# Neurocomputational mechanisms of antidepressant placebo effects

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $509,080

## Abstract

ABSTRACT
Over the last two decades, neuroscientists have used antidepressant placebo probes to examine the biological
mechanisms implicated in antidepressant placebo effects. However, findings from these studies have not yet
elucidated a model-based theory that would explain the mechanism through which antidepressant
expectancies evolve to induce persistent mood changes. Emerging evidence suggests that antidepressant
placebo effects may be informed by models of reinforcement learning, such that an individual’s expectation of
improvement is updated with the arrival of new sensory evidence, by incorporating a reward prediction error
(RPE), which signals the mismatch between the expected (expected value) and perceived improvement.
Consistent with this framework, neuroimaging studies of antidepressant placebo effects have demonstrated
placebo-induced μ-opioid activation and increased blood-oxygen-level dependent (BOLD) responses in regions
tracking expected values [e.g., ventromedial prefrontal cortex (vmPFC)] and RPEs [e.g., ventral striatum (VS)].
In this study, we will demonstrate the causal contribution of reward learning signals (expected values and
RPEs) to antidepressant placebo effects by experimentally manipulating expected values using transcranial
magnetic stimulation (TMS) targeting the vmPFC and μ-opioid striatal RPE signal using pharmacological
approaches. We hypothesized that antidepressant placebo expectancies are represented in the vmPFC
(expected value) and updated by means of μ-opioid-modulated striatal leaning signal (RPE). In a 3x3 factorial
double-blind design, we will randomize 120 unmedicated MDD individuals to one of three between-subject
opioid conditions: the μ-opioid agonist buprenorphine, the μ-opioid antagonist naltrexone, or the inert pill.
Within each arm, individuals will be assigned to receive three within-subject counterbalanced forms of TMS
targeting the vmPFC—intermittent Theta Burst Stimulation (TBS) expected to potentiate the vmPFC,
continuous TBS expected to de-potentiate the vmPFC, or sham TBS. These experimental manipulations will
be used to modulate trial-by-trial reward learning signals and related brain activity during the Antidepressant
Placebo fMRI Task to address the following aims: 1) investigate the relationship between reward learning
signals within the vmPFC-VS circuit and antidepressant placebo effects; 2) examine the causal contribution of
vmPFC expected value computations to antidepressant placebo effects; and 3) investigate the causal
contribution of μ-opioid-modulated striatal RPEs to antidepressant placebo effects. The proposed study will be
the first to investigate the causal contribution of μ-opioid-modulated vmPFC-VS learning signals to
antidepressant placebo responses, paving the way for developing novel treatments modulating learning
processes and objective means of quantifying – and potentially reducing – placebo effects during drug
development.

## Key facts

- **NIH application ID:** 10377379
- **Project number:** 5R01MH122548-03
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Marta Pecina
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $509,080
- **Award type:** 5
- **Project period:** 2020-04-09 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10377379

## Citation

> US National Institutes of Health, RePORTER application 10377379, Neurocomputational mechanisms of antidepressant placebo effects (5R01MH122548-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10377379. Licensed CC0.

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