静息态EEG/MEG的非周期性成分: 分析流程、应用进展和未来前景

doi:10.3724/SP.J.1042.2025.1321

摘要/Abstract

摘要：

功率谱分析是EEG/MEG数据处理中的常用方法, 近年来越来越多的研究者认识到功率谱的非周期性成分具有独特的生理意义与应用价值。随着国际上以频谱参数拟合算法(SpecParam)为代表的工具包的推广使用, 静息态EEG/MEG的非周期分析受到广泛关注。本文首先介绍了在高密度EEG/MEG中进行非周期分析的常规流程。之后总结应用上的两个主要进展: 在发展神经科学方面, 老年人的频谱平坦化与认知表现下降、睡眠质量变差高度相关。在临床应用方面, 非周期性参数可以作为多种神经精神疾病的电生理标志物。目前, 非周期分析还缺少对全脑空间分布的关注, 其神经生理生成机制尚处于探索期, 未来需要结合多模态脑成像技术、实验设计等创新方向进一步筑牢理论基础, 拓展应用范围。

关键词: 非周期性成分, EEG/MEG, 功率谱, 无标度性, 静息态

Abstract:

Power spectral analysis is a common method in EEG/MEG data processing. In recent years, growing numbers of researchers have recognized that the aperiodic components of power spectra hold unique physiological significance and practical value. With the global adoption of toolkits such as SpecParam, the aperiodic analysis of resting-state EEG/MEG has garnered substantial attention. Here we provide a rapid-start guide for beginners in aperiodic analysis, offering tool comparisons and standardized workflows while synthesizing current research on the aperiodic activity of high-density resting-state EEG/MEG. Building on key findings from developmental neuroscience and neuropsychiatric disorders, we propose critical directions for advancing this field.
First, we systematically compare widely-used aperiodic analysis tools (e.g., SpecParam, IRASA) across some dimensions like spectral parameterization approaches, algorithmic foundations, and fitting parameter spaces. Using the representative SpecParam and sleep deprivation dataset, we then demonstrate a whole-brain standardized analysis protocol for high-density EEG/MEG studies. This framework addresses some current limitations in official tool tutorials that predominantly employ single-electrode examples, while highlighting the necessity for future multi-electrode spatial analyses and group comparison. Accompanying analysis code is provided in supplementary materials for replication.
Second, we consolidate major advancements of aperiodic analysis across neuroscience, psychology, and psychiatry. In developmental neuroscience, age-related aperiodic parameter flattening shows robust associations with cognitive decline and sleep deterioration. The aperiodic exponent emerges as a critical biomarker linking advanced cognitive functions, arousal states, and neurodevelopmental trajectories, offering electrophysiological insights into the behavioral mechanisms. In clinical psychiatry, significant aperiodic parameter alterations demonstrate diagnostic potential as the electrophysiological biomarkers for neuropsychiatric disorders. By disentangling periodic and aperiodic components through parameterization, this approach resolves previous contradictory findings while providing novel perspectives for assessing brain dysfunction. These applications underscore aperiodic analysis' cross-population validity and translational promise.
Finally, we identify three critical research frontiers: 1) Current methodologies insufficiently address whole-brain spatial distributions of aperiodic activity, necessitating spatial feature characterization to elucidate neurophysiological generation mechanisms; 2) Standardized analytical pipelines must be established across tools to enhance reproducibility; 3) The physiological interpretation of aperiodic parameters requires expansion through excitation-inhibition (E:I) balance theory, particularly via direct neurotransmitter association studies. These proposed directions aim to bridge existing gaps and propel systematic development of aperiodic analysis methodologies. Future research should integrate multimodal neuroimaging techniques, innovative experimental paradigms, and mechanistic modeling to strengthen the theoretical foundations and clinical applications of EEG/MEG aperiodic analysis.

Key words: aperiodic components, EEG/MEG, Power Spectrum, scale-free, resting-state

中图分类号:

B845

胡静怡, 白朵, 雷旭. (2025). 静息态EEG/MEG的非周期性成分: 分析流程、应用进展和未来前景. 心理科学进展 , 33(8), 1321-1339.

HU Jingyi, BAI Duo, LEI Xu. (2025). Aperiodic components of resting-state EEG/MEG: Analysis procedures, application advances and future prospects. Advances in Psychological Science, 33(8), 1321-1339.

