 to 4 synaptic inputs from 4 separate AN inputs. (C) The noisy synapse model is expanded. AN model outputs interface with a VCN model through the noisy synapses. Up to 4 AN spike trains (green dot raster, where dots represent spike times; time on x-axis, AN input count on y- axis) were input to the synapse model (boxed in grey dashed lines). The synapse model introduced temporal scatter in the evoked vesicle release times and spontaneous vesicle release times. For each VCN output train, 2 or 4 AN input spike trains were used. 4 inputs were used for NH, and 2 inputs for HL conditions (synapses #2 and #3 in the figure are faded, leaving #1 and #4 active to exemplify the input count for HL). Release times, determined for vesicles released due to each spike in the input spike train, may 160 coincide with those of vesicles released due to adjacent spikes. This produces time- varying changes in the number of vesicles released, that is, an evoked vesicle release train. For each AN input spike train, an evoked vesicle release train (magenta dot raster: the count of simultaneously released vesicle on y-axis for each time point on x-axis) was generated. Note that the lowest y-value the dots can take is 1; any value above this reflects simultaneous vesicle release. The bottom rectangle (blue dot raster; axis details same as magenta dot raster) shows vesicle release times, arising not due to AN spike trains but spontaneously. Again, the lowest y-value the dots can take is 1. In response to an individual sound stimulus, evoked vesicle times were computed for each AN input spike train and summed across input spike trains (summation symbol) and added to spontaneous vesicle release counts that were computed once over the stimulus duration. Finally, summed vesicle trains (푉푛푒푡(푡)) were convolved (*) with 푔푚푖푛푖 (orange curve approximating an alpha function, corresponding to conductance change from the release of a single vesicle) to generate a time-varying conductance waveform (not shown), which was input to the Type II VCN model (that is, model of the bushy cell). This process was repeated for many trial presentations of the same stimulus (represented by vertical dotted lines). Putative VCN responses for 100 trials are shown as brown dot raster (rightmost; dots show spike times).

161

Table 5

Synaptic Noise Parameters

Model parameters Baseline Range References

Mean vesicle count 50 5/50 (Bellingham et al., 1998) per spike

Mean spontaneous 30 Hz 0–300 Hz (Xie et al., 2017) vesicle release rate

Synaptic conductance 0.4 0.4–1.3 gain*

Synaptic jitter 0.3 ms 0–3 ms Upper limit for jitter range

of 3 ms is within the range

of temporal dispersion in

evoked EPSCs following

increased vesicle release

failure rates (Isaacson et

al., 1995).

Number of AN inputs 2 (Kim et al., 2004)

* In a previous study on normal-hearing cats (Oleskevich et al., 2002), it was shown that quantal current (mEPSC) at a fixed voltage of -60 mV was -100 pA, which yields a quantal conductance (mEPSG) of 2 nS based on Ohm’s law and assuming a synaptic reversal potential of 0 mV. However, this experiment was performed at 22°C. The quantal conductance was multiplied by 3.03 to adjust to 37°C, yielding 6 nS. Peak 162 conductance in the original VCN model was 26 nS when adjusted to 37°C (gE). Therefore, a synaptic gain of 0.23 would bring gE down to the conductance amplitude evoked by a vesicle. A synaptic gain of 0.4 was chosen to yield enough spike counts for each VCN spike train for SAC computation when number of inputs were set to 2 in the simulated hearing loss condition.

For simulating hearing loss, AN inputs were restricted to 2 because VCN receives reduced inputs after acoustic trauma (Kim et al., 2004). However, for normal hearing case, the number of AN inputs was set to 4.

Stimuli

Stimuli were frozen, broadband (300–15,000 Hz) noise, 1 second long and sampled at 100 kHz. Stimuli were presented at either the same sound level between normal-hearing AN (NH) and hearing-loss AN (HL) conditions or a sound level that produced the same firing rate in the two conditions, estimated from the rate-level function as follows. NH and HL responses were first modeled for 15 repetitions of each sound level (10–100 dB SPL). The sound level at which the firing rate was in the middle of the dynamic range for the HL condition was identified. Then, the sound level for the NH condition that had the same firing rate as that of the HL condition was identified. These levels were termed the “iso-rate” condition (Fig. 22A). Iso-rate sound levels were 91 dB

SPL (HL) and 41 dB SPL (NH). Additionally, stimuli for the NH condition were presented at the same absolute sound level as that of the HL condition, and this was called the “iso-level” condition and fixed at 91 dB SPL.

Lowpass stimuli (100–1500 Hz) were presented to the HL model at 91 dB SPL.

To obtain spontaneous rates, sound input to AN-VCN was a string of zeros spanning the same 1s duration. 163

Low-frequency Temporal Fine Structure (TFS) Sensitivity of AN Model Responses

We wondered if the HL model at high CFs would phase-lock to the fine structure of low-frequency components of broadband stimuli; in other words, if the filter bandwidth of the model was broad enough to allow substantial passage of low-frequency components in its responses. A systems-identification approach based on Wiener kernels was used to determine if low-frequency TFS sensitivity in model AN responses were consistent with published data (Henry et al., 2016). Wiener kernels have been used to describe AN responses arising from mixed degrees of phase-locking to envelope and low- frequency fine structure. Here, we concentrated specifically on the first-order Wiener kernel, h1, which is the linear component of the filter. It was sufficient to compare fine- structure sensitivity between NH and HL responses as HL affects fine structure the most and since there is no representation, in the h1 kernel, of the envelope, which is a second- order process. First-order kernels were computed from the reverse correlation between spikes (~15,000) and the Gaussian noise stimulus (X(t)), normalized for mean stimulus power and firing rate. They represented the mean sound waveform preceding a spike time

(ti). Briefly, h1(τ) was expressed as

푁0 ⁡R1(τ), where N0 was the mean evoked spike rate, A was the mean noise stimulus 퐴

2 power, which was computed as (1/푇) ∑푡 |푋(푡)| where T was the stimulus duration, and

1 푁 R1(τ) = ∑ 푋(푡 − 휏) was the mean stimulus waveform at a time τ before a spike 푁 𝑖=1 𝑖 where N was the total number of spikes. 164

To normalize for variability in h1 with sound stimulus level and mean firing rate (Henry et al., 2016), h1 was multiplied by

√퐴

푁0

Finally, h1(τ) was Fourier transformed to get H1(f), where f was frequency. The magnitude spectrum of H1(f) was expressed in decibels: 20 log10|퐻1(푓)|.

Shuffled Autocorrelograms

A shuffled autocorrelogram (SAC; similar to Chapter 4) was computed from AN model spike trains (Moncada-Torres et al., 2018) and VCN model spike trains to yield information on temporal coding and to provide a metric for ITD sensitivity (See Results).

Computation of a SAC involves counting coincidences of spikes between different trials at different delays and parallels the coincidence mechanism underlying ITD sensitivity.

