Research Article

Can ECAP Measures Be Used for Totally ObjectiveProgramming of Cochlear Implants?

COLETTE M. MCKAY,1,2 KIRPA CHANDAN,1 IDRICK AKHOUN,1 CATHERINE SICILIANO,1 AND KAROLINA KLUK1

1Audiology & Deafness Research Group, School of Psychological Sciences, The University of Manchester, Ellen Wilkinson Building,Manchester, M13 9PL, UK2The Bionics Institute of Australia, 384-388 Albert St., East Melbourne, 3002, Australia

Received: 15 April 2013; Accepted: 6 September 2013; Online publication: 19 September 2013

ABSTRACT

An experiment was conducted with eight cochlearimplant subjects to investigate the feasibility of usingelectrically evoked compound action potential(ECAP) measures other than ECAP thresholds topredict the way that behavioral thresholds changewith rate of stimulation, and hence, whether they canbe used without combination with behavioral mea-sures to determine program stimulus levels forcochlear implants. Loudness models indicate thattwo peripheral neural response characteristics con-tribute to the slope of the threshold versus ratefunction: the way that neural activity to each stimuluspulse decreases as rate increases and the slope of theneural response versus stimulus current function.ECAP measures related to these two characteristicswere measured: the way that ECAP amplitude de-creases with stimulus rate and the ECAP amplitudegrowth function, respectively. A loudness model(incorporating temporal integration and the twoneural response characteristics) and regression analy-ses were used to evaluate whether the ECAP measurescould predict the average slope of the behavioralthreshold versus current function and whether indi-vidual variation in the measures could predict indi-vidual variation in the slope of the threshold function.The average change of behavioral threshold withincreasing rate was well predicted by the model whenusing the average ECAP data. However, the individualvariations in the slope of the thresholds versus rate

functions were not well predicted by individualvariations in ECAP data. It was concluded that theseECAP measures are not useful for fully objectiveprogramming, possibly because they do not accuratelyreflect the neural response characteristics assumed bythe model, or are measured at current levels muchhigher than threshold currents.

Keywords: cochlear implant, thresholds, ECAP,loudness

INTRODUCTION

The introduction of reversed telemetry in cochlearimplant (CI) systems made it possible to efficientlymeasure the response of the auditory nerve to electricalstimulus pulses and led to the expectation that thismeasure would facilitate a fully objective method to setthe signal processor program parameters for individ-uals. However, many studies have now shown that thecorrelation between electrically evoked compoundaction potential (ECAP) thresholds and behavioralthresholds or comfortably loud levels is only moderateat best (Abbas et al. 1999; Hughes et al. 2000; Gordon etal. 2002, 2004a; Thai-Van et al. 2004; Cafarelli Dees et al.2005; McKay et al. 2005; Potts et al. 2007) anddeteriorates as the rate of stimulation used for behav-ioral measures increases. As modern implant systemsuse moderate to high rates of stimulation, the poorpredictive power of ECAP thresholds prevents themfrom being used in isolation to set the program levels foreach patient. Currently, the contour of ECAP thresholdsacross electrodes (or an amended version of this)

Correspondence to: Colette M. McKay & The Bionics Institute ofAustralia & 384-388 Albert St., East Melbourne, 3002, Australia.Telephone: +61-3-96677541; email: cmckay@bionicsinstitute.org

JARO 14: 879–890 (2013)DOI: 10.1007/s10162-013-0417-9D 2013 The Author(s). This article is published with open access at Springerlink.com

879

JAROJournal of the Association for Research in Otolaryngology

together with one or more behavioral measurements isused in combination to set a preliminary program forinfants and children (Smoorenburg et al. 2002; Gordonet al. 2004a, b). However, these ECAP-based maps havebeen shown by some researchers to be suboptimal forspeech understanding (Seyle and Brown 2002;Smoorenburg et al. 2002). In this study, it was hypoth-esized that ECAP measures that characterize neuralresponse properties in individuals for different rates ofstimulation could be used to obtain a better predictionof high-rate behavioral thresholds than using ECAPthresholds alone.

McKay et al. (2005) discussed some possiblereasons for the poor correlation between ECAP andbehavioral thresholds. The standard ECAP amplitude(measured at a low probe rate of 40 or 80 pulses persecond—pps) is related to the neural response to asingle stimulus pulse in isolation. In contrast, thebehavioral threshold for a pulse train is influenced byadditional peripheral and central factors. At theperiphery, the neural response to each pulse in apulse train is influenced by the preceding pulses viarefractory and adaptive mechanisms. The higher thepulse rate, the smaller the neural response to eachpulse in the train (except the first pulse). Theperceptual response to a pulse train is also influencedby central temporal integration. In a temporal inte-gration model, the peripheral neural activity thatoccurs within an integration window of severalmilliseconds is integrated and the output of theintegrator is used to make decisions about threshold,loudness, and intensity discrimination. McKay andMcDermott (1998) and McKay et al. (2013) used sucha model to investigate the effect of interpulse intervalson loudness. The data were fit with a model thatcombined neural adaptation effects with temporalintegration. In that context, and in this paper, theterm “adaption” is used to refer to the lowering inneural response magnitude due to increases in rate orstimulus duration regardless of the mechanismsunderlying the change. In the above model, subjectswho experience a high degree of adaptation arepredicted to have flatter threshold versus rate func-tions than those with lesser degrees of adaptation, as,with an increase in rate of stimulation, the influenceof temporal integration (more stimulus pulses occurin the integration window) would be counterbalancedby a decreasing neural response to each pulse.Another important factor that influences the slopeof the threshold versus rate function is the slope ofthe neural response versus current function. Thesteeper the growth of neural activity with increase incurrent, the smaller the changes of current that areneeded to maintain threshold or equal loudness asrate changes. The fact that equal loudness versus ratefunctions are generally steeper than threshold versus

rate functions is consistent with the neural responseversus current function becoming steeper at highstimulus levels (McKay et al. 2003).

