The Search for a Diagnostic Indicator of Cochlear Deafferentation: Predicting Age and Veteran Status from Auditory Evoked Potential Measures

The Search for a Diagnostic Indicator of Cochlear Deafferentation: Predicting Age and Veteran Status from Auditory Evoked Potential Measures | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search The Search for a Diagnostic Indicator of Cochlear Deafferentation: Predicting Age and Veteran Status from Auditory Evoked Potential Measures View ORCID Profile Brad N. Buran , Morgan Thienpont , Sean D. Kampel , Anne E. Heassler , Nicole K. Whittle , Haley A. Szabo , View ORCID Profile Sarah Verhulst , View ORCID Profile Naomi F. Bramhall doi: https://doi.org/10.1101/2025.11.20.25340672 Brad N. Buran 1 Oregon Hearing Research Center (OHRC), Oregon Health & Science University , Portland, OR Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Brad N. Buran For correspondence: buran{at}ohsu.edu Morgan Thienpont 2 Hearing Technology @ WAVES, Department of Information Technology, Ghent University , Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sean D. Kampel 3 VA Rehabilitation Research & Development (RR&D) National Center for Rehabilitative Auditory Research (NCRAR) , VA Portland Health Care System, Portland, OR Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anne E. Heassler 3 VA Rehabilitation Research & Development (RR&D) National Center for Rehabilitative Auditory Research (NCRAR) , VA Portland Health Care System, Portland, OR Find this author on Google Scholar Find this author on PubMed Search for this author on this site Nicole K. Whittle 3 VA Rehabilitation Research & Development (RR&D) National Center for Rehabilitative Auditory Research (NCRAR) , VA Portland Health Care System, Portland, OR Find this author on Google Scholar Find this author on PubMed Search for this author on this site Haley A. Szabo 3 VA Rehabilitation Research & Development (RR&D) National Center for Rehabilitative Auditory Research (NCRAR) , VA Portland Health Care System, Portland, OR Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sarah Verhulst 2 Hearing Technology @ WAVES, Department of Information Technology, Ghent University , Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sarah Verhulst Naomi F. Bramhall 3 VA Rehabilitation Research & Development (RR&D) National Center for Rehabilitative Auditory Research (NCRAR) , VA Portland Health Care System, Portland, OR 4 Department of Otolaryngology – Head & Neck Surgery, Oregon Health & Science University , Portland, OR Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Naomi F. Bramhall Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Objectives Cochlear synaptopathy, a type of cochlear deafferentation that occurs with aging and following loud noise exposure, is expected to be common in humans and to have negative impacts on auditory perception. However, there is currently no means for diagnosing cochlear deafferentation in living humans. Auditory brainstem response (ABR) wave I amplitude and the envelope following response (EFR) are auditory evoked potentials that have been proposed as potential non-invasive indicators of cochlear deafferentation. However, these measures may be impacted by outer hair cell (OHC) dysfunction, making them difficult to interpret. One potential method for estimating the degree of deafferentation in individual patients is to combine evoked potential and distortion product otoacoustic emission (DPOAE) measurements with a computational model of the auditory periphery (CMAP). The goal of this study was to evaluate the ability of auditory evoked potentials, with and without the CMAP, to predict risk factors for cochlear synaptopathy (age and history of military noise exposure). Design In a population of military Veterans and non-Veterans with up to a mild sensorineural hearing loss, a CMAP was used with Bayesian regression to predict synapse numbers across cochlear frequency (synaptograms) for individual human participants based on their ABR, EFR, and/or DPOAE measurements. Linear regression models were then used to evaluate the ability of the synaptograms and various ABR wave I amplitude, EFR magnitude, and DPOAE measurements to predict age and Veteran status. All Veterans were assumed to have at least some history of military noise exposure. Results High frequency (4 and 5.6 kHz) ABR wave I amplitude measurements and synaptograms generated from high frequency ABR wave I amplitudes performed the best at predicting participant age. Accounting for OHC function (as indicated by DPOAEs) in the generation of the synaptograms or by including DPOAEs in the linear regression models had limited impact on the ability of ABR wave I amplitudes to predict age. DPOAEs were highly predictive of Veteran status, making it difficult to isolate the ability of the auditory evoked potentials to predict Veteran status. Conclusions High frequency ABR wave I amplitudes and synaptograms generated from high frequency ABR wave I amplitudes were able to predict participant age within approximately 6 years, with or without incorporating DPOAE measurements. This suggests that high frequency ABR wave I amplitude measurements are good candidates for non-invasive diagnosis of age-related cochlear deafferentation and it may not be necessary to use the CMAP or measure DPOAEs to predict deafferentation in individual patients. Unfortunately, specific recommendations for predicting noise-induced cochlear deafferentation could not be ascertained from this study due to confounding related to OHC dysfunction. Introduction Noise-induced cochlear synaptopathy, the selective loss of the synapses between the inner hair cells and the afferent auditory nerve fibers (ANFs), was first described in a mouse model in 2009 ( Kujawa & Liberman, 2009 ). This raised concerns that the current guidelines for occupational noise exposure may not be sufficient to prevent noise-induced cochlear synaptopathy in workers and military Service members exposed to noise. A similar loss of synapses was observed with aging in animal models, suggesting that synaptopathy might be widespread among older adults ( Schmiedt et al., 1996 ; Sergeyenko et al., 2013 ). Unfortunately, investigating synaptopathy in humans is complicated because cochlear synapse loss can only be confirmed through post-mortem temporal bone analysis. Temporal bone studies suggest that both noise-induced and age-related synaptopathy occur in humans ( Wu et al., 2019 ; Wu et al., 2021 ), with estimated age-related loss of synapses occurring at a rate of approximately one synapse per IHC for every decade of life ( Wu et al., 2019 ). Given that synaptopathy can only be confirmed after death, there is a critical need to identify synaptopathy in living humans to determine the prevalence of this condition, characterize the perceptual consequences, and develop options for prevention and treatment. The amplitude of wave 1 of the auditory brainstem response (ABR) and the strength of the envelope following response (EFR) are two non-invasive physiological measures that have been identified in mouse models as possible indicators of cochlear synaptopathy ( Kujawa & Liberman, 2009 ; Parthasarathy & Kujawa, 2018 ; Shaheen et al., 2015 ). ABR wave 1 amplitude is a measure of the combined firing of the ANFs in response to an auditory stimulus ( Hashimoto et al., 1981 ; Moller & Jannetta, 1981 ). Mice with noise-induced synaptopathy experience a focal loss of synapses in the cochlear region corresponding to frequencies of 22.6-64 kHz ( Kujawa & Liberman, 2009 ). Thus, ABR stimulus frequencies in that region (e.g., 32 kHz) are maximally sensitive to noise-induced synaptopathy. It is expected that noise exposure in humans will lead to acute focal synaptopathy in the range of 3-6 kHz, the cochlear region most impacted by noise-induced threshold shifts ( Wilson & McArdle, 2013 ). Therefore, it has been assumed that ABR frequencies from 3-6 kHz will be particularly sensitive to noise-induced synaptopathy in humans. However, mouse data suggest that the focal synaptopathy associated with noise exposure broadens over time to impact the lower frequencies ( Fernandez et al., 2015 ). Temporal bone data from adults aged 50-74 years with a history of occupational or military noise exposure also suggest a relatively uniform reduction in synapse numbers across the cochlea compared with adults of the same age with less noise exposure ( Wu et al., 2021 ). Presumably, this broad pattern of synapse loss is due to the time that has passed since the noise exposure in these adults. In mice, aging also results in broad synapse loss across cochlear regions ( Parthasarathy & Kujawa, 2018 ; Sergeyenko et al., 2013 ). Many human studies have used ABR wave I amplitude to investigate relationships with expected risk factors for synaptopathy such as noise exposure and aging. However, the ABR stimulus and recording parameters across these studies have varied considerably (reviewed in Bramhall, 2021 ). There is still no consensus on what ABR frequency or combination of frequencies perform best at identifying synaptopathy in humans. The optimal ABR stimuli may depend on whether there is a focal or broad underlying pattern of synapse loss. Computational simulations indicate an impact of high frequency OHC dysfunction on ABR wave I amplitude, which suggests that co-occurring OHC dysfunction complicates the interpretation of ABR wave I amplitude as an indicator of synaptopathy ( Verhulst et al., 2016 ). The EFR is a measure of how well the auditory system can encode an amplitude modulated stimulus ( Dolphin & Mountain, 1992 ). Mouse studies suggest that the EFR for a tone sinusoidally modulated at 1000-1024 Hz, believed to target the auditory nerve, is particularly sensitive to synaptopathy ( Parthasarathy & Kujawa, 2018 ; Shaheen et al., 2015 ). However, in humans, the EFR in response to high modulation frequencies is quite small and difficult to distinguish from the noise floor ( Garrett & Verhulst, 2019 ; Purcell et al., 2004 ). Instead, most human studies of synaptopathy that have obtained EFR measures have used a modulation frequency of 80-120 Hz because this results in greater EFR magnitudes (e.g., Bramhall, McMillan, & Kampel, 2021 ; Mepani et al., 2021 ; Paul et al., 2017 ). However, these modulation frequencies are thought to target the auditory brainstem rather than the auditory nerve due to the estimated latency of the generators of the EFR for low versus high modulation frequencies ( Herdman et al., 2002 ; Kiren et al., 1994 ; Kuwada et al., 2002 ). Several different EFR carrier frequencies have been used in human studies of synaptopathy, but as with the ABR, it is unclear which carrier frequency is the most sensitive to noise-induced synaptopathy. Initial human studies of synaptopathy used a sinusoidally modulated (SAM) tone as the EFR stimulus. However, it has been suggested that a rectangular amplitude modulated (RAM) tone may be more sensitive to deafferentation, result in higher magnitude responses, and be less impacted by OHC dysfunction than a SAM tone due to a sharper stimulus onset ( Van Der Biest et al., 2023 ; Vasilkov et al., 2021 ). Both the SAM and the RAM EFR have been shown to be sensitive to synaptopathy in animal models ( Garrett et al., 2025 ; Wilson et al., 2021 ). It is important to recognize that while the ABR and EFR are highly correlated with synaptopathy in mouse models, other sources of deafferentation will also impact these measures. For example, loss of inner hair cells (IHCs) or spiral ganglion cells will also result in reduced ABR wave 1 and EFR amplitudes. For this reason, ABR and EFR measures are an indicator of general cochlear