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Cortical auditory evoked potentials to estimate gap-detection thresholds in adults Angel, Rebecca 2016

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CORTICAL AUDITORY EVOKED POTENTIALS TO ESTIMATE GAP DETECTION THRESHOLDS IN ADULTS  by  Rebecca Angel  B.Ss., Memorial University of Newfoundland 2012  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTURAL STUDIES (Audiology and Speech Sciences)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  October 2016  © Rebecca Angel, 2016 ii  Abstract Temporal resolution is the ability of the auditory system to detect small changes over time and is an important component for the detection and decoding of speech by the central auditory nervous system. Temporal resolution is most often measured by asking a person to detect small gaps between sounds, known as behavioural gap-detection tests. However, certain populations may be unable or unwilling to respond reliably due to perceptual or cognitive deficits, or in medico-legal compensation cases. There are limitations to behavioural gap-detection measures because they cannot separate cognitive from perceptual deficits. The present study utilized electrophysiological gap-detection measures as a means of objectively estimating behavioural gap-detection thresholds. Cortical auditory evoked potentials (CAEPs) are neural responses to changes in sound, duration, and frequency that can be measured from the scalp. The specific aim of this study was to collect adult normative data for CAEP gap-detection thresholds to examine if CAEPs could accurately estimate behavioural gap-detection thresholds. Gap-evoked CAEPs could be recorded in participants who were awake and passively listening and could estimate temporal resolution without the need for a participant’s cooperation or attention. The results showed that there was a significant N1-P2 response to gaps at ≥ 4 ms, with 85% of participants having a response to a 10 ms gap. Additionally, the electrophysiological mean gap-detection threshold was within 1 ms of behavioral mean gap-detection threshold. This demonstrated that gap-evoked CAEPs can accurately estimate behavioural gap-detection thresholds in a normal hearing adult population. iii  Preface  This dissertation is an original product of the author, R. Angel, and thesis supervisor, Anthony T. Herdman. All of the work presented henceforth was conducted in the BRANE laboratory at the University of British Columbia, Vancouver campus. All methods were reviewed and approved by the Behavioural Research Ethics Board of the University of British Columbia. The certificate number of the ethics certificate obtained is #H14-00441                 iv  Table of Contents Abstract .................................................................................................................................................. ii Preface ................................................................................................................................................... iii Table of Contents .................................................................................................................................. iv List of Figures ....................................................................................................................................... vi List of Abbreviations ........................................................................................................................... vii Acknowledgements ............................................................................................................................. viii Dedication ............................................................................................................................................. ix Chapter 1: Introduction .......................................................................................................................... 1 1.1 Temporal Processing ......................................................................................................... 2 1.1.1 Temporal resolution ....................................................................................................... 2 1.2 Central Auditory Processing Disorder .............................................................................. 4 1.3 Traumatic Brain Injury ...................................................................................................... 5 1.4 Behavioural Measures ....................................................................................................... 7 1.4.1 Gap-detection testing ..................................................................................................... 8 1.5 Electrophysiological Measures ......................................................................................... 9 1.5.1 Mismatch negativity ..................................................................................................... 10 1.5.2 Cortical auditory evoked potentials.............................................................................. 11 1.6 Aim of Study and Hypotheses......................................................................................... 14 Chapter 2: Methods .............................................................................................................................. 15 2.1 Participants ...................................................................................................................... 15 2.2 General Study Procedures ............................................................................................... 15 2.2.1 Audiometric assessment ............................................................................................... 15 2.2.2 Behavioural gap-detection testing ................................................................................ 16 v  2.2.3 Electrophysiological gap-detection testing .................................................................. 17 2.3 CAEP Rating Procedure .................................................................................................. 19 2.4 Physiological Temporal Resolution Estimated from CAEPs .......................................... 24 2.5 Statistical Analyses of Rater Judgments ......................................................................... 26 2.5.1 Inter-rater reliability of response judgments ................................................................ 26 2.5.2 Inter-rater reliability of confidence judgments ............................................................. 27 Chapter 3: Results ................................................................................................................................ 28 3.1 Behavioural Measurements of Temporal Resolution ...................................................... 28 3.2 Electrophysiological Measurements of Temporal Resolution ........................................ 28 3.2.1 Percent of participants with CAEPs to gaps................................................................. 28 3.2.2 Rater-determined CAEP gap-detection threshold ........................................................ 29 3.2.3 Group-averaged CAEP gap-detection threshold .......................................................... 29 3.2.4 CAEP and behavioural gap-detection comparisons ..................................................... 31 3.2.5 CAEP confidence ratings ............................................................................................. 33 Chapter 4: Discussion .......................................................................................................................... 36 4.1 Temporal Resolution ....................................................................................................... 36 4.1.1 Behavioural gap-detection thresholds .......................................................................... 36 4.1.2 CAEPs .......................................................................................................................... 37 4.2 Inter-rater Reliability Measures ...................................................................................... 40 4.3 Clinical Implications ....................................................................................................... 42 4.4 Caveats ............................................................................................................................ 43 4.5 Conclusions ..................................................................................................................... 45 Bibliography ........................................................................................................................................ 47  vi  List of Figures Figure 1: Rating for CAEP thresholds ........................................................................................ 19  Figure 2: Percentage of participants with CAEPs at various gap durations ............................... 27  Figure 3: Group-average CAEP waveforms .............................................................................. 28  Figure 4: Individual and group-rater CAEP-to-Behavioural Correlations ................................. 30  Figure 5: ‘Response Present’ confidence ratings ....................................................................... 32  Figure 6: ‘Response Absent’ confidence ratings ....................................................................... 33 vii  List of Abbreviations ANSD Auditory Neuropathy Spectrum Disorder CAEP Cortical Auditory Evoked Potential CANS Central Auditory Nervous System CAPD Central Auditory Processing Disorder dB deciBel EEG Electroencephalography ERP Event Related Potential MMN Mismatch Negativity TBI Traumatic Brain Injury   viii  Acknowledgements  I owe particular thanks to my supervisor, Anthony T. Herdman, who inspired and encouraged me to pursue research in this field. He provided me with the confidence I needed to begin and finish this endeavor. I would also like to offer gratitude to my thesis committee, Navid Shahnaz and Sharon Adelman, for providing endless support throughout my time at the School of Audiology and Speech Sciences. Thank you to Susan Small and Osamu Takai for their additional assistance during my data collection and analysis.  I would also like to thank my former classmate and colleague, Rae Riddler for her inspiration and constant reassurance.  