R. Pascual-Marqui, P. Faber, T. Kinoshita, Y. Kitaura, K. Kochi, P. Milz, K. Nishida, M. Yoshimura
{"title":"The dual frequency RV-coupling coefficient: a novel measure for quantifying cross-frequency information transactions in the brain","authors":"R. Pascual-Marqui, P. Faber, T. Kinoshita, Y. Kitaura, K. Kochi, P. Milz, K. Nishida, M. Yoshimura","doi":"10.5167/UZH-123448","DOIUrl":null,"url":null,"abstract":"Identifying dynamic transactions between brain regions has become increasingly important. Measurements within and across brain structures, demonstrating the occurrence of bursts of beta/gamma oscillations only during one specific phase of each theta/alpha cycle, have motivated the need to advance beyond linear and stationary time series models. Here we offer a novel measure, namely, the \"dual frequency RV-coupling coefficient\", for assessing different types of frequency-frequency interactions that subserve information flow in the brain. This is a measure of coherence between two complex-valued vectors, consisting of the set of Fourier coefficients for two different frequency bands, within or across two brain regions. RV-coupling is expressed in terms of instantaneous and lagged components. Furthermore, by using normalized Fourier coefficients (unit modulus), phase-type couplings can also be measured. The dual frequency RV-coupling coefficient is based on previous work: the second order bispectrum, i.e. the dual-frequency coherence (Thomson 1982; Haykin & Thomson 1998); the RV-coefficient (Escoufier 1973); Gorrostieta et al (2012); and Pascual-Marqui et al (2011). This paper presents the new measure, and outlines relevant statistical tests. The novel aspects of the \"dual frequency RV-coupling coefficient\" are: (1) it can be applied to two multivariate time series; (2) the method is not limited to single discrete frequencies, and in addition, the frequency bands are treated by means of appropriate multivariate statistical methodology; (3) the method makes use of a novel generalization of the RV-coefficient for complex-valued multivariate data; (4) real and imaginary covariance contributions to the RV-coherence are obtained, allowing the definition of a \"lagged-coupling\" measure that is minimally affected by the low spatial resolution of estimated cortical electric neuronal activity.","PeriodicalId":298664,"journal":{"name":"arXiv: Neurons and Cognition","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv: Neurons and Cognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5167/UZH-123448","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Identifying dynamic transactions between brain regions has become increasingly important. Measurements within and across brain structures, demonstrating the occurrence of bursts of beta/gamma oscillations only during one specific phase of each theta/alpha cycle, have motivated the need to advance beyond linear and stationary time series models. Here we offer a novel measure, namely, the "dual frequency RV-coupling coefficient", for assessing different types of frequency-frequency interactions that subserve information flow in the brain. This is a measure of coherence between two complex-valued vectors, consisting of the set of Fourier coefficients for two different frequency bands, within or across two brain regions. RV-coupling is expressed in terms of instantaneous and lagged components. Furthermore, by using normalized Fourier coefficients (unit modulus), phase-type couplings can also be measured. The dual frequency RV-coupling coefficient is based on previous work: the second order bispectrum, i.e. the dual-frequency coherence (Thomson 1982; Haykin & Thomson 1998); the RV-coefficient (Escoufier 1973); Gorrostieta et al (2012); and Pascual-Marqui et al (2011). This paper presents the new measure, and outlines relevant statistical tests. The novel aspects of the "dual frequency RV-coupling coefficient" are: (1) it can be applied to two multivariate time series; (2) the method is not limited to single discrete frequencies, and in addition, the frequency bands are treated by means of appropriate multivariate statistical methodology; (3) the method makes use of a novel generalization of the RV-coefficient for complex-valued multivariate data; (4) real and imaginary covariance contributions to the RV-coherence are obtained, allowing the definition of a "lagged-coupling" measure that is minimally affected by the low spatial resolution of estimated cortical electric neuronal activity.
识别大脑区域之间的动态交易变得越来越重要。大脑结构内部和大脑结构之间的测量表明,只在每个θ / α周期的一个特定阶段才会出现β / γ振荡爆发,这促使人们需要超越线性和平稳时间序列模型。在这里,我们提供了一种新的测量方法,即“双频rv耦合系数”,用于评估不同类型的频率-频率相互作用,这些频率-频率相互作用促进了大脑中的信息流。这是测量两个复值向量之间的一致性,由两个不同频段的傅里叶系数集合组成,在两个大脑区域内或跨两个大脑区域。rv耦合用瞬时分量和滞后分量表示。此外,通过使用归一化傅里叶系数(单位模量),相位型耦合也可以测量。双频rv耦合系数基于先前的工作:二阶双谱,即双频相干性(Thomson 1982;Haykin & Thomson 1998);rv系数(Escoufier 1973);Gorrostieta et al (2012);Pascual-Marqui et al(2011)。本文介绍了新的测量方法,并概述了相关的统计检验。“双频rv耦合系数”的新颖之处在于:(1)它可以应用于两个多元时间序列;(2)该方法不局限于单个离散频率,此外,该频段通过适当的多元统计方法进行处理;(3)该方法对复值多变量数据的rv系数进行了新的推广;(4)获得了实协方差和虚协方差对rv相干性的贡献,允许定义“滞后耦合”测量,该测量受估计皮层电神经元活动的低空间分辨率影响最小。