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ORIGINAL RESEARCH article

Front. Psychiatry
Sec. Addictive Disorders
Volume 15 - 2024 | doi: 10.3389/fpsyt.2024.1513793

Specific Endophenotypes in EEG Microstates for Methamphetamine Use Disorder

Provisionally accepted
Xurong Gao Xurong Gao 1Yun-Hsuan Chen Yun-Hsuan Chen 1*Ziyi Zeng Ziyi Zeng 1Wenyao Zheng Wenyao Zheng 1Cheng PengChai Cheng PengChai 1Hemmings Wu Hemmings Wu 2Zhoule Zhu Zhoule Zhu 2Jie Yang Jie Yang 1Lihua Zhong Lihua Zhong 3Hua Shen Hua Shen 3Mohamad Sawan Mohamad Sawan 1*
  • 1 School of Engineering, Westlake University, Hangzhou, Zhejiang, China
  • 2 Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, China
  • 3 Zhejiang Gongchen Compulsory Isolated Detoxification Center, HangZhou, China

The final, formatted version of the article will be published soon.

    microstates, which reflect large-scale resting-state networks of the brain, have been proposed as potential endophenotypes for methamphetamine use disorder (MUD). However, current endophenotypes lack refinement at the frequency band level, limiting their precision in identifying key frequency bands associated with MUD. In this study, we investigated EEG microstate dynamics across various frequency bands and different tasks, utilizing machine learning to classify MUD and healthy controls. During the resting state, the highest classification accuracy for detecting MUD was 85.5%, achieved using microstate parameters in the alpha band. Among these, the coverage of microstate class A contributed the most, suggesting it as the most promising endophenotype for specifying MUD. We accurately categorize the endophenotype of MUD into different sub-frequency bands, thereby providing reliable biomarkers.

    Keywords: EEG, Microstate, Methamphetamine addiction, resting states, detection biomarkers, machine learning, Classification

    Received: 19 Oct 2024; Accepted: 23 Dec 2024.

    Copyright: © 2024 Gao, Chen, Zeng, Zheng, PengChai, Wu, Zhu, Yang, Zhong, Shen and Sawan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yun-Hsuan Chen, School of Engineering, Westlake University, Hangzhou, 310024, Zhejiang, China
    Mohamad Sawan, School of Engineering, Westlake University, Hangzhou, 310024, Zhejiang, China

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