双耳节拍调控疼痛的神经机制

doi:10.3724/SP.J.1041.2026.0500

摘要/Abstract

摘要：

双耳节拍(Binaural Beats, BB)是一种便捷、低成本、可应用于日常生活的神经调控手段, 具有潜在的镇痛效应。本研究采用α频段BB作为刺激, 比较其与单耳节拍(Monaural Beats, MB)和白噪音在疼痛调控及神经机制上的差异。脑电频谱分析表明, 与白噪音相比, BB和MB均显著降低γ频段能量, 表明节律性听觉刺激对高频神经活动具有共性调节作用。脑电微状态分析表明, BB相较于MB和白噪音, 显著增强了微状态A的活动, 减弱了微状态C的活动。中介分析显示, BB通过降低微状态C与D之间的转换概率, 间接调节疼痛诱发P2波幅, 进而影响主观疼痛体验。综上, BB可能通过重塑大脑动态功能网络, 减少对伤害性刺激的注意调动, 展现出潜在的神经调控价值。

关键词: 双耳节拍, 单耳节拍, 疼痛, 脑电微状态, 激光诱发电位

Abstract:

Non-pharmacological interventions for pain have been a crucial topic within psychology and neuroscience disciplines for many years. Among them, rhythmic auditory stimulation has gained increasing attention due to its non-invasive nature, repeatability, and suitability for daily use. Binaural beats (BBs), which comprise a virtual, rhythmic signal generated by presenting slightly different frequencies to each ear, can induce cortical oscillations at matching frequencies and thereby modulate brain activity. BBs engage more complex central auditory processing compared to monaural beats (MBs), which are physically mixed sounds that primarily act as external rhythmic stimuli.

There is an inverse relationship between alpha activity and pain perception, and enhanced alpha oscillations have been proposed to play an analgesic role. Accordingly, this study employs alpha-band BBs as an experimental stimulus, with MBs and white noise as control conditions, to investigate their analgesic effects and underlying neural mechanisms. Using a within-subjects design with three auditory conditions (BBs, MBs, and white noise), we assessed subjective pain ratings (intensity and unpleasantness), laser-evoked potentials (LEPs), and spontaneous EEG dynamics. To comprehensively capture neural modulation, we combined spectral power analysis with EEG microstate analysis to examine the dynamic reorganization of brain networks during auditory stimulation with BBs.

No significant differences in subjective pain ratings were observed across conditions. However, BBs were found to uniquely modulate brain dynamics. Both BBs and MBs significantly decreased gamma-band power during stimulation compared to white noise, indicating a similar effect between these rhythmic auditory inputs on high-frequency activity. Importantly, microstate analysis revealed BB-specific changes: BBs enhanced the occurrence of microstate A (associated with primary auditory processing) while reducing the presence of microstate C (linked to introspective and self-referential processing). Mediation analysis further showed that BBs indirectly modulated pain-related P2 amplitudes, indicative of attentional allocation to nociceptive stimuli, through reducing the transition probability between microstate C and microstate D (involved in attentional reorientation); these factors together may modulate subjective pain experiences. This dynamic pathway suggests that BBs can alter pain processing by reshaping functional brain states and modulating the deployment of attentional resources.

In summary, while BBs did not produce robust behavioral analgesia, they produced significant neural modulatory effects—potentially by reducing dynamic switching between the default mode network and attention-related networks. Our methodology of EEG microstate analysis to investigate pain modulation by rhythmic auditory stimulation offers a novel perspective for evaluating non-pharmacological neuromodulation. Theoretically, our findings call for a shift from frequency-centric views toward a state-dependent framework emphasizing dynamic brain network reorganization. Future studies may explore personalized rhythmic stimulation protocols tailored to individual brain dynamics to enhance the clinical application of BBs in chronic pain and affective disorders.

Key words: binaural beats, monaural beats, pain, microstate analyses, laser-evoked potentials

中图分类号:

B845

李晓云, 吴奇奇, 蒋骊雯, 吕雪靖, 彭微微. (2026). 双耳节拍调控疼痛的神经机制. 心理学报, 58(3), 500-515.

LI Xiaoyun, WU Qiqi, JIANG Liwen, LU Xuejing, PENG Weiwei. (2026). Neural mechanisms of binaural beats in pain modulation. Acta Psychologica Sinica, 58(3), 500-515.

