Hearing Research 359 (2018) 13e22 Contents lists available at ScienceDirect Hearing Research journal homepage: www.elsevier.com/locate/heares Research Paper Depth matters - Towards finding an objective neurophysiological measure of behavioral amplitude modulation detection based on neural threshold determination Saskia M. Waechter a, b, Alejandro Lopez Valdes a, b, Cristina Simoes-Franklin a, c, e, Laura Viani c, d, e, Richard B. Reilly a, b, d, e, * a Trinity Centre for Bioengineering, Trinity College, The University of Dublin, Dublin 2, Ireland School of Engineering, Trinity College, The University of Dublin, Dublin 2, Ireland National Cochlear Implant Program, Beaumont Hospital, Dublin 9, Ireland d Royal College of Surgeons in Ireland, Dublin 2, Ireland e School of Medicine, Trinity College, The University of Dublin, Dublin 2, Ireland b c a r t i c l e i n f o a b s t r a c t Article history: Received 14 January 2017 Received in revised form 7 December 2017 Accepted 11 December 2017 Available online 14 December 2017 With increasing numbers undergoing intervention for hearing impairment at a young age, the clinical need for objective assessment tools of auditory discrimination abilities is growing. Amplitude modulation (AM) sensitivity has been known to be an important factor for speech recognition particularly among cochlear implant (CI) users. It therefore would be useful to develop objective measures of AM detection for future clinical assessment of CI users; this study aimed to verify the feasibility of a neurophysiological approach studying a cohort of normal-hearing participants. The mismatch waveform (MMW) was evaluated as a potential objective measure of AM detection for a low modulation rate (8 Hz). This study also explored the relationship between behavioral AM detection and speech-in-noise recognition. The following measures were obtained for 15 young adults with no known hearing impairment: (1) psychoacoustic sinusoidal AM detection ability for a modulation rate of 8 Hz; (2) neural AM detection thresholds estimated from morphology weighted cortical auditory evoked potentials elicited to various AM depths; and (3) AzBio sentence scores for speech-in-noise recognition. No significant correlations were found between speech recognition and behavioral AM detection measures. Individual neural thresholds were obtained from MMW data and showed significant positive correlations with behavioral AM detection thresholds. Neural thresholds estimated from morphology weighted MMWs provide a novel, objective approach for assessing low-rate AM detection. The findings of this study encourage the continued investigation of the MMW as a neural correlate of low-rate AM detection in larger normal-hearing cohorts and subsequently in clinical cohorts such as cochlear implant users. © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Temporal auditory processing Objective measures Mismatch waveform Amplitude modulation detection Speech recognition 1. Introduction Abbreviations: ACC, acoustic change complex; AFC, alternative forced choice; AM, amplitude modulation; AMD, amplitude modulation depth; ASSR, auditory steady-state response; AUC, area-under-the-curve; BT, behavioral threshold; CI, cochlear implant; EEG, electroencephalography; EFR, envelope following response; ERP, event-related potential; IV, intersection value; LTASS, long-term average speech spectrum; MMW, mismatch waveform; NT, neural threshold; RMS, rootmean square; SNR, signal-to-noise ratio; SIN, speech-in-noise; SD, standard deviation; TFS, temporal fine structure * Corresponding author. Trinity Centre for Bioengineering, 152-160 Pearse Street, Trinity College, The University of Dublin, Dublin 2, Ireland. E-mail address: REILLYRI@tcd.ie (R.B. Reilly). Speech processing in humans is a complex process based on the integration of spectral and temporal information, where temporal information can be divided into the slow amplitude fluctuations in the envelope and the faster temporal changes conveyed in the temporal fine structure (TFS). There is evidence that TFS cues can enhance speech recognition in adverse listening conditions (Hopkins and Moore, 2009). However, the temporal envelope is considered as one of the most important features for speech intelligibility (Drullman, 1995; Shannon et al., 1995). Specifically, envelope fluctuations with rates below 16 Hz are crucial for https://doi.org/10.1016/j.heares.2017.12.005 0378-5955/© 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). 14 S.M. Waechter et al. / Hearing Research 359 (2018) 13e22 phoneme recognition (Drullman, 1995; Xu et al., 2005). The significance of slow envelope fluctuations in speech recognition has prompted many recent investigations of brain activity in response to sounds with low-rate amplitude modulation (AM). A literature review by Edwards and Chang (2013) highlighted the tuning of the human auditory system to low-rate AM in the fluctuation range (~1e10 Hz) and its implications for speech processing. Given the importance of envelope fluctuations for sound perception and particularly for speech processing, this study explored human auditory brain responses to modulated sounds with a low AM rate (8 Hz) and examined their applicability as an objective metric. Such an objective metric may provide a valuable tool for clinical hearing assessment without relying on subjective feedback (Hall and Swanepoel, 2010), addressing the clinical demand for objective metrics of auditory processing due to increasing numbers undergoing intervention for hearing impairment at a young age (Rajan et al., 2017). Experiments were conducted for a normal-hearing cohort to verify the feasibility of a neurophysiological approach based on neural change detection. Paradigms were designed in a way that allows future replication of the same test battery in a CI user cohort. As detailed by Picton (2013), neural responses elicited by temporal auditory features can be employed to assess various aspects of temporal auditory processing. Previous studies have assessed the relationship between behavioral AM detection abilities and corresponding neural measures (Purcell et al., 2004; Manju et al., 2014; Han and Dimitrijevic, 2015; Luke et al., 2015; Dimitrijevic et al., 2016). The main objective of this study was to build on this research by estimating individual neural thresholds (NTs) from late cortical auditory evoked potentials (CAEPs) for low-rate AM detection. These NTs were derived from CAEP data elicited by various amplitude modulation depths (AMDs), and compared to behavioral AM detection thresholds for a modulation rate of 8 Hz. We hypothesized that NTs would be significantly correlated with behavioral AM detection thresholds of the same AM rate. Periodic neural responses such as the auditory steady state response (ASSR) (Manju et al., 2014; Luke et al., 2015) and the envelope following response (EFR) (Purcell et al., 2004), as well as transient CAEPs in the form of the acoustic change complex (ACC) (Han and Dimitrijevic, 2015) have been investigated as potential candidates to determine objective, neural measures of AM detection. The ASSR measures the neural response to a fixed AM rate, whereas the EFR may be evoked by sweeping stimuli, e.g. stimuli with continuously changing AM rates or AMDs. Significant correlations between behavioral AM detection thresholds and electrophysiological measures have been found for the EFR (Purcell et al., 2004) and the ASSR (Manju et al., 2014), for normal-hearing cohorts at modulation rates above 20 Hz and with constant AMDs in the neurophysiological paradigms. Dimitrijevic et al. (2016) investigated EFRs elicited by stimuli with continuously changing AMDs at a fixed AM rate of 41 Hz. Neural AMD thresholds obtained from the EFRs showed significant correlations with behavioral AM detection thresholds. Luke et al. (2015) investigated the electrically evoked ASSR for CI users for modulation rates of 4 Hz and 40 Hz and found a significant correlation between ASSRs at 40 Hz and behavioral AM detection thresholds at 20 Hz. No studies have been reported investigating the relationship between AM detection abilities and EFRs/ASSRs elicited by stimuli with differing AMDs for modulation rates in the fluctuation range, e.g. below 20 Hz. Han and Dimitrijevic (2015) investigated the influence of the AMD on ACCs for differing modulation rates including a low-rate AM of 4 Hz, showing a fast decline in ACC amplitude with decreasing AMD. The present study explored a transient neurophysiological response referred to as the mismatch waveform (MMW) (Lopez Valdes et al., 2014). The MMW was obtained using an auditory oddball paradigm by subtracting the standard CAEP from the deviant CAEP. This difference waveform showed two distinct components: a negative component corresponding to the widely studied mismatch negativity, which is the result of perceptual €ta €nen et al., 2007; change detection in stimulus sequences (N€ aa Fishman, 2014), followed by a positive component which is associated with cognitive processes and may be a result of involuntary attention directed towards the deviant stimulus, similar to the P3a response (He et al., 2009). Previous studies have shown that both components are positively correlated with the magnitude of stimulus change (Katayama and Polich, 1998; He et al., 2009). Thus, both components were investigated as part of the MMW, similar to work by Lopez Valdes et al. (2014), who have demonstrated positive correlations between MMW-based neurophysiological thresholds and psychoacoustic thresholds for spectral-ripple discrimination. This study aimed to expand on those findings, transitioning from spectral processing to temporal processing with the overall goal of designing a combined test battery to assess spectro-temporal auditory processing abilities. While not the main focus of this study, the relationship between the ability to detect low-rate AM with AMDs near threshold and speech recognition scores was also addressed. Although the literature suggests a lack of correlations between psychoacoustic measures (e.g. pitch discrimination, intensity discrimination and modulation detection) and speech recognition measures within normal-hearing cohorts (Strouse et al., 1998; Watson and Kidd, 2002; Goldsworthy et al., 2013), the calculation of correlations was implemented in order to have a fully translatable experimental battery for replication in CI users. Across groups of younger and older normal-hearing and CI participants, Jin et al. (2014) found significant correlations between AM detection thresholds (at 2 and 4 Hz AM rates) and speech-in-noise recognition with modulated noise maskers, but no within-group correlations were reported. Experiments with CI user cohorts have shown significant correlations between speech measures (i.e. vowel, consonant, phoneme, syllable, and sentence recognition) and low-rate (modulation rate fm < 20 Hz) (Gnansia et al., 2014; De Ruiter et al., 2015) as well as high-rate (fm  20 Hz) AM detection abilities (Cazals et al., 1994; Fu, 2002; Luo et al., 2008; Won et al., 2011; De Ruiter et al., 2015). These reported correlations in CI user cohorts provide support for the importance of AM sensitivity in electrical hearing and encourage the investigation of an objective measure of AM sensitivity. 2. Materials & methods 2.1. Participants 15 young adults (9 female, 6 male; 19e28 years, mean: 23.2 ± 2.5 years) with no known hearing impairment participated in this study. Three participants were not native English speakers and their data were excluded from analysis relating to the speech test, but their results from electrophysiological and AM detection paradigms were included. Informed written consent was obtained from all participants prior to participation and all experimental procedures were approved by the Ethics (Medical Research) Committee at Beaumont Hospital, Beaumont, Dublin and the Research Ethics Committee at Trinity College Dublin. Participants were seated in a quiet room and auditory stimuli were presented monaurally to the left ear via headphones (Sennheiser HD 205) for all experimental paradigms. The presentation level of 70 dB SPL was verified with a KEMAR mannequin (45 BC) with pinna simulator (KB 0091), pre-amplifier (26CS) and prepolarized pressure microphone (40A0) (all from G.R.A.S. Sound & Vibration). All stimuli were energy matched by adjusting the rootmean-square (RMS) amplitude. S.M. Waechter et al. / Hearing Research 359 (2018) 13e22 2.2. AM stimuli Stimuli were created in MATLAB (MATLAB Release 2013b, The MathWorks, Inc., Natick, Massachusetts, United States) with a sampling rate of 44100 Hz. The modulation rate was 8 Hz, which provided four full