"Expectancy-Mood Neural Dynamics Predict Mechanisms of Short- and Long-Term Antidepressant Placebo Effects".

Abstract

Objective: Acute experimental models of antidepressant placebo effects suggest that expectancies, encoded within the salience network (SN), are reinforced by sensory evidence and mood fluctuations. However, whether these dynamics extend to longer timescales remains unknown. To answer this question, we investigated how SN and default mode network (DMN) functional connectivity during the processing of antidepressant expectancies facilitates the shift from salience attribution to contextual cues in the SN to belief-induced mood responses in the DMN, both acutely and long-term.

Methods: Sixty psychotropic-free patients with major depressive disorder (MDD) completed an acute antidepressant placebo fMRI experiment manipulating placebo-associated expectancies and their reinforcement while assessing trial-by-trial mood improvement, before entering an 8-week double-blind, randomized, placebo-controlled trial (RCT) of a selective serotonin reuptake inhibitor (SSRI) or placebo.

Results: Learned antidepressant expectancies predicted by a reinforcement learning model modulated SN-DMN connectivity. Acutely, greater modulation predicted higher effects of expectancy and reinforcement manipulations on reported expectancies and mood. Over 8 weeks, no significant drug effects on mood improvement were observed. However, participants who believed they were receiving an antidepressant exhibited significantly greater mood improvement, irrespective of the actual treatment received. Moreover, increased SN-DMN connectivity predicted mood improvement, especially in placebo-treated participants who believed they received an SSRI.

Conclusion: SN-DMN interactions may play a critical role in the evolution of antidepressant response expectancies, drug-assignment beliefs, and their effects on mood.