Lastly, thank you to Hamish Elliott for his continuous support and positive attitude throughout this process.  ix  Dedication  To my family: Each of you has inspired me in a unique and creative way.  I owe my accomplishments to your continuous love and support. 1  Chapter 1: Introduction Speech understanding is the primary basis of most human communication. However, the neural processes that underlie perception of complex acoustic signals, such as speech, are not well understood. Speech consists of spectral and temporal features with a number of acoustic cues (Katz, 2009). Acoustic cues of speech play a vital role in speech perception, which include formant structure, periodicity, frequency transitions, acoustic onsets, and the speech envelope (Kats, 2009). The central auditory nervous system (CANS) must be able to resolve and integrate this information into a meaningful message that the human brain can comprehend. Understanding how the auditory nervous system does this accurately and efficiently could help us to better understand the underlying basis of speech and language development. The speech signal is constantly changing over time, and the auditory system must be able to separate and integrate these temporal changes (Viemeister & Wakefield, 1991). This is referred to as temporal processing, a skill that is important for accurate speech perception. Consequently, if there is a deficit in processing along the auditory pathway, individuals may have difficulty with listening and language skills. For example, stop consonants change their acoustic properties very quickly and are distinguishable in terms of place and manner of articulation (Tallal, Miller, & Fitch, 1993). Voice onset time (VOT) is a defining feature in the production of stop consonants comprised of the time between the release of the stop consonant and the onset of voicing or vibration of the vocal folds (Cho & Ladefoged, 1999). This distinguishing feature enables differentiation between similar consonants like /da/ vs. /ta/ or /ba/ vs. /pa/ (Holt & Lotto, 2010). Our auditory systems must be able to decipher and integrate voice onset time and other important acoustic 2  information; otherwise consonants, phonemes, or syllables could be improperly interpreted. With continuous speech, syllables are generated about every 200 to 400 ms and the temporal structure within each syllable or phoneme contributes significantly to speech understanding (Mauk & Buonomano, 2004). Precise and accurate processing of the timing in the various elements of sound is crucial for phonemic distinctions, and in turn, speech understanding.  1.1 Temporal Processing Subcategories of temporal processing include temporal masking, temporal ordering, temporal integration, and temporal resolution (Musiek et al., 2005). Temporal integration and temporal resolution have similar defining features. Temporal integration is the combination or integration of acoustical information across a period of time (Yost, 2007), whereas temporal resolution refers to the sensitivity of the auditory system to detect small acoustical changes over time (Viemeister & Wakefield, 1991). The auditory system must have the capacity to combine complex acoustic signals while resolving very fine temporal details. The temporal envelope of a sound or how it changes in amplitude over time is an important component to speech understanding (Mauk & Buonomano, 2004). Beyond a peripheral hearing level, the brain has to analyze the sound and then integrate that information in order to localize, separate auditory stimuli, and follow the speech signal (Mauk & Buonomano, 2004). The concept of temporal resolution will be the main focus of this study.  3  1.1.1 Temporal Resolution Temporal resolution is an important characteristic of the central auditory nervous system (CANS) and aids in its ability to segregate acoustic information such as phoneme discrimination, duration discrimination, and prosodic distinction necessary for accurate decoding of speech (Chermak & Musiek, 1997; Viemeister & Wakefield, 1991). Accurate perception of auditory stimuli is dependent on the separation of meaningful information embedded in neuronal activity in subcortical nuclei and cortical areas in the brain (Cacace, 2009).  The central auditory pathway consists of a complex network of nuclei interconnected in the brainstem, thalamus and cerebral cortex (Cacace, 2009). The integration of auditory information occurs in these multiple ascending and descending pathways. In a review on the behavioural, electrophysiological and theoretical literature on the neural basis of temporal processing by Mauk & Buonomano (2004), it is stated that temporal resolution is required for simple to complex forms of sensory processing. Given the complex nature of speech perception, disruptions anywhere along the processing pathway could lead to distortions in the speech signal. This deficit is further exacerbated by the presence of competing signals or background noise (Schoof & Rosen, 2014). Background noise makes analyzing and integrating the speech signal even more difficult. As we know, speech is often present with competing noise therefore our auditory systems have to learn to segregate important acoustic signals, like speech, from competing signals, like background noise. Inaccurate representations or misunderstandings of speech are known to occur in patients with developmental or acquired temporal processing problems (Trainor, Samuel, Desjardins & Sonnadara, 2001). These populations could include individuals with auditory neuropathy spectrum 4  disorder (ANSD) or central auditory processing disorder (CAPD). It is also known to occur in people with dyslexia or specific language impairments, as well as the aging population and individuals following a traumatic brain injury (TBI) (Bertoli, Smurzynski, & Probst, 2002a; Dawes & Bishop, 2009; Hoover, Souza, & Gallun, 2014; Klein & Farmer, 1995; Musiek et al., 2005). For the purpose of my thesis, auditory temporal processing with respect to CAPD and TBI will be further discussed in the following paragraphs.  1.2 Central Auditory Processing Disorder Even individuals with normal peripheral hearing abilities can experience impairments with auditory processing that can lead to difficulty with listening and communication(Chermak, Tucker, & Seikel, 2002; Musiek, Chermak, Weihing, Zappulla, & Nagle, 2011). CAPD is a disorder of the central auditory nervous system and can effect various neural and perceptual processes such as: sound localization, auditory discrimination, pattern recognition, temporal aspects of audition, and auditory performance with degraded or competing acoustic signals (Chermak et al., 2002). Consequently, people with CAPD may have difficulty following directions, paying attention, and associated reading, writing, and other learning deficits. The prevalence of CAPD has yet to be determined, however, it is estimated that CAPD affects 2 to 3% of children and 10 to 20% of adults will have a deficit on various tests used for diagnosing CAPD (Chermak & Musiek, 1997; Cooper & Gates 1991). Central auditory processing test batteries consist of a combination of behavioural tests, which include the following categories: temporal processing, dichotic digits, auditory discrimination, binaural 5  integration, and monaural low redundancy tests. What makes diagnosis of CAPD difficult is the overlap that it has with other clinical entities (Cacace & McFarland, 2009; Dawes & Bishop, 2009; Moore et al, 2010). It has been suggested that CAPD co-exists with other neurological or developmental disorders (Bamiou et al., 2001), such as specific language impairment, dyslexia, or attention deficit/hyperactivity disorder (ADHD). In particular, CAPD and ADHD have overlapping symptoms (Keller, 1992). Children with ADHD or CAPD may be easily distracted, inattentive, have poor listening skills and difficulty following directions. It can be difficult to tease the two disorders apart, and can sometimes coexist. Chermak et al (1999) suggested that despite the argument that CAPD and ADHD are comorbid or a single distinct developmental disorder, the inattentive deficits in each condition result from different sites of processing. Given the overlap in symptomology of CAPD and ADHD, there is a need for a collaborative approach from professionals who work with this population (psychologists, speech-language pathologists, and audiologists) in diagnosing each condition. Furthermore, a means of assessing auditory temporal processing while eliminating attention problems will lead to a more definitive diagnosis of CAPD. 1.3 Traumatic Brain Injury Acquired auditory processing disorders in adults can occur as a result of neurological disorders and insults, such as concussions or TBI. A topic of interest that is lacking in research is TBI and its effect on hearing ability. Temporal processing and TBI research dates back to 1926 when researcher Henry Head studied First World War veterans and discovered many of them had deficits in auditory perception. Today in the United States, TBI affects nearly 1.4 million people each year (CDC, 2006). Common 6  causes of TBI are motor vehicle accidents, assaults, falls, gunshots, or blast waves (CDC, 2006). TBI patients often express a variety of concerns including but not limited to: headaches, dizziness, noise sensitivity, anxiety, fatigue, memory and concentration problems, as well as other auditory complaints (Bergemalm et al., 2005). Although it is known that TBI can cause damage to the auditory pathway anywhere from the outer ear to the cortex, few studies have investigated temporal processing deficits associated with TBI. Taber et al (2006) stated that auditory processing areas of the temporal lobe are a common site of lesion following TBI. Central auditory effects following TBI have not been systematically studied in humans, but we know that if the organization of certain neural pathways is disrupted, the ability to localize sound and make temporal distinctions is greatly reduced (Mauk & Buonomano, 2004; Hoover et al., 2014; Mioni et al., 2014). We also know that this impairment is further exacerbated in the presence of competing stimuli (i.e., background noise; Hoover et al., 2014). Difficulty understanding speech in background noise is a common auditory complaint among TBI patients, as high as 89% according to Oleksiak and colleagues (2012). Oleksiak et al (2012) studied war veterans with mild traumatic brain injury using an extensive CAPD test battery. They discovered that there was low referral rate to audiology with an alarmingly high percentage of hearing related issues. Central auditory effects following a traumatic injury can often be missed or overlooked because despite having normal peripheral hearing sensitivity (i.e., normal pure-tone thresholds), an individual may have limited availability of temporal cues and this can distort the speech signal. It is possible that auditory complaints following TBI are because of an impaired perception of temporal cues (Bergemalm et al., 2005; Munjal et al., 2010). 