1. Introduction
Pain is an unpleasant sensory and emotional experience associated with actual or potential tissue damage. Thus, pain is fundamentally linked to emotional experience and motivational behaviour. This is reflected in the common classification of pain into a sensory-discriminative and a negative affective component, the currency of punishment as primary reinforcement. Pain is modulated on a cognitive level depending on attention, anticipation, emotion and memory of previous pain experience [59]. Empirical studies demonstrate that measures of pain perception are associated with changes of internal bodily reactions, such as heart rate (HR), skin conductance response, baroreflex sensitivity and HR variability (HRV) [4,35,52]. HRV measures are commonly used to estimate the sympathovagal balance [37].
The generation and perception (interoception) of internal states of bodily arousal are central to many theoretical accounts of emotion [18,19,31,57]. James [31] presented an influential psychological theory linking somatic and visceroafferent feedback to subjective emotional experience (feelings). This model argues that an emotive stimulus automatically initiates visceral, vascular or somatic reactions such as changes in blood pressure or HR; and it is the perception of these bodily reactions that crucially constitutes the emotional component of experience. Refinements of this model include the notion of somatic markers, which represent involuntary changes in internal bodily state signaling stimulus significance to guide both emotional and cognitive behaviour (eg, decision making) [18,19].
Such peripheral models of emotion led to an interest in individual differences in the perception and sensitivity to changes in internal bodily state. Individuals differ substantially in measures of interoceptive sensitivity, the ability to perceive consciously signals arising from the body. Interoceptive sensitivity is commonly quantified by measuring a person’s ability to perceive and accurately report one’s heartbeats at rest [7,17,20,47,49,58]. Differences in interoceptive sensitivity are related with both reported emotional experience, and corresponding psychophysiological markers of emotion processing [21,29,44,46,48,64]. Moreover the strength of correspondence between cognitive–affective processing and bodily reactions depends on whether individuals can perceive bodily changes well—or not [21].
Experimental studies of pain demonstrate the importance of bodily changes for the experience of pain, such as changes in HR [35]. It is hypothesized that interoceptive sensitivity interacts with pain in part by facilitated detection of bodily changes accompanying pain experience. This hypothesis is supported by recent research that demonstrates that interoceptive sensitivity is positively associated with the self-regulation of behaviour in situations that are accompanied by somatic and/or physiological changes. These changes during paradigms assessing physical workload [30] or decision making [21,62], can be usefully formulated in terms of somatic markers. Nevertheless, the interaction of pain with interoceptive sensitivity remains poorly understood. To address this shortfall, we conducted a study on healthy participants who differed in levels of interoceptive sensitivity determined from accuracy of heartbeat monitoring. We hypothesized that enhanced interoceptive sensitivity would be associated with measures of pain threshold, tolerance and pain experience. Moreover, we hypothesized that this effect would also be associated with underlying differences in physiological reactivity as measured by HRV to pain stimuli.
2. Methods
2.1. Participants
Sixty participants (mean±SD age 24.4±3.2, 30 men and 30 women) were recruited from an introductory psychology course and by advertising announcements at the university. All participants were screened for health status via a questionnaire. Participants were excluded if they had a history of chronic pain, any common psychiatric disorder, in particular anxiety disorders or depression (or any other axis 1 disorders) according to the Diagnostic and Statistical Manual of Mental Disorders [1]. Drug use (except of contraceptives) was also an exclusion criterion. Experiments were conducted in accordance with the Declaration of Helsinki. Ethical approval from a local ethics board was obtained. All participants provided written informed consent.
2.2. Procedure outline
Upon arrival at the laboratory room in the Department of Psychology, each participant completed a set of questionnaires. Afterwards, they were fitted with physiological recording equipment for HR (electrocardiography) and respiration (respiratory belts), using a portable Biopac system (Biopac MP150, version 2.7.2.). The room was air-conditioned with an average room temperature of 23°C. All experiments were conducted between 9 and 12 o’clock in the morning. The experiment started with a 10-min rest period in which the baseline measures were assessed. This period was followed by the interoceptive sensitivity task. First, interoceptive sensitivity was assessed using 4 heartbeat counting phases (varying in length) in accordance with the Mental Tracking Method suggested by Schandry [58]. Participants were asked to count their own heartbeats silently and to verbally report the number of counted heartbeats at the end of the counting phase. The beginning and the end of the counting intervals were signaled acoustically. Interoceptive sensitivity was estimated as the mean heartbeat perception score according to the following transformation:
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The mean±SD obtained heartbeat perception score was 0.66±0.15. We used a median split procedure to contrast participants with higher interoceptive sensitivity (mean 0.79, high IS) to participants with lower interoceptive sensitivity (mean 0.54, low IS). The distribution of the heartbeat perception scores for both groups is depicted as box-and-whisker plot in Fig. 1.
