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A Bayesian perspective on sensory and cognitive integration in pain perception and placebo analgesia.

Anchisi D, Zanon M - PLoS ONE (2015)

Bottom Line: The placebo effect is a component of any response to a treatment (effective or inert), but we still ignore why it exists.Here we show how Bayesian decision theory can account for such features and we describe a model of pain that we tested against experimental data.Finally, the model can also reflect the strategies used by pain perception, showing that modulation by disparate factors is intrinsic to the pain process.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical and Biological Sciences, Universit degli Studi di Udine, Udine, Italy.

ABSTRACT
The placebo effect is a component of any response to a treatment (effective or inert), but we still ignore why it exists. We propose that placebo analgesia is a facet of pain perception, others being the modulating effects of emotions, cognition and past experience, and we suggest that a computational understanding of pain may provide a unifying explanation of these phenomena. Here we show how Bayesian decision theory can account for such features and we describe a model of pain that we tested against experimental data. Our model not only agrees with placebo analgesia, but also predicts that learning can affect pain perception in other unexpected ways, which experimental evidence supports. Finally, the model can also reflect the strategies used by pain perception, showing that modulation by disparate factors is intrinsic to the pain process.

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Ratings of pain induced by intermediate intensity electrical stimuli paired with no cue.(A) 31 subjects’ pain rating (y axis) scaled, for each subject, to the mean of pain ratings for high intensity stimuli in the same stimulation block); subjects are ordered according to the magnitude of the placebo effect (x axis, the magnitude is relative to the mean of pain ratings for high intensity stimuli paired with red cues). Each subject rated 8 stimuli and is represented with a different color and symbol. The square box delimits subjects with no significant placebo effect (tested at P < 0.05; n = 8; one tail Mann-Whitney rank-sum test). (B) Correlation between individual clustering measures of pain rating (cluster distance, first column; cluster separation index, second column; probability that the data followed a bimodal distribution, third column) and the magnitude of the placebo effect (first row); or the expectation of analgesia (parameter w) estimated by the model on placebo data (second row). Cluster analysis: K-means method for 2 clusters; test of bimodal distribution vs unimodal: Bayesian hypothesis comparison; correlation analysis: Pearson’s product moment correlation coefficient, tested for positive correlation. (n = 31). (See also Table 1).
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pone.0117270.g006: Ratings of pain induced by intermediate intensity electrical stimuli paired with no cue.(A) 31 subjects’ pain rating (y axis) scaled, for each subject, to the mean of pain ratings for high intensity stimuli in the same stimulation block); subjects are ordered according to the magnitude of the placebo effect (x axis, the magnitude is relative to the mean of pain ratings for high intensity stimuli paired with red cues). Each subject rated 8 stimuli and is represented with a different color and symbol. The square box delimits subjects with no significant placebo effect (tested at P < 0.05; n = 8; one tail Mann-Whitney rank-sum test). (B) Correlation between individual clustering measures of pain rating (cluster distance, first column; cluster separation index, second column; probability that the data followed a bimodal distribution, third column) and the magnitude of the placebo effect (first row); or the expectation of analgesia (parameter w) estimated by the model on placebo data (second row). Cluster analysis: K-means method for 2 clusters; test of bimodal distribution vs unimodal: Bayesian hypothesis comparison; correlation analysis: Pearson’s product moment correlation coefficient, tested for positive correlation. (n = 31). (See also Table 1).

Mentions: The main findings of this study concern not only the placebo effect but a wider range of effects also due to past experience. To test these predictions, in our study we focused on the pain rating for stimuli of intensity at midpoint between those used in conditioning and delivered in the absence of a visual cue (midblue stimuli; Fig. 3A, upper right panel; Fig. 3B, blue dotted curve; and Fig. 5). The fBD model predicts that, with a uniform loss function, the probabilities of the perceived pain would follow a bimodal distribution, with pain ratings more likely to be clustered around the two peaks of highest probability (Fig. 5A), and that the degree of clustering is positively correlated with the effectiveness of conditioning/expectation (Fig. 5B). These outcomes were unexpected, but the experimental results supported them. In fact, the subjects’ responses to intermediate-intensity stimuli were mostly clustered toward the two levels perceived within the conditioning stage, as the model predicts, and only in some cases to the midpoint, as one would otherwise expect (Fig. 6A).