7  The auditory implications of sport-related concussion are another important topic to consider given the high proportion of concussions that happen each year. Langlois et al (2006) reported an estimated 1.6 to 3.8 million concussion injuries occur in the United States every year. Turgeon et al (2011) assessed auditory processing in sixteen university athletes, eight of who had reports of sport-related concussions. The researchers found that concussed athletes had deficits for one or more auditory processing tests while all non-concussed athletes had normal results. Further investigation of temporal processing and concussion could provide insight into some of the issues that accompany multiple concussion related injuries. Evaluating and assessing temporal processing or CAPD requires the use of extensive and complex behavioural tasks. The discovered deficits from these tests could be unrelated to audiological deficits alone. In these populations, questions remain whether impairments are specific to the auditory system or a combination of audition and cognition (i.e., memory, motivation, concentration, or attention). Given the difficulty in teasing apart auditory problems from coexisting cognitive or developmental issues, it is important for audiologists to isolate auditory from cognitive confounds, and few studies have looked at objective measures of temporal processing in these vulnerable or complex populations.  1.4 Behavioural Measures Three ways to assess our temporal processing abilities are through detection of amplitude-modulated signals, recognition of double versus single stimuli, and detection of transient gaps in sounds(Picton, 2013). Measures of temporal processing provide 8  insight into the integrity of the central auditory nervous system and speech perception abilities(Picton, 2013). Temporal resolution is typically assessed through a psychoacoustic measurement known as gap detection. Gap-detection tests can evaluate auditory temporal resolution by asking an individual to detect small gaps between sounds (Musiek, 2005; Keith, 2000).   1.4.1 Gap-Detection Testing Behavioural gap detection tests provide a feasible means for evaluating temporal acuity and provide information about the integrity of the CANS. In order to process silent gaps between sounds, the auditory system must be able to detect a difference between the presented stimuli and the silent period (Phillips, 1999). Behavioural gap-detection requires a listener’s ability to identify the smallest gap interval between sounds (Fitzgibbons & Wightman, 1982). Two commonly used clinical gap-detection tests are Gaps-in-Noise (GIN) test (Musiek, 2005) and Random Gap Detection test (RGDT; Keith, 2000). Both are measuring temporal resolution, but differ in stimulus presentation, method of presentation, response acceptance, gap durations, normative data and testing time (Chermak and Lee, 2005). During the RGDT, tones are presented with gaps from 0 to 42 ms, and patients are asked to identify whether they heard one or two sounds (Keith, 2000). Stimuli used in gap-detection testing are often presented using pure-tones, white noise, or narrow band noise.  For the GIN test, patients are asked to respond to a gap embedded in a 6 second noise burst. The gaps range from 0 to 20 ms at random various durations. The GIN test has supporting evidence for a sensitivity of 67% and a specificity of 94% for detecting central auditory nervous system pathology (Shinn, Chermak & 9  Musiek, 2009). Research has shown variability in gap-detection thresholds across normal hearing populations. For inexperienced listeners, average gap-detection thresholds range between 2-7 ms, and can be as high as 20 ms when sounds are presented near threshold (Musiek et al., 2005; Zeng et al., 1999). Behavioural gap-detection thresholds can often easily be obtained in normal hearing populations, however, there are factors such as memory, cognition, motivation, attention, and hearing loss that can affect behavioural testing procedures (Lister et al., 2007). Behavioural gap-detection testing cannot determine whether a patient has challenges detecting the gaps or other cognitive challenges such as paying attention during the testing. As previously stated, cognitive impairments might mask underlying perceptual deficits and it is difficult to tease apart cognitive from perceptual deficits using behavioural measures alone. An alternative method for determining gap-detection thresholds could be to use electrophysiological measures. Cortical auditory evoked potentials (CAEPs) can measure neural detection of a change in an acoustic stimulus (Lister et al., 2007). CAEPs to gap-detection may be a good alternative to behavioural measures alone for diagnosing temporal processing deficits. Current behavioural paradigms cannot distinguish between perceptual and cognitive deficits therefore we need a method to assess auditory temporal resolution that does not rely on behavioural measures alone.  1.5 Electrophysiological Measures Electrophysiological recordings to auditory stimuli provide insight into the different structural and functional processing of the auditory system. Auditory evoked potentials have been extensively studied and used to assess human auditory systems 10  (Picton 2010). Auditory evoked potentials can measure millisecond-by-millisecond neural activity of auditory neurons and can help in the diagnosis and monitoring of temporal processing disorders (Picton, 2010).  Event related potentials (ERPs) are brain-generated small voltages in response to stimuli (Blackwood & Muir, 1990). It is a safe and non-invasive method to studying psychophysiological processes. Measuring gap-detection thresholds using electrophysiological measures holds promise as an objective tool to estimate gap-detection thresholds. Below is a brief discussion on auditory electrophysiological recordings for the purpose of measuring temporal processing. 1.5.1 Mismatch Negativity Mismatch negativity (MMN) has been used as an objective tool for measuring gap detection thresholds. MMN is an evoked potential observed when a deviant stimulus is presented in the middle of standard stimuli. Previous studies have shown that MMN to gaps can elicit a response to gaps of 4 msec (Desjardins, Trainor, Hevenor, and Polak, 1999). Bertoli, Smurzynski & Probst (2002) also recorded MMN to gaps, but stated a number of limitations to using MMN. These include the clinical testing time, the reliability MMN measures, poor signal-to-noise ratio, and the lack of recording and measurement parameter protocols (Stapells, 2009).  Bertoli et al (2002; 2005) also discovered that although MMN revealed responses to gaps, they were often much higher than behavioural thresholds. Although middle latency responses (MLRs) and MMN have been reported in the assessment of temporal processing, they have not been extensively used for objective assessment of temporal resolution (Michaelewski, 2005).  11  1.5.2 Cortical Auditory Evoked Potentials Cortical auditory evoked potentials (CAEPs) are electrophysiological responses recorded from the scalp, which can be used to measure neural detection of a change in an acoustic stimulus (Lister et al., 2007; Martin & Boothroyd, 1999). CAEPs are neural responses to changes in sound, duration, and frequency that can be measured from the scalp (Picton, 2011) and have the potential to distinguish between perceptual and cognitive deficits.  The P1-N1-P2 response can also be referred to as an acoustic change complex (ACC) is associated with the behavioural detection of frequency, intensity, and temporal changes of an acoustic stimulus (Picton, 2011). In adults, a P1-N1-P2 response consists of a positive wave at approximately 50 ms (P1), and negative wave between 80-100ms (N1) and a second positive wave at 180-200 ms (P2; Stapells, 2009). CAEPs can be recorded under active and passive conditions and therefore might be clinically useful for assessing gap-detection thresholds in patients who are unable to reliably respond behaviouraly (Pratt et al., 2005). Some research has investigated CAEP testing as an objective measure of gap-detection thresholds; however, small sample sizes (n<12) have been used amongst various populations (Palmer & Musiek, 2013; 2014; He, Grose & Buchman, 2012; He et al, 2013; Michaelewski et al., 2005; Pratt et al., 2005; Atcherson et al, 2009). A selection of relevant studies are discussed in more detail in the following paragraphs. Michaelewski et al (2005) investigated temporal processing using electrophysiologic and psychoacoustic gap-detection measures in both normal hearing adults and hearing-impaired auditory neuropathy patients. Normal hearing listeners had an N1-P2 response to gaps at 5 ms whereas ANSD listeners required longer gap durations 12  that varied from 10-50 ms. Psychoacoustic behavioural gap detection thresholds were in good agreement with electrophysiological gap-detection thresholds.  