Specific endophenotypes in EEG microstates for methamphetamine use disorder.

IF 3.2 3区医学 Q2 PSYCHIATRY Frontiers in Psychiatry Pub Date : 2025-02-03 eCollection Date: 2024-01-01 DOI:10.3389/fpsyt.2024.1513793

Xurong Gao, Yun-Hsuan Chen, Ziyi Zeng, Wenyao Zheng, Chengpeng Chai, Hemmings Wu, Zhoule Zhu, Jie Yang, Lihua Zhong, Hua Shen, Mohamad Sawan

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Abstract

Background: Electroencephalogram (EEG) 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.

Methods: In this study, we investigated EEG microstate dynamics across various frequency bands and different tasks, utilizing machine learning to classify MUD and healthy controls.

Results: 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.

Discussion: We accurately categorize the endophenotype of MUD into different sub-frequency bands, thereby providing reliable biomarkers.

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甲基苯丙胺使用障碍脑电微观状态的特异性内表型。

背景：脑电图（EEG）微观状态反映了大脑的大规模静息状态网络，已被认为是甲基苯丙胺使用障碍（MUD）的潜在内表型。然而，目前的内表型在频带水平上缺乏细化，限制了其识别与MUD相关的关键频带的精度。方法：在本研究中，我们研究了不同频段和不同任务下的EEG微状态动态，利用机器学习对MUD和健康对照组进行分类。结果：静息状态下，利用α波段微状态参数检测MUD的分类准确率最高，达到85.5%。其中，微态A类的覆盖贡献最大，提示其是最有希望指定MUD的内表型。讨论：我们准确地将MUD的内表型划分为不同的亚频段，从而提供可靠的生物标志物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Frontiers in Psychiatry Medicine-Psychiatry and Mental Health

CiteScore

6.20

自引率

8.50%

发文量

2813

审稿时长

14 weeks

期刊介绍： Frontiers in Psychiatry publishes rigorously peer-reviewed research across a wide spectrum of translational, basic and clinical research. Field Chief Editor Stefan Borgwardt at the University of Basel is supported by an outstanding Editorial Board of international researchers. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide. The journal''s mission is to use translational approaches to improve therapeutic options for mental illness and consequently to improve patient treatment outcomes.