AM cycles for a sound duration of 500 ms. The noise carrier was created by filtering a 500-ms white Gaussian noise stimulus with a long-term average speech spectrum (LTASS) filter (Byrne et al., 1994). The AM signal, s(t), was created by multiplying the noise carrier, c(t), with a sinusoidal signal according to Equation (1): sðtÞ ¼ ½1 þ m*sinð2*p*fm *t þ Фފ*cðtÞ (1) where t denotes time, fm is the modulation rate (8 Hz), Ф is the starting phase (-p/2) and m is the AMD with values between zero and one. The psychoacoustics literature commonly reports the AMD in decibels expressed as 20log(m), but it may also be reported in percentage expressed as 100*m, in line with the literature investigating neurophysiological measures of AM processing (Purcell et al., 2004; Han and Dimitrijevic, 2015; Dimitrijevic et al., 2016). For the neurophysiological paradigm, AMDs were chosen on a linear scale with a constant step size of 25% (100%, 75%, 50% and 25%), thus, the AMD in this study is reported in percentage, expressed as 100*m, unless noted otherwise. The chosen starting phase results in minimum amplitude of the noise signal at stimulus onset. To avoid loudness cues resulting from changes in AMD, the unmodulated and modulated stimuli were energy matched by adjusting the RMS amplitude to a constant value. Additionally, level roving was applied in the psychoacoustic paradigm with a range of ±3 dB to reduce the usefulness of any potentially remaining loudness cues. 15 fatigue, data for this paradigm were acquired in a separate session to the other tests. The percentage of correct responses was calculated for each AMD as the sum of hits and correct rejections divided by the total number of trials, where hits refers to the number of trials in which modulated stimuli were correctly identified as modulated and correct rejections refers to the number of trials in which unmodulated stimuli were correctly identified as unmodulated. Additionally, the sensitivity index, d0 (d-prime), is reported. In the case of extreme values of zero and one for the hit rate or false alarm rate, a correction was applied by adjusting zero to 0.5N and one to 1-0.5N, where N is the number of possible hits or false alarms, respectively (Macmillan and Kaplan, 1985). 2.4. Speech test The AzBio speech test (Spahr et al., 2012) was employed. Recorded sentences were presented with male and female speakers with an American English accent and masked with a ten-talker babble noise. Three SNRs (10, 5 and 0 dB) were used for one sentence list each. Speech recognition scores for a normal-hearing cohort were expected to show ceiling effects for SNRs of 10 dB and 5 dB, but were included in the test battery to facilitate study replication in a CI user cohort, in which speech-in-noise recognition is known to be poorer (Oxenham and Kreft, 2014). Every sentence list included 20 sentences with a mean of seven words per sentence across the three used lists. The number of correctly identified words was counted and the speech recognition score was calculated as the percentage of correctly identified words. All presented words were considered for each sentence's recognition score. The speech signal was presented at a constant level and the noise signal was adjusted according to the SNR. 2.5. Electrophysiology 2.3. Psychoacoustics Behavioral AM detection was evaluated with two paradigms: One paradigm estimated the behavioral threshold (BT) for AM detection with an adaptive procedure, and the second paradigm yielded the percentage of correct discrimination for a set of specific AMDs, providing an estimate of the overall psychometric function. BTs were determined with a three-alternative-forced-choice (3AFC), two-down/one-up paradigm yielding an estimate of the 70.7% correct point on the psychometric function (Levitt, 1971). The inter-stimulus interval for each trial of three consecutive stimuli was set to 100 ms. Participants were provided with visual feedback, with the selected button lighting up green or red for a correct or incorrect response, respectively. The starting AMD was 0 dB, expressed as 20log(m). The step size was 4 dB for the first four reversals and 2 dB thereafter. A run was completed after 12 reversals and the BT was taken as the arithmetic mean of the 20log(m) values at the last eight reversals. Data were acquired for four runs and the final BTs were calculated as the mean across runs. BTs are reported, both in percentage (100*m) and in dB (20log(m)), to facilitate easy comparison with neural thresholds (NTs) and BTs reported in the literature, respectively. To obtain an estimate of the psychometric function, unmodulated and modulated stimuli with a duration of 500 ms were presented in a single interval yes/no task with blocked modulation depths (i.e. 10%, 12.5%, 25%, 50%, 75%, and 100%). The participant had a 2 s time window following stimulus presentation to decide whether the stimulus was modulated or unmodulated by clicking the corresponding button. No feedback was provided. Modulated and unmodulated stimuli had equal probability of presentation. Stimulus presentations were divided into three runs for each AMD with a total of 120 stimulus presentations for each AMD. To avoid 2.5.1. Data acquisition Single-channel EEG data were acquired through a custom-built, single-channel, high sampling rate EEG setup, previously designed and validated to acquire EEG data from CI recipients and which includes an electrical artefact reduction algorithm (Mc Laughlin et al., 2013). In this setup, the recording electrode is positioned at the vertex and referenced to the right mastoid; the right collar bone is used as the system ground. As the long term goal is to use the protocol with CI users, data were sampled at a high rate of 125 kHz for artefact reduction purposes (Mc Laughlin et al., 2013). Such a high sampling rate is unnecessary for EEG signals from normalhearing participants and uneconomical for further postprocessing. Hence, data were down-sampled offline by a factor of 100. The amplifier's high-pass filter was set to 0.03 Hz and the lowpass filter was set to 100 Hz and data were amplified with a gain of 2000. Electrode impedances were measured before, during and after the electrophysiological recordings. Impedances were kept below 5 kU for all electrode combinations. Cortical responses were elicited using an