() Three Distinct Categories of Time Course of Pain Produced by Oral Capsaicin Carey D. Balaban,* Donald H. McBurney,† and Mark A. Affeltranger† Abstract: Humans vary in oral pain tolerance. Our earlier studies noted that the responses of subjects show 1 of 3 qualitative response patterns to a single oral capsaicin concentration, which we termed a tonic pattern (level detection response), a phasic pattern (change detection response), and an integrator pattern (cumulative irritation) response. These patterns were modeled quantitatively as the sum of 3 underlying processes. Two time-varying capsaicin stimulus profiles were designed from the quantitative model. In the ascending step paradigm, 30 ppm capsaicin was presented to 42 subjects for 15 minutes, followed immediately and without explanation by 300 ppm capsaicin for 25 minutes. In the descending step paradigm, 300 ppm capsaicin was presented to 36 other subjects for 24 minutes, followed by 10 ppm for 22 minutes. Subjective burn was rated at 1 minute and then at 3-minute intervals throughout the presentation. Fuzzy cluster analysis identified 3 distinct response phenotypes in each paradigm, corresponding to level detection, change detection, and cumulative irritation response patterns identified previously. Discriminant functions permitted classification of these phenotypes from the response patterns. Thus, these paradigms provide the first quantitative phenotypic description of distinct oral pain responses to a common irritant, capsaicin. Perspective: This study examined the time-dependent behavior of pain produced by oral application of capsaicin. Three distinct temporal response phenotypes were identified objectively: level detection, change detection, and cumulative irritation detection. These time-dependent analyses provide a new dimension to understanding individual differences in pain sensation in clinical settings. © 2005 by the American Pain Society Key words: Capsaicin, pain intensity, oral, phenotypes, mathematical modeling. I t is well recognized that marked individual differences in pain sensation and pain tolerance reflect influences of biologic, social, and cultural determinants.15 It is unclear whether these differences reflect random varia- tions about a mean behavior or distinct response pheno- types for painful stimulation. In previous studies13,14 we noted 3 systematic patterns of human psychophysical re- sponses to a constant oral capsaicin stimulus, which we termed a tonic pattern (level detection response), a pha- sic pattern (change detection response), or a rising pat- tern (cumulative irritation response). We were able to describe these patterns by a mechanistic model13 that represents individual responses to capsaicin as a sum of 3 parallel processes (Fig 1). Each process corresponds to 1 of the 3 temporal patterns just described. The stimulus- response relationship for each process appears to follow a distinct power function.14 This model is also able to predict temporal interactions termed sensitization, desensitization, and cross adap- tation.1,2 The model is also useful for designing time-varying stimuli to test whether our previous obser- vations reflect different phenotypic categories of sub- jects’ oral pain responses. The model assumes that the response to 2 simulta- neous stimuli is the sum of the responses to the individ- ual stimuli. On the basis of individual differences in re- sponses to single capsaicin doses,13,14 we predicted that successive exposure to 2 concentrations of capsaicin would also distinguish 3 response phenotypes. The dura- tions and capsaicin concentrations for stimuli were based on these modeled responses. Here we use fuzzy cluster analysis and discriminant analysis to identify 3 distinct psychophysical response phenotypes in 2 new stimulus paradigms: a double step increase in stimulus intensity and a step decrease from a high to a low intensity. These results yield a new method of classifying human pain responses by temporal pat- tern, rather than threshold or magnitude of response. Methods Subjects Seventy-eight undergraduate students participated in the experiment for course credit. Forty-two subjects par- ticipated in the ascending concentration step experi- Received October 13, 2004; Revised December 22, 2004; Accepted Janu- ary 5, 2005. From the *Departments of Otolaryngology, Neurobiology and Commu- nication Sciences, Eye and Ear Institute, School of Medicine, and the †Department of Psychology, University of Pittsburgh, Pittsburgh, Penn- sylvania. Supported by the Eye & Ear Institute Foundation and by internal funds from the Department of Psychology, University of Pittsburgh. Address reprint requests to Carey D. Balaban, MD, Department of Oto- laryngology, University of Pittsburgh, 107 Eye & Ear Institute, 203 