deafferentation rather than a specific indicator of synaptopathy. Although synaptopathy is hypothesized to have a negative impact on auditory perception, leading to tinnitus, hyperacusis, and difficulty with speech perception in background noise ( Kujawa & Liberman, 2015 ), this has been difficult to confirm in humans due to mixed results across studies (reviewed in Bramhall & McMillan, 2024 ). These mixed findings may be due in part to differences across studies in terms of the physiological measure(s) and the stimulus parameters used as an indicator of deafferentation. Due to our inability to obtain physiological measures and synapse counts in the same individuals, there is no consensus on the best physiological measure and stimulus parameters to use for this purpose. One way to circumvent this problem is to evaluate the relationship between physiological measures and synapse counts using a computational model. A computational model of the human auditory periphery (CMAP; Verhulst et al., 2018 ) was previously used in combination with Bayesian regression to predict synapse numbers in individual human participants based on their measured ABR wave I amplitudes and distortion product otoacoustic emissions (DPOAEs; Buran et al., 2022 ). DPOAEs were included in the modeling as an indicator of OHC dysfunction. Predicted synapse counts were correlated with predicted risk factors (noise exposure and age) and perceptual consequences of synaptopathy (tinnitus and difficulty with speech perception in noise), providing partial validation of this approach. In the current study, we expanded on the computational modeling approach in Buran et al. (2022) by evaluating the ability of both ABR and RAM EFR measures to predict cochlear deafferentation in individual human participants with normal hearing up to a mild hearing loss. Leveraging lessons learned from Buran et al. (2025) , a parallel study in an animal model for which we can validate synapse counts, our objective was to determine the optimal physiological measure and stimulus or combination of measures/stimuli for predicting deafferentation in humans. Given that synapse numbers cannot be confirmed in living humans, age and military Veteran status (i.e., if an individual was a Veteran or a non-Veteran) were used as proxies for synaptopathy. Deafferentation predictions generated from different combinations of physiological measures were compared based on their ability to predict participant age and Veteran status. Methods Participants 127 adults aged 18-59 years participated in a cochlear deafferentation study conducted at the National Center for Rehabilitative Auditory Research (NCRAR). Fifty of the participants were military Veterans and the remaining participants were non-Veterans who had never served in the military. Participants were recruited from previous studies conducted at the NCRAR; a database of Veterans seen at the VA Portland Healthcare System (VAPORHCS); fliers posted at the VAPORHCS, Portland area colleges/universities, and in the Portland community; and recruitment emails sent to Portland area Veterans. Inclusion criteria included: pure tone air conduction thresholds of ≤40 dB HL from 0.25-8 kHz, a normal tympanogram (226 Hz tympanogram, compliance 0.3-1.5 ml, and peak pressure between ±50 daPa), no air-bone gaps > 15 dB and no more than one air-bone gap equal to 15 dB, no history of otologic or neurologic disorder (including traumatic brain injury or concussion), and either a native English speaker or learned English prior to age 5. Only individuals meeting all audiological criteria in at least one ear were invited to participate. All participants provided written informed consent and were paid for their participation. All study procedures were approved by the VAPORHCS Institutional Review Board. EFR data from 88 participants in this sample were described previously ( Bramhall & McMillan, 2024 ). Procedures Testing took place over three test sessions. Only participants who completed all three test sessions were included in this analysis. Except for audiometry and tympanometry, test measures were obtained only in a single ear. If only one ear met the inclusion criteria for the study, all test measures were collected in that ear. If both ears qualified but there was something about one of the ears that would complicate testing, such as cerumen or an ear piercing, the uncomplicated ear was selected as the test ear. In the absence of such a complication, the test ear was determined by rolling a die. Distortion product otoacoustic emissions (DPOAEs) DPOAE testing was conducted using a custom system consisting of an ER10X probe microphone and EMAV software from Boys Town National Research Hospital ( Neely & Liu, 1993 ). DPOAE stimuli were presented at a fixed primary frequency ratio f 2 /f 1 =1.2 and responses were obtained using a primary frequency sweep (DPgram) from 1-8 kHz in 1/3 octave increments and from 9-16 kHz in 1/6 octave increments at two sets of stimulus frequency levels ( L 1 / L 2 ): 65/55 dB forward pressure level (FPL) and 50/40 dB FPL. Using in-the-ear calibration, the voltage was adjusted to set L 1 and L 2 to the desired levels. The ER10X enables FPL calibration, which increases the precision of the stimulus presentation level measurement, particularly for frequencies from 3-7 kHz in adult ear canals where interactions of incident and reflected waves produce pressure nodes or standing waves that can cause errors in standard sound pressure level (SPL) calibration ( Konrad-Martin et al., 2016 ). Other than differences in the degree of measurement error, values in FPL and SPL are equivalent. Measurement based stopping rules were employed in which averaging continued until 48 seconds of artifact-free data were collected, the signal-to-noise ratio (SNR) was ≥ 30 dB, or until the noise floor was below −30 dB SPL. Auditory brainstem response (ABR) ABR data was obtained using an Intelligent Hearing Systems SmartEP system (Miami, FL) and Etymotic Research gold foil ER3-26A tiptrode electrodes (Elk Grove Village, IL) placed in the ear canal. Alternating polarity toneburst stimuli with frequencies of 1, 2.8, 4, 5.6, or 8 kHz were presented at 100 and 110 dB peak-to-peak equivalent SPL (peSPL) with the exception of 8 kHz, which was presented at 100 and 105 dB peSPL due to output level limitations of the transducer. Toneburst durations were as follows: 4 ms for 1 kHz (4 cycles), 3 ms for 2 kHz (6 cycles), 2.5 ms for 2.8 kHz (7 cycles), 2 ms for 4 kHz (8 cycles), 1.43 ms for 5.6 kHz (8 cycles), and 1 ms for 8 kHz (8 cycles). These durations were chosen to maximize both frequency specificity and stimulus brevity. The stimulus repetition rate was 11.1/sec. For the 100 dB peSPL stimuli, 2048 sweeps were averaged, while only 1024 sweeps were averaged for the 105 dB peSPL and 110 dB peSPL stimuli to limit exposure to the high intensity level. At least two replicates of each waveform were obtained. Electrode impedance was ≤ 5.0 kΩ for all but two participants, where impedance was ≤ 6.7 kΩ. Wave I peaks and the following negative troughs were scored by experienced audiologists using a semi-automated peak picking program (adapted from Buran, 2015 ). Disagreements were resolved by a third audiologist. Wave I amplitude was defined as the voltage difference between the positive peak and the following negative trough ( Supplemental Data Figure 1A ). Download figure Open in new tab Supplemental Data Figure 1. Exemplar ABR and EFR data (A) Exemplar ABR waveform data in response to a 4 kHz, 110 dB peSPL tone pip. Two replicates of 1024 averages are collected for each subject. Wave I amplitude is measured as the peak to the following trough (vertical arrow). (B) Power spectral density of the EFR in response to a 4 kHz tone rectangular amplitude modulated at 110 Hz. Dashed lines indicate the modulation frequency and the first four harmonics. Data from both panels are from the same subject. Envelope following response (EFR) EFRs were recorded in a sound booth using Intelligent Hearing Systems SmartEP-CAM (Miami, FL) and electrostatically shielded Etymotic Research ER-3A insert transducers (Grand Prairie, TX). Participants were seated in a comfortable recliner in the reclined position. They were asked to close their eyes during testing and encouraged to sleep if possible. The active electrode was placed at the vertex (Cz), the ground on the low forehead (Fpz), and the reference on the mastoid of the test ear. Electrode impedance was ≤ 5.0 kΩ for all but five participants, where impedance was ≤ 9.3 kΩ. Stimuli consisted of 70 dB peSPL RAM tones 100% modulated at 110 Hz with a 25% duty cycle and a 2.5% Tukey window applied to the onset and offset of each individual cycle of the RAM tone to provide gradual transitions ( Vasilkov et al., 2021 ). Carrier frequencies were 1, 2, and 4 kHz. Stimuli were 500 ms in duration, presented in alternating polarity, and each recording consisted of 1002 sweeps (501 for each polarity). To minimize entrainment of evoked potentials to the stimulus, the interstimulus interval was uniformly jittered between 100 and 120 msec. EFR magnitude was calculated similarly to the bootstrapping approach described in Zhu et al. (2013) . In this approach, 400 trials were drawn with replacement, averaged, and the power spectrum was computed ( Supplemental Data Figure 1B ). Random draws were balanced across positive and inverted polarities (i.e., 200 trials from each polarity). This process was repeated 100 times to generate a distribution of the power for each frequency bin. The average value of the distribution at the modulation frequency and the first four harmonics was used to estimate the raw EFR response. To estimate the noise floor the power in the fourth to seventh discrete Fourier transform (DFT) bin on either side of the frequency of interest was averaged for a total of eight bins. Computational modeling of cochlear synapses A computational approach was used that allows for a combination of DPOAE, ABR, and EFR measurements to predict the number of ANF synapses per IHC in individual human participants across cochlear frequency (i.e., the synaptogram). A previous version of this approach was described in Buran et al. (2022) ; however, that approach used only DPOAE and ABR data to predict each participant’s synaptogram. In this report, that approach was expanded to also incorporate EFR measurements. Refinements to the previous approach are highlighted in the relevant sections. Throughout this report, bold symbols represent vectors. Thus, θ represents a vector and θ [0] represents an element of that vector. Although ABR wave I amplitude and EFR magnitude are used as indicators of cochlear synaptopathy/deafferentation, evoked responses can also be affected by decreased cochlear gain (e.g., due to OHC dysfunction). For this reason, the computational approach must account for potential OHC dysfunction when generating predictions of synaptic loss. The CMAP is a multi-stage model that includes middle ear filtering, cochlear mechanics, synaptic transmission from the IHC to ANF synapse, and the spiking activity of ANFs, the cochlear nucleus (CN) and the inferior colliculus (IC). By altering the parameters of the cochlear mechanics stage, the CMAP can simulate spiking responses in an ear with OHC dysfunction. Wave I of the ABR can be generated by multiplying the simulated ANF responses by the predicted number of synapses per IHC at each characteristic frequency (CF; i.e., synaptogram) and then summing the ANF responses across the entire population. Since the brainstem is thought to contribute to the EFR at low modulation frequencies, the contributions of the CN and IC must be