Synchronization likelihood with explicit time-frequency priors
Synchronization likelihood with explicit time-frequency priors
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DOI：
10.1016/j.neuroimage.2006.06.066
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发表时间：
2006-12-01
期刊：
影响因子：
5.7
通讯作者：
Stam, C. J.
中科院分区：
文献类型：
--
作者：
Montez, T.;Linkenkaer-Hansen, K.;Stam, C. J.
认知处理需要整合在大脑空间上不同区域同时处理的信息。两个大脑区域对彼此活动的影响通常由一个未知的函数控制，该函数可能具有非线性项。如果在不同领域的活动之间的函数关系是由非线性项，相关性的线性措施可能无法检测到令人满意的统计相互依赖。因此，用于检测非线性依赖性的算法可能被证明是非常宝贵的，用于表征某些神经元系统、条件或病理中的功能耦合。同步似然（SL）是基于广义同步概念的方法，并且检测两个信号之间的非线性和线性依赖性（Stam，C. J.，货车Dijk，B.W.，2002.同步似然：多元数据集中广义同步的无偏度量。Physica D，163：236-241.）。SL依赖于对同时发生的模式的检测，这些模式在两个信号中可能是复杂的并且广泛不同。将SL应用于脑电或脑磁图（EEG/MEG）信号的临床研究已经显示出有希望的结果。然而，在该算法的先前实现中，许多参数缺乏关于潜在生理过程的时间-频率特性的严格定义。在这里，我们介绍了一个选择这些参数作为感兴趣的模式的时间-频率内容的函数的基本原理。由此，SL算法的用户可以任意选择的参数的数量从六个减少到两个。我们的建议的优点的经验证据是由一个应用程序的癫痫发作和模拟两个单向耦合Henon系统的EEG数据。(c)2006年爱思唯尔公司All rights reserved.
Cognitive processing requires integration of information processed simultaneously in spatially distinct areas of the brain. The influence that two brain areas exert on each others activity is usually governed by an unknown function, which is likely to have nonlinear terms. If the functional relationship between activities in different areas is dominated by the nonlinear terms, linear measures of correlation may not detect the statistical interdependency satisfactorily. Therefore, algorithms for detecting nonlinear dependencies may prove invaluable for characterizing the functional coupling in certain neuronal systems, conditions or pathologies. Synchronization likelihood (SL) is a method based on the concept of generalized synchronization and detects nonlinear and linear dependencies between two signals (Stam, C.J., van Dijk, B.W., 2002. Synchronization likelihood: An unbiased measure of generalized synchronization in multivariate data sets. Physica D, 163: 236-241.). SL relies on the detection of simultaneously occurring patterns, which can be complex and widely different in the two signals. Clinical studies applying SL to electro- or magnetoencephalography (EEG/MEG) signals have shown promising results. In previous implementations of the algorithm, however, a number of parameters have lacked a rigorous definition with respect to the time-frequency characteristics of the underlying physiological processes. Here we introduce a rationale for choosing these parameters as a function of the time-frequency content of the patterns of interest. The number of parameters that can be arbitrarily chosen by the user of the SL algorithm is thereby decreased from six to two. Empirical evidence for the advantages of our proposal is given by an application to EEG data of an epileptic seizure and simulations of two unidirectionally coupled Henon systems. (c) 2006 Elsevier Inc. All rights reserved.