Lo- throp Street, Pittsburgh, PA 15213. E-mail: cbalaban@pitt.edu 1526-5900/$30.00 © 2005 by the American Pain Society doi:10.1016/j.jpain.2005.01.346 315The Journal of Pain, Vol 6, No 5 (May), 2005: pp 315-322 ment; 36 subjects participated in the descending concen- tration step experiment. The research protocol was reviewed and approved by the University of Pittsburgh Institutional Review Board, and informed consent was obtained from each subject. Capsaicin Stimuli Capsaicin (Sigma, St Louis, Mo; 98%) was dissolved in 95% ethyl alcohol to 6000 ppm. This stock solution was diluted in water to the final concentration. All solutions were stored at �10°C. Twenty-five microliters of the so- lution was pipetted onto 1.27-cm diameter filter paper disks (Schleicher & Schuell, Keene, NH) and allowed to dry. The filter papers were wetted with 50 �L of water just before being placed on the tongue. Subjects rated the burning sensation elicited by a capsaicin stimulus in a single session. For the step-up experiment, 30 ppm cap- saicin was applied to the tongue for 15 minutes, fol- lowed immediately and without explanation by 300 ppm capsaicin for 25 minutes. For the step-down experiment, 300 ppm capsaicin was applied to the tongue for 24 min- utes, followed by 10 ppm for 22 minutes. Protocol Standard, nonmodulus, magnitude estimation instruc- tions were read to the subjects, and any questions were answered. Because it assesses a temporal profile, free magnitude estimation is appropriate to display proper- ties such as an evolving integrator response. Subjects were given practice judging the distance between the experimenter’s hands. They wrote their responses to the capsaicin on index cards, which they turned over after each trial. Filter papers were laid out in a matrix array before the experiment to prevent the subject from sur- mising that all stimuli were the same. There was an ex- cess of both filter papers and index cards to prevent the subject from anticipating the end of the session. The stimuli were placed on the end of the outstretched tongue by means of forceps and left for 1 minute. The subjects gently held the filter paper in place with a tongue depressor. They were asked to keep the tongue extended between the closed lips for the duration of the session, but they were permitted to retract the tongue and close the mouth briefly as needed when the filter papers were changed every minute. At the end of the first 1-minute interval of stimulation and every third minute thereafter, subjects were asked to rate the burning sensation they felt at that moment. A computer beeped to signal the replacement of the filter paper and the rating of the stimuli. Data Transformation Data were transformed by multiplying each response by a constant so that each subject’s mean response be- came 100. This was done so that each subject would make an equal contribution to the average data. Note that this transformation obscures differences between subjects in overall response to capsaicin. Fuzzy C-means cluster analysis was performed in MATLAB (MathWorks, Natick, Mass) by using the “fcm.m” algorithm in the “Fuzzy toolkit.” Fuzzy C-means cluster analysis is a method for partitioning a data set into a specified num- ber of clusters, on the basis of an iterative minimization of the within groups sum of square error for the selected partition.4 In contrast to classic cluster analysis, which forces each observation into a single cluster, fuzzy cluster analysis generates a membership function matrix (values ranging from 0 to 1) that specifies the grade of member- ship of each observation in each cluster and an objective function specifying the location of the center of each Figure 1. The transfer function for the model for psychophysical responses to capsaicin is shown in block diagram form. The responses are modeled as a sum of 3 parallel processes: a tonic process with gain a, a phasic process with gain b, and an integrator process with gain c. The Laplace representations of each transfer function are shown in the boxes associated with each process. 316 Categories of Oral Capsaicin Pain Time Course cluster. Because the sum of the membership matrix en- tries for each observation is 1, the membership matrix entries are analogous to the likelihood of membership in each cluster. The theory and applications of these methods are described in detail in the statistics litera- ture. Discriminant analysis was performed with SYSTAT 10.2 (www.systat.com) software. Model Implementation and Parameter Estimation The model has 3 free parameters: tonic gain, phasic gain, and integrator process gain. The 3 time constants and relative response magnitudes to the 2 concentra- tions of capsaicin were fixed on the basis of previous studies.2,13,14 The input to the model is first