Abstract

The experience of pain varies for every person in both intensity and unpleasantness. The mechanisms supporting these differences in pain sensitivity are not yet understood, in part due to challenges of dissociating perceptual differences from reporting biases. In this preliminary analysis we sought to assess discriminability of graded noxious stimuli in highly sensitive and insensitive individuals. One hundred and three healthy individuals were asked to rate pain intensity and pain unpleasantness in response to a thermal stimulus paradigm containing 4 sequences of 8 semi-randomized stimuli of 35 and 43 to 49°C for a total of 32 stimuli. In order to identify different pain phenotypes, we employed a data-driven approach using a hierarchical cluster analysis of normalized pain intensity and unpleasantness ratings. A scree plot indicated that participants could be optimally divided into three broad clusters based on their pain ratings. These different phenotypes could be described as insensitive, moderately sensitive, and highly sensitive. A regression analysis was conducted for each cluster to calculate the stimulus response curves for each group according to Steven's Power Law. In all three clusters, pain unpleasantness stimulus response curves were characterized by steeper slopes than pain intensity stimulus response curves. The highly sensitive cluster exhibited a greater y-intercept and shallower slope for both intensity and unpleasantness stimulus response curves in relation to the other two groups. These differences in stimulus response curves could potentially support greater discriminability of small differences in noxious stimulus intensity in the highly sensitive cluster. Repeated measures analysis of variance confirmed that the highly sensitive cluster discriminated noxious stimuli of 1°C differences at lower stimulus intensities than other clusters. As such, these preliminary findings suggest that rating biases are not the primary source of observed individual differences in pain sensitivity. This research was supported by NIH NS085391.

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