However, sample size was small (n=12) and there was a lot of variability in the ANSD population, with some subjects not having an evoked response to any of the gap durations. Other studies have also shown auditory neuropathy patients to have gap detection thresholds as large as 80 ms (Kraus et al, 2000; Starr et al, 1991; Zeng et al, 1999). Although this study shows that CAEPs can be used to evaluate gap-detection in both passive and active conditions, further research is needed to establish normative electrophysiological gap-detection thresholds in normal and abnormal populations. A more recent study by Palmer & Musiek (2014) looked at gap detection thresholds in younger and older normal hearing adults using electrophysiological and behavioural measures. Electrophysiological gap-detection threshold and behavioural gap-detection threshold were within 2 ms of each other for both groups, demonstrating that electrophysiological gap detection can accurately predict behavioural gap-detection threshold. Furthermore, older adults showed an elevated gap-detection threshold compared to younger adults. However, similar to other studies of cortical auditory evoked gap-detection thresholds, sample size is small and there is a lack of normative data for comparison. Lister et al (2007) examined the P1-N1-P2 response using within-channel (identical spectral markers) and between-channel (spectrally difference markers) gap-detection parameters. Within-channel gap detection paradigms consists of silent gaps within noise bands of similar frequency while between or across-channel gap detection have noise bands of different frequencies surrounding the gap, making the task more 13  difficult and resulting in larger gap-detection thresholds (Lister & Roberts, 2005). Between-channel gap detection is thought to be a more realistic and representative measure of temporal resolution because temporal cues in phonemes in speech are never identical. They found that gap-detection thresholds were significantly larger for between-channel markers. They also found that for between-channel, the P1-N1-P2 response is present at a gap duration that is well below threshold representing the change in frequency rather than detection of a gap. For the purpose of the present study, we want to isolate the gap-evoked response by using identical markers on either side of the gap for the purpose of within-channel adult normative data. Future studies could investigate between-channel differences.  Studies examining CAEPs to gap-detection lack large sample sizes and tend to differ in stimulus parameters and protocol, making it difficult to generalize results to all normal adult populations. Although it is clear that the P1-N1-P2 response to changes in frequency, intensity, and duration correspond well to psychoacoustic or behavioural measures (Palmer & Musiek, 2013; 2014; Pratt et al, 2005; Michaelewski et al, 2005; Lister et al, 2007; Atcherson et al, 2009) the sample sizes demonstrating these effects are small. Furthermore, normative data for electrophysiological evoked responses to gaps-in-noise is not yet available. We need to understand the normal neural representation of psychophysical gap detection before we begin to explore populations with diagnosed temporal resolution deficits, such as in older adults, individuals with ANSD, CAPD, language impairments and TBI patients. Further systematic investigation of electrophysiological gap detection thresholds is needed for clinical application. CAEPs 14  were selected for this study because they have been previously studied, are clinically used to evaluate hearing thresholds, and can be recorded in reasonable clinical testing time.   1.6 Aim of Study and Hypotheses The current study evaluated the accuracy and precision of gap-evoked CAEPs to estimate behavioural gap-detection thresholds in a normal adult population. The main objective of this study was to collect a large sample of normative data for CAEP gap-detection thresholds in normal-hearing healthy adults. I hypothesized that mean CAEP and behavioural gap-detection thresholds will be similar (within 3 ms of each other) and that the variability of CAEP gap-detection thresholds will be minimal (standard deviation < 4 msec). In addition, I hypothesized that more than 80% of the participants will have CAEP gap detection thresholds less than or equal to 10 ms. This data holds the potential for determining the validity of using CAEPs to estimate gap-detection thresholds in normal adults. These results will help inform future studies investigating the clinical validity in using CAEPs to diagnose temporal processing problems, such as those that can occur in CAPD.   15  Chapter 2: Method  2.1  Participants The proposed study consisted of 47 right-handed participants (31 females) ages between 18-40 years old. Participants had normal hearing with no reported history of: perceptual or cognitive problems, suspected and/or diagnosed learning or communication disorders, no head injury or neurological defects, otitis media, nor ototoxic medications. The study was reviewed by the Behavioural Research Ethics Board at the University of British Columbia and all participants provided informed consent following explanation of the study procedures. Participants received an honorarium for their time.   2.2  General Study Procedures 2.2.1 Audiometric assessment Otoscopic examination was performed on each subject prior to starting the test. Tympanometry was conducted using a conventional 226-Hz prove tone to determine normal middle-ear functioning. Middle-ear functions were normal based on otoscopic examination and immittance measurements. Pure-tone air conduction hearing screening was conducted using ER-3A insert earphones in a soundproof booth to ensure normal hearing sensitivity of less than 20 dB HL between 0.5 to 4 kHz bilaterally. Testing began with a behavioural gap-detection task followed by electrophysiological recordings. Testing took place in the BRANE lab at the University of British Columbia (UBC). Total session time was approximately 2.5 hours, with breaks allotted when needed. Behavioural and electrophysiological procedures are explained below. 16  2.2.2 Behavioural gap-detection testing Stimuli. Auditory stimuli for measuring behavioural gap-detection thresholds were broadband noise pulses of 1-second duration with randomly selected gaps of 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, and 20 msec inserted at 500 msec after noise onset. To compensate for the duration of the gaps, the noise burst following the gap was reduced by the duration of the gap. Thus, the total duration of the stimulus (noise + gap duration) was one second for all stimuli. This was done in order to minimize the chance of participants using overall stimulus duration as a cue for detecting gaps. Stimuli were generated using custom Matlab program with a sampling rate of 44,000 Hz. Stimuli were sent from a computer sound card into an audiometer (Interacoustics) and then delivered to the participant’s left ear through an ER-3A insert earphone at 60 dB SPL. Stimuli were calibrated using a SoundPro© sound level meter (SoundPro©). The right ear was occluded with a foam earplug to minimize ambient stimulus energy crossing over to the non-test ear.  Task. The gap-detection task was completed in a sound-treated booth while a participant remained awake and seated in a comfortable chair. Behavioural gap detection measures were approximated using an adapted manual staircase design. Participants listened to a two second burst of noise with gaps varying in duration from 2 to 20 msec. Participants were asked to raise their hand when they detected a gap in the auditory stimuli. When the participant correctly identified two consecutive gaps of the same duration, the gap duration was decreased using 1 ms step sizes. When the participant incorrectly identified or did not respond to a gap, the gap duration was increased. The procedure was similar to that of Hughson-Weslake pure-tone audiometric threshold 17  search. A participant’s behavioural gap detection threshold was defined as the smallest gap that they could detect 4 out of 6 presentations. I chose this procedure over the Gaps-in-Noise Test (Musiek, 2005; Palmer & Musiek, 2013; 2014), to reduce the time required to estimate behavioural gap-detection thresholds. Although my procedure had not been formally evaluated, pilot testing of four participants revealed very similar results to the GIN test results. To minimize participant fatigue, I opted to use the Hughson-Weslake procedure because it takes less than 5 minutes to perform as compared to the 20-30 minutes for GIN testing. Analysis. Behavioural gap detection thresholds were considered the smallest gap duration a participant could detect in 4 of 6 presentations. The mean and standard deviations were calculated and recorded for all participants.  2.2.3 Electrophysiological gap-detection testing Stimuli. To obtain CAEP gap detection thresholds, stimuli consisted of two 500-ms broadband noise bursts separated by randomly assigned gaps with durations of 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, and 20 msec. The noise burst duration following the gap was reduced by the duration of the gap so that the total duration of the stimulus was 1 second for all stimuli. Stimulus onset asynchrony was randomly set between 1850-2150 ms to obtain clear stimulus onset responses. The stimuli were calibrated using a SoundPro© sound-level meter (SoundPro©) and were presented at 60 dB SPL to a participant’s left ear while the right ear was occluded with a foam earplug. One hundred stimulus trials per gap duration for CAEP testing were randomly presented.  18  Task. Participants were seated in a comfortable chair located in a sound attenuated-booth. EEG signals were continuously recorded using an ActiView2 64-channel system (BioSemi, the Netherlands) while the participants were alert, ignoring the stimuli, and watching a closed-caption movie of their choice.  Recordings. Data were collected using a BIOSEMI (www.biosemi.com) from 64 channels arranged in an expanded 10-20 system. Electrode caps were suitable for each participant’s head size as determined by head circumference and measuring the nasion, inion, Fz, Pz, T8, and T9 electrode-cap positions. The 64 scalp channels were referenced to a common electrode placed between CPz and CP2; and later referenced to linked mastoid electrodes for offline analyses. Additional bipolar electrodes were placed near the right and left outer canthi (horizontal electrooculography) and infra- and supra-orbital margins (vertical electrooculography) to record and aid in identification of eye movements and blinks. EEG signals were amplified and sampled at a rate of 1024 Hz with a band-pass filter of 0.16 to 208 Hz.  