1)天津大学电气自动化与信息工程学院,天津 300072;2)天津中医药大学第一附属医院,天津 300193
国家自然科学基金(62271348,62071324)资助项目。
1)School of Electrical and Information Engineering, Tianjin University, Tianjin300072, China;2)The First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin300193, China
This work was supported by grants from The National Natural Science Foundation of China (62271348, 62071324).
大脑神经活动由周期性的节律振荡和非周期性的神经波动组成。节律振荡表现为神经信号的频谱尖峰,直接反映了大脑神经元集群同步化活动,与大脑认知和行为状态密切相关;非周期性波动表现为以幂率衰减的频谱趋势,综合反映了大脑神经活动的多尺度动态,为全面理解大脑神经动力学提供了新的视角。近年来,国内外研究者在大脑非周期活动特性及其动力学机制等方面取得了进展,发现非周期活动的幂律衰减指数可以有效地表征神经系统兴奋-抑制(E/I)平衡程度,并将其作为多种脑部疾病的生物标志物。基于此,本文归纳总结了大脑非周期活动的特征参数提取方法及其优缺点,详细分析了非周期活动与年龄、认知水平等生理因素的相关性,重点剖析了非周期指数与癫痫、阿尔茨海默病和帕金森病等神经疾病的关联,进一步阐述了神经元及其网络中非周期活动的产生机制,在此基础上对大脑非周期动力学的未来研究方向进行了展望。
Brain’s neural activities encompass both periodic rhythmic oscillations and aperiodic neural fluctuations. Among them, rhythmic oscillations manifest as spectral peaks of neural signals, directly reflecting the synchronized activities of the brain neural population and being intimately tied to cognitive and behavioral states. Conversely, aperiodic fluctuations exhibit a power-law decaying spectral trend, unveiling the multiscale dynamics of brain neural activity. In recent years, researchers have made notable progress in the study of brain aperiodic dynamics. These studies demonstrate that aperiodic activity bears significant physiological relevance, correlating with various physiological states such as external stimuli, drug induction, sleep states, and aging. It serves as a reflection of the brain’s sensory capacity, consciousness level, and cognitive ability. In clinical research, the aperiodic exponent emerges as a significant potential biomarker, capable of reflecting the progression and trends of brain diseases while being intricately intertwined with the excitation-inhibition balance of neural system. The physiological mechanisms underlying aperiodic dynamics span multiple neural scales, with neural activities at the levels of individual neurons, neuronal ensembles, and neural networks each expletively influencing the frequency, oscillatory patterns, and spatiotemporal characteristics of aperiodic activities. Currently, aperiodic dynamics boasts broad application prospects, not only providing a fresh perspective for investigating brain neural dynamics but also holding immense potential as neural markers in neuromodulation technologies or brain-computer interface technologies. This paper summarizes methods for extracting characteristic parameters of aperiodic activity, comparatively analyzes its physiological relevance and potential as a biomarker in brain diseases, summarizes its physiological mechanisms, and finally, based on these findings, elaborates on the research prospects of aperiodic dynamics.
胡植才,张镇,王江,李桂平,刘珊,于海涛.大脑非周期动力学[J].生物化学与生物物理进展,2025,52(1):99-118
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