transformed to reflect the exponential relationship between stimulus concentration and normalized burn sensation for each process.14 For the ascending step stimuli (30 to 300 ppm), the tonic input was simulated as a constant step of 48.7, followed by a step to 91.3. The phasic input (which satu- rates at 100 ppm) was simulated as an initial step of 43.6, followed by a step to 98.8, whereas the input to the integrator was set as a step to 15.8, followed by a step to 63.6. For the descending step stimuli (300 to 10 ppm), the tonic input was simulated as a constant step of 91.3, followed by a step down to 36.1. The phasic input (which saturates at 100 ppm) was simulated as an initial step of 98.8, followed by a step to 19.8, whereas the input to the integrator was set as a step to 63.6, followed by subtrac- tion of a response to a step of size 55.5 (the difference between the 63.6 magnitude for a 300-ppm stimulus and the 8.1 magnitude for a 10-ppm stimulus). The transfer functions of the individual processes (including the time constants) are identical to the model in our previous pub- lications.2,13,14 As in our previous studies, the model was simulated in MATLAB for least squares estimation of the gain of each process by using “leastsq.m” algorithm. Results The group median response to the ascending step showed 2 distinct limbs (Fig 2). During application of the initial 30-ppm stimulus, the response rose with a roughly exponential time course to approximately 60 arbitrary units. Application of the 300-ppm stimulus produced a second, roughly exponential rise to approximately 150 arbitrary units at the conclusion of the trial. The median response to the descending step stimulus profile (Fig 2) rose to a plateau at 150 arbitrary units during application of the initial 300-ppm stimulus, followed by a roughly exponential fall to 0 during application of the 10-ppm stimulus. The behavior of the integrator response has not been tested for a large decrease in capsaicin concentration. One possibility is that the response continues to inte- grate, but at a rate dictated by the reduced stimulus. A second possibility, however, is that the integrator re- Figure 2. Median subject responses during exposure to ascending (upper panel, 30 ppm for 15 minutes followed by 300 ppm for 25 minutes) and descending (lower panel, 300 ppm for 24 minutes followed by 10 ppm for 22 minutes) step concentrations of capsaicin. The filled circles represent the median group response at each time point. The solid line displays the fit of a single model to the responses of each group. The gains in the inset are a least squares estimate of the parameters a (tonic gain), b (phasic gain), and c (integrator gain) from the model, shown schematically in Fig 1 and described in Methods. 317ORIGINAL REPORT/Balaban et al sponse could “discharge” on the precipitous reduction of irritant concentration. The model was first fitted to the data from the descending step experiment to choose between 2 versions of the integrator process. The first version assumed that the integrator component would continue to increase when the stimulus was decreased because of continued integration of the lower intensity. The second version assumed that the integrator would discharge at the drop in concentration with the same rate with which it had charged. The active integrator discharge was implemented by adding an integrator re- sponse to a negative step of the same magnitude as the concentration drop. The discharging version yielded a better fit, particularly of the second (lower) concentra- tion portion of the data. Next, the model was fit simul- taneously to the median data from both experiments, yielding a single set of parameters that maximized the fit to the 2 sets of data. The group median responses for the 2 experiments were fitted well by a weighted sum of tonic (gain � 0.55), phasic (gain � 1.70), and integrator (gain � 0.66) components (Fig 2). Consistent with our previous observations, objective statistical methods identified 3 different temporal re- sponse profiles that characterize groups of subjects. Fuzzy C-means cluster analysis4 was performed on square root transformed responses of individual subjects to identify heterogeneous underlying response patterns. These analyses were done separately for the 2 experi- ments. Subjects were characterized as belonging to a single cluster (group response pattern) if their member- ship set value was greater than 0.45 for that cluster and less than 0.4 for any other cluster. Subjects with approx- imately equal membership values for 2 clusters were not used for characterizing the properties of any cluster. Three distinct response patterns were identified for the ascending capsaicin steps. Thirteen of the 42 subjects fell into Cluster 1, which might be