unattended, auditory oddball paradigm in which modulated (deviant) and unmodulated (standard) noise sounds (details in Section 2.2) were presented for deviants with AMDs of 100%, 75%, 50%, and 25%. Each stimulus had a duration of 500 ms and an inter-stimulus interval of 1 s. Each condition was presented in separate blocks of 160 stimulus repetitions each with a total of four blocks per condition. Each block contained 20 initial presentations of the standard, followed by 140 mixed presentations of the standard (90% occurrence probability) and deviant (10% occurrence probability), resulting in 56 deviant and 584 standard presentations in total for each AMD. The order of AMD blocks was pseudo-randomized for each participant. Participants were seated in a quiet room, watching a silent, 16 S.M. Waechter et al. / Hearing Research 359 (2018) 13e22 captioned movie of their choice and were instructed to keep body movements to a minimum. As a measure of signal quality, an additional brief block of pure tone stimuli was added at the start and end of the EEG recording to elicit the robust N1-P2 complex (500 Hz, 500 ms duration, 1 s inter-stimulus interval). All participants exhibited visible N1-P2 complexes, so no participant's data were excluded from further analysis. 2.5.2. Data processing Offline post-processing of the down-sampled data included zero-phase band-pass filtering between 1 Hz and 15 Hz with a 4th order Butterworth filter, gain removal, epoching ( 300 ms prestimulus to 700 ms post-stimulus), linear de-trending, baseline correction, and separation into standard and deviant epochs. Standard and deviant epochs were averaged to form the respective CAEPs and the MMW was obtained by subtracting the standard from the deviant CAEP. Morphology weighting: The MMW was evaluated in terms of area-under-the-curve (AUC) in the region of 110 ms to 310 ms poststimulus onset (Fig. 3A). Random non-task related fluctuations in the EEG data may result in spurious AUC measurements. For this reason, a morphology weighting approach was developed (Fig. 1). Morphology weighting was achieved by assessing the Pearson correlation between MMWs at differing AMDs and a participantspecific template. These correlation coefficients were associated with weights according to a weighting function (binary or exponential weighting, Fig. 3C). Multiplication of the AUC values with the assigned weights for each AMD and each participant provided the morphology weighted AUC curves. The application of weights to AUC values based on similarity of the MMW with the participant-specific template reduced the influence of random fluctuations on AUC values. Morphology weights calculation: Each participant showed a clear MMW for the 100% AMD, i.e. a negative component followed by a positive component in the time region of interest. Thus, the individual MMW at 100% AMD served as a participant-specific template for the morphology weighting approach and is referred to as the ‘template’ in the following. Subsequently, correlation coefficients between the individual MMWs for lower AMDs (MMW75, MMW50 and MMW25) and this template were calculated. It has been reported that increasing task difficulty may result in increased MMW latencies (Tiitinen et al., 1994; Kimura and Takeda, 2013). MMW latency shifts may lead to lower correlation coefficients despite overall similar morphology. To compensate for such latency shifts, MMW75, MMW50 and MMW25 were aligned with the template prior to the correlation calculation (Fig. 2). To determine the latency shift required for the alignment, the peaks of the template were determined in the corresponding time range of interest and a time window of 5 ms to þ40 ms around these peak latencies served as the search window for peak detection of the remaining MMWs. The search window was chosen to be asymmetrical, as latencies were not expected to decrease for lower AMDs. MMWs were aligned separately, by positive peak and by negative peak, providing two sets of correlation coefficients. Fig. 1. Overview of data processing steps for morphology weighting of mismatch waveforms (MMWs) to obtain weighted area-under-the-curve (AUC) functions. For each participant and each amplitude modulation depth (AMD), (1) the AUC is calculated between 110 ms and 310 ms post-stimulus onset. (2) Separately for the negative and the positive peaks, the MMWs were aligned (Fig. 2) by shifting the waveforms. The correlation coefficients between the template (MMW100) and MMWs for lower AMDs are calculated. Based on the correlation coefficient, a weight is assigned (binary or exponential weighting function). The overall weight is the average of the obtained values for negative and positive peak alignment. (3) The morphology weighted AUC values are obtained by multiplying the determined weights with their respective AUC values. S.M. Waechter et al. / Hearing Research 359 (2018) 13e22 17 Fig. 2. Mismatch waveform (MMW) alignment for an example participant, allowing for latency shifts between differing amplitude modulation depths (AMDs); MMWs for 75%, 50% and 25% AMD were aligned with the template (100% AMD) with regard to the negative peak (B) and the positive peak (C) of the MMW. Correlation coefficients were calculated after the MMWs were aligned. Each correlation coefficient was assigned a weight and to obtain the final weight for each AUC value, the weights derived from negative and positive peak alignments were averaged. The morphology weighted AUC values (Fig. 3D) were obtained by multiplying the unweighted AUC values (Fig. 3B) with the weights. Weighting functions: Weight assignment was based on two weighting approaches, binary and exponential weighting. The binary weighting represents an “all-or-nothing” approach in which a weight of one was assigned to all MMWs with correlation coefficients of 0.85 or above and a weight of zero was assigned otherwise (Fig. 3C). The threshold of 0.85 was determined empirically and preserves MMWs that show the characteristic waveform with little variation, but suppresses MMWs that do not show high similarity to the template. For the less stringent exponential weighting, correlation coefficients were assigned into bins of 0.1 width. The first bin with correlation coefficients between one and 0.9 was assigned a weight of one, and with each bin the weight was halved (i.e. 0.5, 0.25, 0.125) (Fig. 