accounted for when calculating EFR magnitude. Simulated ANF responses are passed through models for the CN and IC. The ANF, CN, and IC responses are then summed across the entire population of CFs and spontaneous rates to generate the EFR. To ensure that the model provided realistic predictions of the synaptogram, synapse count data from human cadavers ( Wu et al., 2019 ) were used in the Bayesian analysis to set reasonable priors (i.e., bounds) on the predictions. The CMAP can generate simulations of the number of low, medium and high spontaneous rate (SR) fibers, enabling incorporation of these fiber subtypes into the ABR simulations. Although our previous work assumed that first all low, then all medium, and finally high SR fibers are lost based on work from animal studies ( Furman et al., 2013 ; Schmiedt et al., 1996 ), this assumption is based on data from rodents and may not hold true in humans. Computational modeling studies of the EFR indicate that a subset of high SR fibers must be lost to align simulated and measured EFR responses in humans ( Encina-Llamas et al., 2019 ), and single-unit recordings in CBA/CaJ mice did not find evidence for selective loss of low SR fibers ( Suthakar & Liberman, 2021 ). Accordingly, in this report, remaining ANFs were distributed across SR according to a fixed ratio based on data from cats raised in a quiet chamber (16% low, 23% medium, and 61% high; Liberman, 1978 ), regardless of the number of fibers remaining. When fitting the CMAP to the DPOAE, ABR, and EFR data, two parameters were adjusted to minimize the difference between simulated and measured data: the pole function ( α ) and the synaptogram ( θ ). The pole function represents the active cochlear amplification process, which affects the overall gain that is fed into the IHC stage of the model. The synaptogram represents the average number of synapses per IHC as a function of frequency, which affects the overall amplitude of auditory evoked responses. Only the middle ear filtering and cochlear mechanics stages of the CMAP are required to predict DPOAEs. Thus, the model fitting was split into multiple steps. The first step predicted the participant-specific pole function required by the cochlear mechanics stage based on measured DPgrams ( Keshishzadeh & Verhulst, 2021 ). The participant-specific pole function was then frozen and the CMAP was used to simulate participant-specific ANF responses for the ABR and EFR stimuli used in this study. Bayesian regression was then used to estimate the synaptogram. For simplicity, all other model parameters (e.g., synaptic transmission) were assumed to be identical across participants. Separate models were fit using various subsets of the data (e.g., only 4 kHz ABR from all participants, only 4 kHz EFR from all participants, both 4 kHz ABR and 4 kHz EFR from all participants, etc.). All models were fit using Bayesian regression to maximize the Student’s T likelihood of free parameters using pymc ( Salvatier et al., 2016 ). In contrast to a Normal likelihood, a Student’s T likelihood makes the regression more robust to outliers. The prior for both the standard deviation and degrees of freedom were set to Half Cauchy with a scale parameter (beta) of 1. Each model was fit four times for 2000 samples following a 1000 sample burn-in period using the No U-Turn Sampler ( Hoffman & Gelman, 2014 ). Posterior samples were combined across all fits (i.e., chains) for inference. Gelman-Rubin statistics were computed to ensure that the four fits, each of which started with a random estimate for each parameter, converged to the same final estimate ( R̂ < 1.1). 1. Correcting for DPOAEs The CMAP does not directly model OHC function, but instead simulates basilar membrane (BM) vibration using a series of longitudinally coupled cochlear mechanical filters. Each filter is centered at a given cochlear frequency with a frequency-specific tuning parameter (i.e., the double pole of the cochlear admittance). The effects of OHC dysfunction (i.e., broader cochlear filtering and reduced sensitivity) can be simulated by reducing the gain of these filters. Thus, the pole function, α , can be adjusted in a CF-dependent manner to reflect frequency-specific OHC damage in individual human participants ( Verhulst et al., 2012 ; Verhulst et al., 2016 ). The pole function was fit as described in Buran et al. (2022) . Briefly, 26 randomly-generated pole functions with pole values spanning 0.036 to 0.302 were used to simulate DPOAE data for the DPOAE frequencies used in this dataset. This range corresponds to OHC dysfunction profiles that vary from a fully intact to a severely damaged cochlea and was defined across 1001 characteristic frequency channels. A four-layer deep neural network was then trained on the simulated DPOAE dataset. The network consisted of a 16 neuron input layer, two hidden layers with 200 neurons each, and a 1001 neuron output layer. This network was then used to predict participant-specific pole functions, α i . This approach follows Buran et al. (2022) , with the main difference being the inclusion of DPOAE data from extended high frequencies (9-16 kHz), which required expanding the input layer from 11 to 16 neurons. 2. Priors for Synapse Counts As in Buran et al. (2022) , the predicted synapse count per IHC for participant i at position g is modeled as: Participant i ’s maximum number of synapses per IHC (i.e., the height of the parabola at its vertex) is exp( θ i [2] ), the vertex is positioned at cochlear frequency g = G ( θ i [1] ) (i.e., the center frequency), and the curvature of the parabola at the vertex is defined by 2 ⋅ exp( θ i [0] ). G ( g ) is the Greenwood equation ( Greenwood, 1990 ), which converts g to the cochlear frequency corresponding to that position. The vector of participant-specific parabola coefficients, θ i , were assumed to be normally distributed around a population mean vector, Θ , with covariance matrix, Σ . Synapse counts per IHC from a set of 19 human cadaver ears ( Wu et al., 2019 ) were used to generate priors for Θ and Σ . 3. Predicting Synapse Counts As in Buran et al. (2022) , the CMAP was used to predict the instantaneous firing rate for high, medium and low SR ANFs distributed across the tonotopic axis of the human cochlea. The ABR response was calculated from the summed response of the simulated ANF activity scaled by the predicted number of synapses at that cochlear frequency as described by Equation 3 in Buran et al. (2022) with one change. In Buran et al. (2022) , simulated ABRs have a sex-specific scaling factor, exp ( c 0 + c 1 ⋅ S i ), where c 0 is the scaling constant needed to correct for differences between the simulated and measured ABR wave I amplitude, c 1 is the difference between males and females, and S i = 0 if the participant is male, 1 if the participant is female. Due to limitations of the CMAP, simulated ABRs do not recapitulate measured frequency-specific differences in the scale of ABR wave I amplitude ( Supplemental Data Figure 2A ). Thus, c 0 in Equation 3 of Buran et al. (2022) was replaced by a frequency-specific scaling constant, c 0, f , to correct for differences between the simulated and measured waveform at frequency f , giving the frequency and sex-specific scaling factor, exp ( c 0, f + c 1 ⋅ S i ) for the ABR. This scaling constant was assigned a Normal prior with the mean set to the natural logarithm of the ratio of measured to simulated ABR wave I amplitudes for the corresponding frequency and the standard deviation was set to 1. Download figure Open in new tab Supplemental Data Figure 2. Differences between simulated and measured evoked potentials Simulated amplitudes (blue dashed line) for ABR wave I (A) and EFR (B) were generated with the CMAP under the assumption that participants had normal-hearing pole functions with a normal complement of auditory nerve fiber synapses. Measured amplitudes (orange solid line) were calculated as the mean measurements collected from the non-Veteran participants in the sample. Error bars indicate standard error of the mean (SEM). Similar to the ABR response, the EFR response, EFR i ( f ), for participant i was modeled as the sum of the unitary contribution of each fiber to the overall EFR magnitude: is the average number of high SR fibers at the characteristic frequency indexed by k for participant i . ( f ) is the unitary contribution to the EFR of a high ( H ) SR ANF for participant i with characteristic frequency indexed by k responding to an EFR stimulus of frequency f . Medium ( M ) and low ( L ) SR fibers are similarly represented. The unitary contribution is represented as a complex number, thereby ensuring that phase information is preserved. Unlike the ABR, the ratio between simulated and measured EFRs is constant across frequency ( Supplemental Data Figure 2B ), so a frequency-specific scaling factor was not used. Thus, e 0 represents the scaling constant needed to correct for differences between the simulated and measured magnitude. The mean of the prior was set to the logarithm of the average ratio of the measured to simulated EFR magnitude across carrier frequency and the standard deviation was set to 1. Since there may be sex differences in EFR magnitude ( Krizman et al., 2012 ), a sex-specific scaling constant (identical across all participants of a given sex), exp( e 0 + e 1 ⋅ S i ), was included. For the difference between females and males, e 1 , a Normal prior with mean of 0 and standard deviation of 1 was used. All other parameters are represented similarly to the ABR response. Statistical Analysis Models for predicting participant’s age To test the relative ability of the synaptogram to predict the age of each participant, linear regression models were constructed based on θ i , the vector of three parameters defining the shape of participant i ’s synaptogram, as . The age of participant i is represented by y i , θ i , j is the j -th parameter of the participant’s synaptogram, and the participant-specific sex is represented as S i , where S i = 0 if the participant is male, 1 if the participant is female. The residual error term is represented by ε i . It was not necessary to include DPOAEs as predictors in the model since the CMAP corrects for DPOAEs. For the age-prediction analysis, only data from the non-Veteran participants were included to avoid confounds due to military noise exposure (another risk factor for synaptopathy). Although the synaptograms were already corrected for sex, it was necessary to include sex as a predictor to facilitate comparisons with models that used the ABR, EFR, and/or DPOAE measurements to predict age. Since there are reported sex-specific differences in ABR wave I amplitude and EFR magnitude in humans ( Krizman et al., 2012 ; Trune et al., 1988 ), sex should be included as a predictor in models incorporating these measurements. The average age of the female participants in the sample differed from the average age of the male participants by 10 years. Thus, any model incorporating sex as a predictor will outperform a similar model that does not include sex as a predictor. To allow for a fair comparison of the relative performance of models that included predicted synapse counts versus models based on raw ABR, EFR, or DPOAE measurements, sex was incorporated into both types of models. A linear regression model, was used to test the relative ability of each raw evoked potential measurement (ABR, EFR, or DPOAE) to predict participant age. The age of participant i is represented by y i , the participant-specific evoked potential measure j is represented by Μ j , i , the number of evoked potentials is represented by p , the participant-specific DPOAE