It can be hypothesized, therefore, that differencesamong individuals of the slopes of the threshold (orequal loudness) versus rate functions are contributed toby differences in the degree of adaptation and thecurrent to neural response slope among individuals.The standard ECAP threshold is usually highly correlat-ed with (although higher than) single pulse or low-rate(e.g., 40 or 80 pps) behavioral thresholds (Brown et al.1996, 1998; Zimmerling and Hochmair 2002), for whichadaptation effects are minimal. It is evident then thatthe poorer correlation of ECAP thresholds with higherrate behavioral thresholds is due to individual variationin the slope of the behavioral threshold versus ratefunction. If that slope can also be predicted usingalternative ECAPmeasures, then it should be possible touse it in combination with the prediction of the low-ratebehavioral threshold to predict the higher rate thresh-olds. In this study, it was hypothesized that the slope ofthe behavioral threshold versus rate function would becorrelated with measures of individual neural adapta-tion and/or the rate of growth of neural response withlevel, as estimated from ECAP measures.

METHODS

Subjects

Eleven post-lingually deaf adult users of cochlearimplants participated in the experiment. All wereusers of the CI24RE implant and Freedom systemmanufactured by Cochlear Ltd, with the exception ofone subject (S5) who had a CI24M implant. Threesubjects (S1, S2, S8) were later removed from thestudy due to absent or poorly defined ECAPs. Table 1shows details of the etiology and implant use for theremaining eight subjects.

Procedures

Electrophysiological measures

ECAP measurements were obtained for each subjectvia neural response telemetry (NRT) and CustomSound Evoked Potential software provided byCochlear Ltd. The biphasic stimulus pulses werepresented in monopolar (MP1) mode and had aphase duration of 25 μs and interphase gap of 7 μs.The active electrode position was initially E12. Inthese implants, current is controlled in logarithmicsteps called currents levels (CLs). Each CL step isequal to 0.18 or 0.16 dB for the CI24M and CI24REimplants, respectively.

880 MCKAY ET AL.: ECAP and Objective CI Programming

First, an ECAP amplitude growth function (ampli-tude versus CL) was obtained using the standardforward masking procedure (Brown and Abbas 1990).This function was used initially to determine thepresence of an ECAP and the CL range over which agood ECAP could be identified. If ECAPs wereunsatisfactory on electrode E12, then electrodes E20or E4 were tested. Three subjects (S1, S2, S8) wereremoved from the study at that point due to theabsence of useful ECAPs on all three electrodes. Inthree other cases, useful ECAPs were found onelectrodes other than E12, so the whole experiment(including behavioral measures) was performed usingthese alternative electrodes: E20 (S7, S10) and E4(S5). The slope of the ECAP amplitude growthfunction (in decibel per decibel—denoted SECAP)was calculated for later use in the loudness modeling,and the visual ECAP threshold (lowest CL at which arecognizable ECAP waveform was apparent) wasnoted for reader reference only.

Secondly, the amplitudes of ECAP evoked by astimulus pulse when preceded by differing numbersand rates of masker pulses were measured. Theamplitude measurements were undertaken for eachof the first 20 pulses in each pulse train, and the pulserates used were 500, 900, 1,200, 1,800, and 2,400 pps.In addition, the responses to three successive pulsesthat were approximately 40 ms after stimulus onsetwere recorded to assess the degree of adaptation forlonger durations. Before starting this procedure, themaximum tolerable CL for the procedure was deter-mined using masker pulse trains of 20 pulses at a rateof 2,400 pps. This CL was used for the first set ofECAP amplitude measurements.

Figure 1 shows a schematic diagram of the stepsused to obtain the ECAP amplitude evoked by the nthpulse in a pulse train. In condition A, the artifact andresponse after n pulses (n−1 maskers and one probe)was obtained. The interpulse intervals were deter-mined by the chosen rate, and all current levels in thepulse train were identical. Similarly, in condition B,the artifact and response after n – 1 pulses wasobtained. Subtracting B from A produced the artifact

and response due to the nth pulse only. The artifactwas then determined by subtracting condition D fromcondition C. The masker pulse in conditions C and Dwas 10 CL greater than the probe CL and precededthe probe in condition C by 500 μs. Finally, theresponse to the nth pulse was determined bysubtracting the probe artifact from the response-plus-artifact obtained from conditions A and B (i.e.,[A − B] − [C − D]). Before the runs of each rate(separated in time), the response to the first pulse(i.e. a single isolated pulse) was determined using theforward masking method described by Brown andAbbas (1990). The masker–probe interval and mask-er–probe CL offset were the same as shown forcondition C in Figure 1. Thus, there were five ormore measurements of ECAP amplitude for the firstpulse (one for each rate under investigation andrepetitions within a measurement run for each ratefor some subjects, to check for response adaptationbetween conditions). These amplitudes were alwayssimilar and were averaged for later analysis.

