Nature CommunicationsNeurosciencePeer-reviewed journal article
Brain and psychological determinants of placebo pill response in chronic pain patients
Étienne Vachon‐PresseauiD, Sara BergeriD, Taha B. AbdullahiD, Lejian Huang, Guillermo CecchiiD, James William GriffithiD, Thomas J. SchnitzeriD, A. Vania ApkarianiD
Abstract
Placebo response is universally observed in randomized controlled trials (RCTs), yet these effects are commonly dismissed as consequences of uncontrollable confounds. In this prospective neuroimaging-based RCT performed in chronic back pain patients, we demonstrate that the intensity, but not quality, of pain is diminished with placebo pill ingestion. The response to placebo pills depended on brain: subcortical limbic volume asymmetry, sensorimotor cortical thickness, and functional coupling of the dorsolateral prefrontal cortex (DLPFC) with the periaqueductal grey (PAG), the rostral anterior cingulate cortex (rACC), and the precentral gyrus (PreCG); and psychological factors. All features were present before exposure to the pill; most remained stable across treatment and washout periods, although specific functional coupling between DLPFC and PAG dissipated with repeated exposure. These brain properties and specific psychological factors, such as interoceptive awareness and openness, were also predictive of the magnitude of response (continuous variable). We used machine learning in a fully cross-validated procedure and demonstrated that psychological factors were sufficient for classifying and predicting response magnitude; and response magnitude could also be predicted from a functional network (nodes mainly located in the limbic community, the DLPFC, the orbitofrontal cortex, and the temporo-parietal junction); the combined model explained 36% of the variance. Together, our results demonstrate that placebo pill analgesia observed in clinical trials depends on a combination of brain properties and specific psychological factors.
- organism: human
- assay: imaging
- research focus: benchmarking
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 120
- FWCI
- 8.32
- Field percentile
- top 2%
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