3C). All correlation coefficients below 0.6 were assigned a weight of zero. Each weighting type was applied individually throughout the data analysis and final correlations between BTs and NTs were compared to assess the influence of the weighting approach. Neural threshold calculation: The NT was taken as the interpolated AMD at which the individual's weighted AUC curve dropped below a derived intersection value (Fig. 3E). A range of different intersection values was investigated. Correlation analysis between BTs and NTs: The potential linear relationship between BTs and NTs was assessed with Pearson's correlation coefficient r. Correlation analysis was carried out for a range of potential intersection values with both weighting functions. For this analysis, all standard epochs were averaged for each AMD. To verify the validity of the novel methodology, the entire analysis was carried out for 300 permutations of different sub-sets of 56 standards for an example intersection value of 0.35 and both weighting functions. 3. Results 3.1. Psychoacoustics The individual mean BTs were between 8.1% ( 21.8 dB) and 16.7% ( 15.5 dB) AMD. The group mean threshold was 12.1% ( 18.3 dB) with a standard deviation of 2.4% ( 33.4 dB) . Behavioral AM detection scores in the psychometric function showed expected ceiling effects at the group level for AMDs of 25% and above, while detection accuracy decreased significantly below 25% AMD (Fig. 4A). Table 1 summarizes the group mean percentage correct values and their standard deviations for the various AMDs. Additionally, the group mean hit rates, false alarm rates and the sensitivity index d0 are reported (Table 1). A non-parametric Friedman test revealed a statistically significant difference in task performance between differing AMDs (Х2(5) ¼ 55.03, p-value<.001). Post-hoc analysis with Wilcoxon signed-rank tests was conducted with Bonferroni correction to adjust for multiple comparisons with an adjusted significance level of 0.003 for 15 comparisons. There were significant differences between 12.5% and all other AMDs (Z  3.24, p-value.001) and between 10% and all other AMDs (Z  3.24, p-value.001). 3.2. Speech test Speech-in-noise recognition scores for the native English speakers revealed ceiling effects at the 10 dB SNR and close to ceiling effects at the 5 dB SNR, but for 0 dB SNR a decline in speech recognition and increased variation among participants occurred (Fig. 4B, Table 2). A non-parametric Friedman test revealed significant differences between SNRs (Х2(2) ¼ 24.00, p-value<.001). Fig. 3. (A) Example area-under-the-curve (AUC) between 110 ms and 310 ms for an individual participant's mismatch waveform (MMW) for the 100% amplitude modulation depth (AMD); (B) unprocessed AUC curves derived from individual MMWs for all participants; (C) exponential and binary weighting function assigning weights to AUC scores depending on correlation coefficients between the associated MMW and the participant's template; (D) weighted AUC curves obtained by multiplying assigned weights with unweighted AUC values for all participants' weighted AUC curves; results displayed for binary weighting function; (E) neural thresholds determined as the intersection point between weighted AUC curves and the chosen intersection value (IV); the neural threshold (NT) represents the highest, interpolated AMD at which the AUC curve drops below the IV. 18 S.M. Waechter et al. / Hearing Research 359 (2018) 13e22 Fig. 4. (A) Psychometric function of amplitude modulation detection showing group mean scores and standard deviations (all participants, n¼15); (B) Boxplots indicating the 25th and 75th percentiles and the median speech-in-noise scores for three signal-to-noise ratios (SNRs) for the native speakers (n¼12); (C) Regression line and correlation results between speech-in-noise scores (0 dB SNR) and the 12.5% AMD of the psychometric function (native speakers, n¼12); both levels represent the tested level at which participants showed poorer task performance and increased performance variability. Table 1 Group mean results of the psychometric function reporting the total percentage of correct responses (hits and correct rejections), the corresponding standard deviation (SD), hit rates, false alarm rates and the sensitivity index d0 for six amplitude modulation depths (AMDs) which are reported in percentage, expressed as 100*m, and in dB, expressed as 20log(m). AMD [%] AMD [dB] Total correct [%] SD Hit rate False alarm rate d0 100 75 50 25 12.5 10 0.985 0.983 0.990 0.973 0.831 0.494 0 2.5 6.0 12.0 18.0 20 98.8 98.7 98.8 97.9 84.9 66.7 1.2 1.2 1.4 2.2 10.3 9.8 0.016 0.018 0.024 0.024 0.131 0.170 4.44 4.41 4.46 4.20 2.41 1.01 Post-hoc analysis with Wilcoxon signed-rank tests was conducted with Bonferroni correction to adjust for multiple comparisons with an adjusted significance level of 0.017 for three comparisons. Significant differences were observed between all three conditions (10 dB vs. 5 dB: Z ¼ 3.06, p-value ¼ .002; 10 dB vs. 0 dB: Z ¼ 5.06, p-value ¼ .002; 5 dB vs. 0 dB: Z ¼ 3.06, p-value ¼ .002). Nonnative speakers showed large variations in their performance as well as overall poorer performance for lower SNRs and were therefore excluded from data analysis relating to speech recognition scores. 3.3. Electrophysiology The group mean MMWs revealed a clear morphology for 100% and 75% AMD, whereas the waveforms for 50% and 25% AMD only showed random fluctuations (Fig. 5A). The individual morphology weighted AUC values showed a strong decline from 100% to 75% and from 75% to 50% AMD, but then remained constant at a low (mostly zero) level for 50% and 25% AMD (Fig. 3D and Fig. 5B). Statistical analysis by means of a non-parametric Friedman test revealed a significant effect of AMD for the binary weighted MMW AUC values (Х2(3) ¼ 37.08, p-value<.001). Post-hoc analysis was carried out with Wilcoxon signed-rank tests, and Bonferroni correction provided an adjusted significance level of 0.008 for six comparisons. Weighted MMW AUC values for 100% AMD differed significantly from those for all other AMDs (100% vs. 25%: Z ¼ 3.41, Table 2 Group mean data and their standard deviations (SDs) for the speech-in-noise test; the table includes scores for native speakers (n¼12) at three tested signal-to-noise ratios (SNRs). SNR Mean SD 10 dB 5 dB 0 dB 98.2 94.0 67.8 1.65 1.67 7.54 