TABLE 1Subject details

Subject Etiology Duration of profound deafness (years) Implanted ear Duration of implant use (years)

S3 Familial progressive 9 R 4S4 Head injury 39 L 4S5 CSOM 14 R 10S6 Progressive 38 L 3S7 Progressive 10 R 5S9 Congenital progressive 22 L 5S10 Viral infection 9 R 5S11 Idiopathic sudden 7 R 5

FIG. 1. A schematic diagram showing the four measurementbuffers used to derive the ECAP waveform of the fifth pulse in apulse train. Condition A includes five equal pulses at the test rate,four being maskers (black) and the last one being the probe pulse(red). Condition B includes only the four masker pulses of A.Subtraction of B from A gives the ECAP and artifact for the fifthpulse. Condition C contains the probe pulse preceded by a maskerpulse that has a level 10 CL higher than the probe pulse and a shortinterpulse interval of 500 μs and D contains the masker pulse of C onits own. Subtraction of D from C gives the probe artifact, which canthen be subtracted from the difference between A and B to obtain theartifact-free probe response to the fifth pulse.

MCKAY ET AL.: ECAP and Objective CI Programming 881

ECAP recordings were sampled at 20 kHz andamplified by 50 dB for CI24RE implants and by 60 dBfor the participant with a CI24M implant. Thesampling delay was 122 μs for CI24RE implants and68 μs for the CI24M implant. ECAPs were measuredin 1,600 μs duration epochs. The probe rate variedwith the masker rate and duration (maximum periodof 1 s) to ensure adequate neural recovery betweenmeasurement epochs. The ECAP amplitude for eachmasker condition (number and rate of masker pulses)was determined from the online average of 50 epochs.The recording electrode was two electrodes moreapical than the stimulus electrode (except when E20was the stimulus electrode, in which case E18 was therecording electrode) and used the MP2 electrode asthe reference. Measurement data were analyzedoffline using MatLab software to produce ECAPwaveforms. The N1 and P1 peaks were visuallydetermined and the ECAP amplitude was defined asthe N1−P1 voltage difference. The noise floor wasapproximately 10 μV in these recordings, and ECAPamplitudes below 10 μV were considered to be noise.

The measure of adaptation (A) for each maskerrate was the average ECAP amplitude obtained fromthe third to the 20th pulse expressed as a fraction ofthe ECAP amplitude from the first pulse (Amp1).

A ¼ Amp−11 1=18*X

3

20

Ampn

!ð1Þ

An A of 0 represents a total abolition of the ECAPresponse and an A of 1 represents no reduction inECAP amplitude relative to the first pulse.

Following the measurements at the maximumtolerable level, the same data were collected at lowerstimulus current levels, provided that ECAP ampli-tudes remained significantly above the noise floor.Levels were chosen that spanned the range whereECAPs for a single pulse were clearly evident in theamplitude growth function.

Behavioral measurements

Detection thresholds were obtained using pulse trains of500 ms duration. Pulse rates of 40, 250, 500, 900, 1,200,1,800, and 2,400 pps were used. Stimulation mode wasMP1+2, pulse duration was 25 μs, and interphase gapwas 7 μs. Note that the stimulus monopolar referenceelectrode (MP1+2) was slightly different to that for theECAP measures (MP1). The two different referenceelectrodes are those typically used in each case, and theECAP measures ideally require different referenceelectrodes (MP1 and MP2) to be used for stimulus andrecording electrodes. Stimuli were controlled andsubject responses recorded, using ImPResS software(developed at Melbourne University in collaboration

with the MRC-CBU in Cambridge, UK). The softwaredirectly controlled the delivery of the electrical stimulusto the implanted electrodes via an interface using aSPEAR research processor (developed at the HearingCRC inMelbourne). A response box was used to visuallyrepresent trial time intervals and to record subjectresponses.

Thresholds were obtained using an adaptive three-interval three-alternative forced choice task, in whichsubjects were asked to nominate which of three intervals(separated by 500 ms) in each trial contained a sound.The stimulus occurred in one randomly selectedinterval only. A two-down one-up adaptive procedurewas used with a step size of 4 CL until two reversals wereobtained and then a step size of 2 CL until a total of 10reversals were obtained. Threshold was calculated as theaverage CL at the final six reversals. After thresholdswere obtained for each rate of stimulation, a second setof thresholds was obtained using the reverse order oftesting to limit any effects of practice due to test order. Ifa repeated threshold was more than 6 CL different fromthe first measurement, a third threshold was measured.The two (or three) thresholds for each rate wereaveraged for further analyses.

Temporal integration model of behavioral threshold

The effect of rate on behavioral threshold has beenmodeled using a temporal integration model and hasalso been shown to successfully explain the effect ofinterpulse intervals on loudness in cochlearimplantees (McKay and McDermott 1998) and toexplain a variety of temporal effects, including thoseof rate, duration, and masker–probe interval, onthresholds in cochlear and auditory midbrainimplantees (McKay et al. 2013). In this model, theslope of the behavioral threshold versus rate functiondepends on two key factors that vary betweenindividuals. This slope is modeled to be flatter whenthe neural response to each pulse decreases morerapidly as rate increases and when neural activityincreases faster with increases in stimulus current.The objective of this experiment was to evaluatewhether these two model parameters can be inferredfrom ECAP measurements and, thus, to be able topredict the way that individual behavioral thresholdschange with rate using only objective measurements.First, the average ECAP data was input into thistemporal integration model to see if it could predictthe slope of the average behavioral threshold versusrate function. Following this, regression analyses wereperformed to see whether individual variability in theECAP measures could predict differences in behav-ioral threshold slopes among subjects.