measure k is represented by ξ k , i , the number of DPOAE measurements is represented by d , and the residual error term is represented by ε i . Since the raw ABR data had two stimulus levels per frequency, each stimulus level was treated as a separate predictor in the model and an interaction term was also included (e.g., p = 3 when using only ABR wave I amplitude at 4 kHz was used to predict age). To establish a baseline for interpreting the prediction error, an intercept-only model (i.e., y i = β 0 + β 2 ⋅ S i + ε i ) was tested in which the predicted age was the average age of all non-Veteran participants in the dataset of the same sex. In addition, because extended high frequency DPOAEs are correlated with age (e.g., Mepani et al., 2021 ), the ability of the DPOAE measurements to predict age was also evaluated (i.e., ). An ideal metric for assessing deafferentation would perform better at predicting age than the intercept-only and the DPOAE-only models. Models for predicting Veteran status To test the ability of predicted synapse counts and raw evoked potentials to predict Veteran status, an approach similar to the one used to predict age was used except linear regression was used to model the log-odds probability of each participant being a Veteran. The linear regression models were formulated identically to those for predicting age, except that y i represented the log-odds probability of participant i being a Veteran. Assessing model performance Model performance was assessed using twenty repeats of 20-fold cross-validation ( Burman, 1989 ) in which models were fit using the Python statsmodels library ( Seabold & Perktold, 2010 ). Cross-validation is a technique used to estimate how well a model will perform on new, unseen data. The data was partitioned into twenty folds (i.e., groups). For each of the twenty iterations, one fold was held out for validation while the model was trained on the pooled data from the other nine folds. The resulting model was then used to predict the ages of the participants from the held-out validation fold. To ensure a stable and robust estimate of model performance, this entire 20-fold process was repeated twenty times, with the participants randomly shuffled each time before partitioning them into folds. For age, prediction error was quantified as the root-mean-squared error (RMSE), , where y k is the age of participant k and is the corresponding age prediction. The mean and standard error of the RMSE was calculated across all repeats and folds. For Veteran status, the area under the receiver-operating characteristic curve (AUC ROC) was calculated. AUC ROC values of 0.5 indicate that the model has no ability to discriminate Veterans from non-Veterans, whereas a value of 1 indicates perfect discrimination. Reanalysis of published mouse data To validate the approach presented in this report of using the ability to predict age as a proxy for the ability to predict deafferentation, mouse data from Buran et al. (2025) was re-analyzed to determine how well synapse counts perform at predicting a mouse’s age (in weeks). For this analysis, noise-exposed mice were excluded. The distributions of measured DPOAE thresholds, ABR wave 1 amplitudes, and synapse counts are shown in the young and aged groups of Figure 1 of Buran et al. (2025) . To test the relative ability of synapse counts to predict age, a linear regression model was constructed, y i = β 0 + β 1 ⋅ ln ⌒ i [ f ] + ε i , where y i is the age of the i -th mouse and the mouse-specific synapse count at frequency f is represented by ⌒ i [ f ] . Model performance was assessed using twenty repeats of 20-fold cross-validation as described above. Download figure Open in new tab Figure 1. The sample generated a large range of DPOAE, ABR, and EFR measurements Plots show variation across individual participants in (A) DPOAE levels, (B) ABR wave I amplitudes, and (C) RAM EFR magnitudes. All RAM EFR data in this study were collected using a modulation frequency of 110 Hz. Data from both non-Veterans (purple) and Veterans (green) are included (N=127). To more closely parallel the linear regression models used to predict age from the human synaptogram coefficients, mouse synaptograms were fit to Equation 1 to obtain mouse-specific synaptograms ( θ i ) and then age was predicted using the linear regression model . Results Measurement characteristics and relationships with age The DPOAE, ABR, and EFR data collected from 127 participants (21 male non-Veterans, 56 female non-Veterans, 38 male Veterans, 12 female Veterans) represents a wide range of cochlear gain loss (OHC dysfunction as indicated by DPOAEs; Figure 1A ), ABR wave I amplitudes ( Figure 1B ) and EFR magnitudes ( Figure 1C ). To investigate relationships between DPOAEs and ABR/EFR, the DPOAE data were split by frequency into regular (DPOAE R , f 2 < 9 kHz) and extended high frequency (DPOAE EHF , f 2 ≥ 9 kHz) DPOAEs and the levels for L 2 = 40 dB SPL were averaged. DPOAE EHF , ABR wave I amplitude for a 4 kHz toneburst (ABR 4 ), and EFR magnitude for a 4 kHz carrier (EFR 4 ) were strongly correlated with age, but DPOAE R was not ( Figure 2 ). Although the majority of participants had normal hearing thresholds (≤ 20 dB HL from 0.25-8 kHz), five non-Veteran participants and 16 Veteran participants had a mild hearing loss (at least one audiometric threshold of 25-40 dB HL from 0.25-8 kHz). Download figure Open in new tab Figure 2. Correlations between auditory physiological measurements (ABR, EFR, and DPOAE) and age DPOAE R levels were not correlated with age (A), but DPOAE EHF levels were (B). ABR wave I amplitudes for a 4 kHz toneburst (ABR 4 ) were correlated with age for both 100 (green) and 110 (orange) dB peSPL stimuli (C). RAM EFR magnitudes for a 4 kHz carrier (EFR 4 ) were not correlated with age (D). DPOAE R is the average DPOAE level for f 2 < 9 kHz, while DPOAE EHF is the average DPOAE level for f 2 ≥ 9 kHz. For both DPOAE R and DPOAE EHF , L 2 = 40 dB SPL. Markers indicate data from individual participants. Pearson’s correlation coefficients and the 95% confidence interval are shown above each plot. A dotted regression line was added if the 95% confidence interval did not overlap with 0. Only data from non-Veterans were included (N=77) due to confounding from military noise exposure in Veterans. Simulated versus measured DPgrams, ABR wave I amplitudes, and EFR magnitudes The ABR, EFR, and DPOAE measurements were used to predict participant synaptograms by fitting the CMAP to various combinations of each evoked potential measure. The first step of fitting the CMAP was to individualize the cochlear gain stage (specifically, the pole function which is an indicator of OHC function), for each participant. Using a neural network model trained on simulated DPgrams generated from random pole functions, a pole function was predicted for each participant and a simulated DPgram was generated from the predicted pole function ( Figure 3A-C ). At frequencies ≤ 4 kHz, simulated DPgrams generally matched measured DPgrams (e.g., f 2 = 4 kHz, Figure 3D ) and simulated DPgrams were strongly correlated with measured DPgrams ( Figure 3E ). However, the difference between simulated and measured DPgrams increased with f 2 frequency ( Figure 3F ), indicating limitations of either the approach to predicting pole functions or the CMAP in accurately simulating high frequency DPOAEs. Download figure Open in new tab Figure 3. Simulated DPgrams approximate observed DPgrams (A-C) Each panel shows the relationship between the simulated DPgram (dotted line) and the measured DPgram (solid line) for an exemplar participant. DPgrams for low (red) and high (blue) L 2 levels are shown separately. (D) For an f 2 of 4 kHz, simulated DPOAE levels match measured DPOAE levels for both a low-( L 2 = 40 dB SPL, red) and high-level ( L 2 = 55 dB SPL, blue) stimulus. Points indicate data from individual participants. (E) The correlations between the simulated and measured DPgrams are high for both low- and high-level stimuli. The correlation coefficient for the simulated vs. actual DPgram is shown separately for each L 2 level (blue versus red). (F) Prediction error was calculated as the root mean squared difference between the simulated and measured DPOAE levels. Average prediction error across all participants is shown separately for each f2 frequency and L 2 level. Data from both non-Veterans and Veterans are included (N=144). Once the CMAP was individualized for each participant, Bayesian regression was used to fit the CMAP to various combinations of each participant’s measured ABR wave I amplitudes and EFR magnitudes. Based on prior histological work in human temporal bones ( Wu et al., 2019 ), the synaptogram was modeled as a parabola and the fits were constrained by the known range of human synapse counts. Synaptograms were predicted using only ABR 4 ( Figure 4A ), only EFR 4 ( Figure 4B ), or both ABR 4 and EFR 4 ( Figure 4C ). To validate how well the predicted synapse counts simulated the evoked potentials, the CMAP (individualized using each participant’s pole function and synaptogram) was used to simulate ABR wave I amplitude and EFR magnitude. The simulations were compared to the measured ABR wave I amplitude ( Figure 4D-F ) and EFR magnitude ( Figure 4G-I ). When fit to only ABR 4, simulated ABR wave I amplitudes were correlated with measured ABR wave I amplitudes ( Figure 4D ); however, simulated EFR magnitudes were not correlated with measured EFR magnitudes ( Figure 4G ). In contrast, when fit to only EFR 4 , both simulated ABR wave I amplitudes and EFR magnitudes were correlated with measured ABR wave I amplitudes and EFR magnitudes ( Figure 4E,H ). When fit to both ABR 4 and EFR 4 , simulated and measured ABR wave I amplitudes were correlated, but not simulated and measured EFR magnitudes. Download figure Open in new tab Figure 4. Predicted synapse counts and simulated evoked potentials after individualizing the CMAP using the DPgram (A-C) Predicted synaptograms (number of synapses per IHC as a function of cochlear frequency) generated using (A) ABR wave I amplitude at 4 kHz (ABR 4 ), (B) RAM EFR magnitude for a 4 kHz carrier (EFR 4 ), or (C) both 4 kHz ABR and EFR (ABR 4 · EFR 4 ). Lines represent individual participants. Vertical dashed line indicates the stimulus frequency. (D-F) Simulated vs. measured ABR wave I amplitudes based on the individualized synaptogram of each participant as shown in A-C. Dots indicate individual participants. A dotted regression line was added if the 95% confidence interval did not overlap with 0. (G-I) Simulated vs. measured EFR magnitude for a 4 kHz carrier based on the individualized synaptogram of each participant as shown in A-C. Dots indicate individual participants. Data from both non-Veterans and Veterans are shown (N=127). Impact of individualizing the CMAP with DPgrams To assess whether individualizing the CMAP for each participant’s DPgram affected the predicted synaptogram, synaptograms were regenerated for each participant under the assumption that all participants had a normal hearing pole function. Each participant’s synaptogram was modeled by a parabola that is defined by a vector of three coefficients, θ i , representing the curvature at the vertex, the center frequency (i.e., cochlear frequency of the vertex), and the maximum number of synapses per IHC. Notably, individualizing the CMAP using the DPgram had little impact on any of the synaptopgram coefficients generated by ABR 4 ( Figure 5 ). In contrast, synaptograms generated by fits to EFR 4 or both ABR 4 and EFR 4 were affected by individualizing the CMAP. This suggests that ABR wave I amplitude, in contrast to