MEG Microstates: An Investigation of Underlying Brain Sources and Potential Neurophysiological Processes.

IF 2.3 3区 医学 Q3 CLINICAL NEUROLOGY Brain Topography Pub Date : 2024-11-01 Epub Date: 2024-08-08 DOI:10.1007/s10548-024-01073-z
Christian Valt, Angelantonio Tavella, Cristina Berchio, Dylan Seebold, Leonardo Sportelli, Antonio Rampino, Dean F Salisbury, Alessandro Bertolino, Giulio Pergola
{"title":"MEG Microstates: An Investigation of Underlying Brain Sources and Potential Neurophysiological Processes.","authors":"Christian Valt, Angelantonio Tavella, Cristina Berchio, Dylan Seebold, Leonardo Sportelli, Antonio Rampino, Dean F Salisbury, Alessandro Bertolino, Giulio Pergola","doi":"10.1007/s10548-024-01073-z","DOIUrl":null,"url":null,"abstract":"<p><p>Microstates are transient scalp configurations of brain activity measured by electroencephalography (EEG). The application of microstate analysis in magnetoencephalography (MEG) data remains challenging. In one MEG dataset (N = 113), we aimed to identify MEG microstates at rest, explore their brain sources, and relate them to changes in brain activity during open-eyes (ROE) or closed-eyes resting state (RCE) and an auditory Mismatch Negativity (MMN) task. In another dataset of simultaneously recorded EEG-MEG data (N = 21), we investigated the association between MEG and EEG microstates. Six MEG microstates (mMS) provided the best clustering of resting-state activity, each linked to different brain sources: mMS 1-2: left/right occipito-parietal; mMS 3: fronto-temporal; mMS 4: centro-medial; mMS 5-6: left/right fronto-parietal. Increases in occipital alpha power in RCE relative to ROE correlated with greater mMS 1-2 time coverage (τ<sub>b</sub>s < 0.20, ps > .002), while the lateralization of deviance detection in MMN was associated with mMS 5-6 time coverage (τ<sub>b</sub>s < 0.16, ps > .012). No temporal correlation was found between EEG and MEG microstates (ps > .05), despite some overlap in brain sources and global explained variance between mMS 2-3 and EEG microstates B-C (rs > 0.60, ps < .002). Hence, the MEG signal can be decomposed into microstates, but mMS brain activity clustering captures phenomena different from EEG microstates. Source reconstruction and task-related modulations link mMS to large-scale networks and localized activities. Thus, mMSs offer insights into brain dynamics and task-specific processes, complementing EEG microstates in studying physiological and dysfunctional brain activity.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":" ","pages":"993-1009"},"PeriodicalIF":2.3000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11408537/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Brain Topography","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10548-024-01073-z","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/8 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Microstates are transient scalp configurations of brain activity measured by electroencephalography (EEG). The application of microstate analysis in magnetoencephalography (MEG) data remains challenging. In one MEG dataset (N = 113), we aimed to identify MEG microstates at rest, explore their brain sources, and relate them to changes in brain activity during open-eyes (ROE) or closed-eyes resting state (RCE) and an auditory Mismatch Negativity (MMN) task. In another dataset of simultaneously recorded EEG-MEG data (N = 21), we investigated the association between MEG and EEG microstates. Six MEG microstates (mMS) provided the best clustering of resting-state activity, each linked to different brain sources: mMS 1-2: left/right occipito-parietal; mMS 3: fronto-temporal; mMS 4: centro-medial; mMS 5-6: left/right fronto-parietal. Increases in occipital alpha power in RCE relative to ROE correlated with greater mMS 1-2 time coverage (τbs < 0.20, ps > .002), while the lateralization of deviance detection in MMN was associated with mMS 5-6 time coverage (τbs < 0.16, ps > .012). No temporal correlation was found between EEG and MEG microstates (ps > .05), despite some overlap in brain sources and global explained variance between mMS 2-3 and EEG microstates B-C (rs > 0.60, ps < .002). Hence, the MEG signal can be decomposed into microstates, but mMS brain activity clustering captures phenomena different from EEG microstates. Source reconstruction and task-related modulations link mMS to large-scale networks and localized activities. Thus, mMSs offer insights into brain dynamics and task-specific processes, complementing EEG microstates in studying physiological and dysfunctional brain activity.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
MEG 微状态:对潜在脑源和潜在神经生理过程的研究。
微状态是通过脑电图(EEG)测量到的大脑活动的瞬时头皮构型。在脑磁图(MEG)数据中应用微状态分析仍具有挑战性。在一个脑磁图数据集(N = 113)中,我们旨在识别静息状态下的脑磁图微态,探索其大脑来源,并将其与睁眼(ROE)或闭眼静息状态(RCE)和听觉错配负性(MMN)任务中的大脑活动变化联系起来。在另一个同时记录 EEG-MEG 数据的数据集中(N = 21),我们研究了 MEG 和 EEG 微状态之间的关联。六个 MEG 微状态(mMS)提供了静息态活动的最佳聚类,每个微状态都与不同的脑源相关联:mMS 1-2:左/右枕顶叶;mMS 3:前颞叶;mMS 4:中央-内侧;mMS 5-6:左/右前顶叶。相对于 ROE,RCE 枕叶α功率的增加与 mMS 1-2 时间覆盖范围的扩大相关(τbs < 0.20,ps > .002),而 MMN 偏差检测的侧化与 mMS 5-6 时间覆盖范围相关(τbs < 0.16,ps > .012)。尽管 mMS 2-3 和 EEG 微状态 B-C 之间存在一些脑源重叠和全局解释方差(rs > 0.60,ps
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Brain Topography
Brain Topography 医学-临床神经学
CiteScore
4.70
自引率
7.40%
发文量
41
审稿时长
3 months
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.
期刊最新文献
Classification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models. Eeg Microstates and Balance Parameters for Stroke Discrimination: A Machine Learning Approach. Individuals' Food Preferences can be Influenced by the Music Styles: An ERP Study. Relational Integration Training Modulated the Frontoparietal Network for Fluid Intelligence: An EEG Microstates Study. Altered Static and Dynamic Functional Network Connectivity and Combined Machine Learning in Stroke.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1