静息状态和心理任务状态下脑电图非周期性成分的测试-重测可靠性

IF 2.3 3区 医学 Q3 CLINICAL NEUROLOGY
Brain Topography Pub Date : 2024-11-01 Epub Date: 2024-07-17 DOI:10.1007/s10548-024-01067-x
Na Li, Jingqi Yang, Changquan Long, Xu Lei
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引用次数: 0

摘要

非周期性活动来源于脑电图(EEG)功率谱,反映了宽带频谱斜率和偏移的变化。研究表明,非周期性成分的测试-再测可靠性并不一致。本研究系统地测量了非周期性成分的测试-再测可靠性在短间隔(90 分钟)和长间隔(一个月)内受数据持续时间(1、2、3、4 和 5 分钟)、状态(闭眼休息、睁眼休息、进行心算、回忆一天中的事件和心唱歌曲)和方法(拟合振荡和一过 F(FOOOF)和线性混合效应回归(LMER))影响的情况。结果表明,非周期性成分在短时间和长时间间隔内的重复测试可靠性为一般、良好或优秀(从 0.53 到 0.91 不等)。建议使用超过 3 分钟的数据持续时间、闭眼休息状态、心算任务状态和 LMER 方法来获得非周期性成分的更高可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Test-Retest Reliability of EEG Aperiodic Components in Resting and Mental Task States.

Test-Retest Reliability of EEG Aperiodic Components in Resting and Mental Task States.

Aperiodic activity is derived from the electroencephalography (EEG) power spectrum and reflects changes in the slope and shifts of the broadband spectrum. Studies have shown inconsistent test-retest reliability of the aperiodic components. This study systematically measured how the test-retest reliability of the aperiodic components was affected by data duration (1, 2, 3, 4, and 5 min), states (resting with eyes closed, resting with eyes open, performing mental arithmetic, recalling the events of the day, and mentally singing songs), and methods (the Fitting Oscillations and One-Over-F (FOOOF) and Linear Mixed-Effects Regression (LMER)) at both short (90-min) and long (one-month) intervals. The results showed that aperiodic components had fair, good, or excellent test-retest reliability (ranging from 0.53 to 0.91) at both short and long intervals. It is recommended that better reliability of the aperiodic components be obtained using data durations longer than 3 min, the resting state with eyes closed, the mental arithmetic task state, and the LMER method.

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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.
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