Mats Lekander

Internet-delivered cognitive behavior therapy (CBT) for social anxiety disorder is an evidence-based treatment in regular health care. Still, a considerable proportion of treated patients do not respond. There is also a debate on the importance of patients expectations’ for therapeutic outcomes. However, experimental evidence is lacking to answer this question. Our aim is thus to investigate the placebo response in social anxiety disorder, and the link between initial placebo responsiveness and subsequent outcome of CBT. First, we will manipulate expectations of anxiety relief. A benzodiazepine (a common anti-anxiety drug) will be administered with correct or incorrect information about clinical efficacy during a public speaking task. Self-reports and moment-to-moment variability in neural response will be measured with functional magnetic resonance imaging (fMRI) during the manipulation of expectations. The balanced placebo design allows us to dissect the total treatment effect into its components: drug, placebo, and interactions between the two. Second, patients undergo internet-delivered CBT after completing the placebo experiments. This project aims to unravel expectations´ influence on treatment response, and has a two-fold significance, 1) the scientific understanding of the neural mechanisms of treatment expectations, central for placebo, and 2) the development of pre-treatment predictors of CBT outcome which could improve precision in clinical decision making.

The question of how emotions are triggered remains obscure. A recent influential theory is built on predicting coding, which states that the brain makes predictions and constantly compares these predictions with the actual incoming sensory information. The active inference theory of emotions posits that emotions arise when there is a mismatch between the bodily (interoceptive) sensations and what was predicted by the brain, i.e. when interoceptive prediction errors occur. This theory is extremely relevant for emotional disorders linked with infections (e.g. post-COVID), since infections can trigger various and intense interoceptive signals that can be difficult to predict by the brain. In this project, we will apply the active inference theory of emotions in an ecologically relevant model of infection, using an innovative experimental approach. We will go beyond the existing correlational designs by applying a causal intervention eliciting real sickness symptoms, and manipulating interoceptive prediction errors directly, in 240 participants. We hypothesize that emotional responses will arise during sickness when interoceptive prediction errors occur, i.e. when interoceptive sensations violate predictions. This project will therefore provide critical information to understand how individuals' predictions shape sickness behavior, and how emotions are generated in infection-related conditions, which could open new therapies for mood disorders associated with immune activation.