Placebo Effects and Neuromodulation for Depression: A Meta-Analysis and Evaluation of Shared Mechanisms

SOURCE: Molecular Psychiatry. 27(3):1658-1666, 2022 Mar.

AUTHORS: Burke MJ; Romanella SM; Mencarelli L; Greben R; Fox MD; Kaptchuk TJ; Pascual-Leone A; Santarnecchi E

ABSTRACT: There is growing evidence that placebo effects can meaningfully modulate the brain. However, there has been little consideration of whether these changes may overlap with regions/circuits targeted by depression treatments and what the implications of this overlap would be on measuring efficacy in placebo-controlled clinical trials. In this systematic review and meta-analysis, we searched PubMed/Medline and Google Scholar for functional MRI and PET neuroimaging studies of placebo effects. Studies recruiting both healthy subjects and patient populations were included.
Neuroimaging coordinates were extracted and included for Activation Likelihood Estimation (ALE) meta-analysis. We then searched for interventional studies of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS) for depression and extracted target coordinates for comparative spatial analysis with the placebo effects maps. Of 1169 articles identified, 34 neuroimaging studies of placebo effects were included. There were three significant clusters of activation: left dorsolateral prefrontal cortex (DLPFC) (x = -41, y = 16, z = 34), left sub-genual anterior cingulate cortex (sgACC)/ventral striatum (x = -8, y = 18, z = -15) and the right rostral anterior cingulate cortex (rACC) (x =
4, y = 42, z = 10). There were two significant deactivation clusters: right basal ganglia (x = 20, y = 2, z = 7) and right dorsal anterior cingulate cortex (dACC) (x = 1, y = -5, z = 45). TMS and DBS targets for depression treatment overlapped with the left DLPFC cluster and sgACC cluster, respectively. Our findings identify a common set of brain regions implicated in placebo effects across healthy individuals and patient populations, and provide evidence that these regions overlap with depression treatment targets. We model the statistical impacts of this overlap and demonstrate critical implications on measurements of clinical trial efficacy for this field.