Evoked potential analysis. The continuous EEG was filtered using a IIR bandpass filter between 1 to 20 Hz. Event-related potentials (ERPs) of -500 to 1500 ms were time locked to the onset of the first noise burst thus the gap-evoked CAEPs were time-locked to the gap onset at 500 ms. Trials with ERPs exceeding ±100 microV between -250 to 1000 ms were rejected from further analyses. A principal component artefact reduction procedure with a principal component threshold of ±100 microV between -1000 to 2000 ms was performed in order to reduce the rising and falling edges of artefacts that might remain within the interval of -250 to 1000 ms window (Picton et al., 2000). The remaining artefact free trials were then split into two buffers for each gap duration (odd-19  numbered trials and even-numbered trials). These buffers were then averaged to obtain two ERP replications for each gap duration. Difference ERPs were also calculated for a priori defined contrasts of each gap duration minus the zero-gap duration (e.g., gap2 minus gap0, gap4 minus gap0, etc.). These replications and difference ERPs were plotted for electrode Cz recordings so that raters could judge wether or not a gap-evoked CAEP was present between 550-800 ms after noise burst onset (i.e., 50-300 ms after gap onset). All data from participants were included in a bank of ERPs so that raters could judge wether gap-evoked CAEPs were present or absent (see rating section below for more details).  2.3 CAEP Rating Procedure Three experienced raters volunteered to view a bank of ERPs recorded at electrode Cz for the presence/absence of an N1-P2 response. The ERPs were displayed on a computer screen in the following format (see Figure 1). Two replications and the average no-gap ERPs were presented as thin and thick gray lines, respectively. On top of these no-gap ERP waveforms, two replications and the averaged gap-evoked ERP were presented as thin and thick red lines, respectively. The gap duration for the gap-evoked ERPs were not provided to the raters and thus the raters were blinded to the gap condition. The difference ERPs between the no-gap and the gap-evoked ERP were presented as blue lines below the standard ERPs. Two vertical dotted lines at 500 and 800 ms designated the interval in which gap-evoked CAEPs would likely occur. This helped guide the raters when looking for gap-evoked CAEPs. The raters were instructed to also consider the ERP replications and difference ERPs that occurred outside this interval in 20  order to rate replicability and residual noise levels. Raters were instructed to rate their confidence in making a gap-evoked CAEP present or absent judgment on a five-point scale (very poor, poor, adequate, good, and excellent). This rating was included to gain insight into how confident the raters felt in making their judgments for all gap duration conditions. Raters also were instructed to rate the replicability of the ERP replications for the gap conditions (i.e., amplitude differences across time) on a five-point scale (very poor, poor, adequate, good, and excellent). This provided information regarding the rater’s judgment of the residual noise within the replications that they were basing their judgment on. In addition, the raters also had the option of rating the ERP as “reject” if they felt there was too much residual noise or too poor of an onset CAEP to have any level of confidence in their judgments. Thus, instead of rejecting participant ERP data before entering them into the bank of ERPs to be rated, all participants’ data were included in the bank of ERPs for rating. A participants’ data was rejected from further analyses if any gap-condition ERP was designated as “reject” by a rater. This occurred for 11 participants. Thus, the final accepted data was from 36 participants.   Overlapping the gap and no-gap CAEPs was done to help the raters see the participant’s onset ERP fluctuations and late-latency CAEPs (P3 and sustained components) that occurred within the time interval of 500 to 800 ms (where the gap-evoked CAEPs might occur). The benefit of doing this helped the raters better identify gap-evoked CAEPs but the cost is that raters could be biased in knowing that every ERP displayed comes from a condition which was evoked by a gap stimulus. Thus, raters would be more likely to judge a gap-evoked CAEP to be present even though gap-evoked CAEPs were not clearly evident in the replications. To help avoid this bias, simulated 21  CAEPs to ten no-gap conditions were created for an additional 47 sham participants and included in the bank of ERPs for raters to judge. This yielded a probability of 470 simulated CAEPs to no-gap stimuli (50% no-gap conditions) and 470 real CAEPs to stimuli with 2 to 20 ms gaps (50% gap conditions) in the bank of CAEP trials. This reduced the raters’ bias based on gap-condition probability. Simulated CAEPs to no-gap stimuli were created for a Cz recording by modeling the P1, N1, P2, and sustained potential (SP) components using a Gaussian window function in Matlab. For each sham participant, a unique CAEP was simulated by independently jittering the latencies of each P1, N1, P2, and SP components. Each component waveform was also multiplied by a factor to yield an amplitude that was randomly assigned to be a within 5 to 15 microvolt range. The following describes the parameters used to simulate each CAEP component. The P1 component (onset and offset responses) was simulated using a 3-point Gaussian window (three standard deviation width of its Fourier Transform) with a duration of 80 ms that began at 40 ± 10 ms post-stimulus onset and offset. The N1 component (onset and offset responses) was simulated using a Gaussian window (three standard deviation width of its Fourier Transform) with a duration of 100 ms that began at 50 ± 10 ms post-stimulus onset and offset. The P2 component (onset and offset responses) was simulated using a Gaussian window (two standard deviation width of its Fourier Transform) with a duration of 140 ms that began at 80 ± 10 ms post-stimulus onset and offset. The SP component was simulated using a Gaussian window (two standard deviation width of its Fourier Transform) with a duration of 1000 ms (duration of original no-gap stimulus) that began at 25 ± 10 ms post-stimulus onset and offset. The P1, N1, P2, and SP components were then summed to generate a 22  simulated CAEP unique for each sham participant. Each sham participant’s CAEP was then temporally jitter by a random latency between -10 to 10 ms for each of the 100 trials. In addition, artificially generated brain noise was added to each trial using a custom Matlab program designed to simulate the power distribution of real brain noise. The noise was gained for each trial in order to yield an overall signal-to-noise ratio that was randomly assigned to yield a signal-detection ratio (SDR; Picton et al., 1983) between 1.0 and 5.0 for the onset CAEPs between 0-300 ms. The SDR was calculated from two replications of the 50-trial averages for each no-gap condition. These simulated CAEPs had variable waveform morphologies, component latencies, and component amplitudes similar to real participant’s CAEPs.              23  Figure 1.  Rating for CAEP thresholds.  Figure 1. Waveform display that was presented to raters for rating participant CAEPs. The thick pink line represents the averaged-trials ERP to a particular gap stimulus. The gap duration was not known to the rater. The thin pink lines represent two replications of half the averaged-trials ERP shown by the thick pink line. The thick grey line represents averaged-trials ERP to the no-gap (control) stimulus. The thin grey lines represent two replications of half the averaged trials shown by the thick grey line.  The blue line represents the ERP difference between the no-gap and gap conditions. The hashed vertical lines at 500 and 800 ms represented interval when a gap-evoked ERP could possibly occur. The SDRon and RNon values were the standard-deviation ratio and residual noise estimates, respectively, for the onset ERP between 0-300ms. The SDRgap and RNgap were the estimates between 500-800 ms for the gap condition. The SDRdiff and RNdiff were the estimates for the difference ERP between 500-800ms. The y-axis scale for ERP amplitude was in microVolts and the x-axis scale for time was in milliseconds.  24  2.4 Physiological Temporal Resolution Estimated from CAEPs Individual-rater determined CAEP gap-detection threshold. Each rater made response judgments on all participants; therefore, I was able to obtain each participant’s CAEP gap detection thresholds as judged by each rater. A participant’s CAEP gap-detection threshold was considered to be the smallest gap duration that was judged by a rater to have evoked a CAEP within the 500-800 ms interval. This yielded a data matrix of CAEP gap-detection thresholds for each participant (n=36 after rejection) by each rater (n=3). This matrix was also used to evaluate the inter-rater reliability on judging CAEP gap-detection thresholds.  Grouped-rater determined CAEP gap-detection threshold.  In addition to individual-rater determined CAEP gap-detection thresholds, I used the raters’ response judgment to estimate a group-rater determined CAEP gap-detection threshold. A group-rater CAEP gap-detection threshold was defined as the lowest gap duration that was judged to evoke a CAEP for each individual participant by at least two of the three raters. This yielded a group-rater CAEP gap-detection threshold for each participant. These were then used to obtain the mean and standard deviations of the group-averaged CAEP gap-detection threshold for all participants. Group-averaged waveform analyses and statistics. I also obtained an objective estimate of a group-averaged CAEP gap-detection threshold by using each participants (n=36 after rejection) ERP waveforms to each gap duration. I statistically compared, across participants, the ERP waveforms for each gap condition to the ERP waveforms for the no-gap condition using Student’s t-test (a priori) at each sample point between 500-800 ms. To minimize the risk of finding multiple false positives due to multiple t-tests, I 25  performed a false-discovery rate correction as described by Benjamin & Hochberg (1995).  