Certainty in ascending sensory signals - The unexplored driver of analgesic placebo response

Test-retest and interrater reliability of experimental within-subject variability of pain reports as assessed by the focused analgesia selection test

Introduction

Within-subject variability (WSV) of pain intensity reports has been shown to predict the placebo response. The focused analgesia selection test (FAST), which allows to experimentally assess WSV of pain reports, has been used as a screening tool to identify participants who are likely to have a strong placebo response in drug-development clinical trials. Yet, the reliability of FAST has not been reported.

Objectives

To assess test-retest and interrater reliability of the FAST outcomes. To mimic pharma-sponsored clinical trials, we enlisted inexperienced assessors who underwent limited training.

Methods

Healthy volunteers performed the FAST twice within a week and were randomly assigned to either the test-retest group or the interrater group. T-tests, partial Pearson correlations, intraclass correlations (ICC), and Bland-Altman plots were generated to assess FAST outcomes' reliability.

Results

Sixty-three participants completed the study and were assigned to the test-retest (N = 33) or interrater (N = 30) arms. No statistically significant differences in the FAST outcomes were detected between the 2 sessions, except for the FAST covariance (FAST CoV) in the interrater assessment (P = 0.009). Test-retest reliabilities of the FAST-main outcomes were r = 0.461, ICC = 0.385 for the FAST R 2 and r = 0.605, ICC = 0.539 for the FAST ICC and in the interrater cohort, they were FAST R 2: r = 0.321, ICC = 0.337 and FAST ICC: r = 0.355, ICC = 0.330.

Conclusion

Using inexperienced assessors, the FAST outcomes test-retest ranged from moderate to strong, whereas the interrater reliability ranged from weak to poor. These results highlight the importance of adequately training study staff members before using this tool in multicentre clinical trials.

Pain perception as hierarchical Bayesian inference: A test case for the theory of constructed emotion

An intriguing perspective about human emotion, the theory of constructed emotion considers emotions as generative models according to the Bayesian brain hypothesis. This theory brings fresh insight to existing findings, but its complexity renders it challenging to test experimentally. We argue that laboratory studies of pain could support the theory because although some may not consider pain to be a genuine emotion, the theory must at minimum be able to explain pain perception and its dysfunction in pathology. We review emerging evidence that bear on this question. We cover behavioral and neural laboratory findings, computational models, placebo hyperalgesia, and chronic pain. We conclude that there is substantial evidence for a predictive processing account of painful experience, paving the way for a better understanding of neuronal and computational mechanisms of other emotions.

Does Active Inference Provide a Comprehensive Theory of Placebo Analgesia?

Placebo interventions generate mismatches between expected pain and sensory signals from which pain states are inferred. Because we lack direct access to bodily states, we can only infer whether nociceptive activity indicates tissue damage or results from noise in sensory channels. Predictive processing models propose to make optimal inferences using prior knowledge given noisy sensory data. However, these models do not provide a satisfactory explanation of how pain relief expectations are translated into physiological manifestations of placebo responses. Furthermore, they do not account for individual differences in the ability to endogenously regulate nociceptive activity in predicting placebo analgesia. The brain not only passively integrates prior pain expectations with nociceptive activity to infer pain states (perceptual inference) but also initiates various types of actions to ensure that sensory data are consistent with prior pain expectations (active inference). We argue that depending on whether the brain interprets conflicting sensory data (prediction errors) as a signal to learn from or noise to be attenuated, the brain initiates opposing types of action to facilitate learning from sensory data or, conversely, to enhance the biasing influence of prior pain expectations on pain perception. Furthermore, we discuss the role of stress, anxiety, and unpredictability of pain in influencing the weighting of prior pain expectations and sensory data and how they relate to the individual ability to regulate nociceptive activity (endogenous pain modulation). Finally, we provide suggestions for future studies to test the implications of the active inference model of placebo analgesia.

Correlations between within-subject variability of pain intensity reports and rubber hand illusion proprioceptive drift

INTRODUCTION

Consistent with the Bayesian brain hypothesis, the within-subject variability of pain intensity reports as captured with the Focused Analgesia Selection Test (FAST) might be a surrogate measure of the certainty in ascending noxious signals. The outcomes of a non-pain-related task, the rubber hand illusion, were hypothesized to reflect the same construct. This study aimed to explore whether within-subject differences in variability of pain intensity reports and the outcomes of the rubber hand illusion might be related.

METHODS

Nonclinical participants underwent the classic rubber hand illusion under synchronous (experimental) and asynchronous (control) conditions. Two outcomes were assessed: proprioceptive drift and feeling of ownership. Thereafter, participants underwent the FAST to assess the within-subject variability of pain reports in response to heat stimuli. Intraclass correlation (ICC) and the correlation coefficient (R2) were the main outcomes. Spearman's correlations were used to assess associations between the outcomes of the 2 tasks.

RESULTS

Thirty-six volunteers completed the study. Both FAST outcomes-ICC (Spearman's r = 0.355, p = 0.033) and R2 (Spearman's r = 0.349, p = 0.037)-were positively correlated with proprioceptive drift in the synchronous but not asynchronous conditions (p > 0.05). The subjective feeling of ownership and FAST outcomes did not correlate (p > 0.05).

CONCLUSIONS

The associations between the 2 tasks' outcomes imply that both tasks at least partly assess similar constructs. Current knowledge suggests that this construct represents the person's certainty in perceiving ascending sensory signals, or, in Bayesian terminology, the certainty of the likelihood.

Osteopathy and Mental Health: An Embodied, Predictive, and Interoceptive Framework

Globally, mental and musculoskeletal disorders present with high prevalence, disease burden, and comorbidity. In order to improve the quality of care for patients with persistent physical and comorbid mental health conditions, person-centered care approaches addressing psychosocial factors are currently advocated. Central to successful person-centered care is a multidisciplinary collaboration between mental health and musculoskeletal specialists underpinned by a robust therapeutic alliance. Such a collaborative approach might be found in osteopathy, which is typically utilized to treat patients with musculoskeletal disorders but may arguably also benefit mental health outcomes. However, research and practice exploring the reputed effect of osteopathy on patients with mental health problems lack a robust framework. In this hypothesis and theory article, we build upon research from embodied cognition, predictive coding, interoception, and osteopathy to propose an embodied, predictive and interoceptive framework that underpins osteopathic person-centered care for individuals with persistent physical and comorbid mental health problems. Based on the premise that, for example, chronic pain and comorbid depression are underlined by overly precise predictions or imprecise sensory information, we hypothesize that osteopathic treatment may generate strong interoceptive prediction errors that update the generative model underpinning the experience of pain and depression. Thus, physical and mental symptoms may be reduced through active and perceptual inference. We discuss how these theoretical perspectives can inform future research into osteopathy and mental health to reduce the burden of comorbid psychological factors in patients with persistent physical symptoms and support person-centered multidisciplinary care in mental health.