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神经功率谱可被参数化为周期性和非周期性成分
作者:小柯机器人 发布时间:2020/11/25 16:32:31

美国加州大学圣迭戈分校Bradley Voytek、Thomas Donoghue等研究人员合作发现,神经功率谱可被参数化为周期性和非周期性成分。2020年11月23日出版的《自然—神经科学》杂志发表了这项成果。

研究人员发现,标准的分析方法可以将周期性参数(中心频率、功率、带宽)与非周期性参数(偏移、指数)混淆,从而影响生理学解释。为了克服这些局限性,研究人员引入了一种算法来将神经功率谱参数化,作为非周期性成分和假定周期性振荡峰的组合。该算法不需要先验的频段说明。研究人员在模拟数据上验证了该算法,并演示了其不同的应用。

据了解,电生理信号表现出周期性和非周期性的性质。周期性振荡与许多生理、认知、行为和疾病状态有关。越来越多的证据表明,非周期性成分具有公认的生理学解释,并且会随着年龄、任务需求和认知状态而动态变化。通常使用经典定义的频带来分析电生理神经活动,而不考虑非周期性成分。

附:英文原文

Title: Parameterizing neural power spectra into periodic and aperiodic components

Author: Thomas Donoghue, Matar Haller, Erik J. Peterson, Paroma Varma, Priyadarshini Sebastian, Richard Gao, Torben Noto, Antonio H. Lara, Joni D. Wallis, Robert T. Knight, Avgusta Shestyuk, Bradley Voytek

Issue&Volume: 2020-11-23

Abstract: Electrophysiological signals exhibit both periodic and aperiodic properties. Periodic oscillations have been linked to numerous physiological, cognitive, behavioral and disease states. Emerging evidence demonstrates that the aperiodic component has putative physiological interpretations and that it dynamically changes with age, task demands and cognitive states. Electrophysiological neural activity is typically analyzed using canonically defined frequency bands, without consideration of the aperiodic (1/f-like) component. We show that standard analytic approaches can conflate periodic parameters (center frequency, power, bandwidth) with aperiodic ones (offset, exponent), compromising physiological interpretations. To overcome these limitations, we introduce an algorithm to parameterize neural power spectra as a combination of an aperiodic component and putative periodic oscillatory peaks. This algorithm requires no a priori specification of frequency bands. We validate this algorithm on simulated data, and demonstrate how it can be used in applications ranging from analyzing age-related changes in working memory to large-scale data exploration and analysis. A method for parameterizing electrophysiological neural power spectra into periodic and aperiodic components is introduced, addressing limitations of common approaches. The method is validated in simulation and demonstrated on real data applications.

DOI: 10.1038/s41593-020-00744-x

Source: https://www.nature.com/articles/s41593-020-00744-x

期刊信息

Nature Neuroscience:《自然—神经科学》,创刊于1998年。隶属于施普林格·自然出版集团,最新IF:21.126
官方网址:https://www.nature.com/neuro/
投稿链接:https://mts-nn.nature.com/cgi-bin/main.plex