Neurocomputational Mechanisms of Mood Improvement
Neurocomputational Mechanisms of Mood Improvement
The central goal of this application is to demonstrate the causal contribution of reward learning signals (expected values and reward prediction errors [RPE]) to antidepressant responses (Aim1) by experimentally manipulating expected values using transcranial magnetic stimulation (TMS) targeting the vmPFC (Aim 2) and μ-opioid striatal RPE signal using pharmacological approaches (Aim 3).
The central goal of this study is to demonstrate the causal contribution of reward learning signals (expected values and RPEs) to antidepressant responses (Aim1) by experimentally manipulating expected values using transcranial magnetic stimulation (TMS) targeting the vmPFC (Aim 2) and μ-opioid striatal RPE signal using pharmacological approaches (Aim 3). In a 3x3 factorial double-blind trial, the investigators will randomize 120 unmedicated major depressive disorder (MDD) adults (18-55 years) to one of three between-subject opioid conditions: the μ-opioid agonist buprenorphine (n=40), the μ-opioid antagonist naltrexone (n=40), or the inert pill (n=40). Within each arm, individuals will be assigned to receive three within-subject counterbalanced sessions of TMS targeting the vmPFC-intermittent TBS (iTBS) expected to potentiate the vmPFC, continuous TBS (cTBS) expected to de-potentiate the vmPFC, and sham TBS (sTBS). These experimental manipulations will be used to modulate trial-by-trial reward learning signals and related brain activity during the Antidepressant fMRI Task. Understanding the mechanisms underlying antidepressant responses is essential to identify novel therapeutic targets for depression.
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