EFR magnitude, is a robust measure of auditory nerve function even in the presence of OHC dysfunction (up to a mild hearing loss). Download figure Open in new tab Figure 5. Change in the shape of the synaptogram after individualizing the CMAP based on the DPgram The synaptogram coefficients were transformed into three parameters: curvature at the vertex (A), center frequency (B), and maximum number of synapses per IHC (C). The change in each parameter resulting from individualizing the CMAP relative to the value when the synaptogram was fit using a normal hearing pole function is plotted in A-C. Dots indicate individual participants and colors indicate the evoked potentials that were used to predict synapse numbers. Data from both non-Veterans and Veterans were included (N=127). Validating synapse predictions using age Proof of concept using mouse data Since there is currently no means of directly validating predictions of cochlear deafferentation in living humans, many studies have turned to proxy measures such as performance on complex speech perception tasks (e.g., Garrett et al., 2025 ; Grant et al., 2020 ; Harris et al., 2021 ; Mepani et al., 2021 ). However, post-mortem temporal bone data demonstrate that age is a key predictor of cochlear synapse counts ( Wu et al., 2019 ). As a proof-of-concept, cochlear synapse counts from mouse ( Buran et al., 2025 ) were used to assess how well the number of synapses per IHC can predict a mouse’s age ( Figure 6 ). The ages of the mice ranged from 10-104 weeks. For this analysis, mice that were noise exposed were excluded. Notably, there was considerable variability in cochlear synapse numbers within age groups (e.g., 14 to 22 synapses/IHC for the youngest age group). Despite this variability, there was a strong, inverse correlation between synapse count and age ( Figure 6A ). Using a simple linear regression model, mouse age was predicted using synapse counts at 16 kHz ( Figure 6B ). The prediction error for linear regression models using synapse counts at various cochlear frequencies to predict age was quantified as the root-mean-squared error (RMSE) of the difference in predicted vs. actual age ( Figure 6C ). To provide a baseline against which predictions could be compared, an intercept-only model was fit in which the predicted synapse count was set to the average number of synapses per IHC in the sample (blue bar in Figure 6C ). Except for synapse counts at 5.6 kHz (the most apical region of the cochlea), synapse counts across cochlear frequencies did a good job of predicting the age of each mouse, with prediction errors ranging from 22.6-30.5 weeks. To provide a more direct parallel to our method of predicting age from the individualized synaptograms in humans, synapse count data from the mice were also fit to Equation 1 . The resulting mouse-specific synaptograms performed well at predicting the age of each mouse (prediction error of 22.3 weeks). Download figure Open in new tab Figure 6. Proof of concept for validating synapse predictions using age Plots show reanalysis of synapse count data from Buran et al. (2025) , collected from CBA/CaJ mice spanning a large range of ages. (A) Synapses per IHC at 16 kHz were inversely correlated with the age of the mouse. (B) Cross validation using a linear regression model shows that predicted synapse count at 16 kHz can be used to predict mouse age. (C) Prediction error for predicting mouse age from synapse counts for each cochlear frequency. To parallel the analysis of synaptograms vs. age analysis done in humans, synaptograms were also fit to the synapse count data for mouse. The resulting mouse-specific vector of three coefficients ( θ i ) was then used to predict mouse age. The shaded blue region indicates baseline performance, as indicated by an intercept-only model in which the age is set to the average age of the mice in the sample. Data from 31 mice were included. Results that overlap with the blue region (e.g., 5.6 kHz) indicate that the model was not predictive of mouse age. Better performance is indicated by smaller prediction error. Predicting age from the individualized synaptograms Consistent with expectations, each of the three coefficients defining the synaptograms for the non-Veteran participants fit to ABR 4 were correlated with participant age ( Figure 7A-C ). With advancing age, the predicted maximum number of synapses decreased, the center frequency shifted towards the base (i.e., became lower in frequency), and the curvature at the vertex increased (i.e., the parabola became narrower). Download figure Open in new tab Figure 7. Relationship between predicted synaptograms and age Synaptograms were generated for each human participant using ABR 4 after individualizing the CMAP for each participant using their DPgram. (A-C) Relationship between each coefficient of the synaptogram and participant age: curvature at the vertex (A), center frequency (B), and the maximum number of synapses per IHC (C). See Priors for Synapse Counts in Methods for details regarding the transformation of each coefficient into interpretable values. (D) Cross validation using a linear regression model incorporating the three synaptogram coefficients as predictors was used to predict participant age (y-axis). These predictions were compared with actual age (x-axis). The predicted age was set to the average across all repeats and folds. Regression lines in each plot show the best fit to the data. Markers indicate data from individual participants. Pearson’s correlation coefficients and 95% confidence intervals are provided above each plot. Only data from non-Veterans were included (N=77). While in Buran et al. (2022) age was predicted with a single synaptogram coefficient, the maximum number of synapses per IHC; in this study, all three coefficients were used in a linear regression model to predict the age of each participant ( Figure 7D ). Although the CMAP includes a correction for sex, all regression models included sex as a predictor (including the baseline model) to facilitate comparison with age predictions generated using the raw ABR, EFR, and/or DPOAE data, which may require a correction for sex. Thus, the baseline model (blue bar in Figure 8 ) represents age predictions based on the average age for all males or all females in the sample, depending on the participant’s sex (as opposed to the average age of the full sample). Prediction error was then quantified for each synaptogram as generated by the individualized CMAP fit to various subsets of the evoked potential measurements (green markers in Figure 8A-C ). To assess whether individualization of the CMAP led to improved age prediction performance, the models were refit using a CMAP with a normal hearing pole function (orange markers in Figure 8A-C ). Out of all the ABR/EFR measurements or combinations of measurements used to generate synaptograms, synaptograms generated from ABR 5.6 (individualized for the DPgram) performed the best at predicting age (RMSE of 5.94 years, standard error of the mean [SEM] = ± 0.14 years). ABR 4 (with a normal hearing pole function) and ABR 4 · EFR 4 (individualized) were the next-best performers (RMSE = 6.18 years, SEM = ± 0.14 years and RMSE = 6.02 years, SEM = ± 0.13 years, respectively). Synaptograms generated from EFR measurements alone did not perform better than the baseline model ( Figure 8B ). Only ABR 5.6 and ABR 4 · EFR 4 demonstrated a benefit of individualizing the CMAP, with improvements in prediction error of 0.26 (± 0.04 SEM) and 0.35 (± 0.06 SEM) years, respectively. Download figure Open in new tab Figure 8. Comparison of linear regression models for their ability to predict participant age All models were evaluated using cross-validation. Prediction error is reported as the root-mean-squared error (RMSE) of the difference between the participant’s actual versus predicted age. (A-C) Models that used the synaptogram ( θ i ) to predict participant age are grouped by the combination of evoked potentials used to generate the predicted synaptograms: ABR (A), EFR (B), or combination of ABR and EFR (C). The stimuli used to generate the predictions are indicated as θ i ( stimuli ). For all ABR models, both stimulus levels were used for generating the synapse predictions. For ABR all , the click ABR, all six ABR tone frequencies (1 through 8 kHz), and both stimulus levels were used for generating the synapse predictions. For EFR all , all three EFR carrier frequencies were used for generating the synapse predictions. Predictions were generated using a CMAP individualized for each participant’s DPgram (individualized; green points). Predictions were also generated under the assumption that each participant had normal cochlear gain (NH; orange points). (D-G) Models that used raw DPOAE or evoked potential measures to predict participant age are grouped by measure: DPOAE (D), ABR (E), EFR (F), combination of ABR and EFR (G). For all panels, the shaded blue region indicates baseline performance +/- standard error of the mean (SEM) for a model where the predicted age was based on sex only. Results that overlap with the blue region (e.g., NH EFR 1 ) indicate that the model was not predictive of participant age. Better performance is indicated by smaller prediction error. Markers indicate the RMSE of the corresponding model averaged across all repeats and folds and error bars indicate the SEM of the RMSE. Only data from non-Veterans were included (N=77). Predicting age from raw physiological responses As an alternative to the phenomenologically-inspired data reduction approach of combining multiple measures into a vector of three coefficients describing each participant’s synaptogram, simple linear regression models can be used to predict participant age based on one or more auditory physiological measurements (ABR, EFR, and DPOAE; Figure 8D-G ). As for the synaptogram prediction models, the age prediction performance of the baseline model (average age for males/females in the sample) was plotted against the results for all other regression models (blue bar in Figure 8 ). A regression model with only DPOAE R and DPOAE EHF as predictors ( Figure 8D ) provides a reference for comparing the models that include evoked potentials as predictors when adjusted for DPOAE function. With the exception of the ABR 2 and ABR 2.8 models, linear regression models incorporating ABR measurements generally performed better than the baseline model and the DPOAE-only model ( Figure 8E ). High frequency ABR models (ABR 4 , ABR 5.6 , and ABR 8 ) outperformed low frequency ABR models (ABR 1 , ABR 2 , ABR 2.8 ), the click ABR model (ABR c ), and a model which combined all the ABR stimuli (ABR all ). The best-performing models were ABR 4 and ABR 5.6 , adjusted for DPOAEs (green markers in Figure 8E ), which were able to predict participant age within 6.01 (±0.17 SEM) and 6.02 (± 0.15 SEM) years, respectively. Including DPOAE R and DPOAE EHF in these models improved the prediction error by 0.06 years (±0.05 SEM) for ABR 4 and 0.10 years (±0.05 SEM) for ABR 5.6 (green markers vs orange markers in Figure 8E ). In contrast, models incorporating EFR measurements did not outperform the DPOAE-only model, even when adjusted for DPOAEs (green markers in Figure 8F ). EFRs models that did not include an adjustment for DPOAEs (orange markers in Figure 8F ) performed similarly to the baseline model, suggesting that the improved age prediction performance of the DPOAE-adjusted EFR models were driven largely by the inclusion of DPOAEs as a predictor. Finally, a model that included both ABR 4 and EFR 4 and was adjusted for DPOAEs performed slightly better (RMSE = 5.93 years, ±0.17 SEM) than the ABR 