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A brainnetome atlas-based methamphetamine dependence identification using neighborhood component analysis and machine learning on functional MRI data.
Zhou, Yanan; Tang, Jingsong; Sun, Yunkai; Yang, Winson Fu Zun; Ma, Yuejiao; Wu, Qiuxia; Chen, Shubao; Wang, Qianjin; Hao, Yuzhu; Wang, Yunfei; Li, Manyun; Liu, Tieqiao; Liao, Yanhui.
Afiliação
  • Zhou Y; Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Tang J; Department of Psychiatry, Brain Hospital of Hunan Province (The Second People's Hospital of Hunan Province), Changsha, China.
  • Sun Y; Department of Psychiatry, School of Medicine, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China.
  • Yang WFZ; Department of Psychiatry, School of Medicine, Sir Run-Run Shaw Hospital, Zhejiang University, Hangzhou, China.
  • Ma Y; Department of Psychological Sciences, College of Arts and Sciences, Texas Tech University, Lubbock, TX, United States.
  • Wu Q; Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Chen S; Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Wang Q; Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Hao Y; Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Wang Y; Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Li M; Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Liu T; Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
  • Liao Y; Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
Front Cell Neurosci ; 16: 958437, 2022.
Article em En | MEDLINE | ID: mdl-36238830
Addiction to methamphetamine (MA) is a major public health concern. Developing a predictive model that can classify and characterize the brain-based biomarkers predicting MA addicts may directly lead to improved treatment outcomes. In the current study, we applied the support vector machine (SVM)-based classification method to resting-state functional magnetic resonance imaging (rs-fMRI) data obtained from individuals with methamphetamine use disorder (MUD) and healthy controls (HCs) to identify brain-based features predictive of MUD. Brain connectivity analyses were conducted for 36 individuals with MUD as well as 37 HCs based on the brainnetome atlas, and the neighborhood component analysis was applied for feature selection. Eighteen most relevant features were screened out and fed into the SVM to classify the data. The classifier was able to differentiate individuals with MUD from HCs with a high prediction accuracy, sensitivity, specificity, and AUC of 88.00, 86.84, 89.19, and 0.94, respectively. The top six discriminative features associated with changes in the functional activity of key nodes in the default mode network (DMN), all the remaining discriminative features are related to the thalamic connections within the cortico-striato-thalamo-cortical (CSTC) loop. In addition, the functional connectivity (FC) between the bilateral inferior parietal lobule (IPL) and right cingulate gyrus (CG) was significantly correlated with the duration of methamphetamine use. The results of this study not only indicated that MUD-related FC alterations were predictive of group membership, but also suggested that machine learning techniques could be used for the identification of MUD-related imaging biomarkers.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article