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Background:The abuse of methamphetamine (MA) worldwide has gained international attention as the most rapidly growing illicit drug problem. The classification and treatment response prediction of MA addicts are thereby paramount, in order for effective treatments to be more targeted to individuals. However, there has been limited progress.Methods:In the present study, 43 MA-dependent participants and 38 age- and gender-matched healthy controls were enrolled, and their resting-state functional magnetic resonance imaging data were collected. MA-dependent participants who showed 50% reduction in craving were defined as responders to treatment. The present study used the machine learning method, which is a support vector machine (SVM), to detect the most relevant features for discriminating and predicting the treatment response for MA-dependent participants based on the features extracted from the functional graph metrics.Results:A classifier was able to differentiate MA-dependent subjects from normal controls, with a cross-validated prediction accuracy, sensitivity, and specificity of 73.2% [95% confidence interval (CI) = 71.23–74.17%), 66.05% (95% CI = 63.06–69.04%), and 80.35% (95% CI = 77.77–82.93%), respectively, at the individual level. The most accurate combination of classifier features included the nodal efficiency in the right middle temporal gyrus and the community index in the left precentral gyrus and cuneus. Between these two, the community index in the left precentral gyrus had the highest importance. In addition, the classification performance of the other classifier used to predict the treatment response of MA-dependent subjects had an accuracy, sensitivity, and specificity of 71.2% (95% CI = 69.28–73.12%), 86.75% (95% CI = 84.48–88.92%), and 55.65% (95% CI = 52.61–58.79%), respectively, at the individual level. Furthermore, the most accurate combination of classifier features included the nodal clustering coefficient in the right orbital part of the superior frontal gyrus, the nodal local efficiency in the right orbital part of the superior frontal gyrus, and the right triangular part of the inferior frontal gyrus and right temporal pole of middle temporal gyrus. Among these, the nodal local efficiency in the right temporal pole of the middle temporal gyrus had the highest feature importance.Conclusion:The present study identified the most relevant features of MA addiction and treatment based on SVMs and the features extracted from the graph metrics and provided possible biomarkers to differentiate and predict the treatment response for MA-dependent patients. The brain regions involved in the best combinations should be given close attention during the treatment of MA.

Background Methamphetamine (MA) abuse is one of the most rapidly growing illicit drug problems worldwide. Impulsivity has been considered as a core impairment underpinning addictive behavior. Studies have demonstrated that MA addicts have white matter abnormalities based on ROIs. There are few studies on whole brain, and the association between whole brain tracts and impulsivity in MA dependence remain unclear. Tract-based spatial statistics (TBSS) was used to detect four DTI measures, and these were correlated with the Barratt Impulsivity Scale (BIS) to verify and expand the previous results. Methods A total of 28 MA addicts and 22 healthy controls were recruited. MRI was performed to evaluate the brain structural changes, the BIS was used to evaluate impulsivity behavior, white matter differences were compared between MA addicts and healthy controls, and then determine correlation between diffusion parameters and BIS scores. Results MA addicts had significantly lower FA, and higher AD, RD, and MD in a wide range of white matter, which mainly included: corona radiata, internal capsule, superior longitudinal fasciculus, external capsule, inferior fronto-occipital fascicules, posterior thalamic radiation, sagittal stratum, fornix and stria terminalis, cerebral peduncle, superior cerebellar peduncle, corpus callosum, and corticolspinal tract compared with controls. The MA group had significantly higher total score, attention and motor scores compared to healthy controls. Higher MD in the right corticospinal tract was significantly associated with higher total scores. Conclusion MA addicts exhibit a globally diminished white matter integrity. furthermore, they present with high levels of impulsivity, and this dysfunction is associated with MD in corticospinal tracts. Future studies on larger sample sizes, gender effects and longitudinal studies are needed.