Specific endophenotypes in EEG microstates for methamphetamine use disorder
BackgroundElectroencephalogram (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 precisio...
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| Format: | Article |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Psychiatry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1513793/full |
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| author | Xurong Gao Yun-Hsuan Chen Ziyi Zeng Wenyao Zheng Chengpeng Chai Hemmings Wu Zhoule Zhu Jie Yang Lihua Zhong Hua Shen Mohamad Sawan |
| author_facet | Xurong Gao Yun-Hsuan Chen Ziyi Zeng Wenyao Zheng Chengpeng Chai Hemmings Wu Zhoule Zhu Jie Yang Lihua Zhong Hua Shen Mohamad Sawan |
| author_sort | Xurong Gao |
| collection | DOAJ |
| description | BackgroundElectroencephalogram (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.MethodsIn this study, we investigated EEG microstate dynamics across various frequency bands and different tasks, utilizing machine learning to classify MUD and healthy controls.ResultsDuring 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.DiscussionWe accurately categorize the endophenotype of MUD into different sub-frequency bands, thereby providing reliable biomarkers. |
| format | Article |
| id | doaj-art-25a22c754fab4a1e942ce74cc82d7a97 |
| institution | Kabale University |
| issn | 1664-0640 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychiatry |
| spelling | doaj-art-25a22c754fab4a1e942ce74cc82d7a972025-02-03T11:18:20ZengFrontiers Media S.A.Frontiers in Psychiatry1664-06402025-02-011510.3389/fpsyt.2024.15137931513793Specific endophenotypes in EEG microstates for methamphetamine use disorderXurong Gao0Yun-Hsuan Chen1Ziyi Zeng2Wenyao Zheng3Chengpeng Chai4Hemmings Wu5Zhoule Zhu6Jie Yang7Lihua Zhong8Hua Shen9Mohamad Sawan10CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, ChinaCenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, ChinaCenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, ChinaCenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, ChinaCenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, ChinaDepartment of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, ChinaDepartment of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, ChinaCenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, ChinaDepartment of Education and Correction, Zhejiang Gongchen Compulsory Isolated Detoxification Center, Hangzhou, ChinaZhejiang Liangzhu Compulsory Isolated Detoxification Center, Hangzhou, ChinaCenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, ChinaBackgroundElectroencephalogram (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.MethodsIn this study, we investigated EEG microstate dynamics across various frequency bands and different tasks, utilizing machine learning to classify MUD and healthy controls.ResultsDuring 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.DiscussionWe accurately categorize the endophenotype of MUD into different sub-frequency bands, thereby providing reliable biomarkers.https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1513793/fullEEGmicrostatemethamphetamine addictionresting statesdetection biomarkersmachine learning |
| spellingShingle | Xurong Gao Yun-Hsuan Chen Ziyi Zeng Wenyao Zheng Chengpeng Chai Hemmings Wu Zhoule Zhu Jie Yang Lihua Zhong Hua Shen Mohamad Sawan Specific endophenotypes in EEG microstates for methamphetamine use disorder Frontiers in Psychiatry EEG microstate methamphetamine addiction resting states detection biomarkers machine learning |
| title | Specific endophenotypes in EEG microstates for methamphetamine use disorder |
| title_full | Specific endophenotypes in EEG microstates for methamphetamine use disorder |
| title_fullStr | Specific endophenotypes in EEG microstates for methamphetamine use disorder |
| title_full_unstemmed | Specific endophenotypes in EEG microstates for methamphetamine use disorder |
| title_short | Specific endophenotypes in EEG microstates for methamphetamine use disorder |
| title_sort | specific endophenotypes in eeg microstates for methamphetamine use disorder |
| topic | EEG microstate methamphetamine addiction resting states detection biomarkers machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1513793/full |
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