Aberrant effective connectivity in gray matter volume-altered brain regions in males with tobacco use disorder: A multimodal study combining meta-analysis and spectral DCM.

BACKGROUND AND OBJECTIVE: Tobacco use disorder (TUD) is associated with widespread structural alterations and functional dysregulation in the brain. However, the dynamic causal interactions among structurally abnormal regions remain unclear. This study integrated neuroimaging meta-analysis with spectral dynamic causal modeling (spDCM) to characterize effective connectivity patterns in smokers and evaluate their potential as functional biomarkers for predicting nicotine dependence severity.

METHODS: First, we conducted a meta-analysis using Anisotropic Effect Size-Signed Differential Mapping (AES-SDM) to identify robust gray matter volume (GMV) abnormalities in TUD. These regions subsequently served as regions of interest (ROIs) for spDCM analysis on resting-state fMRI data from 66 TUD patients and 54 healthy controls (HC). Group-level differences in effective connectivity (EC) were examined using Parametric Empirical Bayes (PEB). Subsequently, a leave-one-out cross-validation (LOOCV) framework evaluated the strength of aberrant connections in predicting Fagerström Test for Nicotine Dependence (FTND) scores. Finally, exploratory correlations between abnormal ECs and smoking motives measured by the Reasons for Smoking Questionnaire (RRSQ) were performed.

RESULTS: The meta-analysis revealed increased GMV in the left putamen (L.PUT) and middle occipital gyrus (L.MOG), alongside decreased GMV in the left insula (L.INS), superior temporal gyrus (L.STG), and bilateral medial orbitofrontal cortex (mOFC) in the TUD group. spDCM identified significant disruptions in EC (Pp > 0.95). Specifically, intrinsic self-connection strength was reduced in the L.PUT and L.STG but increased in the L.INS. Regarding directed connectivity, significant reductions were observed in pathways projecting to the L.INS (from L.MOG and R.mOFC) and from the L.INS to the L.STG. Predictive analysis showed that L.PUT self-connection significantly predicted FTND scores. While the multivariate model also demonstrated significant predictive capacity, it did not outperform the univariate striatal biomarker. Exploratory analyses showed preliminary correlations with specific smoking motives that did not survive correction.

CONCLUSION: By integrating VBM meta-analysis and spDCM, this study revealed significant disruptions in both directed connectivity between structurally altered regions and their intrinsic self-connection. These aberrant neurodynamic patterns are closely linked to the pathophysiological mechanisms of tobacco addiction. Crucially, the aberrant self-connection of the L.PUT significantly predicts the severity of nicotine dependence. This study offers an integrated perspective for investigating the mechanisms underlying tobacco addiction.