To elaborate, for each pair of spike trains, one of the spike trains was time-shifted (that is, delayed) against the other where the number of coincidences were computed for the case where spike train #1 in the pair was advanced relative to spike train #2 (positive delay), and for the case where spike train #2 was advanced relative to spike train #1 (negative delay). Importantly, this procedure yielded the same number of coincidences for both positive and negative delays, making SACs symmetrical about 0 delay in the plot of

SACs vs. delays (Fig. 24–26). Coincidence counting from a pair of spike trains at different delays is akin to a coincidence detector receiving left and right inputs in which one of the inputs is time-shifted due to ITDs. Thus, a SAC can be used to quantify the extent to which ITD may be extracted from peripheral responses. SACs were normalized using the normalization constant (N)(N-1)(rate)2(bin width)(stimulus duration), where N 165 is the number of trials, rate was the average firing rate, bin width was set to 50 µs, and the duration was 1 s. N was set to 1000 and 500 for AN and VCN models, respectively.

To assess temporal coding for lowpass stimuli, low-resolution SACs were computed for

VCN model responses from 100 iterations of sound presentation and SAC bin width of

250 µs.

SACs for AN were additionally Fourier transformed to obtain response-response coherence. SACs are equivalent to cross-correlation function that plots correlations between two waveforms at various lags (Joris et al., 2006a). Response-response coherence analysis decomposes the frequency components of the cross-correlation of pairs of spike trains, obtained from multiple presentations of the stimulus, without needing an explicit knowledge of the stimulus. The amplitude at any frequency reflects the stimulus-induced synchronization at a synchronization rate corresponding to that frequency, and here, it is expressed in dB. The amplitudes can be large for frequencies well above the stimulus frequency bandwidth, and the maximum amplitude provides the upper bound on the information that can be transmitted (McGillivray et al., 2012).

Wiener kernel, h1 in the frequency domain, described in the previous section, is

st st equivalent to a 1 order stimulus-response coherence. Although h1 reflects 1 order

(linear) filter properties of the AN, the response-response coherence need not. This is because the response-response correlograms are influenced by higher-order Wiener kernels (the actual filter is described not only by h1 but by a series of such kernels such as h2, h3, etc.) as well and have harmonics corresponding to the distortions. Therefore, the response-response coherence at a frequency is always greater in magnitude than stimulus- 166 response coherence at that frequency for a repeated presentation of the frozen stimulus

(Roddey et al., 2000).

Binaural Model of ILD Sensitivity

A binaural model described by Brown and Tollin (2016) was used to approximate

ILD sensitivity based on AN responses. Here, broadband noise stimuli were filtered using rabbit DTFs for different azimuths as in Chapter 4. These filters gave the stimuli the appropriate frequency-specific ITDs and ILDs for a given direction. Rectified spike count differences were computed between left and right AN responses within a sliding time window over the duration of the stimulus, then summed across the duration of the stimulus. Summed difference spike counts were computed for different azimuths within the front horizontal plane and represented tuning to ILD since ILD varied monotonically with azimuth. Summed difference spike counts were normalized by dividing by the maximum value across azimuths. The temporal window of integration was represented by the width of the sliding time window, which was set to 3 ms. For each azimuth, the stimulus was repeated 100 times.

Results

AN Model Response Characterization: Rate-Level Function and TFS Sensitivity

To compare with the results in AN fibers after noise-induced SNHL (Henry et al.,

2016), two aspects of frequency tuning in the AN model were examined between NH and

HL in the iso-rate condition: 1) the presence of low-frequency temporal fine-structure sensitivity in the HL condition at high sound levels for high CFs, where OHCs are reduced and filter bandwidth expands to low frequencies; and 2) the absence of low- 167 frequency sensitivity in the NH condition at low sound levels for high CFs. Figure 22B shows time- and frequency-domain plots of the first-order Wiener kernel. In the frequency domain, the kernel represents the passband of the linear filter, which describes the frequencies for which the AN phase-locks to the temporal fine structure. The HL responses for a CF of 6 kHz exhibited an increase in sensitivity to low-frequency fine- structure (blue vs. red curves) with a peak at 1.6 kHz. Henry et al. (2016) also reported the presence of peak phase-locking to be between 1 and 2 kHz in high-CF AN fibers. We avoided examining envelope sensitivity, since loss of envelope sensitivity in AN fibers is variable (either retained sharply, or in more severe cases, broadened to low CFs). Low- frequency TFS coding by high-CF neurons is a more consistent effect of SNHL. In the

NH model at low sound level, there was no low-frequency TFS sensitivity. Additionally, when sounds were presented at a high level, the NH model showed only moderate TFS sensitivity. Thus, the AN model performed as expected for both NH and HL conditions.

168

Figure 22

Rate-level Functions and Systems-Identification of TFS Sensitivity

(A) Rate-level function for a model AN fiber with a CF of 6 kHz and spontaneous rate of 60 Hz in the NH and HL conditions (dashed green and blue lines, respectively). Red dots with horizontal arrow indicate levels at which NH and HL rates are in the middle of the dynamic range (iso-rate). Red dots with vertical arrow indicate rates when sound level for the NH and HL models are the same (iso-level; saturated range in NH and dynamic range in HL). SNHL simulation produced a 55-dB shift in response threshold. (B) For the same model, normalized h1 in the time domain (left) and frequency domain (right), with HL responses having a maximum TFS sensitivity at 1.6 kHz (red asterisk), ~2 octaves below its actual CF, and not observed in the iso-rate NH condition. Mild TFS sensitivity was exhibited in NH for the iso-level condition.

ILD Sensitivity Present for the AN Model in the HL Condition, Consistent with Data

Pairs of “left” and “right” DTF-filtered broadband stimuli have ILD appropriate for each azimuth. ILDs span –15 to +15 dB over the range of azimuths between –90° and

+90°. Low- and high-CF AN model fibers were presented with 100 trials of DTF-filtered pairs of stimuli per azimuth. If sound level in the left DTF-filtered sound was higher than in the right DTF-filtered sound, it was expected that there would be firing rate differences 169 between left and right AN model responses. Therefore, an ILD-based metric was computed from left and right AN model responses using a sliding time-window within which differences in spike counts between left and right spike trains were tallied. When the difference was negative, it was assigned a value of 0 (that is, rectified differences) so that only positive differences were summed. This was because the phenomenological model treated negative values as impossible as output spike counts from actual neurons can never be less than zero. Summed spike count differences were normalized by the maximum value across azimuths. This method phenomenologically captures ILD sensitivity in the IC (Brown et al., 2016) and only depends on AN responses. In Figure 23

(left), the low-CF binaural model had minimal sensitivity to ILD because filtering at low

CFs in the AN model removes high-frequency components containing ILDs. ILDs were large at high frequencies and small at low frequencies largely due to head size (Day et al.,

2012). For high-CF, NH model responses had pronounced ILD sensitivity, which was matched to that of HL model responses (Fig. 23, right).

170

Figure 23

ILD Information in AN Responses is Unaffected by Hearing Loss

Normalized ILD model responses derived from the low- (left) and high- (right) CF AN models in the iso-rate condition. ILD sensitivity was preserved even after HL in the high- CF model, while the weak ILD sensitivity of the low-CF model was inherited even in the HL condition from the weak ILDs produced by DTFs at low frequencies.

