Mapping Smoking Addiction Using Effective Connectivity Analysis

dc.creatorTang, Rongxiang
dc.creatorRazi, Adeel
dc.creatorFriston, Karl J.
dc.creatorTang, Yi-Yuan (TTU)
dc.date.accessioned2023-01-27T16:36:58Z
dc.date.available2023-01-27T16:36:58Z
dc.date.issued2016
dc.description© 2016 Tang, Razi, Friston and Tang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution and reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en_US
dc.description.abstractPrefrontal 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.en_US
dc.identifier.citationTang R, Razi A, Friston KJ and Tang Y-Y (2016) Mapping Smoking Addiction Using Effective Connectivity Analysis. Front. Hum. Neurosci. 10:195. doi: 10.3389/fnhum.2016.00195en_US
dc.identifier.urihttps://doi.org/10.3389/fnhum.2016.00195
dc.identifier.urihttps://hdl.handle.net/2346/90483
dc.language.isoengen_US
dc.subjectDynamic Causal Modeling (DCM)en_US
dc.subjectSmoking Addictionen_US
dc.subjectMedial Prefrontal Cortex (mPFC)en_US
dc.subjectPosterior Cingulate Cortex (PCC)en_US
dc.subjectEffective Connectivity Analysisen_US
dc.titleMapping Smoking Addiction Using Effective Connectivity Analysisen_US
dc.typeArticleen_US

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