described as a tonic response profile (Fig 3). The responses of these subjects showed a simple exponential rise to an initial plateau in the range of 50 to 100 arbitrary units during application of the initial 30-ppm stimulus, followed by a similar rise to a plateau of 120 to 200 arbitrary units. By contrast, the 12 subjects in Cluster 2 showed a more phasic response pattern (Fig 4) that rose within 5 minutes to a median plateau of approximately 60 arbitrary units, followed by a jump to a slowly increasing response pattern after ap- plication of the second capsaicin concentration. Finally, the 9 subjects in Cluster 3 showed a “rising pattern” observed in our previous studies.13,14 These responses showed a simple exponential rise to the first stimulus, followed by a rising response during application of the higher stimulus concentration (Fig 5). The remaining 8 subjects showed patterns that were almost equally likely Figure 3. Level detection response cluster. The upper panel shows the median burn sensation that characterizes the pattern termed Cluster 1 (13 subjects) from the group of subjects given ascending capsaicin stimulus concentrations. The lower panel shows the median burn sensation from the corresponding cluster of subjects (n � 13) from the group exposed to descending capsaicin stimulus concentrations. The curves on each panel show the fit of a model with a single set of parameters to the responses. The least squares estimates of the 3 gains in the model are listed in the figure. 318 Categories of Oral Capsaicin Pain Time Course to be (1) in cluster 1 or cluster 2 (5 subjects) or (2) in cluster 1 or cluster 3 (3 subjects). The responses to the descending capsaicin steps also showed 3 distinct patterns. The median response for the 13 subjects in Cluster 1 (Fig 3) was virtually identical to the median response for all subjects (Fig 2). The response rose rapidly to a plateau during application of 300 ppm capsaicin. When the stimulus concentration dropped to 10 ppm, the response declined within about 12 minutes to a plateau between 0 and 50 arbitrary units. The 7 subjects in Cluster 2 (Fig 4) showed a response that rose rapidly to a peak, followed by a flat to declining response during the 300-ppm stimulation. When the stimulus con- centration dropped to 10 ppm, the response declined precipitously, reaching 0 by the end of the session in all subjects. The 8 subjects in Cluster 3 (Fig 5) showed a slow rise during the 25-minute exposure to 300 ppm capsaicin, followed by a slow decline during exposure to 10 ppm capsaicin. Among the 8 individuals with intermediate re- sponse patterns to descending steps, 7 showed a pattern intermediate between clusters 1 and 2, and 1 subject showed a pattern intermediate between clusters 2 and 3. To test the hypothesis that the clusters from both ex- periments constitute the same 3 subject types with dis- tinct response characteristics, a least squares regression fit was performed to obtain a single set of parameters for a model fit to the response to both the ascending con- centration subjects and descending concentration sub- jects. Responses of Cluster 1 subjects from both experi- ments (Fig 3) were described by a single model with a tonic gain of 0.68, a phasic gain of 1.65, and an integra- tor gain of 0.61, which behaves as a level detector. Re- sponses of Cluster 2 subjects from both experiments (Fig 4) were described by a single model with a phasic gain of 3.72 and an integrator gain of 0.28, which behaves in a manner that is highly sensitive to changes in concentra- tion. Responses of Cluster 3 subjects from both experi- ments (Fig 5) were attributed to a sum of a small tonic gain (0.15), a moderate phasic gain (0.65), and a large integrator gain (1.90), which reflects high sensitivity to cumulative irritation. Thus, these findings support our earlier suggestion that there are 3 phenotypic patterns for subjective burning sensations elicited by oral irrita- tion by capsaicin. It is noteworthy that this classification approach ignores individual differences in perceived in- tensity. This contrasts sharply with all previous studies, because they are based on sensitivity and intensity, not the time course. Within each 2-step stimulus paradigm, the subject’s re- sponse phenotype could be identified by a discriminant analysis of the responses across time points. This ap- proach yielded 2 canonical variables for classifying sub- ject responses from each experimental paradigm (Tables 1 and 2). These factor scores yielded 100% correct classi- Figure 4. Change detection response cluster. The upper panel shows the median burn sensation that characterizes the pattern termed Cluster 2 (n � 12 subjects) from the group of subjects given ascending capsaicin stimulus concentrations. The lower panel shows the median burn sensation from the corresponding cluster of subjects (n � 7) from the group exposed to descending capsaicin stimulus concentrations. The curves on each panel show the fit of a model with a single set of parameters to the responses. The least squares estimates of the 3 gains in the model are listed in the figure. 