ITD Sensitivity Present for the AN Model in the HL Condition

To test if information about ITD is present in AN model responses after SNHL,

SACs were computed (Fig. 24) from a simulation of ITD tuning based on independent responses from identical left and right AN fibers. The free parameter in the SAC was the bin width of the SAC and ITD was represented by the SAC delay. This allowed systematic manipulation of coincidence window size to determine its effect on ITD sensitivity. In addition to high-CF (6 kHz) AN responses, low-CF (600 Hz) responses were modeled to determine potential CF-dependence on ITD sensitivity following SNHL simulation for iso-rate and iso-level conditions (Fig. 24A). For a 50-µs coincidence window, coincidences in the HL condition consistently varied with ITD for both iso-rate and iso-level conditions and for both low and high CFs (Fig. 24B). In all cases, SACs in 171 the HL condition had large peak heights. At iso-rate sound levels, ITD tuning was strikingly greater in the HL than in the NH condition for high CF (Fig. 24B, top right), while it was similar for low CF, except for a substantial reduction in side-lobe height in the HL condition (Fig. 24B, top left). These differences are accounted for by a widening of the AN filter bandwidth with 1) increased sound level and 2) OHC and IHC loss. For iso-level sound level, the high-CF model in the NH condition showed slight sharpening of the peak compared to that at iso-rate sound level, but the peak was small compared to that in the HL condition. Slight sharpening of the peak in the NH condition was likely due to the filter representing tall stereocilia, which is broadband at high sound levels. For low CF, widening of the filter bandwidth (Fig. 24B, bottom left) with sound level has the effect of reducing ringing in the SACs and pushes the sidelobes in the NH condition down to that in the HL condition (Fig. 24B, middle left).

The auditory response-response coherence bandwidths were compared across the three conditions: NH iso- rate and level, HL, similar to Wiener kernel, h1. The amplitude spectrum derived from SAC differs from filter characteristics in one major respect:

Wiener kernel was first-order and represented the linear component of the AN filter, that is, frequencies of TFS to which the model fiber phase-locked. In contrast, response- response coherence shows higher-order non-linear filter components in addition to first- order filter component. This way, coherence describes the AN filter better than Wiener kernels, though it would not isolate TFS sensitivity from just SACs alone (Joris et al.,

2006a). In the frequency-domain, response-response coherence reflects the strength of synchronization (amplitude) to different frequency components. Bottom left panel in 172

Figure 24B, for the NH iso-rate condition for a low-CF model AN fiber, shows a main peak for the first-order component at 650 Hz, close to its actual CF at 600 Hz, with an additional smaller peak, at 1300 Hz (a harmonic of the first-order component). In these two cases, the main peaks were approximately 2 octaves wider and the peak heights lower in amplitude than that of NH iso-rate condition. For high-CF (CF of 6 kHz), AN fiber of HL, which had a wider bandwidth than that of NH, was flat with a corner frequency at ~ 2 kHz, where the roll-off begins. In the iso-rate condition, NH was nearly the same amplitude as HL between 0.25 and 0.5 kHz, but rolled off, considerably, above this frequency. This is consistent with broader and shallower SACs in the time domain.

For high-CF NH fibers in the iso-level condition, the amplitudes were consistently low; this reduction in low-frequency representation compared to iso-rate condition explains the sharpening of the SACs in the time domain for the iso-level condition relative to iso- rate condition. Therefore, high-CF fibers in HL had sharp, high-amplitude SAC peak because they synchronized highly to high-frequency components.

For both low and high CFs, SAC peak heights progressively flattened to a value of 1 for coincidence window sizes at and above 500 µs (Fig. 24C). It is well known that

SAC peak heights reduce with an increase in SAC histogram binwidth (Joris et al.,

2006a). Since SAC peaks indicate the degree of temporal coding, a large peak in the HL as compared to NH condition suggests that temporal coding is enhanced for AN model fibers in the HL condition. Such enhanced temporal coding has been observed in AN fibers of the chinchilla following noise-induced hearing loss (Henry et al., 2014; Kale et al., 2010). 173

Figure 24

ITD Present in AN Responses in Hearing Loss is Reduced by Wider Coincidence Window

(A) Rate-level functions for low- and high-CF AN models in the NH and HL conditions. Sound levels for the iso-level and iso-rate conditions are indicated, similar to Figure 22. (B) Normalized SAC plots for low- (left column) and high-CF (right column) models, with sound levels under iso-rate (top row) and iso-level (middle row) conditions. NH and HL models shown in blue and red curves, respectively. ITD sensitivity remains in the HL condition for both CFs, unlike our experimental data. ITD sensitivity in the HL condition predicted to be even sharper than in the NH model at high CF due to broadening of the auditory filter with HL (coincidence window = 50 µs). The two panels in the bottom row show response-response coherence as the Fourier transform of SACs in NH iso- rate (blue) and level (orange) conditions and HL iso-rate (red) condition with amplitude/gain 174 in dB vs. frequency for low-CF (left panel) and high-CF (right panel) model AN fibers. (C) SAC plots for low- (top) and high-CF (bottom) models in the HL condition using different coincidence windows (colored curves). SACs are weakly modulated by delay at and above a window of 1 ms.

Spike-timing Precision of VCN in the Absence of Synaptic Noise

A majority of IC neurons had poor temporal precision in their outputs and loss of

ITD sensitivity following hearing loss (Chapter 4). A complete lack of ITD sensitivity may be due to weak spike-timing precision in the inputs to the SOC. If there is adequate spike-timing precision in the responses of AN fibers following hearing loss, then how is it compromised centrally? For this, we input responses of the AN model in the HL or NH condition into a VCN spherical bushy cell model, which is the main excitatory input to the SOC. We compared model responses between HL and NH conditions. The synapse onto the VCN was modeled simply as a passive relay of spikes from multiple AN fiber inputs through a convolution of input spike times with a miniature postsynaptic conductance. The time-varying conductance served as the synaptic input from which the synaptic current could be calculated. The synaptic gain (which scales the conductance waveform) and the number of AN inputs could be varied in this model. Model VCN responses in the NH condition had greater spike-timing precision than that of model AN fibers, consistent with reported data (Joris et al., 1994b; Louage et al., 2005) (Fig. 25A).

For a fixed gain and number of inputs, VCN responses in the HL condition exhibited greater spike-timing precision (peak SAC) than in the NH condition (Fig. 25B). We hypothesized that increased gain and decreased number of inputs may reduce VCN model spike-timing precision. Consistent with this hypothesis, there was a large reduction in 175 spike-timing precision with increased gain and decreased inputs (Fig. 25C). However, even with increased gain and decreased number of inputs, the spike-timing precision of the model in the HL condition remained slightly greater than in the NH condition. The

VCN model is equipped with low- and high-threshold K+ conductances, which have a role in maintaining temporal precision (Svirskis et al., 2002). Reducing peak KHT conductance, peak KLT conductance, or both peak KHT and KLT conductances, to 1/10th of their original values failed to reduce spike-timing precision (Fig. 25D). In this model, no extra source of stochasticity was present other than that carried by inputs from the AN model. Taken together, the results indicate that spike-timing precision in the model depends on synaptic gain and number of inputs but not potassium channel conductances.

Furthermore, changes in gain and number of inputs were not sufficient to account for loss of ITD sensitivity. Realistic synapses have additional sources of noise, such as temporal jitter in synaptic transmission and spontaneous background vesicle release. Therefore, we asked if VCN model responses would differ in their spike-timing precision if these additional sources of noise were present.

