Mapping smoking addiction using effective connectivity analysis

Rongxiang Tang, Adeel Razi, Karl J. Friston, Yi Yuan Tang

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

Prefrontal and parietal cortex, including the default mode network (DMN; medial prefrontal cortex (mPFC), and posterior cingulate cortex, PCC), have been implicated in addiction. Nonetheless, it remains unclear which brain regions play a crucial role in smoking addiction and the relationship among these regions. Since functional connectivity only measures correlations, addiction-related changes in effective connectivity (directed information flow) among these distributed brain regions remain largely unknown. Here we applied spectral dynamic causal modeling (spDCM) to resting state fMRI to characterize changes in effective connectivity among core regions in smoking addiction. Compared to nonsmokers, smokers had reduced effective connectivity from PCC to mPFC and from RIPL to mPFC, a higher self-inhibition within PCC and a reduction in the amplitude of endogenous neuronal fluctuations driving the mPFC. These results indicate that spDCM can differentiate the functional architectures between the two groups, and may provide insight into the brain mechanisms underlying smoking addiction. Our results also suggest that future brain-based prevention and intervention in addiction should consider the amelioration of mPFC-PCC-IPL circuits.

Original languageEnglish
Article number195
JournalFrontiers in Human Neuroscience
Volume10
Issue numberMAY2016
DOIs
StatePublished - May 4 2016

Keywords

  • Dynamic causal modeling (DCM)
  • Effective connectivity analysis
  • Medial prefrontal cortex (mPFC)
  • Posterior cingulate cortex (PCC)
  • Smoking addiction

Fingerprint Dive into the research topics of 'Mapping smoking addiction using effective connectivity analysis'. Together they form a unique fingerprint.

  • Cite this