319ORIGINAL REPORT/Balaban et al fication of the subjects that were classified by the C- means fuzzy cluster analysis (Fig 6). Among subjects who could not be classified unambiguously by the C-means analysis, 4 of 8 ascending step responses were classified as a response phenotype by the discriminant function, whereas only 1 of 8 was classified for the descending responses; these discriminant classifications were consis- tent with the membership set values from fuzzy C-means analysis. Discussion Our findings show that time-dependent behavior adds a new dimension to classification of oral sensitivity to chemical stimuli. All previous classification schemata for individual difference in response to chemical stimuli (whether tastants or irritants) have been based on in- stantaneous measures of magnitude or threshold. Be- cause pain responses, like all biologic processes, take place over time, instantaneous measures of magnitude face the problem of choice of the appropriate moment. Our approach, by contrast, permits an objective classifi- cation based on the temporal response profile of each subject to a single experimental exposure, independent of the absolute magnitude or choice of a single moment. The labeled magnitude scale is a visual analog scale anchored by verbal descriptors and having ratio-level properties.9 Bartoshuk3 has modified the upper limit to read, “strongest imaginable sensation of any kind,” to permit a more valid comparison between subjects. We believe this generalized labeled magnitude scale is still subject to ceiling effects, especially in studies that do not present examples of extremely strong stimuli at the be- ginning of the session. Because capsaicin irritation can Figure 5. Cumulative irritation response cluster. The upper panel shows the median burn sensation that characterizes the pattern termed Cluster 3 (n � 9 subjects) from the group of subjects given ascending capsaicin stimulus concentrations. The lower panel shows the median burn sensation from the corresponding cluster of subjects (n � 8) from the group exposed to descending capsaicin stimulus concentrations. The curves on each panel show the fit of a model with a single set of parameters to the responses. The least squares estimates of the 3 gains in the model are listed in the figure. Table 1. Discriminant Function for Ascending Capsaicin Steps Paradigm TERM FACTOR 1 COEFFICIENT FACTOR 2 COEFFICIENT Constant 31.477 15.248 1 min 0.514 0.198 4 min 0.240 0.005 7 min �0.098 �0.687 10 min 0.200 1.002 13 min �0.476 �0.802 16 min �0.153 0.402 19 min �0.131 �0.718 22 min 0.421 �0.099 25 min �0.770 �0.076 28 min �0.096 0.182 31 min �1.323 �0.194 34 min �0.253 0.181 37 min 0.152 �0.972 40 min �0.547 0.248 320 Categories of Oral Capsaicin Pain Time Course grow considerably over time, especially for integrators and for higher concentrations,14 we believe that free magnitude estimation is preferable in studies of the time course of capsaicin irritation. Of course, the normaliza- tion of data used in the present study thereby ignores individual differences in magnitude of pain. In our previous studies, the time course of the subjec- tive burning sensation to oral capsaicin was modeled explicitly as a linear sum of 3 underlying processes: a tonic mechanism, a phasic mechanism, and an integrator component. This study provided one refinement of the model structure for a previously unexplored experimen- tal condition, a precipitous decrease in capsaicin concen- tration. Under these conditions, the integrator process appears to discharge. When the discharging of the inte- grating component is incorporated, the results of this study support the adequacy of this linear modeling ap- proach because the 3 phenotypic responses could be modeled effectively as distinct linear sums of the 3 com- ponent processes. It is also noteworthy that the model used in this study was highly constrained and had only 3 free parameters. Specifically, the time constants of these processes and the psychophysical scaling factors of each process for capsaicin concentration were fixed by esti- mates from our earlier studies. Because our initial study found that the tonic component and the integrator com- ponent show long-term adaptation to capsaicin expo- sure, the response phenotypes likely represent an inter- action between genetics and history of exposure. Our previous data14 indicate that response phenotypes are stable for separate applications of different