In the model, the input to the temporal integration(TI) window is the neural response to each stimulus

882 MCKAY ET AL.: ECAP and Objective CI Programming

pulse. Thus, the way that the neural response to eachpulse changes with rate of stimulation is an importantmodel parameter in this experiment. In the presentpaper, the ECAP amplitudes for each subject wereassumed to be proportional to the number of neuronsthat responded to the corresponding stimulus pulse.This is a first approximation only, since other factorssuch as synchrony will also affect the ECAP amplitude.The response to the first pulse in a pulse train wasassigned a nominal value of 1, and the responses tothe second and subsequent pulses were assigned avalue of the mean ECAP amplitude (across subjects)relative to that of the first pulse for that particularpulse and stimulus rate. Thus, all neural responseswere denoted as a fraction of the response to the firstpulse for any particular stimulus CL and rate.

The TI window used in this paper is the same aswas successfully used by McKay et al. (2013) to explaina range of CI data and follows the form used byauthors in the acoustic hearing field (Oxenham andMoore 1994; Oxenham 2001; Plack et al. 2002):

W tð Þ ¼ 1−wð Þexp t=Tb1ð Þ þ wexp t=Tb2ð Þ; t < 0W tð Þ ¼ exp −t=Tað Þ; t≥0;

ð2Þ

where W(t) is the weight applied at time t relative tothe peak of the function, Ta and Tb1 together definethe short time constant associated with temporalresolution, Tb2 defines a longer tail of the windowassociated with forward masking and the effect ofduration, and w is the weighting of the long versusshort time constants. Oxenham (2001) derived theintegration window shape to best fit forward maskingdata in normal hearing listeners: the best-fitting values ofthe parameters wereTa=3.5ms,Tb1=4.6ms,Tb2=16.6ms,andw=0.17. SinceMcKay andMcDermott (1998) showedthat TI windows fitted to CI data had similar widths tothose of normal hearing subjects and did not varysignificantly among CI subjects, it was assumed that theabove parameters would be appropriate to use in themodel here and that variations in the TI window did notcontribute to variations in the effect of rate on threshold.

The output of the window was the weighted sum ofall neural responses as per Eq. 2. A function ofwindow output versus time was calculated by movingthe peak of the window (t=0) along the length of thestimulus. Detection threshold was assumed to be thestimulus level that evoked a fixed criterion maximumwindow output (assuming that internal noise wasconstant across experimental conditions—that is, acriterion signal-to-noise ratio).

In the final model step, to predict the averageeffect of rate of stimulation on behavioral threshold,the change in input stimulus level for each rate higheror lower than 500 pps that was required to achieve thesame criterion window output as the 500 pps stimulus

was calculated. This step required a function to relatestimulus current to neural response. The slope indecibel per decibel of the neural response versuscurrent function, S, was first used as a free fittingparameter in the model. The way that S changes withlevel can be hypothesized to be related to the way thatloudness grows with level. McKay et al. (2003) showedthat the slope of the loudness versus current function(on log–log scales) was fairly constant and similaracross subjects for low currents (below the thresholdof a 500-pps stimulus, approximately) and increasedwith level above a certain kneepoint current. Loudnesscan be plausibly assumed to be a power function of thetemporal window output (summed neural response),since most decision criteria for changes in the outputwindow necessary for detection of intensity changes cansuccessfully be expressed as ratios (McKay et al. 2013). Itfollows that for threshold currents for rates of 500 ppsand above, neural response is predicted to be a powerfunction of current (a constant S, similar across subjects).In contrast, S is predicted to increase at stimulus levelscorresponding to threshold currents for rates lower than500 pps and for suprathreshold stimulus levels, by anamount depending on where the subject-dependentkneepoint is situated. The fitted values of S found byMcKay and McDermott (1998) from psychophysical dataranged from 0.5 to 6.1 and significantly increased withlevel for the low-rate stimuli used in that experiment.

Although S can theoretically be estimated using theECAP growth function (SECAP), in practice, SECAP canonly be measured using low-rate or single pulsestimuli, since the ECAP amplitudes are too small atlow levels and high rates. SECAP was measured in thisstudy using the standard forward masking procedureusing a single pulse masker and, thus, was measuredat stimulus levels above even the lowest rate thresholdlevel. Thus, it was not expected that SECAP could beinput into the model to predict behavioral thresholdchanges with increases in rate, since it was expectedthat S would be level- and hence rate-dependent.Instead, S was used as a free fitting parameter in themodel, when using the average measured ECAPamplitudes as model input. The resultant fitted Svalues and their changes with stimulus level were thencompared to those previously obtained elsewhereusing solely psychophysical data. SECAP is predictedto be greater than the fitted S in the low-rate regionand considerably greater than the fitted S in the high-rate region and is therefore not expected to becorrelated with the slope of the threshold versus ratefunction above 500 pps. It may, however, be at leastcorrelated with (although higher than) S at 40 ppsand may consequently be correlated with behavioralthreshold differences between 40 and 250 pps.

In summary, the average ECAP amplitudes relative tothat of the first pulse were input into the model which

MCKAY ET AL.: ECAP and Objective CI Programming 883

was then fit to the average behavioral threshold versusrate function using the S parameter. The fitted S valueswere compared to previously published values frompsychophysical data at different stimulus levels. Next,the individual A and SECAP measures were used inregression analyses to see if they could predict individualdifferences in the slope of the behavioral thresholdversus rate function. It was hypothesized that differencesin the way that A changes with rate would predictdifferences in behavioral threshold slope in the high-rate region (above 500 pps), where S is predicted to berelatively constant and similar across subjects, and whereadaptation effects would be likely to dominate. Incontrast, it was hypothesized that SECAP would becorrelated to the behavioral threshold slope in the verylow-rate region (below 250 pps), where SECAP wasmeasured, and where A is assumed to be close to 1.