A Bayesian perspective on sensory and cognitive integration in pain perception and placebo analgesia.

Anchisi D, Zanon M - PLoS ONE (2015)

Ratings of pain induced by intermediate intensity electrical stimuli paired with no cue.(A) 31 subjects’ pain rating (y axis) scaled, for each subject, to the mean of pain ratings for high intensity stimuli in the same stimulation block); subjects are ordered according to the magnitude of the placebo effect (x axis, the magnitude is relative to the mean of pain ratings for high intensity stimuli paired with red cues). Each subject rated 8 stimuli and is represented with a different color and symbol. The square box delimits subjects with no significant placebo effect (tested at P < 0.05; n = 8; one tail Mann-Whitney rank-sum test). (B) Correlation between individual clustering measures of pain rating (cluster distance, first column; cluster separation index, second column; probability that the data followed a bimodal distribution, third column) and the magnitude of the placebo effect (first row); or the expectation of analgesia (parameter w) estimated by the model on placebo data (second row). Cluster analysis: K-means method for 2 clusters; test of bimodal distribution vs unimodal: Bayesian hypothesis comparison; correlation analysis: Pearson’s product moment correlation coefficient, tested for positive correlation. (n = 31). (See also Table 1).
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pone.0117270.g006: Ratings of pain induced by intermediate intensity electrical stimuli paired with no cue.(A) 31 subjects’ pain rating (y axis) scaled, for each subject, to the mean of pain ratings for high intensity stimuli in the same stimulation block); subjects are ordered according to the magnitude of the placebo effect (x axis, the magnitude is relative to the mean of pain ratings for high intensity stimuli paired with red cues). Each subject rated 8 stimuli and is represented with a different color and symbol. The square box delimits subjects with no significant placebo effect (tested at P < 0.05; n = 8; one tail Mann-Whitney rank-sum test). (B) Correlation between individual clustering measures of pain rating (cluster distance, first column; cluster separation index, second column; probability that the data followed a bimodal distribution, third column) and the magnitude of the placebo effect (first row); or the expectation of analgesia (parameter w) estimated by the model on placebo data (second row). Cluster analysis: K-means method for 2 clusters; test of bimodal distribution vs unimodal: Bayesian hypothesis comparison; correlation analysis: Pearson’s product moment correlation coefficient, tested for positive correlation. (n = 31). (See also Table 1).
Mentions: The main findings of this study concern not only the placebo effect but a wider range of effects also due to past experience. To test these predictions, in our study we focused on the pain rating for stimuli of intensity at midpoint between those used in conditioning and delivered in the absence of a visual cue (midblue stimuli; Fig. 3A, upper right panel; Fig. 3B, blue dotted curve; and Fig. 5). The fBD model predicts that, with a uniform loss function, the probabilities of the perceived pain would follow a bimodal distribution, with pain ratings more likely to be clustered around the two peaks of highest probability (Fig. 5A), and that the degree of clustering is positively correlated with the effectiveness of conditioning/expectation (Fig. 5B). These outcomes were unexpected, but the experimental results supported them. In fact, the subjects’ responses to intermediate-intensity stimuli were mostly clustered toward the two levels perceived within the conditioning stage, as the model predicts, and only in some cases to the midpoint, as one would otherwise expect (Fig. 6A).

Bottom Line: The placebo effect is a component of any response to a treatment (effective or inert), but we still ignore why it exists.Here we show how Bayesian decision theory can account for such features and we describe a model of pain that we tested against experimental data.Finally, the model can also reflect the strategies used by pain perception, showing that modulation by disparate factors is intrinsic to the pain process.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical and Biological Sciences, Universit degli Studi di Udine, Udine, Italy.

ABSTRACT
The placebo effect is a component of any response to a treatment (effective or inert), but we still ignore why it exists. We propose that placebo analgesia is a facet of pain perception, others being the modulating effects of emotions, cognition and past experience, and we suggest that a computational understanding of pain may provide a unifying explanation of these phenomena. Here we show how Bayesian decision theory can account for such features and we describe a model of pain that we tested against experimental data. Our model not only agrees with placebo analgesia, but also predicts that learning can affect pain perception in other unexpected ways, which experimental evidence supports. Finally, the model can also reflect the strategies used by pain perception, showing that modulation by disparate factors is intrinsic to the pain process.

Show MeSH
Related in: MedlinePlus