Fig. 1.:
Distribution of the heartbeat perception scores contrasting participants with high vs low IS.
Then pain thresholds were assessed (twice on each hand side, alternating between the left- and right-hand side, 60-s break after one assessment on right- and left-hand side), which took an average of 4min. Afterwards, pain tolerance measures were taken (twice on each hand, alternating between left and right hands, 60-s break in between, mean duration 3min). The average duration of the pain assessment block was 10min (range 9–11min). Autonomic measures were recorded during the whole duration of both pain threshold and tolerance measurements. Subsequent analysis of autonomic responses was performed over an average length of 10min. Subjective pain intensity and unpleasantness was assessed immediately at the end of each pain trial with a 9-point self-rating scale.
2.3. Pain assessment
Pain thresholds and tolerance were measured with a pressure algometer (FDN200; Wagner Instruments, Greenwich, CT) that exerts forces up to 20kg/cm2 (corresponding to ∼2000kPa). This device is used to identify the pressure and/or force eliciting a pressure–pain threshold and tolerance level. This validated method has a high interrater reliability in the rate of force application [3,8,33]. Before testing, all involved investigators were familiar with the algometer after practice sessions. The handheld algometer had a 1cm2 round rubber application surface, which was placed over the thenar eminence of the hand [53,54]. The pressure pain threshold was determined with 3 series of ascending stimulus intensities, each applied as a slowly increasing ramp of 50kPa/s (∼0.5kg/cm2 per second). This procedure leads to high reliability of the algometer assessment, in accordance to previous studies [8,43]. Each trial was stopped when the participant experienced the pressure applied by the algometer as painful. Pain tolerance levels were then assessed; again, each trial was aborted as soon as participants experienced the pressure as unbearable. We had previously established the reliability of this assessment in a group of 34 healthy participants in a pilot study at the University of Potsdam (Supplementary Material). Test–retest reliability scores (Cronbach’s alphas) between both assessments were sufficiently high (threshold: α=.90, P<.001; tolerance: α=.84, P<.001).
2.4. Autonomic measures
Mean HR and respiration rate during baseline and pain assessment were recorded with a Biopac system (Biopac MP150, version 2.7.2.) and the corresponding software AcqKnowledge (Biopac Systems, Santa Barbara, CA). Signals were sampled at 500Hz and analysed by a computer-based data acquisition system. HRV was analysed stepwise as described by Herbert et al. [28]. The details are described in the Supplementary Material.
In short, R-R intervals were imported into a HR analysis programme (HRV Analysis Software, version 1.1., SP1, The Biomedical Signal Analysis Group, Department of Applied Physics, University of Kuopio, Finland [39]). This programme has been successfully used in previous studies [24,42]. A fast Fourier transformation was run on R-R interval data in order to obtain power spectrum density values for high-frequency (HF; 0.15–0.40Hz), low-frequency (LF; 0.04–0.15Hz), and very low-frequency (VLF; <0.04Hz) spectra. These ranges were based on previously established standards defined by the Task Force of the European Society of Cardiology and North American Society of Pacing and Electrophysiology [36]. Because the power in these frequency bands can vary widely within and between individuals, to generate more exact measures of the specific autonomic activity, the absolute power of LF and HF was normalised by dividing the power in each band by the total power minus the DC noise (0.04–0.40Hz). While the HF band reflects pure vagal tone, the LF component seems to be influenced by both parasympathetic and sympathetic outflow [6,37]. Sympathetic influences on the LF component seem to become more explicit if LF power is expressed in normalised units (LF n.u.) [36], while the LF/HF ratio is often considered to be an estimate of sympathovagal balance [37].
2.5. Pain unpleasantness and pain intensity
Perceived pain unpleasantness and pain intensity were assessed after each pain trial (both threshold and tolerance) with a 9-point rating scale. Participants were asked to rate their feelings concerning pain unpleasantness (1: not unpleasant at all; 9: maximum unpleasantness) and pain intensity (1: barely perceivable; 9: maximum intensity) while exposed to pain stimuli.