图/表 8

图1 研究设计和实验流程。(A)实验总体流程示意图。(B)三种听觉刺激条件: 双耳节拍(BB)、单耳节拍(MB)和白噪音(WN)。(C)疼痛评分任务的单次试验流程。

图2 激光诱发的疼痛评分和脑响应。(A)激光诱发疼痛强度和不愉悦度评分。(B)激光诱发电位(LEP)波形图与的地形图。绿色标记的电极表示该成分纳入分析的电极区域; 波形图中灰色阴影区域表示纳入分析的时间窗。(C)激光诱发的N2和P2波幅。柱状图显示不同条件下的平均波幅, 数据以M ± SE表示。彩图见电子版, 下同。

图3 听觉干预期间的脑电频谱分析结果。(A)不同干预条件下的全脑平均频谱图, 以及各频段的意见能量地形分布图。(B)γ频段不同脑区频谱功率的影响。柱状图显示不同条件下的平均功率, 数据以M ± SE表示。# p < 0.10; * p < 0.05。

图4 听觉干预期间的脑电微状态特征。(A)不同听觉干预条件下的微状态地形图。(B)微状态平均持续时间。(C)微状态发生率。(D)微状态时间覆盖率。柱形图显示不同干预条件下各微状态特征, 数据以M ± SE表示。*p < 0.05; **p < 0.01; ***p < 0.001。

表1 听觉干预期间脑电微状态的动态特征统计结果

微状态特征	微状态类型主效应			干预条件主效应			交互效应
微状态特征	F_{3, 123}	p	η²_p	F_{2, 82}	p	η²_p	F_{6, 246}	p	η²_p
平均持续时间	1.57	0.218	0.04	1.71	0.191	0.04	7.63	<0.001	0.16
发生率	7.18	<0.001	0.15	0.99	0.373	0.02	25.08	<0.001	0.38
时间覆盖率	2.35	0.095	0.05	2.02	0.140	0.05	18.78	<0.001	0.31

表2 听觉干预期间脑电微状态转换率的统计结果

微状态转换率	F_{2, 82}	p	η²_p
微状态A→微状态B	33.41	<0.001	0.45
微状态B→微状态A	32.54	<0.001	0.44
微状态A→微状态C	6.01	0.004	0.13
微状态C→微状态A	4.14	0.019	0.09
微状态A→微状态D	23.45	<0.001	0.36
微状态D→微状态A	29.83	<0.001	0.42
微状态B→微状态C	18.11	<0.001	0.31
微状态C→微状态B	16.75	<0.001	0.29
微状态B→微状态D	4.21	0.018	0.09
微状态D→微状态B	3.47	0.036	0.08
微状态C→微状态D	27.67	<0.001	0.40
微状态D→微状态C	31.79	<0.001	0.44

图5 听觉干预期间的脑电微状态间的转换概率。(A)双耳节拍(BB)、单耳节拍(MB)和白噪音(WN)条件下的微状态转换概率比较; (B) BB与MB条件下的微状态转换概率比较; (C) BB与WN条件下的比较; (D) MB与WN条件下的比较; (E)不同听觉干预条件下微状态间的转换概率图示。注: 黄色和蓝色箭头分别表示两种干预条件下, 微状态间的转换概率显著增加或减少; 灰色虚线箭头表示转换概率在两条件之间无显著差异。每对微状态之间的平均转换概率差异以柱形图展示, 数据以M ± SE表示。*p < 0.05; **p < 0.01; ***p < 0.001。

图6 脑电微状态与疼痛诱发反应的相关性及中介效应分析。(A)微状态C↔D转换概率与激光诱发P2波幅之间的相关性。与MB条件相比, BB条件下微状态C↔D转换概率的降低显著正相关于P2波幅的降低。散点图展示个体数据及回归线, 灰色阴影区域表示95%置信区间; (B)中介模型结果。听觉干预类型(BB vs. MB)通过减少微状态C与D之间的转换概率, 间接降低P2波幅, 进而减轻主观疼痛评分。图中标示路径系数及显著性水平。*: p < 0.05; **: p < 0.01; ***: p < 0.001。

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