图/表 6

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工具包名称	频谱分离参数化情况		算法基础	拟合空间	优点	缺点	参考文献
工具包名称	非周期性成分	周期性成分	算法基础	拟合空间	优点	缺点	参考文献
SpecParam (FOOOF)	√	√	高斯拟合、幂律函数	对数功率−频率	不需要预先定义特定的感兴趣频带和控制非周期性成分。	①拟合频率范围局限在窄带的低频或高频时性能较差 ②模型驱动, 受预设参数影响大 ③难以表征无法明确区分的振荡峰值(重叠峰) ④跨频率范围边界的振荡会导致指数误差增大	Donoghue et al., 2020
SPRiNT	√	√	高斯拟合、幂律函数	对数功率−频率	将Specparam与短时傅里叶变换相结合, 实现了时间分辨频谱参数化	①高估峰值带宽, 低估峰值数量 ②存在非周期性拐点(knee)时, 性能下降	Wilson et al., 2022
PAPTO	√	√	高斯拟合、幂律函数	对数功率−频率	对皮层瞬时β节律活动敏感	-	Brady & Bardouille, 2022
IRASA/MRCSA	√	×	重采样	对数功率−对数频率	①可估计频率范围边界上的周期性成分 ②MRCSA可用于分析两个同时记录的神经信号的交叉频谱	①缺乏明确的参数化以便进一步分析 ②受到重采样因子大小影响 ③难以表征无法明确区分的振荡峰值(重叠峰) ④存在大带宽峰值或非周期性拐点(knee)时, 性能下降	Wen & Liu, 2016b; Racz et al., 2022
BOSC	√	×	卡方分布	对数功率−对数频率	能够正确拒绝非振荡的瞬态事件	①通常假设非周期分量是不变的 ②缺乏去除频谱峰值干扰的系统预处理步骤	Whitten et al., 2011
MODAL	×	√	频率滑动法	对数功率−对数频率	确保仅在感兴趣频带中功率增加的时间段内获得相位和频率估计。	通常假设非周期分量是不变的	Watrous et al., 2018
ξ-α	√	×	t检验	原始数据	关注α节律峰值	-	Pascual-marqui et al., 1988
ξ-π	√	√	单模态与多模态	原始数据	①对不规则频谱形状具有较好鲁棒性 ②对所有类型周期信号进行无偏识别	-	Hu et al., 2024

工具包名称	频谱分离参数化情况		算法基础	拟合空间	优点	缺点	参考文献
工具包名称	非周期性成分	周期性成分	算法基础	拟合空间	优点	缺点	参考文献
SpecParam (FOOOF)	√	√	高斯拟合、幂律函数	对数功率−频率	不需要预先定义特定的感兴趣频带和控制非周期性成分。	①拟合频率范围局限在窄带的低频或高频时性能较差 ②模型驱动, 受预设参数影响大 ③难以表征无法明确区分的振荡峰值(重叠峰) ④跨频率范围边界的振荡会导致指数误差增大	Donoghue et al., 2020
SPRiNT	√	√	高斯拟合、幂律函数	对数功率−频率	将Specparam与短时傅里叶变换相结合, 实现了时间分辨频谱参数化	①高估峰值带宽, 低估峰值数量 ②存在非周期性拐点(knee)时, 性能下降	Wilson et al., 2022
PAPTO	√	√	高斯拟合、幂律函数	对数功率−频率	对皮层瞬时β节律活动敏感	-	Brady & Bardouille, 2022
IRASA/MRCSA	√	×	重采样	对数功率−对数频率	①可估计频率范围边界上的周期性成分 ②MRCSA可用于分析两个同时记录的神经信号的交叉频谱	①缺乏明确的参数化以便进一步分析 ②受到重采样因子大小影响 ③难以表征无法明确区分的振荡峰值(重叠峰) ④存在大带宽峰值或非周期性拐点(knee)时, 性能下降	Wen & Liu, 2016b; Racz et al., 2022
BOSC	√	×	卡方分布	对数功率−对数频率	能够正确拒绝非振荡的瞬态事件	①通常假设非周期分量是不变的 ②缺乏去除频谱峰值干扰的系统预处理步骤	Whitten et al., 2011
MODAL	×	√	频率滑动法	对数功率−对数频率	确保仅在感兴趣频带中功率增加的时间段内获得相位和频率估计。	通常假设非周期分量是不变的	Watrous et al., 2018
ξ-α	√	×	t检验	原始数据	关注α节律峰值	-	Pascual-marqui et al., 1988
ξ-π	√	√	单模态与多模态	原始数据	①对不规则频谱形状具有较好鲁棒性 ②对所有类型周期信号进行无偏识别	-	Hu et al., 2024