2.6. Data analyses
We calculated Pearson’s correlation coefficient between interoceptive sensitivity and mean pressure pain thresholds as well as tolerance levels. Pressure pain thresholds as well as tolerance levels were analysed by a repeated measures analysis of variance (ANOVA) with the factors Measurement (threshold, tolerance) and Group (high/low IS).
HR, respiration and HRV measures (HF, LF, LF/HF ratio) were analysed by repeated measures ANOVA with the factors Experimental Condition (baseline, pain assessment) and Group (high/low IS). Where appropriate, degrees of freedom were adjusted according to the Greenhouse–Geisser correction [26]. Correlation analyses were assessed between interoceptive sensitivity and HRV measures, referring to the whole sample. All results are presented with focus on the factor Group.
In a last step, we focused in more detail on the relationship between pain measures, interoceptive sensitivity and autonomic changes. Two hierarchical regression analyses (criteria: pain threshold, pain tolerance) were carried out. Sex, age, change in sympathovagal balance (HF/LF ratio), interoceptive sensitivity score and computed interaction terms between interoceptive sensitivity and change in sympathovagal balance were included as predictors.
3. Results
3.1. Pain assessment
Mean pain threshold was 4.6kg/cm2, and mean pain tolerance score was 6.7kg/cm2. We observed a significant effect of Group (F(1,58)=6.02, P<.05, η2=.10, ε=.68) indicating lower threshold and tolerance scores in participants with high IS (mean 5.1kg/cm2) as compared to low IS (6.2kg/cm2) (Fig. 2). We observed significant inverse correlations between interoceptive sensitivity and pain threshold (r=−.42, P<.01), as well as pain tolerance (r=−.33, P<.05). Scatter plots depict these correlation coefficients (Fig. 3).
Fig. 2.:
Pain threshold and tolerance scores contrasting participants with high vs low IS (∗ P < .05).
Fig. 3.:
Scatter plots between heartbeat perception scores (a) pain threshold and (b) pain tolerance scores.
3.2. Pain unpleasantness and intensity
We observed a significant Group (F(1,58)=4.19, P<.05, η2=.07, ε=.53) and Group×Measurement interaction (F(1,58)=5.49, P<.05, η2=.09, ε=.64) indicating that the high IS group evaluated stimuli at threshold pain level as significantly more unpleasant (mean 7.0) when compared to the low IS group (mean 6.3, Fig. 4). Referring to perceived pain intensity (1: lowest intensity, 9: highest intensity), no significant main or interaction effect with regard to Group occurred.
Fig. 4.:
Perceived pain unpleasantness for threshold and tolerance levels stimuli contrasting participants with high vs low IS (∗ P < .05).
3.3. HR, respiration, and HRV
HR (F(1,58)=57.31, P<.001, η2=.50, ε=1.00) and respiration rate (F(1,58)=4.67, P<.05, η2=.11, ε=.64) were significantly increased during pain perception (mean 82.5beats/min and mean 16.6breaths/min) as compared to baseline (mean 7.1beats/min and 14.1breaths/min). Additionally, a significant Experimental Condition×Group interaction (F(1,58)=8.74, P<.01, η2=.13, ε=.83) was observed indicating that the increase in HR was more pronounced in the high IS group (baseline 69.1bpm, pain assessment 86.4bpm) as compared to the low IS group (baseline 71.5bpm, pain assessment 8.2, Tukey tests, P<.05).
For HRV, mean HF n.u. was significantly decreased during pain assessment (mean 28.3) compared to baseline (mean 46.6, F(1,58)=4.06, P<.001, η2=.41, ε=1.00) (Fig. 4). Additionally, a significant Experimental Condition×Group interaction (F(1,58)=8.74, P<.01, η2=.13, ε=.83) was found. This interaction suggests that the decrease in HF n.u. was more distinct in the high IS group in comparison with the low IS group (Tukey test, P<.05). In other words, the parasympathetic decrease was more pronounced in the high IS group. The opposite pattern was observed for LF n.u.