RESULTS

Behavioral measures

Figure 2 shows the thresholds across rates for eachsubject. Panel A shows the thresholds in dB re 1 µA,while panel B shows the thresholds in decibel relativeto the 500-pps threshold, replotted to more easilycompare the slopes of the functions. Four sets of data(green symbols) are included in these graphs that areadditional to the eight sets corresponding to theECAP data sets, to illustrate the variation of the slopesacross subjects and electrodes. It is clear from Figure 2that the slopes vary more across subjects in the low-frequency region (G500 pps) than in the high-frequency region. The similarity of slopes above500 pps across the subjects is illustrated by the smallstandard deviation of difference in threshold between500 and 2,400 pps (mean difference=5.8 dB, SD=1.2 dB,range 3.6 to 7.2). In contrast, the difference betweenthresholds at 40 and 500 pps although smaller on averagevaried more between subjects (mean difference=3.7 dB,SD=2.6 dB, range from −0.1 to 7.9).

ECAP measurements

Figure 3 shows example ECAP waveforms obtainedfrom S11 at the highest current level tested (210 CLsor 57.6 dB re 1 µA), with a masker of 2,400 pps andvarying numbers of masker pulses. The thick blackline is the ECAP obtained from a single pulse usingthe standard forward masking method. The thincolored lines are the ECAP waveforms from the thirdto the 19th pulse in the pulse train, obtained using themethod illustrated in Figure 1, and the thick red lineis the ECAP to the 20th pulse. The responses to the2,400-pps pulse train showed an immediate reductionin amplitude relative to the first pulse, and an immediate

increase in latency that did not vary much during thepulse train. The increase in latency after the first pulsewas consistent across subjects when using high-ratemaskers, and for some subjects, the latency alternatedin step with the amplitude alternations. These changesin latency could possibly be associated with a change inresponse synchronicity if the first pulse induces aminimum post-stimulus response time in each activatedfiber. If so, the ECAP amplitude variations do notnecessarily reflect only changes in the number ofneurons that responded, which was an assumption ofthe model. Another possible explanation for the changeinmorphology from the first to subsequent pulses is thattheremay be residual stimulus artifact. An assumption of

FIG. 2. A The behavioral thresholds of each subject in decibel re1 mA as a function of stimulus rate. B The behavioral thresholdsreplotted in decibel re the 500-pps threshold. In both plots, the greendata are additional behavioral data sets that were not used later forcomparison with ECAP data (including a set for S2 who was laterremoved from the study due to inadequate ECAPs). The additionaldata are included to better illustrate the variability in slopes amongsubjects and electrodes.

884 MCKAY ET AL.: ECAP and Objective CI Programming

the artifact rejection method is that the stimulus artifactof each pulse in the pulse train is identical to that of thefirst pulse. If this assumption is violated, then themorphology of the second and subsequent pulseresponses may be distorted by residual artifact.

Figure 4 shows an example (S6) of the ECAPamplitudes for the first 20 pulses for different rates ofstimulation at the highest level tested (190 CLs or 54.4 dBre 1 µA). In general, the amplitudes decreased withincreasing pulse rate similarly in all subjects. Figure 5shows the mean ECAP amplitudes relative to that of thefirst pulse (averaged across subjects) versus time afteronset and includes the data collected around 40 ms. Thelatter data were not collected for S4 as he was no longeravailable. It can be seen that the majority of the responsereduction occurred over the first 20 pulses for each rate,with only small further reductions at the 40-ms duration.

Figure 6 shows individual subject A values and the meansand standard deviations for different rates measured atthe highest level. The mean data show a relatively lineardecrease in average ECAP amplitude with logarithmicincrements in rate above 500 pps.

Since the ECAP data were measured at currentlevels somewhat higher than those associated withbehavioral thresholds, an important question iswhether the A values vary with stimulus level, andmore importantly in the present experiment, whethervalues at high levels are correlated with values at lowlevels. Valid measures of A could only be obtainedat lower current levels in a subset (five) of subjects

FIG. 3. An example set of ECAP waveforms derived for S11 usingmaskers of 2,400 pps and a stimulus level of 210 CLs (57.6 dB re1 mA), showing the ECAP to the first pulse (thick black line) and thethird to the 20th pulse (the last denoted by a thick red line).

FIG. 4. A representative data set for S6, showing ECAP amplitudesversus position of the probe pulse in the pulse train for different ratesof stimulation and 190 CL (54.4 dB re 1 mA).

FIG. 5. Mean ECAP amplitudes normalized to that of the first pulsefor each pulse between pulse 3 and pulse 20, and for threeconsecutive pulses near 40 ms duration. The amplitudes are shownversus time after masker onset. These normalized amplitudes wereinput into the loudness model (see text).

FIG. 6. Mean ECAP amplitudes for the third to the 20th pulse as aproportion of the amplitude to the first pulse (values of A) plottedagainst the masker rate. Individual subject data and mean data acrosssubjects are shown. Error bars represent standard deviations.