实验条件	分析工具	被试与涉及疾病	主要结果			参考文献
实验条件	分析工具	被试与涉及疾病	非周期性指数	非周期性偏移量	调整后的周期性成分	参考文献
静息态睁眼	线性回归	雷特综合征(n = 57)、健康对照组(n = 37)	↑	-	增加的δ功率与较低的认知评估相关	Roche et al., 2019
	SpecParam	小儿脆性X染色体综合征(n = 11)、年龄匹配组(n = 12)、认知匹配组(n = 12)	↓	-	小儿脆性X染色体综合征个体额叶γ功率增加	Wilkinson & Nelson, 2021
		帕金森病(分为用药与停药; n = 15)、健康对照组(n = 16)	↑	↑	帕金森病组θ范围的混合功率高于健康对照组	Wang et al., 2022
		注意力缺陷与多动障碍(n = 33)、健康对照组(n = 33)	↑	-	注意力缺陷与多动障碍组的β功率高于健康对照组	Dakwar-kawar et al., 2024
		首发精神分裂症谱系障碍(n = 43)、健康对照组(n = 28)	-	↓	-	Earl et al., 2024
静息态闭眼		阿尔茨海默症(n = 36)、额颞叶痴呆(n = 23)、健康对照组(n = 29)	↑	↑	与额颞叶痴呆组相比, 阿尔茨海默症组的θ功率更高	Wang et al., 2024
		阿尔茨海默症(n = 43)、健康对照组(n = 33)	-	-	阿尔茨海默症组功率显著降低; 阿尔茨海默症与非周期性脑电活动的变化无关	Kopčanová et al., 2024
		全面发作性癫痫(n = 51)、健康对照组(n = 49)	↑	-	全面发作性癫痫组频谱指数与α功率的负相关程度下降	Kopf et al., 2024
	IRASA	路易体痴呆(n = 21)、帕金森病(n = 28)、轻度认知障碍(n = 27)、健康对照组(n = 22)	↑	-	与健康对照、轻度认知障碍与帕金森病组相比, 路易体痴呆个体表现出更高的θ功率	Rosenblum et al., 2023

实验条件	分析工具	被试与涉及疾病	主要结果			参考文献
实验条件	分析工具	被试与涉及疾病	非周期性指数	非周期性偏移量	调整后的周期性成分	参考文献
静息态睁眼	线性回归	雷特综合征(n = 57)、健康对照组(n = 37)	↑	-	增加的δ功率与较低的认知评估相关	Roche et al., 2019
	SpecParam	小儿脆性X染色体综合征(n = 11)、年龄匹配组(n = 12)、认知匹配组(n = 12)	↓	-	小儿脆性X染色体综合征个体额叶γ功率增加	Wilkinson & Nelson, 2021
		帕金森病(分为用药与停药; n = 15)、健康对照组(n = 16)	↑	↑	帕金森病组θ范围的混合功率高于健康对照组	Wang et al., 2022
		注意力缺陷与多动障碍(n = 33)、健康对照组(n = 33)	↑	-	注意力缺陷与多动障碍组的β功率高于健康对照组	Dakwar-kawar et al., 2024
		首发精神分裂症谱系障碍(n = 43)、健康对照组(n = 28)	-	↓	-	Earl et al., 2024
静息态闭眼		阿尔茨海默症(n = 36)、额颞叶痴呆(n = 23)、健康对照组(n = 29)	↑	↑	与额颞叶痴呆组相比, 阿尔茨海默症组的θ功率更高	Wang et al., 2024
		阿尔茨海默症(n = 43)、健康对照组(n = 33)	-	-	阿尔茨海默症组功率显著降低; 阿尔茨海默症与非周期性脑电活动的变化无关	Kopčanová et al., 2024
		全面发作性癫痫(n = 51)、健康对照组(n = 49)	↑	-	全面发作性癫痫组频谱指数与α功率的负相关程度下降	Kopf et al., 2024
	IRASA	路易体痴呆(n = 21)、帕金森病(n = 28)、轻度认知障碍(n = 27)、健康对照组(n = 22)	↑	-	与健康对照、轻度认知障碍与帕金森病组相比, 路易体痴呆个体表现出更高的θ功率	Rosenblum et al., 2023