This approach addresses the errors that can occur with multiple significance testing, similar to controlling for the ‘familywise error rate (FWER)’. The procedure helps to control for the false-discovery rate for independent test statistics (Benjamin & Hochberg, 1995). Statistical analyses were performed using the Matlab statistics toolbox and custom software statistics developed in the BRANE lab. The smallest gap duration that was found to evoke a CAEP significantly different from the no-gap condition was determined to be the objective group-averaged CAEP gap-detection threshold. Electrophysiological gap-detection validity. Because behavioural gap detection thresholds are considered the current gold standard for evaluating auditory temporal resolution, I compared the means and standard deviations of the CAEP and behavioural gap-detection thresholds. Electrophysiological gap-detection thresholds were compared with behavioural gap-detection thresholds to derive physiological to psychophysical gap-detection threshold correction factors. Behavioural and CAEP gap-detection thresholds were also correlated using a Pearson’s correlation to evaluate the concurrent validity of using gap-evoked CAEPs and behavioural tests to assess auditory temporal resolution in normal adults. This was not an a priori determined analysis and thus the study design was not intended to provide evidence for correlations between behavioural and CAEP gap-detection thresholds. Therefore a challenge with this study design was that I collected data from participants with normal auditory temporal resolutions. The behavioural gap-detection thresholds had small variability as compared to the CAEP gap-detection thresholds. Having a small variability within the predicted results is known to cause a restriction of range problem that can reduce the inferences made from correlation results. 26  A Pearson correlation was computed to determine whether a significant relationship existed between CAEP and behavioural gap-detection thresholds for each individual rater. A group-rater CAEP gap-detection threshold correlation was also computed. A significant CAEP was considered present when 2 out of 3 raters judged a ‘response present’. Concurrent validity was considered to be strong in the CAEP-to-behavioural gap-detection threshold correlation is > 0.7.  2.5 Statistical Analyses of Rater Judgments 2.5.1 Inter-rater reliability of response judgments To determine the agreement among raters, I first performed a percent agreement among raters. To do this, the number of ratings in agreement was divided by the total number of ratings, and then this fraction was converted into a percentage. However, this method does not account for the amount of chance that can occur when raters have to make a guess due to uncertainty on some ratings (Fleiss & Cohen, 1973).  In addition, I performed an inter-rater reliability analysis using a Light’s Kappa statistic (Light, 1971). This k statistic can be used for 3 or more raters by computing a kappa for all coder pairs. The mean of these estimates is used to provide an overall index of agreement. This kappa statistic extension comes from the idea of taking pairs of raters’ judgment of the condition, and seeing whether both raters assign the condition to the same or different categories (Light, 1971). Inter-rater reliability was computed for raters 1-2, 1-3, and 2-3, as well as an overall group analysis by averaging together the inter-rater reliabilities (Light, 1971). 27  2.5.2 Inter-rater reliability of confidence judgments Each rater was also required to judge their confidence, on a 5-point Likert scale, for making their decision on response present or absent. An inter-rater reliability analysis using a Light’s Kappa statistic was performed to determine the amount of consistency on confidence for judging response present or absent between each pair of raters. I also compared the individual confidence measures to show that as the gap duration increases, confidence ratings should also increase. It is also informative to see how confident each rater was when they made their decision and how each rater compares to one another in confidence when they agree or disagree. I performed two a priori defined one-way Analysis of Variance (ANOVA) for comparisons of confidence ratings for “response present” and “response absent” across raters and gap conditions. Post-hoc comparisons of significant ANOVAs were performed using a Tukey-Kramer test. I also performed an ANOVA to compare confidence ratings between “response present” and “response absent” trials as averaged across raters and gap conditions. ANOVA and post-hoc results were considered significant for p-values less than .05.      28  Chapter 3: Results 3.1 Behavioural Measurements of Temporal Resolution Behavioural gap-detection thresholds. The group-mean behavioural gap-detection threshold for 47 participants was 4.09 ± 0.85 ms. For the purpose of comparing behavioural thresholds with electrophysiological thresholds; the group-mean for 36 participants (11 rejected recordings) was 4.1 ± 0.87 ms. The behavioural gap-detection thresholds ranged from 3 ms to 6 ms. 3.2 Electrophysiological Measurements of Temporal Resolution 3.2.1 Percent of participants with CAEPs to gaps.  The percentage of participants with CAEPs for various gap durations is displayed in Figure 2. The percentage of participants with gap-evoked CAEPs was 87%, 77%, and 91% for each rater, respectively. Averaging across raters revealed that 85% of the participants had a CAEP to a 10 ms gap. A percent agreement was computed between each rater pair on their response present/absent judgments. The percent agreement for Raters #1 and #2, #1 and #3, and #2 and #3, was 73%, 58%, and 61%, respectively. To determine the inter-rater reliability between the three raters, a Light’s kappa coefficient was calculated. The inter-rater reliability (i.e., kappa) for Raters #1 and #2, #1 and #3, and #2 and #3 was 0.67, 0.37, 0.45, respectively. The rater-averaged inter-rater reliability was 0.50. Even though the rater-averaged kappa had a moderate value, there was a large amount of variability in inter-rater kappa values.   29   Figure 2.  Percentage of Participants with CAEPs at various gap durations  Figure 2. Percent of participants that were judged to have CAEPs at various gap durations (ms). The blue, green, and red lines represent the individual rater judgments. The black line represents the average of the individual rater lines.  3.2.2 Rater-determined CAEP gap-detection threshold. CAEP gap-detection threshold as determined by three individual raters were 4.5 ± 2.6 ms, 4.9 ± 2.8ms, 3.0 ± 1.9 ms, respectively. Results for the group-rater determined CAEP gap-detection threshold revealed a mean of 3.4 ± 1.4 ms (i.e., at least two raters judged responses present).   3.2.3 Group-averaged CAEP gap-detection thresholds.  Performing a statistical analysis on the ERPs to gaps from the group of 36 participants revealed gap durations of 4 ms evoking significantly different post-gap ERPs 30  as compared to the no-gap stimulus. CAEPs after gaps of less than 4 ms did not reveal significant changes as compared to the no-gap control stimulus. Thus, the group-averaged CAEP gap detection threshold was determined to be 4 ms. Figure 3 displays the group-averaged CAEPs to the various gap and no gap stimuli between 500 to 800 ms after gap onset. Thus, the results from the individual- and group-rater CAEP gap-detection thresholds were similar. Figure 3. Group-average CAEP waveforms  Figure 3. The group-average CAEP waveforms for each gap duration (0 to 20ms). A stimulus with a 20 ms gap is designated at the top of the figure to depict timing of events. The grey bars above each CAEP represent significant intervals (p<.05 FDR corrected) from the no-gap (0 gap) condition. Onset CAEPs are evident for 31  all waves. ERPs to gaps of ≥ 4ms were significantly different from ERP to no-gap stimulus between 500 to 800 ms. Thus, the group-mean gap-detection threshold was defined as 4 ms.   3.2.4 CAEP and behavioural gap-detection comparisons Validity of CAEP gap-detection thresholds. A paired-samples t-test was conducted to compare the group-rater determined CAEP gap-detection thresholds with behavioural gap-detection thresholds. Paired samples t-tests revealed a significant difference between CAEP (3.4 ± 1.4 ms) and behavioural (4.1 ± 0.87 ms) thresholds (t = -2.87, df=35, p = 0.013). Twenty-seven participants (77%) had CAEP gap-detection thresholds within 2 ms of their behavioural threshold. This finding shows a good correspondence between group-rater CAEP and behavioural gap-detection thresholds. However, correlation results did not reveal a significant correlation between CAEP and behavioural gap-detection threshold (r = -0.23, p = 0.17). Figure 4 shows individual rater-correlations.             32  Figure 4.  Individual and Group-rater CAEP-to-Behavioural Correlations  Figure 4. Correlation measures between behavioural and electrophysiological gap-detection thresholds (in milliseconds) for individual rater judgments. The correlation (r) and significance values (p) are presented for each individual rater-defined thresholds. In addition, the best fit linear line (red) and equations are provided. The bottom right graph represents the group-rater defined electrophysiological gap-detection thresholds.  There were no significant correlations (p>.05) between behavioural and electrophysiological measures for any individual rater not for the group analysis.  33  3.2.5 CAEP Confidence Ratings A Light’s kappa coefficient showed the amount of agreement between raters on how confident they were when making a decision. The inter-rater reliability between Rater’s #1 and #2 was 0.29, between Rater’s #1 and #3 was 0.14, and between #2 and #3 was 0.15. Averaged across raters, Light’s kappa was 0.19, which represents a weak inter-rater reliability on confidence measures (Cohen, 1960).  As the gap duration increased, the confidence judgments also increased for all raters. Figure 5 displays the confidence ratings for ‘response present’ for each gap duration for all raters.  Confidence ratings were significantly higher for longer gap durations as compared to shorter gap durations (F=14.42; df=9.664; p<.001). Post-hoc analyses of response “present” CAEPs revealed that raters had significantly higher confidence ratings for gaps of 10-20 ms (3.6 ± 1.2) as compared to those for gaps of 2-8 ms (2.6 ± 1.0). In addition, confidence ratings among raters were statistically different (F=50.20; df=2,671; p<.001). Post-hoc analyses revealed that Rater #3 had significantly lower confidence ratings than Rater #1 or Rater #2 when judging responses to be “present”. Rater #1 and #2 did not significantly differ in their overall confidence ratings. Figure 6 displays the confidence ratings for response “absent” judgments.  