Qike Cao, Yulin Wang, Yufang Ji, Zhihui He, Xu Lei
{"title":"静息态脑电图显示失眠抑郁症的异常微状态特征","authors":"Qike Cao, Yulin Wang, Yufang Ji, Zhihui He, Xu Lei","doi":"10.1007/s10548-023-00949-w","DOIUrl":null,"url":null,"abstract":"<p><p>Previous research revealed various aspects of resting-state EEG for depression and insomnia. However, the EEG characteristics of depressed subjects with insomnia are rarely studied, especially EEG microstates that capture the dynamic activities of the large-scale brain network. To fill these research gaps, the present study collected resting-state EEG data from 32 subclinical depression subjects with insomnia (SDI), 31 subclinical depression subjects without insomnia (SD), and 32 healthy controls (HCs). Four topographic maps were generated from clean EEG data after clustering and rearrangement. Temporal characteristics were obtained for statistical analysis, including cross-group variance analysis (ANOVA) and intra-group correlation analysis. In our study, the global clustering of all individuals in the EEG microstate analysis revealed the four previously discovered categories of microstates (A, B, C, and D). The occurrence of microstate B was lower in SDI than in SD and HC subjects. The correlation analysis showed that the total Pittsburgh Sleep Quality Index (PSQI) score negatively correlated with the occurrence of microstate C in SDI (r = - 0.415, p < 0.05). Conversely, there was a positive correlation between Self-rating Depression Scale (SDS) scores and the duration of microstate C in SD (r = 0.359, p < 0.05). These results indicate that microstates reflect altered large-scale brain network dynamics in subclinical populations. Abnormalities in the visual network corresponding to microstate B are an electrophysiological characteristic of subclinical individuals with symptoms of depressive insomnia. Further investigation is needed for microstate changes related to high arousal and emotional problems in people suffering from depression and insomnia.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":" ","pages":"388-396"},"PeriodicalIF":2.3000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Resting-State EEG Reveals Abnormal Microstate Characteristics of Depression with Insomnia.\",\"authors\":\"Qike Cao, Yulin Wang, Yufang Ji, Zhihui He, Xu Lei\",\"doi\":\"10.1007/s10548-023-00949-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Previous research revealed various aspects of resting-state EEG for depression and insomnia. However, the EEG characteristics of depressed subjects with insomnia are rarely studied, especially EEG microstates that capture the dynamic activities of the large-scale brain network. To fill these research gaps, the present study collected resting-state EEG data from 32 subclinical depression subjects with insomnia (SDI), 31 subclinical depression subjects without insomnia (SD), and 32 healthy controls (HCs). Four topographic maps were generated from clean EEG data after clustering and rearrangement. Temporal characteristics were obtained for statistical analysis, including cross-group variance analysis (ANOVA) and intra-group correlation analysis. In our study, the global clustering of all individuals in the EEG microstate analysis revealed the four previously discovered categories of microstates (A, B, C, and D). The occurrence of microstate B was lower in SDI than in SD and HC subjects. The correlation analysis showed that the total Pittsburgh Sleep Quality Index (PSQI) score negatively correlated with the occurrence of microstate C in SDI (r = - 0.415, p < 0.05). Conversely, there was a positive correlation between Self-rating Depression Scale (SDS) scores and the duration of microstate C in SD (r = 0.359, p < 0.05). These results indicate that microstates reflect altered large-scale brain network dynamics in subclinical populations. Abnormalities in the visual network corresponding to microstate B are an electrophysiological characteristic of subclinical individuals with symptoms of depressive insomnia. Further investigation is needed for microstate changes related to high arousal and emotional problems in people suffering from depression and insomnia.</p>\",\"PeriodicalId\":55329,\"journal\":{\"name\":\"Brain Topography\",\"volume\":\" \",\"pages\":\"388-396\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Brain Topography\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s10548-023-00949-w\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/3/9 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain Topography","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10548-023-00949-w","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/3/9 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
摘要
以往的研究揭示了抑郁症和失眠症静息态脑电图的各个方面。然而,对抑郁伴失眠者的脑电图特征,尤其是捕捉大规模脑网络动态活动的脑电图微观状态的研究却很少。为了填补这些研究空白,本研究收集了 32 名失眠亚临床抑郁症患者(SDI)、31 名无失眠亚临床抑郁症患者(SD)和 32 名健康对照组(HC)的静息态脑电图数据。在对干净的脑电图数据进行聚类和重新排列后,生成了四张地形图。获得的时间特征用于统计分析,包括跨组方差分析(ANOVA)和组内相关分析。在我们的研究中,所有个体在脑电图微状态分析中的全局聚类显示了之前发现的四类微状态(A、B、C 和 D)。微状态 B 在 SDI 受试者中的出现率低于 SD 和 HC 受试者。相关性分析表明,匹兹堡睡眠质量指数(PSQI)总分与 SDI 中微态 C 的出现呈负相关(r = - 0.415,p
Resting-State EEG Reveals Abnormal Microstate Characteristics of Depression with Insomnia.
Previous research revealed various aspects of resting-state EEG for depression and insomnia. However, the EEG characteristics of depressed subjects with insomnia are rarely studied, especially EEG microstates that capture the dynamic activities of the large-scale brain network. To fill these research gaps, the present study collected resting-state EEG data from 32 subclinical depression subjects with insomnia (SDI), 31 subclinical depression subjects without insomnia (SD), and 32 healthy controls (HCs). Four topographic maps were generated from clean EEG data after clustering and rearrangement. Temporal characteristics were obtained for statistical analysis, including cross-group variance analysis (ANOVA) and intra-group correlation analysis. In our study, the global clustering of all individuals in the EEG microstate analysis revealed the four previously discovered categories of microstates (A, B, C, and D). The occurrence of microstate B was lower in SDI than in SD and HC subjects. The correlation analysis showed that the total Pittsburgh Sleep Quality Index (PSQI) score negatively correlated with the occurrence of microstate C in SDI (r = - 0.415, p < 0.05). Conversely, there was a positive correlation between Self-rating Depression Scale (SDS) scores and the duration of microstate C in SD (r = 0.359, p < 0.05). These results indicate that microstates reflect altered large-scale brain network dynamics in subclinical populations. Abnormalities in the visual network corresponding to microstate B are an electrophysiological characteristic of subclinical individuals with symptoms of depressive insomnia. Further investigation is needed for microstate changes related to high arousal and emotional problems in people suffering from depression and insomnia.
期刊介绍:
Brain Topography publishes clinical and basic research on cognitive neuroscience and functional neurophysiology using the full range of imaging techniques including EEG, MEG, fMRI, TMS, diffusion imaging, spectroscopy, intracranial recordings, lesion studies, and related methods. Submissions combining multiple techniques are particularly encouraged, as well as reports of new and innovative methodologies.