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脑电图微状态突出特定的正念特质。

Electroencephalography microstates highlight specific mindfulness traits.

作者信息

Zarka D, Cevallos C, Ruiz P, Petieau M, Cebolla A M, Bengoetxea A, Cheron G

机构信息

Laboratory of Neurophysiology and Movement Biomechanics, Research Unit in Sciences of Osteopathy, Faculty of Human Motor Sciences, Université Libre de Bruxelles, Brussels, Belgium.

Research Unit in Sciences of Osteopathy, Faculty of Human Motor Sciences, Université Libre de Bruxelles, Brussels, Belgium.

出版信息

Eur J Neurosci. 2024 Apr;59(7):1753-1769. doi: 10.1111/ejn.16247. Epub 2024 Jan 14.

DOI:10.1111/ejn.16247
PMID:38221503
Abstract

The present study aimed to investigate the spontaneous dynamics of large-scale brain networks underlying mindfulness as a dispositional trait, through resting-state electroencephalography (EEG) microstates analysis. Eighteen participants had attended a standardized mindfulness-based stress reduction training (MBSR), and 18 matched waitlist individuals (CTRL) were recorded at rest while they were passively exposed to auditory stimuli. Participants' mindfulness traits were assessed with the Five Facet Mindfulness Questionnaire (FFMQ). To further explore the relationship between microstate dynamics at rest and mindfulness traits, participants were also asked to rate their experience according to five phenomenal dimensions. After training, MBSR participants showed a highly significant increase in FFMQ score, as well as higher observing and non-reactivity FFMQ sub-scores than CTRL participants. Microstate analysis revealed four classes of microstates (A-D) in global clustering across all subjects. The MBSR group showed lower duration, occurrence and coverage of microstate C than the control group. Moreover, these microstate C parameters were negatively correlated to non-reactivity sub-scores of FFMQ across participants, whereas the microstate A occurrence was negatively correlated to FFMQ total score. Further analysis of participants' self-reports suggested that MBSR participants showed a better sensory-affective integration of auditory interferences. In line with previous studies, our results suggest that temporal dynamics of microstate C underlie specifically the non-reactivity trait of mindfulness. These findings encourage further research into microstates in the evaluation and monitoring of the impact of mindfulness-based interventions on the mental health and well-being of individuals.

摘要

本研究旨在通过静息态脑电图(EEG)微状态分析,探究作为一种特质的正念背后的大规模脑网络的自发动力学。18名参与者参加了标准化的基于正念的减压训练(MBSR),18名匹配的候补名单个体(CTRL)在静息状态下接受听觉刺激时被记录下来。使用五因素正念问卷(FFMQ)评估参与者的正念特质。为了进一步探索静息态微状态动力学与正念特质之间的关系,还要求参与者根据五个现象维度对他们的体验进行评分。训练后,MBSR参与者的FFMQ得分显著提高,并且在观察和非反应性FFMQ子得分方面高于CTRL参与者。微状态分析揭示了所有受试者全局聚类中的四类微状态(A - D)。MBSR组的微状态C的持续时间、出现率和覆盖率低于对照组。此外,这些微状态C参数与参与者FFMQ的非反应性子得分呈负相关,而微状态A的出现率与FFMQ总分呈负相关。对参与者自我报告的进一步分析表明,MBSR参与者在听觉干扰的感觉 - 情感整合方面表现更好。与先前的研究一致,我们的结果表明微状态C的时间动态具体构成了正念的非反应性特质的基础。这些发现鼓励进一步研究微状态,以评估和监测基于正念的干预措施对个体心理健康和幸福感的影响。

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引用本文的文献

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Using Electroencephalography to Advance Mindfulness Science: A Survey of Emerging Methods and Approaches.利用脑电图推进正念科学:新兴方法与途径综述
Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Apr;10(4):342-349. doi: 10.1016/j.bpsc.2024.09.012. Epub 2024 Oct 5.
2
EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning.基于脑电图,通过深度学习和传统机器学习研究正念冥想训练对状态和特质的影响。
Front Hum Neurosci. 2023 Aug 31;17:1033420. doi: 10.3389/fnhum.2023.1033420. eCollection 2023.