Abnormal resting-state functional connectome in methamphetamine-dependent patients and its application in machine-learning-based classification

Brain resting-state functional connectivity (rsFC) has been widely analyzed in substance use disorders (SUDs), including methamphetamine (MA) dependence. Most of these studies utilized Pearson correlation analysis to assess rsFC, which cannot determine whether two brain regions are connected by direct or indirect pathways. Moreover, few studies have reported the application of rsFC-based graph theory in MA dependence. We evaluated alterations in Tikhonov regularization-based rsFC and rsFC-based topological attributes in 46 MA-dependent patients, as well as the correlations between topological attributes and clinical variables. Moreover, the topological attributes selected by least absolute shrinkage and selection operator (LASSO) were used to construct a support vector machine (SVM)-based classifier for MA dependence. The MA group presented a subnetwork with increased rsFC, indicating overactivation of the reward circuit that makes patients very sensitive to drug-related visual cues, and a subnetwork with decreased rsFC suggesting aberrant synchronized spontaneous activity in subregions within the orbitofrontal cortex (OFC) system. The MA group demonstrated a significantly decreased area under the curve (AUC) for the clustering coefficient (Cp) (P(perm) < 0.001), shortest path length (Lp) (P(perm) = 0.007), modularity (P(perm) = 0.006), and small-worldness (σ, P(perm) = 0.004), as well as an increased AUC for global efficiency (E.glob) (P(perm) = 0.009), network strength (Sp) (P(perm) = 0.009), and small-worldness (ω, P(perm) < 0.001), implying a shift toward random networks. MA-related increased nodal efficiency (E.nodal) and altered betweenness centrality were also discovered in several brain regions. The AUC for ω was significantly positively associated with psychiatric symptoms. An SVM classifier trained by 36 features selected by LASSO from all topological attributes achieved excellent performance, cross-validated prediction area under the receiver operating characteristics curve, accuracy, sensitivity, specificity, and kappa of 99.03 ± 1.79, 94.00 ± 5.78, 93.46 ± 8.82, 94.52 ± 8.11, and 87.99 ± 11.57%, respectively (P(perm) < 0.001), indicating that rsFC-based topological attributes can provide promising features for constructing a high-efficacy classifier for MA dependence.