concentra- tions of capsaicin, separated by at least a 1-week interval. Fast et al8 and Prutkin et al16 have extensively docu- mented the importance of 6-N-propylthiouracil (PROP) and/or phenylthiocarbamide (PTC) sensitivity as a predic- tor of individual differences in taste and oral irritant sen- sitivity. The inability to taste PROP or PTC was originally believed to be a simple mendelian recessive trait,5,19 with homozygous PROP tasters displaying highest sensi- tivity.11,17 Genetic linkage studies7,10,12,17,18 have pre- sented evidence for loci on chromosomes 7q, 16p, and 5p. Positional cloning studies by Drayna et al6,7 and Kim et al12 have identified 3 distinct polymorphisms within the PTC gene on chromosome 7q, which affect amino acids 49, 262, and 296 of the TAS2R receptor protein. They also demonstrated that distinct haplotypes are as- sociated with different sensitivities to PTC and PROP, so that expression of the PAV haplotype is associated with high PROP sensitivity. Because one of our previous stud- ies13 indicated that PROP taster status is associated with Table 2. Discriminant Function for Descending Capsaicin Steps Paradigm TERM FACTOR 1 COEFFICIENT FACTOR 2 COEFFICIENT Constant �38.521 �0.064 1 min 0.236 0.561 4 min 0.725 �0.419 7 min 0.630 �0.434 10 min �0.288 0.683 13 min �0.260 �0.241 16 min �0.355 0.762 19 min 0.056 �0.173 22 min 1.702 �1.051 25 min �0.711 0.801 28 min 0.867 �0.315 31 min 0.737 0.176 34 min �0.426 0.214 37 min 0.683 �0.233 40 min 0.833 �0.544 43 min 0.025 0.200 46 min 0.091 0.061 Figure 6. Discriminant factor scores (Table 1) provide a distinct classification of 3 subject response populations. A P � .6827 (1 standard deviation) confidence ellipse, centered around the mean, is indicated for each cluster. These discriminant functions might be used to classify subjects as level detectors (Cluster 1), change detectors (Cluster 2), or cumulative irritation detectors (Cluster 3). 321ORIGINAL REPORT/Balaban et al a rising capsaicin response phenotype, a temporal classi- fication of oral irritant responses might be a powerful approach for identifying genetic linkages of individual differences in oral sensation. Acknowledgments We wish to thank Kristin McGinnis, Alexander Puskar, and Adrienne Hamer-Deithorn for assistance in data col- lection. References 1. Affeltranger MA: An examination of cross-adaptation and cross-sensitization effects between orally presented va- nilloids: Use of a dynamic model (dissertation). Pittsburgh, PA, University of Pittsburgh, 2002 2. Balaban CD, McBurney DH, Stoulis M: Time course of burn to repeated applications of capsaicin. Physiol Behav 66:109-112, 1999 3. Bartoshuk LM: Comparing sensory experiences across indi- viduals: Recent psychophysical advances illuminate genetic variation in taste perception. Chem Senses 25:447-460, 2000 4. Bezdek JC: Pattern recognition with fuzzy objective func- tion algorithms. New York, NY, Plenum Press, 1981 5. Blakesley AF, Fox AL: Our different taste worlds. J Hered- ity 23:97-110, 1932 6. Drayna D, Coon H, Kim U, Elsner T, Cromer K, Otterud B, Baird L, Peiffer AP, Leppert M: Genetic analysis of a complex trait in the Utah Genetic Reference Project: A major locus for PTC taste ability on chromosome 7q and a secondary locus on chromosome 16p. Hum Genet 112:567-572, 2003 7. Drayna D, Kim U, Wooding S, Jorde L, Floriano W, God- dard WA: Genetics of PTC taste sensitivity in humans. Pre- sented at the 2004 Meeting of the Association for Chemo- reception Sciences, Sarasota, Fla. 8. 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McBurney DH, Balaban CD, Popp JR, Rosenkranz JE: Ad- aptation to capsaicin burn: Effects of concentration and in- dividual differences. Physiol Behav 172:205-216, 2001 15. Melzack R: The Puzzle of Pain. New York, NY, Basic Books, 1973 16. Prutkin J, Duffy VB, Etter L, Fast K, Gardner E, Lucchina LA, Snyder DJ, Tie K, Weiffenbach J, Bartoshuk LM: Genetic variation and inferences about perceived taste intensity in mice and men. Physiol Behav 69:161-173, 2000 17. Reed DR, Bartoshuk LM, Duffy VB, Marino S, Price A: Propylthiouracil tasting: Determination of underlying threshold distributions using maximum likelihood. Chem Senses 20:529-533, 1995 18. Reed DR, Nanthakumar E, North M, Bell C, Bartoshuk LM, Price RA: Localization of a gene for bitter taste percep- tion to hyman chromosome 5p15. Am J Hum Genet 64:1478- 1480, 1999 19. Snyder LH: Studies in human inheritance. IX. The in- heritance of taste deficiency in man. Ohio J Sci 32:436- 440, 1932 322 Categories of Oral Capsaicin Pain Time Course Three Distinct Categories of Time Course of Pain Produced by Oral Capsaicin Methods Subjects Capsaicin Stimuli Protocol Data Transformation Model Implementation and Parameter Estimation Results Discussion Acknowledgments References