Zhang Mengzhe, Sun Jieping, Tao Qiuying, Dang Jinghan, Wang Weijian, Han Shaoqiang, Wei Yarui, Cheng Jingliang, Zhang Yong
Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Front Psychiatry. 2025 Aug 14;16:1622162. doi: 10.3389/fpsyt.2025.1622162. eCollection 2025.
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.
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).
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.
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.
在烟草使用障碍（TUD）中，包括突显网络（SN）、中央执行网络（CEN）和默认模式网络（DMN）在内的三重网络的因果或直接连接性改变以及与吸烟动机相关的神经生物学特征仍不清楚，这阻碍了TUD靶向干预措施的发展。
我们招募了93名男性吸烟者和55名男性非吸烟者，并获取了他们的静息态功能磁共振成像（rs-fMRI）和与吸烟相关的临床量表。我们将动态因果模型（DCM）应用于rs-fMRI，以表征TUD中三重网络七个主要枢纽之间有效连接性（EC）的变化。采用留一法（LOO）交叉验证来研究改变的EC是否能预测吸烟动机（通过罗素吸烟原因问卷评估）。
与对照组相比，TUD组在SN内表现出抑制性外在有效连接性。网络之间的异常EC主要表现为TUD个体中DMN和ECN活动之间不协调的切换，岛叶在此过程中作为因果枢纽。此外，右侧背外侧前额叶皮质（R-DLPFC）和内侧前额叶皮质（MPFC）的EC增加可预测与身体依赖相关的吸烟动机。
本研究揭示了三重网络中异常的因果连接性，并阐明了由身体依赖驱动的吸烟行为的潜在神经机制。这些发现表明，基于网络的指标可能是TUD的潜在生物标志物，并有助于识别吸烟行为动机的异质性。
