title: Mapping Smoking Addiction Using Effective Connectivity Analysis.
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.
authors.affiliation: Department of Psychology, Washington University in St. Louis St. Louis, MO, USA.
authors.forename: Rongxiang
authors.initials: R
authors.lastname: Tang
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College LondonLondon, UK; Department of Electronic Engineering, NED University of Engineering and TechnologyKarachi, Pakistan.
authors.forename: Adeel
authors.initials: A
authors.lastname: Razi
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London London, UK.
authors.forename: Karl J
authors.initials: KJ
authors.lastname: Friston
authors.affiliation: Department of Psychological Sciences, Texas Tech University Lubbock, TX, USA.
authors.forename: Yi-Yuan
authors.initials: YY
authors.lastname: Tang
keywords: dynamic causal modeling (DCM); effective connectivity analysis; medial prefrontal cortex (mPFC); posterior cingulate cortex (PCC); smoking addiction