LF/HF ratio significantly increased during pain assessment (mean 3.27, F(1,58)=64.11, P<.001, η2=.52, ε=1.00) as compared to baseline (mean 1.47) indicating an increase in sympathetic influence on sympathovagal balance (Fig. 5). Additionally, a significant Experimental Condition×Group interaction (F(1,58)=8.74, P<.01, η2=.13, ε=.83) occurred: The change in sympathovagal balance was more pronounced in the high IS group as compared to the low IS group, and both groups differed significantly in LF/HF ratio during pain assessment (Tukey post hoc tests, P<.05).
Fig. 5.:
HRV measures (HF n.u. indicates high frequency normalised units; LF/HF ratio, low frequency/high frequency ratio) contrasting participants with high vs low IS (∗ P < .05).
3.4. Interactions between change in sympathovagal balance, interoceptive sensitivity and pain assessment
Because the change in sympathovagal balance and its interaction to interoceptive sensitivity was one key finding, we performed a correlation analysis between the interoceptive sensitivity score and the change in LF/HF ratio. Interestingly, the correlation analyses yielded a significant positive correlation of r=.41 (P<.01) which denotes a more substantial change in sympathovagal balance in relationship to interoceptive sensitivity. We depicted a scatterplot of this relationship in Fig. 6. However, we did not observe any significant correlation between the change in LF/HF ratio and pain threshold (r=−.10, P<NS) as well as pain tolerance (r=.02, P=NS).
Fig. 6.:
Scatter plots between heartbeat perception score and change in sympathovagal balance.
In a last step, 2 hierarchical regression analyses (forward stepping) were performed using either pain threshold or pain tolerance as criterion and sex, age, change in sympathovagal balance (HF/LF ratio), interoceptive sensitivity and interaction terms between interoceptive sensitivity and change in sympathovagal balance as predictors.
Pain threshold was explained by differences in interoceptive sensitivity (T=−3.81, β=.43, P<.001) and age (T=2.71, β=.31, P<.01, F(2,57)=1.70, P<.001, R=.52, R2=.27). All other predictors were not included. Concerning pain tolerance, the only predictor included was interoceptive sensitivity (T=2.97, β=.33, P<.05, F(1,58)=6.97, P<.05, R=.31, R2=.11).
4. Discussion
In accordance with our hypotheses, we found evidence for a positive relationship between interoceptive sensitivity and the perception of pain stimuli, both on a threshold as well as on a tolerance level. These differences were accompanied by a larger decrease in parasympathetic activity and a more pronounced change in sympathovagal balance during pain assessment in the high IS group as compared to the low IS group. When introducing experimentally induced mechanical pain, the ‘normal’ reactivity pattern involves both an increase in sympathetic as well as a decrease in parasympathetic activity. In conclusion, the present results demonstrate interaction of interoceptive sensitivity with changes in sympathovagal balance, pain threshold and pain tolerance. However, pain measures per se as used in the paradigm were not predicted by sympathovagal changes.
It is important to note that humans also differ in their sensitivity to other interoceptive conditions, such as esophageal or rectal distension or thermal discomfort [27,40]. There is sparse evidence that pain perception is associated with interoception of inputs from different organ systems like the cardiovascular system or the gastrointestinal system [63], suggesting that interoceptive sensitivity may covary across different modalities. It is nevertheless an open question whether cardiac sensitivity is also correlated to other interoceptive conditions and other pain qualities such as visceral pain [40], as induced by rectal balloon distension or stomach distension. In this study, pain led to a clear increase in sympathetic activation known to be associated with activation of the right anterior insula [12,14], a key structure for interoceptive sensitivity as measured by heartbeat detection [17,51]. If the interoceptive condition is associated with another autonomic pattern, such as a parasympathetic increase, the observed interaction between cardiac sensitivity and pain sensitivity may be undetectable.
When we interpret pain both as a distinct sensation and as a motivation reflecting homeostatic behavioural drive (a homeostatic emotion), such as suggested by Craig [10], we would expect that interoceptive sensitivity covaries with particular emotional conditions. We thereby extend findings of Dunn et al. [21], who stress the notion that interoceptive sensitivity determines the strength of the relationship between bodily reactions and cognitive–affective processes as demonstrated by emotional and decision-making paradigms. Our data further demonstrate that both pain threshold and tolerability are fundamentally associated with interoceptive processes. We interpret our findings as indicating that better detection of internal signals and evoked bodily changes seems to increase pain perception for pressure pain. This was reflected in negative relationships between interoceptive sensitivity and pain measures, which remained significant in regression analyses that accounted for multiple interrelations between observed variables.