176

Figure 25

Influence of Synaptic and Channel Parameters for a Noise-Free Synapse on Time-locking in VCN Responses

(A) Normalized SACs from model AN responses and from model VCN responses (4 convergent AN inputs and a synaptic gain of 0.8) in the NH condition showing enhancement of spike-timing precision (larger peak SACs) in the VCN. (B) NH and HL VCN responses; Synaptic gain and number of inputs the same in both cases. (C) Normalized SACs for VCN responses in the HL condition show dependence on gain and number of inputs (black vs. red dashed lines). (D) Normalized SACs for VCN model in the HL condition show that manipulations of K+ conductances do not alter spike-timing precision. The gain was 1.3 and number of inputs was 2.

177

Synaptic Noise Reduces VCN Model Spike-timing Precision

Very few reports are available on the effect of hearing loss on temporal jitter in the output of cochlear nucleus neurons. Specifically, with age-related hearing loss, AN fibers maintain temporal coding (Heeringa et al., 2020), while temporal precision of vesicle release (synchronized to current input) is reduced at the endbulb of Held (Xie et al., 2017). Therefore, we tested the possibility that synaptic noise may contribute to VCN jitter. The AN model in the HL condition, which produces sharp, time-locked output, offers an ideal computational scenario to study how different synaptic parameters influence spike-timing precision in the VCN. We modeled variability in two types of vesicle release: evoked and spontaneous. The four independent parameters constraining synaptic noise in the model were mean evoked vesicle release count elicited by an action potential (eVRC), mean spontaneous vesicle release count (sVRC), standard deviation of per-spike vesicle release times (synaptic jitter), and per-vesicle gain in the conductance amplitude (synaptic gain) (See Methods for the rationale).

The primary aim was to see which of the 4 parameters reduced temporal coding to the greatest extent (Table 5, baseline parameters). In contrast to the VCN model in the

HL condition with a noise-free synapse, which had a SAC peak height of approximately

5.5 (Fig. 25C), the SAC peak height for the model with a noisy synapse and baseline parameters was only approximately 2 (Fig. 26A, left, black line). Increasing synaptic gain and decreasing number of inputs only slightly reduced spike-timing precision (Fig. 26A, left). Changes in synaptic gain alone could also change spike-timing precision (Fig. 26A, right). Therefore, we fixed gain to 0.4 and number of inputs to 2 to assess changes in 178 temporal coding due to changes in other parameters. Varying synaptic jitter in evoked vesicle release was varied from 0 to 2 ms strikingly reduced spike-timing precision with the sharpest decline occurring between 0.1 to 0.5 ms (Fig. 26B, left). Our baseline jitter of 0.3 ms falls in the middle of this range.

In our data, there was no neural sensitivity to ITDs in lowpass noise after hearing loss (Chapter 4 text). We therefore tested the hypothesis that increasing synaptic jitter may also underlie loss of ITDs in lowpass noise. For lowpass noise stimuli (results not shown), a 3-ms synaptic jitter completely prevented VCN from exhibiting time-locked responses (peak height of 1.1), while a sub-ms jitter produced a sharp time-locking with a peak height of 3.1 (as AN model for HL was high-CF, responses are time-locked to TFS).

Additionally, for 0.3-ms jitter, when eVRC was decreased from 50 to 5 vesicles per spike, spike-timing precision was also reduced (Fig. 26B, right).

In contrast, increasing the sVRC from 30 Hz to 300 Hz did not alter spike-timing precision (Fig. 26C). This was because every input spike, evoked at least 10 times a larger vesicle output than did spontaneous release. For instance, if the input firing rate were around 60 Hz, expected evoked release would be 3000 vesicles in a 1-s duration versus a spontaneous release of only 30–300 vesicles. sVRCs are normally between 0.2

Hz and 20 Hz (Malagon et al., 2016; Xie et al., 2017) and unlikely to ever exceed 100 Hz in physiological conditions. sVRCs may have a greater impact on synapses with low-rate eVRCs (synapses receiving AN inputs with low spontaneous or driven rates). The effect of varying sVRC was also tested on the spontaneous firing rates in the VCN model output. The sVRC values of 0 Hz, 30 Hz, 300 Hz and 3 kHz led to VCN output 179 spontaneous rates of 112 Hz, 108 Hz, 112 Hz and 127 Hz, respectively. Above 3 kHz, sVRCs reduced spontaneous rate. Thus, sVRC had little effect on VCN model spontaneous rate, and VCN model spontaneous rate was largely determined by spontaneous firing of AN inputs.

There is a possibility that the source of decreased spike-timing precision may be from the postsynaptic neuron through a reduction of KLT and KHT channels, although there is no evidence that this occurs in hearing loss. Figure 26D shows the effect of decreasing K+ conductances in the VCN model. Reducing both KLT and KHT channels to 1/10th their baseline conductances reduced spike-timing precision, while individual manipulations did not. Furthermore, spontaneous and evoked firing rates nearly doubled when both KLT and KHT conductances were reduced when compared to the case where only either of the conductances was manipulated. Therefore, the effect of low- and high- voltage-activated K+ conductances was to 1) improve temporal coding, and 2) decrease output firing rates. Altogether, our modeling results suggest that temporal coding in the

VCN is degraded the most by increased synaptic jitter (i.e., spread in evoked vesicle release timing), and to a lesser extent, by reduced KHT and KLT conductances, increased synaptic gain, reduced number of AN inputs, and reduced number of evoked vesicle release.

180

Figure 26

Influence of Synaptic and Channel Parameters for a Noisy Synapse on Time-locking in

VCN Responses

(A) Effect of synaptic gain on spike-timing precision. Left, synaptic gain and input numbers were varied, but SAC curves highly overlapped between the two conditions. Right, more extreme reduction in synaptic gain increased the peak height (black curve). The blue curve is duplicated in both left and right panel. (B) Effect of synaptic jitter and eVRC on spike-timing precision. Left, peak SAC (correlation index) vs. synaptic jitter for 181 values from 0 to 2 ms (0, 0.05, 0.1, 0.5, 1, and 2 ms). A ms-scale jitter completely abolished spike-timing precision. Right, decreasing eVRC from 50 vesicles/spike to 5 vesicle/spike (blue curve: 5 vesicles per spike) with a 0.3-ms jitter reduced peak SAC. (C) sVRCs did not affect spike-timing precision: there was strong overlap between the SAC curves for 30 and 300 Hz sVRCs. (D) KHT and KLT conductance manipulations revealed dependence of spike-timing precision on KHT and KLT channels. KHT conductance or KLT conductance reduction to 1/10th of the baseline value alone did not alter the SAC, curves overlap (purple, KHT; red dashed line, KLT; blue, baseline). When both KHT and KLT conductance were reduced (yellow curve) the peak height decreased by ~0.5.

Discussion

We modeled various mechanisms affecting ITD sensitivity that could be potentially tested in future in vitro or in vivo experiments in the brainstem. AN model responses were consistent with known effects of noise-induced HL on AN fibers, especially high-CF fibers. Model high-CF fibers were tuned to low-frequency TFS, as previously reported (Henry et al., 2016). While the model preserved ILD information, consistent with our data on SNHL, it also preserved ITD information in its responses due to the presence of highly time-locked responses to high-intensity, broadband sounds, consistent with previous reports on enhanced temporal coding in HL (Henry et al., 2014;

Kale et al., 2010). That ITD information was present, in theory, in the auditory periphery, but was not seen in any of our IC neurons in HL, raises the possibility that HL-related central changes potentially increase temporal jitter. At the level of the cochlear nucleus, which provides input to the binaural coincidence detectors in the SOC, temporal jitter would scramble spike times, and as a result, ITDs would no longer be present in the inputs. Central changes may refer to several different aspects: reduced inputs (Kim et al.,

2004), increased spontaneous rates (Mulders et al., 2009), altered synaptic processes (Xie 182 et al., 2017), or altered excitation-inhibition balance (Barsz et al., 2007). Here, we focused on synaptic mechanisms that increase temporal jitter in two versions of the synapse: 1) passive synapse (Fig. 25), and 2) noisy synapse (Fig. 26).