Confidence ratings were significantly different as compared across gap durations (F=2.54; df=9,194; p=.015). This significance was mainly driven by lower confidence ratings (1.7 ± 0.7) for the 14 ms gap duration as compared to the ratings (2.89 ± 1.0) for the 2 and 4 ms gap durations. There was no trend in confidence ratings across gap durations and thus this effect might be a false positive finding. Post-hoc analyses revealed that Rater #1 had significantly lower confidence ratings than Rater #2 or Rater #3 when 34  judging responses to be “absent”. Rater #2 and #3 did not significantly differ in their overall confidence ratings. Averaged across gap duration and raters, confidence judgments were statistically higher when raters judged responses to be present (confidence ratings = 3.2 ± 1.2) than absent (confidence ratings = 2.6 ± 1.1; F=48.27; df=1,876; p<.001).  Figure 5. ‘Response Present’ Confidence Ratings  Figure 5.  Confidence ratings for response “present” judgments across various gap durations (0 to 20 ms). Blue, green, and red lines represent individual confidence ratings and the black line represents the average of the three raters.  As gap duration increased, rater’s confidence in deciding response present also increased.  35  Figure 6. ‘Response Absent’ Confidence Ratings  Figure 6. Confidence ratings for response “absent” judgments across various gap durations (0 to 20 ms). Blue, green, and red lines represent individual confidence ratings and the black line represents the average of the three raters.  Raters were overall less confident and had variable ratings across gap durations when deciding response absent.   36  Chapter 4: Discussion The main focus of my thesis was to compare electrophysiological gap-detection thresholds with behavioural gap-detection thresholds in a moderate sample size of normal hearing adults. The goal was to determine whether or not gap-evoked CAEPs can be used to accurately and precisely predict behavioural gap detection thresholds. Behavioural gap-detection is a measure of temporal resolution and is included in the audiological test battery for assessing central auditory processing disorder. Behavioural gap-detection tests involve a paradigm that measures a listener’s ability to identify the smallest gap interval between sounds (Fitzgibbons & Wightman, 1982).  However, there are a variety factors such as memory, cognition, motivation, hearing loss, and attention that make it difficult to determine the underlying physiology using behavioural testing procedures alone (Lister et al., 2007). Current behavioural gap-detection tests cannot distinguish perceptual from cognitive deficits, therefore we need a method for assessing auditory temporal resolution that does not rely on behavioural measures alone. This is where electrophysiological testing may help and was the focus of my study. The results of the present study are discussed below. The main findings offer a possible solution to the issues that surround behavioural gap-detection testing. 4.1 Temporal Resolution 4.1.1 Behavioural gap-detection thresholds.  The mean behavioural gap-detection threshold was 4.09 ± 0.85ms. This is consistent with the normative data in the literature proposed by Musiek (2005). This is clinically promising as my behavioural gap-detection procedure was a modified Hughson-Weslake procedure that required less than 5 minutes to conduct. The 37  behavioural data obtained in my study corresponds well to other previous studies assessing gap detection using the standard GIN test and other adapted GIN procedures (Palmer & Musiek, 2013; 2014; Pratt et al, 2005; Michaelewski et al, 2005; Lister et al, 2007; Atcherson et al, 2009). I feel my procedure could reduce cognitive fatigue that can occur when clients perform other gap detection tests. Given that clinical testing time could be reduced with my procedure, this would allow for more opportunities to test other auditory functions within a single clinical visit.  4.1.2 CAEPs. The mean CAEP gap-detection threshold from a moderate sample size of 36 participants was 3.4 ± 1.4ms. This is comparable to the statistical result from the group-mean data, which showed that 4 ms was the smallest gap duration to have a significant N1-P2. Three experienced raters judged that 85% of participants had gap detection thresholds of 10ms or less. This is consistent with previous literature that had smaller sample sizes (Michaelewski et al, 2005; Pratt et al, 2005, Lister et al, 2007; Atcherson et al, 2009). Bertoli et al (2001; 2002) used mismatch negativity to evaluate gap detection and found that 9 out of 10 participants had a clear N1 peaks for 15 ms gaps and half of the participants had a clear N1 for gaps of 9ms. A study by Rupp et al (2002) used middle latency responses and found that participants had a clear N1 to 9 ms gaps. Michalewski et al (2005) found CAEP gap-detection thresholds for 5 ms gaps, however they do not state the percentage of participants that had an N1-P2 response. Palmer and Musiek (2013) found the mean electrophysiological gap-detection threshold to be 5.09 ms. CAEP gap detection thresholds from my study correspond well to the previously discussed literature and have a larger sample size of normal hearing adults. However, the mean CAEP gap-38  detection threshold in this study is slightly lower than those previously reported. This is likely due to the variability among raters at low gap durations. Because Rater #3 was more liberal in his/her judgments, rating about 70% of participants as having an N1-P2 response to 2 ms gaps, group-mean thresholds were lower than what has been previously reported.  Average CAEP gap-detection thresholds were comparable to behavioural gap-detection thresholds, with CAEP thresholds being within 1 ms of behavioural thresholds in normal hearing listeners. The mean difference between the two measures was 0.8 ± 1.8 ms. However, there was an insignificant relationship between behavioural and physiological gap-detection thresholds. This lack of correlation is most likely due to the inclusion of only individuals with normal temporal processing functions. Thus, a restriction of range problem likely occurred(Wiseman, 1967). Although CAEP and behavioural measures are very close to one another in mean values, it is obvious that CAEP thresholds are more variable than behavioural thresholds. With small amounts of variability, a significant correlation between two variables is much less likely (Field, 2009). The variability in CAEP thresholds can be attributed to a number of things including but not limited to participants’ arousal state, attention, and myogenic or electrophysiological noise. While it is clear that electrophysiological gap-detection testing can predict behavioural thresholds in normal hearing listeners, a larger sample size is required to confirm using CAEPs as an appropriate screening method for those with temporal processing deficits. Interestingly, 90% participants showed significant CAEPs to 12 ms gaps, which is well below what is considered the normative behavioural 39  gap-detection threshold of 20 ms (Keith, 2000). Given that the electrophysiological thresholds in this study and similar studies are much lower than current normative values, it would be of value to establish new normative values for gap-detection thresholds. However, we cannot determine whether or not a 20 ms cut-off for ‘normal’ gap-detection threshold is too conservative until we establish what the ‘abnormal’ gap-detection thresholds. Other studies have used different approaches and methods for determining electrophysiological gap-detection thresholds. Some studies utilize gap durations that corresponded to a participant’s behavioural threshold (Palmer & Musiek, 2013; Pratt et al, 2005). The study by Palmer & Musiek (2013) included a gap that was well above their threshold (20 ms), a gap duration that was 2 milliseconds above their behavioural threshold, as well as a gap duration below their threshold (2 ms). However, this does not provide us with a true CAEP gap-detection threshold. Procedures that include fewer gap durations that correspond to behavioural gap-detection thresholds would cut down on testing time, but it does not provide us with a true behavioural-to-electrophysiological comparison. Other gap-detection studies utilize spectrally different markers on either side of the gap. In particular, a study by Lister, Maxfield, & Pitt (2007) utilized two different gap conditions – a ‘within-channel’ and a ‘between-channel’ recording. Within-channel condition refers to the noise on either side of the gap as being the same, and between-channel (or across-channel) condition refers to the noise on either side of the gap as being spectrally different. P2 components were significantly larger in amplitude for the between-channel gap-detection condition. Although a salient indicator of temporal coding, the P2 response is likely increased due to the change in stimulus frequency after 40  the gap and not to the gap in the noise alone. Despite the fact that between-channel gap detection is likely more representative of temporal cues in speech perception, my study utilized spectrally similar markers (a within-channel paradigm), in order to avoid a second acoustic feature that could interfere with estimating true gap-detection thresholds for the purpose of establishing normative data.  When using CAEPs to establish threshold of any type, the procedure in terms of stimulus parameters and methods can vary among researchers. There are also no current standardized amplitude values or residual noise levels for determining the presence or absence of an N1-P2 response to gaps. Majority of studies require a reviewer’s judgment, which can vary greatly depending on the rater’s experience and biases. Some previous studies investigating CAEP gap-detection testing fail to state the number of rater’s involved in determining the CAEP gap-detection threshold or utilizes only two raters (Musiek & Palmer, 2013; 2014; Musiek, 2005; Michaelweski et al, 2005; Atcherson et al, 2009). The present study used three raters, allowing for further comparisons and discussion of inter-rater reliability. The results for inter-rater agreement are discussed below. 