Evidence map›Paper›PMID 39963515›Full record

ArticleFrontiers in psychiatry2024

Specific endophenotypes in EEG microstates for methamphetamine use disorder.

Xurong Gao, Yun-Hsuan Chen, Ziyi Zeng, Wenyao Zheng, Chengpeng Chai, Hemmings Wu, Zhoule Zhu, Jie Yang, Lihua Zhong, Hua Shen and 1 more

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Article in Frontiers in psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Xurong GaoCenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.
Yun-Hsuan ChenCenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.
Ziyi ZengCenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.
Wenyao ZhengCenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.
Chengpeng ChaiCenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.
Hemmings WuDepartment of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Zhoule ZhuDepartment of Neurosurgery, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Jie YangCenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.
Lihua ZhongDepartment of Education and Correction, Zhejiang Gongchen Compulsory Isolated Detoxification Center, Hangzhou, China.
Hua ShenZhejiang Liangzhu Compulsory Isolated Detoxification Center, Hangzhou, China.
Mohamad SawanCenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

classificationdetection biomarkersEEGmachine learningmethamphetamine addictionmicrostateresting states

Identifiers

PMID39963515
PMCPMC11831278

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.