The Higher Parietal Cortical Thickness in Abstinent Methamphetamine Patients Is Correlated With Functional Connectivity and Age of First Usage

Mapping structural covariance networks of emotional withdrawal symptoms in males with methamphetamine use disorder during abstinence

Individuals with methamphetamine use disorder (MUD) often experience anxiety and depressive symptoms during abstinence, which can worsen the likelihood of relapse. Thus, it is essential to understand the neuro-mechanism behind methamphetamine use and its associated emotional withdrawal symptoms in order to develop effective clinical strategies. This study aimed to evaluate associations between emotional withdrawal symptoms and structural covariance networks (SCNs) based on cortical thickness (CTh) across the brain. The CTh measures were obtained from Tl-weighted MRI data from a sample of 48 males with MUD during abstinence and 48 male healthy controls. The severity of anxiety and depressive symptoms was assessed by the Hamilton Anxiety Scale (HAMA) and depression (HAMD) scales. Two important nodes belonging to the brain reward system, the right rostral anterior cingulate cortex (rACC) and medial prefrontal cortex (medPFC), were selected as seeds to conduct SCNs and modulation analysis by emotional symptoms. MUDs showed higher structural covariance between the right rACC and regions in the dorsal attention, right frontoparietal, auditory, visual and limbic networks. They also displayed higher structural covariance between the right medPFC and regions in the limbic network. Moreover, the modulation analysis showed that higher scores on HAMA were associated with increased covariance between the right rACC and the left parahippocampal and isthmus cingulate cortex in the default mode network. These outcomes shed light on the complex neurobiological mechanisms underlying methamphetamine use and its associated emotional withdrawal symptoms and may provide new insights into the development of effective treatments for MUD.

Aberrant topology of white matter networks in patients with methamphetamine dependence and its application in support vector machine-based classification

Brain white matter (WM) networks have been widely studied in neuropsychiatric disorders. However, few studies have evaluated alterations in WM network topological organization in patients with methamphetamine (MA) dependence. Therefore, using machine learning classification methods to analyze WM network topological attributes may give new insights into patients with MA dependence. In the study, diffusion tensor imaging-based probabilistic tractography was used to map the weighted WM networks in 46 MA-dependent patients and 46 control subjects. Using graph-theoretical analyses, the global and regional topological attributes of WM networks for both groups were calculated and compared to determine inter-group differences using a permutation-based general linear model. In addition, the study used a support vector machine (SVM) learning approach to construct a classifier for discriminating subjects with MA dependence from control subjects. Relative to the control group, the MA-dependent group exhibited abnormal topological organization, as evidenced by decreased small-worldness and modularity, and increased nodal efficiency in the right medial superior temporal gyrus, right pallidum, and right ventromedial putamen; the MA-dependent group had the higher hubness scores in 25 regions, which were mainly located in the default mode network. An SVM trained with topological attributes achieved classification accuracy, sensitivity, specificity, and kappa values of 98.09% ± 2.59%, 98.24% ± 4.00%, 97.94% ± 4.26%, and 96.18% ± 5.19% for patients with MA dependence. Our results may suggest altered global WM structural networks in MA-dependent patients. Furthermore, the abnormal WM network topological attributes may provide promising features for the construction of high-efficacy classification models.

Brain morphometric abnormalities and their associations with affective symptoms in males with methamphetamine use disorder during abstinence

BACKGROUND

Methamphetamine (METH) use induces neurotoxic effects in brain structures and affective symptoms that persist during abstinence. However, the brain morphometry of individuals with METH use disorder (MUD) remains unclear, as well as their associations with affective symptoms during abstinence.

METHODS

Forty-eight abstinent males with MUD and 66 age-, sex-, and education-matched healthy controls (HCs) underwent high-resolution T1-weighted magnetic resonance imaging. Cortical thickness, surface area, volume, local gyrification index (LGI), and subcortical volume were obtained with FreeSurfer software. Brain morphometry differences between groups and their associations with affective symptoms and drug abuse history within the males with MUD were examined, with intracranial volume, age, and years of education as covariates.

RESULTS

Compared with the HCs, the individuals with MUD showed a significantly higher LGI in the right cuneus gyrus, left lingual gyrus, bilateral supramarginal gyrus, right inferior parietal gyrus (IPG), and right dorsal anterior cingulate cortex (clusterwise p < 0.05, Monte Carlo-corrected), as well as a smaller volume of the left nucleus accumbens (NAcc) (p < 0.05, FDR-corrected). However, there were no significant group differences in cortical thickness, area or volume. In addition, the LGI in the right IPG was positively associatedwith the severity of depression and anxiety symptoms in MUDs (p < 0.05, FDR-corrected).

CONCLUSION

Brain morphometric abnormalities in abstinent males with MUD were characterized by hypergyrification across multiple mid-posterior brain regions anda smaller volume of the left NAcc.Gyrification of the right IPG may be a potential neural substrate underlying the affective symptoms experienced by MUDs during abstinence.