p-value ¼ .001, 100% vs. 50%: Z ¼ 3.41, p-value ¼ .001, 100% vs. 75%: Z ¼ 3.35, p-value ¼ .001) and 75% AMD scores differed significantly from those for 25% AMD (Z ¼ 2.80, p-value ¼ .005), but not 50% AMD (Z ¼ 2.58, p-value ¼ .010). No significant difference was found between scores for 25% and 50% AMD (Z ¼ 0.54, p-value ¼ .593). For binary weighting, five out of the 15 MMW75 values were assigned a weight of zero, suggesting that five participants showed only a clear MMW response for the MMW100. For the MMW50 and MMW25 only two non-zero weights were observed for each set of 15 MMWs (Fig. 5B). Closer analysis of the MMWs associated with non-zero weights at 25% and 50% AMD showed that participant ‘NH2’ demonstrated clearer MMWs than other participants and AUC values different from zero for all AMDs. Further inspection of the data suggested that non-zero AUC values for participant ‘NH4’ at 50% AMD and for ‘NH16’ at 25% AMD were unexpected based on the observed waveform since the MMW did not resemble the template. However, minimal random fluctuations with the shape of the template in the region of interest led to high correlation values, therefore, assigning greater weights to these AUC values. 3.4. Correlation analysis Speech test vs. psychoacoustics: Only conditions without evident floor or ceiling effects in the group mean scores were included in the correlation analysis, namely the 0 dB condition of the speech test which was compared to the AM detection scores of the psychometric function at 10% and 12.5% AMD, and the BTs. Potential linear relationships between experimental measures were investigated with Pearson's correlation coefficients. No significant correlations were found between speech scores at 0 dB SNR and BTs (r ¼ 0.15, p-value ¼ .638) and AM detection at 10% AMD (r ¼ 0.24, p-value ¼ .461). Comparison of the speech scores at 0 dB SNR and the behavioral AM detection scores for the 12.5% AMD (Fig. 4C) suggested a moderately strong linear relationship between the two measures (r ¼ 0.65, p-value ¼ .021), but the correlation did not remain significant after adjusting the significance level to 0.017 with the conservative Bonferroni correction for multiple comparisons. Behavioral vs. neural thresholds: Individuals' BTs and NTs for AM detection showed statistically significant correlations for a range of tested intersection values (Table 3). For intersection values of 0.25 or below and 0.5 and above, NT calculation failed for one or more participants. To validate the applied procedure, the correlation analysis was carried out for 300 permutations of different sub-sets of 56 standards and for an example intersection value of 0.35. The distributions of Pearson's correlation coefficients and their respective p-values across 300 permutations are shown in Fig. A1 S.M. Waechter et al. / Hearing Research 359 (2018) 13e22 19 Fig. 5. (A) Group mean mismatch waveform (MMW) data for the four tested amplitude modulation depths (AMDs) with indicated region of interest (110 mse310 ms); (B) Boxplots visualizing variation in group data for weighted area-under-the-curve (AUC) values (shown for binary weighting) across AMDs; (C) Example correlations between behavioral and neural thresholds (BT and NT, respectively) for the binary weighting (‘o’, dashed line) and exponential weighting (‘þ’, solid line) approach. NTs were calculated with an intersection value of 0.35 and all standard epochs were averaged to obtain the MMW. Table 3 Overview of the correlations between neural thresholds (NTs) and behavioral thresholds; The NTs included in this analysis were estimated based on the reported intersection values (IVs), and all standard epochs were averaged to calculate the standard response. For high (0.5) and low (0.2) IVs, NT calculation failed for some participants. This number of participants is indicated by the last column. The IV 0.35 (bold) indicates the IV for which the correlation analysis was additionally carried out for 300 permutations of randomly chosen subsets of standard epochs (see Fig. A.1, Appendix). Binary Exponential IV r p no NT IV r p no NT 0.10 0.15 0.20 0.25 0.30 0.325 0.35 0.40 0.45 0.50 0.593 0.615 0.636 0.588 0.612 0.625 0.639 0.386 0.647 0.651 .026 .019 .015 .021 .015 .013 .010 .156 .009 .012 1 1 1 0 0 0 0 0 0 1 0.10 0.15 0.20 0.25 0.30 0.325 0.35 0.40 0.45 0.50 0.562 0.579 0.609 0.558 0.580 0.592 0.605 0.339 0.611 0.603 .037 .030 .021 .031 .024 .020 .017 .216 .016 .023 1 1 1 0 0 0 0 0 0 1 (Appendix). The central tendency of the right-skewed distributions of the correlation coefficients and p-values can be expressed by the median. For the specified intersection value of 0.35, median correlation coefficients of r ¼ 0.603 (p-value ¼ .018) and r ¼ 0.559 (pvalue ¼ .033) were obtained for the linear relationship between BTs and NTs based on binary and exponential weighting, respectively. The non-parametric Mann-Whitney test showed that the distributions of Pearson's correlation coefficients between BTs and NTs across 300 permutations are statistically significantly different for the binary and exponential morphology weighting (p < .001). evaluate the perception of acoustic changes related to AM detection. Previous studies have assessed the MMW as a measure of auditory temporal resolution via gap detection (Desjardins et al., 1999; Trainor et al., 2001; Uther et al., 2003). Overall, the successful elicitation of neural responses provides evidence for the feasibility of the application of the MMW for this type of acoustic change. The morphology weighting approach reduced AUC values for the lower AMDs (Fig. 3B and D), showing that MMWs at lower AMDs do not strongly resemble the template, which is in line with observations of random fluctuations of the individual MMWs at 25% and 50% AMD. Fig. 6 shows a comparison of the psychometric function of behavioral AM detection and the weighted AUCs of neural responses at the corresponding AMDs, highlighting the difference in response development with decreasing AMD. AUC values declined at much higher AMDs compared to behavioral responses. The psychometric function showed ceiling effects for AMDs of 25% and above and deteriorating performance for AMDs below 25%, which agrees with the literature for 4 Hz AM detection (Han and Dimitrijevic, 2015). In contrast, the MMW amplitudes decreased from 100% to 75% AMD, and for an AMD of 