MCKAY ET AL.: ECAP and Objective CI Programming 885

and mostly at the lower rates, due to the amplitudesbeing in the noise floor. Figure 7 shows the relationbetween values of A obtained at the higher andlower levels. The regression shown in the figureshows that the A values were significantly correlatedacross levels (r2(17)=0.916, PG0.001) when combiningdata across subjects and electrodes and were greater onaverage (less reduction in amplitude) for the highercurrents than for the lower currents by a constantamount of 0.045. A two-tailed paired t test showed thatthis latter difference, although smaller compared to thedifferences between subjects and rates, was significant(T17=−3.127, P=0.006). The difference in CL betweenthe higher and lower levels differed between subjectsbut was always in the range of 5–10 CL. It should benoted, however, that the current levels at which A valuescould be obtained were higher than the high-ratebehavioral thresholds. Therefore, when using themeasured A values to characterize individual adapta-tion, we are making an assumption that the values atbehaviorally relevant levels are at least correlated with,and similar to, those at the measured levels for eachsubject.

For each subject, SECAP was derived from the slope indecibel per decibel of the ECAP amplitude growthfunction (with 40 pps measurement rate), fitted with alinear regression. SECAP values are shown in Table 2, alongwith the ECAP threshold measured visually (V-NRT). Theaverage SECAP was 4.1 (dB/dB) with a standard deviationof 1.4 dB/dB.

The loudness model: average data

The average ECAP amplitudes (across subjects, measuredat the higher level, as shown in Fig. 5) were used as input

to the loudness model, while using S as a free fittingparameter, to assess whether themodel could explain theaverage slope of the behavioral threshold versus rate data.

For each rate, the value of 1 was applied to theneural response to the first pulse and a value equal tothe mean relative ECAP amplitude (Fig. 5) for each ofthe responses to pulses 3 to 20. Since the response tothe second pulse was not measured for technicalreasons, its response was input as equal to thesubsequent even numbered pulse (pulse 4). Theamplitudes for pulse 21 and subsequent pulses wereassigned to the average amplitude of the three pulsesat 40 ms duration for each rate above 500 pps. Thepossibility of further adaptation after 40 ms wasignored since the TI window output had generallyreached its maximum by 40 ms, and the similar dataof Hay-McCutcheon et al. (2005) was consistent withthe present data in showing very little adaption afterthe 20th pulse. The TI window maximum output(across the 500 ms) was then calculated for each rateand converted to a decibel scale. Finally, the fittingconstant, S, the slope of the neural response versuscurrent function, was adjusted so that the currentadjustment needed to maintain a constant criterionTI window output for different rates was equal to thecurrent adjustment in the behavioral data. Since thevariation in behavioral threshold versus rate functionsacross subjects differed in the range above and below500 pps, and the behavior of the fitting constant, S,was also predicted to differ in the range above andbelow 500 pps, the TI window output at 500 pps wasused as the criterion output for adjustment ofcurrents above and below this rate.

Figure 8 shows the average behavioral thresholds(filled circles) relative to the 500-pps threshold,together with the fitted model (open symbols). For500 pps and above, a constant value of S (1.55—redtriangles) produced a very good fit to the data.However, this S value did not predict the 40- and250-pps thresholds. To correctly fit the change from500 to 250 pps thresholds, a higher S value of 2.68 wasneeded (blue triangle), and to fit the change in

FIG. 7. Values of A measured at the higher stimulus level plottedagainst the value of A measured at the lower stimulus level for thesame subject/rate. The black solid line is the regression line as perthe equation, and the red dotted line is the equality line.

TABLE 2ECAP thresholds and slope of the ECAP growth function

(SECAP) in decibel per decibel

Subject–electrode V-TNRT (CL and dB re 1 μA) SECAP

S3-E12 180, 53.1 3.3S4-E12 185, 53.9 4.5S5-E4 195, 54.3 2.5S6-E12 155, 49.2 4.6S7-E20 175, 52.3 2.0S9-E12 160, 50.0 6.5S10-E20 175, 52.3 4.5S11-E12 175, 52.3 5.1

886 MCKAY ET AL.: ECAP and Objective CI Programming

threshold from 250 to 40 pps, an S value of 3.22 wasneeded (pink triangle). Of course, it is possible toexactly fit the threshold at each rate by independentlyadjusting S for each point. However, the fitted S valuesare very similar to the average values found using adifferent set of subjects by McKay and McDermott(1998) using solely psychophysical data of a differentnature (average S of 1.45 at threshold and 3.3 athigher levels). Furthermore, the behavior of the Svalues at different rates (constant at threshold forrates above 500 pps and increasing as rate decreases(or level increases) below 500 pps) matches thepredicted behavior of S based on loudness growthwith level (McKay et al. 2003). Alternatively, toproduce a model output using a constant S thatmatches the much shallower slope below 500 ppscompared to above 500 pps, an amplitude versus ratefunction that drops more steeply with rate at very lowrates compared to higher rates would be required.This would be inconsistent with the ECAP amplitudedata (Figs. 5 and 6) and also inconsistent with what isknown physiologically.

As mentioned above, the measured SECAP wasexpected to be somewhat higher than the true S atbehavioral threshold at 40 pps. The average SECAP of4.1 dB/dB was indeed somewhat higher than the value(3.22) fit by the model to the average change inbehavioral threshold between 250 and 40 pps and isbroadly consistent with the value expected by an extrap-olation of the fitted S values to the higher current level at

which the ECAP was measured. This consistency lendssupport for the general validity of the loudness model.

Analysis of individual variability

Since the behavior of both the psychophysical andelectrophysiological data differed in nature above andbelow 500 pps, the ECAP predictors of the slopes of thebehavioral thresholds versus rate functions were sepa-rately investigated in the two different rate ranges.