In the passive synapse model, synaptic gain and number of inputs, and not K+ conductances, largely influenced output temporal jitter in the VCN; reducing gain and increasing number of inputs reduced temporal jitter.

The noisy synapse model provided 4 synaptic parameters that could be independently varied. These parameters were set based on literature on the endbulbs of

Held, which are the synapses between AN fiber and spherical bushy cells of the VCN. Of these, synaptic jitter (the standard deviation of evoked vesicle release time) had the most effect on temporal precision. Reducing mean evoked release counts also decreased spike timing precision. Varying other parameters, namely, synaptic gain and mean spontaneous vesicle release rates had minimal effect on spike-timing precision in the ongoing responses. Spontaneous release rates did not influence spike-timing precision and were kept to physiologically observed rates.

Varying potassium conductances gave mixed results: neither the KHT conductance nor the KLT conductance altered spike-timing precision significantly, but manipulations of both the KHT and the KLT conductances did. Reducing KLT and KHT conductances increased spontaneous rates and decreased spike-timing precision.

However, KLT and KHT manipulations did not completely abolish spike-timing precision as did increasing synaptic jitter. Synaptic jitter in the VCN has not yet been reported for hearing loss induced by noise exposure, but remains a possible candidate for 183 explaining failure to encode ITDs for several reasons: 1) asynchronous release of EPSCs is increased in two different models of hearing loss, congenital (Oleskevich et al., 2002) and age-related (Xie et al., 2017), making it likely that it might be related to input deprivation, which is also a hallmark of noise-induced hearing loss; 2) asynchronous

EPSCs would be a natural source of input decorrelation; 3) to date, no hearing-loss- related postsynaptic changes, such as a stark reduction in KHT and/or KLT channels, have been found.

The other candidate for reduced spike-timing precision, the amount of evoked vesicle release, decreases with age-related hearing loss, as indicated by reduced EPSC peak amplitudes (Xie et al., 2017). This is modeled by the eVRC parameter in our model.

In summary, EPSC peak-to-tail ratio is reduced by decreased synchronous release counts and increased asynchronous release rates. Small amounts of asynchronous vesicle release are seen in normal endbulbs (Xie et al., 2017), but their physiological role is still unclear.

This is because asynchronous vesicle release in normal endbulbs only occurs for high- frequency current stimulation and its occurrence rate may be more likely to be an in vitro parameter than an in vivo parameter for describing temporal jitter. Simulating asynchronous release in the endbulb may be more relevant for computational studies on temporal coding involving cochlear implants which employ electrical stimulation pulse rates that often exceed 400 Hz (Buechel et al., 2018).Such a study on endbulb may be novel because previous models have so far treated this synapse only as a passive relay

(Chung et al., 2015).

184

Limitations of the AN Model Implementation

Although the AN model by Bruce et al. (2018) permits flexible manipulation of several key parameters, it does not model functional IHC sensitivity loss related to damage to short stereocilia or cell death—at a cihc of 0, IHC activity related to short stereocilia is still maintained in the model. Thus, a cihc of 0 does not reflect a complete functional loss of IHC. In contrast, a cohc of 0 models a complete functional loss of OHC.

It is therefore somewhat circuitous to model a complete functional loss of OHCs, but not that of IHCs. In hearing loss rabbits that we used for assessing hair cell damage from noise overexposure, many had IHC loss that ranged from 0% to 80% (Chapter 3). This pattern was seen regardless of the actual ABR threshold shift as long as the shift was above a critical value. However, we could not functionally recreate this scenario in the

AN model. It is computationally time-consuming to run the VCN model when it receives multiple AN inputs.

The AN model also does not model cross-CF interactions and distortions. It assumes a simple feedforward response derived from an IHC-AN synaptic relay from the cochlear region described by the CF. However, this is more of a problem for studying responses to narrowband stimuli than the broadband noise stimulus used in the present study. Finally, the model has poor phase-locking to sinusoidally amplitude-modulated tones for CFs below 7 kHz (Ashida et al., 2019), but this issue should not affect our analysis on broadband sounds.

Finally, the AN model conforms to the firing properties of AN fibers from the cat, not the rabbit. However, features like Q10 values (ratio of CF to the bandwidth at 10 dB 185 relative to the threshold at CF) may not vary between cat and rabbit (Borg et al., 1988;

Miller et al., 1997). Additionally, tone thresholds in AN highly depend on morphological features like axonal caliber (Liberman, 1982). But it is unclear if there are large differences in auditory nerve fibers across species in terms of number of hair cells contacted, axonal diameter, hair cell innervation density, or spontaneous rates (Borg et al., 1988; Liberman, 1982; Nayagam et al., 2011). The CFs chosen in this study were either 600 Hz or 6 kHz, which are within the audible range of both cats and rabbits

(Heffner et al., 1980; Heffner et al., 1985).

Limitations of the ILD Model

The key limitation of our ILD model is that it does not directly model the LSO, the first ILD processing center in the auditory system. In the LSO, ILDs are encoded from coincidence detection and not from a difference in sound levels—a difference in levels is converted to a short time-delay at this brainstem nucleus (Franken et al., 2015;

Tollin et al., 2005). Basically, the model is a phenomenologically inspired alternative to a much simpler difference counting of left and right spike counts. When the sound level is selected close to the middle of the dynamic range, left and right model spike trains would have different firing rates and the difference would capture ILD. But this does not directly explain the neural mechanisms underlying retention of ILD sensitivity in HL.

However, manipulations of the temporal integration window in the model suggest a simple counting algorithm with the difference representing the inhibition received by the

EI cells in the IC. If LSO coincidence detection is compromised in HL, then either LSO or the IC must incorporate a mechanism to generate ILD sensitivity without temporally 186 precise coincidence detection. Brown and Tollin (2016) have proposed an integration window of 3 ms for ILD sensitivity in the IC; the same window would hamper ITD sensitivity in the MSO or LSO. Thus, if the coincidence window were to widen in LSO, it would not affect ILD sensitivity. Furthermore, the IC may have additional ILD processing not inherited from the SOC, thought to arise from monaural excitation from contra- and ipsi- inhibitory projections from the DNLL (reviewed by Pollak, 2012).

Limitations of SAC-based ITD Extraction from the AN Model

The peak height of the SAC depends on binwidth. Widening the binwidth resulted in poor spike-timing precision. In principle, counting coincidences is an EE (ipsilateral excitatory, contralateral excitatory) process (spikes occur coincidentally in both left and right excitatory inputs). Therefore, the SAC assumes an EE process. This is not a serious pitfall because it only models coincidences as EE-type inputs and not EI-type inputs.

While the SAC can be used to assess temporal coding precision (Joris et al., 2006b), it produces large peak heights if there is only a strong onset response. It is not sensitive to the duration of time-locking in the ongoing responses as the same peak height can be achieved by a much stronger onset response. Alternatively, more physiologically explicit models of ITD encoding may require additional assumptions about hearing loss-related central changes.