50% no clear MMW was detectable in the group mean data. Similarly, Han and Dimitrijevic (2015) showed declines in ACC amplitudes between 100% and 50% AMD, and only a weakly discernible ACC for 25% AMD for an AM rate of 4 Hz. MMWs have been elicited when acoustic changes were only just perceptible (Kraus et al., 1993) or even consciously imperceptible 4. Discussion There were five main findings: (1) MMWs can be elicited by change detection from unmodulated to modulated noises. (2) The MMW amplitude decreases with decreasing AMD. (3) Morphology weighting of MMWs allows the objective estimation of NTs. (4) NTs are significantly correlated with BTs. (5) No significant correlations were observed between AM detection and speech scores at 0 dB SNR. 4.1. Objective measure 4.1.1. Mismatch waveforms To our knowledge, this is the first application of the MMW to Fig. 6. Comparison of the group mean psychometric function of AM detection (dashed line, left y-axis) and the group mean weighted area-under-the-curve (AUC) values obtained from the neurophysiological data (solid line, right y-axis) for different AMDs. 20 S.M. Waechter et al. / Hearing Research 359 (2018) 13e22 (Allen et al., 2000). In light of this, the disappearance of the MMW for AMDs at which behavioral performance showed ceiling effects raises questions about the underlying mechanisms that result in MMW elicitation for this acoustic change type. Auditory information may have to be accumulated for a longer time period to result in AM detection for low AMDs than for high AMDs. Increasing reaction times with decreasing AMD support this interpretation (Han and Dimitrijevic, 2015). In the case of the MMW, prolonged temporal integration of stimulus information for low AMDs may result in temporally jittered neural change detection, preventing a clear MMW. 4.1.2. Neural thresholds The morphology weighting approach reduced AUC values at low AMDs based on dissimilarities with the individual MMW template. This allowed objective NT calculation, where the NT is determined as the AMD at which the weighted AUC curve drops below a specified intersection value. Correlation analyses revealed significant correlations between BTs and NTs for both weighting methods, binary and exponential weighting, and for a range of intersection values (Table 3). The intersection value of 0.4 constitutes an exception with non-significant correlation results. Closer inspection of the data reveals that this is due to an outlier with a very low NT (participant ‘NH2’). The ‘best’ intersection value of 0.35 was selected to further validate the analysis procedure by repeating the analysis with 300 permutations of randomly chosen sub-sets of 56 standards. The resulting distributions of correlation coefficients and p-values showed that results are repeatable and do not strongly rely on the choice of standard epochs. Non-parametric statistical analysis of the distributions showed that binary weighting yields significantly higher correlations than exponential weighting, which is likely a result of the more stringent rejection of MMWs with poor resemblance to the template. 4.1.3. Intersection value As stated in Section 4.1.2, the statistical significance of the correlation between BTs and NTs was not strongly dependent on the intersection value. To objectively determine a specific intersection value for data analysis, different approaches may be applied. Unless a thresholding paradigm is employed, task difficulty levels in psychoacoustic paradigms are commonly selected with the goal of deriving a measure of performance between floor and ceiling. Such difficulty levels provide a means of comparing task performance across participants and a means for performing correlation analysis across paradigms. The 75% AMD fulfils these requirements for NTs: some participants showed clear waveforms for the 75% AMD condition while in others MMW morphologies were poorer. At the 50% AMD level, no clear MMWs were recognizable. Given the suitability of the 75% AMD level for correlation analysis, one could determine the intersection value as the group mean AUC value at this level. This would provide a value of 0.325 (binary weighting) or 0.323 (exponential weighting), which lies in the range of the intersection values with the highest correlations (see Table 3). 4.1.4. AM loudness cues The challenge of loudness balancing AM stimuli is usually overcome by energy adjustment and/or level roving for behavioral testing (Viemeister, 1979; Bacon and Viemeister, 1985; Shen and Richards, 2013; Shen, 2014). Unfortunately, level roving cannot be employed in MMW paradigms as it may result in MMWs elicited purely through intensity change detection (von Wedel, 1982; Martin and Boothroyd, 2000; Harris et al., 2007). The loudness may change with the overall presentation level, the modulation rate (Zhang and Zeng, 1997; Moore et al., 1999) and the AMD (Moore et al., 1999). Despite energy adjustment of AM stimuli, subjective perception of loudness differences cannot be prevented without individual behavioral loudness balancing, which would be required for each tested AMD. Moore et al. (1999) reported an average difference of approximately 1.5 dB in the RMS level required to achieve equal loudness for unmodulated and modulated speech-shaped noise at a modulation rate of 8 Hz and with 100% AMD. For 50% AMD, the RMS-level difference decreased to less than 0.5 dB. Neurophysiological studies have reported ACC responses elicited by intensity changes of 2 dB in a vowel change stimulus (Martin and Boothroyd, 2000) and for pure tone intensity increments (Harris et al., 2007). These findings do not support the interpretation that unwanted overall loudness cues between modulated and unmodulated stimuli had a strong influence on the MMW, but some influence cannot be ruled out. 4.1.5. Limitations Some limitations of this study should be acknowledged. The use of only 56 deviant presentations for each condition may be a confounding factor in NT estimation. Kraus et al. (1993) averaged neural responses for 200 deviant presentations to show neural change detection near the perception threshold. Increasing the number of deviant trials would likely have a positive impact on the SNR of the acquired neural responses. However, this study included four acoustic change conditions (four AMDs), compared to one acoustic change