Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials
Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials
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DOI：
10.1016/j.ecosta.2019.06.003
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发表时间：
2020-07-01
影响因子：
1.9
通讯作者：
Ombao, Hernando
中科院分区：
文献类型：
--
作者：
Fontaine, Charles;Frostig, Ron D.;Ombao, Hernando
基于使用傅立叶系数的大小而不是相干性的参数copula模型（双变量模型和vine模型），开发了表征自发脑信号之间非线性谱依赖性的工具。其动机是在大鼠身上进行实验，研究中风对不同微电极记录的局部场电位之间的连接结构（依赖性）的影响。主要涉及以下几个问题。首先是根据每个历元（小时间窗口）建模的累积分布函数之间的差异，确定给定频带内微电极状态中的变化点。所提出的方法是一种迭代算法，利用二元Kolmogorov-Smirnov统计量在所有epoch范围内比较每个连续的二元copuls。其次是确定这种变化是否只存在于某些微电极中，还是在整个网络中普遍存在。这些问题是通过比较每个时代的vine -copula模型来解决的。通过对大鼠局部场电位数据的分析，提供了必要的框架，并证明了方法的有效性。(C) 2019经济计量经济学与统计。Elsevier B.V.版权所有。
Tools for characterizing non-linear spectral dependence between spontaneous brain signals are developed, based on the use of parametric copula models (both bivariate and vine models) applied on the magnitude of Fourier coefficients rather than using coherence. The motivation is an experiment on rats that studied the impact of stroke on the connectivity structure (dependence) between local field potentials recorded by various microelectrodes. The following major questions are addressed. The first is to determine changepoints in the regime within a microelectrode for a given frequency band based on a difference between the cumulative distribution functions modeled for each epoch (small window of time). The proposed approach is an iterative algorithm which compares each successive bivariate copulas on all the epochs range, using a bivariate Kolmogorov-Smirnov statistic. The second is to determine if such changes are present only in some microelectrodes versus generalized across the entire network. These issues are addressed by comparing Vine-copulas models fitted for each epoch. The necessary framework is provided and the effectiveness of the methods is shown through the results for the local field potential data analysis of a rat. (C) 2019 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.