Test-retest reliability of Bayesian estimations of the effects of stimulation, prior information and individual traits on pain perception

Previous research has demonstrated inter-individual variability in the influence of different components such as nociception or expectations on pain perception. Identifying the relative influence of these components could serve as a patient stratification tool. Nevertheless, this would only be of use if the influence of each component is stable in time. In this study 30 healthy participants underwent a cognitive pain paradigm in which they rated pain after viewing a probabilistic cue informing of forthcoming pain intensity and then receiving electrical stimulation. The trial information and pain ratings were then used in a Bayesian probability model to compute the relative weight each participant put on stimulation, cue and cue uncertainty when rating their perceived pain intensity, as well as to extract the general trait-like bias of participants and the weight they put on it. The same procedure was repeated two weeks later. Test-retest reliability of all measures was assessed. Results showed that the effect of the stimulation and the trait like bias had a good reliability and that the estimations for the effect of the cue had a moderate reliability. These findings support the hypothesis that inter-individual differences in the weight placed on different pain factors is stable in time and could therefore be a possible target for patient stratification.