{"title":"甲基苯丙胺使用障碍脑电微观状态的特异性内表型。","authors":"Xurong Gao, Yun-Hsuan Chen, Ziyi Zeng, Wenyao Zheng, Chengpeng Chai, Hemmings Wu, Zhoule Zhu, Jie Yang, Lihua Zhong, Hua Shen, Mohamad Sawan","doi":"10.3389/fpsyt.2024.1513793","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>In this study, we investigated EEG microstate dynamics across various frequency bands and different tasks, utilizing machine learning to classify MUD and healthy controls.</p><p><strong>Results: </strong>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.</p><p><strong>Discussion: </strong>We accurately categorize the endophenotype of MUD into different sub-frequency bands, thereby providing reliable biomarkers.</p>","PeriodicalId":12605,"journal":{"name":"Frontiers in Psychiatry","volume":"15 ","pages":"1513793"},"PeriodicalIF":3.8000,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11831278/pdf/","citationCount":"0","resultStr":"{\"title\":\"Specific endophenotypes in EEG microstates for methamphetamine use disorder.\",\"authors\":\"Xurong Gao, Yun-Hsuan Chen, Ziyi Zeng, Wenyao Zheng, Chengpeng Chai, Hemmings Wu, Zhoule Zhu, Jie Yang, Lihua Zhong, Hua Shen, Mohamad Sawan\",\"doi\":\"10.3389/fpsyt.2024.1513793\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>In this study, we investigated EEG microstate dynamics across various frequency bands and different tasks, utilizing machine learning to classify MUD and healthy controls.</p><p><strong>Results: </strong>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.</p><p><strong>Discussion: </strong>We accurately categorize the endophenotype of MUD into different sub-frequency bands, thereby providing reliable biomarkers.</p>\",\"PeriodicalId\":12605,\"journal\":{\"name\":\"Frontiers in Psychiatry\",\"volume\":\"15 \",\"pages\":\"1513793\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2025-02-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11831278/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Psychiatry\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3389/fpsyt.2024.1513793\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"PSYCHIATRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Psychiatry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3389/fpsyt.2024.1513793","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"PSYCHIATRY","Score":null,"Total":0}
Specific endophenotypes in EEG microstates for methamphetamine use disorder.
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.
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.
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