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分解 EEG 阿尔法功率中的年龄效应。

Decomposing age effects in EEG alpha power.

机构信息

Department of Psychology, University of Zurich, Methods of Plasticity Research, Zurich, Switzerland; University Research Priority Program (URPP) Dynamics of Healthy Aging, Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland.

Department of Psychology, University of Zurich, Methods of Plasticity Research, Zurich, Switzerland; University Research Priority Program (URPP) Dynamics of Healthy Aging, Zurich, Switzerland.

出版信息

Cortex. 2023 Apr;161:116-144. doi: 10.1016/j.cortex.2023.02.002. Epub 2023 Feb 22.

Abstract

Increasing life expectancy is prompting the need to understand how the brain changes during healthy aging. Research utilizing electroencephalography (EEG) has found that the power of alpha oscillations decrease from adulthood on. However, non-oscillatory (aperiodic) components in the data may confound results and thus require re-investigation of these findings. Thus, the present report analyzed a pilot and two additional independent samples (total N = 533) of resting-state EEG from healthy young and elderly individuals. A newly developed algorithm was utilized that allows the decomposition of the measured signal into periodic and aperiodic signal components. By using multivariate sequential Bayesian updating of the age effect in each signal component, evidence across the datasets was accumulated. It was hypothesized that previously reported age-related alpha power differences will largely diminish when total power is adjusted for the aperiodic signal component. First, the age-related decrease in total alpha power was replicated. Concurrently, decreases of the intercept and slope (i.e. exponent) of the aperiodic signal component were observed. Findings on aperiodic-adjusted alpha power indicated that this general shift of the power spectrum leads to an overestimation of the true age effects in conventional analyses of total alpha power. Thus, the importance of separating neural power spectra into periodic and aperiodic signal components is highlighted. However, also after accounting for these confounding factors, the sequential Bayesian updating analysis provided robust evidence that aging is associated with decreased aperiodic-adjusted alpha power. While the relation of the aperiodic component and aperiodic-adjusted alpha power to cognitive decline demands further investigation, the consistent findings on age effects across independent datasets and high test-retest reliabilities support that these newly emerging measures are reliable markers of the aging brain. Hence, previous interpretations of age-related decreases in alpha power are reevaluated, incorporating changes in the aperiodic signal.

摘要

预期寿命的延长促使人们需要了解大脑在健康衰老过程中的变化。利用脑电图 (EEG) 的研究发现,阿尔法振荡的功率从成年期开始下降。然而,数据中的非振荡(非周期性)成分可能会混淆结果,因此需要重新研究这些发现。因此,本报告分析了来自健康年轻和老年个体的静息状态 EEG 的一个试点和另外两个独立样本(总 N=533)。使用一种新开发的算法,该算法允许将测量信号分解为周期性和非周期性信号成分。通过在每个信号成分中使用年龄效应的多元顺序贝叶斯更新,在整个数据集上积累了证据。假设当总功率调整为非周期性信号成分时,先前报告的与年龄相关的阿尔法功率差异将大大减少。首先,复制了与年龄相关的总阿尔法功率下降。同时,观察到非周期性信号成分的截距和斜率(即指数)下降。对非周期性调整后的阿尔法功率的研究结果表明,这种功率谱的一般移动导致在常规的总阿尔法功率分析中对真实年龄效应的高估。因此,强调了将神经功率谱分离为周期性和非周期性信号成分的重要性。然而,即使考虑到这些混杂因素,顺序贝叶斯更新分析也提供了强有力的证据,表明衰老与非周期性调整后的阿尔法功率降低有关。虽然非周期性成分和非周期性调整后的阿尔法功率与认知能力下降的关系需要进一步研究,但独立数据集之间的一致发现以及高测试-重测可靠性支持这些新出现的测量是衰老大脑的可靠标志物。因此,重新评估了先前关于阿尔法功率与年龄相关下降的解释,纳入了非周期性信号的变化。

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