Baycrest Centre, Rotman Research Institute of Baycrest Toronto, ON, Canada.
Front Syst Neurosci. 2011 Nov 23;5:96. doi: 10.3389/fnsys.2011.00096. eCollection 2011.
Variability in source dynamics across the sources in an activated network may be indicative of how the information is processed within a network. Information-theoretic tools allow one not only to characterize local brain dynamics but also to describe interactions between distributed brain activity. This study follows such a framework and explores the relations between signal variability and asymmetry in mutual interdependencies in a data-driven pipeline of non-linear analysis of neuromagnetic sources reconstructed from human magnetoencephalographic (MEG) data collected as a reaction to a face recognition task. Asymmetry in non-linear interdependencies in the network was analyzed using transfer entropy, which quantifies predictive information transfer between the sources. Variability of the source activity was estimated using multi-scale entropy, quantifying the rate of which information is generated. The empirical results are supported by an analysis of synthetic data based on the dynamics of coupled systems with time delay in coupling. We found that the amount of information transferred from one source to another was correlated with the difference in variability between the dynamics of these two sources, with the directionality of net information transfer depending on the time scale at which the sample entropy was computed. The results based on synthetic data suggest that both time delay and strength of coupling can contribute to the relations between variability of brain signals and information transfer between them. Our findings support the previous attempts to characterize functional organization of the activated brain, based on a combination of non-linear dynamics and temporal features of brain connectivity, such as time delay.
在激活网络中的不同源之间，源动力学的可变性可能表明信息在网络内是如何被处理的。信息论工具不仅允许我们描述局部脑动力学，还可以描述分布式脑活动之间的相互作用。本研究遵循这样的框架，探索了在一个从人类脑磁图（MEG）数据重建的神经磁源的非线性分析的非数据驱动管道中，信号变异性与相互依存关系的不对称性之间的关系，这些数据是作为对人脸识别任务的反应而收集的。使用传递熵分析了网络中非线性相互依存关系的不对称性，传递熵量化了源之间的预测信息传递。使用多尺度熵估计源活动的变异性，多尺度熵量化了信息生成的速度。基于具有耦合时滞的耦合系统动力学的合成数据分析支持了经验结果。我们发现，从一个源到另一个源的信息传递量与这两个源的动力学变异性之间的差异有关，净信息传递的方向取决于计算样本熵的时间尺度。基于合成数据的结果表明，时间延迟和耦合强度都可以有助于大脑信号变异性与它们之间的信息传递之间的关系。我们的发现支持了以前基于非线性动力学和大脑连通性的时间特征（如时间延迟）的组合来描述激活大脑的功能组织的尝试。