4 model adjusted for DPOAEs, with the DPOAE adjustment accounting for an improvement in prediction error of 0.35 years (± 0.06 SEM) ( Figure 8G ). In contrast, including all of the ABR and EFR measurements in a model resulted in age prediction performance that was similar to the baseline model ( Figure 8G ). Validating synapse predictions using Veteran status Predicting Veteran status from the individualized synaptograms Because high levels of noise exposure are common during military service, Veterans typically have higher lifetime noise exposure than non-Veterans, which places them at higher risk for noise-induced synaptopathy ( Bramhall et al., 2017 ). As an alternative method of validating the predicted synapse counts derived from evoked potential measures and the CMAP, the ability of synaptograms to predict whether a participant was a Veteran or a non-Veteran was evaluated. A binomial classifier was built using a linear regression model with the three synaptogram coefficients as predictors. The output of the model was the probability that a participant was a Veteran. The set of predicted probabilities for each participant was then used to build a ROC curve. Prediction error was quantified as the AUC ROC, with 0.5 indicating a random guess and 1 indicating perfect performance. Although the CMAP accounts for sex, all models, including the baseline model, included sex as a predictor to facilitate comparison with predictions generated using the raw ABR, EFR, and DPOAE data, which may require an adjustment for sex. Given that there were sex imbalances in both the Veteran (more males than females) and the non-Veteran (more females than males) groups, a baseline model that used sex to predict Veteran status outperformed chance (blue bar in Figure 9 ). Download figure Open in new tab Figure 9. Comparison of linear regression models for their ability to predict Veteran status All models were evaluated using cross-validation. Prediction error is reported as the AUC of the ROC. (A-C) Models that used the synaptogram ( θ i ) to predict Veteran status are grouped by the combination of evoked potentials used to generate θ i : ABR (A), EFR (B), or combination of ABR and EFR (C). The stimuli used to generate the predictions are indicated as θ i ( stimuli ). For all ABR models, both stimulus levels were used for generating the synapse predictions. For ABR all , the click ABR, all six ABR tone frequencies (1 through 8 kHz), and both stimulus levels were used for generating the synapse predictions. For EFR all , all three EFR carrier frequencies were used for generating the synapse predictions. Predictions were generated using a CMAP individualized for each participant’s DPgram (individualized; green points). Predictions were also generated under the assumption that each participant had normal cochlear gain (NH; orange points). (D-G) Models that used raw DPOAE or evoked potential measures to predict Veteran status are grouped by measure: DPOAE (D), ABR (E), EFR (F), combination of ABR and EFR (G). For all panels, the shaded blue region indicates baseline performance +/- standard error of the mean (SEM) for a model where the predicted Veteran status was based on sex only. Better performance is indicated by a higher ROC AUC. Markers indicate the ROC AUC of the corresponding model averaged across all repeats and folds and error bars indicate the SEM of the ROC AUC. Data from both non-Veterans and Veterans are included (N=127). Regardless of the measures used to predict synapse counts, all models that included synaptograms performed better than the baseline model ( Figure 9A-C ). The best performing model used synaptopgrams that were fit using EFR all and were not individualized based on the DPgram (AUC=0.86 ±0.01 SEM; Figure 9B ). Other top performing models included those where synaptograms were fit without individualizing based on the DPgram using either ABR all (AUC=0.83 ±0.01 SEM; Figure 9A ), EFR 2 (AUC = 0.84 ±0.01 SEM; Figure 9B ), EFR 4 (AUC = 0.83 ±0.01 SEM; Figure 9B ), or a combination of ABR all and EFR all (AUC=0.84 ±0.01 SEM, Figure 9C ). Individualizing the synaptogram based on the DPgram generally did not improve model performance. Predicting Veteran status from raw physiological responses As with the age prediction analysis, linear regression models including ABR, EFR, and/or DPOAE measurements as predictors were also evaluated for their ability to predict Veteran status. Models including only DPOAEs and sex performed well at predicting Veteran status (AUC=0.82 ±0.01 SEM, Figure 9D ). All evoked potential models, regardless of whether they included DPOAEs, outperformed the baseline model that predicted Veteran status based on sex ( Figure 9E-G ). However, the evoked potential models that included DPOAEs performed similarly to the DPOAE-only models and the evoked potential models without DPOAEs were worse at predicting Veteran status than the DPOAE-only models. Models that included both ABR and EFR measurements ( Figure 9G ) did not perform better than the ABR-only or EFR-only models. Discussion In a sample of 127 adults with relatively good hearing (77 non-Veterans and 50 Veterans) and a wide range of DPOAE, ABR, and EFR responses, a CMAP was used with Bayesian regression to predict the number of synapses per IHC as a function of cochlear frequency (synaptogram) for each participant. The CMAP can combine multiple DPOAE, ABR, and EFR measurements in response to different stimuli, thereby reducing a heterogenous set of measurements to a set of predicted synapse counts across cochlear frequency, the synaptogram. This study expanded on Buran et al. (2022) , where the CMAP was used with ABR wave I amplitude at multiple frequencies and levels to predict synaptograms in individuals with normal hearing thresholds, by 1) incorporating EFR measurements, 2) including participants with up to a mild sensorineural hearing loss, allowing for better assessment of the cochlear gain individualization, 3) incorporating extended high frequency (EHF) DPOAEs, 4) collecting a broader range of ABR frequencies similar to animal studies of synaptopathy (e.g., Buran et al., 2025 ), and 5) incorporating the full shape of the synaptogram into comparisons with age and Veteran status rather than just using the coefficient representing the maximum number of synapses per IHC. Since the models did not include participant age or Veteran status as a predictor, the models are a measure of how well the incorporated coefficients can predict two key risk factors for cochlear synaptopathy: age and history of military noise exposure. Mouse data validates the ability of synapse counts to accurately predict age Reanalysis of data from a study that histologically quantified cochlear synapses in 31 mice encompassing a large age span ( Buran et al., 2025 ) demonstrates that, despite variability in synapse counts across individual mice (14-22 synapses per IHC in the youngest mice), synapse counts can be used to predict an individual mouse’s age significantly better than chance ( Figure 6C ). The observed variability in synapse counts among mice of the same age ( Figure 6A ) raises the possibility that much of the observed inter-subject variability in ABR wave 1/I amplitude seen in animals/humans may be due to variability in the actual degree of cochlear deafferentation across subjects rather than anatomical variation. High frequency ABR wave I amplitudes performed best at predicting participant age Linear regression models incorporating either synaptograms generated from ABR 5.6 , synaptograms generated from ABR 4 and EFR 4 , or high frequency raw ABR wave I amplitudes (ABR 4 , ABR 5.6 , and ABR 8 ) performed best at predicting participant age. Combining multiple evoked potential measurements, either as independent predictors in a linear regression model or when predicting synapse counts using the CMAP, generally did not offer large improvements in prediction of age. Given previous studies showing a decrease in SAM ( Bramhall et al., 2023 ) and RAM ( Van Der Biest et al., 2023 ) EFR magnitude with age, it was surprising that the RAM EFR was not a better predictor of age. However, cochlear deafferentation has been demonstrated to lead to central gain in the auditory brainstem ( Chambers et al., 2016 ; Salvi et al., 2017 ) and the 110 Hz modulation frequency used in this study results in auditory brainstem contributions to the EFR response ( Kuwada et al., 2002 ). Therefore, central gain may confound interpretation of the RAM EFR modulated at 110 Hz. In Buran et al. (2025) , the RAM EFR modulated at 110 Hz performed more poorly than ABR wave 1 amplitude at predicting synapse counts in mice. In contrast, the RAM EFR modulated at 1000 Hz, which is believed to be generated primarily by the auditory nerve ( Shaheen et al., 2015 ), performed better than both RAM EFR modulated at 110 Hz and ABR wave I amplitude at predicting synapse counts in mice with broad synaptopathy across cochlear frequency. The discrepancy between the results of the current study and the results of Van Der Biest et al. may be due to how the relationship between age and RAM EFR magnitude was evaluated. In the current study, age was treated as a continuous variable, while the Van Der Biest et al. study compared RAM EFR magnitudes for groups of young and older adults. If the data from the current study is split into two age groups following Van Der Biest et al. ( 45 years of age) a difference in RAM EFR magnitude is observed between the two groups (two-sample t-test, t(39) = 2.32, p=0.03). Including DPOAEs improved age predictions for the best performing synaptogram models, but had limited effect on the best performing raw ABR wave I amplitude models In terms of predicting age, there was a benefit to individualizing the synaptogram to the participant’s DPgram when ABR 5.6 or ABR 4 combined with EFR 4 were used to generate the synaptogram, but not when ABR 4 was used by itself. One possible explanation for this finding is that suprathreshold ABR wave I amplitude for a 4 kHz toneburst is less impacted by OHC dysfunction than ABR wave I amplitude for a 5.6 kHz toneburst and EFR magnitude for a 4 kHz carrier. Since OHC dysfunction generally begins in the base (i.e., high frequency) and progresses apically (i.e., towards low frequencies), auditory nerve fibers responding to a 5.6 kHz toneburst are more likely to be affected by OHC dysfunction than fibers responding to a 4 kHz toneburst. The age predictions from the raw EFR data support this hypothesis, showing that age predictions improve for EFR models that include DPOAEs. However, the raw ABR 4 and raw ABR 5.6 models perform almost identically, with very small improvements in age predictions when DPOAEs are included. In contrast to the synaptogram models, there was little improvement in age predictions when DPOAEs were included in the high frequency ABR models ( Figure 8E orange versus green). This is consistent with the results of Buran et al. (2025) where the ability to predict synapse counts in mice (which had a large range of auditory thresholds) with ABR wave 1 amplitude measurements was similar regardless of whether an adjustment for DPOAEs was used. This suggests that, at least for individuals with relatively good hearing (no more than a mild hearing loss), high frequency suprathreshold ABR wave I amplitudes can be used as an indicator of cochlear deafferentation without adjusting for OHC function. This may be a bit surprising given the results of Verhulst et al. (2016 )in which computational modeling showed that the ABR is sensitive to high frequency cochlear gain loss. However, their simulations demonstrated that there is a bigger impact of cochlear gain