Frontiers in psychiatry · 2025
Abnormal resting-state effective connectivity of triple network predicts smoking motivations among males.
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Zhang Mengzhe, Sun Jieping, Tao Qiuying, Dang Jinghan, Wang Weijian, Han Shaoqiang, Wei Yarui, Cheng Jingliang, Zhang Yong
Аннотация
BACKGROUND: 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 are still unclear, which hampered the development of a targeted intervention for 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 modeling (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 with the control group, the 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 the 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 the potential neural mechanism of smoking behavior driven by physical dependence. These findings suggested that a network-derived indicator could be a potential bio-marker of TUD and help to identify the heterogeneity in the motivation of smoking behavior.
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 modeling (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 with the control group, the 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 the 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 the potential neural mechanism of smoking behavior driven by physical dependence. These findings suggested that a network-derived indicator could be a potential bio-marker of TUD and help to identify the heterogeneity in the motivation of smoking behavior.