Above 500 pps, the behavioral slopes show only a smallvariability across subjects. According to the model, thisvariability could be due either to individual differences inS, or differences in the slope of theA versus rate function.The former cannot be measured electrophysiologically,since the neural response is not measurable via the usualNRT measurements at the low stimulus currents neededfor high-rate thresholds. However, it can be hypothesizedthat a steeper A versus rate function will result in a flatterbehavioral threshold versus rate function. To test thishypothesis, a regression analysis was performed to assesswhether the difference between 500 and 2,400 ppsbehavioral thresholds (in decibels) could be predictedby the difference in A between 500 and 2,400 pps (in dB:20log(A500/A2,400)).

Although the trend in the relationship was in thehypothesized direction, it did not account for asignificant amount of the variability in the thresholdversus rate slope (r2(7)=0.07, P=0.53). This may bebecause there is very little variation between subjectsin the slopes of A or threshold between 500 and2,400 pps (leading to poor statistical power), orbecause other factors (such as differences in S acrosssubjects) have more influence on behavioral thresh-old slopes than the slope of the A versus rate function.

For the rate range below 500 pps, it is highly likely thatthe variation in S across subjects and how it increases withlevel contribute significantly to the variation in slope ofthe threshold versus rate functions. Between 40 and250 pps, for example, it is not expected that anysignificant reduction in A would occur. The hypothesiswas tested that higher SECAP values would be associatedwith flatter behavioral threshold slopes between 40 and250 pps (assuming that SECAP is at least correlated withthe appropriate S at the 40-pps behavioral thresholdlevel). Again, although the regression showed a trend inthe hypothesized direction, it did not account for asignificant amount of the variability (r2(7)=0.04, P=0.65),indicating that SECAP may not be a good predictor of S atthe appropriate lower level, as discussed above.

A third hypothesis was tested that a larger A at 500 pps(expressed as 20log(A500))) is associated with a greaterdifference between 40 and 500 pps thresholds. Onceagain, the relationship had a trend in the hypothesizeddirection but did not account for a significant amount ofvariance (r2(7)=0.04, P=0.61).

FIG. 8. Mean behavioral thresholds (solid circles and line) andmodel fits to the data (colored open symbols) for different ratesplotted as a function of the 500-pps threshold. The open symbols aremodel fits to the data using the measured values of A and using S as afitting parameter. A constant S of 1.55 (red downward triangles)produces an excellent fit for all data points above 500 pps, but failsto predict the shallower slope below 500 pps. Increasing values of Sat higher levels (blue upward triangle and pink circle) are needed tofit the behavioral data.

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DISCUSSION

The present experiment attempted to find parame-ters of ECAP measurements that would predict theway that behavioral thresholds change with rate ofstimulation. If successful, the prediction of the slopeof the threshold versus rate function, along with thehigh correlations found between ECAP thresholdsand low-rate behavioral thresholds, would allow high-rate behavioral thresholds to be predicted using solelyobjective ECAP measures.

The average way that the amplitudes of ECAPdeclined with increasing rate of stimulation was inputinto a loudness model that successfully predicted theaverage slope of the threshold versus rate function, givenplausible values of the slope of current versus excitationfunction that were consistent with previous models andthe direct measurement of ECAP amplitude growthfunctions. However, the variability between individualsof the behavioral threshold versus rate slope was not wellpredicted by variability in individual ECAP measures.Importantly, the hypothesis that differences in the effectof rate on ECAP amplitude (or the effect of neuraladaptation) would explain variation in the slope of thebehavioral threshold versus rate function was notsupported: for rates above 500 pps, both these slopesshowed little variation between subjects, whereas forrates below 500 pps, there was little evidence of neuraladaptation in spite of large variations in behavioralslopes between subjects. The consistency of fitted Svalues when using the ECAP average amplitudes asmodel input with the previously published S valuesderived from psychophysical data only, supports theidea that ECAP amplitudes generally reflect neuralresponse amplitudes, although it is possible that ECAPamplitudes measured at high rates could be underesti-mates: the shift in response latency after the first pulse ina pulse train (see Fig. 3) most likely reflects increasedneural response jitter that could both increase thelatency and decrease the amplitude of the responseaveraged across the stimulus epochs. Thus, the ECAPamplitude may not reflect the number of neurons thatwere activated by the pulse and, hence, may underesti-mate the predicted loudness from the model.

Hay-McCutcheon et al. (2005) also measuredindividual differences in neural adaptation viaECAPs measured using a 1,000-pps pulse train andtested a hypothesis that their measure (analogous tothe normalized amplitudes at 40 ms duration in thecurrent data) would be correlated with the perceptualeffect of stimulus duration on loudness and thresh-olds. The fact that no significant correlation wasfound is consistent with the current data, but may bealso explained in part by the lack of consideration ofdifferences in current-to-excitation slopes among thesubjects in that experiment. If these slopes varied

more than the adaptation effects for a 1,000-pps stimulus,it would lead to the observed lack of correlation in thatexperiment.

A conclusion from the current data is that the poorcorrelations between ECAP thresholds and high-ratebehavioral thresholds that are documented in manypublished reports have been caused in major part byindividual variation in how low rates affect behavioralthresholds, since this is the region where the majority ofvariation in the behavioral data is seen. Since neuraladaptation does not seem to be a major factor at theselow rates, alternativemechanismsmust contribute to thevariation in behavioral threshold slopes.