loss on wave V amplitude than wave I amplitude. Further, they reported that the effect of simulated synaptopathy on ABR wave I amplitude was much greater than the effect of cochlear gain loss, with the effect of cochlear gain loss decreasing as synaptopathy increased. Veteran status was best predicted by synaptograms generated from all EFR measurements Synaptograms generated from all the RAM EFR measurements (1, 2, and 4 kHz carriers), without individualization for the DPgram, performed best at predicting Veteran status. This is surprising given that the synaptograms generated from EFR measurements did not perform well at predicting age. In addition, the results of Buran et al. (2025) indicate that in mice with acute noise-induced cochlear synaptopathy, ABR wave 1 amplitude is a better predictor of synapse number than RAM EFR modulated at 110 Hz. It is possible that the EFR magnitude is well correlated with a Veteran characteristic other than synaptopathy (e.g., central gain) that is allowing the EFR synaptograms to perform so well at predicting Veteran status. DPOAEs performed well at predicting Veteran status The linear regression models that included only DPOAEs and sex as predictors performed quite well at predicting Veteran status. In addition, prediction of Veteran status improved for all of the single stimulus raw ABR and EFR regression models when DPOAEs were included as a predictor. This is not surprising given that a history of military noise exposure is likely to negatively impact DPOAEs. Previous synaptopathy studies comparing young Veterans and non-Veterans showed that Veterans tended to have poorer DPOAEs even when their audiometric thresholds were normal ( Bramhall, McMillan, & Mashburn, 2021 ). Given that none of the regression models using raw evoked potential measurements show a clear improvement in the ability to predict Veteran status over the DPOAE models, the Veteran status analysis does not provide any clear evidence of which evoked potential measurement is the best indicator of noise-induced cochlear deafferentation. Limitations of the prediction error for DP-grams The greater prediction error in the simulated DP-grams for f 2 frequencies greater than 4 kHz may have adversely impacted our ability to properly individualize the CMAP for each participant for high frequency ABR and EFR stimuli. This may impact the ability of the CMAP to separate the effects of OHC dysfunction from the effects of cochlear deafferentation. However, as shown by computational simulations, the impact of the most severe cochlear gain loss configurations on ABR wave I amplitude was small compared to the impact of cochlear synaptopathy ( Verhulst et al., 2016 ). Auditory nerve fibers, even at the highest stimulus levels, are not sensitive to frequencies greater than half an octave above their CF ( Liberman, 1978 ). Thus, conclusions for ABR 4 are less likely to be impacted by the poorer performance of the individualization algorithm at frequencies greater than 4 kHz as compared to ABR 5.6 and ABR 8 . Utility of using the CMAP to predict synapse counts Although the age prediction results suggest that using the CMAP may not improve synapse prediction over use of raw ABR measurements and it’s unclear how to interpret the results of the Veteran status prediction analysis, the CMAP still has advantages when working with multiple ABR and/or EFR measurements collected in response to different stimuli. For example, the model to predict age that included the full set of raw ABR and EFR measurements did not perform much better than chance. This is likely due to overfitting, where the large number of parameters relative to the size of the sample results in the model having greater difficulty recognizing meaningful patterns ( Hastie et al., 2009 ). While there are statistical techniques that can be used to address overfitting (e.g., feature selection, dimensionality reduction, or regularization), these techniques can make it more challenging to interpret the results, are sometimes unstable when predictors are correlated, and increase the risk of losing relevant information. A simpler, more interpretable approach is to analyze each predictor independently against the outcome; however, this leads to decreased statistical power due to the necessity of correcting for multiple comparisons. By defining structured relationships between OAEs, ABRs, and EFRs, the CMAP offers a simple approach to address the collinearity issue by pinpointing the theoretical source of the individual differences – the participant’s synaptogram. The utility of using the CMAP when incorporating multiple ABR and EFR measurements is demonstrated by the superior ability of models that included synaptograms generated from all the ABR and EFR measurements to predict age compared to models that included all the raw ABR and EFR measurements ( Figure 8C versus 8G). Using the CMAP to predict synaptograms in individual participants also allows for data to be combined in a meta-analysis across studies that collected different ABR or EFR stimuli. For example, synaptograms could be predicted for all the participants in human studies that investigated the relationship between synaptopathy and tinnitus even though the studies used different ABR and EFR measurements to estimate the degree of cochlear deafferentation. Synaptograms could then be evaluated for relationships with tinnitus across the participants for all the studies. Conclusions The results of this study suggest that, of the synaptograms and raw ABR and EFR measurements that were evaluated, synaptograms generated from ABR wave I amplitudes for a 5.6 kHz toneburst and raw ABR wave I amplitudes for 4 and 5.6 kHz tonebursts were the single best predictors of age. This suggests that ABR wave I amplitudes for 4 and 5.6 kHz tonebursts are better than other evoked potential measures at predicting age-related cochlear deafferentation. Combining 4 kHz ABR wave I amplitude and RAM EFR measurements may slightly improve the ability to detect cochlear deafferentation, but the additional benefit may not outweigh the extra time required to collect both measurements. Although individualizing the synaptogram for the DPgram improved age predictions using the ABR 5.6 synaptogram, adjusting for DPOAEs had limited benefit for the raw ABR 4 or ABR 5.6 regression models in terms of improving the ability to predict age. This suggests that, in individuals with up to a mild hearing loss, it may not be necessary to adjust high frequency ABR wave I amplitudes for DPOAEs when estimating cochlear deafferentation. Although recommendations for an evoked potential measure to detect noise-induced cochlear deafferentation cannot be generated from the results of the current study due to confounding from OHC dysfunction, the findings of Buran et al. (2025) indicate that, in mice, ABR wave 1 amplitude is the best evoked potential measure for detecting the focal synaptopathy that results from acute noise exposure. Given these data, measurement of high frequency suprathreshold ABR wave I amplitude is recommended for future human studies of synaptopathy/deafferentation. Synaptograms generated using the CMAP and raw ABR wave I amplitude measurements (for a 4 or 5.6 kHz toneburst) performed similarly at predicting age, partially validating the ability of the CMAP to accurately predict the synaptogram from ABR wave I amplitude data. However, the similar age prediction performance of the synaptograms and the raw ABR wave I amplitude data suggest that use of the CMAP is not necessary to estimate deafferentation in individual human participants. Synapse prediction using the CMAP may be better suited for meta-analyses where the collected evoked potential measurements differ across studies. Data Availability All data produced in the present study are available upon reasonable request to the corresponding author. Acknowledgements This work was supported by the National Institutes of Health, National Institute on Deafness and Other Communication Disorders - Award #R01DC020423 (to N.F.B). and by resources and facilities at the VA National Center for Rehabilitative Auditory Research (NCRAR) [Center Award #C2361C/I50 RX002361] at the VA Portland Health Care System in Portland, OR. The opinions and assertions presented are private views of the authors and are not to be construed as official or as necessarily reflecting the views of the National Institutes of Health or the Department of Veterans Affairs. Footnotes Financial disclosures/conflicts of interest: This study was funded by the National Institutes of Health, National Institute on Deafness and Other Communication Disorders - Award #R01DC020423 (to N.F.B). and by resources and facilities at the VA National Center for Rehabilitative Auditory Research (NCRAR) [Center Award #C2361C/I50 RX002361] at the VA Portland Health Care System in Portland, OR. There are no conflicts of interest, financial, or otherwise. References ↵ Bramhall , N. F. , Konrad-Martin , D. , McMillan , G. P. , & Griest , S. E . ( 2017 ). Auditory Brainstem Response Altered in Humans With Noise Exposure Despite Normal Outer Hair Cell Function . Ear Hear , 38 ( 1 ), e1 – e12 . doi: 10.1097/AUD.0000000000000370 OpenUrl CrossRef PubMed ↵ Bramhall , N. F . ( 2021 ). Use of the auditory brainstem response for assessment of cochlear synaptopathy in humans . J Acoust Soc Am , 150 ( 6 ), 4440 . doi: 10.1121/10.0007484 OpenUrl CrossRef PubMed ↵ Bramhall , N. F. , McMillan , G. P. , & Kampel , S. D . ( 2021 a). Envelope following response measurements in young veterans are consistent with noise-induced cochlear synaptopathy . Hear Res , 408 , 108310 . doi: 10.1016/j.heares.2021.108310 OpenUrl CrossRef PubMed ↵ Bramhall , N. F. , McMillan , G. P. , & Mashburn , A. N . ( 2021 b). Subclinical Auditory Dysfunction: Relationship Between Distortion Product Otoacoustic Emissions and the Audiogram . Am J Audiol , 1 – 16 . doi: 10.1044/2020_AJA-20-00056 OpenUrl CrossRef ↵ Bramhall , N. F. , Theodoroff , S. M. , McMillan , G. P. , Kampel , S. D. , & Buran , B. N . ( 2023 ). Associations Between Physiological Correlates of Cochlear Synaptopathy and Tinnitus in a Veteran Population . J Speech Lang Hear Res , 66 ( 11 ), 4635 – 4652 . doi: 10.1044/2023_JSLHR-23-00234 OpenUrl CrossRef PubMed ↵ Bramhall , N. F. , & McMillan , G. P . ( 2024 ). Perceptual Consequences of Cochlear Deafferentation in Humans . Trends Hear , 28 , 23312165241239541 . doi: 10.1177/23312165241239541 OpenUrl CrossRef ↵ Buran , B. N . ( 2015 ). Auditory-wave-analysis: v1.1 . Retrieved April 14 , 2017 from http://zenodo.org/record/17365#.VoMCrjbUi70 ↵ Buran , B. N. , McMillan , G. P. , Keshishzadeh , S. , Verhulst , S. , & Bramhall , N. F . ( 2022 ). Predicting synapse counts in living humans by combining computational models with auditory physiology . J Acoust Soc Am , 151 ( 1 ), 561 . doi: 10.1121/10.0009238 OpenUrl CrossRef PubMed ↵ Buran , B. N. , Elkins , S. , He , W. , & Bramhall , N. F . ( 2025 ). Predicting cochlear synaptopathy in mice with varying degrees of outer hair cell dysfunction using auditory evoked potentials . bioRxiv , 2025 . 2005 . 