4.2 Inter-rater Reliability Measures Three rater’s viewed the bank of ERPs. They were blinded to the gap condition and asked to state whether a response was present or absent. For the purpose of determining threshold, at least two rater’s had to state response present in order to accept it as a true response. Inter-rater agreement was determined for each pair of raters on their response detection. The agreement between rater’s 1 and 2 was much better compared to rater’s 1 and 3, and 2 and 3. In fact, the inter-rater reliability between rater’s 1 and 3, and 41  2 and 3 was poor. Rater #3 was much more liberal in his/her response judgments. He/she determined that more than 70% of participants had an N1-P2 response to gaps of 2 ms. Rater #3 was experienced in recognizing auditory and visual cortical evoked potentials to stimulus onsets but was only minimally trained in detecting auditory evoked potentials to gaps. In clinical settings, clinicians will differ in their threshold detection because not all clinicians can be provided with the exact same training and experience. The variability in rater’s response detection offers an interesting dimension and discussion to the present research. Clearly, it was very difficult for raters to determine a significant response from the background noise in the recordings, regardless of how experienced they were. This difficulty increased with decreasing gap durations as recognized in each rater’s confidence ratings. The inter-rater reliability for confidence ratings was also poor. This means that when detecting a response present or absent, rater’s varied in their confidence scale from 1 (very poor) to 5 (excellent), as seen in Figures 5 and 6. When rater’s chose ‘response present’ at small gap durations, their confidence was much lower than it was for higher gap durations (i.e., > 10ms). Rater’s 1 and 2 were similar in their confidence ratings, whereas rater 3 was less confident overall. When selecting ‘no response’, raters’ confidence decreased with increasing gap duration. This means that at higher gap durations (i.e., >16ms), raters’ were less confident in choosing no response, even though they were blinded to the condition. This further exacerbates the point that noise can greatly impact a response judgment. Rater 3 was much more variable in his/her confidence ratings for the ‘no response’ condition. Correct rejection and false positive rates were computed for each rater individually for the simulated CAEP data. False positive refers to a rater selecting ‘response present’ when there is no response (i.e., 42  simulated no-gap condition). In other words, incorrectly identifying an N1-P2 response, as there should be no CAEP for the simulated no-gap condition. Correct rejection refers to a rater’s ability to select ‘no response’ for the no-gap condition. Rater #3 had a higher false-positive rate (20%), indicating that he/she was more likely than the other two raters to identify a CAEP for the control condition.  4.3 Clinical Implications The corroboration between my results and previous findings of CAEPs and gap-detection indicates the clinical viability of using CAEPs to assess normal adults for temporal processing. However, given the restriction of range problem that occurred when analyzing correlation, a large-sample study should be conducted to evaluate the sensitivity and specificity of using CAEPs to assess temporal processing in individuals with a wider range of temporal processing deficits. The results from the present study will be very useful for comparing normal adults to adults with known processing deficits (i.e., CAPD, ANSD, elderly patients, or traumatic brain injury patients). Furthermore, it would be useful to collect normative physiological data for a child population, as CAPD is also prevalent among school-age children (Chermak & Musiek, 1997). Given the complex nature of central auditory processing, it will be difficult to generalize results to all ‘abnormal’ populations, as the site of lesion will vary given the cause of the deficit. Future research should aim to isolate unique populations for the purpose of collecting and comparing gap-detection thresholds. Previous studies have studied auditory temporal resolution in patients with TBI, language disorders, auditory neuropathy, and older adults (Bergemalm et al., 2005; Bertoli, Smurzynski, & Probst, 2002b; Lew, Jerger, Guillory, & Henry, 2007; Lister, Besing, & Koehnke, 2002; Michalewski, Starr, Nguyen, Kong, & 43  Zeng, 2005). However, more extensive research with larger sample sizes is needed on each of these sub-populations. Another topic of interest for future research regarding temporal processing involves temporal modulation transfer function (TMTF). It has been suggested TMTF is another viable measure of temporal resolution. A study by Shen and Richards (2013) investigated TMTF and gap-detection threshold as measures of temporal resolution. TMTF relates to a listener’s ability to detect sinusoidal amplitude modulation to the frequency of the modulation (Veimeister, 1979). The modulation threshold refers to the depth of modulation required for a person to just discriminate between a modulated and unmodulated waveform (Veimeister, 1979). Thus, TMTF is not only a measure of temporal resolution but also intensity resolution (Strickland and Veimeister, 1997). Behavioural measures of TMTF are clinically relevant in assessing temporal processing, however, it is a time-consuming process that can take more than 2 hours of listening time. Two new and more efficient procedures for estimating TMTF have been developed. For specific details about the two paradigms for assessing TMTF, refer to Shen and Richards (2013). Based upon the literature to date, TMTF is a valuable measure of some aspects of temporal resolution that is worth mentioning for the purpose of future direction. It would be of value for future research to investigate measures of TMTF, and how it relates to a person’s gap-detection threshold, for the purpose of providing additional information about an individuals temporal processing abilities. 4.4 Caveats Based on my study’s results, a correction factor for CAEP-to-behavioural gap-detection thresholds would be 0.8 ms, but this would only be for adults with normal temporal resolution. The established range of gap detection thresholds for normal adults 44  is clinically relevant, but future studies should include a wider variability of responses so that we may yield a significant relationship between both measures and then establish correction factors. EEG recordings do not come without limitations. The procedure for determining physiological gap-detection threshold took roughly 1.5 hours. This length of time is impractical and often led to patient fatigue. The length of recording time also interfered with electrode impedance and an increase in participant myogenic noise. In clinical settings, a streamlined approach will need to be established for determining whether a person’s gap-detection threshold is normal or abnormal. When a study with a similar paradigm is applied to a large sample of adults with known temporal processing deficits, this more efficient clinical method may be established. The next step is to investigate the effects of hearing loss, head injury, central lesions, and confirmed central auditory processing on these electrophysiological measures of gap-detection.  Furthermore, a high level of background EEG noise is a major source of unreliability in recording CAEPs. In the present study, it was difficult for raters to confidently determine an N1-P2 response to short gap-durations (< 8 ms), therefore making the established subjective threshold somewhat unreliable across raters. In the study by Michaelewksi et al (2005), they stated that only 8 out of 12 participants showed clear peaks to the largest 50 ms gap, but in my study, 32 out of 36 participants were judged to have a clear response to 12 ms gaps. However, this statistic might be improved in a clinical setting because clinicians would not be blinded to the gap-duration condition; unlike the raters being blinded to the gap-durations in my study. For research purposes, it is important that rater’s are not biased by the gap-duration when making a decision but 45  clinically, clinicians will be given more information for the purpose of making a reliable and accurate decision. Clinicians will also be more experienced with CAEPs and hopefully properly trained on the decision-making process.  This means that clinicians are more likely to identify CAEPs to gaps and that CAEP gap-detection thresholds will be lower than those reported in this study. In the future and with more CAEP gap-detection data, statistical measures will be implemented to help guide raters in making better-informed and more accurate decisions regarding response detection.  4.5 Conclusions The current study investigated electrophysiological measures of auditory temporal resolution. CAEPs can be used as an objective measure of gap-detection thresholds in normal hearing adult populations because behavioural and physiological gap-detection results were similar.  Results from this study with a moderately large sample (n=36) provide further supporting evidence for utilizing CAEP gap-detection thresholds as a method to estimate temporal resolution in adults. Temporal resolution is just one component of auditory temporal processing. Auditory processing with regards to speech perception is very complex in nature with many contributing pathways and connections, many of which are still unknown. The auditory system’s ability to detect rapid changes over time is certainly important for accurate speech detection, but it is just one piece of the puzzle. There are many conditions that will fall under the umbrella of central auditory processing disorders, and testing temporal processing through gap detection is just one measure included in an extensive test battery. While being able to accurately and objectively determine an 46  individual’s temporal resolution ability is important for diagnosis, it is only scratching the surface of a very complex and intricate disorder. Overall, my study has contributed to the current literature by further establishing the validity of using electrophysiological measures to assess temporal resolution. Future research will evolve from the present findings, hopefully allowing for comparisons between individuals with normal and abnormal temporal processing as measured in a gap-detection paradigm. Further research will help to determine the clinical utility of gap-evoked CAEPs in abnormal populations.                47  Bibliography American Speech-Language-Hearing Association, & EBSCOhost. Perspectives on hearing and hearing disorders in childhood. Perspectives on Hearing and Hearing Disorders in Childhood,  Atcherson, S. R., Gould, H. J., Mendel, M. I., & Ethington, C. A. (2009). 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