Limitations of the VCN Model Implementation

Here we considered a feedforward input structure from model AN to VCN connection. The number of inputs did not go over 4, consistent with the number of inputs

VCN bushy cells normally receive (Ashida et al., 2019). Since inputs to VCN bushy cells 187 are reduced following hearing loss, we fixed the number to 2 in most simulations. The inputs were from model AN fibers matched in CF due to cochleotopic representation in the cochlear nucleus (reviewed by Ryugo et al., 2003). However, the feedforward model failed to simulate two key aspects of a VCN spherical bushy cell. First, the cell receives inputs from multiple AN fibers with different SRs (spontaneous rates), not the same SRs

(Liberman, 1991). Here, however, we modeled matched-SR inputs to the VCN model.

Same-SR inputs recreate the input properties of globular, not spherical, bushy cells.

Second, the number of AN inputs (~4) has been suggested to be insufficient to produce significant temporal coding (Joris et al., 2008). Inhibition may fine-tune output temporal precision in VCN bushy cells by discarding imprecise excitatory impulses. Therefore, reduced inhibition in VCN after hearing impairment (Boettcher et al., 1993) may lead to increased temporal jitter in the VCN output. Indeed, application of glycine or GABA antagonists resulted in increased firing rates and decreased coupling to AN inputs in spherical bushy cells (Dehmel et al., 2010; Keine et al., 2015).

Manipulations of KLT conductances were carried out in our model without covarying the Ih conductances, so resting potential may have varied in our simulations looking at KLT effects (This point is mentioned in Chung et al., 2015). This is because the KLT and H-currents push membrane potential in opposing directions, and this interplay determines the resting potential (Rothman et al., 2003). However, only dual reduction in KHT and KLT conductances in the model yielded a reduction in spike- timing precision, albeit only with the inclusion of synaptic noise (Fig. 26D). In contrast, preserving baseline conductances of either KHT or KLT was sufficient for maintaining 188 spike-timing precision because output firing rate was the same as baseline level in the model. Increases in firing rate caused by joint reduction of KHT and KLT conductances interacted with the synaptic jitter (0.3 ms) to weaken spike-timing precision. In the absence of synaptic noise, a reduction in both KLT and KHT also increased firing rates

(result not shown), but since there was no synaptic noise, VCN spikes were time-locked to the AN input spike times (Fig. 25D).

We did not model globular bushy cells, the main source to the MNTB, which provides sharply timed inhibitory inputs to the LSO and MSO and is central to ILD and envelope ITD sensitivity in the LSO. Since our aim was to see which synaptic factors could theoretically be involved in reducing ITD sensitivity, we only modeled spherical bushy cells, which project bilaterally to the MSO.

Limitations in Choosing Synaptic Noise Parameters

In the noisy synapse model, parameters were chosen from data on endbulbs.

Specifically, endbulbs, under lower release probability, release vesicles, when excited by synaptic input, according to a Poisson distribution (Isaacson et al., 1995). This is because there is a surplus pool of vesicles which are readily releasable (>100) (Lin et al., 2011).

Spontaneous vesicle release is mediated via a Ca2+-dependent process that may or may not use voltage-gated calcium channels (Dai et al., 2015). Lower rates of spontaneous release (<10 Hz) may be present in endbulbs of both young and older animals (Xie et al.,

2017). However, we chose a value of 30 Hz. Synaptic jitter, that is, the temporal spread of vesicles evoked by each spike, was chosen to be on the microsecond scale in normal hearing. We picked vesicle times from a normal distribution with a mean of 0 and 189 standard deviation equal to the synaptic jitter. However, the value of the jitter was arbitrary since the literature is unclear on the exact temporal dispersion.

The synaptic gain parameter that modulated values of peak synaptic conductance

(peak conductance amplitude induced by a synaptic vesicle) was arbitrarily chosen. The peak conductance elicited by a single vesicle has not been directly reported. So, we used a previously reported mEPSC peak (peak current evoked by a single vesicle) of 100 pA to compute the peak conductance using Ohm’s law. However, we used a large value for mEPSC peak. Although these were values from mEPSCs from spontaneously released mEPSCs at 10 to 15 Hz (Xie et al., 2017), it was assumed that each mEPSC was actually induced by the release of a single vesicle. If spontaneous release of one vesicle was not entirely independent of the other (due to proximity or sharing of the same active zone), then each mEPSC could have arisen from a small fraction of simultaneously released vesicles. It is possible that mEPSC peaks reflecting individual vesicles may be even lower. The lower bound reported in previous studies was <50 pA (Isaacson et al., 1995;

Xie et al., 2017), even in aged endbulbs. Further, neither young nor aged endbulbs have mEPSC >150 pA. Therefore, synaptic gain values greater than 1 (mEPSC peaks of at least 300 pA or higher), may not be physiological (Table 5).

In vitro studies have shown that when the endbulb is subject to high-frequency afferent shocks, short-term synaptic depression occurs for input rates of 300 Hz or higher, where the neuron does not display fast spiking activity; the origins of this depression have been attributed to vesicle pool depletion and AMPA receptor desensitization

(Oleskevich et al., 2000; Yang et al., 2008). The noisy synapse model did not incorporate 190 either of these features and hence VCN responses did not show these effects. Under pool- depleting conditions, the vesicle release process would exhibit binomial rather than

Poisson statistics (Malagon et al., 2016). The short-term depression improves temporal precision in the outputs of the VCN neurons receiving different rates of timing information by suppressing high-rate inputs (Yang et al., 2009). However, this depression has been reported in the endbulb only in vitro (Borst, 2010).

The model also did not incorporate vesicle reuptake. Vesicle reuptake in the endbulb of Held largely determines recovery from short-term depression due to vesicle pool depletion. However, vesicle release probability is too low (Borst, 2010) to cause substantial pool depletion, in vivo.

Physiological Implications of the Computational Results for Improving ITD Sensitivity

Cochlear implant stimulation has the capacity to induce sharp temporal fluctuations that can accentuate ITD sensitivity (Buechel et al., 2018; Smith et al., 2007;

Smith et al., 2008). VCN bushy cells exhibit primary-like discharge pattern, that is, they have a response pattern very similar to AN fibers, having a strong onset followed by exponential decay of firing rate. Inputs that sharply fluctuate may narrow the temporal spread of vesicle release and improve spike-timing precision in both AN fibers and VCN bushy cells, for instance, acoustic pulses. Furthermore, improvement in spike-timing precision may arise from pharmacologically decreasing the activation voltage of high- threshold K+ conductance (Chambers et al., 2017).

191

Chapter 6: General Discussion

In this study, we recorded from the IC neurons of severely noise-deafened and normal-hearing rabbits and report for the first time a specific loss of sensitivity to ITDs after hearing loss. ILD sensitivity was retained.

In Chapter 3, we showed how noise overexposure affected hair cell survival in the cochleae of Dutch-belted rabbits. Hair cell loss was correlated with ABR and DPOAE threshold shifts. The noise overexposure at a level of 133 dB SPL and duration of 90 minutes produced variable ABR threshold shifts of 5 to 80 dB. The variability was much larger in males than females. Males exhibited greater degree of asymmetric hearing loss, following which the same animal could have a small threshold shift in one ear and a large threshold shift in the other.