2009 .653157. doi: 10.1101/2025.05.09.653157 OpenUrl Abstract / FREE Full Text ↵ Burman , P . ( 1989 ). A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods . Biometrika , 76 ( 3 ), 503 – 514 . doi: 10.1093/biomet/76.3.503 OpenUrl CrossRef Web of Science ↵ Chambers , A. R. , Resnik , J. , Yuan , Y. , Whitton , J. P. , Edge , A. S. , Liberman , M. C. , & Polley , D. B . ( 2016 ). Central Gain Restores Auditory Processing following Near-Complete Cochlear Denervation . Neuron , 89 ( 4 ), 867 – 879 . doi: 10.1016/j.neuron.2015.12.041 OpenUrl CrossRef PubMed ↵ Dolphin , W. F. , & Mountain , D. C . ( 1992 ). The envelope following response: scalp potentials elicited in the Mongolian gerbil using sinusoidally AM acoustic signals . Hear Res , 58 ( 1 ), 70 – 78 . doi: 10.1016/0378-5955(92)90010-k OpenUrl CrossRef PubMed Web of Science ↵ Encina-Llamas , G. , Harte , J. M. , Dau , T. , Shinn-Cunningham , B. , & Epp , B . ( 2019 ). Investigating the Effect of Cochlear Synaptopathy on Envelope Following Responses Using a Model of the Auditory Nerve . J Assoc Res Otolaryngol , 20 ( 4 ), 363 – 382 . doi: 10.1007/s10162-019-00721-7 OpenUrl CrossRef PubMed ↵ Fernandez , K. A. , Jeffers , P. W. , Lall , K. , Liberman , M. C. , & Kujawa , S. G . ( 2015 ). Aging after noise exposure: acceleration of cochlear synaptopathy in “recovered” ears . J Neurosci , 35 ( 19 ), 7509 – 7520 . doi: 10.1523/JNEUROSCI.5138-14.2015 OpenUrl Abstract / FREE Full Text ↵ Furman , A. C. , Kujawa , S. G. , & Liberman , M. C . ( 2013 ). Noise-induced cochlear neuropathy is selective for fibers with low spontaneous rates . J Neurophysiol , 110 ( 3 ), 577 – 586 . doi: 10.1152/jn.00164.2013 OpenUrl CrossRef PubMed Web of Science ↵ Garrett , M. , Vasilkov , V. , Mauermann , M. , Devolder , P. , Wilson , J. L. , Gonzales , L. , Henry , K. S. , & Verhulst , S . ( 2025 ). Deciphering Compromised Speech-in-Noise Intelligibility in Older Listeners: The Role of Cochlear Synaptopathy . eNeuro , 12 ( 2 ). doi: 10.1523/ENEURO.0182-24.2024 OpenUrl Abstract / FREE Full Text ↵ Garrett , M. , & Verhulst , S . ( 2019 ). Applicability of subcortical EEG metrics of synaptopathy to older listeners with impaired audiograms . Hear Res , 380 , 150 – 165 . OpenUrl CrossRef PubMed ↵ Grant , K. J. , Mepani , A. M. , Wu , P. , Hancock , K. E. , de Gruttola , V. , Liberman , M. C. , & Maison , S. F. ( 2020 ). Electrophysiological markers of cochlear function correlate with hearing-in-noise performance among audiometrically normal subjects . J Neurophysiol , 124 ( 2 ), 418 – 431 . doi: 10.1152/jn.00016.2020 OpenUrl CrossRef PubMed ↵ Greenwood , D. D . ( 1990 ). A cochlear frequency-position function for several species--29 years later . J Acoust Soc Am , 87 ( 6 ), 2592 – 2605 . doi: 10.1121/1.399052 OpenUrl CrossRef PubMed Web of Science ↵ Harris , K. C. , Ahlstrom , J. B. , Dias , J. W. , Kerouac , L. B. , McClaskey , C. M. , Dubno , J. R. , & Eckert , M. A . ( 2021 ). Neural Presbyacusis in Humans Inferred from Age-Related Differences in Auditory Nerve Function and Structure . J Neurosci , 41 ( 50 ), 10293 – 10304 . doi: 10.1523/JNEUROSCI.1747-21.2021 OpenUrl Abstract / FREE Full Text ↵ Hashimoto , I. , Ishiyama , Y. , Yoshimoto , T. , & Nemoto , S . ( 1981 ). Brain-stem auditory-evoked potentials recorded directly from human brain-stem and thalamus . Brain , 104 ( Pt 4 ), 841 – 859 . OpenUrl CrossRef PubMed ↵ Hastie , T. , Tibshirani , R. , & Friedman , J . ( 2009 ). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.) . Springer-Verlag . ↵ Herdman , A. T. , Lins , O. , Van Roon , P. , Stapells , D. R. , Scherg , M. , & Picton , T. W. ( 2002 ). Intracerebral sources of human auditory steady-state responses . Brain Topogr , 15 ( 2 ), 69 – 86 . doi: 10.1023/a:1021470822922 OpenUrl CrossRef PubMed Web of Science ↵ Hoffman , M. D. , & Gelman , A . ( 2014 ). The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo . J Mach Learn Res , 15 , 1593 – 1623 . OpenUrl CrossRef ↵ Keshishzadeh , S. , & Verhulst , S . ( 2021 ). Individualized Cochlear Models Based on Distortion Product Otoacoustic Emissions . Conf Proc IEEE Eng Med Biol Soc(2021) , 403 - 407 . doi: 10.1109/EMBC46164.2021.9629808 OpenUrl CrossRef ↵ Kiren , T. , Aoyagi , M. , Furuse , H. , & Koike , Y . ( 1994 ). An experimental study on the generator of amplitude-modulation following response . Acta Otolaryngol Suppl , 511 , 28 – 33 . OpenUrl PubMed ↵ Konrad-Martin , D. , Poling , G. L. , Dreisbach , L. E. , Reavis , K. M. , McMillan , G. P. , Lapsley Miller , J. A. , & Marshall , L . ( 2016 ). Serial Monitoring of Otoacoustic Emissions in Clinical Trials . Otol Neurotol , 37 ( 8 ), e286 – 294 . doi: 10.1097/MAO.0000000000001134 OpenUrl CrossRef ↵ Krizman , J. , Skoe , E. , & Kraus , N . ( 2012 ). Sex differences in auditory subcortical function . Clin Neurophysiol , 123 ( 3 ), 590 – 597 . doi: 10.1016/j.clinph.2011.07.037 OpenUrl CrossRef PubMed ↵ Kujawa , S. G. , & Liberman , M. C . ( 2009 ). Adding insult to injury: cochlear nerve degeneration after “temporary” noise-induced hearing loss . J Neurosci , 29 ( 45 ), 14077 – 14085 . doi: 10.1523/JNEUROSCI.2845-09.2009 OpenUrl Abstract / FREE Full Text ↵ Kujawa , S. G. , & Liberman , M. C . ( 2015 ). Synaptopathy in the noise-exposed and aging cochlea: Primary neural degeneration in acquired sensorineural hearing loss . Hear Res , 330 ( Pt B ), 191 – 199 . doi: 10.1016/j.heares.2015.02.009 OpenUrl CrossRef PubMed ↵ Kuwada , S. , Anderson , J. S. , Batra , R. , Fitzpatrick , D. C. , Teissier , N. , & D’Angelo , W. R . ( 2002 ). Sources of the scalp-recorded amplitude-modulation following response . J Am Acad Audiol , 13 ( 4 ), 188 – 204 . OpenUrl CrossRef PubMed ↵ Liberman , M. C. ( 1978 ). Auditory-nerve response from cats raised in a low-noise chamber . J Acoust Soc Am , 63 ( 2 ), 442 – 455 . OpenUrl CrossRef PubMed Web of Science ↵ Mepani , A. M. , Verhulst , S. , Hancock , K. E. , Garrett , M. , Vasilkov , V. , Bennett , K. , de Gruttola , V. , Liberman , M. C. , & Maison , S. F. ( 2021 ). Envelope following responses predict speech-in-noise performance in normal-hearing listeners . J Neurophysiol , 125 ( 4 ), 1213 – 1222 . doi: 10.1152/jn.00620.2020 OpenUrl CrossRef PubMed ↵ Moller , A. R. , & Jannetta , P. J . ( 1981 ). Compound action potentials recorded intracranially from the auditory nerve in man . Exp Neurol , 74 ( 3 ), 862 – 874 . OpenUrl CrossRef PubMed ↵ Neely , S. , & Liu , Z. ( 1993 ). EMAV: Otoacoustic emission averager . In Tech Memo No. 17 . Boys Town National Research Hospital Omaha . ↵ Parthasarathy , A. , & Kujawa , S. G . ( 2018 ). Synaptopathy in the Aging Cochlea: Characterizing Early-Neural Deficits in Auditory Temporal Envelope Processing . J Neurosci , 38 ( 32 ), 7108 – 7119 . doi: 10.1523/JNEUROSCI.3240-17.2018 OpenUrl Abstract / FREE Full Text ↵ Paul , B. T. , Waheed , S. , Bruce , I. C. , & Roberts , L. E . ( 2017 ). Subcortical amplitude modulation encoding deficits suggest evidence of cochlear synaptopathy in normal-hearing 18-19 year olds with higher lifetime noise exposure . J Acoust Soc Am , 142 ( 5 ), EL434 . doi: 10.1121/1.5009603 OpenUrl CrossRef PubMed ↵ Purcell , D. W. , John , S. M. , Schneider , B. A. , & Picton , T. W . ( 2004 ). Human temporal auditory acuity as assessed by envelope following responses . J Acoust Soc Am , 116 ( 6 ), 3581 – 3593 . doi: 10.1121/1.1798354 OpenUrl CrossRef PubMed Web of Science ↵ Salvatier , J. , Wiecki , T. , & Fonnesbeck , C . ( 2016 ). Probabilistic programming in Python using PyMC3 . PeerJ Comput Sci , 2 , e55 . OpenUrl CrossRef ↵ Salvi , R. , Sun , W. , Ding , D. , Chen , G.-D. , Lobarinas , E. , Wang , J. , Radziwon , K. , & Auerbach , B. D . ( 2017 ). Inner hair cell loss disrupts hearing and cochlear function leading to sensory deprivation and enhanced central auditory gain . Front Neurosci , 10 , 621 . doi: 10.3389/fnins.2016.00621 OpenUrl CrossRef PubMed ↵ Schmiedt , R. A. , Mills , J. H. , & Boettcher , F. A . ( 1996 ). Age-related loss of activity of auditory-nerve fibers . J Neurophysiol , 76 ( 4 ), 2799 – 2803 . doi: 10.1152/jn.1996.76.4.2799 OpenUrl CrossRef PubMed Web of Science ↵ Seabold , S. , & Perktold , J . ( 2010 ). Statsmodels: Econometric and statistical modeling with python . Proceedings of the 9th Python in Science Conference . doi: 10.25080/Majora-92bf1922-011 OpenUrl CrossRef ↵ Sergeyenko , Y. , Lall , K. , Liberman , M. C. , & Kujawa , S. G . ( 2013 ). Age-related cochlear synaptopathy: an early-onset contributor to auditory functional decline . J Neurosci , 33 ( 34 ), 13686 – 13694 . doi: 10.1523/JNEUROSCI.1783-13.2013 OpenUrl Abstract / FREE Full Text ↵ Shaheen , L. A. , Valero , M. D. , & Liberman , M. C . ( 2015 ). Towards a Diagnosis of Cochlear Neuropathy with Envelope Following Responses . J Assoc Res Otolaryngol , 16 ( 6 ), 727 – 745 . doi: 10.1007/s10162-015-0539-3 OpenUrl CrossRef PubMed ↵ Suthakar , K. , & Liberman , M. C . ( 2021 ). Auditory-nerve responses in mice with noise-induced cochlear synaptopathy . J Neurophysiol , 126 ( 6 ), 2027 – 2038 . doi: 10.1152/jn.00342.2021 OpenUrl CrossRef ↵ Trune , D. R. , Mitchell , C. , & Phillips , D. S . ( 1988 ). The relative importance of head size, gender and age on the auditory brainstem response . Hear Res , 32 ( 2 ), 165 – 174 . OpenUrl CrossRef PubMed Web of Science ↵ Van Der Biest , H. , Keshishzadeh , S. , Keppler , H. , Dhooge , I. , & Verhulst , S. ( 2023 ). Envelope following responses for hearing diagnosis: Robustness and methodological considerations . J Acoust Soc Am , 153 ( 1 ), 191 . doi: 10.1121/10.0016807 OpenUrl CrossRef PubMed ↵ Vasilkov , V. , Garrett , M. , Mauermann , M. , & Verhulst , S . ( 2021 ). Enhancing the sensitivity of the envelope-following response for cochlear synaptopathy screening in humans: The role of stimulus envelope . Hear Res , 400 , 108132 . doi: 10.1016/j.heares.2020.108132 OpenUrl CrossRef PubMed ↵ Verhulst , S. , Dau , T. , & Shera , C. A . ( 2012 ). Nonlinear time-domain cochlear model for transient stimulation and human otoacoustic emission . J Acoust Soc Am , 132 ( 6 ), 3842 – 3848 . doi: 10.1121/1.4763989 OpenUrl CrossRef PubMed Web of Science ↵ Verhulst , S. , Jagadeesh , A. , Mauermann , M. , & Ernst , F . ( 2016 ). Individual Differences in Auditory Brainstem Response Wave Characteristics: Relations to Different Aspects of Peripheral Hearing Loss . Trends Hear , 20 . doi: 10.1177/2331216516672186 OpenUrl CrossRef PubMed ↵ Verhulst , S. , Altoe , A. , & Vasilkov , V . ( 2018 ). Computational modeling of the human auditory periphery: Auditory-nerve responses, evoked potentials and hearing loss . Hear Res , 360 , 55 – 75 . doi: 10.1016/j.heares.2017.12.018 OpenUrl CrossRef PubMed ↵ Wilson , R. H. , & McArdle , R . ( 2013 ). Characteristics of the audiometric 4,000 Hz notch (744,553 veterans) and the 3,000, 4,000, and 6,000 Hz notches (539,932 veterans) . J Rehabil Res Dev , 50 , 111 – 132 . OpenUrl PubMed ↵ Wilson , J. L. , Abrams , K. S. , & Henry , K. S . ( 2021 ). Effects of Kainic Acid-Induced Auditory Nerve Damage on Envelope-Following Responses in the Budgerigar (Melopsittacus undulatus) . J Assoc Res Otolaryngol , 22 ( 1 ), 33 – 49 . doi: 10.1007/s10162-020-00776-x OpenUrl CrossRef PubMed ↵ Wu , P. Z. , Liberman , L. D. , Bennett , K. , de Gruttola , V. , O’Malley , J. T. , & Liberman , M. C. ( 2019 ). Primary Neural Degeneration in the Human Cochlea: Evidence for Hidden Hearing Loss in the Aging Ear . Neuroscience , 407 , 8 – 20 . doi: 10.1016/j.neuroscience.2018.07.053 OpenUrl CrossRef PubMed ↵ Wu , P. Z. , O’Malley , J. T. , de Gruttola , V. , & Liberman , M. C. ( 2021 ). Primary Neural Degeneration in Noise-Exposed Human Cochleas: Correlations with Outer Hair Cell Loss and Word-Discrimination Scores . J Neurosci , 41 ( 20 ), 4439 – 4447 . doi: 10.1523/JNEUROSCI.3238-20.2021 OpenUrl Abstract / FREE Full Text ↵ Zhu , L. , Bharadwaj , H. , Xia , J. , & Shinn-Cunningham , B . ( 2013 ). A comparison of spectral magnitude and phase-locking value analyses of the frequency-following response to complex tones . J Acoust Soc Am , 134 ( 1 ), 384 – 395 . doi: 10.1121/1.4807498 OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted November 21, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. 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