We again observed, as have others, that autonomic reactivity was positively correlated to interoceptive sensitivity, suggesting that autonomic reactivity might be enhanced in participants with high interoceptive sensitivity [28,45,50]. Thus, the enhanced ability of high IS individuals to perceive their bodily changes might be further augmented by the more pronounced nature of such changes. Thus, such a mechanism would facilitate the establishment and short-term consolidation of somatic markers that could be used to guide individual motivational behaviours in parallel with the sensory signaling of pain. Our findings suggest a contextual embellishment of the somatic marker hypothesis by Damasio [18,19]. Furthermore, our regression analyses demonstrated that interoceptive sensitivity (rather than differences in autonomic reactivity) explained variance of the pain measures obtained. For this reason, we suggest that future studies should focus on investigating subgroups of participants selected priori with respect to differential autonomic reactivity and interoceptive sensitivity.
Internal signals like one’s heartbeats are centrally processed via dedicated pathways and both their neural representations as well as their conscious perception provide key information accessible to many cognitive and emotional processes [9,44]. Here, the insular cortex plays a major role as a cortical projection area of viscerosensory input [11]. Craig [13] suggests 3 sequential processing steps involving different portions of the insula consistent with the view for a posterior-to-anterior progression in the insula. Raw interoceptive signals such as those coming from visceral changes and, importantly, pain, first project to the posterior insula and become progressively integrated with contextual motivational and hedonic information as they progress towards the anterior insula. There is ample evidence that the activation of the insula is positively correlated with interoceptive sensitivity [17,47,51]. What is more, the cortical potential reflecting the processing of heartbeats (heartbeat-evoked potential) was found to be associated with activation in the right insula [25,47]. It is therefore likely that this structure serves as an interface between interoception, interoceptive sensitivity and pain processing.
The insular cortex and the anterior cingulate play crucial roles connecting interoceptive processes and emotions [9,15,16]. Their activation was found to be modulated by cognitive [5,56] and emotional factors in several studies [5,14,34,41]. We used HRV as one approach to assess sympathovagal balance and associated changes during pain assessment, but there are other methods using the cardiac vagal tone, cardiac output measures derived from impedance cardiography or baroreceptor sensitivity. Interestingly, recent research highlights that HRV measures might be differentially associated with activation of the insula [61] suggesting a lateralization within the insula with the right insula strongly associated with measures of sympathetic influence. One might speculate whether the observed shift in sympathovagal balance as assessed with the LF/HF ratio is also reflected in a similar activation change within the right and left insula.
Interoceptive sensitivity is also associated with more intense negative and positive feelings, as in response to emotional pictures [29,44,46]. Differences in the affective evaluation of pain stimuli highlight the impact of interoceptive processes on cognitive–affective aspects of pain [23,32,55,60,65]. Theories of embodied cognition hold that higher cognitive processes operate on perceptual symbols. Perceptual symbols involve the reactivation of previous sensory-motor states occurring during experience with the world [2,22,38]. It is stated that mental representations in bodily formats including motoric, somatosensory, affective and interoceptive information have an important role in cognition and emotion. Our results support this notion by demonstrating that interoceptive processes and sensitivity to interoceptive signals are crucial variables for explaining interindividual differences in respect to the perception of pain and its cognitive–affective evaluation [21].
Our study provides the first empirical evidence that interoceptive sensitivity and associated activation of interoceptive representations and metarepresentations of bodily signals profoundly interact with the processing and the subjective experience of painful stimuli. Interoceptive processes and sensitivity to these processes (as assessed by a heartbeat detection task) is associated with our pain experience, extending theoretical concepts of embodied cognition and embodied emotion to the field of pain perception. For this reason, assessing interoceptive sensitivity in clinical samples—for example, in somatoform patients or in depression—might be a powerful approach to explain different observations with respect to autonomic reactivity and might provide ideas for novel therapeutic interventions, as based on interoceptive sensitivity training.
Conflict of interest statement
The authors report no conflict of interest.
Acknowledgments
We thank Jennifer Meyer, Kevin Görsch, Alexander Dreyer, and Sarah Wankner for their support in data assessment and data processing, and Julia Schneider for her editorial support.
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Appendix A Supplementary data
Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.pain.2012.04.030.
Appendix A Supplementary data
Supplementary data 1: Supplementary material
