Network Alterations in Comorbid Chronic Pain and Opioid Addiction: An Exploratory Approach.

Academic Article


  • The comorbidity of chronic pain and opioid addiction is a serious problem that has been growing with the practice of prescribing opioids for chronic pain. Neuroimaging research has shown that chronic pain and opioid dependence both affect brain structure and function, but this is the first study to evaluate the neurophysiological alterations in patients with comorbid chronic pain and addiction. Eighteen participants with chronic low back pain and opioid addiction were compared with eighteen age- and sex-matched healthy individuals in a pain-induction fMRI task. Unified structural equation modeling (SEM) with Lagrange multiplier (LM) testing yielded a network model of pain processing for patient and control groups based on 19 a priori defined regions. Tests of differences between groups on specific regression parameters were determined on a path-by-path basis using z-tests corrected for the number of comparisons. Patients with the chronic pain and addiction comorbidity had increased connection strengths; many of these connections were interhemispheric and spanned regions involved in sensory, affective, and cognitive processes. The affected regions included those that are commonly altered in chronic pain or addiction alone, indicating that this comorbidity manifests with neurological symptoms of both disorders. Understanding the neural mechanisms involved in the comorbidity is crucial to finding a comprehensive treatment, rather than treating the symptoms individually.
  • Authors

  • Smallwood, Rachel F
  • Price, Larry R
  • Campbell, Jenna L
  • Garrett, Amy S
  • Atalla, Sebastian W
  • Monroe, Todd B
  • Aytur, Semra
  • Potter, Jennifer S
  • Robin, Donald
  • Publication Date

  • 2019
  • Has Subject Area

    Published In


  • automated search strategy
  • chronic low back pain
  • fMRI
  • opioid addiction
  • pain induction
  • unified structural equation modeling
  • vector autoregressive modeling
  • Digital Object Identifier (doi)

    Start Page

  • 174
  • Volume

  • 13