Dissecting Antidepressant Placebo Expectancy-Mood Dynamics
openNIMH - National Institute of Mental Health
Abstract
Despite the high prevalence of major depressive disorder (MDD) and its projected rise as the leading cause of
global disease burden by 2030, treatment efficacy remains suboptimal. First-line antidepressants have modest
efficacy (~50%), and high placebo response rates (~40%) contribute to the failure of antidepressant trials and
hinder new drug development. While research underscores the role of antidepressant expectancies in
modulating mood across various brain regions, there is a critical need to elucidate how expectancy-driven
neural dynamics interact with downstream mood regulation processes to induce sustained mood improvement.
Our recent work provides the first computational account of antidepressant placebo effects, where
reinforcement learning (RL) model-predicted expectancies—encoded in the salience network (SN)—trigger
mood changes perceived as reward signals, which reinforce antidepressant expectancies through an
expectancy-mood loop. Furthermore, we and others have demonstrated that enhanced functional connectivity
(FC) between the SN and default mode network (DMN) during expectancy processing and at rest predicts
long-term antidepressant placebo effects. This evidence suggests that antidepressant expectancies,
originating from contextual treatment cues, are represented in the SN and influence mood regulation through
top-down connections with the DMN. To test this hypothesis, this study will investigate the causal roles of the
SN, DMN, and SN-DMN FC in antidepressant placebo effects using Theta Burst Stimulation (TBS). In a 2x3
factorial design, 200 patients with MDD will be randomized to three counter-balanced TBS conditions
(intermittent, continuous, and sham, within-subject) targeting either the SN or DMN (between-subject). These
acute experimental manipulations will modulate trial-by-trial expectancy and mood ratings and the neural
encoding of model-based expectancies and mood reward signals during the “antidepressant placebo fMRI
task”, which manipulates placebo-associated expectancies using visually cued fast-acting antidepressant
infusions and sham visual neurofeedback. Led by experts in placebo effects, reinforcement learning,
depression, and neuromodulation, this study combines a robust theoretical framework, state-of-the-art
neuroimaging, precision functional mapping for personalized TBS targeting, and accelerated TBS, ensuring
scientific rigor. The insights gained from this study will deepen our understanding of the neural mechanisms
behind placebo effects, enhancing clinical trial design, advancing neuroimaging predictors of treatment
response, and accelerating the development of expectancy-based interventions for MDD.
Up to $729K
health research