Comparison directed of linear signal processing techniques to infer interactions in multivariate neural systems
Comparison directed of linear signal processing techniques to infer interactions in multivariate neural systems
复制标题
DOI：
10.1016/j.sigpro.2005.07.011
复制
发表时间：
2005-11-01
影响因子：
4.4
通讯作者：
Witte, H
中科院分区：
文献类型：
--
作者：
Winterhalder, M;Schelter, B;Witte, H
在过去的几十年里，已经开发了几种技术来分析多元动态系统中的相互作用。这些分析技术已应用于从经济学到生物医学等各个研究领域记录的经验数据。对不同大脑结构之间相互作用的研究引起了神经科学的浓厚兴趣。电磁信号中包含的信息可用于量化这些结构之间的信息传输。在研究这种相互作用时，我们必须面对一个逆问题。通常，生成经验数据的基本过程的独特特征和不同概念属性以及因此适当的分析技术是事先未知的。这些方法的性能主要根据它们所开发的模型系统进行评估。为了在经验时间序列的应用中得出可靠的结论，了解时间序列分析技术的属性和性能至关重要。为此，本研究研究了四种代表性多元线性信号处理技术在时域和频域的性能。在不同模型系统的基础上，比较了部分交叉谱分析和衡量格兰杰因果关系的三个不同量，即格兰杰因果关系指数、部分定向相干性和定向传递函数。为了捕捉脑神经网络动力学的不同特性，我们研究了多元线性、多元非线性以及多元非平稳模型系统。在应用丘脑电图和皮层电图记录镇静状态下幼猪的神经数据时，丘脑和皮质脑结构之间的定向相互作用和随时间变化的相互作用被研究。通过格兰杰因果指数和部分定向相干性分析了局部活动的时间依赖性变化和相互作用的变化。根据我们研究的模型系统，这两种方法都被证明最适合应用于脑神经网络。这项研究的结果有助于实现了解深度镇静异常状态下神经结构关系的长期目标。 (c) 2005 Elsevier B.V. 保留所有权利。
Over the last decades several techniques have been developed to analyze interactions in multivariate dynamic systems. These analysis techniques have been applied to empirical data recorded in various branches of research, ranging from economics to biomedical sciences. Investigations of interactions between different brain structures are of strong interest in neuroscience. The information contained in electromagnetic signals may be used to quantify the information transfer between those structures. When investigating such interactions, one has to face an inverse problem. Usually the distinct features and different conceptual properties of the underlying processes generating the empirical data and therefore the appropriate analysis technique are not known in advance. The performance of these methods has mainly been assessed on the basis of those model systems they have been developed for. To draw reliable conclusions upon application to empirical time series, understanding the properties and performances of the time series analysis techniques is essential. To this aim, the performances Of four representative multivariate linear signal processing techniques in the time and frequency domain have been investigated in this study. The partial cross-spectral analysis and three different quantities measuring Granger causality, i.e. a Granger causality index, partial directed coherence, and the directed transfer function are compared on the basis of different model systems. To capture distinct properties in the dynamics of brain neural networks, we have investigated multivariate linear, multivariate nonlinear as well as multivariate non-stationary model systems. In an application to neural data recorded by electrothalamography and electrocorticography from juvenile pigs under sedation, directed as well as time-varying interactions have been studied between thalamic and cortical brain structures. The time-dependent alterations in local activity and changes in the interactions have been analyzed by the Granger causality index and the partial directed coherence, Both methods have been shown to be most suitable for this application to brain neural networks based on our model systems investigated. The results of this investigation contribute to the long-term goal to understand the relationships in neural structures in an abnormal state of deep sedation. (c) 2005 Elsevier B.V. All rights reserved.