Placebo Response is Driven by UCS Revaluation: Evidence, Neurophysiological Consequences and a Quantitative Model

Placebo Effects in Clinical Trials Evaluating Patients with Uncontrolled Persistent Asthma

Rationale: Patients with uncontrolled, persistent asthma can show substantial health improvements when administered placebo.Objectives: We analyzed five randomized, placebo-controlled clinical trials that assessed subjects with uncontrolled, persistent asthma to determine the magnitudes of placebo effects across different clinical outcomes.Methods: Placebo effects for objective asthma-related outcomes, healthcare utilization outcomes, and patient-reported outcomes were estimated, with adjustments for regression to the mean.Results: Statistically significant improvements in all clinical outcomes were seen in patients randomized to placebo across all trials. Placebo effects were largest for healthcare utilization outcomes, including exacerbations (median reduction, 0.44 events/yr; 31% risk reduction; range, 19-56%), emergency department visits (median reduction, 0.19 events/yr; 50% risk reduction; range, 36-82%), and hospitalizations for asthma (median reduction, 0.26 events/yr; 66% risk reduction; range, 61-74%). Patient-reported outcomes exhibited intermediate placebo effects. Median improvements in the Asthma Control Questionnaire and St. George's Respiratory Questionnaire scores in placebo-treated patients were 0.53 units (25% improvement; range, 18-30%) and 8.3 units (19.5% improvement; range 19-20%), respectively. Forced expiratory volume in 1 second exhibited the smallest relative placebo effects (median increase, 77 ml; 4.2% improvement; range, 3.4-4.9%). Subgroup analyses did not reveal patient subgroups that were more susceptible to placebo effects. Pre- and postrandomization counts for asthma exacerbations showed patterns consistent with the expected negative binomial distribution except for significant departures in prerandomization exacerbations for two trials.Conclusions: Patients with uncontrolled asthma derived consistent benefit from randomization to placebo. Observed placebo effects may represent beneficial effects of both sham therapy and a structured asthma regimen dictated by the study protocol. In the case of healthcare utilization outcomes, recall errors in self-reported healthcare events may have introduced biases that inflated placebo effect estimates.

Understanding and exploiting prediction errors minimization within the brain in pharmacological treatments

The human brain can be conceptualized as an inference machine that actively predicts and explains its sensations and perceptions: it makes predictions through a probabilistic model. Such a model is continuously and implicitly updated by the computation and minimization of weighted prediction errors, as shown by numerous studies and experimental results. Nevertheless, such an algorithmic functioning of the brain has not been exploited in the neuropharmacological practice. In this manuscript, we show by theoretical analysis and model fitting of previously published data in two different contexts, how it is possible to increase the effectiveness of neuropharmacological and immunosuppressive drugs, through the modulation of the weighted prediction errors. Moreover, on the basis of the proposed model, we derive an optimized drug administration schedule able to increase the drug effectiveness of one order of magnitude, in psoriasis treatment. We make important testable predictions, evidencing the impact and the potential benefit of prediction errors modulation within the brain, in the pharmacotherapeutic practice. Finally, our results lead to a novel formal theory of implicit learning, and shed lights on the actual roles of classical conditioning and UCS revaluation in behavioral and pharmacological conditioning experiments. The potential practical implications of our results are many: the reduction of drugs side effects; the maximization of the therapeutic outcome; a more effective treatment for chronic pain, certain neuropsychiatric diseases, autoimmune diseases and allergic diseases.

A System Computational Model of Implicit Emotional Learning

Nowadays, the experimental study of emotional learning is commonly based on classical conditioning paradigms and models, which have been thoroughly investigated in the last century. Unluckily, models based on classical conditioning are unable to explain or predict important psychophysiological phenomena, such as the failure of the extinction of emotional responses in certain circumstances (for instance, those observed in evaluative conditioning, in post-traumatic stress disorders and in panic attacks). In this manuscript, starting from the experimental results available from the literature, a computational model of implicit emotional learning based both on prediction errors computation and on statistical inference is developed. The model quantitatively predicts (a) the occurrence of evaluative conditioning, (b) the dynamics and the resistance-to-extinction of the traumatic emotional responses, (c) the mathematical relation between classical conditioning and unconditioned stimulus revaluation. Moreover, we discuss how the derived computational model can lead to the development of new animal models for resistant-to-extinction emotional reactions and novel methodologies of emotions modulation.