The loudness model suggested that a potentiallyimportant factor influencing the low-rate behavioralthreshold slopes is the slope of the neural activityversus current function. If activity grows faster in onesubject as current is increased, then the behavioralthresholds for different rates will be closer (that is thethreshold versus rate function will be flatter) than fora different subject with slower activity growth withlevel. The higher currents associated with low-ratethresholds, as well as the steeper slope of the activityversus current function at higher currents, contributeto the generally flatter threshold function at low ratescompared to high rates (McKay et al. 2013).Therefore, it is plausible that differences betweensubjects in low-rate threshold versus rate slopes maybe largely due to differences in the slope of activitygrowth with current at high current levels. However, ameasure of ECAP amplitude growth (SECAP) did notaccount for a significant amount variation of thebehavioral threshold slopes in the low-frequencyregion. The predictive failure of this objective mea-sure could be due to multiple factors: the model maybe incorrect, although it is unclear what alternativeparameters might be influencing the effect of rate onlow-rate thresholds, or the fact that SECAP must bemeasured at suprathreshold levels may render it apoor estimate of S at perceptual threshold.

The slope of the activity growth with current may beinfluenced by which neural elements are being activat-ed. McKay et al. (2003) hypothesized that the steepincrease in loudness above a certain kneepoint currentin each individual may be due to activation of morecentral and tightly packed neural material in theinternal auditory meatus. In subjects with poor spiralganglion cell survival, it will be necessary to increase thecurrent to higher levels to recruit sufficient activity forcomfortable loudness, thus making it likely that thesesubjects will have a kneepoint in loudness growth at apoint lower in their overall dynamic range than thosewith good spiral ganglion cell survival. Thus, subjectswith poorer neural survival may have flatter thresholdversus rate functions, particularly in the low-rate region,than those with better neural survival.

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The effect of cochlear health on pulse train thresholdshas been studied in guinea pigs with cochlear implants(Middlebrooks 2004; Kang et al. 2010; Pfingst et al. 2011).In general, the findings showed that animals withdeafened cochleae (absence of hair cells and dendritesand reduced spiral ganglion cell [SGC] density) generallyhad higher thresholds to pulse trains and the slopes ofthe threshold versus rate functions below 1,000 pps wereflatter compared with animals that had remainingcochlear function. This animal data support the proposalthat the flatter threshold versus rate slope for rates below1,000 pps in some animals was due to activation of morecentral and tightly packed neural material in the internalauditory meatus in animals with poor SGC survival. Boththe animal and human CI studies show an imperfectrelationship, between absolute low-rate behavioral thresh-old and slope of the threshold versus rate function(Middlebrooks 2004; Bonnet et al. 2012; Zhou et al.2012), and this can be attributed (particularly in humans)to the additional influence on thresholds of the distanceof the electrode from the spiral ganglion or internalauditory meatus. Thus, the ECAP threshold itself is not areliable objective correlate of the slope of the thresholdversus rate function in the low-rate region. Instead, anobjective measure that is highly correlated with spiralganglion cell survival may help to predict the low-rateslope of the threshold versus rate function. One ECAP-dependent measure that has been correlated with spiralganglion cell survival in guinea pigs is the effect of pulseduration or interphase gap on the ECAP growth function(Prado-Guitierrez et al. 2006). Animals with bettersurvival showedmore shift in the ECAP amplitude growthfunctions (change in current for equal ECAP amplitude)when pulse timing parameters were changed thananimals with poor survival. Thus, we could hypothesizethat human subjects who show greater influence of pulsetiming parameters on their ECAP amplitude growthfunctions (better neural survival) would have a steeperbehavioral threshold versus rate function in the low-rateregion. It remains to be tested whether this correlationexists, and, if so, whether it would allow prediction ofhigh-rate behavioral thresholds with sufficient accuracyfor completely objective cochlear implant fitting.

CONCLUSIONS

The variation between individuals in the way that ECAPamplitude changed when preceded by masker pulsetrains of different rates did not predict the variation inthe way that behavioral thresholds changed with rateand, thus, did not assist in improving the predictabilityof behavioral thresholds from ECAP measurementsalone. A majority of the variation in the behavioralthreshold versus rate slope occurred in the low rates(G500 pps) where there was little effect ofmasker rate on

ECAP amplitude, leading to the proposition that the lowcorrelations between ECAP thresholds and high-ratebehavioral thresholds are instead due to individualdifferences in how neural activity grows with current,the latter mostly affecting the behavioral slope in the low-rate region. However, ECAP amplitude growth functionslopes (objective correlates of neural activity growth)were not correlated with behavioral threshold versuscurrent slopes in the low-rate region, probably due to thesuprathreshold currents needed for the ECAPmeasures.

All regressions that estimated the influence of individualdifferences in the degree of adaptation or differences inneural activity versus current slope on the way thatbehavioral thresholds changed with rate provided relation-ships with a trend in the hypothesized direction, suggestingthat statistically significant correlations might be found inan experiment with higher power. However, even if thiswere so, it is unlikely that the ECAP measures wouldprovide sufficient predictive power to be used in individualobjective programming, given the low r values found here.

Taken together, the findings suggest that it is unlikelythat ECAP measures additional to ECAP threshold canbe combined with ECAP thresholds to predict high-ratebehavioral thresholds with sufficient accuracy to elimi-nate the need for behavioral measures when fitting acochlear implant.

ACKNOWLEDGMENTS

This study was funded by Cochlear Europe as a PhDstudentship to the second author and by a project grant fromthe UK Medical Research Council to the first author. Theauthors thank the cochlear implant users who generously gavetheir time for this study. The Bionics Institute acknowledgesthe support it receives from the Victorian Governmentthrough its Operational Infrastructure Support Program.

Open Access This article is distributed under the termsof the Creative Commons Attribution License which permitsany use, distribution, and reproduction in any medium,provided the original author(s) and the source are credited.

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