Abnormal Resting-state Effective Connectivity of Triple Network Predicts Smoking Motivations among males

Mengzhe ZhangJiePing SunQiuying TaoJinghan DangWeijian WangShaoqiang HanYarui WeiJingliang Cheng^*Yong Zhang^*

• First Affiliated Hospital of Zhengzhou University, Zhengzhou, China

The causal or direct connectivity alterations of triple network including Salience Network (SN), Central Executive Network (CEN) and Default Mode Network (DMN) in tobacco use disorder (TUD) and the neurobiological features associated with smoking motivation is still unclear, which hampered the development of targeted intervention of TUD. Method: We recruited 93 male smokers and 55 male non-smokers, and obtained their resting-state functional MRI (rs-fMRI) and smoking related clinical scales. We applied dynamic causal modelling (DCM) to rs-fMRI to characterize changes of effective connectivity (EC) among seven major hubs from triple networks in TUD. Leave-one-out (LOO) cross-validation was used to investigate whether the altered EC could predict the smoking motivations (evaluated by Russell Reason for Smoking Questionnaire). Results: Compared to control group, TUD group displayed inhibitory extrinsic effective connectivity within SN. The abnormal ECs between networks were mainly characterized by uncoordinated switching between DMN and ECN activities in TUD individuals, with insula acting as a causal hub in this process. Moreover, increased EC from right dorsolateral prefrontal cortex (R-DLPFC) and medial prefrontal cortex (MPFC) could predict the smoking motivations related to physical dependence. Conclusions: This study revealed aberrant causal connectivity in triple network and clarified potential neural mechanism of smoking behavior driven by physical dependence. These findings suggested network-derived indicator could be a potential bio-marker of TUD and helped to identify the heterogeneity in the motivation of smoking behavior.