The relationship between tone-evoked threshold shifts and hair cell loss in frequency-matched cochlear locations revealed critical threshold shifts above which OHC survival reduced sharply to <20% and IHCs showed variable survival with lower bounds overlapping with OHC survival. Widespread damage to the cochlea and elevated thresholds were found at frequencies well above our noise band (530–1061 Hz).

However, dysfunction of hair cells subthreshold to cell loss was not known since we did not measure disruption or loss of stereocilia. In the chinchilla strain of rabbits, IHC stereocilia loss can account for threshold shifts of 40 dB or higher (Borg et al., 1995). We found that ABRs and DPOAEs predicted critical threshold shifts for OHC loss equally well. 192

ABR Wave I amplitudes are used to assess damage to auditory nerve or loss of

IHC-AN synapses (Kujawa et al., 2009). We did not, however, characterize postexposure ABR Wave I amplitudes for the following reasons. 1) The post-exposure ABR signals were not detectable up to the acoustic limit in many cases, and where measurable, they were close to the acoustic limit. Typically, at the threshold of ABRs, the signals are still substantially contaminated by noise: measured ABR Wave I amplitudes would likely be a noise-altered version of the actual Wave I amplitude. 2) For a minority of measurable ABR signals, where threshold shifts were at least 10 dB below the acoustic limit, such that, ABRs would be clearly above noise and Waves I–V measurable, for sounds presented at the acoustic limit. 3) As it was not possible to quantify auditory nerve damage or loss of IHC-AN synapses, using our immunostaining protocol, it would not have been possible to associate Wave I amplitudes with synaptopathy and nerve damage in our animal model, which was the primary goal: to make paired measurements of structural and functional damage in the (same) cochlea (Chapter 3). 4) Sounds are still robustly encoded in the firing rates of IC neurons after a near-complete auditory nerve denervation with intact hair cells, such that animals recover their normal behavioral tone detection and neural frequency response thresholds within a month (Chambers et al.,

2016). Whereas, frequency response thresholds in neurons of our deaf rabbits were clearly and consistently elevated. It is likely that hair cell damage and not nerve damage, largely determines the thresholds of rate coding of sounds in the IC. We therefore expected threshold elevations in the IC neurons of our deaf rabbits to depend mainly on hair cell damage and did not investigate nerve damage. 193

In Chapter 4, we reported data from neural recording experiments on rabbits with noise-induced hearing loss. Our sample was likely biased toward high-CF neurons.

Reasons for this potential CF bias were a) that threshold shifts (median of 75 dB) of low-

CF neurons placed the expected neural response thresholds above the acoustic limit for sound levels, and b) our tetrode penetrations were oblique and gave a higher percentage of neurons with CFs > 2 kHz, even in normal-hearing rabbits. Previously, depth of electrode penetration had been used as a way to determine CF in neurons of the VCN, whose CFs could not be determined after acoustic trauma (Vogler et al., 2011). However, depth was not an ideal way to infer CF in the ICC since tetrode tracks were oblique to the cochleotopic axis of the ICC. Instead, we used the upper-frequency edge, which is log- log linear in its relationship with CF in normal-hearing units from the ICC and does not likely change post-overexposure.

Azimuth tuning in the population of neurons was reduced because of 1) increased number of neurons that were irresponsive to sound, and 2) increased number of monaural neurons, which had weak azimuth tuning [Monaural IC neurons typically display weaker binaural directional cue sensitivity (Chase et al., 2008, their Fig. 9B)]. Note that some monaural neurons had strong tuning to binaurally presented ITD+ILD stimulus from their sensitivity to sound level variation across azimuths in one ear (see e.g., in Chapter 4, Fig.

16B). Therefore, for monaural neurons, tuning strength to binaural ITD+ILD is not a reflection of actual tuning to azimuths. Remaining binaural neurons were sensitive only to ILDs. No hearing loss units had ITD sensitivity; this could not be accounted for by sounds being below or near-threshold, or having a broadband or a low-pass spectrum, or 194 a potential high-frequency bias in the CFs, or the range of ITDs tested, or monaural sensitivity. Since there was no clear relationship between output spike-timing precision for monaural sounds and ITD sensitivity, we were unable to associate loss of ITD sensitivity with decreased output spike-timing precision.

The lack of ITD sensitivity in binaural neurons did not decrease their sensitivity to azimuths as they still retained ILD sensitivity. A recent study showed that ILD sensitivity is present in low-CF units for narrowband noise centered at high frequencies

(Dorkoski et al., 2020). The sensitivity of low-CF neurons to high-frequency noise may be due to distortion induced by those sounds in the cochlear apical regions. Potential apical distortion products would be expected to be masked by the low-frequency components of broadband sounds, and therefore, low-CF neurons do not show ILD sensitivity for broadband sounds (Fig. 27). In other words, ITD remains the primary cue for representing azimuth in low-CF neurons in response to broadband sounds. However, with hearing loss, low-CF neurons may lose their ability to encode ILDs in response to high-frequency sounds since apical distortions would not be boosted enough to be sensed by surviving IHCs, due to the loss of OHCs.

195

Figure 27

Dependence of ILD Sensitivity on CF in IC Neurons of Normal-Hearing Rabbits

Normalized mutual information in responses of IC neurons (n = 60) to broadband noise (70 dB SPL) that was filtered to allow only ILDs corresponding to azimuths between –90° and 90°, while ITD was fixed at 0°. Sensitivity to ILD increased at higher CFs. Shaded region corresponds to CFs <2 kHz.

The high-CF neurons in the normal-hearing group were sensitive to ITDs, but their azimuth sensitivity was not solely determined by ITDs. The relative sensitivities to

ITDs and ILDs can be complex in the IC (Chase et al., 2005; Chase et al., 2008); ITDs and ILDs can either be exclusively (single dominant cue) or jointly (dual cue) encoded in the firing rate. Consistent with this, our data from high-CF neurons show that azimuth tuning can be shaped by neural sensitivity to either exclusively ILDs (cases where ILD- only tuning curves matched those in ITD+ILD condition) or a combination of ILDs and

ITDs (cases where neither ILD-only nor ITD-only neural tuning curves matched those in

ITD+ILD condition).

Mutual information (MI), which quantifies azimuth sensitivity in neurons, has been previously used to characterize the effect of cue encoding on azimuth sensitivity of neurons for broadband sounds in higher auditory areas, especially those with high-CFs. In 196 the IC, high-CF neurons are not affected by misalignment of the two cues (that is, the

ITD and ILD of a sound are not combined in the way they would be for a sound in the free field) (Slee et al., 2011): MI does not change when cues are misaligned. However, neurons of the brachium of the IC (which receives inputs mainly from the ICC and projects to superior colliculus) are sensitive to the alignment of ITD and ILD. MI is larger when ITDs and ILDs are naturally aligned. This suggests that information about azimuth improves in high-CF neurons when both ITD and ILD match the azimuth (Slee et al.,

2014). Therefore, although no differences may arise in coding azimuths from complete lack of ITD sensitivity in the high-CF IC neurons after hearing loss, reduced azimuth sensitivity may be seen in higher areas, like the brachium.