type in Kraus et al. (1993), which required a fine balance between the number of deviant presentations and participant fatigue. The low number of recording channels can be an advantage or a limitation. The chosen single-channel setup is clinically friendly, which is important for future extension to clinical cohorts. However, it also introduces the possibility of slight misplacement of the recording electrode, resulting in altered MMW amplitudes across participants, but not influencing recordings within participants. The discrepancy between the magnitudes of BTs and NTs, despite significant correlations, needs to be explained and may be due to several factors: (1) the difference in the experimental paradigm may in part account for a threshold difference (3AFCdiscrimination easier than single-interval discrimination). (2) MMW amplitudes are known to decrease with increasing task difficulty, and therefore, for the 50% and 25% AMD conditions the MMW may exist but is not distinguishable from the noise floor. The SNR of the EEG data can be improved by increasing the number of deviant repetitions, potentially resulting in distinguishable MMWs at low AMDs, which would in turn lead to lower NTs and decrease the gap between BTs and NTs. Future research has to identify where the large discrepancy between BTs and NTs originates from. 4.2. Psychoacoustics The AM detection thresholds found in this study were higher than those commonly reported in the literature, although similar AM detection thresholds were recently presented for 4-Hz AM, with an average threshold of 13% for a stimulus duration of 1 s, equating to 4 AM cycles, as in the present study (Han and Dimitrijevic, 2015). Modulation rates below 10 Hz yield constant AM thresholds, provided the stimulus duration is chosen sufficiently long with regard to the number of AM cycles at a given AM rate (Viemeister, 1979; Bacon and Viemeister, 1985; Sheft and Yost, 1990). Previous studies investigating AM detection thresholds for an 8-Hz modulation rate and broadband noise carriers for young normal-hearing cohorts reported mean thresholds of approximately 8% (Jin et al., 2014) and 5%e6% (Viemeister, 1979; Bacon and Viemeister, 1985; Takahashi and Bacon, 1992). In all reported studies broadband noise stimuli, with and without AM, and with a duration of 500 ms were presented monaurally via headphones S.M. Waechter et al. / Hearing Research 359 (2018) 13e22 and reported thresholds provide an estimate of the AMD required for 70.7% correct identification. Presentation levels differed across studies, but do not significantly affect AM detection thresholds unless presentation levels are very low (Viemeister, 1979). A potential cause for the higher thresholds reported here, is the carrier bandwidth. A reduced carrier bandwidth is associated with poorer AM detection thresholds (Bacon and Viemeister, 1985; Bacon and Gleitman, 1992; Strickland and Viemeister, 1997). The LTASS weighted noise carrier in this study emphasized frequencies below 1 kHz while higher frequency content was lower in level. Similar to band-limited carriers, this LTASS weighted carrier may result in poorer AM detection thresholds than for broadband noise carriers. Higher average AM detection thresholds may also be caused by the level roving (±3 dB), as the loudness changes may distract from the task at hand, particularly at low AMDs (Chatterjee and Oberzut, 2011). 4.3. Speech identification and AM detection The lack of significant correlations between speech-in-noise recognition and behavioral AM detection abilities is not surprising and in line with the literature. Previous studies in normalhearing cohorts have already demonstrated the difficulty in teasing out relationships between speech measures and various psychoacoustic measures (Strouse et al., 1998; Watson and Kidd, 2002; Goldsworthy et al., 2013). Watson and Kidd (2002) proposed that speech-in-noise recognition in normal-hearing cohorts largely depends on a combination of pattern recognition abilities and the ability to infer the meaning of degraded speech from splinters of information. In contrast to this, deficits in spectral and temporal auditory processing caused by hearing impairment may negatively impact speech processing, which is supported by 21 significant correlations between psychoacoustic and speech measures (Dreschler and Plomp, 1980, 1985; Festen and Plomp, 1983; Glasberg and Moore, 1989). Moreover, various studies for CI cohorts have shown significant correlations between speech measures and AM detection (Cazals et al., 1994; Fu, 2002; Luo et al., 2008; Won et al., 2011; Gnansia et al., 2014; De Ruiter et al., 2015), upholding the hypothesis that AM detection may play a role in speech processing in the case of electric hearing. As stated previously, the aim of the test battery design presented in this study was its future implementation in a CI user cohort, thus, the inclusion of the speech measure was deemed justified. 5. Conclusions The morphology weighting procedure allowed the calculation of NTs and may provide a useful analysis tool in CAEP research at the individual level. Significant correlations between BTs and NTs encourage further research into the application of the MMW as an objective measure of low-rate AM detection. However, future work should address the discrepancy between the magnitudes of BTs and NTs. Acknowledgments We would like to thank all participants who volunteered their time for the study. This work was supported by funding from Trinity Centre for Bioengineering and Cochlear Ltd. Appendix Fig. A1. Distributions of Pearson's correlation coefficient r (left) between behavioral and neural thresholds of amplitude modulation detection and their respective p-values (right) based on binary (top) and exponential (bottom) weighting functions for the morphology weighting. Distributions were based on 300 permutations of randomly chosen sub-sets of 56 standard epochs for the calculation of the mismatch waveform. The median of each distribution is indicated by the circle. 22 S.M. Waechter et al. / Hearing Research 359 (2018) 13e22 References Allen, J., Kraus, N., Bradlow, A., 2000. Neural representation of consciously imperceptible speech sound differences. Percept. Psychophys. 62, 1383e1393. Bacon, S.P., Viemeister, N.F., 1985. 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