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去趋势波动分析:神经元振荡的无标度观点。

Detrended fluctuation analysis: a scale-free view on neuronal oscillations.

机构信息

Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University Amsterdam Amsterdam, Netherlands.

出版信息

Front Physiol. 2012 Nov 30;3:450. doi: 10.3389/fphys.2012.00450. eCollection 2012.

Abstract

Recent years of research have shown that the complex temporal structure of ongoing oscillations is scale-free and characterized by long-range temporal correlations. Detrended fluctuation analysis (DFA) has proven particularly useful, revealing that genetic variation, normal development, or disease can lead to differences in the scale-free amplitude modulation of oscillations. Furthermore, amplitude dynamics is remarkably independent of the time-averaged oscillation power, indicating that the DFA provides unique insights into the functional organization of neuronal systems. To facilitate understanding and encourage wider use of scaling analysis of neuronal oscillations, we provide a pedagogical explanation of the DFA algorithm and its underlying theory. Practical advice on applying DFA to oscillations is supported by MATLAB scripts from the Neurophysiological Biomarker Toolbox (NBT) and links to the NBT tutorial website http://www.nbtwiki.net/. Finally, we provide a brief overview of insights derived from the application of DFA to ongoing oscillations in health and disease, and discuss the putative relevance of criticality for understanding the mechanism underlying scale-free modulation of oscillations.

摘要

近年来的研究表明,持续振荡的复杂时间结构是无标度的,其特征是长程时间相关性。去趋势波动分析(DFA)已被证明特别有用,它揭示了遗传变异、正常发育或疾病可能导致振荡的无标度幅度调制的差异。此外,幅度动态与时间平均的振荡功率显著无关,这表明 DFA 提供了对神经元系统功能组织的独特见解。为了促进对神经元振荡缩放分析的理解并鼓励更广泛的使用,我们提供了 DFA 算法及其基础理论的教学解释。应用 DFA 于振荡的实用建议,得到了来自神经生理生物标记工具箱(NBT)的 MATLAB 脚本和指向 NBT 教程网站 http://www.nbtwiki.net/ 的链接的支持。最后,我们简要概述了从 DFA 应用于健康和疾病中的持续振荡中得出的见解,并讨论了临界性对理解振荡的无标度调制机制的潜在相关性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/69d4/3510427/e9711f479f32/fphys-03-00450-g001.jpg

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