Psychophysical studies have shown mixed results regarding the relationship of hearing loss with the loss of binaural cue sensitivity, although reports are more consistent for overall decline in azimuth sensitivity with hearing impairment. Listeners with hearing loss had an increase in the error of about 15° compared to normal-hearing listeners and showed errors for all azimuths (Lorenzi et al., 1999a). One very early study showed individuals with hearing loss had elevated ITD discrimination thresholds but had normal

ILD discrimination thresholds (Hawkins et al., 1980). While this appears to match our conclusions, other psychophysical reports (Spencer et al., 2016a) weaken the generality of the (1980) conclusions. While most studies are consistent with an elevation of ITD discrimination thresholds (Dai et al., 2018), discrimination threshold estimates may differ depending on the type of reference sound used and individual hearing loss (Best et al.,

2019). ITD discrimination thresholds increase with the degree of hearing loss (Best et al., 197

2019). It is quite likely that severity of hearing loss may explain the complete lack of ITD sensitivity in our neural sample, in addition to the fact that we used broadband noise as opposed to stimuli that induce greater spike-timing precision. There are also mixed psychophysical results regarding ILD, with some reporting mild increases in ILD thresholds (about 2–8 dB vs. 1 dB normal hearing) and others, no difference (Gabriel et al., 1992; Smith-Olinde et al., 1998; Spencer et al., 2016a).

Noise is a distracting feature that prevents the listener from fully attending to the target. However, with hearing loss, parsing multiple sources is extremely difficult.

Temporal coding following hearing loss deteriorates in noise (Henry et al., 2012).

Elevated ITD discrimination thresholds, which are a psychophysical hallmark of temporal coding, correlate with decreased attentional capacity in listeners with hearing loss (Dai et al., 2018). However, Best and Swaminathan (2019) did not find any increase in ITD discrimination thresholds in the presence of noise, either for speech or narrowband stimuli in hearing-impaired relative to normal-hearing listeners. Again, these differences may arise from the types of stimuli used.

Surprisingly, in quiet, temporal coding in the auditory nerve is generally robust following acoustic trauma, even when tuning curve tips are completely abolished (Henry et al., 2012; Henry et al., 2014; Kale et al., 2010). This means that spike-timing information at the auditory periphery is not fully utilized by the central auditory system, resulting in temporal processing deficits, such as the complete lack of ITD sensitivity in

IC neurons in our study. Since no de novo ITD sensitivity arises in the IC (McAlpine et al., 1998; Wang et al., 2014), the SOC is the main source of ITD information. Thus, the 198 inputs to the SOC or the SOC itself could have caused the lack of ITD sensitivity in the

IC.

We also described a model to test the above implication (Chapter 5). We first simulated the effects of hearing loss using a model of the AN (Bruce et al., 2018) and verified that both ITD and ILD information were available in its responses after simulated hearing loss. Next, we placed the AN model in a feedforward circuit with a noisy synapse to interface with a VCN bushy cell model. One dominant source of noise in the synapse model was jitter in the timing of vesicular release. This synaptic jitter caused a loss of spike-timing precision in the outputs of the VCN model.

The model did not include inhibition. Precisely-timed preceding inhibition may shape ITD sensitivity in LSO (Beiderbeck et al., 2018) likely through post-inhibitory facilitation (Dodla et al., 2006), wherein a short duration of inhibition creates a transient period (sub-ms) of excitability in the neuron, which could aid in potentiating responses to excitatory inputs. MNTB is a major source of this inhibition. Not much is known about the temporal firing properties of this nucleus after acoustic trauma. In the MNTB of aged animals, the normal tonotopic gradient of high-threshold K+ channels (normally low in low-frequency areas and high in high-frequency areas) is lost (von Hehn et al., 2004), and durations of postsynaptic currents and action potentials increase (Grimsley et al., 2014).

Similar widening of EPSCs has been reported in LSO after acoustic trauma, but they return to normal after 2 months (Pilati et al., 2016). It is unknown if such widening of

EPSCs occurs in the MNTB after acoustic trauma and if it is a permanent feature. 199

Functional implications of the reported changes in MNTB with age are not well understood, nor is their translation to acoustic trauma.

The other manipulation that reduced spike-timing precision in VCN was the combined reduction of a low- and a high-voltage-activated (or low-threshold) K+ conductances. KLT conductance is responsible for time-locking to stimuli and coincidence detection in the SOC (Gai et al., 2014; Khurana et al., 2011; Mathews et al.,

2010; Roberts et al., 2013; Svirskis et al., 2002). The KLT conductance improves spike- timing precision by increasing action potential threshold, acts with the hyperpolarization- activated current, Ih, to suppress slow fluctuations in the membrane potential (Rothman et al., 2003), and limits action potentials to the rising or falling phases of the input signal

(Gai et al., 2009; Gai et al., 2014). The KHT channels repolarize membrane potential quickly and support fast temporal processing in the auditory system (Chambers et al.,

2017).

Although there is no direct evidence for the loss of KLT or KHT channels following acoustic trauma, pharmacologically decreasing the activation of high-threshold

K+ conductances (increasing the relative strength of K+ conductances at low membrane potentials using a drug, AUT00063) appears to be an effective method to reduce occurrences of temporally imprecise spiking and at the same time prevents hearing-loss- induced elevations in spontaneous rates (Anderson et al., 2018; Chambers et al., 2017;

Glait et al., 2018; Rybalko et al., 2014). AUT00063 can be injected intraperitoneally and improves synchronization to pulse stimuli in the IC neurons of hearing-impaired mice

(Ouabain-induced cochlear nerve damage, hair cells intact), in vivo, within 15 to 20 200 minutes of injection (Chambers et al., 2017). Although the results are promising with this drug in animal models, it does not appear to translate well to humans with tinnitus (Hall et al., 2019). The efficacy of this drug in enhancing temporal processing is being tested in humans (Carlyon et al., 2018) and its possible benefit to ITD coding has not yet been explored.

Future studies could investigate ITD coding for varying levels of hearing loss to identify the specific degree of hearing loss at which ITD sensitivity is lost. Furthermore,

ITD sensitivity could be measured for other kinds of stimuli that may have better temporal envelopes, such as acoustic clicks. Cochlear synaptopathy (damage to the synapses between IHCs and AN fibers) underlies deficits in speech-in-noise detection and other temporal coding deficits (Bharadwaj et al., 2015; Kujawa et al., 2009;

Parthasarathy et al., 2018), and may affect ITD sensitivity.

It may also be interesting to fit cochlear implants on rabbits with severe noise- induced hearing loss to see if loss of ITD sensitivity, similar to our rabbits, is observed.

Behavioral studies could look at the extent to which ILDs dominate sound localization in rabbits with hearing loss. Most importantly, it is imperative that studies identify the neural mechanism underlying loss of ITD sensitivity in various brainstem nuclei. Several unanswered questions remain: does VCN have increased temporal jitter, in vivo, after severe acoustic trauma? Does MNTB also have increased temporal jitter with severe hearing loss? Do SOC nuclei have reduced KLT channels? Do they receive less inhibition from the trapezoid body after hearing impairment? Finally, it would be interesting to show if primary neurons in LSO still encode ILDs, since they encode ILDs 201 through coincidence detection (Franken et al., 2015). Sound localization is an important function of the auditory system. Its proper restoration in hearing-impaired individuals requires a better understanding of alterations in neural processes at the level of the brainstem and above.

202

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